feat: ML1

11 files changed, 55351 insertions, 0 deletions

diff --git a/ML/01_intro/.ipynb_checkpoints/01_a_typical_ML_workflow-checkpoint.ipynb b/ML/01_intro/.ipynb_checkpoints/01_a_typical_ML_workflow-checkpoint.ipynb
new file mode 100644
index 0000000..2649b38
--- /dev/null
+++ b/ML/01_intro/.ipynb_checkpoints/01_a_typical_ML_workflow-checkpoint.ipynb
@@ -0,0 +1,1747 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A typical machine workflow\n",
+    "\n",
+    "_this notebook gives an example of a machine learning workflow. We will also illustrate the machine learning challenges through some examples_\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This project requires Python 3.7 or above:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "\n",
+    "assert sys.version_info >= (3, 7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Scikit-Learn ≥1.0.1 is required:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from packaging import version\n",
+    "import sklearn\n",
+    "\n",
+    "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's define the default font sizes, to plot pretty figures:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rc('font', size=12)\n",
+    "plt.rc('axes', labelsize=14, titlesize=14)\n",
+    "plt.rc('legend', fontsize=12)\n",
+    "plt.rc('xtick', labelsize=10)\n",
+    "plt.rc('ytick', labelsize=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make this notebook's output stable across runs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "np.random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A small code to begin\n",
+    "## _Load the data -> optionnaly visualize them -> Select and learn a model -> make a prediction_\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[6.30165767]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "# Download and prepare the data\n",
+    "data_root = \"data/\"\n",
+    "lifesat = pd.read_csv(data_root + \"lifesat.csv\")\n",
+    "X = lifesat[[\"GDP per capita (USD)\"]].values\n",
+    "y = lifesat[[\"Life satisfaction\"]].values\n",
+    "\n",
+    "# Visualize the data\n",
+    "lifesat.plot(kind='scatter', grid=True,\n",
+    "             x=\"GDP per capita (USD)\", y=\"Life satisfaction\")\n",
+    "plt.axis([23_500, 62_500, 4, 9])\n",
+    "plt.show()\n",
+    "\n",
+    "# Select a linear model\n",
+    "model = LinearRegression()\n",
+    "\n",
+    "# Train the model\n",
+    "model.fit(X, y)\n",
+    "\n",
+    "# Make a prediction for Cyprus\n",
+    "X_new = [[37_655.2]]  # Cyprus' GDP per capita in 2020\n",
+    "print(model.predict(X_new)) # outputs [[6.30165767]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Replacing the Linear Regression model with k-Nearest Neighbors (in this example, k = 3) regression in the previous code is as simple as replacing these two\n",
+    "lines:\n",
+    "\n",
+    "```python\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "model = LinearRegression()\n",
+    "```\n",
+    "\n",
+    "with these two:\n",
+    "\n",
+    "```python\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "\n",
+    "model = KNeighborsRegressor(n_neighbors=3)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[6.33333333]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Select a 3-Nearest Neighbors regression model\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "\n",
+    "model = KNeighborsRegressor(n_neighbors=3)\n",
+    "\n",
+    "# Train the model\n",
+    "model.fit(X, y)\n",
+    "\n",
+    "# Make a prediction for Cyprus\n",
+    "print(model.predict(X_new)) # outputs [[6.33333333]]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generating the data and figures — please skip"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the code I used to generate the `lifesat.csv` dataset. You can safely skip this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a function to save the figures:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "# Where to save the figures\n",
+    "IMAGES_PATH = Path() / \"images\" / \"fundamentals\"\n",
+    "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
+    "    path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
+    "    if tight_layout:\n",
+    "        plt.tight_layout()\n",
+    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load and prepare Life satisfaction data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create `lifesat.csv`, I downloaded the Better Life Index (BLI) data from [OECD's website](http://stats.oecd.org/index.aspx?DataSetCode=BLI) (to get the Life Satisfaction for each country), and World Bank GDP per capita data from [OurWorldInData.org](https://ourworldindata.org/grapher/gdp-per-capita-worldbank). The BLI data is in `datasets/lifesat/oecd_bli.csv` (data from 2020), and the GDP per capita data is in `datasets/lifesat/gdp_per_capita.csv` (data up to 2020).\n",
+    "\n",
+    "If you want to grab the latest versions, please feel free to do so. However, there may be some changes (e.g., in the column names, or different countries missing data), so be prepared to have to tweak the code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the code for downloading and preparing the datasets is omitted. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datapath = Path() / \"datasets\" / \"lifesat\"\n",
+    "oecd_bli = pd.read_csv(datapath / \"oecd_bli.csv\")\n",
+    "gdp_per_capita = pd.read_csv(datapath / \"gdp_per_capita.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Preprocess the GDP per capita data to keep only the year 2020:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GDP per capita (USD)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Afghanistan</th>\n",
+       "      <td>1978.961579</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Africa Eastern and Southern</th>\n",
+       "      <td>3387.594670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Africa Western and Central</th>\n",
+       "      <td>4003.158913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albania</th>\n",
+       "      <td>13295.410885</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Algeria</th>\n",
+       "      <td>10681.679297</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             GDP per capita (USD)\n",
+       "Country                                          \n",
+       "Afghanistan                           1978.961579\n",
+       "Africa Eastern and Southern           3387.594670\n",
+       "Africa Western and Central            4003.158913\n",
+       "Albania                              13295.410885\n",
+       "Algeria                              10681.679297"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdp_year = 2020\n",
+    "gdppc_col = \"GDP per capita (USD)\"\n",
+    "lifesat_col = \"Life satisfaction\"\n",
+    "\n",
+    "gdp_per_capita = gdp_per_capita[gdp_per_capita[\"Year\"] == gdp_year]\n",
+    "gdp_per_capita = gdp_per_capita.drop([\"Code\", \"Year\"], axis=1)\n",
+    "gdp_per_capita.columns = [\"Country\", gdppc_col]\n",
+    "gdp_per_capita.set_index(\"Country\", inplace=True)\n",
+    "\n",
+    "gdp_per_capita.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Preprocess the OECD BLI data to keep only the `Life satisfaction` column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Air pollution</th>\n",
+       "      <th>Dwellings without basic facilities</th>\n",
+       "      <th>Educational attainment</th>\n",
+       "      <th>Employees working very long hours</th>\n",
+       "      <th>Employment rate</th>\n",
+       "      <th>Feeling safe walking alone at night</th>\n",
+       "      <th>Homicide rate</th>\n",
+       "      <th>Household net adjusted disposable income</th>\n",
+       "      <th>Household net wealth</th>\n",
+       "      <th>Housing expenditure</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Personal earnings</th>\n",
+       "      <th>Quality of support network</th>\n",
+       "      <th>Rooms per person</th>\n",
+       "      <th>Self-reported health</th>\n",
+       "      <th>Stakeholder engagement for developing regulations</th>\n",
+       "      <th>Student skills</th>\n",
+       "      <th>Time devoted to leisure and personal care</th>\n",
+       "      <th>Voter turnout</th>\n",
+       "      <th>Water quality</th>\n",
+       "      <th>Years in education</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Australia</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>13.04</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>63.5</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>32759.0</td>\n",
+       "      <td>427064.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>49126.0</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>502.0</td>\n",
+       "      <td>14.35</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Austria</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>6.66</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>80.6</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>33541.0</td>\n",
+       "      <td>308325.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>50349.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>492.0</td>\n",
+       "      <td>14.55</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>17.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Belgium</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>4.75</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>70.1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>30364.0</td>\n",
+       "      <td>386006.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>49675.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>503.0</td>\n",
+       "      <td>15.70</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>19.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Brazil</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>7.13</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>35.6</td>\n",
+       "      <td>26.7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>395.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>16.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>3.69</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>82.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>30854.0</td>\n",
+       "      <td>423849.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>47622.0</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>523.0</td>\n",
+       "      <td>14.56</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>17.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Indicator  Air pollution  Dwellings without basic facilities  \\\n",
+       "Country                                                        \n",
+       "Australia            5.0                                 NaN   \n",
+       "Austria             16.0                                 0.9   \n",
+       "Belgium             15.0                                 1.9   \n",
+       "Brazil              10.0                                 6.7   \n",
+       "Canada               7.0                                 0.2   \n",
+       "\n",
+       "Indicator  Educational attainment  Employees working very long hours  \\\n",
+       "Country                                                                \n",
+       "Australia                    81.0                              13.04   \n",
+       "Austria                      85.0                               6.66   \n",
+       "Belgium                      77.0                               4.75   \n",
+       "Brazil                       49.0                               7.13   \n",
+       "Canada                       91.0                               3.69   \n",
+       "\n",
+       "Indicator  Employment rate  Feeling safe walking alone at night  \\\n",
+       "Country                                                           \n",
+       "Australia             73.0                                 63.5   \n",
+       "Austria               72.0                                 80.6   \n",
+       "Belgium               63.0                                 70.1   \n",
+       "Brazil                61.0                                 35.6   \n",
+       "Canada                73.0                                 82.2   \n",
+       "\n",
+       "Indicator  Homicide rate  Household net adjusted disposable income  \\\n",
+       "Country                                                              \n",
+       "Australia            1.1                                   32759.0   \n",
+       "Austria              0.5                                   33541.0   \n",
+       "Belgium              1.0                                   30364.0   \n",
+       "Brazil              26.7                                       NaN   \n",
+       "Canada               1.3                                   30854.0   \n",
+       "\n",
+       "Indicator  Household net wealth  Housing expenditure  ...  Personal earnings  \\\n",
+       "Country                                               ...                      \n",
+       "Australia              427064.0                 20.0  ...            49126.0   \n",
+       "Austria                308325.0                 21.0  ...            50349.0   \n",
+       "Belgium                386006.0                 21.0  ...            49675.0   \n",
+       "Brazil                      NaN                  NaN  ...                NaN   \n",
+       "Canada                 423849.0                 22.0  ...            47622.0   \n",
+       "\n",
+       "Indicator  Quality of support network  Rooms per person  Self-reported health  \\\n",
+       "Country                                                                         \n",
+       "Australia                        95.0               NaN                  85.0   \n",
+       "Austria                          92.0               1.6                  70.0   \n",
+       "Belgium                          91.0               2.2                  74.0   \n",
+       "Brazil                           90.0               NaN                   NaN   \n",
+       "Canada                           93.0               2.6                  88.0   \n",
+       "\n",
+       "Indicator  Stakeholder engagement for developing regulations  Student skills  \\\n",
+       "Country                                                                        \n",
+       "Australia                                                2.7           502.0   \n",
+       "Austria                                                  1.3           492.0   \n",
+       "Belgium                                                  2.0           503.0   \n",
+       "Brazil                                                   2.2           395.0   \n",
+       "Canada                                                   2.9           523.0   \n",
+       "\n",
+       "Indicator  Time devoted to leisure and personal care  Voter turnout  \\\n",
+       "Country                                                               \n",
+       "Australia                                      14.35           91.0   \n",
+       "Austria                                        14.55           80.0   \n",
+       "Belgium                                        15.70           89.0   \n",
+       "Brazil                                           NaN           79.0   \n",
+       "Canada                                         14.56           68.0   \n",
+       "\n",
+       "Indicator  Water quality  Years in education  \n",
+       "Country                                       \n",
+       "Australia           93.0                21.0  \n",
+       "Austria             92.0                17.0  \n",
+       "Belgium             84.0                19.3  \n",
+       "Brazil              73.0                16.2  \n",
+       "Canada              91.0                17.3  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
+    "oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
+    "\n",
+    "oecd_bli.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's merge the life satisfaction data and the GDP per capita data, keeping only the GDP per capita and Life satisfaction columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GDP per capita (USD)</th>\n",
+       "      <th>Life satisfaction</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>South Africa</th>\n",
+       "      <td>11466.189672</td>\n",
+       "      <td>4.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colombia</th>\n",
+       "      <td>13441.492952</td>\n",
+       "      <td>6.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Brazil</th>\n",
+       "      <td>14063.982505</td>\n",
+       "      <td>6.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mexico</th>\n",
+       "      <td>17887.750736</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chile</th>\n",
+       "      <td>23324.524751</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              GDP per capita (USD)  Life satisfaction\n",
+       "Country                                              \n",
+       "South Africa          11466.189672                4.7\n",
+       "Colombia              13441.492952                6.3\n",
+       "Brazil                14063.982505                6.4\n",
+       "Mexico                17887.750736                6.5\n",
+       "Chile                 23324.524751                6.5"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,\n",
+    "                              left_index=True, right_index=True)\n",
+    "full_country_stats.sort_values(by=gdppc_col, inplace=True)\n",
+    "full_country_stats = full_country_stats[[gdppc_col, lifesat_col]]\n",
+    "\n",
+    "full_country_stats.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To illustrate the risk of overfitting, I use only part of the data in most figures (all countries with a GDP per capita between `min_gdp` and `max_gdp`). Later in the chapter I reveal the missing countries, and show that they don't follow the same linear trend at all."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GDP per capita (USD)</th>\n",
+       "      <th>Life satisfaction</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Russia</th>\n",
+       "      <td>26456.387938</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Greece</th>\n",
+       "      <td>27287.083401</td>\n",
+       "      <td>5.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Turkey</th>\n",
+       "      <td>28384.987785</td>\n",
+       "      <td>5.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Latvia</th>\n",
+       "      <td>29932.493910</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hungary</th>\n",
+       "      <td>31007.768407</td>\n",
+       "      <td>5.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         GDP per capita (USD)  Life satisfaction\n",
+       "Country                                         \n",
+       "Russia           26456.387938                5.8\n",
+       "Greece           27287.083401                5.4\n",
+       "Turkey           28384.987785                5.5\n",
+       "Latvia           29932.493910                5.9\n",
+       "Hungary          31007.768407                5.6"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min_gdp = 23_500\n",
+    "max_gdp = 62_500\n",
+    "\n",
+    "country_stats = full_country_stats[(full_country_stats[gdppc_col] >= min_gdp) &\n",
+    "                                   (full_country_stats[gdppc_col] <= max_gdp)]\n",
+    "country_stats.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "country_stats.to_csv(datapath / \"lifesat.csv\")\n",
+    "full_country_stats.to_csv(datapath / \"lifesat_full.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
+    "                   x=gdppc_col, y=lifesat_col)\n",
+    "\n",
+    "min_life_sat = 4\n",
+    "max_life_sat = 9\n",
+    "\n",
+    "position_text = {\n",
+    "    \"Turkey\": (29_500, 4.2),\n",
+    "    \"Hungary\": (28_000, 6.9),\n",
+    "    \"France\": (40_000, 5),\n",
+    "    \"New Zealand\": (28_000, 8.2),\n",
+    "    \"Australia\": (50_000, 5.5),\n",
+    "    \"United States\": (59_000, 5.3),\n",
+    "    \"Denmark\": (46_000, 8.5)\n",
+    "}\n",
+    "\n",
+    "for country, pos_text in position_text.items():\n",
+    "    pos_data_x = country_stats[gdppc_col].loc[country]\n",
+    "    pos_data_y = country_stats[lifesat_col].loc[country]\n",
+    "    country = \"U.S.\" if country == \"United States\" else country\n",
+    "    plt.annotate(country, xy=(pos_data_x, pos_data_y),\n",
+    "                 xytext=pos_text, fontsize=12,\n",
+    "                 arrowprops=dict(facecolor='black', width=0.5,\n",
+    "                                 shrink=0.08, headwidth=5))\n",
+    "    plt.plot(pos_data_x, pos_data_y, \"ro\")\n",
+    "\n",
+    "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
+    "\n",
+    "save_fig('money_happy_scatterplot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GDP per capita (USD)</th>\n",
+       "      <th>Life satisfaction</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Turkey</th>\n",
+       "      <td>28384.987785</td>\n",
+       "      <td>5.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hungary</th>\n",
+       "      <td>31007.768407</td>\n",
+       "      <td>5.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>France</th>\n",
+       "      <td>42025.617373</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New Zealand</th>\n",
+       "      <td>42404.393738</td>\n",
+       "      <td>7.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Australia</th>\n",
+       "      <td>48697.837028</td>\n",
+       "      <td>7.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Denmark</th>\n",
+       "      <td>55938.212809</td>\n",
+       "      <td>7.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>60235.728492</td>\n",
+       "      <td>6.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               GDP per capita (USD)  Life satisfaction\n",
+       "Country                                               \n",
+       "Turkey                 28384.987785                5.5\n",
+       "Hungary                31007.768407                5.6\n",
+       "France                 42025.617373                6.5\n",
+       "New Zealand            42404.393738                7.3\n",
+       "Australia              48697.837028                7.3\n",
+       "Denmark                55938.212809                7.6\n",
+       "United States          60235.728492                6.9"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "highlighted_countries = country_stats.loc[list(position_text.keys())]\n",
+    "highlighted_countries[[gdppc_col, lifesat_col]].sort_values(by=gdppc_col)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
+    "                   x=gdppc_col, y=lifesat_col)\n",
+    "\n",
+    "X = np.linspace(min_gdp, max_gdp, 1000)\n",
+    "\n",
+    "w1, w2 = 4.2, 0\n",
+    "plt.plot(X, w1 + w2 * 1e-5 * X, \"r\")\n",
+    "plt.text(40_000, 4.9, fr\"$\\theta_0 = {w1}$\", color=\"r\")\n",
+    "plt.text(40_000, 4.4, fr\"$\\theta_1 = {w2}$\", color=\"r\")\n",
+    "\n",
+    "w1, w2 = 10, -9\n",
+    "plt.plot(X, w1 + w2 * 1e-5 * X, \"g\")\n",
+    "plt.text(26_000, 8.5, fr\"$\\theta_0 = {w1}$\", color=\"g\")\n",
+    "plt.text(26_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=\"g\")\n",
+    "\n",
+    "w1, w2 = 3, 8\n",
+    "plt.plot(X, w1 + w2 * 1e-5 * X, \"b\")\n",
+    "plt.text(48_000, 8.5, fr\"$\\theta_0 = {w1}$\", color=\"b\")\n",
+    "plt.text(48_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=\"b\")\n",
+    "\n",
+    "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
+    "\n",
+    "save_fig('tweaking_model_params_plot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "θ0=3.75, θ1=6.78e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import linear_model\n",
+    "\n",
+    "X_sample = country_stats[[gdppc_col]].values\n",
+    "y_sample = country_stats[[lifesat_col]].values\n",
+    "\n",
+    "lin1 = linear_model.LinearRegression()\n",
+    "lin1.fit(X_sample, y_sample)\n",
+    "\n",
+    "t0, t1 = lin1.intercept_[0], lin1.coef_[0][0]\n",
+    "print(f\"θ0={t0:.2f}, θ1={t1:.2e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
+    "                   x=gdppc_col, y=lifesat_col)\n",
+    "\n",
+    "X = np.linspace(min_gdp, max_gdp, 1000)\n",
+    "plt.plot(X, t0 + t1 * X, \"b\")\n",
+    "\n",
+    "plt.text(max_gdp - 20_000, min_life_sat + 1.9,\n",
+    "         fr\"$\\theta_0 = {t0:.2f}$\", color=\"b\")\n",
+    "plt.text(max_gdp - 20_000, min_life_sat + 1.3,\n",
+    "         fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\", color=\"b\")\n",
+    "\n",
+    "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
+    "\n",
+    "save_fig('best_fit_model_plot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "37655.1803457421"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cyprus_gdp_per_capita = gdp_per_capita[gdppc_col].loc[\"Cyprus\"]\n",
+    "cyprus_gdp_per_capita"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.301656332738056"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cyprus_predicted_life_satisfaction = lin1.predict([[cyprus_gdp_per_capita]])[0, 0]\n",
+    "cyprus_predicted_life_satisfaction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
+    "                   x=gdppc_col, y=lifesat_col)\n",
+    "\n",
+    "X = np.linspace(min_gdp, max_gdp, 1000)\n",
+    "plt.plot(X, t0 + t1 * X, \"b\")\n",
+    "\n",
+    "plt.text(min_gdp + 22_000, max_life_sat - 1.1,\n",
+    "         fr\"$\\theta_0 = {t0:.2f}$\", color=\"b\")\n",
+    "plt.text(min_gdp + 22_000, max_life_sat - 0.6,\n",
+    "         fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\", color=\"b\")\n",
+    "\n",
+    "plt.plot([cyprus_gdp_per_capita, cyprus_gdp_per_capita],\n",
+    "         [min_life_sat, cyprus_predicted_life_satisfaction], \"r--\")\n",
+    "plt.text(cyprus_gdp_per_capita + 1000, 5.0,\n",
+    "         fr\"Prediction = {cyprus_predicted_life_satisfaction:.2f}\", color=\"r\")\n",
+    "plt.plot(cyprus_gdp_per_capita, cyprus_predicted_life_satisfaction, \"ro\")\n",
+    "\n",
+    "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Using KNN**\n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.333333333333333"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#With KNN\n",
+    "knn_model = KNeighborsRegressor(n_neighbors=3)\n",
+    "\n",
+    "# Train the model\n",
+    "knn_model.fit(X_sample, y_sample)\n",
+    "\n",
+    "# Make a prediction for Cyprus\n",
+    "knn_model.predict([[cyprus_gdp_per_capita]])[0, 0]\n",
+    "\n",
+    "#print(model.predict(X_new)) # outputs [[6.33333333]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Partial conclusion\n",
+    "\n",
+    "The model makes good prediction. Otherwise, you may need to **use more attributes** (employment rate, meteo, health, pollution, etc.), **get more or better quality training data**, or perhaps **select a more powerful model** (in that case, a polynomial regression model). \n",
+    "\n",
+    "\n",
+    "## In summary ## \n",
+    "\n",
+    "Study the data -> Select the model -> Train the model on the training data -> Apply it to make predictions on new cases (aka _inference_) hoping that the model will generalize well. \n",
+    "\n",
+    "## Now let's look at what can go wrong in learning and prevent making accurate predictions. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's consider more data !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GDP per capita (USD)</th>\n",
+       "      <th>Life satisfaction</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>South Africa</th>\n",
+       "      <td>11466.189672</td>\n",
+       "      <td>4.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colombia</th>\n",
+       "      <td>13441.492952</td>\n",
+       "      <td>6.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Brazil</th>\n",
+       "      <td>14063.982505</td>\n",
+       "      <td>6.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mexico</th>\n",
+       "      <td>17887.750736</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chile</th>\n",
+       "      <td>23324.524751</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Norway</th>\n",
+       "      <td>63585.903514</td>\n",
+       "      <td>7.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Switzerland</th>\n",
+       "      <td>68393.306004</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ireland</th>\n",
+       "      <td>89688.956958</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Luxembourg</th>\n",
+       "      <td>110261.157353</td>\n",
+       "      <td>6.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              GDP per capita (USD)  Life satisfaction\n",
+       "Country                                              \n",
+       "South Africa          11466.189672                4.7\n",
+       "Colombia              13441.492952                6.3\n",
+       "Brazil                14063.982505                6.4\n",
+       "Mexico                17887.750736                6.5\n",
+       "Chile                 23324.524751                6.5\n",
+       "Norway                63585.903514                7.6\n",
+       "Switzerland           68393.306004                7.5\n",
+       "Ireland               89688.956958                7.0\n",
+       "Luxembourg           110261.157353                6.9"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_data = full_country_stats[(full_country_stats[gdppc_col] < min_gdp) |\n",
+    "                                  (full_country_stats[gdppc_col] > max_gdp)]\n",
+    "missing_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "position_text_missing_countries = {\n",
+    "    \"South Africa\": (20_000, 4.2),\n",
+    "    \"Colombia\": (6_000, 8.2),\n",
+    "    \"Brazil\": (18_000, 7.8),\n",
+    "    \"Mexico\": (24_000, 7.4),\n",
+    "    \"Chile\": (30_000, 7.0),\n",
+    "    \"Norway\": (51_000, 6.2),\n",
+    "    \"Switzerland\": (62_000, 5.7),\n",
+    "    \"Ireland\": (81_000, 5.2),\n",
+    "    \"Luxembourg\": (92_000, 4.7),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "full_country_stats.plot(kind='scatter', figsize=(8, 3),\n",
+    "                        x=gdppc_col, y=lifesat_col, grid=True)\n",
+    "\n",
+    "for country, pos_text in position_text_missing_countries.items():\n",
+    "    pos_data_x, pos_data_y = missing_data.loc[country]\n",
+    "    plt.annotate(country, xy=(pos_data_x, pos_data_y),\n",
+    "                 xytext=pos_text, fontsize=12,\n",
+    "                 arrowprops=dict(facecolor='black', width=0.5,\n",
+    "                                 shrink=0.08, headwidth=5))\n",
+    "    plt.plot(pos_data_x, pos_data_y, \"rs\")\n",
+    "\n",
+    "X = np.linspace(0, 115_000, 1000)\n",
+    "plt.plot(X, t0 + t1 * X, \"b:\")\n",
+    "\n",
+    "lin_reg_full = linear_model.LinearRegression()\n",
+    "Xfull = np.c_[full_country_stats[gdppc_col]]\n",
+    "yfull = np.c_[full_country_stats[lifesat_col]]\n",
+    "lin_reg_full.fit(Xfull, yfull)\n",
+    "\n",
+    "t0full, t1full = lin_reg_full.intercept_[0], lin_reg_full.coef_[0][0]\n",
+    "X = np.linspace(0, 115_000, 1000)\n",
+    "plt.plot(X, t0full + t1full * X, \"k\")\n",
+    "\n",
+    "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
+    "\n",
+    "save_fig('representative_training_data_scatterplot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bad algorithms / overfitting\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn import preprocessing\n",
+    "from sklearn import pipeline\n",
+    "\n",
+    "full_country_stats.plot(kind='scatter', figsize=(8, 3),\n",
+    "                        x=gdppc_col, y=lifesat_col, grid=True)\n",
+    "\n",
+    "poly = preprocessing.PolynomialFeatures(degree=10, include_bias=False)\n",
+    "scaler = preprocessing.StandardScaler()\n",
+    "lin_reg2 = linear_model.LinearRegression()\n",
+    "\n",
+    "pipeline_reg = pipeline.Pipeline([\n",
+    "    ('poly', poly),\n",
+    "    ('scal', scaler),\n",
+    "    ('lin', lin_reg2)])\n",
+    "pipeline_reg.fit(Xfull, yfull)\n",
+    "curve = pipeline_reg.predict(X[:, np.newaxis])\n",
+    "plt.plot(X, curve)\n",
+    "\n",
+    "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
+    "\n",
+    "save_fig('overfitting_model_plot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country\n",
+       "New Zealand    7.3\n",
+       "Sweden         7.3\n",
+       "Norway         7.6\n",
+       "Switzerland    7.5\n",
+       "Name: Life satisfaction, dtype: float64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_countries = [c for c in full_country_stats.index if \"W\" in c.upper()]\n",
+    "full_country_stats.loc[w_countries][lifesat_col]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GDP per capita (USD)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Malawi</th>\n",
+       "      <td>1486.778248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rwanda</th>\n",
+       "      <td>2098.710362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>2744.690758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Africa Western and Central</th>\n",
+       "      <td>4003.158913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Papua New Guinea</th>\n",
+       "      <td>4101.218882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lower middle income</th>\n",
+       "      <td>6722.809932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Eswatini</th>\n",
+       "      <td>8392.717564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Low &amp; middle income</th>\n",
+       "      <td>10293.855325</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>13753.707307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Botswana</th>\n",
+       "      <td>16040.008473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>World</th>\n",
+       "      <td>16194.040310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New Zealand</th>\n",
+       "      <td>42404.393738</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sweden</th>\n",
+       "      <td>50683.323510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Norway</th>\n",
+       "      <td>63585.903514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Switzerland</th>\n",
+       "      <td>68393.306004</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            GDP per capita (USD)\n",
+       "Country                                         \n",
+       "Malawi                               1486.778248\n",
+       "Rwanda                               2098.710362\n",
+       "Zimbabwe                             2744.690758\n",
+       "Africa Western and Central           4003.158913\n",
+       "Papua New Guinea                     4101.218882\n",
+       "Lower middle income                  6722.809932\n",
+       "Eswatini                             8392.717564\n",
+       "Low & middle income                 10293.855325\n",
+       "Arab World                          13753.707307\n",
+       "Botswana                            16040.008473\n",
+       "World                               16194.040310\n",
+       "New Zealand                         42404.393738\n",
+       "Sweden                              50683.323510\n",
+       "Norway                              63585.903514\n",
+       "Switzerland                         68393.306004"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_w_countries = [c for c in gdp_per_capita.index if \"W\" in c.upper()]\n",
+    "gdp_per_capita.loc[all_w_countries].sort_values(by=gdppc_col)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "country_stats.plot(kind='scatter', x=gdppc_col, y=lifesat_col, figsize=(8, 3))\n",
+    "missing_data.plot(kind='scatter', x=gdppc_col, y=lifesat_col,\n",
+    "                  marker=\"s\", color=\"r\", grid=True, ax=plt.gca())\n",
+    "\n",
+    "X = np.linspace(0, 115_000, 1000)\n",
+    "plt.plot(X, t0 + t1*X, \"b:\", label=\"Linear model on partial data\")\n",
+    "plt.plot(X, t0full + t1full * X, \"k-\", label=\"Linear model on all data\")\n",
+    "\n",
+    "ridge = linear_model.Ridge(alpha=10**9.5)\n",
+    "X_sample = country_stats[[gdppc_col]]\n",
+    "y_sample = country_stats[[lifesat_col]]\n",
+    "ridge.fit(X_sample, y_sample)\n",
+    "t0ridge, t1ridge = ridge.intercept_[0], ridge.coef_[0][0]\n",
+    "plt.plot(X, t0ridge + t1ridge * X, \"b--\",\n",
+    "         label=\"Regularized linear model on partial data\")\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "\n",
+    "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
+    "\n",
+    "save_fig('ridge_model_plot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "22b0ec00cd9e253c751e6d2619fc0bb2d18ed12980de3246690d5be49479dd65"
+   }
+  },
+  "nav_menu": {},
+  "toc": {
+   "navigate_menu": true,
+   "number_sections": true,
+   "sideBar": true,
+   "threshold": 6,
+   "toc_cell": false,
+   "toc_section_display": "block",
+   "toc_window_display": true
+  },
+  "toc_position": {
+   "height": "616px",
+   "left": "0px",
+   "right": "20px",
+   "top": "106px",
+   "width": "213px"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/ML/01_intro/01_a_typical_ML_workflow.ipynb b/ML/01_intro/01_a_typical_ML_workflow.ipynb
new file mode 100644
index 0000000..3e34a93
--- /dev/null
+++ b/ML/01_intro/01_a_typical_ML_workflow.ipynb
@@ -0,0 +1,1759 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A typical machine workflow\n",
+    "\n",
+    "_this notebook gives an example of a machine learning workflow. We will also illustrate the machine learning challenges through some examples_\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This project requires Python 3.7 or above:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "\n",
+    "assert sys.version_info >= (3, 7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Scikit-Learn ≥1.0.1 is required:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'sklearn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpackaging\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m version\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m version.parse(sklearn.__version__) >= version.parse(\u001b[33m\"\u001b[39m\u001b[33m1.0.1\u001b[39m\u001b[33m\"\u001b[39m)\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'sklearn'"
+     ]
+    }
+   ],
+   "source": [
+    "from packaging import version\n",
+    "import sklearn\n",
+    "\n",
+    "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's define the default font sizes, to plot pretty figures:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rc('font', size=12)\n",
+    "plt.rc('axes', labelsize=14, titlesize=14)\n",
+    "plt.rc('legend', fontsize=12)\n",
+    "plt.rc('xtick', labelsize=10)\n",
+    "plt.rc('ytick', labelsize=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make this notebook's output stable across runs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "np.random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A small code to begin\n",
+    "## _Load the data -> optionnaly visualize them -> Select and learn a model -> make a prediction_\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[6.30165767]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "# Download and prepare the data\n",
+    "data_root = \"data/\"\n",
+    "lifesat = pd.read_csv(data_root + \"lifesat.csv\")\n",
+    "X = lifesat[[\"GDP per capita (USD)\"]].values\n",
+    "y = lifesat[[\"Life satisfaction\"]].values\n",
+    "\n",
+    "# Visualize the data\n",
+    "lifesat.plot(kind='scatter', grid=True,\n",
+    "             x=\"GDP per capita (USD)\", y=\"Life satisfaction\")\n",
+    "plt.axis([23_500, 62_500, 4, 9])\n",
+    "plt.show()\n",
+    "\n",
+    "# Select a linear model\n",
+    "model = LinearRegression()\n",
+    "\n",
+    "# Train the model\n",
+    "model.fit(X, y)\n",
+    "\n",
+    "# Make a prediction for Cyprus\n",
+    "X_new = [[37_655.2]]  # Cyprus' GDP per capita in 2020\n",
+    "print(model.predict(X_new)) # outputs [[6.30165767]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Replacing the Linear Regression model with k-Nearest Neighbors (in this example, k = 3) regression in the previous code is as simple as replacing these two\n",
+    "lines:\n",
+    "\n",
+    "```python\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "model = LinearRegression()\n",
+    "```\n",
+    "\n",
+    "with these two:\n",
+    "\n",
+    "```python\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "\n",
+    "model = KNeighborsRegressor(n_neighbors=3)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[6.33333333]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Select a 3-Nearest Neighbors regression model\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "\n",
+    "model = KNeighborsRegressor(n_neighbors=3)\n",
+    "\n",
+    "# Train the model\n",
+    "model.fit(X, y)\n",
+    "\n",
+    "# Make a prediction for Cyprus\n",
+    "print(model.predict(X_new)) # outputs [[6.33333333]]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generating the data and figures — please skip"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the code I used to generate the `lifesat.csv` dataset. You can safely skip this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a function to save the figures:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "# Where to save the figures\n",
+    "IMAGES_PATH = Path() / \"images\" / \"fundamentals\"\n",
+    "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
+    "    path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
+    "    if tight_layout:\n",
+    "        plt.tight_layout()\n",
+    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load and prepare Life satisfaction data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create `lifesat.csv`, I downloaded the Better Life Index (BLI) data from [OECD's website](http://stats.oecd.org/index.aspx?DataSetCode=BLI) (to get the Life Satisfaction for each country), and World Bank GDP per capita data from [OurWorldInData.org](https://ourworldindata.org/grapher/gdp-per-capita-worldbank). The BLI data is in `datasets/lifesat/oecd_bli.csv` (data from 2020), and the GDP per capita data is in `datasets/lifesat/gdp_per_capita.csv` (data up to 2020).\n",
+    "\n",
+    "If you want to grab the latest versions, please feel free to do so. However, there may be some changes (e.g., in the column names, or different countries missing data), so be prepared to have to tweak the code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the code for downloading and preparing the datasets is omitted. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datapath = Path() / \"datasets\" / \"lifesat\"\n",
+    "oecd_bli = pd.read_csv(datapath / \"oecd_bli.csv\")\n",
+    "gdp_per_capita = pd.read_csv(datapath / \"gdp_per_capita.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Preprocess the GDP per capita data to keep only the year 2020:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GDP per capita (USD)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Afghanistan</th>\n",
+       "      <td>1978.961579</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Africa Eastern and Southern</th>\n",
+       "      <td>3387.594670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Africa Western and Central</th>\n",
+       "      <td>4003.158913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albania</th>\n",
+       "      <td>13295.410885</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Algeria</th>\n",
+       "      <td>10681.679297</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             GDP per capita (USD)\n",
+       "Country                                          \n",
+       "Afghanistan                           1978.961579\n",
+       "Africa Eastern and Southern           3387.594670\n",
+       "Africa Western and Central            4003.158913\n",
+       "Albania                              13295.410885\n",
+       "Algeria                              10681.679297"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdp_year = 2020\n",
+    "gdppc_col = \"GDP per capita (USD)\"\n",
+    "lifesat_col = \"Life satisfaction\"\n",
+    "\n",
+    "gdp_per_capita = gdp_per_capita[gdp_per_capita[\"Year\"] == gdp_year]\n",
+    "gdp_per_capita = gdp_per_capita.drop([\"Code\", \"Year\"], axis=1)\n",
+    "gdp_per_capita.columns = [\"Country\", gdppc_col]\n",
+    "gdp_per_capita.set_index(\"Country\", inplace=True)\n",
+    "\n",
+    "gdp_per_capita.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Preprocess the OECD BLI data to keep only the `Life satisfaction` column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Indicator</th>\n",
+       "      <th>Air pollution</th>\n",
+       "      <th>Dwellings without basic facilities</th>\n",
+       "      <th>Educational attainment</th>\n",
+       "      <th>Employees working very long hours</th>\n",
+       "      <th>Employment rate</th>\n",
+       "      <th>Feeling safe walking alone at night</th>\n",
+       "      <th>Homicide rate</th>\n",
+       "      <th>Household net adjusted disposable income</th>\n",
+       "      <th>Household net wealth</th>\n",
+       "      <th>Housing expenditure</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Personal earnings</th>\n",
+       "      <th>Quality of support network</th>\n",
+       "      <th>Rooms per person</th>\n",
+       "      <th>Self-reported health</th>\n",
+       "      <th>Stakeholder engagement for developing regulations</th>\n",
+       "      <th>Student skills</th>\n",
+       "      <th>Time devoted to leisure and personal care</th>\n",
+       "      <th>Voter turnout</th>\n",
+       "      <th>Water quality</th>\n",
+       "      <th>Years in education</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Australia</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>13.04</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>63.5</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>32759.0</td>\n",
+       "      <td>427064.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>49126.0</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>502.0</td>\n",
+       "      <td>14.35</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Austria</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>6.66</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>80.6</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>33541.0</td>\n",
+       "      <td>308325.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>50349.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>492.0</td>\n",
+       "      <td>14.55</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>17.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Belgium</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>4.75</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>70.1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>30364.0</td>\n",
+       "      <td>386006.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>49675.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>503.0</td>\n",
+       "      <td>15.70</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>19.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Brazil</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>7.13</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>35.6</td>\n",
+       "      <td>26.7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>395.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>16.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>3.69</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>82.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>30854.0</td>\n",
+       "      <td>423849.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>47622.0</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>523.0</td>\n",
+       "      <td>14.56</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>17.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Indicator  Air pollution  Dwellings without basic facilities  \\\n",
+       "Country                                                        \n",
+       "Australia            5.0                                 NaN   \n",
+       "Austria             16.0                                 0.9   \n",
+       "Belgium             15.0                                 1.9   \n",
+       "Brazil              10.0                                 6.7   \n",
+       "Canada               7.0                                 0.2   \n",
+       "\n",
+       "Indicator  Educational attainment  Employees working very long hours  \\\n",
+       "Country                                                                \n",
+       "Australia                    81.0                              13.04   \n",
+       "Austria                      85.0                               6.66   \n",
+       "Belgium                      77.0                               4.75   \n",
+       "Brazil                       49.0                               7.13   \n",
+       "Canada                       91.0                               3.69   \n",
+       "\n",
+       "Indicator  Employment rate  Feeling safe walking alone at night  \\\n",
+       "Country                                                           \n",
+       "Australia             73.0                                 63.5   \n",
+       "Austria               72.0                                 80.6   \n",
+       "Belgium               63.0                                 70.1   \n",
+       "Brazil                61.0                                 35.6   \n",
+       "Canada                73.0                                 82.2   \n",
+       "\n",
+       "Indicator  Homicide rate  Household net adjusted disposable income  \\\n",
+       "Country                                                              \n",
+       "Australia            1.1                                   32759.0   \n",
+       "Austria              0.5                                   33541.0   \n",
+       "Belgium              1.0                                   30364.0   \n",
+       "Brazil              26.7                                       NaN   \n",
+       "Canada               1.3                                   30854.0   \n",
+       "\n",
+       "Indicator  Household net wealth  Housing expenditure  ...  Personal earnings  \\\n",
+       "Country                                               ...                      \n",
+       "Australia              427064.0                 20.0  ...            49126.0   \n",
+       "Austria                308325.0                 21.0  ...            50349.0   \n",
+       "Belgium                386006.0                 21.0  ...            49675.0   \n",
+       "Brazil                      NaN                  NaN  ...                NaN   \n",
+       "Canada                 423849.0                 22.0  ...            47622.0   \n",
+       "\n",
+       "Indicator  Quality of support network  Rooms per person  Self-reported health  \\\n",
+       "Country                                                                         \n",
+       "Australia                        95.0               NaN                  85.0   \n",
+       "Austria                          92.0               1.6                  70.0   \n",
+       "Belgium                          91.0               2.2                  74.0   \n",
+       "Brazil                           90.0               NaN                   NaN   \n",
+       "Canada                           93.0               2.6                  88.0   \n",
+       "\n",
+       "Indicator  Stakeholder engagement for developing regulations  Student skills  \\\n",
+       "Country                                                                        \n",
+       "Australia                                                2.7           502.0   \n",
+       "Austria                                                  1.3           492.0   \n",
+       "Belgium                                                  2.0           503.0   \n",
+       "Brazil                                                   2.2           395.0   \n",
+       "Canada                                                   2.9           523.0   \n",
+       "\n",
+       "Indicator  Time devoted to leisure and personal care  Voter turnout  \\\n",
+       "Country                                                               \n",
+       "Australia                                      14.35           91.0   \n",
+       "Austria                                        14.55           80.0   \n",
+       "Belgium                                        15.70           89.0   \n",
+       "Brazil                                           NaN           79.0   \n",
+       "Canada                                         14.56           68.0   \n",
+       "\n",
+       "Indicator  Water quality  Years in education  \n",
+       "Country                                       \n",
+       "Australia           93.0                21.0  \n",
+       "Austria             92.0                17.0  \n",
+       "Belgium             84.0                19.3  \n",
+       "Brazil              73.0                16.2  \n",
+       "Canada              91.0                17.3  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
+    "oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
+    "\n",
+    "oecd_bli.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's merge the life satisfaction data and the GDP per capita data, keeping only the GDP per capita and Life satisfaction columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GDP per capita (USD)</th>\n",
+       "      <th>Life satisfaction</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>South Africa</th>\n",
+       "      <td>11466.189672</td>\n",
+       "      <td>4.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colombia</th>\n",
+       "      <td>13441.492952</td>\n",
+       "      <td>6.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Brazil</th>\n",
+       "      <td>14063.982505</td>\n",
+       "      <td>6.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mexico</th>\n",
+       "      <td>17887.750736</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chile</th>\n",
+       "      <td>23324.524751</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              GDP per capita (USD)  Life satisfaction\n",
+       "Country                                              \n",
+       "South Africa          11466.189672                4.7\n",
+       "Colombia              13441.492952                6.3\n",
+       "Brazil                14063.982505                6.4\n",
+       "Mexico                17887.750736                6.5\n",
+       "Chile                 23324.524751                6.5"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,\n",
+    "                              left_index=True, right_index=True)\n",
+    "full_country_stats.sort_values(by=gdppc_col, inplace=True)\n",
+    "full_country_stats = full_country_stats[[gdppc_col, lifesat_col]]\n",
+    "\n",
+    "full_country_stats.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To illustrate the risk of overfitting, I use only part of the data in most figures (all countries with a GDP per capita between `min_gdp` and `max_gdp`). Later in the chapter I reveal the missing countries, and show that they don't follow the same linear trend at all."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GDP per capita (USD)</th>\n",
+       "      <th>Life satisfaction</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Russia</th>\n",
+       "      <td>26456.387938</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Greece</th>\n",
+       "      <td>27287.083401</td>\n",
+       "      <td>5.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Turkey</th>\n",
+       "      <td>28384.987785</td>\n",
+       "      <td>5.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Latvia</th>\n",
+       "      <td>29932.493910</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hungary</th>\n",
+       "      <td>31007.768407</td>\n",
+       "      <td>5.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         GDP per capita (USD)  Life satisfaction\n",
+       "Country                                         \n",
+       "Russia           26456.387938                5.8\n",
+       "Greece           27287.083401                5.4\n",
+       "Turkey           28384.987785                5.5\n",
+       "Latvia           29932.493910                5.9\n",
+       "Hungary          31007.768407                5.6"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min_gdp = 23_500\n",
+    "max_gdp = 62_500\n",
+    "\n",
+    "country_stats = full_country_stats[(full_country_stats[gdppc_col] >= min_gdp) &\n",
+    "                                   (full_country_stats[gdppc_col] <= max_gdp)]\n",
+    "country_stats.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "country_stats.to_csv(datapath / \"lifesat.csv\")\n",
+    "full_country_stats.to_csv(datapath / \"lifesat_full.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
+    "                   x=gdppc_col, y=lifesat_col)\n",
+    "\n",
+    "min_life_sat = 4\n",
+    "max_life_sat = 9\n",
+    "\n",
+    "position_text = {\n",
+    "    \"Turkey\": (29_500, 4.2),\n",
+    "    \"Hungary\": (28_000, 6.9),\n",
+    "    \"France\": (40_000, 5),\n",
+    "    \"New Zealand\": (28_000, 8.2),\n",
+    "    \"Australia\": (50_000, 5.5),\n",
+    "    \"United States\": (59_000, 5.3),\n",
+    "    \"Denmark\": (46_000, 8.5)\n",
+    "}\n",
+    "\n",
+    "for country, pos_text in position_text.items():\n",
+    "    pos_data_x = country_stats[gdppc_col].loc[country]\n",
+    "    pos_data_y = country_stats[lifesat_col].loc[country]\n",
+    "    country = \"U.S.\" if country == \"United States\" else country\n",
+    "    plt.annotate(country, xy=(pos_data_x, pos_data_y),\n",
+    "                 xytext=pos_text, fontsize=12,\n",
+    "                 arrowprops=dict(facecolor='black', width=0.5,\n",
+    "                                 shrink=0.08, headwidth=5))\n",
+    "    plt.plot(pos_data_x, pos_data_y, \"ro\")\n",
+    "\n",
+    "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
+    "\n",
+    "save_fig('money_happy_scatterplot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GDP per capita (USD)</th>\n",
+       "      <th>Life satisfaction</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Turkey</th>\n",
+       "      <td>28384.987785</td>\n",
+       "      <td>5.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hungary</th>\n",
+       "      <td>31007.768407</td>\n",
+       "      <td>5.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>France</th>\n",
+       "      <td>42025.617373</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New Zealand</th>\n",
+       "      <td>42404.393738</td>\n",
+       "      <td>7.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Australia</th>\n",
+       "      <td>48697.837028</td>\n",
+       "      <td>7.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Denmark</th>\n",
+       "      <td>55938.212809</td>\n",
+       "      <td>7.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>60235.728492</td>\n",
+       "      <td>6.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               GDP per capita (USD)  Life satisfaction\n",
+       "Country                                               \n",
+       "Turkey                 28384.987785                5.5\n",
+       "Hungary                31007.768407                5.6\n",
+       "France                 42025.617373                6.5\n",
+       "New Zealand            42404.393738                7.3\n",
+       "Australia              48697.837028                7.3\n",
+       "Denmark                55938.212809                7.6\n",
+       "United States          60235.728492                6.9"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "highlighted_countries = country_stats.loc[list(position_text.keys())]\n",
+    "highlighted_countries[[gdppc_col, lifesat_col]].sort_values(by=gdppc_col)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nZv/fDcwo7hpHjHBtgZatbDirbM4ql5RatrJiqWyHDkn5/vtS9uwppaurlCBl3bpSPvSQlD/9JGV6evEjXEttuG8DccCDQLY7dTfgTZTL2FMW1HEY6CyE8DEp697AluIuuJFlpcXcGo1GUwakhB07YMkSNaGXvay5VStlVx86FNq3BxcLHWwtVbgGYKyU8kezsgNCiDPALCxQuFLK34UQi4CtwA1gG8rdrEgSTyUyYO4ADJEGhjQfov1BNRqNzblxQ61ky1ayhw6pmA233qoCvw8ZkuvGVlosVbj+QGFJkw+gXMYsQkr5MvCypecH+waz88xO7vruLrzdvBnUfBCGCAMxoTGFzthrNBpNWbh2zYXvvlMKdtky5Zvs6an8jV94QXmMBAWVvx1LFW4iyof24Xzl47HhCrR61euxefxmNh7ZiDHJyIJdC1iwcwEBXgHc3vJ2DJEGejTqgauLq61E0Gg0lZQzZ9QS6CVL4H//i+L6dRWeMjZWmQr69lXubtbEUoX7DPCjEKIPsMlU1hmoC9h0RbOLcCGqYRRRDaOYHjOdVQdXYUwyMn/nfD7f9jkhfiGMCh9F8+vN6SF7IISwpTgajaYCc+CAGsUuWaICmGdlqWXKsbEnePjh+nTrplby2QqLFK6U8jchRBhqhJu98GEhysvAbosX3F3diQmNISY0hqsZV1m+dznGZCMztszgeuZ1pv0zjbiIOAyRBlrUyr8+Q6PRVDWkhK1blYJdskSFggQVleyFF9RItm1b+PXX/URHFx2rxFpYHA/XpFift6EspcLH3YeR4SMZGT6Si2kXeeO7N9iasZXJv03mtd9e4+bgm4mLiGN0xGga+DdwtLgajcZOZGSoEI/Zk15Hjyovgu7d1eq5IUPUUmZHUKTCFUK0A7ZLKbNM+0UipdxqdclKQYBXAANCBjAlegonLp9gwc4FGJONPPPLMzzzyzN0b9QdQ4SBEa1GEOgT6EhRNRoAzqWmc/TCNerX8CbQr2pPAFujLy5fhv/9TynZ5ctVCEtvb+jXTy0tHjhQxYxwNMWNcLcAwcBp074ECjOQSsBpZq1CqoUwvvN4xncez/7z+5mXPI+5SXN5cPmDPLLiEfrd1A9DpIHBzQfj5+Gcq140lZul248xcfEO3F1cyMjKYsrtrRnctp6jxXII5emLkyfh+++Vkl21Cq5fV0p1+HBlKujTJ2/ENWegOIXbBBXzIHu/whFaM5QXur/A892eJ/FUIvFJ8cQnx7N833J83H0Y3HwwhggD/UL74eHq4WhxNVWAc6npTFy8g7SMLNJQ4bOeWbyDqNBaVW6kW5a+2LMn11SwaZOy0TZtqkJLDh2qfGVdnWb4V5AiFa6U8h/zf4EjUhYMvCCEaGgLwayJEIK2wW1pG9yWN/u8yfrD6zEmGVm4ayHzkudRw6sGI1qNwBBpoHuj7riIKpdbU2Mnjl64hruLS46CAXB3ceHohWtOo3DtZe6wpC+ysuCPP3Invf76S53Xvj28+qpSsuHhamFCRcDSSbODQAjKvJCDECLQdMyJf1Py4iJc6NaoG90adeOD/h+w8u+VGJOMGJOMfLb1M+pVq8eo8FEYIg20C2mn3cw0VqV+DW8ysrLylGVkZVG/hreDJMqLPc0dRfVFbR/vHHvs99+rTMJubirf2iOPqEwYDSroPLilCldQeNxbP8BuGRusjburOwOaDWBAswFczbjKsj3LMCYb+XDzh7y76V3CAsOIi4gjLiKO5rWaO1pcTSUg0M+TKbe35pl8Ss0ZRrf2NneY94XLdQ8u7g2k/tUwWjTx5PJlteigf3/lVTBggFqUUNEpVuEKIT4w7UrgTSHEVbPDrsAtVIJcZ6DczEZFjGJUxCjOXzvPt7u/xZhk5NVfX+WVX1+hfUj7HDezetWr5gSHpngsfRUf3LYeUaG1nM5LwRrmjtKYI44ehaMb6hGyIYR1awU3MgRZQRAXp5Rsr16WZymuKJQ0wo00/RVAS+C62bHrqEA0U20gl0Op6V2T+9rdx33t7uNYyrEcN7OnVj7F0yufpkfjHsRFxDGi1QiL0/NoKjelfRUP9PN0GkWbTXnNHSX1gZSwc2fupNcff6jysDAXnnxC2WM7dbI88lZFpFiFK6XsCSCE+BIYL6VMKe78yki96vV4ossTPNHlCfad20d8cjzGJCP/+eE/PPLjI8SExhAXEcfg5oPx9fB1tLgaB1BZPA/KY+4oqg86N6nF3iRPliyBefNu4dgxdX6nTvDmm0rJtqhCi0ItteE+B1QH8ihcIUR9IENKecragjkjzQKb8VKPl3ix+4tsO7ktx81s2d5l+Lj7MLTFUAwRBvre1Bd3VxsuyNZYjD1m3G3teVDeeyjN9WU1d5j3QVaGC2n/1CLl7xBafOLOhXPg4QFt217jxRd9GDQI6tYt9W1UCixVuHOA+cBn+cr7oVLm9LWmUM6OEIJ2Ie1oF9KOt297m7X/rCU+OZ6FuxZiTDJS07smI1uNxBBpoGvDro4Wt8pirxl3W3oelPceynJ9WcwdvtKbs9uDSfmrDtcO1kZmuOHimcHQwZJRIyAmBrZuTSI6OrpU9VY2LLWWdAB+K6R8relYlcVFuNCjcQ9mxs7kxIQTLItbRkxoDN/s+IYes3vQaHojZh6YybYT2yjEjVljI8xfcS+n3yAtI4tnFu/gXGq61dvKfhX3cnehmqcbXu4uVvE8KO892LoP/vkHPvhATW61aOrJye/bcP1EDWq0OU59w2YWrD3N4gWu3HEHVK9ulSYrPJaOcN2Awp4eryLKqyQerh7EhsUSGxbLletX+H7P9xiTjSzat4j5n86nRa0WOW5mzQLLGDK+AuKIuAH2XmBgC8+D8t6DtfvAPN3MkiWwfbsqDw+HZ59VngWNmwuOX6pO/RpBFcp+bS8sVbi/Aw+ZNnMeBv6wqkSVBF8PX+Ii44iLjGPpyqWcqnkKY5KRSQmTeDnhZTrW7UhcRByjIkZRt1rlNWg5Km6AIxYYWNvzoLz3YI0+ME83s2SJGtUKAVFRMHWqUrKhoeZXeFK7ula0RWGpSeF5YIwQYr0Q4jXTth64C/iv7cSrHPi7+/NA+wdIuCeBw08cZuptU8mUmTz585PUf7c+vb/uzayts7hw7YKjRbUq9nytz4+tXvPtSXnvoazXX7kC330HY8aotDI9e8LMmdC6NcyapYLGrF0LEybkV7aakrA0APkmIUQX4GlguKl4GzBOSploK+EqI/Wr12fCrROYcOsE9pzdk+Nmdv+y+xm3fBwDmqmkmbFhsfi4O1moo1Li6LgBzrrAoDSU9x4svX7PwXTmLc5k42pPfl3jSlqa7dPNVEVKE4A8EfiXDWWpcjSv1ZxJ0ZN4ucfL/HniT+KT4pm3cx5L9yzFz8OPoS2GEhcRx21Nb6uQbmbOEDfAGRcYlJby3kNR1x84oMwEX8xNZ9d2D5ACN/+rxAzL4sn7/eja1bbpZqoiFivcbIQQwUCeWIZSysNWk6gKIoSgQ90OdKjbgSm3TWHt4bU50czm7JhDoHcgd4TfgSHSwK0Nbq0w0cycOW5AVURK+PPP3Jxe2elmPIPS8b/1H3yancK9Tgr7PFxo3bEX7u76c7I2FilcIYQ/8AFwB/mUrYkSo4UJIZqjfHmzaQq8JKWcbokMVQVXF1eiG0cT3TiaD/t/yE8HfiI+OZ7Z22fzyZZPaFC9QU7ettZBrZ0+mllleK2vyFy/rtLNzF+UyQ/LBKdOuOSkm5k+HZp1TGHiTxu5nH4j5xpnCxdZmbB0hDsVaAMMBb4FxgL1UGnSJ1hSgZRyD9AWQAjhChwDviuVtFUMTzdPBjcfzODmg0m9nsrSv5YSnxzPu5veZcqGKbSs1RJDpIG4iDhuqnmTo8UtksrwWl+RKCzdjHCDaqFnCO54kneeqMO/okMAOJfqScYK5w0XWdmwVOH2B+KklGuFEJnAn1LK+UKIE8B/gEWlbLc3cCBfkHNNMfh5+HFn6zu5s/WdnL16lkW7FmFMMvLimhd5cc2L3FLvFgwRBkZFjCLYL9jR4mrsTHa6mS++iGTbttx0M7GDM0lI345L/dO4uCvFOnnVcfp3qJnzQ6jNPvZDWLL6SQiRCrSSUh4WQhwBRkgpfxdCNAZ2SilLFbVFCPEFsFVK+VEhxx4AHgAICgpqP2/ePIvqTE1Nxc9Jp1FtKduptFOsObOGVadXsT91Py64cHPAzfSq04vutbvj51Z8u1W138qDreXKzJJcz8zCw9UFV5eiTUaHD3uzfn0t1q2rxe7d1ZFSEBx8he7dzxMVdZbw8Etcz8rk4JkrZJp9z12FoEltX7zdcy2BlrZZHpz18wQlm7ePr1X6oGfPnn9KKQtdgWupwk1ERQtLEEL8DOwEngSeAJ6QUlocf10I4QEcB8JLCnrToUMHuWXLFovqTUhIcNp12vaSbfeZ3TluZgcuHMDD1YOBzQYSFxFHbFgs3u4FXxN1v5UeW8pV3EKRrCzYvDl3EcKePeqaDh3UAoShQ+HMmQR69syV7VxqOlFvryYtI9ds4OXuwvqJvew+inXWzxNg6YqVTNxwwyoLdIQQRSpcS00Ks4HWQALwFvAD8Ahq4cT4UsrTHzW6rRIRxuxJy9otebXnq7wS/Qpbjm/BmGRk3s55fPfXd1TzqMawlsMwRBjo3bQ3bi6ldlDR2JjCQhw+NT+Za3/XYfVP7nz/vTIduLmpxQiPPlow3UxCQt46tcmgZLKXnqdluNo8vKalCx/eM9tfLYRogQpas09KmVTKNuOA+FJeoykFQgg61utIx3odmdp3KgmHEohPjmfRrkV8nfg1tX1q57iZVZaAOo6I12BtsheKXE1z4drfdbi6L4i0v+sw+rpbTrqZoUNVupmAAMvr1Z4ixXP0wjXyGxBs5alRpMI1TY6FSClPm2yu46WUlyHH77bUvrdCCF/gNtREm8YOuLq40rtpb3o37c3HAz5mxf4VxCfH8/m2z/n4j48J8gzi3sx7MUQaiAyKLLlCJ8RR8RqsydGj8NNCX/7+pj1X/6kJWS64+KZRLfw4//dCCEMGuJcr3Yz2FCma+jW8CyRstJWnRnEj3GuoJJGngTHAROByeRqTUl4BAstTh6bseLp5MrTFUIa2GMrl9Mss+WsJH//2Me9seIe31r9FeO3wHDezJjWaOFpci6io2Ray081kL0JQUxXu1G3kj1fngwQ0P4Nb3Qu8M6I1g9vq5V62JNDPk/o1vPFyv2Fzs0txCncDsEQI8Scqp9kHQohrhZ0opRxrdck0NqWaZzXuanMXDS40ILxjOAt3LSQ+OZ7nVz/P86ufp3P9zhgiDNwRfgdBfkGOFrdIHB2voTRkZsLGjbmTXgcOqPLOnc3TzbhzLrU+Ry8E6td/O+Lv7c76id1tbnYpTuHeBTwFhKKy9gYCtg/zpLE7tX1rM67jOMZ1HMc/F/9hXvI84pPjeex/j/H4T4/Tu0lvDJEGhrUYhr+Xv6PFzYMzxGsojmvX4JdflIJdtgzOnFHpZnr3hqefVpNeISF5r9Gv/47BHv1epMI1eRE8DSCEOIha+HDOptJoHE6jgEZM7DqRiV0nsvP0zhw3s3uX3suDPzxIbFgscRFxDAwbiJeb43NYO+Ms/LlzaoXXkiXw009w9Sr4+6vJrqFDVboZnQGhamKpl0IBg54Qwl1KmWF9kTTOQnidcCb3msxrPV9j87HNGJOMzN85n8W7F1PdszrDWw4nLiKOXk16OdTNzBlm4Q8dyrXHrl2rzAf16sE99ygl26OHGtlqqjaWBq95DDgmpVxs+v9zVEDyA8BgU5wETSVFCEGn+p3oVL8T0/pNI+FQAsYkI4t3L2b29tnU8a3DqPBRGCINdKrXySEBdez9Gi4lJCYqBbt0acF0M0OHQvv2KjuCRpONpcOSx1ABaxBCdEdFDTMAtwPTgFibSKdxOtxc3OjTtA99mvZhxsAZrNi3AmOykU///JQPN39Ik4AmOdHMwuuEO1pcq3Ljhhq9fvRRKPfcY0m6GY0mL5Yq3HrAQdP+IGChlHKBECIJlblXUwXxcvNiWMthDGs5jJT0FJb8tQRjkpG317/NG+veILJOJIZIA6MjRtM4oLHd5LLmIogrV5QddulS+OEHOH8e3N3rEhMDL72kMiLUqWMlwTWVHksVbgpQBziCWrjwjqk8A5W5V1PFqe5Znbvb3M3dbe7mVOqpHDez51Y9x3OrnuPWBrdiiDAwMnwkdXxtp6GssQji9GmlXJcsgZUryUk3M2iQGsV6e6+nf/9utrkBTaXG0tQBPwOfCSFmodzEVpjKw8kd+Wo0AAT5BfHILY+wfux6Do4/yJu93+Ry+mUeWfEIdafVpf/c/nyd+DUp6SlWbbc8SSv374dp06BbNwgOhn//W6UE/89/YPVqpYS/+gqGDwdv70yryq2pOlg6wn0YeB1oiArNeN5U3g4dF0FTDI0DGvNs12d5tuuzJJ1KIj45nvjkeMYsGYOXmxexYbEYIgz0b9a/3G5mpVkEkZ1uJnsRws6dqrxtW2UqGDoU2rTRk14a62KpW1gK8Ggh5S9bXSJNpSUyKJLIoEhe7/U6m45uwphkZMGuBSzatQh/T39ub3k7cZFx9GzcE1eXErM2FaCkRRDZ6WayPQuOHQNXVzWqnT5dmQsaNy7/fWo0RVFc8Jqa2SNZIUTN4ioxG/FqNCUihKBLgy50adCF92LeY/XB1TlJM7/Y/gXBfsGMCh9F2PUwesgeFruZFbYIYlK/Nqz60ZMlS+DHH1W6GR8f6NdPjWIHDoRAHd1DYyeKG+GeEUKESClPA2ehQEAdUDEWJBYkkdRUTGwd9tDNxY2+N/Wl7019+WTgJ/y470eMyUZmbplJemY60w5NwxBhIC4yjla1W5VY3+C29bjJrxbzFmaycY0XY6a45KSbuf12pWT79AFv51j5q6liFKdwewHnzfYrR+BUjcXYO+yht7s3t7e6ndtb3c6ltEu8ueRNtmZs5Y11bzB57WTaBLXJcTNr6N8wz7V//ZVrKti0Sf0w3HSTCtI9dCh06aLMBxqNIykulsKvZvsJdpFG4zQ4Ouyhv5c/McExvBX9FidTT7Jw50KMyUYm/jKRib9MJKp+N7rwOOm7Yvh5uU+edDOTJyt7bHi4nvTSOBeWLu3NCUaerzwQOC2l1GOHSoYzhT0M9gvm0U6Pcn+bR5n7/Qn+b85Jfn+3PutTaoNLBoGttjLmvzd4dmwrWtzknEkKNRqw3C2sqHGCJ3DdSrJonAhnCXt48aKa7FqyBFasgNTUEPz8Qhg+QHJz9GFOhnzBd4e+5KtLh1kwz5tBzQdhiDAQExqDp5sOcahxLopVuEKIJ027EnjQlC49G1egG/CXjWTTOBBHhj08cgS+/x6+/LI1iYkqhkFwMBgMyh7bqxd4egqUW/gk3pUvsfHIxhw3swU7FxDgFcCIliOIi4yjR6MeZXIz02isTUkj3GzfWwHcB5gvsbkOHAIetL5YGnMclSDRXmEPs9PNZC9C+PNPVd6woScTJigle8st4FLEukgX4UJUwyiiGkYxPWY6qw6uyslYPGvbLEL8QnKimXWo28Eh0cw0GihB4WbHwRVCrAGGSykv2EUqTQ6OTpBoq7CHmZmwYUOukv37b1XeuTO89Zaa9Dp58g+io6NLVa+7qzsxoTHEhMZwNeMqy/cux5hsZMaWGUz/fTqhNUNz3Mxa1Gph7dvSaIrF0pVmPW0tiKYgjvYUsDbXrqlgMNnpZs6ezU03M3GiCg5jnm7m5Mnytefj7sPI8JGMDB/JxbSLfLv7W4xJRiavncyrv73KzcE3Y4g0MCp8FA38G5SvMY3GAiwO0y+ECANGoAxneWLXW5pEUggRAMwCIlB24bFSyo2WylDVcCZPgbJy7pyKvLV0ad50M336ZdIpOo2RQ91oHGL7ewnwCmDszWMZe/NYTlw+wYKdCzAmG3l65dM8vfJpujfqjiHCwIhWIwj00UvPNLbBomhhQoiBwA5ULNyxQHNgADAMqFWK9t4H/ielbAG0AXaXStoqhrN4CpSWgwdVbIKePSEoSKWZ2bwZ7r1XjXBnrTxGctjPfH1yHTEzVvP99mN2lS+kWgjjO4/n9/t+Z9+j+3it52ucvnKaB5c/SPC0YGKNsRiTjKReTy25Mo2mFFg6wn0VeEVK+aYQ4jIqo+9x4BvAohGqEMIf6A7cAyClvI52KSsWZ0yQWBhSqhQz2Tm9EhNVeUQEPPecssdmp5s5l5pO1NvOYyYJrRnKC91f4Pluz5N4KpH4JBXNbPm+5fi4+zC4+WAMEQb6hfbDw1UnJdOUDyFlySt2Te5graWUfwshzgPdpZTJQohIYLmUsmEJVSCEaAt8CuxCjW7/BMZLKa/kO+8B4AGAoKCg9vPmzbPoRlJTU/Hzcz6n98wsyZUrqfj6+uHqUrbZ8cwsyfXMLDxcXcpcR1GUpt8ysyTXMpSjioeLGzuTA1i3rhbr19fi1CkvhJBERl4iKuosUVHnqFfvWoE6rmVkcvDMFTLNnjtXIWhS2xdv97yuW476TLNkFsmXkll1ZhUJpxNIuZFCNbdqdK/dnT51+tDUtSnVqzln2l1n/R5A1ZGtZ8+ef0opOxR2zFKFewLoLaXcJYTYCTwvpVwihLgZ+E1KWc2COjoAm4AoKeXvQoj3gRQp5YtFXdOhQwe5ZcuWEuUDSEhIKPWMtq3J9jB4rGUGH+x2t7uHgSVY2m9Ltx/jiTlJpB6ozdV9QVw7UIesNA+8vKBvXzWKtSTdjBrhriYtI9dU4uXuwvqJvQqMcJ3hM83IzGDl3ysxJhlZ8tcSrmRcoZZHLca0H0NcRBztQto5lZuZM/RZUVQV2YQQRSpcS00KvwNdUaPT5cA0IUQblA3X0kmvo8BRKeXvpv8XAc9aeG2Fw9zDIFPKnOwDFc3D4PRpiF+YwfPT3bhy8DbIdMXF6zreoaep3vw0mz4Op2GQ5fdTUcwk2bi7ujOg2QAGNBvA1YyrLNuzjA8SPuCD3z9g2sZphAWG5biZhQWGOVpcjZNjqcJ9Esgeb08CqqEy9u41HSsRKeVJIcQRIURzU1r13igFbnfssZCgInsY7N+f6x+7YQNI6Y67f3WqtT2MT9hJPOtfQLhIfDxcuXC9KQ0p3f3Ya0GFtfFx92FUxCiCzgbRplMbFu9ejDHJyCu/vsKkXyfRPqR9jptZverO9SajcQ4s9cP922z/KvBQGdt7FJgrhPAA/gbuLWM9ZcZeCwkqkodBVlZuupmlS/Omm3n5ZYjue537f1jD9cy85qfMLFnm+7HVggp7UcO7Bve1u4/72t3HsZRjOW5mE36ewFM/P0WPxj0wRBi4vdXt1PQuNn6/pgphqVtYbSFEbbP/I4UQk4UQcaVpTEq5XUrZQUrZWko51N4r18qTZLC0ZL86e7m74CoEXu4uTvXqfP06/PwzvPdeMxo2VEtn335b2WDff1+5dm3bphRujy4eTB3ZBjezp8XdVfDOCOe5H0dSr3o9nujyBH/c/wd7H9nLpOhJnLh8ggd+eIDgqcEMjh/MvOR5XLl+peTKNJUaS00KC1AuYF8IIWoBv6Hcwh4VQtSVUk6zlYDWxN6v+dmvzps3rmP94K4OV04pKSri1tKlsHy5+t/LK5gBA3LTzUgPZW6pVsMbzEwF2fey83gKIAmv6+/w+3FGmgU246UeL/Fi9xfZfnI7xiQj8cnxLNu7DF93X4a0GIIhwkDfm/ri7uruaHE1dsZShdsa5WEAarXZfillRyHEEOAdoEIoXEe85gf6eeLt7uow5XTihIq8tWQJrFoFGRlQuzaMHKk8Czw81tOvX3egZHNLoJ8n3cNqF9GSxhwhBDeH3MzNITfz9m1vs/aftcQnx7Nw10KMSUYCvQMZ0WoEhkgDXRt2xUVY9LKpqeBY+il7A9nLbvoA35v2twIVZhG6+Wt+NU83p3vNtxZ//aUCwHTuDHXrwoMPwr59MH48rF2rlPCsWSp2gaen+gGyp7mlquEiXOjRuAczY2dyYsIJlsUto19oP77Z8Q09Zveg0fRGPP3z02w7sQ1L3DQ1FRdLR7j7gOFCiMVAX9SoFiAIuGgDuWyGs82QW8NjIisLfv8917Ng715V3rGjSjczdCi0alV8upmK7FVRkfBw9SA2LJbYsFiuXL/C93u+Jz45num/T2fqxqm0qNUix80stGaoo8XVWBlLFe4rQDzKdLDKzJe2H7DNFoLZEmeZIS+Px0RaGqxerRTs99/DqVPg5qbiF4wfD4MHQ/36lstSkbwqKgu+Hr7ERcYRFxnHuavnWLx7MfHJ8byc8DIvJbxEx7odc9zMQqqFlFyhxumxyKQgpfwWFSWsAxBjdugXLPTD1eSlLK/wFy7A3LnK/lq7tprkmjcPevQAoxHOnFGeB+PGlU7ZQtUxtzgrgT6BPND+AdaMWcPhJw4z9bapZMpMnvjpCeq9W4/eX/fm862fc+GaDkldkbE4PKOU8hRwKl/Z70WcXimx5oIJS1/hjxxRXgVLl0JCQm66mTvvVKaCnj3B00o60dnMLVWV+tXrM+HWCUy4dQJ7zu4hPjkeY5KR+5bdx0PLH2JAswEYIg3EhsXi4+7jaHE1pcBihVvVsfaCiaJe4esFeJOUlLsIITvdTIsW8NRTSsl27Fh0upny4izmFo2iea3mTIqexMs9Xmbria05qYOW7lmKn4cfQ1sMxRBhoE/TPtrNrAKgFa4F2CLzgnlMATdcSPnHnxYynE5tPfn7bzXB1bmzWowwZAg0b27NO9JUNIQQtK/bnvZ12zPltimsPbwWY5KRRbsWMWfHHGr51GJkq5G0vNGS7rK7djNzUrTCNaMok4EtZvCvXgUO16P9oWBW/Ci4cN6FUx7Qpw88+6xy2QoOzpUr8Yh+zdcoXF1ciW4cTXTjaD4a8BE/7f8JY7KR2dtnc+3GNaYenMro8NEYIg20DmrtVNHMqjpa4ZoozmRgrRn87HQzS5aodDPXroG/vyuxsWoUGxMD1fIFunR0EkmNc+Ph6sGg5oMY1HwQqddTeeu7t9ieuZ13N73LlA1TaFW7FXERccRFxHFTzZscLW6Vx+L3DiFEkBDiKSHEJ6blvQghooQQTWwnnn0oyWOgPDP4Bw/CokX1iY5WcQruuQe2bIGxY1W6mTNnYM4c5XmQX9nqxQia0uDn4UefoD78YPiBExNO8MnAT6jlU4sX17xI6IehdJ7Vmfc3vc/J1HJm59SUGYtGuEKI9sAq4CAQjlr4cBa4DQgDDLYS0B5YYjKwdAY/O91M9iKEHTsAQomMhP/+V016tWtX/CKE0sil0RRGLZ9aPNjhQR7s8CBHLh1hXvI84pPjefynx3ny5yfp1aQXhggDw1oOI8ArwNHiVhksNSlMBd6XUr5symmWzU84IMSitbHUZFDUDH5Ghloym+1ZcPiw8iKIioJp0yAoaBN33tnZZnJpNMXRwL8BT0c9zdNRT7P7zO4cN7Ox34/lweUPMrDZQAyRBgY2G4i3u362bImlJoX2wFeFlJ9ALe+t0JTFZJCaCosXw113qcy0vXvDZ5+pGLJffAEnT8Jvv8GTT0K9eml2k0ujKY6WtVvyas9X2ffoPjbft5lxHcax8ehGRi4cSdDUIMYsGcNP+3/iRtYNR4taKbF0hHsNqFFIeQvgtPXEcRyWmAxOnYJly9QoduVKSE+HmjXVMtqhQ+G228DX1/5yaTSlRQhBx3od6VivI1P7TuXXf37NcTP7OvFravvU5o7wOzBEGuhSv4v2dLASlircpcDLQoiRpv+lEKIx8Daw2BaCOYLCTAb79uWm/1bpZqBxY3joIaVko6JUDAN7y6XRWAtXF1d6NelFrya9+HjAx/xv//8wJhv5fNvnfPzHxzQOaJzjZhYZFOlocSs0lqqKp4AfgTOAD7AOZUpYD7xgG9EcQ1aW8iLIVrK7TFnXbr4ZJk1S7lutW1s26aXRVDQ83TwZ0mIIQ1oM4XL6ZZbuWYoxycg7G97hrfVvEVEnIsfNrEmNCu+gZHcsDV6TIqXsCgwFJgLvAzFSyh5SygqfNyQ73cy4cdCgAXTqpFZ4BQerdDOHDsHWrfDSS9CmjVa2DmHbNvU64eOj8gEdPmzb9vbtAy8v+Ne/Cj+eng7//jc0aqT8+dq2Vek0KhHVPKvxr9b/4sc7f+TEhBPMGDCDAK8Anl/9PE0/aMqtn9/Kh79/yKnUUyVXpgGKUbhCiEwhRB3T/hdCiGpSytVSyqlSyilSyl/sJ6b1SUmB+fMhLk5F3urXD776Crp0ga+/VunBV62Cxx5T3ymNAzl6FAYMgIkT1eqRpk1VoF9b8vDDKmhFUdy4oX6df/0VLl1S8txxh/p1roTU9q3NQx0fYu29azk0/hBv93mbqxlXeex/j1H33br0m9OP2dtncyntkqNFdWqKG+FeIzc1+hjAy/bi2Jbjx2HmTLWiq1YtGD1aKdWRI9Vk2NmzsGiR8jyoqROtOg8TJsD996vZSW9v9cH98Yft2ps3DwIClOtJUfj6KhtT48bKBzA2Fpo0yY02VIlpFNCIZ6KeYfuD20l+KJnnuj7H/vP7uXfpvQRNDWLEghF8u/tb0m6UzTunMlOcDXcDsEQI8ScggA+EENcKO1FKOdaSxoQQh4DLQCZwQ0rZoXTilg4pVbqZ7EUImzer8tBQFaR76FAVIMbV1ZZSaMpFSooyqGensQBlaPey8Pc/NhbWrSv8WNeuaq11/vZeeklFd581y3I5T51SMoaHW35NJSC8TjiTe03mtZ6vsfnYZoxJRubvnM/i3Yup7lmd4S2HY4gw0LNJT0eL6hQUp3DvQk2WhQISCASssaa0p5TyrBXqKZSsLNi0KXcRgnm6mddfV0q2ZUtth60wZGe+bN06tyw9Xc1egjIzbNigRppffAHu+UIU5leoJfHii8o2W5oI7hkZKkDxmDEqjmYVRAhBp/qd6FS/E9P6TSPhUALGJCOLdy9m9vbZ1PGtQ1RAFF6hXnSq16nKupkVqXBNAcefBhBCHATipJTn7CVYaUhLg40ba2I05k0306sXPP64ehOtp+O9VEwOHVIf4Hff5ZYNGKDsQomJcOyYWub3+uvKHhQXV/a2tm+HX35RE3SWkpWlbFAeHvDRR2VvuxLh5uJGn6Z96NO0DzMGzmDFvhUYk40s3b2U7z7/jiYBTYiLiMMQaSC8TtV6I7DILUxKaS3/Dwn8LISQwP9JKT8ta0UXLsDy5WoUu2IFXLnSmmrV1Hdx6FDo3x/8/a0ktcZxpKcrz4RsDh5Ufntz5qhZz759VXlMDHz5ZUGF27+/UsiF0a1bXs+ChASl4Bs2VP+npkJmpvIN3Lq14PVSqtHwqVPw448FR9cavNy8GNZyGMNaDmP5L8s5V/scxiQjb69/mzfWvUHroNbERcQxOmI0jQMaO1pcmyOKSssshHgSmCGlTDPtF4mU8l2LGhOinpTymMn7YSXwqJTyt3znPAA8ABAUFNR+3rx5OcdOn/Zk/fparFtXi8REfzIzXQgMTCcq6izt2h2lS5c0PDycL810amoqfn5+JZ/oAJxdtvp79tDyrbf485NPEFlZtHrlFU7278+J2FgazpnD1caNOdu1K97HjtH4iy/Y/eKLZW7PJS0Nt6tXc/5vMH8+XidPsveJJ8gICMgjl5+fH2Hvvovf/v0kTptGprdzxCBw9s8zW7YL1y+QcCaBVadXsTNlJwAR1SPoVacX0bWjqeFR2MJW+8hWXnr27PlnkfNTUspCN1RksECz/aK2v4uqo7gNmAQ8Vdw57du3lzt2SPnqq1K2ayelGlJI2bKllM89J+WmTVJmZkoppZRr1qyRzoqWrWysWbNGyqwsKf/zHyl9faVs2FDKjz/OPeHjj6X86iu1v2WLlA8/bF0BXn5ZyjvvzFsWEyMP/PvfUh46pB5GT08lW/Y2Z451ZSglTv95FsLBCwflm2vflJEzIiWTkK6vuMqYOTHyq+1fyUtplxwqW1kAtsgidFpxNtwmhe2XFSGEL+Aipbxs2u8LvFrcNUlJuau6unTR6WaqJEIoX76ZMwseu/VWePdduPtuFdE9Ksq6bU+aVLBsxQoOJyTQtFEj9fuvKTeNAxrzbNdnebbrsySfTiY+KR5jspExS8bg5ebFoLBBxEXE0b9Zf7zcKrZ3armiAAghGgHvSCnvsOD0IOA70+ykG2CUUv6vuAu8vNQ8hHm6GY0mh7ZtVai2bt2U3fWppxwtkaacRNSJ4PXerzO512Q2Hd2EMcnIgl0LWLhrIf6e/tze8nbiIuPo2bgnri4Vz5+zvGFXAoDbLTlRSvk30KY0lTdrpvzdNZoieecdR0ugsQFCCLo06EKXBl14L+Y9Vh9cjTHJyMJdC/li+xcE+wUzKnwUhkgDHet2rDBuZjq1p0ajcWrcXNzoe1NfZg+dzamnTrFo5CJubXArM7fMpNOsTjT7sBkvrn6R3Wd2O1rUEtEKV6PRVBi83b25vdXtLL5jMaeeOsWXQ76kaY2mvLHuDVrNaEXbmW2Zsn4Khy/ZOLhRGdEKV6PRVEj8vfy5p+09/HzXzxx78hgfxHyAt7s3E3+ZSKPpjej2ZTc++eMTzl612cLWUlOswhVCfF/chgrTqNHYHnuFZzx/HoYNU8FpGjUCo9E27WisSrBfMI92epSN/97IgccO8Hqv1zl/7TzjfhxHyLQQBhoHMmfHHC6nXy65MhtS0gj3XAnbQeBrWwqo0dg1POPDD6tluqdOwdy5KrXHzp22aUtjE5rWaMp/u/2X5IeSSXwwkae6PMXO0zu567u7CJoaxOhFo1n611LSb1gjNEzpKNZLQUpZ4TPyaioB5uEZQYVnfOUV67dz5YrKDJqcDH5+KprY4MHwzTfw1lvWb09jU4QQtA5qTeug1rze+3U2HtlIfHI883fOZ/7O+QR4BTCi5QgMkQayZFbJFVoBG2fj0mjKiT3DM+7dq6IehYXllrVpo4KMayo0LsKFqIZRRDWM4r1+77Hq4Crik+OZt3Mes7bNItAjkLuv301cRBwd6nawmZuZVrga56a48IyXLqlUybt2qZicEREFry9NeMbUVKhePW+Zvz9cdqzdT2Nd3F3diQmNISY0hpkDZ/LD3h/4MOFDPv7jY97b9B6hNUMxRBiIi4yjRS3rhtvUXgoa5yY7POPFi7lbz54qOpiPjwoZN2KEddry81MjanNSUlTOMk2lxNvdm5HhI3k1/FVOPXWKzwd/TiP/RkxeO5mWH7ek3f+1Y+qGqRxNOWqV9rTC1Tg3RYVnHDxYhUOsXbv46/v3V4q0sK1//7znhoWpXGX79uWWJSZWuSwOVZUArwDG3jyWX+7+haNPHGV6v+m4u7rz9MqnafheQ3rM7sH/bfk/zl0te1hwrXA1zk3HjsqGevw4HDkCBoMKNm5p0rkVK5SpoLAtf5ZdX18YPlyl2LlyBdavV/bju+6y/n1pnJqQaiGM7zye3+/7nX2P7uPVnq9y5soZHlz+IMHTghkUPwhjkpHU66mlqlcrXI1z06uXmvgKC1OTXHfdZdsAGzNmwLVrUKeOCmb+ySd6hFvFCa0ZygvdX2DnuJ1s/892nuz8JIknE7nz2zsJmhqEYbGBZXuWcT3zeol16UkzjXNTXHhGW1CzpkqIp9HkQwhBm+A2tAluw5t93mT94fXEJ8ezYOcC4pPjqeFVg5GtRhZbh1a4morNgAEqF9mePfCf/8A99zhaIk0VwEW40K1RN7o16sb7Me+z8u+VxCfHMzdpbrHXaYWrqdj8+KOjJdBUcdxd3RnQbAADmg3gauxVfJ/3LfJcbcPVaDQaK+Hj7lPsca1wNRqNxk5ohavRaDR2wrltuHv2QHS0Rae2vXgRzFJZOxNatrLhrLI5q1ygZSsr9pJNj3A1Go3GXhSVP90Ztvbt21ucC96aeeWtjZatbDirbM4ql5RatrJiTdmALbIInWb3Ea4QwlUIsU0IUYowThqNRlPxcYRJYTzg/Ok1NRqNxsrYVeEKIeoDA4FZ9mxXo9FonAF7j3CnA88A9slnodFoNE6E3dzChBCxwGkp5Z9CiOhiznsAeMD0b6oQYo+FTdQCnCcfcl60bGXDWWVzVrlAy1ZWrClbo6IOCDWpZnuEEG8CdwE3AC+gOvCtlPJfVqp/i5SygzXqsjZatrLhrLI5q1ygZSsr9pLNbiYFKeVzUsr6UsrGwGhgtbWUrUaj0VQE9MIHjUajsRMOWdorpUwAEqxc7adWrs+aaNnKhrPK5qxygZatrNhFNrvZcDUajaaqo00KGo1GYy+KWvPriA1oAKwBdgE7gfGm8knAMWC7aRtgds1zwH5gD9DPrDzGVLYfeNasvAnwu6l8PuBhoWxewGYg0STbK8XVB3ia/t9vOt64rDKXQ7bZwEGzfmtrKhfAB6Z2dgDtzOoaA+wzbWPMytsDSaZrPsD0dmShfK7ANuAHZ+mzYmRzlj47ZLp2O6a1+UBNYKWpnZVADSeSbRIO/o6arg0AFgF/oVa0dnGWfpNSOp3CDcm+aaAasBdoZfownyrk/FYoJeNp+pAOoL5Arqb9poCH6ZxWpmsWAKNN+zOBhyyUTQB+pn130wPRuaj6gHHATNP+aGB+WWUuh2yzgRGFnD8AWGG6rjPwu9kX+m/T3xqm/eyHc7PpXGG6tn8pPtcnASO5Ss3hfVaMbM7SZ4eAWvnKpmBSTMCzwNtOJNskHPwdNZ3/FXCfad8DpYCdot+kdEDwmuKQUp6QUm417V9G/ULVK+aSIcA8KWW6lPIg6lfnFtO2X0r5t5TyOjAPGCKEEEAv1C8gqA9nqIWySSlldhJ6d9Mmi6lviOl/TMd7m9ovlczllK0ohgBfm67bBAQIIUKAfsBKKeV5KeUF1GggxnSsupRyk1RP3ddY2G/5l3OX8BnYrc8Kk60E7NZnJciQ3T/5+83RshUns12+o0IIf6A78DmAlPK6lPIiTtRvTqVwzRFCNAZuRo3WAB4RQuwQQnwhhKhhKqsHHDG77KiprKjyQOCilPJGvnJLZXIVQmwHTqM+hAPF1Jcjg+n4JVP7pZW5TLJJKbP77XVTv70nhPDML5uFMtQz7ZdFtunkXc5d3Gdg1z4rRLZsHN1noH4wfxZC/GlafQkQJKU8Ydo/CQQ5kWzg+O9oE+AM8KUpIuEsIYQvztNvzqlwhRB+wGLgcSllCvAJcBPQFjgBTHOEXFLKTCllW6A+6he6hSPkKIz8sgkhIlC2sxZAR9Tr0UR7ymS+nNue7VpCMbI5tM/M6CqlbAf0Bx4WQnQ3P2gaYTnKxagw2ZzhO+oGtAM+kVLeDFxBmRBycHC/OZ/CFUK4o5TtXCnltwBSylMmhZIFfIZSdqCM9A3MLq9vKiuq/BzqtcEtX3mpML2mrEEZ5IuqL0cG03F/U/ullbmsssWYTDRSSpkOfEnZ++2Yab+0skUBg4UQh1CvjL2A93GOPisgmxBijhP0GQBSymOmv6eB70xynDK91mL6e9pZZHOS7+hR4KjZ290ilAJ2in4DnG7STKDsItPzlYeY7T+BsgkBhJPXIP83yhjvZtpvQq5BPtx0zULyGuTHWShbbSDAtO8NrAVii6oPeJi8E0ALyipzOWQLMevX6cBbpv8HkneyYLPMnSw4iJooqGHarykLnywYYIlsZjJGkzsx5fA+K0Y2h/cZ4AtUM9vfgJrRf4e8kz9TnEg2h39HTeevBZqb9ieZ+szh/ZYjX2kfTltuQFfUcH8HZu4lwDcoV4wdwPf5PtznUbbUPZjNGJqu22s69rxZeVNTp+03fbCeFsrWGuU+tANIBl4qrj6Uq9ZCU/lmoGlZZS6HbKtN/ZYMzCHXk0EAH5vaSQI6mNU11iTzfuBes/IOpnoOAB9RWneYvErN4X1WjGwO7zNT/ySS6+b3vKk8EFiFclX6hVwl4AyyOfw7arq2LbDFJMcSlMJ0eL9lb3qlmUaj0dgJp7PhajQaTWVFK1yNRqOxE1rhajQajZ3QClej0WjshFa4Go1GYye0wtVoLEAI0VgIIYUQNsl7JYRwF0Lsyb+izN4IISKFEMdMS2I1VkYr3EqGECLIFANgnxAiTQhxWgixQQjxqGnJdPZ5h0wKRJrOOyKE+E4IMaiQOqXZdlkIsUUIMdy+d+ZwjqCi2W0HEEJEm/qjlpXqfwA4LqX8zVR/kQpeCJEghPjI7P82QoilQoiTps/ysBBisRCikdk55p/hVSHE30IIoxCim3ndUsokYBMqiprGymiFW4kwBfzZilr58yJqWWMn4A2gNzA43yWvopRIGGpl1yHgO/Mvsxn3m87tiHJ6XyiE6GL1mygGIYSHPdszR6plqydlblAVq2GKkPUYpihXpby2NsqpPxW1cqoFKjv2AVRmbHOyP8OWwL+B68CvQoin8533JfCQ2fJajbUoy8ocvTnnhlpqeATwLeK4MNs/ROHxSx9ArfbraVYmMYsRiwr/eBV4s4h2GpuuMQDrgDRUQOi++c5rBSwHLqPWt8cDwWbHZwM/oALIHEUFmynq3jujVoldQUUZWw3UNR2LQS35vACcB34CWpZGXrNzOpjtm2+zLWmrCNk7oCKWBRTWXiHnJwAfmfaHApmUEKQ7/2doVv4GcAMINSvzMPVBH0c/05Vt0yPcSoIQIhAVx/NjKeWVws6Rpm9TCXyOUha3F3WClDIDyEAp3uKYgoqK3xYVznKpEKKeSd4Q4DfUMslbgD6An+kc8+eyB2rpcgxqlF4AIUQbVMCe/aigNJ1RmQKyR2i+qLgIt6CW8V4ClhUyYi5S3nwcIbd/wlGjxvGlbMucbsABqQIPlZaTqDfVEaaRcmmZZrp+aHaBVPFpt6P6XmNF9CtD5SEUtTZ8j3mhEOIoKuo9wBwp5YPFVSKlzBRC7EWtZy+AKT7s06jX1VUlyPSJlHKB6brxqB+Eh4AXTH8TpZQ54Q+FEHejRoUdUGvpQY20xkoVvasongG2SynNY7PuNrunxfnu4V4gBaUU11kobw6mPjpv+ve0lPJsGdoypxFwvJj7KxIp5SYhxBuowNofCyH+QI2A50op/7Hg+nNCiNMU/LyPo0bZGiuiR7iVn26oEdtmVHAYSxAUjBn6jRAiFWVKeBJljlhRQj0bs3ekCtv3O8qMACo3VHchRGr2Rm7Q55vM6kguQdmCClS/usibEeIm0wTRASFECnAK9ew3LIW8FlGKtszxRv2wlAkp5fNAMMoclISyz+4SQhT6RlCY2BT8vK+Z5NJYET3CrTzsR31p8gRFlyqtCUKIq5ZUIoRwRU2ibc536Gngf0CKVHFQy4sLyn77VCHHTpntF2oeKSU/oGzA/0HFL72BSlRqi0m4srR1FvWjYU6K6a9/IecHoEwVOUgpz6Eiay0UQjyHih73IiW8hZi8LGqjQiWaUxNl59dYET3CrSSYvnA/o9Kc+JV0fjHcR27mU3NOSin3l1LZds7eMdkXbyH3VX8ryv75j6le8+1yKWXehgpuXgCTbbsF8IaU8hcp5W5UgtLCBhvFyZuf66a/rmVsK7/8zc1t11LK8yhF3D7f/VRHmY/ymI7MMdlgD6Bs4iUxATVhtyRfeQTqM9JYET3CrVyMA9YDfwohJqHct26gvrRtUArZnGpCiGDU5FcDYCTwKGoG/FcryPOQyR6cZJKtESoVC6g4pPcD84UQb6NyUTUF7gAmlFLpvgNsEkJ8aqo3DWVK+Rk12jwL3C+EOILKQfUOql9KI29+/kG9UQwUQixDvYJfKEVb5qxBmXtaY/LzNfEu8KwQ4jjK3BGIGrWeQY1ms1MFjUZlrdiLMg8MQsWafTlfOwGmz9sDZbYZA9wNPCOlPJB9ksm9sB4FnxdNeXG0m4TerLuhbHnvo0wM6Sj/zD9QubqqmZ13iFyXpnSUYloCDC6kzkJdioqRobHpmjtRGQHSyBd82nReM9RI+gJKYe0BPsTk4oTJLczCNruivB6uARdRgaazszf0QnlDpJn+9jP1yz2Wykshbloo5XcCNUKcbUlbxcgfD7yTr8wV9QO4w1THUZRibWx2TlNUVoS/yHWJ2w48Tl43QHMXtjRUFoN4oHshsjwH/M/Rz3Jl3HQAco3VMY2QDgIdpZRbHCxOiTiDvEKIcNRIN1SqxKkOweSFsg+Ik1Kud5QclRVtw9VonAAp5U7UBGITB4vSCHhdK1vboG24Go2TIKX82glk2IuyBWtsgDYpaDQajZ3QJgWNRqOxE1rhajQajZ3QClej0WjshFa4Go1GYye0wtVoNBo7oRWuRqPR2In/B963DKuwxCR2AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 360x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
+    "                   x=gdppc_col, y=lifesat_col)\n",
+    "\n",
+    "X = np.linspace(min_gdp, max_gdp, 1000)\n",
+    "\n",
+    "w1, w2 = 4.2, 0\n",
+    "plt.plot(X, w1 + w2 * 1e-5 * X, \"r\")\n",
+    "plt.text(40_000, 4.9, fr\"$\\theta_0 = {w1}$\", color=\"r\")\n",
+    "plt.text(40_000, 4.4, fr\"$\\theta_1 = {w2}$\", color=\"r\")\n",
+    "\n",
+    "w1, w2 = 10, -9\n",
+    "plt.plot(X, w1 + w2 * 1e-5 * X, \"g\")\n",
+    "plt.text(26_000, 8.5, fr\"$\\theta_0 = {w1}$\", color=\"g\")\n",
+    "plt.text(26_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=\"g\")\n",
+    "\n",
+    "w1, w2 = 3, 8\n",
+    "plt.plot(X, w1 + w2 * 1e-5 * X, \"b\")\n",
+    "plt.text(48_000, 8.5, fr\"$\\theta_0 = {w1}$\", color=\"b\")\n",
+    "plt.text(48_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=\"b\")\n",
+    "\n",
+    "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
+    "\n",
+    "save_fig('tweaking_model_params_plot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "θ0=3.75, θ1=6.78e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import linear_model\n",
+    "\n",
+    "X_sample = country_stats[[gdppc_col]].values\n",
+    "y_sample = country_stats[[lifesat_col]].values\n",
+    "\n",
+    "lin1 = linear_model.LinearRegression()\n",
+    "lin1.fit(X_sample, y_sample)\n",
+    "\n",
+    "t0, t1 = lin1.intercept_[0], lin1.coef_[0][0]\n",
+    "print(f\"θ0={t0:.2f}, θ1={t1:.2e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
+    "                   x=gdppc_col, y=lifesat_col)\n",
+    "\n",
+    "X = np.linspace(min_gdp, max_gdp, 1000)\n",
+    "plt.plot(X, t0 + t1 * X, \"b\")\n",
+    "\n",
+    "plt.text(max_gdp - 20_000, min_life_sat + 1.9,\n",
+    "         fr\"$\\theta_0 = {t0:.2f}$\", color=\"b\")\n",
+    "plt.text(max_gdp - 20_000, min_life_sat + 1.3,\n",
+    "         fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\", color=\"b\")\n",
+    "\n",
+    "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
+    "\n",
+    "save_fig('best_fit_model_plot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "37655.1803457421"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cyprus_gdp_per_capita = gdp_per_capita[gdppc_col].loc[\"Cyprus\"]\n",
+    "cyprus_gdp_per_capita"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.301656332738056"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cyprus_predicted_life_satisfaction = lin1.predict([[cyprus_gdp_per_capita]])[0, 0]\n",
+    "cyprus_predicted_life_satisfaction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ceAZHHTUiU6bnHQkdoojcEZwq8AcR2Rx1uRAYgq9UcRoQ9TGcJpXxzGgR1WeereCjDzsDnWncbiO7DfmSNvuuYfbtB9FhN7u3uLiiRhkNmWQtxAOCVwH2A7ZHXduOaRvenAG7HCcumZxNzVQ4TW1tDntfovHMiIjq1KkmoRURUR0wqIJO3/2Cwj5f0Xj3TQC0KmrEim+37HSIuxoJHWJEKVtEHgIuVtUNWbHKceKQ6e5sJsJpamtzqvdFj2fGE1E96qhKEdXC5uUMu+FLtpZVft5chw7lmrDbkF4F7FY9UUS6i0in9JrkOLGJ7s5u3LaDrWUVXPH0h6wrTbCvZIpEup9NGxfQqqgRTRsX1CmcprY21+a+khLbn+Xoo21LgVNOMSWZ8eOtdbhunTnIc84xRel0f9aGQNhJlUeBfwDVt2b5HnAKcFQ6jXLqD9kUN8jW6pB0htPU1uYw90WLqE6dCrODHYr22ovQIqr5EjqUL4R1iIOpKf0F8Bbgguq7KNmejc3m6pB0hdPU1uZ493Vu1Yw33qgUTYgWUb3+enOC++2XmmhCPoQO5Qthu8yNiB101DROutPAyUb3tTr1sYtXW5uj72shRZQt7EK3uSPot1cRI0fCXXfBPvvYzoAlJTBrFlx1Fey/f/4pyNQnwrYQZwE/DY5oLgB2wc0PnVyJG9THLl5tbF61ClbP7kav2Z15Y3oB27YJW9pacHSuRVQbMmEd4mTgdRHpD7wepB0JHIhtQO/sYuRS3KA+dvHC2Pz55/D44z24+upoEdVCfvITmxg5/PDYIqq7qkhtJggrEDtTRA4Ffg6cGCR/AJyvqnMzZZyTv+SbuEF9pLzcurpVRVT3TElEtb6tqsl3UhGInQucXtuKRGQfbKY6wh7Ar1X1ttqW6eSW+th9zTVbthBTRHXUKJsZ7tDhv/zwh4eGKqs+rqrJd0I7xAgi0hnbrH4nqro02X2qOg8YGJRRCJQAz6Zav5Nf1Mfua7ZZu9Z2lJs2zeICN2+G3XYz8dTqIqrFxbEnpWJ1i3dVkdpMEsohikhr4A7gh1RzhgGpqt2MBhZWkxhznAbDwoWVXeG33zZNwW7dqJWIarxucT6K1NZ3wrYQbwYGAOOBZ4BzgG7YNqSX1aLeCcDjtbjPcfKSiIhqZJP1Tz6x9AMOgKuvNic4aFDqITHJusU+jpteJIbma81MIsuBU1X1LRHZABykqgtE5FTgHFUdE7pCkSbACqCfqq6KcX0SMAmgU6dOg5544omkZZaWltKyZcuwJmQctycx+WRPXWzZvl2YM6ctM2a045132rN2bREFBUr//t9w6GFrOXjoanp2305hQXgvWN2eLWXlLFqzifKo32mhCH06tKBZY+uYlVco28sraFJYkFJdtbEn12zcuJFGTZvX+bOOGjXqPVUdXD09rEMsBfZX1aUisgw4SVVniUhv4BNVbRHWEBEZB1ygqkmX+w0ePFhnR9YjJaC4uJiRI0eGNSHjuD2JySd7UrVl/XoTS4gWUW3RwtYPjxtnmyu9vaz2M7/V7VlXuo1hN7xeRYChaeMCZlx5ZFZagvn0XU2bU8LyT9/j3nlFdZ5RF5GYDjHsSpWF2KwwwGfABBERLATn6xRtORXvLjv1iCVL4M47YfRoE0U4/XST05owwSZL1q6Fp56CM84AitK7gqc+rs7JBJGhgwrVjK6MCjuGOAXoDxQDfwSeBy7EHOrFYSsTkRbAGOAnqRjp7JrkKuA4WkR12jQ7B1sjfPnl1hIcMgQKYjQnMjHz6+FNlc81mkzMqIcNzL416vx1EdkXE3z4QlU/CluZqm4C2iXN6OzyZDvguKyMnaIJ0SKqhx0GN91kTnDvvZOXk6mZ3109vClbM+pxu8wiUi4iHYPzB0Vk58pJVV2qqs+k4gwdJyzZEo7YsAGmT+/AxImmHzhmDPz1rzBwoL2uXGkhM5dfHs4ZgndxM0XkuRaIZPS5JmohbsE2kVoNnAlcCWxMa+2OE4NMBhyXlFgLcNo0eP11KCvrR/v2cOKJ1gocMwaaN6+b/d7FzQxjB3bjP1/P59HhB2bsuSZyiO8AU0XkPWxPlTtEZEusjKp6Ttotc3ZZ0tk9UoWPP64cD4wWUf3Zz6Bnzw+44IIDE4qo1oZdvYubKQoLhAE92mSs/EQO8QzgcmAvbNe9dkDmxO4cJ6CuAcc7dsCMGTVFVIcOrSmiWlz8bdqdoVN/iesQg6DpnwOIyCIsMHtdtgxzdm1S7XZu2gT//rc5wBdesP1DmjSxUJkrr4QTToAuXbJkvFNvCTvL3Kd6mog0VtWy9JvkOEaybueqVaYYM20avPoqbNsGbV1E1akDYcUdfgaUqOrTwfu/AmeKyEJgbKBk4zgZ5/PPK7vCERHV3r3hvPPMCcYTUXWcMIQNzP4ZJuiAiIzAVG9OA34A3AIcnxHrnF2eiIhqRDRh/nxLT0VE1XHCEtYhdgMWBecnAP9U1SdF5CNs5z3HSRuxRVSVwYfu4IafwKknN6ZHj1xb6TREwjrEDUBHYBm29C6y9WgZtvOe49SJRCKq3Qd+zbR171HavIIH11ew77r+9OjhMvlO+gnrEF8B/iIi72NhOC8F6f2obDk6TkpERFSnTrUwmVgiqhu3b2PYDbMoK6ygLAj6cpl8J1OEdYgXANcBPTHpr4jCzUG4co0TkooKC4x+4IE+XHhhpYhq//4webI5wYMOqjoeuHyVy+Q72SNs2M0G4KIY6dek3SKnQbFtmy2Ri4wHrlgBBQU9GTECbr3VnGCfGkFdlbhMvpNN4jpEEdk90hIUkd0TFRLVYnScKiKqL70EpaVVRVRbt57B2LGHhyrLZfKdbJKohbhGRLqo6mpgLbZ8rzoSpPvip12cJUsq4wPffNOWz3XuDKedZk7wyCOhaTD9Vly8I6WyXSzByRaJHOKRVKphH0lsh+g0AEIJsT72mA30LV0KPXui113HnP0n7owPnDvXsoURUa0NLpbgZINEa5nfiDovzoo1TtYJJcT62GMwaZLFwgAsWcKWMyZxk8ITMpFhw1ITUXWcfCXs0r1yINJ9jk5vB6xWVe8y10OSbXEJJqJaeMlkWkScYUBz3cz97SZz26cT6dgx66Y7TkYIG3YTb2FUEbA9TbY4WSaeEOt7n21l4eyinSKqW8uWxry/5ddLaenO0GlAJHSIInJpcKrAecF2pBEKgeHA5xmyzckwkZAWVShb24rNX3Ri1YJOfO93rQETUb34Ytj+SE+arlpSs4CePbNsseNklmQtxEjsoQA/Bsqjrm0HFgPnpd8sJ9Ps2AEfv1dE30XDefmFAsq+Md38vgds56yLqoqoMvC6qmOIYDr7112XG+MdJ0MkdIgRHUQRmQ6cqKrrs2LVLkwmt96MFlF9/nn4+msoKmrJEUeUc8iozUw4qZB+e8Woc+JEe73kElNe7dnTnGEk3XEaCGFXqozKtCFOZrbeXLkSnn++CzffbAoy0SKq48ebiGrLloVAkp2VJk50B+g0eMJOqiAifYGTsPXMTaKv+SZTdSfMjG9Yaoqo7pMeEdVly+zVtbecBkrYsJvjgKeBD4BBwP+APbFZ5tB6iCLSBngA+A42UXOOqv43NZMbJnXZerO83BxfxAlGRFQHDYLf/ha6dv0f55xzcN1FVM84w16Li+tYkOPkJ2FbiL8DfquqfxCRjdiOfCuAR4BUHNrtwMuqepKINCFpP23XIVURg9giqjBqlG2vOXZsZUOuuHhT1hSlMzkG6jiZJqxD3Af4R3BeBjRX1a0i8jvgBeBPyQoQkdbACOAsAFXdjscw7iSMiEE8EdVjj7Wu8DHHQOvWufsMmRgDdZxsEtYhbqRSGfsrTCT24+D+tiHL6AOsAR4SkQHAe8DFqropvLkNm1giBgsWVHaFIyKq3bubiOr48XDEEbbdZq5J5xio4+QKUU2u2SAiU4EXVfV+EbkR21zqb8D3saV7R4UoYzAwEximqrNE5HZgg6r+qlq+ScAkgE6dOg164oknktpXWlpKy5Ytk+bLBuUVyqZNpbRo0ZLCgtT7qRUVMG9eK2bMaM+MGe1ZvLgFAHvsUcqwYWsZNmwtffuWptQFDvt8yiuULWUWatqscWEN+wdecgkAc267rca9W8rKWbRmE+VRf0+FIvTp0IJmjauu7Myn7yufbAG3JxnpsmfUqFHvqerg6ulhHeIeQEtV/VBEmmM77Q0D5gOXqmrstV1Vy+gMzFTV3sH74cAvVPW4ePcMHjxYZ8+endS+4uJiRo4cmTRfpol0GX+2Xxl3fNY4dJcxWkT1X/+Cr76CwkIYMcK6wmPHJhZRTUaY5zNtTgmXPTmHHcEwZuNC4ZaTB1S1/7nn7PWEE2rcv650G8NueJ2tZZXjoE0bFzDjyiNrtBDz5fuC/LIF3J5kpMseEYnpEMPGIX4Zdb4Z+GmqBqjqShFZJiL7BPs4jwY+TbWcfCW6y1iuytayioRdxvXr4YUXzAm+/HJNEdXjjoPdE8ryptf2K56au9MZApSVKz9/qpr9MRxhBBdydRoCYcNuOgCo6prg/QHAKcAnqprKnioXAY8FM8xfAmenZm7dyOQMaJiwmWgR1TfesHCZeCKq2WT5+i0USgFVV2ZCYYFUDfuZN89e99knZjku5OrUd8JOqjyJhdg8KCLtgTexsJuLRKSrqt4SphBVnQPUaKZmg0zPgMYKm9leXsG6Jc255oGaIqpXXGFO8OCD0yeiWlu6t21GuVbUSC+v0KphPz/5ib0miEN0IVenPhP2p9gfmxABW62yQFX7AT8CfpIJw9JJdHd247YdO7uz60q3pa2OSJexqKCQLz7pwLf/6cfXfx3D6OFN+P3voVUrE1GdPx8+/RSuvx6GDs29M4zYftNJA2gUZUvjQuGmk7zL6+xahG0hNgMi0l/fBf4VnL8P5P06rrqsAgnDhg22mdK0ad1Y+WJX7vxWaNZMOeIoYdw4OP546NChztWkhXjDBpHu7icrNgBKv66t3Rk6uxxhHeIXwIki8jRwFHBTkN4J+CYDdqWVTGxlWVJiM8JTp8L06VBWBu3bww9OFPbY4yMuvfQAmufZOpxkwwbtWhYxom+eeG7HyQFhO2y/BW7A9A9nquqsIP172PrmvCbSnW3auIBWRY1o2rgg5RlQVfjoI7j2Whv3694dzj8fFi0yEdW33jJlmQcfhMMPX5d3zjAbwwaOU98JG3bzjIj0BLoCc6MuvYaJPuQ9tZkB3bED3n67cmZ40SJLHzrUxgDHj4d99yVr64TrQlqGDX75ywxZ5zj5QWj5L1VdBayqljYrTva8JMwMaGmprROuKqIKo0fDL35hoXhdutSu/lwKH6Rl2OC7302zVY6TX4R2iA2ZlSttEca0aVVFVI8/3kJjTES1bnXkWvggLYHTc+bY68CBmTDRcXLOLusQP/+cnZusz5plY4TRIqrDh5ucVjrIF+GDOgdOB2uZXQ/RaajsMg4xmYjquHFwwAGxxwPr2tXNdNhPKnjgtOPEp0E7xC1b4NVXK0VU16wx6fyRI2uKqMYjHV3dTIT9OI6TflLZU6UTppS9J/ArVV0rIsOAFaq6KFMGpkq0iOq//21OsbYiqunq6rrwgePUD8KKOwwC/gMsAvphgdlrgTFAX+C0TBkYhpKSZtxyS00R1XPOMSeYTEQ1Xpc4nV3dsON3LsHvOLkjbAvxZuB2Vb0m2FMlwr/JsmINmMObPbtyUuTTT4cC0L8/TJ5sTvCgg8LFBybqEqe7q5ts/C7XM9FJuf76XFvgOBklrEMcBPxfjPSvsOV7GSeRiOqoUV9w2WV7pyyimqxLnM2ubr7MRCfksMNybYHjZJSwDnELsfdO2RdYnT5zqlJeDo8+GltEdfx4GxfcfXcoLi6hT5+9Uy4/TJc4Wxp/+TQTHZd33rFXd4xOAyWsQ5wGXCMiJwfvVUR6Y+ubM7Z0b84c2wo4UyKqYbvE2QhVqRcz0Vdfba8eh+g0UMKKO1wO7I7tmtcceBtYgCndZGyBa+fOFjtYUgL33WctwnQqSqdD9KEh2uI4uyphxR02AIeLyJHAQZgjfV9VX8ukcd26mZBCJskn2ft8ssVxdkXithBFpFxEOgbnD4pIK1V9XVVvVtUbM+0Ms0m7lkUM6NEmLxxQPtmSd5x1VqXizltvxd3bJSnnnQe//33azHIaDom6zFuAiKTBmVRuVO848endG5o1MzWMTp3MiZWWJrsrdYYPr9z0KhFTpsDhh1dNu/de+NWvYmbPGe+/DyNGMPyYY+y53X577HyffgqDB5v6SNu2pkD0adTmlapw5ZXQrp0dV15paU4oEnWZ3wGmish7gAB3iMiWWBlV9ZxMGOfkGTE2qI/Jc8/ZD7WkxKSCrr0W/vjHqnl27EifekZ9Z+1aC5249Vbe/uUvOeLQQ2H58th5u3aFp56CXr0sIPeuu2DCBPjwQ7t+//0WoDt3rgXijhljm3qfd17WPk59JlEL8Qws8LoNoEA7oEOcw9kVGDgwNemvbt1sreTHH9t7EbjrLoacfjrsHYRJPf+8ldmmjYXzRH7YAB98YBH2rVrBKafA1q2V14qLbTlShGXL4MQTbfOadu3gwgvhs8/MEfz3v9ZibdPG8kZ3vQH+8hfYay+L4Ro7FlasqLwmYi3Kvfe2+y+4IP0trj/9yf5xTJyINmlin3e//WLnbdPGWuEiZkdhISxYUHn94Yfhssvs2XTrZudTpqTX3gZMXIeoqqtU9eeq+n1gKXCqqp4Q68ieuU5Oee01O8KybBm8+CIceGBl2tSpvH/33dbN++ADW195332wbp1tczp2rEXhb99uwaZnnGEqvSefDE/HifAqLzfxyl69YPFia5lOmGBO5d574dBDrdv+zTc1bm3z/vtw1VXw5JMW7d+rl90bzfPPw//+Z876ySdtkXws/v53c1jxjqVLY983c6Y548MO47Dvf99UiOPl3Wl4Gwu5uOiiynAogE8+gQEDKt8PGGBpTijCzjKnuAbEaZBce629JlPOHj/eusOtW8Nxx1X9wV51FTsKCmyc8f77zQlGQgnOPNOWB86caS2gsjLTYBSBk06yllQs3n3XWnU33VTZDa8+bhiHTq+9Zk75oIMs4Q9/sLG5xYutJQYmlR5xaqNGWYDs0UfXLOy00+xIleXLbQzx1VeZuX49I55/Hk491Rbmx+Obb2DTJmsR9upVmV5aWlW9pHVrS1OtH3td5Ji4DlFELgXuVtWtwXlcVDXOX2qNMhcDG4FyYIeq5mTTeifDTJ0a32n26GEtOIAlS+wHfeedlde3bzfnJmJdvugfcfQPP5ply+xaLcYkm6xbV7Xcli2ty11SUukQO3euvN68efoniZo1g+9/Hw4+mIriYrjmGtvC8dtvE0sztWhhQwIdOtjwQMeOZv+GDZV5NmywNHeGoUj0F3QR8DCwNTiPhwKhHGLAKFVdm0J+pyER/cPs0cPUOCZPrpnvjTfMKUW3bJYuhT33rJm3Rw+7FmuiJokj2N6unTnmCJs2Wfe9Wy1ENR57zFq88fj0U+jZs2Z6//5V7UzFeVVUwObN9qw6doR+/WxCZcgQuz53rqU5oUg0hthHVddFncc79sieuU6D4txzbYwvsofDpk3wwguwcaON+zVqBHfcYV3nZ56xrnEshgyxnb9+8QsrY+vWyu5mp07WJd2+Peatq0ePhocesm7wtm3WvR86tLJ1mAoTJ1rrMd4RyxkCnH02PPsszJmD7NhhMZKHHx67dfjqqzb2Wl5urb9LL7UufmQS5kc/sqGFkhJrad9yi00iOaGoU9yDiPQCblLVH4a8RYFXRESB+1T1/hhlTgImAXTq1IniEOtmS0tLQ+XLFg3VnoHBpMScBGUdsnUr8+bOZX2M7utIYNasWZS2br3Tnt0vuojeZ55J8+XLKS8q4tsDDmCeCOXNm9Nq8mT63nwzza66iq+HDoXDD2fLkiUsKi6mzZw57LdtG/8Nyim6+mr2vvNOWnftCsCq0aNZUFaGFBbync6d2a19exBhxrRp7LtyJdvKy1lUXEzpPvvQ7Iwz6HHccTTauJEN/fox/9JL2RaUG7F5SxAGE31v2igooOtZZ9FrzBgO3bKFtf3788Ull+y04eCzzmLJxImsHjOGDjNm0OfBBylas4byoiI27rsvX/7+92yaOdPK2mcf9hgwgC6Bg/zq2GP5cp99ar3+vKH+LcdFVWt9AAOA8hTydwteO2L7O49IlH/QoEEahunTp4fKly0arD2ff25HHcmn55NPtqi6PclIlz3AbI3hc7IaGauqJcHrahF5FhgCvJlNG5w6UNulco5TTwirdlNnRKSFiLSKnANHAR9nq34nDTz3nB2O00DJZguxE/Cs2AxaI+DvqvpyFut36sott9jrCR6L7zRMEjpEEflXkvt3C1uRqn6JjTk6juPkJclaiOtCXM+bLUgdx3HqQkKHqKpZ31HPcRwnV2RtUsVxHCffcUE6JzyPPJJrCxwno7hDdMLTo0euLXCcjOJdZic8//iHHY7TQPEWohOee+6x11NOya0djpMhvIXoOI4T4A7RcRwnwB2i4zhOgDtEx3GcAJ9UccLz1FO5tsBxMoo7RCc87dvn2gLHySjeZXbCM2WKb3ruNGjcITrhcYfoNHDcITqO4wS4Q3Qcxwlwh+g4jhPgDtFxHCfAw26c8Lz4Yq4tcJyM4g7RCU/z5rm2wHEyineZnfDcfbcdjtNAcYfohOfJJ+1wnAZK1h2iiBSKyAci8ny263Ycx0lELlqIFwOf5aBex3GchGTVIYpId+A44IFs1us4jhOGbLcQbwOuACqyXK/jOE5SshZ2IyLHA6tV9T0RGZkg3yRgUvC2VETmhSi+PbC2zkamj4Ztj0hdS8in55NPtoDbk4x02dMrVqKoahrKTo6I/AE4A9gBNAV2A55R1dPTUPZsVR1c13LShduTmHyyJ59sAbcnGZm2J2tdZlW9SlW7q2pvYALwejqcoeM4TrrwOETHcZyAnCzdU9VioDiNRd6fxrLSgduTmHyyJ59sAbcnGRm1J2tjiI7jOPmOd5kdx3EiqGpeHEAPYDrwKfAJcHGQ/hugBJgTHMdG3XMVsACYB3wvKv3oIG0B8Iuo9D7ArCD9H0CTBPY0Bd4F5gb2/DZRGUBR8H5BcL13be1M0Z4pwKKo5zMwSBfgjqDsD4GDoso6E/giOM6MSh8EfBTccwdBDyKBTYXAB8DzuXw2CezJ5bNZHOSfA8wO0nYHXg3KfhVom2N7fkMOfltB/jbAU8Dn2Mq1Q3P5fHbeVxcnls4D6BL5oEArYD6wf/ClXR4j//6YcygKvoyF2A+iMDjfA2gS5Nk/uOdJYEJwfi/w0wT2CNAyOG8cfNmHxCsDOB+4NzifAPyjtnamaM8U4KQY+Y8FXgruOwSYFfWj/DJ4bRucR/7w3g3ySnDvMUm+s0uBv1PpgHLybBLYk8tnsxhoXy3tRgInAvwCuCHH9vyGHPy2gjwPAz8OzptgDjJnzydy5E2XWVW/UtX3g/ON2H+NbgluGQc8oarbVHUR9p9gSHAsUNUvVXU78AQwTkQEOBL7rwT2hYxPYI+qamnwtnFwaIIyxgXvCa6PDupMyc5a2JPo+fwtuG8m0EZEugDfA15V1a9VdT32n/jo4NpuqjpT7S/qb4meT/VlmEmeb0afTSx7kpDRZ5Ok3shzqP58cmFPIjsz9tsSkdbACOCvAKq6XVW/IQ+eT944xGhEpDdwINYKArhQRD4UkQdFpG2Q1g1YFnXb8iAtXno74BtV3VEtPZEdhSIyB1iNPeyFCcrYWW9w/dugzlTtDG2Pqkaez3XB87lVRIqq2xOy3m7BeVh7bqPqMsxEzzfjzyaGPRFy8WzA/lm9IiLvBauvADqp6lfB+UqgU47tgdz8tvoAa4CHAuWrB0SkBbl9PkAeOkQRaQk8DVyiqhuAe4A9gYHAV8At2bJFVctVdSDQHfvvuG+26g5jj4h8Bxvr2Rc4GOs6XJlpO6KXYWa6rjAksCfrzyaKw1X1IOAY4AIRGRF9MWi5ZDPEI5Y9ufptNQIOAu5R1QOBTVgXeSc5eD5AnjlEEWmMOcPHVPUZAFVdFTiCCuAvmGMCGwzuEXV79yAtXvo6rKndqFp6UoLm/HRs4DdeGTvrDa63DupM1c5U7Dk6GGpQVd0GPETtn09JcB7GnmHAWBFZjHWbjgRuJ3fPpoY9IvJojp4NAKpaEryuBp4N6l4VdOcIXlfn0p4c/raWA8ujejhPYQ4yZ89nJ8kGGbN1YIOffwNuq5beJer8/2FjGwD9qDrw+yU26NsoOO9D5cBvv+Cef1J14Pf8BPZ0ANoE582At4Dj45UBXEDViYMna2tnivZ0iXp+twF/DN4fR9WB6He1ciB6ETYI3TY4311jD0QfG8+eKLtGUjmJkZNnk8CenDwboAXQKur8HWx29iaqThrcmGN7cvLbCvK8BewTnP8meDY5eT5V7Mqm00vygA7HmsgfEhUGADyCTZ9/CPyr2pc4GRvXm0fULFJw3/zg2uSo9D2CB7Ug+AKLEtjTHwvh+BD4GPh1ojKwsJh/BunvAnvU1s4U7Xk9eD4fA49SORMtwF1B2R8Bg6PKOiewcwFwdlT64KCchcCfCRGqQFUHlJNnk8CenDyb4DnMpTJEanKQ3g74DxYi8hqVP95c2ZOT31aQfyAwO6h7KubQcvJ8og9fqeI4jhOQV2OIjuM4ucQdouM4ToA7RMdxnAB3iI7jOAHuEB3HcQLcITq7LCLSW0RURDKyR4eINBaRedVXqWQbETlAREqC5XFOAtwh1gNEpFOwFvcLEdkqIqtF5B0RuShY6hjJtzj4gWuQb5mIPCsiJ8QoU6OOjSIyW0ROzO4nyznLMJWlOQAiMjJ4Hu3TVP4kYIWqvhmUH9cBi0ixiPw56v0AEZkmIiuD73KpiDwtIr2i8kR/h5tF5EsR+buIDI8uW1U/AmZiakBOAtwh5jmB0MX72MqCX2FLnIYC1wOjgbHVbvkd9iPvi60KWQw8G/1ji+LcIO/BWNDuP0Xk0LR/iASISJNs1heN2rK1lVopSpA2AgWYnxEouqR4bwcsQLkUW6WxL7Zj5UJst8poIt/hfsD/AduBN0Tk59XyPQT8NGp5nROLuqwu8SPzB7bsaBnQIs51iTpfTGx9u0nYKqBRUWlKlFYgJie2GfhDnHp6B/ecBrwNbMXEPY+qlm9/4AVgI7YW9XGgc9T1KcDzmNDCckyUId5nPwRbbbIJU8h5HegaXDsaW/61Hvga+DewXyr2RuUZHHUefUwJU1cc2wdjyjttYtUXI38x8OfgfDxQTnKR1SrfYVT69dh2v3tFpTUJnsF3c/03nc+HtxDzGBFph2m+3aWqm2Ll0eCvPQl/xX7MP4iXQVXLgDLMMSbiRkyBeCAmiTZNRLoF9nYB3sSWTA0Bvgu0DPJE/60dgS1FPBpr5dZARAZgAhYLMPGGQzAl5kgLpwW2PnkItlzvW+C5GC3OuPZWYxmVz6cf1uq6OMW6ohkOLFQT4kiVlVjv7aSgpZkqtwT3j48kqOkXzsGevRMHbz7nN3th6zjnRSeKyHJMYRjgUVU9L1EhqlouIvOx9aY1CHQCf451x/6TxKZ7VPXJ4L6LMYf9U+CXwetcVd0psyUiP8JaVYOxta5gLZVz1FRo4nEFMEdVo7X7Pov6TE9X+wxnAxswp/V2SHt3Ejyjr4O3q1V1bS3qiqYXsCLB54uLqs4UkesxkdS7ROR/WAvyMVVdEuL+dSKymprf9wqslerEwVuI9ZPhWIvnXUw4IQxCTX25R0SkFOsqX4p1t19KUs5/IydqslGzsG4y2D4WI0SkNHJQKeC5Z1QZHydxhmACwa/H/TAiewYTCAtFZAOwCvt77pmCvaFIoa5ommGOv1ao6mSgMzbc8RE2PvipiMRsUccym5rf95bALicO3kLMbxZgf9RVhGnVZN0Rkc1hChGRQmyS5d1ql34OvAxsUNPJqysF2Pjh5TGurYo6j9n9T5HnsTHIn2BadzuwDcoyMUlTm7rWYk49mg3Ba+sY+dtgXfGdqOo6TDnmnyJyFaZ29CuStOKDWfIOmFRXNLtj48xOHLyFmMcEP4hXMJn3lsnyJ+DHVO5yFs1KVV2QojM8JHISjG8NobIr+z42/rYkKDf62JiizR9gwrM1CMZW9wWuV9XXVPUzbGOyWP/gE9lbne3Ba2Et66pu/z7RY6eq+jXmKAdV+zy7YcMjVYZGognGABdiY7LJuAyb0JlaLf072HfkxMFbiPnP+cAM4D0R+Q0WHrMD+1ENwBxmNK1EpDM2OdIDOBm4CJvBfCMN9vw0GI/8KLCtFyZFD6ZZdy7wDxG5Ads3Yw/gh8BlKTrFm4CZInJ/UO5WbKjgFay1thY4V0SWYftl3IQ9l1Tsrc4SrEV+nIg8h3Ux16dQVzTTseGM/gRxjgF/An4hIiuw7nw7rNW3BmsNRrZEmICpf8/Hur8nYFqE11Srp03wfTfBhiXOBH4EXKGqCyOZgvCtbtT8e3GiyfU0tx/JD2ws6XasC70Ni0/7H7ZnSKuofIupDBnZhjmOqcDYGGXGDNlIYEPv4J6JmOLyVqqJhwb59sZaousxhzIPuJPKPZqnEAi4hqjzcGzWegvwDSYaGlHBPhKbzd4avH4veC5nhbWXGGEwmHP6CmthTQlTVwL7HwduqpZWiP2D+jAoYznm+HpH5dkDU53+nMqQoznAJVQNs4oOEdqKKUY/DoyIYctVwMu5/lvO98MFYp1QBC2MRcDBqjo7x+YkJR/sFZF+WEtxL7UN03JCEEXwBXCqqs7IlR31AR9DdJwMoaqfYBNMfXJsSi/gOneGyfExRMfJIKr6tzywYT42FukkwbvMjuM4Ad5ldhzHCXCH6DiOE+AO0XEcJ8AdouM4ToA7RMdxnAB3iI7jOAH/HwdtuzKyRlLQAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 360x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
+    "                   x=gdppc_col, y=lifesat_col)\n",
+    "\n",
+    "X = np.linspace(min_gdp, max_gdp, 1000)\n",
+    "plt.plot(X, t0 + t1 * X, \"b\")\n",
+    "\n",
+    "plt.text(min_gdp + 22_000, max_life_sat - 1.1,\n",
+    "         fr\"$\\theta_0 = {t0:.2f}$\", color=\"b\")\n",
+    "plt.text(min_gdp + 22_000, max_life_sat - 0.6,\n",
+    "         fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\", color=\"b\")\n",
+    "\n",
+    "plt.plot([cyprus_gdp_per_capita, cyprus_gdp_per_capita],\n",
+    "         [min_life_sat, cyprus_predicted_life_satisfaction], \"r--\")\n",
+    "plt.text(cyprus_gdp_per_capita + 1000, 5.0,\n",
+    "         fr\"Prediction = {cyprus_predicted_life_satisfaction:.2f}\", color=\"r\")\n",
+    "plt.plot(cyprus_gdp_per_capita, cyprus_predicted_life_satisfaction, \"ro\")\n",
+    "\n",
+    "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Using KNN**\n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.333333333333333"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#With KNN\n",
+    "knn_model = KNeighborsRegressor(n_neighbors=3)\n",
+    "\n",
+    "# Train the model\n",
+    "knn_model.fit(X_sample, y_sample)\n",
+    "\n",
+    "# Make a prediction for Cyprus\n",
+    "knn_model.predict([[cyprus_gdp_per_capita]])[0, 0]\n",
+    "\n",
+    "#print(model.predict(X_new)) # outputs [[6.33333333]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Partial conclusion\n",
+    "\n",
+    "The model makes good prediction. Otherwise, you may need to **use more attributes** (employment rate, meteo, health, pollution, etc.), **get more or better quality training data**, or perhaps **select a more powerful model** (in that case, a polynomial regression model). \n",
+    "\n",
+    "\n",
+    "## In summary ## \n",
+    "\n",
+    "Study the data -> Select the model -> Train the model on the training data -> Apply it to make predictions on new cases (aka _inference_) hoping that the model will generalize well. \n",
+    "\n",
+    "## Now let's look at what can go wrong in learning and prevent making accurate predictions. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's consider more data !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GDP per capita (USD)</th>\n",
+       "      <th>Life satisfaction</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>South Africa</th>\n",
+       "      <td>11466.189672</td>\n",
+       "      <td>4.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colombia</th>\n",
+       "      <td>13441.492952</td>\n",
+       "      <td>6.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Brazil</th>\n",
+       "      <td>14063.982505</td>\n",
+       "      <td>6.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mexico</th>\n",
+       "      <td>17887.750736</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chile</th>\n",
+       "      <td>23324.524751</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Norway</th>\n",
+       "      <td>63585.903514</td>\n",
+       "      <td>7.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Switzerland</th>\n",
+       "      <td>68393.306004</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ireland</th>\n",
+       "      <td>89688.956958</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Luxembourg</th>\n",
+       "      <td>110261.157353</td>\n",
+       "      <td>6.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              GDP per capita (USD)  Life satisfaction\n",
+       "Country                                              \n",
+       "South Africa          11466.189672                4.7\n",
+       "Colombia              13441.492952                6.3\n",
+       "Brazil                14063.982505                6.4\n",
+       "Mexico                17887.750736                6.5\n",
+       "Chile                 23324.524751                6.5\n",
+       "Norway                63585.903514                7.6\n",
+       "Switzerland           68393.306004                7.5\n",
+       "Ireland               89688.956958                7.0\n",
+       "Luxembourg           110261.157353                6.9"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_data = full_country_stats[(full_country_stats[gdppc_col] < min_gdp) |\n",
+    "                                  (full_country_stats[gdppc_col] > max_gdp)]\n",
+    "missing_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "position_text_missing_countries = {\n",
+    "    \"South Africa\": (20_000, 4.2),\n",
+    "    \"Colombia\": (6_000, 8.2),\n",
+    "    \"Brazil\": (18_000, 7.8),\n",
+    "    \"Mexico\": (24_000, 7.4),\n",
+    "    \"Chile\": (30_000, 7.0),\n",
+    "    \"Norway\": (51_000, 6.2),\n",
+    "    \"Switzerland\": (62_000, 5.7),\n",
+    "    \"Ireland\": (81_000, 5.2),\n",
+    "    \"Luxembourg\": (92_000, 4.7),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UFZGUlBROnTpl0Sk3NTXV2M7UKdegtCQlJTF8+PBS75TraKydnWrAaQvnT6OFdFuFlPIV4BVr25dXatSoAZBv1eySRErJDz/8wPXr1wkPD+fHH3/M0+bmzZv4+Pjg7u7On3/+yZo1a+jZsycA//d//8fOnTvZu3dvuVvKVJQPLl92IzAQ5s2Dxx6DIUNg8GCw02KoogIjpeTy5cv5OuUaXDyEEAQFBaHT6ejatauZmahWrVp5vmRGRkYqZcYKrJ2hQ2g5ZJ7JdX4KKoNwoalbty4AcXFxdhuzf//+xjoYgYGBrFixgiZNmlhs+8knnzB9+nQmTZpE586dGTp0KImJiQCsXbuWM2fOmFX8fvHFF3nxxRft8RgKhUWio+HUKejZE2rVSmfIEGjcWLumPgcUtiYzM5MzZ85YVFwM/ysBPD09CQsLo3379kyYMMGotDRq1AgPDw/HPUA5xdo/9ZnAz0KI7sAe/bl2gB/QuyQEK88YUj4fOHDALuOdPXs232vLly/PE6lgGsmUm4KiGkzHMYR5KxT24Jln4MgRTbERAj76yNESKcoDSUlJFpWWqKgoM6fcunXrotPpGDFihNlqS/369dVKth2xNg/Nr0KIULQVGkNivXXAJ1JKlRyviPz999+OFkGhKJP8/TfMnq0lwfPxgQ8+gCpVQOWEVBQWg1OuJcXl4sWLxnYuLi40atSI8PBwBg4caOaUW61aNQc+gcKA1YuxesVldgnKUqFwc3MzS3SkUCgKJi1N26pX18xIx45BVBTccw+EhTlaOkVpJzU1NV+n3JSUFGO76tWrEx4eTu/evc1WW4KDg+0WlaooGvkqNEKIlsBBKWWOfj9fpJRqqaGQNG3alL/++svRYpQpVq9ezYoVKwrMmaPIn6vJ6Zy/nkp9bw9qVHFztDhm3Em2tDQtf8ygQbBgATRrBmfO2HBFxtcXTEJkjdSpAybf0hWlGyklV65csbjacvbsWTOn3MDAQHQ6HZ07dzZTXGrXrl1mM+VWdApaodkP+AKX9fsSLaFebiSgFnoLSZs2bfjrr7+QUparP56goCDi4+OJj4+nZs2axvN33303Bw8eJDo6mqCgoCL1PWrUKEaNGmUjSSsWGw7G8fx3/+Dq5ERmTg7vDGrGgBb1HC0WkL9sZ87Azp0wYQK4u8O0adDS5KuVTc1LlpSZgs4rHEpWVla+TrnXr183tvPw8CAsLIy2bdsybtw4M6dcT09PBz6BoiQoSKEJBq6Y7CtsSGhoKKBlgvT29nawNLYlODiYtWvXMnnyZAAOHz5stqSrsC9Xk9N5/rt/SMvMIY0cAGZ+9w8dQmo6fKWmINkWL3Zj0SJ46CHw9oapUx0qqsIBJCUlceLECYtOuZmZtxPU+/r6otPpGDZsmNlqi7+/v3LKrUDk+5uWUp6Tt+siSCBGf85so3DVthV6DKHbFy5ccLAktmfMmDGsXLnSeLxixQrGjh1rPE5PT+e5554jICCAOnXq8OSTTxoTS/Xp04fp06cb2w4fPpxHHnkE0CKy7rvvPuO1o0eP0qNHD3x8fKhTpw7/+c9/jP1HRETg5+eHn58fERERpKenl+gzl2bOX0/FNdc/dVcnJ85fT83njsJxNTmdQ7GJXE0u/BybypZ5zZOLq9uRdbE656+nMnOmFopdpvV9X18t7Cr35uvraMlKDTk5OcTExPDLL7/w4Ycf8vTTT3P//ffj5+dH9erVjasr7777Lv/++y9hYWFMnz6d5cuXs2fPHq5fv86FCxfYsWMHixcvZsqUKfTq1YvAwEClzFQwrHUKjgbqopmfjAghauivKZNTITFVaBobEmbYgCtXrrBlyxZGjhzpsD/mdu3asWrVKv79919CQ0P56quv+O2335gzZw4As2bN4vTp0xw8eBBXV1dGjhzJa6+9xptvvsnnn39Os2bN6Nu3LxcuXODPP//k0KFDeca4efMm3bt357nnnmPjxo1kZmYaSzm88cYb7Nmzh4MHDyKE4MEHH2TevHm8/vrrdp2H0kJ9bw8yc3LMzmXm5FDfu/h5MIpryqrp4UHK9UrgmYVz5Qxy0l1JS3bR+9IUWzzHo0xZRtLS0syccg2p/nM75VarVo3w8HB69eplttrSoEED5ZSrKBBrFRqB5ZWYKoDVFbcVtympFZqpU6eyevVqevfubcxI7AgMqzSdO3cmPDycevW0DzkpJUuXLuWff/7Bx8cH0BLzjRw5kjfffBNfX18WL17MuHHjSE1NZf369Xh5eeXp/8cff8TX19e4muPu7k7btm0BzXn4o48+MtaoeuWVV3jiiScqrEJTo4ob7wxqxsxcikdxzU3FNWVJCQN6uVHFpT3pPXfg6gYej//Gu4OLL1tRuIhW4+V/dh+5/CClJCEhwaJvS3R0NKbFkA2ZcpVTrsJWFKjQCCE+1O9K4E0hhKkjhDNwDypTcJEwKDS2LH+QmJjI6tWradu2rUOVGdAUmk6dOhEdHW1mbrpy5QopKSm0atXKeE5KSXZ2tvG4f//+TJ48mbCwMDMTkymxsbE0bNjQ4rX4+HgCAwONx4GBgQ4pM+EI8osWGtCiHh1Cato0yslgLjIoM3DblJW7f4NcWYke/LzejTlzNMvLnDlQvbo7zdrc79gIrDp1mHTpEoctnFfkJSsri/Pnz7Nx48Y8isu1a9eM7dzd3QkLC6NNmzaMGTPGqLSEhoYqp1yFzbnTCs1d+p8CCAcyTK5loBWafK8E5Cr3GFYdjh49arM+P/xQ0z8XL15ssz6LSmBgIMHBwfz888989tlnxvM1a9bEw8ODo0ePGldtcjN79mzCw8OJjo5m7dq1jBgxIk8bf39/vvrqK4v3+/n5ce7cOWNph5iYGLNSDeWVO5l/alRxs6myYK0pa8PBOGZ++w+VnJ1IOFCXhE13MXCgoGlTGDjQ0Mq2shWW3d9+y3cdO9K8eXM4eNBhcpQ2bty4YdEp99SpU2ZOuXXq1EGn0zFkyBCz1ZaAgADlx6KwGwUqNFLKrgBCiC+AKVLKG3aRqgKxf/9+m/STkZHBK6+8QtWqVbn77rtt0mdx+eyzz7h+/TqVK1c2pgl3cnJi4sSJTJ06lY8//pjatWsTFxfHkSNH6NWrF7/++itffPEFhw4d4syZMwwcOJBOnTrlUX769evHtGnTWLBgAU899RQZGRkcO3aMtm3bMmLECObNm0ebNm0QQvDaa68xevRoR0yB3XBEJJM1pqyTZ9MZ0d8Dz+Z1qdI0jkph5wlskEDdoA5A6ciFk5WVxfjx44Hb0YcVCSkl58+ft2gmMl3ZdHZ2JiQkBJ1OR//+/QF46KGHCAsLK3eRmoqyibU+NC8AVQEzhUYIUR/IlFJWPA83G2FwZC0uhtWK/FYtHEF+JqG3336b1157jXbt2pGQkEC9evV46qmnaN++PWPHjuXjjz+mXr161KtXj0cffZQJEyawZcsWsz68vLzYunUrU6ZM4dVXX8XNzY2IiAjatm3LnDlzuHHjBs2aNQNgyJAhRofkskBREuAVxvxTlPEKY8pKTdWik5o1gxSnVFw8shBOmlzCWeLhmWmVXPZi0aJFXLx4ESGETR30Ac1kpXcA7gGsAWoZztuZtLQ0oqKizBxyDU65t27dMrarWrUq4eHh9OjRI49TbqVKlYztIiMjadeund2fQ6HID2HqpJVvIyG2AV9LKT/Ndf5RYJiUsmdJCBcWFiZPnDhREl2XCsLDwzl+/DjW/A4KQkppXNbNyckptENdZGQkXbp0KZYMijtjzTwXNWroanI6Hd7+H2mZtxUad1cnfnv+/gIVB2vGK6xMgwbB3r1aJt+bGUWTqzgU5n2+fPkyDRo04NatW1SpUoVFixaZ+XzZipycHFxcXFi4cKExP1NJUZBTbo6JidCQKTf3VqdOHav+h6j/G/ZDzfVthBB/SSlbW7pm7QpNa7TClLnZBbxbVMEqOq1ateL48ePF7mf79u0AfPrppyo6oAxTHLNRUSKZrBnPmjYxMfD++/Daa1CtGjz/PKSkgKsr1KhUMhFWtiIiIsLoC+Ls7ExwcMnkEL1x4wZSShYvXmwThSYrK4uzZ89aVFyuXr1qbOfu7k5oaCitWrVi1KhRhIeHGzPlVq5cudhyKBSlCWsVGhcsG7zd8zmvsIKmTZsCcOvWrWL9czGUAxgzZoxN5CovzJ07l6ioKL788kuL15s0acKiRYvo0qXLHdtaQ3FrJRXHbASFj2SyZryC2lR1c8PVFa5cgSVLoHdveOABrVhkceSyF3v27GH9+vVkZGixDpmZmSWm0Fy/fh0PDw/Onj3LyZMnrfbVuXnzZr5OuQa5AWrXro1Op2PQoEF5nHKdVQlyRQXBWoVmL/CUfjPlGWCfTSWqQBgiby5cuEBISEiR+jhy5AiXL1/mhRdewM2tdHxQ2Js1a9Ywf/58jh8/jpeXFy1atGD27DsXhrdlhJktaiXZIgFeYSKZrBnPUpuMLMnTI6tybzt4911o1Qri46GgTAG2jrAqLtnZ2YwfP96YoRo0x3pDOgVbk5iYiKurK2lpaSxfvtyY1Ro0k3FcXJzF1Za4uDhjO2dnZxo2bIhOp6Nv375GpSUsLMyY00mhqMhYq9DMBv4nhGjG7bxT9wN3A91LQrCKgGlyvaIqNIbla9NyARWJ+fPn89Zbb7FkyRJ69epFpUqV2Lx5Mxs2bLDbkrqtIoxKKgFeccYztHnuqyNkxvvgFpjAO4PuYleGE2FhJn05Nu1RoVm6dCnnz583O1ejRo0SW80wFEzMyMjgk08+wcPDw7jycuLECZKTk41tvby8CA8Pp1u3bmarLQ0bNjRzylUoFOZYpdBIKfcIIdoDM4CH9acPAE9LKfPmpVdYRXGzBV+4cIHIyEgGDx5s10R6mzZt4uGHH+b06dMOze+SlJTEyy+/zBdffMHDDz9sPN+/f3/69+/P3LlzycjIYOzYsXz//fcEBASwYsUKWrfW/MmCgoJYtmwZ3bvn1cn37NnDtGnTOHbsGIGBgSxcuDBfp7zimopMsbd5xprxBrSox7YVdVi81plDxzNo3NCNAS20a8U1szmCa9euMWPGDLPIHoCAgACb9H/16tU8Ky379+/nxg0tSNTw3gYEBKDT6XjkkUfMFBdfX1/lC6dQFAFrV2jQKy7lO5mHnSmuQvPaa68B8NZbb9lMpjvxySef8Mwzmn+4u7u73ca1xB9//EFaWhoDb2dny8MPP/zAf//7X7744gvmzJnDpEmT2LNnT4H9xsXF0bdvX1atWsUDDzzA9u3bGTRoEMePH6dWrVp52tu6VpK9zTOWxrt0CV59FZ56Cu66C2ZMc2HQQxDe4HY7W5jZHEFKSgohISEcP36crKwsY5bqwuSgyc7OztcpNyEhwdjOzc2N0NBQ6tevz9WrV8nMzMTJyYmRI0eyatUqmz+bQlGRsVqhMSCE8AXM1j2llDE2k6gCYbB7nzp1qtD3Jicns2TJEsLDw/PN92JLpJRMnjyZRYsWUb9+fY4dO2axxpI9uXr1KjVr1sTFJf/X+L777qNPnz6A5jS9YMGCO/b75Zdf0qdPH+N9PXr0oHXr1vz888+MGzcuT3t7m4pKklu3oHJlLULp66+hTRtNofH31zYDjkjkZyvq16/PwYMHeeutt3jhhRcIDg7m7NmzxszSpiQnJ1t0yj158qSZU26tWrXQ6XQMHDjQbLUlMDAQZ2dn3nvvPWOR1ZycHL7//nsyMjKUCUmhsCFWKTRCiGrAh8BQcikzeu5oeBZChAFfm5xqALwspVxgjQzlEcOy8r59hferXrJkCQCff/65TWWyREZGBj179mTnzp306NGDn376qVRUva1RowYJCQlkZWXlq9T4+voa9z09PUlLSyuwPcC5c+dYt24dGzduNJ7LzMyka9eu+d5TWiN5CsOgQVq49aZN4OMDsbFgWm7H1LxkSzObozA4jkdFRbFnzx4SEhJYtGiRmeJi6mfj5ORkdMrt3bu3mVPunUy+htUZ0742bdrEgw8+WDIPp9Dw9bVc2bxOHbh40f7yKEoUa1do3gOaAw8B/wUeAeoBUwCrvFGllCeAFgBCCGcgDvi+UNKWUw4fzlMSr0CysrKYMWMGTk5OJZ6pMykpibCwMC5dusTUqVN5//33S419v3379ri5ubF+/XoGDx5ss379/f0ZM2YMn3766Z0bm1DaInnuREoKbNgAw4drhSJ79YKMDK0KthDmykxu89JLfRvb1MxmD9LT042Zcvfu3UtOTg6+vr5Uq1Ytj1OuTqeja9eueZxyixpJeCnXh+rNmzdZsmSJUmhKGkvKTEHnFWUaaxWa3sAIKeUuIUQ28JeU8mshxAXgCeDbQo7bDTgtpTxXyPvKHf7+/sTGxhbqnv/+978AfPttYae9cJw/fx5/vZ3h008/5bHHHivR8QpLtWrVeO2113jmmWdwcXGhZ8+euLq6sm3bNnbs2FHkar6jR4+mTZs2bNmyhe7du5OZmcmePXsICQmhfv36Nn4Kx/HVV/DooxAcDO3aweOPW25nybz0+k/HeKlfY17/8VipM7MlJSXx+++/50nxf+bMGbNMuQANGjSgVatWZopL3bp1ba60m/rVGNi6dSuJiYlUr17dpmMpFBUVaxWa6oBB+UgCagBRwB/AsiKMOxxYa+mCEOJx4HHQ7NKRkZFF6L7sYFBorH1OKSXDhg0DtJortpif5OTkPP2cPn3aqMC88847hISElMrfRatWrXjsscd4/vnnGTFiBB4eHoSGhjJ69Gj279/PpUuXjHJf1C8x79y5E2dnZ9LS0jh06BAuLi6cPXvWrO3LL7/MzJkziY6OxsnJCZ1Ox9SpU6lTjBo8lubZnqSkOLNkSUNatbpO585XqOsneOf9KiTfukFkZP4f4KmZ2Twbnkm2SYkOZyHwuRHFoq7uZGTnUMnZCefEU0RGFt4frChkZ2dz6dIlYmJijFtsbCwxMTEkJiYa27m6uuLv74+/vz/t27cnICCAgIAAnnjiCQDeeOMNs35PnjzJyZMnbS5vTEyM0V/GkO8mKCiInTt3Uq1aNZuPZw8c/T5bQ5cCrpV22U0pC3Nt4N6HH6aSPk2BKRne3vyu/zJeUlhby+kQWrXtSCHEL8BRYBowFZgqpfQvsAPzvioB8UCTOxW1LO+1nABeffVV5s6dS3p6ulUOgrt376Zjx44sXLiQZ5991iYy5K4TsnXrVnr21MpzHTp0yFjkUVE8HFWP5fJlqF0bcnKgZUsYMQJ0vayPUCpqnShbkJyczMmTJy065aanpxvb1axZE51Oh0ctf6p4uPN3ThBOPvV5/5HuDGxlHo4dFRVFo0aNePPNN5k1a1aJym/g2LFjXL9+nX379jF16lSOHz9OmGkinzJImagvVNBKWzFr6NmTMjHXBkp4zm1Ry2k50AyIBN4CfgQmAU5ofjSFoTfwt6rQrVGvnvYhcvHixXzzYFy7do3z58/TrFkzJkyYAFBi5p/PPvvM2HdsbGy5MrFURCZNgh9/hKgocHGBv/6CxNR0OrxtfYRSSUdxSSm5cOGCxRBoU3Osk5MTDRo0QKfT0atXLzOn3Jo1axoVr2d0Gfx92IUc4IX1R+kUVsdM1uXLlwO3S4bYA0MVb4MpKyoqqswrNApFacPaxHofmOz/TwihQytYeUpKWTiPVhhBPuamioghF018fHy+Cs2CBQuYN28ezZs3JyoqimeffbbI/iGmZGVlcf/99xMaGkrnzp2ZMWMG77//PrVq1eLUqVNldim8rGHL5HQpKbByJYweDVWqwMCBoNNpqzMAzs5FSwRoiyiujIwMo1Nu7u3mzZvGdlWqVEGn09G5c2cz35aQkJACnXINz2WKped64403cHJyMvqH2RNDRvCoqCi7j10hqVMHLl0iHngDWGR6XlHuyFeh0Tv/1pVSXhZCfI5mcroJxrwzhc49I4SoDPRAcySu0KSmprJjxw5jkrfZs2cjpWTs2LGMHz/erO3WrVuRUnLw4EEAvvvuO5o2bcpjjz1WLOfF559/nv379/Pnn3/y+++/8++//9KpUye2bt1aKvJjxMXFsWDBAv78808iIyNLTXSVLbF1crp//tGS4Xl5wahR0K2btplS1ESA1kZxXbt2zaLScubMGWMSO9Dyweh0OsaNG2emuPj5+RXpd23Ncx05cgTAqnxEJYEhMePu3buZMqWwi9uKQqP3m9u3YQOLBw5k3tWreHt7O1goRUlR0ApNKlAFuAyMA54HbhbQ/o5IKW+hORRXeHbs2MFDDz2Eq6srQgj+9z+tRNY9uUoV5+TkGBNyGYiLi2PKlCkMHz68yMntfv75ZxYvXmwszvfvv//St29fNm7c6FDFITs7m19++YVJkyZx5swZQFvFysnJKXdVg22RnC4nRzMr1a0LL72kRSv9/Te0aJH/PbYwIWVnZxMTE5NHafn333+5cuWKsV2lSpUIDQ2lefPmDBs2zKi0hIaG2jwxo+G5Yo/9hZebi8XnMuRvMjjW2xvD39Yvv/zikPErKidOnEBKyY4dO8zKpCjKFwUpNL8D64UQfwEC+FAIkWqpoZTykZIQrjzTqVMnnJycSElJMZ7z8PDIY3Y6fvw4TrmW0T09PVm2bFmRPxBiY2MZMWKEWaVh0KJ/jh49StOmTYvUb3G4cOECH3/8sVkV4scff5yZM2faJROyIyhqcjop4cwZaNgQnJzgxg1tRcbA3XffeWxrTUi3bt3K1yk3LS3N2M7Hx4fw8HAGDBhgVFrCw8MJCgqyqyI6oEU9tl87yZcd787zXFJKFi1aRPXq1aldu7bdZMqNTqfj+PHjDhu/ImL4UvjDDz8ohaak0Zv5LJ4vYQpSaMYAzwEhgERbWUkvoL2iEFSpUoW2bdvy66+/Gs+5urrmccL9448/zI7d3d158MEHGTFiRJHGzczMpH///nkK8xmubd26tUQUmvT0dObMmcO6des4efIklSpVIicnh61bt/Lss88aQ2WrVq3Kp59+ykMPPVQqzF4lSVFNP2+/Da+8AufOaYlQV60qOLAgPwwmpIKccmNibluWnZycCA4ORqfT0aNHDzMzUc2aNQsvQAnh7CRo7l89z/n9+/cD8N5779lZInN69+7N8ePHyczMLBUZtysCR48eBWDz5s0OlqQCcPEiUVFRfPTRRyxcuNCuQ+er0OijkGYACCGi0RLrXbWXYBWBMWPG8Ndff5kpF7kVmu3bt5tdr1mzZqEz2JoyY8YMTp06ZebL4O7uTsOGDfn0009p3759kfvOj/379zNkyBAuXbqEk5MTy5Yt4+LFi7z++uvGNo8++ijPP/88jRo1svn4pRVrTT8pKfDFF9C9O4SFweDBULWqtoH1ykxGRganT5+2qLgYKkEDVK5cGZ1OR8eOHfM45Tq6IGlx+OijjwAYNGiQQ+UwpEE4d+6c0UlYUbJER0cDcOPGDaKjowkODnawROWb//u//2PdunV2V2iQUhZpA1yLeq+1W2hoqCzPXLp0Sbq5uUm0FTDp7u4uL126ZNamfv36xuseHh7ywIEDRR7vhx9+kJ6enmb91axZU86ePVtmZ2cX82nykpaWJp977jnp4eFhHNN08/T0lGvWrJFpaWk2H7s0smPHDovnE26myYMx12XCTfN5yMnRfl6+LKW7u5Tz5lk3zrVr1+Tvv/8uP//8czlz5kw5YMAAGRoaKp2dnc3mv169erJbt27ymWeekR999JHcunWrjI2NlTmGgcso+c0zIP39/e0rjAV27dolAblp0yZHi1Is8pvn0kZSUpJ0dXU1/s9ZsmSJo0UqNGVlrqWUMicnR9asWVN6enqWSP/AfpmPzmBtccpngTgp5Xf648+AcUKI08AAqdVpUhSS2rVrExYWxj///ANoJh/TpfvExERjDRhPT09ef/11WhTk7VkAMTExjBo1ipSUFFxdXXFxcWHGjBnMmjWLvXv35vHTKS6GVZnLly/n8dVxdXVl/fr1xmrWFR1L0UOzZkF0tFbxulYtOHYMgoJuX8/JyTFzyjVN8X/58mVjO1dXV0JDQ7nrrrsYMmSImVNuVcMSTwXh5MmTeHg4vtaUYVXm1KlTPPDAAw6Wpvxz6tQpPDw8yMzMJCUlhe+//96YKVphe/744w9SU1NJTU0lJyfH5p8tBWFtYr1n0QpSIoTohFZ1eyQwCHgf6Fci0lUARo8ezcsvv0xaWho+Pj5mv/y9e/ca/wG3a9eOqVOnFmmMzMxM+vXrR3JyMh4eHjz00EO8//77xhw4tsTgK7No0aI8ioyBnJwcfvnlF6XQmCCllvSuVSvNhFS9ulbxOjk5hagozSl3xYrbJqITJ06YOeV6e3sTHh5Ov379CA8PNyouQUFBBVYWr0iUFnOmoXzG77//zuTJkx0sTfnHEOFkYNeuXWRnZ5e7qMnSwrJly0hNTaVSpUokJSXZNUze2v909YBo/X5/YJ2U8hshxGFgV4lIVkEYOHAgr7zyCkCeOkG//fYbN2/exNvbm6+//tq6cGpf3zwe5tOAw0Dr1q1ZunQpd1sTBlPIMQD2+/gwpGrVPKsyTk5OVKlShezsbLKysmjYsCENGjQongzlCCklS5de4sknjzNm4kE8XU9z9vQpjh8/zpIlt+u3CiGMTrndunXL45RbHvP0lEdU6LZ9OX78uFk1dWdnZ/7++2/atGnjQKnKJ5mZmaxbt46cnBxcXV25aue8P9YqNDeA2kAsWmK8d/XnM4Gy6yVYCggJCaF27dqcO3eOwMBAs2uGhHrffvut9VEkuRQNCZwG/gs89OeftvnQyzVGJvAi8P61a4jERKpUqYKLiws1a9akadOmdOjQgRYtWtCsWTOCgoLsugRZmsjKyjKusBw9epwffjjOjRvHuXDhOElJSQCs+hSEqxsNQkLp0KEDjz76qFFpadSoUZl2ylXcJiQkRGULthMHDhwwW6FJS0tj8+bNSqEpAbZt22b8jHFycuLq1at2dXy3VqH5BfhUCPE3Whj3Jv35JtxeuVEUkeHDh/P222+b/eJzcnI4fPgwM2fOpGvXrkXuWwA/Gw9K5ht8FLAXrUR6m6VLadasGY0bN6Zy5colMl5pJzEx0WIkUVRUlFl0mbOzH76+OgYNHc6PMQKq1cO1Rn2cvWrgUsmFD+1Q/FHhGHr37s1HH31EVlaWMgmWMLlz/mRmZrJ+/XpeeuklB0lUflm6dKmxjIkQgusWqm6XJNb+JT2DVgojABgspbymP98SVZep2AwZMoS3336b4M8/B5Mwt+eAOe+8AytWGFN4l0bCAWM2nUcfdaAk9iMnJ4fY2FgzZ1zDdslkBcvV1ZVGjRrRpEkTPD3v5/Ll9nz7rY7GjcPIyKhKzZpwKDaRP5ft5WZ61u37rEiwpyi7GJz7Y2JilPm1hLlw4QJeXl6kpqaSlZWFu7u7MYxbYTtu3bpllucnOzuba9euFXCH7bG2OOUNII/3mpTyFZtLVAFp2bIlderUoZ7JB6ETMNdwYCnrosIupKSkcOrUqTxKy4kTJ8z8hAxOuX379jWpAq3j/Plg2rVzwdMT5s07TFzcXTRufDuHDBQ9wZ4jCQoKIiUlhejoaONK3LJly/jyyy+JjIx0rHBlANMilUqhKVl++uknMjIyGDBgAFlZWURHR6t6TiXADz/8gKurqzFYITMzk6tX7Zu6rqDilD6GlRghhE9BnZis2CiKgBCCdevW0axTJ0eLUiGRUnL58mWLZqJz584Z7e9CCIKCgtDpdHTt2tXMKbdWrVp5/JP279cKQy5ZAk88Affdd5UuXfKOb4vaSo4gOzubhQsX8uKLLxa5D0P+iIrmV2Wq0PTs2dPB0pRvOnfuDEB4eDgHDhzA19fXwRKVT5YsWWI0N4EW8VpqFBrgihCirpTyMpCA5l+aG6E/r+Lfioo+YqijrfqzRx0NB9bqKA6ZmZmcOXPGouKSmJhobOfp6UlYWBjt27dnwoQJ1AtqSFXfQNrf3ZT6tarn27+U8MknWn2lp57SQrC//hr6WZHUwNraSqWJGTNm8M477/D0009TvXp1s2u///47U6ZM4eTJk4SGhrJw4ULuvfdeALp06UKHDh2IjIzk77//5sMPP+TDDz/k8OHDAPTo0YPExET27dsHQMeOHZk+fToPPfQQb731Fp9++imXL1/G39+fN954g4EDB5KRkYGvry87d+40ynD58mWCgoI4d+6cscp1acGQMmHPnj08/fTTDpamYhAQEMCBAwccLUa5JCEhgd9//z3P+QsXLthVjoIUmvuBayb7lhQaRXGxtTnJHr42pdifByApKSlfp9ysrNt+KnXr1kWn0zFixAiz1Zb69esbVww2HIzj+e/+wfX0LTJ3/cE7g5oxoEU9s/FSUsDTU/O53rIFnJ01hUYIGDrUerktJdgrzbRu3ZouXbrw3nvvMW/ePOP5a9eu0bdvXz788ENGjBjBunXr6Nu3L1FRUdSoUQOAVatWsWnTJsLCwkhNTWXy5MkkJCRQrVo1/vnnH1xcXLh58yYuLi7s37+fjh01lb9hw4bs2rULX19f1q1bx+jRo4mKiqJu3boMHz6cL7/8kt69ewOwdu1aunXrVuqUGbgdur1lyxYHS1JxMJQ7kFKqFAc2JikpiUaNGnHu3DlSUlKoWrUq6enpZkk+7UFBtZx2muxH2kUahcJKDE65lhSXiyYKl4uLC40aNSI8PJyBAwea+LeEUa1atQLHuJqczvPf/UNaZo6xIvbM7/6hQ0hNo+KxYgVERMDJk1pG36++0pSbisJrr71Ghw4dmDJlivHcTz/9RKNGjRgzZgwAI0aM4MMPP2Tjxo2MHz8egPHjx9OkSRMAvLy8aNOmDb/++it+fn40b96c6tWr89tvv+Hm5kajRo2MitCQIUOM4wwbNow333yTP//8kwcffJBx48YxZMgQY/bdVatWMXPmTHtMQ5EIDg5Wzql2xJDnKzU1Fc+K9EdqBxo2bMixY8c4cOAALVu2ZPbs2QQHB9u9Zpa1pQ+yAYP5yfR8DeCylFKZnEqSUm7KKUlSU1PzdcpNSUkxtqtevTrh4eH07t3bbLUlODi4yBWNz19PxdXJyajMALgIJ77/OZPu97gRFAT33AMjRoDBp7ei/Z9s2rQp/fr146233iI8PByA+Pj4PDmVAgMDiYuLMx77+/ubXe/cuTORkZHUr1+fzp074+3tzc6dO3FzczP6QACsXLmS+fPnc/bsWQCSk5NJSEgAoG3btnh6enLw4EHq1q1LVFQUAwYMKInHtgkPPPAAixcvVllr7YThC0xSUpJSaEqIM2fOANCzZ88il+kpDtaGbee3PucGZNhIFkVuZMWw8kkpuXLlisXVlrNnz5o55QYGBqLT6ejcubNZin9LTrnFxVL0UVqyC8+MqcykSfD++xAervnNVGReffVVWrZsyfTp0wHw8/Pj3LlzZm1iYmLM6hbl/l117tyZ6dOnExAQwKxZs/D29mbixIm4ubnxzDPPAFp16okTJ7J9+3bat2+Ps7MzLVq0MEuaNm7cOLZu3UpCQgKDBw8u1YkIW7ZsCcD58+fzKIAK22Oq0JRE2ReFVjcLcFjkXoEKjRBimn5XAk8KIZJNLjsDHYHjeW5UKCyQlZWVr1OuaQImDw8PwsLCaNu2LePGjTPLlGvPb1aG6KMnXrpG5pWq1OxxjHeG66g6QNC2rd3EKPWEhIQwbNgwPvzwQ+666y769OnD5MmTWbNmDUOHDuW7777j2LFj9CvAO/ree+/lxIkTXLx4kXvuuYdKlSpx7tw5rl+/ztdffw1oeS6EEEafmC+++IIjR46Y9TN69GjefPNNjh49yqpVq0ruoW2AIdLp9OnTSqGxAwaF5saNGw6WpPzy669aRjJHFb690wqNIfeMAB4Dsk2uZQBngSdtL1YFooxGDBVEUlISJ06csOiUm5mZaWzn6+uLTqdj2LBhZmYif39/m4TxXk1OL3LUUEIC1KypRR/tCKvDrivw4zRffL3LjtOuPXn55ZeNCkSNGjX48ccfmTJlCk899RQhISH8+OOPBZbvqFy5Mi1btsTd3Z1KlSoB0L59e44ePUrt2rUBaNy4MdOnT6d9+/Y4OTkxduxYOnToYNaPv78/jRo14urVq0ZH4tJK+/btWblyJffcc4+jRakQGD5kDWVGFLbH0TmoClRopJTBAEKIHcDDUkr75jGuCJTyiKH8yMnJ4fz58xZXW0xD9VxcXAgJCUGn0/Hggw+aOeXmDvW1JcboJJO8Lrmjk/Ljl1+gf3/49Vdo2xbefcsFLTu9SlFvwODDYsDf39+s+vd9993HX3/9ZfHe/P7p/fHHH2bH3377bZ42b7zxBm+88UaBstWuXZvevXuX+kgWNzc3o+O0ouQxNTkpSobU1FSjs78jsDZTcNGLCSnKNGlpaWZOuYZU/7mdcqtVq0Z4eDi9evUyW21p0KBBkZ1yi4o10UmmSAmRkeDuDu3ba9vTT4PBzK5K7ZQdzp49y65du/ikojs2KfKgFJqSxbD63qtXL4fJYPW/aiFEKDAYrZ5TJdNrUspHrOyjOrAMaIrml/OIlPKPAm9SlDgFOeVGR0ebOV0aMuV27tzZTHGpXbt2qflGbCk6qaDaSNnZMGECtGgB69eDlxd88EHRxy+OqUtRdF566SU++OADhg8fbvdwUUXpR/nQlCyxsbGAFvnoKKwN2+4LfAccAFoB+4CGaFFOuwox3kJgs5RysBCiEqBi5+yIoY5JbqXlyJEjZn/k7u7uhIWF0aZNG8aMGWNUWkJDQ8tEuKM1tZG+/hq++AJ+/llbgfnxR7BFlfvimLoUxeP111/n9ddfd7gdX1E68fLyArB7wcSKwunTpwEtJ42jsHaF5jXgVSnlm0KIm8AYIB5YBVi1wiKEqAZ0AsYDSCkzUCHfJcKNGzcsOuWeOnXKzCm3Tp06xtWW+++/36i4BAQElOnaOvnVRkq/4Ua6K7i5aasyKSma82/t2mCLLxWFNXUpFAr7Ycj1k9v/S2EbDAqNI4utCmlFrhN9uHYzKeUZIcQ1oJOU8ogQ4i7gJyllgBV9tACWAseA5sBfwBQp5a1c7R4HHgeoVatWq2+++aaQj1QxMJiJYmJizLbY2FhjojEAJycn6tWrR0BAgNnm7+9v/MaSnJxMlSpVHPUohSY7R5KRnUMlZyecnfI3c5m2Ox9bmccea01ExEn69r2IlFppgsKMmZqpBfl5uDpbHDc1M5voK7fINvmbchaC4FqV8XB1LnPzXFax5zx/+eWXXLhwgRkzZpT4WBEREfTo0YO+ffvavO+uXbvy5ZdfUq+e9auJZfF97tq1K82aNWPhwoWOFqVQlIW5nj9/Phs3bmT79u0l+oW4a9euf0kpW1u6Zq1CcwHoJqU8JoQ4CsyWUq4XQtwN/Cql9LKij9bAHqCDlHKvEGIhcENK+VJ+94SFhckTJ07cUb7yTFpaGlFRUWYOuQan3Fu3buuCVatWNUs0Z+qUawiDzY/IyEi6WCoDXQopjEnnf/+Dq1dhyBDN8ffNN2H4cCjsF4gNB+OY/s1BsvRWLFdnwftDmucZ92pyOh3e/h9pmbfNXe6uTvz2/P3UqOJWpua5LFOYed69ezczZ87k6NGjODs7Ex4ezoIFC2jTpk2hxz179izBwcFkZmbiUgKe5F26dGH06NE89thjNu9bCMGpU6eMuXGsoSy+z0IIgoODjRltywplYa4bNGiQx+eyJBBC5KvQWPtXtxe4D2115SfgfSFEc2AgVpqcgPPAeSnlXv3xt8AsK+8t9yQkJOTrlJtj4g9iyJTbsWNHM8WlTp06pcYpt6SwxqRjuvLyzjtaVPzgwdq5F18s2pgzvz1kVGYAMrMlM77Na0rKz9SlzE2lkxs3btCvXz8WL17M0KFDycjIYNeuXbi5la7fl5SyxD8kKhL2rgBdUYiOjsbHx8ehMlir0EwDDOtdcwEvYBBwUn/tjkgpLwohYoUQYVLKE0A3NAWpwpCVlcXZs2ctKi5Xr141tnN3dyc0NJRWrVoxatQo48pLo0aNqFy5sgOfwDYUNQroTtFLW7bAtGmwezd4e8OyZVpyvOLoeeevp+IsnDDPKQnOTsJi1NSAFvXoEFJTRTmVAU6ePAloxTNBy1Dds2dPQPvi8N///pdWrVqxevVqRo8ezZEjR2jSpAmfffYZGzduZP369cydO5eoqCi+/PJLOnXqBGDMr7R161aefPJJo28BaNmOd+zYQZcuXdizZw/Tpk3j2LFjBAYGsnDhQuO38C5dutChQwciIyP5+++/OXz4sJnsp0+fZuLEiRw6dAghBL169WLRokXGsYOCgpg0aRIrV67k3LlzPPDAA6xYscJYCuLdd99l/vz5CCHMKqWXd6pWraqinEoAg8LdrVs3h8phbR6aMyb7KcBTRRxvMrBaH+F0BphQxH5KNTdv3szXKTcj47YfdO3atdHpdAwaNMhstSUgIKDcFqsrThSQpeillERXKmVq0Ut162oKzJUrmkJTv37x5a3v7UG2zMlzPjtHmkVNmVKjiptSZMoAoaGhODs7M27cOIYPH067du3w9vYGbhfLbNWqFTt37qRBgwb8+uuvNGnShJ07d5oVzDTw66+/EhwcTGJiotHkdOjQIeP1pUuXMn/+fFq2bElcXBx9+/Zl1apVPPDAA2zfvp1BgwZx/PhxY2mHVatWsWnTJsLCwvKs0EgpeeGFF+jUqRM3btxg0KBBzJ07lwULFhjbfPPNN2zevBl3d3c6dOjA8uXLefLJJ9m8eTPvvfce27dvJzg4mIkTJ9p6akstdevWVQpNCWD4Qp47c7e9sTZsuxaAlPKK/vguYBhwVEq51trBpJQHAYu2r7KGlJK4uDiLqy2mVYWdnZ1p2LAhOp2Ovn37mmXKdfTynL0pbhRQbpNOWooTsUu7sAQnFi6EZs1g507bylyjihvvDm7OtFw+NO8OVqaksk7VqlXZvXs3b7/9NhMnTuTixYv06dOHTz/9lM6dO7NhwwamT5/Orl27eOGFF9i2bRtPPfUUO3fuJCIiolBj7d69mzlz5rB7926qVq3K4sWL6dOnD3369AGgR48etG7dmp9//plx48YBMH78+HyzroaEhBj9XWrVqsW0adN49dVXzdo8++yz+Pn5AdC/f38OHjwIaIrOhAkTjPlC5s6dy9q1Vv8bL9P4+/tT0f0yS4LSEOEE1pucvkEL0f5cCFET+BUtbHuyEMJPSvl+SQnoaNLT080y5Zo65SYn367VWbVqVXQ6Hd26dTNbbWnYsOEdnXIrCoVNeGeJakn1GO5Wh4Hjkqnv7cHPzZwoiS8FpmYxgxnpaPwNQNLEr5pSZsoJ4eHhLF++HIDjx48zevRoIiIieP3113nuuee4cOEC2dnZDB06lFdffZWzZ8+SlJREixYtrB4jNjaWoUOHsmLFCkJDQwGtcvi6devYuHGjsV1mZiZdu95Oyu7v759vn5cuXWLKlCns2rWLmzdvkpOTY1xdMuDr62vc9/T0JD4+HoD4+HhatWplvFaRCmMGBQU5WoRyicHJuqwoNM3QIpRAyxYcJaVsI4R4EHgXKPMKzdWrVy2utpw5c8bMKTcgIACdTscjjzxiprj4+vqWe6fc4mJNwjtLZGeDwQK3cSN89ZULM6dVx9MTSqIUTn5msU6htWw/mKLUoNPpGD9+PP/3f/9HSEgInp6efPTRR3Tq1ImqVavi6+vL0qVLue+++yyGpVr6+09NTeWhhx4iIiKC3r17G8/7+/szZswYPv3003zlKej/yYsvvogQgsOHD+Pj48P69euZNGmSVc9Zt25dY1ZXgJiYGKvuKw8YlMTMzEy7l2Qpzxj80cqKQuMBGJYjugM/6Pf/BvL/GlHKyM7Oztcp1zR3i5ubG6Ghodx9992MHDnSLFNueXDKdRRFiQLavx8eflgrSdCyJbz0Esybp9VdKglUcryKw/Hjx/npp58YNmwY9evXJzY2lrVr19KuXTtA86P5+OOPWbRoEaA56n788ce89JLlTBO1atXCycmJM2fOGFdiDF98Zs6cadZ29OjRtGnThi1bttC9e3cyMzPZs2cPISEh1LfC+evmzZtUq1aNatWqERcXx7vvvmv1cw8dOpQJEyYwduxYgoKC8piqyjMGp+mkpKQCq78rCse2bdsAzbHekVir0JwCHhZCfAf0RFuVAagDJJaAXMUiOTnZolPuyZMnzZxya9WqhU6nY+DAgWarLYGBgeXWKdfRWBMFFBcHN25AeDg0agR33QWGhR19ORYzbFk7yRZmMUXZwMvLi7179zJ//nwSExOpXr06/fr1MyoHnTt3Zu3atcbopc6dO/Pee+8Zj3Pj6enJ7Nmz6dChA5mZmWzevJmvvvoKDw8Ps6RomzZtomPHjmzYsIGZM2cyYsQInJ2dueeee1i8eLFVsr/yyiuMHTuWatWqERISwpgxY/jAygJkvXv3JiIigvvvvx8nJyfmzZvH6tWrrbq3rNO/f38yMzPzmOcUxWP37t2OFgGwPrHew8BaNAVou5Syp/78bLREeX1KQriCEutJKYmPj7e42nL+/HljOycnJ6NTrukWFhZGjRo1SkLsMkdpStqUkwMNG2qKzC+/3Lm9rWsn3Sk5XnEoTfNcnlHzbB/UPNuP0j7XQgjatm3Lnj177ty4+GMVL7GelPK/QogAwA84ZHJpG1rRyhIjPT3dmCk392bqlOvl5YVOp6Nr1655nHJLW6IshTm//w6rVsEnn4CTk5Y/xpr6ZiVhHlLJ8RQKhcJ60tLSAC1Sz9FYnZ9bSnkJuJTr3N58mtuE6OhoPD09zZxy/f390el0TJgwwUxxqVu3rnLKLUNkZmoJ71xc4NQpzUdm1iwIDIRu3TRl5VBswWakkjIPqeR4CoVCYR2GYp86nc6xglAIhcYRuLm5MXXqVDOn3NJeoEtxZ2JioEMH+M9/tCilkSO1GkuGhTRrzUhFjZqyBpUcT6FQKO5MaclBA6VcofHz8+O1115ztBgKG3D+PJw+DZ07g78/9OqlrcYAmEZPFsaMpMxDCoVC4VgMOWgaWuMnUMKUaoVGUX545BE4eRLOnLntJ2PANEqpsGakgsxDtox+UigUJUtkZCSjR482C+qwFaY1txS2xeAIbCjZ4UiUQqMoEQ4ehFdfheXLtVDr+fOhShVNmTElt3nppb6NC21GsmQesnX0k0KhsI6goCCWLVtG9+7dHS2Kwg5s374dKDgRpL3Im+4yH4QQdYQQzwkhFuvLHyCE6CCECC458RRlicxMuHlT28/Kgr174fhx7bhpU8idddzUvHQzPYu0zBxe/+kYL/VrjLurE15uLri7OhXajGSp35nf/cPV5HTbPKhCoSg0WVlZjhZBUQJcunSpwDId9sQqhUYI0Qo4AYwCHgWq6i/1AN4oGdEUZYnUVC13zOuva8etW8O5c9C2bf73GMxLprg6OdHUrxq/PX8/Xz7Wlt+ev7/QKyv59Xv+emqh+lEoFEVn+fLldOjQgalTp1KjRg3mzp1Leno6zz33HAEBAdSpU4cnn3yS1FTLf5dvvfUWDRs2xMvLi8aNG/P999+b9X3ffffx3HPP4e3tTXBwMJs2bTJej46OpnPnznh5edGjRw+zTPAK22HIY1caQrbB+hWa94CFUsq7AdOvuVsAx9YLVziM2FgwFOn18IAnnoCePW9fv1OplIKilGpUcaO5f/Ui+b6UZPSTQqGwnr1799KgQQMuXbrE7NmzmTVrFidPnuTgwYNERUURFxeXb+BHw4YN2bVrF0lJSbzyyiuMHj2aCxcumPUdFhZGQkICM2fO5NFHHzV+wI4cOZJWrVqRkJDASy+9xIoVK+zyvBUNw++jdWuLee7sjrUKTSvA0htxAa38gaIC8t578OijkJioHb/wAhTGbG6IUiqOecme/SoUisLh5+fH5MmTcXFxwd3dnaVLl/LBBx/g4+ODl5cXL774Il999ZXFe4cMGYKfnx9OTk4MGzaMRo0a8eeffxqvBwYGMnHiRJydnRk3bhwXLlzg0qVLxMTEsG/fPl5//XXc3Nzo1KkT/fv3t9cjVyhKU8g2WO8UnApYKn6hAy7bThxFaSYqCp56SnPwvesuLRHetGmgr/dWJEoqiZ1KjqdQOB5T34orV66QkpJCq1atjOeklGRnZ1u8d+XKlcyfP9+YuC05OdnMdOTr62vc9/T0NGvj7e1tVkg4MDDQrMK4wjaUppBtsF6h2QC8IoQYoj+WQogg4G1KuPSBwrFkZMDVq1C3Lvj4aH4xsbGaQlO3rm3GKKkkdio5nkLhWEwjX2rWrImHhwdHjx6lXr2C/eLOnTvHxIkT2b59O+3bt8fZ2ZkWLVpgTe3BunXrcv36dW7dumVUamJiYkpFFE5549ixYwAEBAQ4WBINa01OzwE+wBXAE9gNRKFV2p5TIpIpHI6UcO+9Wg4Z0BSaEyegT4mUIlUoFOUZJycnJk6cyNSpU7l8WVvYj4uLY8uWLXna3rp1CyGEMbfJF198wZEjR6waJzAwkNatW/PKK6+QkZHB7t272bhxo+0eRGGkS5cuPPvss1SqVMnRogBWKjRSyhtSyvuAh4DngYXAA1LKzlLKWyUon8LOxMRovjFSarWWpk+HKVNuX1dfchQKRVF5++23CQkJoV27dlStWpXu3btz4sSJPO0aN27M9OnTad++PXXq1OHw4cN06GB9/MmaNWvYu3cvPj4+vPrqq4wdO9aWj6HQ07t3bxYuXOhoMYyI/JbwhBDZQF0p5WUhxOfAFCnlTXsKFxYWJi297ArbEhkZSefOXRBCy+D75JPwzz/QuLGjJStfREZG0qVLF0eLUe5R82wf1DzbDzXXtxFC/CWltBhWVdAKTSpgqAQ5DnC3tWAKx3PlCkyf3pyvv9aOR4/WyhMoZUahUCgUZYmCnIJ/B9YLIf4CBPChEMJiBiQp5SPWDCaEOAvcBLKBrPy0LEXJkpGhKS06HdSooZmXDIEG7u5QSvy7FAqFQqGwmoIUmjFozsAhgARqYJ5Ur6h0lVKqtI0OZMQI+PtvOHUKXFxg/vxDajlToVAoFGWafBUaKeUlYAaAECIaGCGlvGovwRS2Iy4OPvwQXnpJKxA5bRokJ4Ozs6MlUygUCoXCNlgb5RRsI2VGAr8IIf4SQjxug/4UBWAwI8XEaMnwdu/Wjjt0gF69VMSSQqFQlEWEEERFRTlajFJHQVFO04BPpJRp+v18kVLOt2owIepJKeOEELWBrcBkKeWvudo8DjwOUKtWrVbffPONNV0rTMjKEsyadRfh4Td59NFoAK5dq4SPT4bF9snJyVSpUsXiNYXtUPNsH9Q824fyPs/Dhw9nxowZZpmNHUXuue7atStffvnlHRMUlke6du2ab5RTQQpNNNBaSnlVv58fUkpZ6EIOQoi5QLKU8r382qiwbetJT4d9++C++7TjSZOgaVMtBPtOqJBA+6Dm2T6oebYP5X2eg4KCWLZsGd0LU6CuhMg910IITp06RUhISImOm5WVhYuLtQUF7EORwrZNzUz6/fw2q5QZIURlIYSXYR/oCViX+lFxR2bPhm7dQJ+Ak48/tk6ZUSgUCoV1jB8/njlzbifHj4yMpH79+oBWqNHHx4e///4bgPj4eGrVqkVkZCQAe/bs4d5776V69eo0b97ceB60jLtz5szh3nvvpUqVKvTv35+rV68yatQoqlatypNPPmmsaWXg559/pkGDBtSsWZMZM2aQk5MDQE5ODvPmzSMwMJDatWszduxYkpKS8shrICgoiG3btgEwd+5cBg8ezOjRo6latSrLly8nOjqaTp064eXlRffu3XnmmWcYPXq0zebUllhb+sAiQohAIYS1NqE6wG4hxCHgT+AnKeXm4oxfkblyBSIi4Phx7fiZZ2DjRtBnClcoFAqFHWnYsCFvv/02o0ePJiUlhQkTJjBu3Di6dOlCXFwcffv2Zc6cOVy7do333nuPQYMGceXKFeP9X331FatWrSIuLo7Tp0/Tvn17JkyYwLVr1wgICODVV181G+/7779n//79/P3332zYsIHPP/8cgOXLl7N8+XJ27NjBmTNnSE5OZtKkSVY/x4YNGxg8eDCJiYmMGjWKkSNHcs8993D16lXmzp3LqlWrbDNhJUCxFBqgOjDImoZSyjNSyub6rYmU8o1ijl0hSUvTfkoJn38Ov/+uHQcHQ8+eytFXoVAoHMXEiRMJCQmhbdu2XLhwgTfe0D7mvvzyS/r06UOfPn1wcnKiR48etG7dmp9//tl474QJE2jYsCHVqlWjd+/eNGzYkO7du+Pi4kKXLl04cOCA2VjPP/88Pj4+BAQEEBERwdq1awFYvXo106ZNo0GDBlSpUoU333yTr776iqysLKueoX379jz00EM4OTlx5coV9u3bx2uvvUalSpW47777GDBggI1my/YUV6FR2JGhQ2HYMG2/dm0tHPsRq1IaKhQKhcIeTJw4kSNHjjB58mTc3NwArXr4unXrqF69unHbvXs3Fy5cMN5Xp04d476Hh4fZsZubG8nJyWbj+Pv7G/cDAwOJj48HNFNXYGCg2bWsrCwuXbpklfym/cbHx+Pj44Onp6fF66UNpdCUJL6+2pJJ7s3X16rb09Ph+++11RiAjh2hS5fbx15eJSO2QqFQKPJSuXJlUlJSjMcXL140u56cnExERASPPvooc+fO5dq1a4CmBIwZM4bExETjduvWLWbNmlVkWWJjY437MTEx+Pn5AeDn58e5c+fMrrm4uFCnTp088mdnZ5uZvUBzODZQt25drl27ZnaP6bilDaXQlCT5acQFaMpSSm7dukVMTAyvv36Ahx/exoIFPyGlZPJkmDpVmZUUCoXCHmRmZpKWlmbcWrRowc8//8y1a9e4ePEiCxYsMGs/ZcoUWrduzbJly+jbty9P6iMzRo8ezcaNG9myZQvZ2dmkpaURGRnJ+fPniyzbu+++y/Xr14mNjWXhwoUM0y/fjxgxgg8++IDo6GiSk5N58cUXGTZsGC4uLoSGhpKWlsZPP/1EZmYm8+bNIz09/wIAgYGBtG7dmrlz55KRkcEff/zBxo0biyxzSVNgPJYQ4oc73F/VhrJUCLKAxUDMjBnEx8dz6dIlEhISuH79OomJSdy8mYyTk8DNrRIuLi5UriyYNi2JiRNvluucDwqFQlHa6NOnj9nx9OnTad68OUFBQQQFBTFhwgTef/99QHOm3bx5M4cPHwZg/vz5tGjRgtWrVzNq1Cg2bNjAzJkzGTFiBM7Oztxzzz0sXry4yLI9+OCDtGrViqSkJMaPH8+jjz4KwCOPPEJ8fDydOnUiLS2NXr168dFHHwFQrVo1PvnkEx577DGys7OZOXNmnqin3KxevZrx48dTo0YN7rnnHoYNG0a2IWtrKSPfPDQAQogvrOlESjnBZhKZUObz0FhYSkkHxgPfu7kVqBmb4uHhwa1bt8yWAm1Jec8nUVpQ82wf1DzbBzXP9qM0zfWwYcPQ6XR5oq7sRUF5aApcoSkpRaUi4wasBVKuXePHH39k+vSlnD+/m8qVXbh165bFe1JTU3Fy0qyDderUoVu3brRt25YGDRrQoEEDgoOD8fDwsN9DKBQKhaJCsG/fPnx8fAgODuaXX35hw4YNxfL9KUlKVwrACkAabqxlBMOFJ0OHDsXLayh//XWdmjW/4/PPl/LPP//g5OREamqq8Z7w8HBat27N9u3biY+PZ82aNaxZs8Zi/23atKF79+7odDqjwuPr62tUiBQKhUKhsJaLFy/y8MMPc/XqVerXr8/ixYu5++67HS2WRZRCU5LUqZPHAXgfbXiEL3BfDyNGQO/e0Lu3N/AYTz75GPHx8Xz11VcsXbqUc+fOkZmZSd++fXn33XeNfUgpuXLlCtHR0Zw5c4YTJ06wfft2fvvtN/bt28e+ffssiuPv78+ePXuM3vAKhUKhUBRE//796d+/v6PFsAql0JQkFy+Sk6NFJtWvDzNmwH0Sft8D7dpZvsXPz49p06Yxbdo0oqKiWLNmDX379jVrI4Sgdu3a1K5dm7Zt2wJaymoD6enpnDt3jjNnznDmzBn++usvtm3bRnx8PAX5TCkUCoVCUVZRCk0JERsL/v7g5ATnz4OhvpcQ0L69dX2EhITw8ssvF3psNzc3QkNDCQ0NLfS99qBLly6MHj2axx57zCb9zZkzhyVLluDi4pInLwRoeRgaN25MUlISzs7ONhlToVAoFKUL5VhRArz9NoSFQUKCdrxuHegj+0otu3fv5t5776VatWr4+PjQoUOHfE1XhWHu3Lk2KWQWHR2Nk5MTTz31lNn5mJgY3n//fY4dO2ZRmQEICAggOTlZKTMKhUJRjlEKjQ1IS4Nly+DMGe34wQfhjTfAEHhU2v1xb926Rb9+/Zg8eTLXrl0jLi6OV155xZi2uzSwcuVKvL29+frrr83C3WNiYqhRowa1a9e2eJ+19UsUCoVCUbYp5R+1ZYPr17Vq1/raYOh0mt9M5cqOlctaDNkqDQmfPDw86NmzJ82aNQOKXo5+8+bN/Oc//+Hrr7+mSpUqNG/e3Njm3LlzdOjQAS8vL3r27EmCYTnLAlJKVq5cybx583B1dTVmqty2bRs9evQgPj6eKlWqMH78eM6ePYsQgs8++4yAgADuv/9+4zmDcnPt2jUmTJiAn58f3t7ePPTQQwBcv36dfv36UatWLby9venXr1+xMnkqFAqFwn4ohaaIzJkD48dr+3XrwqFD8OKLDhWpyNSvXx9nZ2fGjRvHpk2buH79utn1opajf+CBB4xpt5OTkzl06JDx2po1a/jiiy+4fPkyGRkZvPfee/n2s3v3bs6fP8/w4cMZOnQoK1asAKB79+5s2rQJPz8/kpOTWb58ufGenTt38u+//7Jly5Y8/Y0ZM4aUlBSOHj3K5cuXmTp1KqApbhMmTODcuXPExMTg4eFh1XMqFAqFwvEohaYQHDlye9/FRdtycrRjna7s1liqXLkyu3fvRgjBxIkTqVWrFgMGDDBWZy1uOXpLTJgwgdDQUDw8PBg6dCgHDx7Mt+2KFSvo3bs33t7ejBw5ks2bN3P58uUC+587dy6VK1fOk3DwwoULbNq0iSVLluDt7Y2rqyudO3cGoEaNGgwaNAhPT0+8vLyYPXs2O3fuLPIzKhQKhcJ+KIXGStauhbvugr17teO5czW/mdLuH2Mt4eHhLF++nPPnz3PkyBHi4+OJiIgAil+O3hK+JhXHPT09SU5OttguNTWVdevWMWrUKADat29PQEBAvokFDeRX4j42NhYfHx+8vb3zXEtJSeGJJ54gMDCQqlWr0qlTJxITE0tt3RKFQqFQ3KacfBzbnsxM+PRTiIzUjvv1g4ULoXFjh4plF3Q6HePHj+eIfkmqOOXoi1t/6vvvv+fGjRs8/fTT+Pr64uvrS1xcnNHslB/5jevv78+1a9dITEzMc+3999/nxIkT7N27lxs3bvDrr78CqNw9CoVCUQZQCk0uDCYkgHnz4KuvtH0vL3j2We1necMQ+mxwgI2NjWXt2rW002f/K045+jp16nD27FlyTCe2EKxYsYJHHnmEw4cPc/DgQQ4ePMhvv/3GoUOHjFVtC0PdunXp3bs3Tz/9NNevXyczM9OouNy8eRMPDw+qV6/OtWvXHFZ8TaFQKBSFRyk0JsyfD/fcoyk1rq7wxx9QjOruZQYPDw/27t1L27ZtqVy5Mu3ataNp06a8r0+e88gjjzBmzBg6depEcHAw7u7uFsvR16tXj8qVK5tFPQ0ZMgTQ/FNatmxZKLni4uLYvn07ERERxtUZX19fWrVqxQMPPHDHVZr8WLVqFa6uruh0OmrXrs2CBQsAiIiIIDU1lZo1a9KuXTseeOCBIvWvUCgUCvsjSvNyelhYmDxx4kSJjrF3L7RoAW5u8M03sHkzLFgAVauW6LClitJUmr48o+bZPqh5tg9qnu2HmuvbCCH+klK2tnStQq/Q7N2r1VRavVo7HjoUPv+8YikzCoVCoVCUB+yu0AghnIUQB4QQP9p7bClh6VJNaQHNvLRihabIKBQKhUKhKLs4YoVmCvCvPQc0+KgKAd99B99/f/t47FioUsWe0igUCoVCobA1dlVohBD1gb7AMnuNuXIlBARo5QlAKxT5ww/2Gl2hUCgUCoU9sPcKzQJgJlC0GF4r+eMPMJTgad4c+vfXCkiC5h9TVjP6KhQKhUKhsIyLvQYSQvQDLksp/xJCdCmg3ePA4/rDdCHEkfzaFobPPrNFL+WWmkD+1SEVtkLNs31Q82wf1DzbDzXXtwnM74LdwraFEG8CY4AswB2oCvxXSjm6gHv25xeepbAdap7tg5pn+6Dm2T6oebYfaq6tw24mJynlC1LK+lLKIGA48L+ClBmFQqFQKBQKa6nQeWgUCoVCoVCUD+zmQ2OKlDISiLSi6dKSlUShR82zfVDzbB/UPNsHNc/2Q821FZTq0gcKhUKhUCgU1qBMTgqFQqFQKMo8pVKhEUI8IIQ4IYSIEkLMcrQ8ZQEhhL8QYocQ4pgQ4qgQYor+vI8QYqsQ4pT+p7f+vBBCfKif43+EEC1N+hqnb39KCDHO5HwrIcRh/T0fClFxM/rkLuEhhAgWQuzVz83XQohK+vNu+uMo/fUgkz5e0J8/IYToZXJevf+AEKK6EOJbIcRxIcS/Qoj26n22PUKIqfr/GUeEEGuFEO7qfbYNQojPhRCXTdOP2OMdzm+Mco+UslRtgDNwGmgAVAIOAY0dLVdp34C6QEv9vhdwEmgMvAPM0p+fBbyt3+8DbAIE0A7Yqz/vA5zR//TW73vrr/2pbyv09/Z29HM7cL6nAWuAH/XH3wDD9ftLgKf0+08DS/T7w4Gv9fuN9e+2GxCsf+ed1ftvNscrgMf0+5WA6up9tvkc1wOiAQ/98TfAePU+22x+OwEtgSMm50r8Hc5vjPK+lcYVmnuAKCnlGSllBvAV8KCDZSr1SCkvSCn/1u/fRKuXVQ9t7lbom60AHtLvPwislBp7gOpCiLpAL2CrlPKalPI6sBV4QH+tqpRyj9T+Slaa9FWhELlKeOi/Fd0PfKtvknueDfP/LdBN3/5B4CspZbqUMhqIQnv31fsPCCGqoX0YfAYgpcyQUiai3ueSwAXwEEK4AJ7ABdT7bBOklL8C13Kdtsc7nN8Y5ZrSqNDUA2JNjs/rzymsRL8MfDewF6gjpbygv3QRqKPfz2+eCzp/3sL5isgCzEt41AASpZRZ+mPTuTHOp/56kr59Yee/ohEMXAG+0Jv2lgkhKqPeZ5sipYwD3gNi0BSZJOAv1PtcktjjHc5vjHJNaVRoFMVACFEF+A6IkFLeML2m1+JVWFsxECYlPBwtSznHBW2pfrGU8m7gFtrSuRH1PhcfvW/Fg2gKpB9QGXjAoUJVIOzxDlekv5PSqNDEAf4mx/X15xR3QAjhiqbMrJZS/ld/+pJ+aRL9z8v68/nNc0Hn61s4X9HoAAwQQpxFWz6/H1iItjxsyOtkOjfG+dRfrwZcpfDzX9E4D5yXUu7VH3+LpuCo99m2dAeipZRXpJSZwH/R3nH1Ppcc9niH8xujXFMaFZp9QCO9l30lNMezHxwsU6lHb8f+DPhXSjnf5NIPgMErfhywweT8WL1nfTsgSb9EuQXoKYTw1n976wls0V+7IYRopx9rrElfFQZpuYTHKGAHMFjfLPc8G+Z/sL691J8fro8aCQYaoTn4qfcfkFJeBGKFEGH6U92AY6j32dbEAO2EEJ76eTDMs3qfSw57vMP5jVG+cbRXsqUNzdv7JJp3/GxHy1MWNuA+tGXFf4CD+q0Pmn17O3AK2Ab46NsLYJF+jg8DrU36egTNqS8KmGByvjVwRH/Px+gTM1bUDejC7SinBmj/wKOAdYCb/ry7/jhKf72Byf2z9XN5ApMIG/X+G+ehBbBf/06vR4vwUO+z7ef5VeC4fi5WoUUqqffZNnO7Fs03KRNt1fFRe7zD+Y1R3jeVKVihUCgUCkWZpzSanBQKhUKhUCgKhVJoFAqFQqFQlHmUQqNQKBQKhaLMoxQahUKhUCgUZR6l0CgUCoVCoSjzKIVGoVCUO4QQQUIIKYRoXUL9u+orSHcqif4LIcddQog4fVkIhaJCoxQahcKBCCHqCCE+EEKcEkKkCSEuCyF+F0JM1pexMLQ7q/+Alvp2sUKI74UQ/S30KU22m0KI/UKIh+37ZA4nFq0C/UEAIUQX/XzUtFH/jwPxUis+WKACJYSIFEJ8bHLcXAixQQhxUf+7jBFCfCeECDRpY/o7TBFCnBFCrBFCdDTtW0p5GNiDVv1doajQKIVGoXAQ+iKif6PVznkJLbV/W+A/aBlbB+S65TW0D+lQtIyrZ4HvTT8sTZiob9sGOASsE0K0t/lDFIA+M6xDkFJmSykvytsFFm2GPivrs+grgRfy3lpoCc+S0Sq264AxaInRquZqbvgdhqMlZMsAdgohZuRq9wXwlEmpAoWiYuLozH5qU1tF3YBNaCsJlfO5Lkz2zwLPWWjzOFqG6K4m5yQw2OTYFUgB3sxnnCD9PSOB3UAaWubYnrnaNQZ+Am6i1YZZC/iaXF8O/Ag8j5YV9XIBz94O+B9a0ckk/b6f/toDwC7gOnANLfV7eGHkNWnT2mTfdFtuzVj5yN4ardJ6dUvjWWgfCXys338IyAYq3WEMs9+hyfn/AFlAiMm5Svo56O7od1ptanPkplZoFAoHIISoAfQCFkkpb1lqI6W0Jo33Z2gfxoPyayC1ooOZaIpNQbwDfIhWcmArsEEIUU8vb13gV7Q06/egFTWsom9j+n+kM9AMTVHoZmkQIURztFpBUWiFENsBX6NV2Aat4vMC/Thd0BSejRZWfPKVNxex3J6fJmirHlMKOZYpHYHTUsrEAtrkx0W0lfHB+pWewvK+/v6HDCeklBloprXORehPoSg3qCVKhcIxhKDVbjlhelIIcR6orj/8Ukr5ZEGdSCmzhRAn0Wrv5EEI4QbMQDNnbL+DTIullN/o75uCpnA9BczR/zwkpXzepO+xaKsardHq+oC2UvCIlDK9gHFmAgellI+bnPvX5Jm+y/UME4AbaErHbivlNaKfo2v6w8tSyoQijGVKIBBfwPPli5RyjxDiP8AKYJEQYh/aCs5qKeU5K+6/KoS4TN7fdzzaKpFCUWFRKzQKRemiI9qKw59ohQCtQaCZKExZJYRIRjM1TUMzV226Qz9/GHaklDnAXjQzE0AroJMQItmwoa18ADQ06ePIHZQZgLvRTEyWH0aIhnoH2NNCiBvAJbT/VQGFkNcqCjGWKR5oiluRkFLOBnzRzIWH0fxjjgkhLK5oWRKbvL/vVL1cCkWFRa3QKBSOIQrtQ0lnelJKGQ0ghEixphMhhDOak/CfuS7NADYDN6SUl4strfYh/xPwnIVrl0z2LZrPCsmPaD44TwBxaD4jx9B8RWxNUcZKQFPKTLmh/1nNQvvqaKYsI1LKq2hVq9cJIV4ADqA5hhe4iqaP0qoFnMl1yQfNz0qhqLCoFRqFwgHoP9B+ASaZhmcXgcfQPjC/zXX+opQyqpDKTDvDjt6/4x5um4L+RvM/Oafv13S7WUiZDwD3W7qg9y3SAf+RUm6TUv4LeGH5y1dB8uYmQ//TuYhj5ZY/zNR3SEp5DU3RaZXreaqimRfNTIum6H1gTqP5JN2J6WgOyetznW+K9jtSKCosaoVGoXAcTwO/AX8JIeaihVdnoX0oNkdTeEzxEkL4ojn3+gNDgMloETQ7bSDPU3p/nMN62QKBxfpri9DCiL8WQrwNXEHz4xgKTC+kUvMusEcIsVTfbxqaqe0XtNWSBGCiECIWqKdvbyn8uiB5c3MObUWsrxBiI5qJ5nohxjJlB5o5sBn6PDd65gOzhBDxaOawGmirLlfQVmMQQvRDC7n/CjiJZj7qD/QBXsk1TnX977sSmllvHDAWmCmlPG1opA//r0fe90WhqFg4OsxKbWqryBuaL8VCNBNUOlp+kn3AC4CXSbuz3A45Tkf74F8PDLDQp8WQ3wJkCNLfMwr4HU3BOAH0ztWuEdpK0HU0heAE8BH6EGT0YdtWjnkfWtRUKpAIbAPq6q/djxZNlab/2Us/L+OtlRcLYdRoysUFtBWO5daMVYD8a4F3c51zRlMw/9H3cR5NcQkyadMAWIIWZm4IWT8IRGAepm8aYp4GROvH7GRBlheAzY5+l9WmNkdvQkprIkMVCkV5Rf8NPxpoI6Xc72Bx7khpkFcI0QRtpSZESnnjTu1LUA434BQwQkr5m6PkUChKA8qHRqFQKAqJlPIomoN0sINFCQTeUMqMQqF8aBQKhaJISClXlgIZTqL54igUFR5lclIoFAqFQlHmUSYnhUKhUCgUZR6l0CgUCoVCoSjzKIVGoVAoFApFmUcpNAqFQqFQKMo8SqFRKBQKhUJR5lEKjUKhUCgUijLP/wPuVqqUqi9eEAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "full_country_stats.plot(kind='scatter', figsize=(8, 3),\n",
+    "                        x=gdppc_col, y=lifesat_col, grid=True)\n",
+    "\n",
+    "for country, pos_text in position_text_missing_countries.items():\n",
+    "    pos_data_x, pos_data_y = missing_data.loc[country]\n",
+    "    plt.annotate(country, xy=(pos_data_x, pos_data_y),\n",
+    "                 xytext=pos_text, fontsize=12,\n",
+    "                 arrowprops=dict(facecolor='black', width=0.5,\n",
+    "                                 shrink=0.08, headwidth=5))\n",
+    "    plt.plot(pos_data_x, pos_data_y, \"rs\")\n",
+    "\n",
+    "X = np.linspace(0, 115_000, 1000)\n",
+    "plt.plot(X, t0 + t1 * X, \"b:\")\n",
+    "\n",
+    "lin_reg_full = linear_model.LinearRegression()\n",
+    "Xfull = np.c_[full_country_stats[gdppc_col]]\n",
+    "yfull = np.c_[full_country_stats[lifesat_col]]\n",
+    "lin_reg_full.fit(Xfull, yfull)\n",
+    "\n",
+    "t0full, t1full = lin_reg_full.intercept_[0], lin_reg_full.coef_[0][0]\n",
+    "X = np.linspace(0, 115_000, 1000)\n",
+    "plt.plot(X, t0full + t1full * X, \"k\")\n",
+    "\n",
+    "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
+    "\n",
+    "save_fig('representative_training_data_scatterplot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bad algorithms / overfitting\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn import preprocessing\n",
+    "from sklearn import pipeline\n",
+    "\n",
+    "full_country_stats.plot(kind='scatter', figsize=(8, 3),\n",
+    "                        x=gdppc_col, y=lifesat_col, grid=True)\n",
+    "\n",
+    "poly = preprocessing.PolynomialFeatures(degree=10, include_bias=False)\n",
+    "scaler = preprocessing.StandardScaler()\n",
+    "lin_reg2 = linear_model.LinearRegression()\n",
+    "\n",
+    "pipeline_reg = pipeline.Pipeline([\n",
+    "    ('poly', poly),\n",
+    "    ('scal', scaler),\n",
+    "    ('lin', lin_reg2)])\n",
+    "pipeline_reg.fit(Xfull, yfull)\n",
+    "curve = pipeline_reg.predict(X[:, np.newaxis])\n",
+    "plt.plot(X, curve)\n",
+    "\n",
+    "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
+    "\n",
+    "save_fig('overfitting_model_plot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country\n",
+       "New Zealand    7.3\n",
+       "Sweden         7.3\n",
+       "Norway         7.6\n",
+       "Switzerland    7.5\n",
+       "Name: Life satisfaction, dtype: float64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_countries = [c for c in full_country_stats.index if \"W\" in c.upper()]\n",
+    "full_country_stats.loc[w_countries][lifesat_col]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GDP per capita (USD)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Malawi</th>\n",
+       "      <td>1486.778248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rwanda</th>\n",
+       "      <td>2098.710362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>2744.690758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Africa Western and Central</th>\n",
+       "      <td>4003.158913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Papua New Guinea</th>\n",
+       "      <td>4101.218882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lower middle income</th>\n",
+       "      <td>6722.809932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Eswatini</th>\n",
+       "      <td>8392.717564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Low &amp; middle income</th>\n",
+       "      <td>10293.855325</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>13753.707307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Botswana</th>\n",
+       "      <td>16040.008473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>World</th>\n",
+       "      <td>16194.040310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New Zealand</th>\n",
+       "      <td>42404.393738</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sweden</th>\n",
+       "      <td>50683.323510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Norway</th>\n",
+       "      <td>63585.903514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Switzerland</th>\n",
+       "      <td>68393.306004</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            GDP per capita (USD)\n",
+       "Country                                         \n",
+       "Malawi                               1486.778248\n",
+       "Rwanda                               2098.710362\n",
+       "Zimbabwe                             2744.690758\n",
+       "Africa Western and Central           4003.158913\n",
+       "Papua New Guinea                     4101.218882\n",
+       "Lower middle income                  6722.809932\n",
+       "Eswatini                             8392.717564\n",
+       "Low & middle income                 10293.855325\n",
+       "Arab World                          13753.707307\n",
+       "Botswana                            16040.008473\n",
+       "World                               16194.040310\n",
+       "New Zealand                         42404.393738\n",
+       "Sweden                              50683.323510\n",
+       "Norway                              63585.903514\n",
+       "Switzerland                         68393.306004"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_w_countries = [c for c in gdp_per_capita.index if \"W\" in c.upper()]\n",
+    "gdp_per_capita.loc[all_w_countries].sort_values(by=gdppc_col)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "country_stats.plot(kind='scatter', x=gdppc_col, y=lifesat_col, figsize=(8, 3))\n",
+    "missing_data.plot(kind='scatter', x=gdppc_col, y=lifesat_col,\n",
+    "                  marker=\"s\", color=\"r\", grid=True, ax=plt.gca())\n",
+    "\n",
+    "X = np.linspace(0, 115_000, 1000)\n",
+    "plt.plot(X, t0 + t1*X, \"b:\", label=\"Linear model on partial data\")\n",
+    "plt.plot(X, t0full + t1full * X, \"k-\", label=\"Linear model on all data\")\n",
+    "\n",
+    "ridge = linear_model.Ridge(alpha=10**9.5)\n",
+    "X_sample = country_stats[[gdppc_col]]\n",
+    "y_sample = country_stats[[lifesat_col]]\n",
+    "ridge.fit(X_sample, y_sample)\n",
+    "t0ridge, t1ridge = ridge.intercept_[0], ridge.coef_[0][0]\n",
+    "plt.plot(X, t0ridge + t1ridge * X, \"b--\",\n",
+    "         label=\"Regularized linear model on partial data\")\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "\n",
+    "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
+    "\n",
+    "save_fig('ridge_model_plot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.2"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "22b0ec00cd9e253c751e6d2619fc0bb2d18ed12980de3246690d5be49479dd65"
+   }
+  },
+  "nav_menu": {},
+  "toc": {
+   "navigate_menu": true,
+   "number_sections": true,
+   "sideBar": true,
+   "threshold": 6,
+   "toc_cell": false,
+   "toc_section_display": "block",
+   "toc_window_display": true
+  },
+  "toc_position": {
+   "height": "616px",
+   "left": "0px",
+   "right": "20px",
+   "top": "106px",
+   "width": "213px"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/ML/01_intro/CM.md b/ML/01_intro/CM.md
new file mode 100644
index 0000000..ab4c831
--- /dev/null
+++ b/ML/01_intro/CM.md
@@ -0,0 +1,76 @@
+# Machine Learning
+- 1ère application mainstream : spam
+- Prgrammer les ordinateurs pour qu'ils puissent apprendre sur les données
+
+## Définition
+Le champs d'étude qui permet aux ordinateurs d'apprendre sans être explicitement programmés pour
+
+=> Découverte de patterns cachés et data mining
+
+## Classification des modèles
+- Supervision de l'entrainement
+
+## Apprentissage supervisé : Régression
+- Prédiction d'une valeur numeric cible
+
+**Target** = valeur à prédire
+**Label** = Etiquette de classification
+
+## Apprentissage non-supervisé
+Données non-étiquetées
+Apprentissage :
+- Clustering (formation de groupes de similitudes)
+- Construction de règles d'association
+- Réduction de dimensions pour la visualisation
+- Détection d'anomalies
+
+## Apprentissage semi-supervisé
+QUelques instances labellisées
+
+## Apprentissage auto-supervisé
+Ex : génération du "reste" d'une image
+
+## Apprentissage par transfert
+Exploitation d'un modèle sur d'autres données => fine tuning du modèle
+
+## Apprentissage par renforcement
+Apprentissage sur les expériences prouvées comme fonctionelles et en innovant
+
+# Batch learning
+- "Mesures hors-ligne"
+- Déclin de pertinence au fil du temps
+
+# Online Learning
+- "Mesures en-ligne"
+- Mise à jour en continu du modèle
+
+# Basé sur modèle ou sur instances - Généralisation
+## Basé sur instances
+Rapprochement à des classes existantes : KNN
+
+## Basé sur modèle
+Ex de modèles : régression linéaire / droite affine, à base de règle, deep learning
+
+# Défis et limites
+- Mauvaises données, outliers, valeurs manquantes
+- Features inutiles / correlations absurdes
+- mauvais algos, surapprentissage (overfitting) donc mauvaise généralisation sur de nouvelles instances => Résolu par régularisation, ajustement des hyperparametres
+- underfitting : modèle trop simple qui n'arrive pas à généraliser
+
+# Evaluation d'un modèle
+Capacité d'adaptation du modèle
+Séparer les données en un ensemble d'entrainement et de test (référence statique)
+Modèle très bon sur données test mais pas entrainement = overfitting
+
+## Tuning d'hyperparametres
+dataset de validation en plus
+souvent pas assez de données pour les 3 dataset
+
+## No Free Lunch Theorem
+Sans hypothèse sur les données, impossible de prédire quel sera le meilleur modèle (KNN, NN...)
+
+
+# Conclusion
+- Def ML
+- Apprentissages de modèles
+- Problématiques overfitting/underfitting
diff --git a/ML/01_intro/data/lifesat.csv b/ML/01_intro/data/lifesat.csv
new file mode 100644
index 0000000..b920e96
--- /dev/null
+++ b/ML/01_intro/data/lifesat.csv
@@ -0,0 +1,28 @@
+Country,GDP per capita (USD),Life satisfaction
+Russia,26456.3879381321,5.8
+Greece,27287.0834009302,5.4
+Turkey,28384.9877846263,5.5
+Latvia,29932.4939100562,5.9
+Hungary,31007.7684065437,5.6
+Portugal,32181.1545372343,5.4
+Poland,32238.157259275,6.1
+Estonia,35638.4213511812,5.7
+Spain,36215.4475907307,6.3
+Slovenia,36547.7389559849,5.9
+Lithuania,36732.034744031,5.9
+Israel,38341.3075704083,7.2
+Italy,38992.1483807498,6.0
+United Kingdom,41627.129269425,6.8
+France,42025.6173730617,6.5
+New Zealand,42404.3937381567,7.3
+Canada,45856.6256264804,7.4
+Finland,47260.800458441,7.6
+Belgium,48210.0331113444,6.9
+Australia,48697.8370282475,7.3
+Sweden,50683.3235097178,7.3
+Germany,50922.3580234484,7.0
+Austria,51935.6038618156,7.1
+Iceland,52279.7288513646,7.5
+Netherlands,54209.5638357302,7.4
+Denmark,55938.2128086032,7.6
+United States,60235.7284916969,6.9
diff --git a/ML/01_intro/datasets/lifesat/gdp_per_capita.csv b/ML/01_intro/datasets/lifesat/gdp_per_capita.csv
new file mode 100644
index 0000000..49bb9c9
--- /dev/null
+++ b/ML/01_intro/datasets/lifesat/gdp_per_capita.csv
@@ -0,0 +1,7110 @@
+Entity,Code,Year,"GDP per capita, PPP (constant 2017 international $)"
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+Afghanistan,AFG,2020,1978.96157872183
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+Early-demographic dividend,,2004,5926.09801458174
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+Early-demographic dividend,,2007,6740.43968609283
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+Early-demographic dividend,,2015,8518.31158773067
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+Early-demographic dividend,,2019,9534.21063505113
+Early-demographic dividend,,2020,8964.6909169774
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+Euro area,,1990,32667.5955645947
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+Guyana,GUY,1990,4843.09227308768
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+Guyana,GUY,1999,7852.72126026137
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+Guyana,GUY,2001,7969.70874586926
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+Guyana,GUY,2007,8972.30598598409
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+Guyana,GUY,2010,9789.01670024955
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+Guyana,GUY,2014,11244.4452370602
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+Guyana,GUY,2019,13082.2026195835
+Guyana,GUY,2020,18679.9802308198
+Haiti,HTI,1990,3229.61955595456
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+Haiti,HTI,1992,2995.78184793736
+Haiti,HTI,1993,2779.60330261844
+Haiti,HTI,1994,2401.8268154123
+Haiti,HTI,1995,2591.08980324112
+Haiti,HTI,1996,2649.52567126416
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+Haiti,HTI,1998,2682.8135351668
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+Haiti,HTI,2018,2992.31041114318
+Haiti,HTI,2019,2905.43856536495
+Haiti,HTI,2020,2773.08136431323
+Heavily indebted poor countries (HIPC),,1990,1757.48852223556
+Heavily indebted poor countries (HIPC),,1991,1714.81942994975
+Heavily indebted poor countries (HIPC),,1992,1649.85154080141
+Heavily indebted poor countries (HIPC),,1993,1613.4207835841
+Heavily indebted poor countries (HIPC),,1994,1566.83754219433
+Heavily indebted poor countries (HIPC),,1995,1598.66207977412
+Heavily indebted poor countries (HIPC),,1996,1634.78905292263
+Heavily indebted poor countries (HIPC),,1997,1654.35096877934
+Heavily indebted poor countries (HIPC),,1998,1664.9192208575
+Heavily indebted poor countries (HIPC),,1999,1672.00633389637
+Heavily indebted poor countries (HIPC),,2000,1668.86745200323
+Heavily indebted poor countries (HIPC),,2001,1689.31125758377
+Heavily indebted poor countries (HIPC),,2002,1695.46390443124
+Heavily indebted poor countries (HIPC),,2003,1719.11654678634
+Heavily indebted poor countries (HIPC),,2004,1761.21813337527
+Heavily indebted poor countries (HIPC),,2005,1813.83404330648
+Heavily indebted poor countries (HIPC),,2006,1873.0157173773
+Heavily indebted poor countries (HIPC),,2007,1936.64061439575
+Heavily indebted poor countries (HIPC),,2008,1998.78612748736
+Heavily indebted poor countries (HIPC),,2009,2033.6650385254
+Heavily indebted poor countries (HIPC),,2010,2096.78354202238
+Heavily indebted poor countries (HIPC),,2011,2141.56031374767
+Heavily indebted poor countries (HIPC),,2012,2206.97208201063
+Heavily indebted poor countries (HIPC),,2013,2276.23332594606
+Heavily indebted poor countries (HIPC),,2014,2345.83303249306
+Heavily indebted poor countries (HIPC),,2015,2390.34647060241
+Heavily indebted poor countries (HIPC),,2016,2430.92214371884
+Heavily indebted poor countries (HIPC),,2017,2490.7292894789
+Heavily indebted poor countries (HIPC),,2018,2536.70484432927
+Heavily indebted poor countries (HIPC),,2019,2576.64430999862
+Heavily indebted poor countries (HIPC),,2020,2518.51013946003
+High income,,1990,32045.805755473
+High income,,1991,32339.2945969564
+High income,,1992,32867.6161819374
+High income,,1993,33027.6142284352
+High income,,1994,33858.1398107543
+High income,,1995,34564.4332692323
+High income,,1996,35390.5928374617
+High income,,1997,36395.7842293755
+High income,,1998,37178.9923078246
+High income,,1999,38183.1409676576
+High income,,2000,39535.7639188054
+High income,,2001,39878.1958750699
+High income,,2002,40247.7581722379
+High income,,2003,40941.9457997135
+High income,,2004,42090.7752470087
+High income,,2005,43051.802059704
+High income,,2006,44144.3633457965
+High income,,2007,45077.7338882581
+High income,,2008,44960.4412603546
+High income,,2009,43208.635658403
+High income,,2010,44257.533040905
+High income,,2011,44972.8593223518
+High income,,2012,45310.1450008023
+High income,,2013,45716.597108004
+High income,,2014,46431.1805567475
+High income,,2015,47302.856059263
+High income,,2016,47879.4431085279
+High income,,2017,48780.5509017054
+High income,,2018,49680.4358455199
+High income,,2019,50296.32710103
+High income,,2020,47785.7042413795
+Honduras,HND,1990,3990.02906523073
+Honduras,HND,1991,3749.17320680419
+Honduras,HND,1992,3866.11578810241
+Honduras,HND,1993,4002.96060044613
+Honduras,HND,1994,3899.71405099788
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+Honduras,HND,1998,4077.5503060541
+Honduras,HND,1999,3935.73122753833
+Honduras,HND,2000,4108.77497718928
+Honduras,HND,2001,4109.76969711082
+Honduras,HND,2002,4154.92535715546
+Honduras,HND,2003,4235.62313709617
+Honduras,HND,2004,4390.47334152231
+Honduras,HND,2005,4546.24063500184
+Honduras,HND,2006,4733.54786940426
+Honduras,HND,2007,4914.32374334188
+Honduras,HND,2008,5011.60729945583
+Honduras,HND,2009,4787.86375108506
+Honduras,HND,2010,4866.97393107266
+Honduras,HND,2011,4956.40285182158
+Honduras,HND,2012,5065.45699900649
+Honduras,HND,2013,5113.45915051909
+Honduras,HND,2014,5177.41519718935
+Honduras,HND,2015,5283.41691357908
+Honduras,HND,2016,5395.61455082235
+Honduras,HND,2017,5561.99454219124
+Honduras,HND,2018,5680.36288396582
+Honduras,HND,2019,5736.18163330613
+Honduras,HND,2020,5138.38539820015
+Hong Kong,HKG,1990,28798.4585956938
+Hong Kong,HKG,1991,30189.158562389
+Hong Kong,HKG,1992,31803.2870900616
+Hong Kong,HKG,1993,33200.2074000953
+Hong Kong,HKG,1994,34420.2465393573
+Hong Kong,HKG,1995,34546.4140309109
+Hong Kong,HKG,1996,34453.8365759066
+Hong Kong,HKG,1997,35910.6565279492
+Hong Kong,HKG,1998,33517.192385152
+Hong Kong,HKG,1999,34030.7705333159
+Hong Kong,HKG,2000,36317.1206975424
+Hong Kong,HKG,2001,36252.6608856787
+Hong Kong,HKG,2002,36690.398955264
+Hong Kong,HKG,2003,37886.4665283544
+Hong Kong,HKG,2004,40862.6723656296
+Hong Kong,HKG,2005,43690.3905052586
+Hong Kong,HKG,2006,46463.5837078363
+Hong Kong,HKG,2007,49043.9481656221
+Hong Kong,HKG,2008,49788.8139701131
+Hong Kong,HKG,2009,48459.9645781681
+Hong Kong,HKG,2010,51360.9669751956
+Hong Kong,HKG,2011,53472.9932695113
+Hong Kong,HKG,2012,53785.1530062167
+Hong Kong,HKG,2013,55230.8594343348
+Hong Kong,HKG,2014,56359.2989916423
+Hong Kong,HKG,2015,57215.948033846
+Hong Kong,HKG,2016,58096.2992560876
+Hong Kong,HKG,2017,59849.2481763342
+Hong Kong,HKG,2018,61062.9476532626
+Hong Kong,HKG,2019,59586.2040065092
+Hong Kong,HKG,2020,56153.9714986058
+Hungary,HUN,1991,16424.9091218342
+Hungary,HUN,1992,15927.852682374
+Hungary,HUN,1993,15854.1600476276
+Hungary,HUN,1994,16343.7631939198
+Hungary,HUN,1995,16610.3165037528
+Hungary,HUN,1996,16652.5852250299
+Hungary,HUN,1997,17210.6349309628
+Hungary,HUN,1998,17923.5052290465
+Hungary,HUN,1999,18526.3450154209
+Hungary,HUN,2000,19406.5213432499
+Hungary,HUN,2001,20243.5422548712
+Hungary,HUN,2002,21263.8088400796
+Hungary,HUN,2003,22195.8194585598
+Hungary,HUN,2004,23317.8691246987
+Hungary,HUN,2005,24355.7819172586
+Hungary,HUN,2006,25377.0522205332
+Hungary,HUN,2007,25477.9492943916
+Hungary,HUN,2008,25792.7025200493
+Hungary,HUN,2009,24102.0136912384
+Hungary,HUN,2010,24427.6287331225
+Hungary,HUN,2011,24971.4773629145
+Hungary,HUN,2012,24754.2259132575
+Hungary,HUN,2013,25284.3700544339
+Hungary,HUN,2014,26424.7204043413
+Hungary,HUN,2015,27499.3244662857
+Hungary,HUN,2016,28170.8213622975
+Hungary,HUN,2017,29465.1262413898
+Hungary,HUN,2018,31095.557503677
+Hungary,HUN,2019,32553.5236997988
+Hungary,HUN,2020,31007.7684065437
+IBRD only,,1990,5099.56529606863
+IBRD only,,1991,5041.54619618555
+IBRD only,,1992,4988.44547400916
+IBRD only,,1993,5039.69095769607
+IBRD only,,1994,5080.72537803819
+IBRD only,,1995,5192.95029548103
+IBRD only,,1996,5383.74601079151
+IBRD only,,1997,5564.73195650949
+IBRD only,,1998,5597.9329257487
+IBRD only,,1999,5737.52184101524
+IBRD only,,2000,5998.38479548308
+IBRD only,,2001,6138.94926915507
+IBRD only,,2002,6341.5812504077
+IBRD only,,2003,6644.17411812382
+IBRD only,,2004,7069.1168190892
+IBRD only,,2005,7477.35353925192
+IBRD only,,2006,7985.97266997362
+IBRD only,,2007,8573.7896043381
+IBRD only,,2008,8950.5025699929
+IBRD only,,2009,9058.64239194898
+IBRD only,,2010,9633.83233926391
+IBRD only,,2011,10104.8921902527
+IBRD only,,2012,10513.207255922
+IBRD only,,2013,10935.7210212184
+IBRD only,,2014,11327.6673078502
+IBRD only,,2015,11695.2724360905
+IBRD only,,2016,12127.6756576366
+IBRD only,,2017,12628.3347886596
+IBRD only,,2018,13105.4075664796
+IBRD only,,2019,13486.6663566891
+IBRD only,,2020,13066.0712195316
+IDA & IBRD total,,1990,4536.20962761484
+IDA & IBRD total,,1991,4479.48989548645
+IDA & IBRD total,,1992,4427.08747823883
+IDA & IBRD total,,1993,4454.6894517709
+IDA & IBRD total,,1994,4473.8803643377
+IDA & IBRD total,,1995,4561.31303608225
+IDA & IBRD total,,1996,4716.1836844533
+IDA & IBRD total,,1997,4855.87115033297
+IDA & IBRD total,,1998,4878.35866909197
+IDA & IBRD total,,1999,4985.4059532942
+IDA & IBRD total,,2000,5190.05815797412
+IDA & IBRD total,,2001,5302.17735228325
+IDA & IBRD total,,2002,5464.48128148703
+IDA & IBRD total,,2003,5705.40459126683
+IDA & IBRD total,,2004,6048.76967835981
+IDA & IBRD total,,2005,6376.31608764298
+IDA & IBRD total,,2006,6781.15623289015
+IDA & IBRD total,,2007,7246.49030978034
+IDA & IBRD total,,2008,7539.8715079758
+IDA & IBRD total,,2009,7626.36929768101
+IDA & IBRD total,,2010,8075.67206725467
+IDA & IBRD total,,2011,8438.74092827998
+IDA & IBRD total,,2012,8755.34269835152
+IDA & IBRD total,,2013,9086.53221744502
+IDA & IBRD total,,2014,9393.80117081524
+IDA & IBRD total,,2015,9672.45221610323
+IDA & IBRD total,,2016,9991.43491426674
+IDA & IBRD total,,2017,10367.1703163772
+IDA & IBRD total,,2018,10724.1301303154
+IDA & IBRD total,,2019,10996.8118303763
+IDA & IBRD total,,2020,10631.7564173745
+IDA blend,,1990,3164.22392793037
+IDA blend,,1991,3148.27103228807
+IDA blend,,1992,3165.41324436277
+IDA blend,,1993,3079.21973282258
+IDA blend,,1994,3037.80806592673
+IDA blend,,1995,3032.95179053062
+IDA blend,,1996,3092.97751632382
+IDA blend,,1997,3072.82024100903
+IDA blend,,1998,3078.02072728911
+IDA blend,,1999,3073.57433877456
+IDA blend,,2000,3111.30624329716
+IDA blend,,2001,3167.82694563034
+IDA blend,,2002,3288.88625738637
+IDA blend,,2003,3377.0119387689
+IDA blend,,2004,3546.04956721549
+IDA blend,,2005,3671.81843096302
+IDA blend,,2006,3794.19297080728
+IDA blend,,2007,3914.83977930927
+IDA blend,,2008,3965.40049525044
+IDA blend,,2009,4082.16776492349
+IDA blend,,2010,4205.45696771089
+IDA blend,,2011,4293.10417627791
+IDA blend,,2012,4382.15473089611
+IDA blend,,2013,4519.42292557933
+IDA blend,,2014,4667.6875806833
+IDA blend,,2015,4744.85507724962
+IDA blend,,2016,4746.33522849475
+IDA blend,,2017,4792.80944453195
+IDA blend,,2018,4874.5853207636
+IDA blend,,2019,4872.31174319057
+IDA blend,,2020,4730.22846642724
+IDA only,,1990,1749.30765168408
+IDA only,,1991,1717.94321467852
+IDA only,,1992,1672.08673717747
+IDA only,,1993,1654.29659082851
+IDA only,,1994,1611.32701524802
+IDA only,,1995,1646.66454651766
+IDA only,,1996,1685.57987650009
+IDA only,,1997,1714.62313278072
+IDA only,,1998,1736.53757777876
+IDA only,,1999,1767.36752421514
+IDA only,,2000,1794.1010199874
+IDA only,,2001,1839.80689352928
+IDA only,,2002,1862.83112086542
+IDA only,,2003,1913.6057574199
+IDA only,,2004,1979.24442503089
+IDA only,,2005,2060.59709167372
+IDA only,,2006,2151.00989016079
+IDA only,,2007,2252.16284823712
+IDA only,,2008,2344.77537032885
+IDA only,,2009,2407.42797386757
+IDA only,,2010,2500.26839421721
+IDA only,,2011,2580.57833532157
+IDA only,,2012,2679.9046938397
+IDA only,,2013,2787.93659184773
+IDA only,,2014,2896.31145697271
+IDA only,,2015,2988.54956681384
+IDA only,,2016,3080.13499731751
+IDA only,,2017,3199.05773182022
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+Late-demographic dividend,,1991,5218.76838579073
+Late-demographic dividend,,1992,5090.41860555975
+Late-demographic dividend,,1993,5173.55987876598
+Late-demographic dividend,,1994,5256.23583787637
+Late-demographic dividend,,1995,5448.91634519641
+Late-demographic dividend,,1996,5633.08287446302
+Late-demographic dividend,,1997,5848.72434260814
+Late-demographic dividend,,1998,5899.64397291242
+Late-demographic dividend,,1999,6110.24725831823
+Late-demographic dividend,,2000,6486.40932288695
+Late-demographic dividend,,2001,6760.18081965958
+Late-demographic dividend,,2002,7102.48288295078
+Late-demographic dividend,,2003,7531.11759563736
+Late-demographic dividend,,2004,8079.97799662662
+Late-demographic dividend,,2005,8625.25701799332
+Late-demographic dividend,,2006,9342.92237939351
+Late-demographic dividend,,2007,10198.0873914419
+Late-demographic dividend,,2008,10809.4724319827
+Late-demographic dividend,,2009,11029.8162709707
+Late-demographic dividend,,2010,11798.1432070488
+Late-demographic dividend,,2011,12521.1975399706
+Late-demographic dividend,,2012,13149.2623058946
+Late-demographic dividend,,2013,13763.8250360106
+Late-demographic dividend,,2014,14334.479492163
+Late-demographic dividend,,2015,14837.7627757009
+Late-demographic dividend,,2016,15353.2105923075
+Late-demographic dividend,,2017,16026.3936476925
+Late-demographic dividend,,2018,16763.1761094736
+Late-demographic dividend,,2019,17431.2669768095
+Late-demographic dividend,,2020,17315.0656649401
+Latin America & Caribbean,,1990,10810.7329924697
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+Latin America & Caribbean,,1993,11389.3142654322
+Latin America & Caribbean,,1994,11784.0151933395
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+Least developed countries: UN classification,,1991,1512.3749843667
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+Least developed countries: UN classification,,1993,1434.14450773433
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\ No newline at end of file
diff --git a/ML/01_intro/datasets/lifesat/lifesat.csv b/ML/01_intro/datasets/lifesat/lifesat.csv
new file mode 100644
index 0000000..b920e96
--- /dev/null
+++ b/ML/01_intro/datasets/lifesat/lifesat.csv
@@ -0,0 +1,28 @@
+Country,GDP per capita (USD),Life satisfaction
+Russia,26456.3879381321,5.8
+Greece,27287.0834009302,5.4
+Turkey,28384.9877846263,5.5
+Latvia,29932.4939100562,5.9
+Hungary,31007.7684065437,5.6
+Portugal,32181.1545372343,5.4
+Poland,32238.157259275,6.1
+Estonia,35638.4213511812,5.7
+Spain,36215.4475907307,6.3
+Slovenia,36547.7389559849,5.9
+Lithuania,36732.034744031,5.9
+Israel,38341.3075704083,7.2
+Italy,38992.1483807498,6.0
+United Kingdom,41627.129269425,6.8
+France,42025.6173730617,6.5
+New Zealand,42404.3937381567,7.3
+Canada,45856.6256264804,7.4
+Finland,47260.800458441,7.6
+Belgium,48210.0331113444,6.9
+Australia,48697.8370282475,7.3
+Sweden,50683.3235097178,7.3
+Germany,50922.3580234484,7.0
+Austria,51935.6038618156,7.1
+Iceland,52279.7288513646,7.5
+Netherlands,54209.5638357302,7.4
+Denmark,55938.2128086032,7.6
+United States,60235.7284916969,6.9
diff --git a/ML/01_intro/datasets/lifesat/lifesat_full.csv b/ML/01_intro/datasets/lifesat/lifesat_full.csv
new file mode 100644
index 0000000..125f4d4
--- /dev/null
+++ b/ML/01_intro/datasets/lifesat/lifesat_full.csv
@@ -0,0 +1,37 @@
+Country,GDP per capita (USD),Life satisfaction
+South Africa,11466.189671582,4.7
+Colombia,13441.4929522564,6.3
+Brazil,14063.9825053838,6.4
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+Chile,23324.5247506509,6.5
+Russia,26456.3879381321,5.8
+Greece,27287.0834009302,5.4
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+Portugal,32181.1545372343,5.4
+Poland,32238.157259275,6.1
+Estonia,35638.4213511812,5.7
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+Slovenia,36547.7389559849,5.9
+Lithuania,36732.034744031,5.9
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+Italy,38992.1483807498,6.0
+United Kingdom,41627.129269425,6.8
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+New Zealand,42404.3937381567,7.3
+Canada,45856.6256264804,7.4
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+Iceland,52279.7288513646,7.5
+Netherlands,54209.5638357302,7.4
+Denmark,55938.2128086032,7.6
+United States,60235.7284916969,6.9
+Norway,63585.9035142969,7.6
+Switzerland,68393.306003945,7.5
+Ireland,89688.9569584859,7.0
+Luxembourg,110261.157353302,6.9
diff --git a/ML/01_intro/datasets/lifesat/oecd_bli.csv b/ML/01_intro/datasets/lifesat/oecd_bli.csv
new file mode 100644
index 0000000..7dd996f
--- /dev/null
+++ b/ML/01_intro/datasets/lifesat/oecd_bli.csv
@@ -0,0 +1,2370 @@
+"LOCATION","Country","INDICATOR","Indicator","MEASURE","Measure","INEQUALITY","Inequality","Unit Code","Unit","PowerCode Code","PowerCode","Reference Period Code","Reference Period","Value","Flag Codes","Flags"

+"AUS","Australia","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,5.4,,

+"AUT","Austria","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,3.5,,

+"BEL","Belgium","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,3.7,,

+"CAN","Canada","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,6,,

+"CZE","Czech Republic","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,3.1,,

+"DNK","Denmark","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.2,,

+"FIN","Finland","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,3.9,,

+"FRA","France","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,7.6,,

+"DEU","Germany","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,2.7,,

+"GRC","Greece","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,29.8,,

+"HUN","Hungary","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.7,,

+"ISL","Iceland","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.7,,

+"IRL","Ireland","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,7.8,,

+"ITA","Italy","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,12.3,,

+"JPN","Japan","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.4,,

+"KOR","Korea","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,2.6,,

+"LUX","Luxembourg","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.7,,

+"MEX","Mexico","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,5.5,,

+"NLD","Netherlands","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.8,,

+"NZL","New Zealand","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.7,,

+"POL","Poland","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,5.7,,

+"PRT","Portugal","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,10,,

+"SVK","Slovak Republic","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,9.9,,

+"ESP","Spain","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,23.1,,

+"SWE","Sweden","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,3.2,,

+"TUR","Turkey","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,12.5,,

+"GBR","United Kingdom","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.5,,

+"USA","United States","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,7.7,,

+"CHL","Chile","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,8.7,,

+"EST","Estonia","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,3.8,,

+"ISR","Israel","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.2,,

+"LVA","Latvia","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,9.6,,

+"SVN","Slovenia","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,5.8,,

+"OECD","OECD - Total","JE_LMIS","Labour market insecurity","L","Value","TOT","Total","PC","Percentage","0","Units",,,7,,

+"AUS","Australia","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,5.9,,

+"AUT","Austria","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,4.2,,

+"BEL","Belgium","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,4.7,,

+"CAN","Canada","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,6.7,,

+"CZE","Czech Republic","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,2.8,,

+"DNK","Denmark","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,4.3,,

+"FIN","Finland","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,4.9,,

+"FRA","France","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,8.1,,

+"DEU","Germany","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,4.9,,

+"GRC","Greece","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,23.7,,

+"HUN","Hungary","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,4.7,,

+"ISL","Iceland","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,0.7,,

+"IRL","Ireland","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,10.5,,

+"ITA","Italy","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,12.1,,

+"JPN","Japan","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,2.4,,

+"KOR","Korea","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,3.1,,

+"LUX","Luxembourg","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,1.4,,

+"MEX","Mexico","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,5.5,,

+"NLD","Netherlands","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,4.9,,

+"POL","Poland","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,6,,

+"PRT","Portugal","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,10.2,,

+"SVK","Slovak Republic","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,8.4,,

+"ESP","Spain","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,21.1,,

+"SWE","Sweden","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,3.9,,

+"TUR","Turkey","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,10.2,,

+"GBR","United Kingdom","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,4.9,,

+"USA","United States","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,7.8,,

+"CHL","Chile","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,7,,

+"EST","Estonia","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,4.1,,

+"LVA","Latvia","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,11.1,,

+"SVN","Slovenia","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,5,,

+"OECD","OECD - Total","JE_LMIS","Labour market insecurity","L","Value","MN","Men","PC","Percentage","0","Units",,,7.2,,

+"AUS","Australia","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,5,,

+"AUT","Austria","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.7,,

+"BEL","Belgium","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.7,,

+"CAN","Canada","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,5.4,,

+"CZE","Czech Republic","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,3.5,,

+"DNK","Denmark","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,4.1,,

+"FIN","Finland","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.9,,

+"FRA","France","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,7,,

+"DEU","Germany","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,4.2,,

+"GRC","Greece","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,36,,

+"HUN","Hungary","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,4.6,,

+"ISL","Iceland","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.7,,

+"IRL","Ireland","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,5.2,,

+"ITA","Italy","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,12.5,,

+"JPN","Japan","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.3,,

+"KOR","Korea","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.1,,

+"LUX","Luxembourg","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.9,,

+"MEX","Mexico","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,5.5,,

+"NLD","Netherlands","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,4.7,,

+"POL","Poland","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,5.3,,

+"PRT","Portugal","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,9.7,,

+"SVK","Slovak Republic","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,11.4,,

+"ESP","Spain","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,25,,

+"SWE","Sweden","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.4,,

+"TUR","Turkey","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,14.9,,

+"GBR","United Kingdom","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,4.2,,

+"USA","United States","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,7.6,,

+"CHL","Chile","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,10.3,,

+"EST","Estonia","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,3.6,,

+"LVA","Latvia","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,8,,

+"SVN","Slovenia","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,6.5,,

+"OECD","OECD - Total","JE_LMIS","Labour market insecurity","L","Value","WMN","Women","PC","Percentage","0","Units",,,7.2,,

+"AUS","Australia","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,2.29,,

+"AUT","Austria","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,2.67,,

+"BEL","Belgium","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,2.19,,

+"CAN","Canada","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,4.17,,

+"CZE","Czech Republic","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,1.5,,

+"DNK","Denmark","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,3.65,,

+"FIN","Finland","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,3.41,,

+"FRA","France","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,4.11,,

+"DEU","Germany","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,2.41,,

+"GRC","Greece","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,23.78,,

+"HUN","Hungary","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,1.62,,

+"ISL","Iceland","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,0.59,,

+"IRL","Ireland","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,4.1,,

+"ITA","Italy","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,6.98,,

+"JPN","Japan","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,2.65,,

+"KOR","Korea","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,2.6,,

+"LUX","Luxembourg","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,2.02,,

+"MEX","Mexico","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,6.26,,

+"NLD","Netherlands","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,3.07,,

+"POL","Poland","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,2.92,,

+"PRT","Portugal","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,7.46,,

+"SVK","Slovak Republic","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,5.02,,

+"ESP","Spain","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,13.05,,

+"SWE","Sweden","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,2.15,,

+"TUR","Turkey","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,11.48,,

+"GBR","United Kingdom","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,3.1,,

+"USA","United States","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,4.35,,

+"CHL","Chile","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,6.64,,

+"EST","Estonia","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,2.43,,

+"LVA","Latvia","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,4.59,,

+"SVN","Slovenia","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,3.84,,

+"OECD","OECD - Total","JE_LMIS","Labour market insecurity","L","Value","HGH","High","PC","Percentage","0","Units",,,5,,

+"AUS","Australia","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,11.02,,

+"AUT","Austria","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,7.31,,

+"BEL","Belgium","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,6.97,,

+"CAN","Canada","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,12.97,,

+"CZE","Czech Republic","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,25.14,,

+"DNK","Denmark","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,6.78,,

+"FIN","Finland","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,7.43,,

+"FRA","France","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,15.09,,

+"DEU","Germany","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,10.43,,

+"GRC","Greece","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,32.72,,

+"HUN","Hungary","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,10.66,,

+"ISL","Iceland","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,0.62,,

+"IRL","Ireland","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,16.32,,

+"ITA","Italy","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,16.78,,

+"JPN","Japan","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,4.35,,

+"KOR","Korea","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,1.11,,

+"LUX","Luxembourg","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,1.59,,

+"MEX","Mexico","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,4.22,,

+"NLD","Netherlands","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,7.18,,

+"POL","Poland","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,15,,

+"PRT","Portugal","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,10.76,,

+"SVK","Slovak Republic","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,72.16,,

+"ESP","Spain","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,36.71,,

+"SWE","Sweden","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,9.51,,

+"TUR","Turkey","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,11.22,,

+"GBR","United Kingdom","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,7.02,,

+"USA","United States","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,17.91,,

+"CHL","Chile","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,8.2,,

+"EST","Estonia","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,8.03,,

+"LVA","Latvia","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,23.29,,

+"SVN","Slovenia","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,10.55,,

+"OECD","OECD - Total","JE_LMIS","Labour market insecurity","L","Value","LW","Low","PC","Percentage","0","Units",,,12.35,,

+"AUS","Australia","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.7,,

+"AUT","Austria","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,1.3,,

+"BEL","Belgium","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2,,

+"CAN","Canada","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.9,,

+"CZE","Czech Republic","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,1.6,,

+"DNK","Denmark","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2,,

+"FIN","Finland","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.2,,

+"FRA","France","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.1,,

+"DEU","Germany","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,1.8,,

+"GRC","Greece","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,1.8,,

+"HUN","Hungary","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,1.2,,

+"ISL","Iceland","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.1,,

+"IRL","Ireland","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,1.3,,

+"ITA","Italy","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.5,,

+"JPN","Japan","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,1.4,,

+"KOR","Korea","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.9,,

+"LUX","Luxembourg","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,1.7,,

+"MEX","Mexico","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,3.2,,

+"NLD","Netherlands","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.6,,

+"NZL","New Zealand","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.5,,

+"NOR","Norway","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.2,,

+"POL","Poland","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.6,,

+"PRT","Portugal","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,1.5,,

+"SVK","Slovak Republic","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,3,,

+"ESP","Spain","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,1.8,,

+"SWE","Sweden","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2,,

+"CHE","Switzerland","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.3,,

+"TUR","Turkey","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,1.5,,

+"GBR","United Kingdom","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,3.1,,

+"USA","United States","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,3.1,,

+"BRA","Brazil","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.2,,

+"CHL","Chile","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,1.3,,

+"EST","Estonia","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.7,,

+"ISR","Israel","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.5,,

+"LVA","Latvia","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.2,,

+"SVN","Slovenia","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.5,,

+"OECD","OECD - Total","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.4,,

+"AUT","Austria","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.9,,

+"BEL","Belgium","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.9,,

+"CAN","Canada","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.2,,

+"CZE","Czech Republic","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.7,,

+"DNK","Denmark","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.5,,

+"FIN","Finland","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.5,,

+"FRA","France","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.5,,

+"DEU","Germany","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.2,,

+"GRC","Greece","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.5,,

+"HUN","Hungary","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.7,,

+"ISL","Iceland","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0,,

+"IRL","Ireland","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,1,,

+"ITA","Italy","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.7,,

+"JPN","Japan","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,6.4,,

+"KOR","Korea","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,2.5,,

+"LUX","Luxembourg","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.5,,

+"MEX","Mexico","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,25.5,,

+"NLD","Netherlands","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.1,,

+"NOR","Norway","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0,,

+"POL","Poland","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,3,,

+"PRT","Portugal","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,1,,

+"SVK","Slovak Republic","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.2,,

+"ESP","Spain","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.1,,

+"SWE","Sweden","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0,,

+"CHE","Switzerland","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.1,,

+"TUR","Turkey","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,8,,

+"GBR","United Kingdom","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.3,,

+"USA","United States","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.1,,

+"BRA","Brazil","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,6.7,,

+"CHL","Chile","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,9.4,,

+"EST","Estonia","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,7,,

+"LVA","Latvia","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,13.9,,

+"RUS","Russia","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,14.8,,

+"SVN","Slovenia","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.4,,

+"ZAF","South Africa","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,37,,

+"OECD","OECD - Total","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.4,,

+"AUS","Australia","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,20,,

+"AUT","Austria","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,21,,

+"BEL","Belgium","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,21,,

+"CAN","Canada","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,22,,

+"CZE","Czech Republic","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,24,,

+"DNK","Denmark","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,23,,

+"FIN","Finland","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,23,,

+"FRA","France","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,21,,

+"DEU","Germany","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,20,,

+"GRC","Greece","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,23,,

+"HUN","Hungary","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,19,,

+"ISL","Iceland","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,24,,

+"IRL","Ireland","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,20,,

+"ITA","Italy","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,23,,

+"JPN","Japan","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,22,,

+"KOR","Korea","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,15,,

+"LUX","Luxembourg","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,21,,

+"MEX","Mexico","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,20,,

+"NLD","Netherlands","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,19,,

+"NZL","New Zealand","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,26,,

+"NOR","Norway","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,17,,

+"POL","Poland","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,22,,

+"PRT","Portugal","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,21,,

+"SVK","Slovak Republic","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,23,,

+"ESP","Spain","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,21,,

+"SWE","Sweden","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,19,,

+"CHE","Switzerland","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,22,,

+"TUR","Turkey","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,20,,

+"GBR","United Kingdom","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,26,,

+"USA","United States","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,19,,

+"CHL","Chile","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,18,,

+"EST","Estonia","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,17,,

+"LVA","Latvia","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,23,,

+"RUS","Russia","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,18,,

+"SVN","Slovenia","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,18,,

+"ZAF","South Africa","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,18,,

+"OECD","OECD - Total","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,20,,

+"AUS","Australia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,63.5,,

+"AUT","Austria","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,80.6,,

+"BEL","Belgium","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,70.1,,

+"CAN","Canada","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,82.2,,

+"CZE","Czech Republic","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,72.3,,

+"DNK","Denmark","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,83.5,,

+"FIN","Finland","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,85.1,,

+"FRA","France","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,70.5,,

+"DEU","Germany","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,72.5,,

+"GRC","Greece","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,60,,

+"HUN","Hungary","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,56.3,,

+"ISL","Iceland","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,86,,

+"IRL","Ireland","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,75.9,,

+"ITA","Italy","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,58.4,,

+"JPN","Japan","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,72.5,,

+"KOR","Korea","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,66.6,,

+"LUX","Luxembourg","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,75.8,,

+"MEX","Mexico","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,41.8,,

+"NLD","Netherlands","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,82,,

+"NZL","New Zealand","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,65.7,,

+"NOR","Norway","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,90.1,,

+"POL","Poland","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,67.3,,

+"PRT","Portugal","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,73.4,,

+"SVK","Slovak Republic","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,63.5,,

+"ESP","Spain","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,82.1,,

+"SWE","Sweden","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,75.6,,

+"CHE","Switzerland","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,85.3,,

+"TUR","Turkey","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,59.8,,

+"GBR","United Kingdom","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,77.7,,

+"USA","United States","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,73.9,,

+"BRA","Brazil","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,35.6,,

+"CHL","Chile","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,47.9,,

+"EST","Estonia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,69,,

+"ISR","Israel","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,69.8,,

+"LVA","Latvia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,62.4,,

+"RUS","Russia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,52.8,,

+"SVN","Slovenia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,86.1,,

+"ZAF","South Africa","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,36.1,,

+"OECD","OECD - Total","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,68.4,,

+"AUS","Australia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,80.1,,

+"AUT","Austria","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,84.1,,

+"BEL","Belgium","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,81.6,,

+"CAN","Canada","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,92.2,,

+"CZE","Czech Republic","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,82.7,,

+"DNK","Denmark","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,92.3,,

+"FIN","Finland","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,94.5,,

+"FRA","France","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,76.1,,

+"DEU","Germany","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,77.9,,

+"GRC","Greece","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,69.8,,

+"HUN","Hungary","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,65.6,,

+"ISL","Iceland","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,95.1,,

+"IRL","Ireland","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,83.2,,

+"ITA","Italy","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,64.8,,

+"JPN","Japan","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,81.1,,

+"KOR","Korea","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,73.4,,

+"LUX","Luxembourg","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,80.3,,

+"MEX","Mexico","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,45.8,,

+"NLD","Netherlands","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,91.4,,

+"NZL","New Zealand","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,81.3,,

+"NOR","Norway","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,96.5,,

+"POL","Poland","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,73.9,,

+"PRT","Portugal","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,84.8,,

+"SVK","Slovak Republic","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,71.7,,

+"ESP","Spain","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,84.7,,

+"SWE","Sweden","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,87.9,,

+"CHE","Switzerland","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,88.8,,

+"TUR","Turkey","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,67.3,,

+"GBR","United Kingdom","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,81.3,,

+"USA","United States","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,84,,

+"BRA","Brazil","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,44.7,,

+"CHL","Chile","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,52.6,,

+"EST","Estonia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,80.7,,

+"ISR","Israel","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,78,,

+"LVA","Latvia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,72.4,,

+"RUS","Russia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,63.1,,

+"SVN","Slovenia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,93.1,,

+"ZAF","South Africa","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,45.4,,

+"OECD","OECD - Total","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,75.9,,

+"AUS","Australia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,48.8,,

+"AUT","Austria","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,77.4,,

+"BEL","Belgium","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,59.1,,

+"CAN","Canada","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,73.1,,

+"CZE","Czech Republic","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,62.6,,

+"DNK","Denmark","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,74.9,,

+"FIN","Finland","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,76.2,,

+"FRA","France","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,65.5,,

+"DEU","Germany","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,67.4,,

+"GRC","Greece","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,50.8,,

+"HUN","Hungary","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,48.2,,

+"ISL","Iceland","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,77.8,,

+"IRL","Ireland","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,68.9,,

+"ITA","Italy","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,52.6,,

+"JPN","Japan","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,64.8,,

+"KOR","Korea","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,60,,

+"LUX","Luxembourg","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,71.5,,

+"MEX","Mexico","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,38.3,,

+"NLD","Netherlands","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,72.8,,

+"NZL","New Zealand","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,51.8,,

+"NOR","Norway","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,83.5,,

+"POL","Poland","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,61.2,,

+"PRT","Portugal","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,63.1,,

+"SVK","Slovak Republic","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,55.9,,

+"ESP","Spain","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,79.7,,

+"SWE","Sweden","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,63.2,,

+"CHE","Switzerland","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,81.9,,

+"TUR","Turkey","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,52.4,,

+"GBR","United Kingdom","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,74.3,,

+"USA","United States","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,63.8,,

+"BRA","Brazil","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,27.3,,

+"CHL","Chile","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,43.5,,

+"EST","Estonia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,59.2,,

+"ISR","Israel","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,62.1,,

+"LVA","Latvia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,54.4,,

+"RUS","Russia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,44.5,,

+"SVN","Slovenia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,79.3,,

+"ZAF","South Africa","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,27.5,,

+"OECD","OECD - Total","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,61.2,,

+"AUT","Austria","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.6,,

+"BEL","Belgium","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,2.2,,

+"CAN","Canada","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,2.6,,

+"CZE","Czech Republic","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.4,,

+"DNK","Denmark","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.9,,

+"FIN","Finland","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.9,,

+"FRA","France","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.8,,

+"DEU","Germany","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.8,,

+"GRC","Greece","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.2,,

+"HUN","Hungary","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.2,,

+"ISL","Iceland","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.6,,

+"IRL","Ireland","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,2.1,,

+"ITA","Italy","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.4,,

+"JPN","Japan","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.9,,

+"KOR","Korea","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.5,,

+"LUX","Luxembourg","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.9,,

+"MEX","Mexico","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1,,

+"NLD","Netherlands","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.9,,

+"NZL","New Zealand","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,2.4,,

+"NOR","Norway","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,2.1,,

+"POL","Poland","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.1,,

+"PRT","Portugal","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.7,,

+"SVK","Slovak Republic","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.1,,

+"ESP","Spain","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.9,,

+"SWE","Sweden","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.7,,

+"CHE","Switzerland","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.9,,

+"TUR","Turkey","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1,,

+"GBR","United Kingdom","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.9,,

+"USA","United States","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,2.4,,

+"CHL","Chile","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.2,,

+"EST","Estonia","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.6,,

+"ISR","Israel","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.2,,

+"LVA","Latvia","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.2,,

+"RUS","Russia","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.9,,

+"SVN","Slovenia","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.5,,

+"OECD","OECD - Total","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.8,,

+"AUS","Australia","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,32759,,

+"AUT","Austria","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,33541,,

+"BEL","Belgium","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,30364,,

+"CAN","Canada","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,30854,,

+"CZE","Czech Republic","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,21453,,

+"DNK","Denmark","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,29606,,

+"FIN","Finland","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,29943,,

+"FRA","France","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,31304,,

+"DEU","Germany","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,34294,,

+"GRC","Greece","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,17700,,

+"IRL","Ireland","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,25310,,

+"ITA","Italy","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,26588,,

+"JPN","Japan","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,29798,,

+"KOR","Korea","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,21882,,

+"LUX","Luxembourg","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,39264,,

+"NLD","Netherlands","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,29333,,

+"NOR","Norway","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,35725,,

+"POL","Poland","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,19814,,

+"PRT","Portugal","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,21203,,

+"SVK","Slovak Republic","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,20474,,

+"ESP","Spain","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,23999,,

+"SWE","Sweden","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,31287,,

+"CHE","Switzerland","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,37466,,

+"GBR","United Kingdom","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,28715,,

+"USA","United States","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,45284,,

+"EST","Estonia","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,19697,,

+"LVA","Latvia","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,16275,,

+"SVN","Slovenia","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,20820,,

+"OECD","OECD - Total","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,33604,,

+"AUS","Australia","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,427064,,

+"AUT","Austria","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,308325,,

+"BEL","Belgium","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,386006,,

+"CAN","Canada","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,423849,,

+"DNK","Denmark","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,118637,,

+"FIN","Finland","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,200827,,

+"FRA","France","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,280653,,

+"DEU","Germany","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,259667,,

+"GRC","Greece","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,150134,,

+"HUN","Hungary","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,104458,,

+"IRL","Ireland","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,217130,,

+"ITA","Italy","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,279889,,

+"JPN","Japan","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,305878,,

+"KOR","Korea","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,285980,,

+"LUX","Luxembourg","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,769053,,

+"NLD","Netherlands","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,157824,,

+"NZL","New Zealand","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,388514,,

+"NOR","Norway","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,228936,,

+"POL","Poland","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,210991,,

+"PRT","Portugal","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,232666,,

+"SVK","Slovak Republic","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,119696,,

+"ESP","Spain","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,373548,,

+"GBR","United Kingdom","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,548392,,

+"USA","United States","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,632100,,

+"CHL","Chile","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,100967,,

+"EST","Estonia","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,159373,,

+"LVA","Latvia","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,70160,,

+"SVN","Slovenia","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,203044,,

+"OECD","OECD - Total","IW_HNFW","Household net wealth","L","Value","TOT","Total","USD","US Dollar","0","Units",,,408376,,

+"AUS","Australia","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,73,,

+"AUT","Austria","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,72,,

+"BEL","Belgium","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,63,,

+"CAN","Canada","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,73,,

+"CZE","Czech Republic","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,74,,

+"DNK","Denmark","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,74,,

+"FIN","Finland","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,70,,

+"FRA","France","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,65,,

+"DEU","Germany","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,75,,

+"GRC","Greece","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,53,,

+"HUN","Hungary","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,68,,

+"ISL","Iceland","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,86,,

+"IRL","Ireland","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,67,,

+"ITA","Italy","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,58,,

+"JPN","Japan","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,75,,

+"KOR","Korea","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,67,,

+"LUX","Luxembourg","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,66,,

+"MEX","Mexico","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,61,,

+"NLD","Netherlands","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,76,,

+"NZL","New Zealand","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,77,,

+"NOR","Norway","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,74,,

+"POL","Poland","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,66,,

+"PRT","Portugal","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,68,,

+"SVK","Slovak Republic","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,66,,

+"ESP","Spain","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,62,,

+"SWE","Sweden","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,77,,

+"CHE","Switzerland","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,80,,

+"TUR","Turkey","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,52,,

+"GBR","United Kingdom","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,75,,

+"USA","United States","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,70,,

+"BRA","Brazil","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,61,,

+"CHL","Chile","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,63,,

+"EST","Estonia","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,74,,

+"ISR","Israel","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,69,,

+"LVA","Latvia","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,70,,

+"RUS","Russia","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,70,,

+"SVN","Slovenia","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,69,,

+"ZAF","South Africa","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,43,,

+"OECD","OECD - Total","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,68,,

+"AUS","Australia","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,78,,

+"AUT","Austria","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,76,,

+"BEL","Belgium","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,68,,

+"CAN","Canada","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,76,,

+"CZE","Czech Republic","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,81,,

+"DNK","Denmark","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,77,,

+"FIN","Finland","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,72,,

+"FRA","France","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,69,,

+"DEU","Germany","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,79,,

+"GRC","Greece","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,63,,

+"HUN","Hungary","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,75,,

+"ISL","Iceland","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,88,,

+"IRL","Ireland","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,73,,

+"ITA","Italy","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,67,,

+"JPN","Japan","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,83,,

+"KOR","Korea","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,76,,

+"LUX","Luxembourg","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,70,,

+"MEX","Mexico","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,79,,

+"NLD","Netherlands","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,80,,

+"NZL","New Zealand","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,82,,

+"NOR","Norway","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,76,,

+"POL","Poland","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,73,,

+"PRT","Portugal","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,71,,

+"SVK","Slovak Republic","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,72,,

+"ESP","Spain","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,68,,

+"SWE","Sweden","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,78,,

+"CHE","Switzerland","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,84,,

+"TUR","Turkey","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,71,,

+"GBR","United Kingdom","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,80,,

+"USA","United States","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,75,,

+"BRA","Brazil","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,71,,

+"CHL","Chile","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,72,,

+"EST","Estonia","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,77,,

+"ISR","Israel","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,72,,

+"LVA","Latvia","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,72,,

+"RUS","Russia","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,76,,

+"SVN","Slovenia","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,73,,

+"ZAF","South Africa","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,49,,

+"OECD","OECD - Total","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,76,,

+"AUS","Australia","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,68,,

+"AUT","Austria","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,68,,

+"BEL","Belgium","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,59,,

+"CAN","Canada","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,71,,

+"CZE","Czech Republic","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,66,,

+"DNK","Denmark","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,71,,

+"FIN","Finland","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,69,,

+"FRA","France","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,62,,

+"DEU","Germany","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,72,,

+"GRC","Greece","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,44,,

+"HUN","Hungary","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,61,,

+"ISL","Iceland","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,83,,

+"IRL","Ireland","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,62,,

+"ITA","Italy","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,49,,

+"JPN","Japan","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,67,,

+"KOR","Korea","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,57,,

+"LUX","Luxembourg","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,63,,

+"MEX","Mexico","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,45,,

+"NLD","Netherlands","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,71,,

+"NZL","New Zealand","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,72,,

+"NOR","Norway","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,72,,

+"POL","Poland","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,59,,

+"PRT","Portugal","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,65,,

+"SVK","Slovak Republic","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,60,,

+"ESP","Spain","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,57,,

+"SWE","Sweden","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,75,,

+"CHE","Switzerland","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,75,,

+"TUR","Turkey","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,32,,

+"GBR","United Kingdom","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,70,,

+"USA","United States","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,65,,

+"BRA","Brazil","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,51,,

+"CHL","Chile","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,53,,

+"EST","Estonia","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,71,,

+"ISR","Israel","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,66,,

+"LVA","Latvia","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,68,,

+"RUS","Russia","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,65,,

+"SVN","Slovenia","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,66,,

+"ZAF","South Africa","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,38,,

+"OECD","OECD - Total","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,60,,

+"AUS","Australia","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.31,,

+"AUT","Austria","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.84,,

+"BEL","Belgium","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,3.54,,

+"CAN","Canada","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.77,,

+"CZE","Czech Republic","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.04,,

+"DNK","Denmark","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.31,,

+"FIN","Finland","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,2.13,,

+"FRA","France","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,4,,

+"DEU","Germany","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.57,,

+"GRC","Greece","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,15.65,,

+"HUN","Hungary","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.72,,

+"ISL","Iceland","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.26,,

+"IRL","Ireland","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,3.23,,

+"ITA","Italy","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,6.59,,

+"JPN","Japan","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.03,,

+"KOR","Korea","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.05,,

+"LUX","Luxembourg","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,2.35,,

+"MEX","Mexico","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.07,,

+"NLD","Netherlands","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.97,,

+"NZL","New Zealand","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.74,,

+"NOR","Norway","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.66,,

+"POL","Poland","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.52,,

+"PRT","Portugal","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.43,,

+"SVK","Slovak Republic","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.78,,

+"ESP","Spain","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,7.66,,

+"SWE","Sweden","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.12,,

+"CHE","Switzerland","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.82,,

+"TUR","Turkey","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,2.39,,

+"GBR","United Kingdom","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.13,,

+"USA","United States","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.66,,

+"EST","Estonia","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.92,,

+"ISR","Israel","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.49,,

+"LVA","Latvia","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,3.35,,

+"RUS","Russia","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.59,,

+"SVN","Slovenia","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,3.17,,

+"ZAF","South Africa","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,16.46,,

+"OECD","OECD - Total","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.78,,

+"AUS","Australia","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,1.38,,

+"AUT","Austria","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,1.99,,

+"BEL","Belgium","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,3.71,,

+"CAN","Canada","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,0.88,,

+"CZE","Czech Republic","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,0.85,,

+"DNK","Denmark","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,1.34,,

+"FIN","Finland","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,2.44,,

+"FRA","France","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,4.2,,

+"DEU","Germany","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,1.8,,

+"GRC","Greece","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,12.62,,

+"HUN","Hungary","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,1.59,,

+"ISL","Iceland","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,0.24,,

+"IRL","Ireland","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,3.83,,

+"ITA","Italy","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,6.17,,

+"JPN","Japan","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,1.38,,

+"KOR","Korea","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,0.05,,

+"LUX","Luxembourg","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,2.69,,

+"MEX","Mexico","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,0.06,,

+"NLD","Netherlands","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,1.81,,

+"NZL","New Zealand","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,0.72,,

+"NOR","Norway","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,0.8,,

+"POL","Poland","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,1.55,,

+"PRT","Portugal","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,4.25,,

+"SVK","Slovak Republic","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,4.91,,

+"ESP","Spain","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,6.65,,

+"SWE","Sweden","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,1.3,,

+"CHE","Switzerland","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,1.66,,

+"TUR","Turkey","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,1.65,,

+"GBR","United Kingdom","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,1.3,,

+"USA","United States","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,0.68,,

+"EST","Estonia","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,2.22,,

+"ISR","Israel","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,0.49,,

+"LVA","Latvia","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,4.06,,

+"RUS","Russia","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,1.6,,

+"SVN","Slovenia","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,3.1,,

+"ZAF","South Africa","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,14.42,,

+"OECD","OECD - Total","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,1.73,,

+"AUS","Australia","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.23,,

+"AUT","Austria","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.66,,

+"BEL","Belgium","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,3.35,,

+"CAN","Canada","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.64,,

+"CZE","Czech Republic","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.28,,

+"DNK","Denmark","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.29,,

+"FIN","Finland","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.8,,

+"FRA","France","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,3.79,,

+"DEU","Germany","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.3,,

+"GRC","Greece","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,19.42,,

+"HUN","Hungary","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.87,,

+"ISL","Iceland","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.27,,

+"IRL","Ireland","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.55,,

+"ITA","Italy","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,7.16,,

+"JPN","Japan","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.58,,

+"KOR","Korea","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.05,,

+"LUX","Luxembourg","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.99,,

+"MEX","Mexico","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.07,,

+"NLD","Netherlands","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.15,,

+"NZL","New Zealand","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.75,,

+"NOR","Norway","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.51,,

+"POL","Poland","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.47,,

+"PRT","Portugal","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,4.61,,

+"SVK","Slovak Republic","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,4.63,,

+"ESP","Spain","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,8.82,,

+"SWE","Sweden","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.92,,

+"CHE","Switzerland","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,2,,

+"TUR","Turkey","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,3.94,,

+"GBR","United Kingdom","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.95,,

+"USA","United States","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.63,,

+"EST","Estonia","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.61,,

+"ISR","Israel","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.48,,

+"LVA","Latvia","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.65,,

+"RUS","Russia","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.57,,

+"SVN","Slovenia","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,3.26,,

+"ZAF","South Africa","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,19.05,,

+"OECD","OECD - Total","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.84,,

+"AUS","Australia","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,49126,,

+"AUT","Austria","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,50349,,

+"BEL","Belgium","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,49675,,

+"CAN","Canada","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,47622,,

+"CZE","Czech Republic","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,25372,,

+"DNK","Denmark","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,51466,,

+"FIN","Finland","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,42964,,

+"FRA","France","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,43755,,

+"DEU","Germany","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,47585,,

+"GRC","Greece","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,26064,,

+"HUN","Hungary","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,22576,,

+"ISL","Iceland","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,61787,,

+"IRL","Ireland","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,47653,,

+"ITA","Italy","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,36658,,

+"JPN","Japan","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,40863,,

+"KOR","Korea","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,35191,,

+"LUX","Luxembourg","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,63062,,

+"MEX","Mexico","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,15314,,

+"NLD","Netherlands","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,52877,,

+"NZL","New Zealand","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,40043,,

+"NOR","Norway","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,51212,,

+"POL","Poland","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,27046,,

+"PRT","Portugal","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,25367,,

+"SVK","Slovak Republic","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,24328,,

+"ESP","Spain","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,38507,,

+"SWE","Sweden","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,42393,,

+"CHE","Switzerland","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,62283,,

+"GBR","United Kingdom","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,43732,,

+"USA","United States","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,60558,,

+"CHL","Chile","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,25879,,

+"EST","Estonia","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,24336,,

+"ISR","Israel","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,35067,,

+"LVA","Latvia","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,23683,,

+"SVN","Slovenia","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,34933,,

+"OECD","OECD - Total","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,43241,,

+"AUS","Australia","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,95,,

+"AUT","Austria","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,92,,

+"BEL","Belgium","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,91,,

+"CAN","Canada","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,93,,

+"CZE","Czech Republic","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,91,,

+"DNK","Denmark","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,95,,

+"FIN","Finland","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,95,,

+"FRA","France","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,90,,

+"DEU","Germany","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,90,,

+"GRC","Greece","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,80,,

+"HUN","Hungary","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,86,,

+"ISL","Iceland","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,98,,

+"IRL","Ireland","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,95,,

+"ITA","Italy","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,92,,

+"JPN","Japan","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,89,,

+"KOR","Korea","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,78,,

+"LUX","Luxembourg","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,93,,

+"MEX","Mexico","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,81,,

+"NLD","Netherlands","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,91,,

+"NZL","New Zealand","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,96,,

+"NOR","Norway","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,94,,

+"POL","Poland","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,86,,

+"PRT","Portugal","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,88,,

+"SVK","Slovak Republic","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,91,,

+"ESP","Spain","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,93,,

+"SWE","Sweden","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,91,,

+"CHE","Switzerland","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,93,,

+"TUR","Turkey","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,86,,

+"GBR","United Kingdom","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,94,,

+"USA","United States","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,91,,

+"BRA","Brazil","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,90,,

+"CHL","Chile","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,85,,

+"EST","Estonia","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,92,,

+"ISR","Israel","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,88,,

+"LVA","Latvia","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,86,,

+"RUS","Russia","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,89,,

+"SVN","Slovenia","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,92,,

+"ZAF","South Africa","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,88,,

+"OECD","OECD - Total","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,89,,

+"AUS","Australia","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,96,,

+"AUT","Austria","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,92,,

+"BEL","Belgium","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,92,,

+"CAN","Canada","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,92,,

+"CZE","Czech Republic","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,90,,

+"DNK","Denmark","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,96,,

+"FIN","Finland","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,94,,

+"FRA","France","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,90,,

+"DEU","Germany","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,90,,

+"GRC","Greece","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,79,,

+"HUN","Hungary","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,84,,

+"ISL","Iceland","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,97,,

+"IRL","Ireland","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,94,,

+"ITA","Italy","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,92,,

+"JPN","Japan","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,85,,

+"KOR","Korea","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,76,,

+"LUX","Luxembourg","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,94,,

+"MEX","Mexico","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,79,,

+"NLD","Netherlands","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,92,,

+"NZL","New Zealand","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,95,,

+"NOR","Norway","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,95,,

+"POL","Poland","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,84,,

+"PRT","Portugal","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,88,,

+"SVK","Slovak Republic","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,91,,

+"ESP","Spain","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,93,,

+"SWE","Sweden","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,91,,

+"CHE","Switzerland","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,94,,

+"TUR","Turkey","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,86,,

+"GBR","United Kingdom","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,94,,

+"USA","United States","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,89,,

+"BRA","Brazil","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,90,,

+"CHL","Chile","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,85,,

+"EST","Estonia","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,91,,

+"ISR","Israel","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,88,,

+"LVA","Latvia","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,83,,

+"RUS","Russia","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,89,,

+"SVN","Slovenia","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,91,,

+"ZAF","South Africa","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,88,,

+"OECD","OECD - Total","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,88,,

+"AUS","Australia","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,94,,

+"AUT","Austria","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,92,,

+"BEL","Belgium","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,90,,

+"CAN","Canada","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,94,,

+"CZE","Czech Republic","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,91,,

+"DNK","Denmark","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,95,,

+"FIN","Finland","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,96,,

+"FRA","France","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,91,,

+"DEU","Germany","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,90,,

+"GRC","Greece","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,80,,

+"HUN","Hungary","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,87,,

+"ISL","Iceland","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,98,,

+"IRL","Ireland","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,96,,

+"ITA","Italy","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,91,,

+"JPN","Japan","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,93,,

+"KOR","Korea","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,81,,

+"LUX","Luxembourg","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,92,,

+"MEX","Mexico","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,83,,

+"NLD","Netherlands","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,90,,

+"NZL","New Zealand","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,96,,

+"NOR","Norway","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,94,,

+"POL","Poland","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,88,,

+"PRT","Portugal","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,87,,

+"SVK","Slovak Republic","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,91,,

+"ESP","Spain","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,94,,

+"SWE","Sweden","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,92,,

+"CHE","Switzerland","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,93,,

+"TUR","Turkey","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,86,,

+"GBR","United Kingdom","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,94,,

+"USA","United States","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,92,,

+"BRA","Brazil","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,91,,

+"CHL","Chile","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,84,,

+"EST","Estonia","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,92,,

+"ISR","Israel","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,88,,

+"LVA","Latvia","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,89,,

+"RUS","Russia","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,90,,

+"SVN","Slovenia","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,92,,

+"ZAF","South Africa","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,88,,

+"OECD","OECD - Total","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,90,,

+"AUS","Australia","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,96,,

+"AUT","Austria","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,92,,

+"BEL","Belgium","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,95,,

+"CAN","Canada","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,94,,

+"CZE","Czech Republic","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,95,,

+"DNK","Denmark","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,96,,

+"FIN","Finland","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,97,,

+"FRA","France","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,94,,

+"DEU","Germany","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,92,,

+"GRC","Greece","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,88,,

+"ISL","Iceland","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,99,,

+"IRL","Ireland","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,95,,

+"ITA","Italy","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,94,,

+"MEX","Mexico","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,87,,

+"NZL","New Zealand","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,98,,

+"NOR","Norway","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,96,,

+"PRT","Portugal","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,93,,

+"SVK","Slovak Republic","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,93,,

+"SWE","Sweden","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,92,,

+"TUR","Turkey","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,90,,

+"USA","United States","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,95,,

+"CHL","Chile","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,93,,

+"EST","Estonia","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,92,,

+"ISR","Israel","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,90,,

+"LVA","Latvia","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,91,,

+"RUS","Russia","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,92,,

+"OECD","OECD - Total","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,92,,

+"FIN","Finland","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,92,,

+"FRA","France","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,80,,

+"GRC","Greece","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,67,,

+"HUN","Hungary","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,85,,

+"ITA","Italy","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,90,,

+"JPN","Japan","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,88,,

+"MEX","Mexico","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,78,,

+"NOR","Norway","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,91,,

+"PRT","Portugal","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,84,,

+"ESP","Spain","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,91,,

+"TUR","Turkey","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,82,,

+"BRA","Brazil","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,86,,

+"CHL","Chile","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,76,,

+"EST","Estonia","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,89,,

+"LVA","Latvia","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,82,,

+"OECD","OECD - Total","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,83,,

+"AUS","Australia","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,81,,

+"AUT","Austria","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,85,,

+"BEL","Belgium","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,77,,

+"CAN","Canada","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,91,,

+"CZE","Czech Republic","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,94,,

+"DNK","Denmark","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,81,,

+"FIN","Finland","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,88,,

+"FRA","France","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,78,,

+"DEU","Germany","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,87,,

+"GRC","Greece","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,73,,

+"HUN","Hungary","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,84,,

+"ISL","Iceland","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,77,,

+"IRL","Ireland","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,82,,

+"ITA","Italy","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,61,,

+"KOR","Korea","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,88,,

+"LUX","Luxembourg","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,77,,

+"MEX","Mexico","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,38,,

+"NLD","Netherlands","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,78,,

+"NZL","New Zealand","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,79,,

+"NOR","Norway","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,82,,

+"POL","Poland","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,92,,

+"PRT","Portugal","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,48,,

+"SVK","Slovak Republic","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,91,,

+"ESP","Spain","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,59,,

+"SWE","Sweden","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,83,,

+"CHE","Switzerland","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,88,,

+"TUR","Turkey","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,39,,

+"GBR","United Kingdom","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,81,,

+"USA","United States","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,91,,

+"BRA","Brazil","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,49,,

+"CHL","Chile","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,65,,

+"EST","Estonia","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,89,,

+"ISR","Israel","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,87,,

+"LVA","Latvia","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,88,,

+"RUS","Russia","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,94,,

+"SVN","Slovenia","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,88,,

+"ZAF","South Africa","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,73,,

+"OECD","OECD - Total","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,78,,

+"AUS","Australia","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,82,,

+"AUT","Austria","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,88,,

+"BEL","Belgium","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,76,,

+"CAN","Canada","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,90,,

+"CZE","Czech Republic","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,95,,

+"DNK","Denmark","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,79,,

+"FIN","Finland","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,85,,

+"FRA","France","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,79,,

+"DEU","Germany","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,88,,

+"GRC","Greece","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,72,,

+"HUN","Hungary","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,86,,

+"ISL","Iceland","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,77,,

+"IRL","Ireland","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,79,,

+"ITA","Italy","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,59,,

+"KOR","Korea","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,90,,

+"LUX","Luxembourg","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,77,,

+"MEX","Mexico","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,38,,

+"NLD","Netherlands","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,79,,

+"NZL","New Zealand","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,78,,

+"NOR","Norway","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,81,,

+"POL","Poland","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,92,,

+"PRT","Portugal","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,43,,

+"SVK","Slovak Republic","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,93,,

+"ESP","Spain","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,56,,

+"SWE","Sweden","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,82,,

+"CHE","Switzerland","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,89,,

+"TUR","Turkey","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,43,,

+"GBR","United Kingdom","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,81,,

+"USA","United States","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,90,,

+"BRA","Brazil","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,46,,

+"CHL","Chile","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,65,,

+"EST","Estonia","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,85,,

+"ISR","Israel","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,87,,

+"LVA","Latvia","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,84,,

+"RUS","Russia","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,93,,

+"SVN","Slovenia","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,89,,

+"ZAF","South Africa","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,73,,

+"OECD","OECD - Total","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,78,,

+"AUS","Australia","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,80,,

+"AUT","Austria","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,82,,

+"BEL","Belgium","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,78,,

+"CAN","Canada","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,93,,

+"CZE","Czech Republic","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,92,,

+"DNK","Denmark","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,83,,

+"FIN","Finland","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,91,,

+"FRA","France","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,78,,

+"DEU","Germany","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,85,,

+"GRC","Greece","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,74,,

+"HUN","Hungary","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,82,,

+"ISL","Iceland","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,77,,

+"IRL","Ireland","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,85,,

+"ITA","Italy","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,63,,

+"KOR","Korea","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,85,,

+"LUX","Luxembourg","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,76,,

+"MEX","Mexico","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,37,,

+"NLD","Netherlands","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,78,,

+"NZL","New Zealand","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,79,,

+"NOR","Norway","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,83,,

+"POL","Poland","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,92,,

+"PRT","Portugal","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,52,,

+"SVK","Slovak Republic","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,90,,

+"ESP","Spain","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,62,,

+"SWE","Sweden","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,84,,

+"CHE","Switzerland","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,86,,

+"TUR","Turkey","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,35,,

+"GBR","United Kingdom","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,81,,

+"USA","United States","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,91,,

+"BRA","Brazil","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,52,,

+"CHL","Chile","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,65,,

+"EST","Estonia","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,92,,

+"ISR","Israel","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,88,,

+"LVA","Latvia","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,91,,

+"RUS","Russia","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,95,,

+"SVN","Slovenia","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,87,,

+"ZAF","South Africa","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,74,,

+"OECD","OECD - Total","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,79,,

+"AUS","Australia","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,502,,

+"AUT","Austria","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,492,,

+"BEL","Belgium","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,503,,

+"CAN","Canada","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,523,,

+"CZE","Czech Republic","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,491,,

+"DNK","Denmark","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,504,,

+"FIN","Finland","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,523,,

+"FRA","France","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,496,,

+"DEU","Germany","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,508,,

+"GRC","Greece","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,458,,

+"HUN","Hungary","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,474,,

+"ISL","Iceland","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,481,,

+"IRL","Ireland","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,509,,

+"ITA","Italy","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,485,,

+"JPN","Japan","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,529,,

+"KOR","Korea","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,519,,

+"LUX","Luxembourg","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,483,,

+"MEX","Mexico","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,416,,

+"NLD","Netherlands","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,508,,

+"NZL","New Zealand","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,506,,

+"NOR","Norway","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,504,,

+"POL","Poland","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,504,,

+"PRT","Portugal","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,497,,

+"SVK","Slovak Republic","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,463,,

+"ESP","Spain","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,491,,

+"SWE","Sweden","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,496,,

+"CHE","Switzerland","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,506,,

+"TUR","Turkey","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,425,,

+"GBR","United Kingdom","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,500,,

+"USA","United States","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,488,,

+"BRA","Brazil","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,395,,

+"CHL","Chile","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,443,,

+"EST","Estonia","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,524,,

+"ISR","Israel","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,472,,

+"LVA","Latvia","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,487,,

+"RUS","Russia","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,492,,

+"SVN","Slovenia","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,509,,

+"OECD","OECD - Total","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,486,,

+"AUS","Australia","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,498,,

+"AUT","Austria","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,496,,

+"BEL","Belgium","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,504,,

+"CAN","Canada","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,521,,

+"CZE","Czech Republic","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,489,,

+"DNK","Denmark","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,503,,

+"FIN","Finland","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,511,,

+"FRA","France","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,492,,

+"DEU","Germany","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,509,,

+"GRC","Greece","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,451,,

+"HUN","Hungary","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,472,,

+"ISL","Iceland","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,473,,

+"IRL","Ireland","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,511,,

+"ITA","Italy","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,489,,

+"JPN","Japan","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,531,,

+"KOR","Korea","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,510,,

+"LUX","Luxembourg","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,483,,

+"MEX","Mexico","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,416,,

+"NLD","Netherlands","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,505,,

+"NZL","New Zealand","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,503,,

+"NOR","Norway","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,498,,

+"POL","Poland","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,502,,

+"PRT","Portugal","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,497,,

+"SVK","Slovak Republic","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,458,,

+"ESP","Spain","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,492,,

+"SWE","Sweden","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,488,,

+"CHE","Switzerland","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,505,,

+"TUR","Turkey","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,420,,

+"GBR","United Kingdom","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,498,,

+"USA","United States","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,487,,

+"BRA","Brazil","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,394,,

+"CHL","Chile","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,446,,

+"EST","Estonia","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,521,,

+"ISR","Israel","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,470,,

+"LVA","Latvia","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,478,,

+"RUS","Russia","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,489,,

+"SVN","Slovenia","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,502,,

+"OECD","OECD - Total","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,485,,

+"AUS","Australia","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,506,,

+"AUT","Austria","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,488,,

+"BEL","Belgium","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,501,,

+"CAN","Canada","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,526,,

+"CZE","Czech Republic","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,493,,

+"DNK","Denmark","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,505,,

+"FIN","Finland","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,535,,

+"FRA","France","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,499,,

+"DEU","Germany","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,507,,

+"GRC","Greece","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,466,,

+"HUN","Hungary","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,477,,

+"ISL","Iceland","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,488,,

+"IRL","Ireland","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,507,,

+"ITA","Italy","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,482,,

+"JPN","Japan","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,527,,

+"KOR","Korea","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,529,,

+"LUX","Luxembourg","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,484,,

+"MEX","Mexico","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,416,,

+"NLD","Netherlands","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,511,,

+"NZL","New Zealand","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,509,,

+"NOR","Norway","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,511,,

+"POL","Poland","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,506,,

+"PRT","Portugal","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,496,,

+"SVK","Slovak Republic","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,468,,

+"ESP","Spain","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,491,,

+"SWE","Sweden","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,504,,

+"CHE","Switzerland","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,508,,

+"TUR","Turkey","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,429,,

+"GBR","United Kingdom","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,502,,

+"USA","United States","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,488,,

+"BRA","Brazil","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,396,,

+"CHL","Chile","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,439,,

+"EST","Estonia","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,528,,

+"ISR","Israel","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,473,,

+"LVA","Latvia","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,496,,

+"RUS","Russia","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,494,,

+"SVN","Slovenia","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,517,,

+"OECD","OECD - Total","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,487,,

+"AUS","Australia","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,550,,

+"AUT","Austria","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,541,,

+"BEL","Belgium","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,559,,

+"CAN","Canada","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,558,,

+"CZE","Czech Republic","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,548,,

+"DNK","Denmark","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,543,,

+"FIN","Finland","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,562,,

+"FRA","France","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,558,,

+"DEU","Germany","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,563,,

+"GRC","Greece","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,506,,

+"HUN","Hungary","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,534,,

+"ISL","Iceland","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,509,,

+"IRL","Ireland","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,550,,

+"ITA","Italy","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,524,,

+"JPN","Japan","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,568,,

+"KOR","Korea","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,562,,

+"LUX","Luxembourg","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,549,,

+"MEX","Mexico","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,447,,

+"NLD","Netherlands","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,556,,

+"NZL","New Zealand","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,554,,

+"NOR","Norway","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,539,,

+"POL","Poland","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,549,,

+"PRT","Portugal","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,551,,

+"SVK","Slovak Republic","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,514,,

+"ESP","Spain","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,535,,

+"SWE","Sweden","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,543,,

+"CHE","Switzerland","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,558,,

+"TUR","Turkey","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,460,,

+"GBR","United Kingdom","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,546,,

+"USA","United States","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,534,,

+"BRA","Brazil","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,445,,

+"CHL","Chile","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,492,,

+"EST","Estonia","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,562,,

+"ISR","Israel","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,514,,

+"LVA","Latvia","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,521,,

+"RUS","Russia","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,520,,

+"SVN","Slovenia","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,552,,

+"OECD","OECD - Total","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,539,,

+"AUS","Australia","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,0,,

+"AUT","Austria","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,447,,

+"BEL","Belgium","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,452,,

+"CAN","Canada","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,488,,

+"CZE","Czech Republic","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,440,,

+"DNK","Denmark","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,471,,

+"FIN","Finland","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,487,,

+"FRA","France","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,442,,

+"DEU","Germany","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,468,,

+"GRC","Greece","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,420,,

+"HUN","Hungary","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,418,,

+"ISL","Iceland","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,453,,

+"IRL","Ireland","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,472,,

+"ITA","Italy","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,445,,

+"JPN","Japan","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,489,,

+"KOR","Korea","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,480,,

+"LUX","Luxembourg","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,428,,

+"MEX","Mexico","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,385,,

+"NLD","Netherlands","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,468,,

+"NZL","New Zealand","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,459,,

+"NOR","Norway","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,472,,

+"POL","Poland","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,466,,

+"PRT","Portugal","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,456,,

+"SVK","Slovak Republic","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,414,,

+"ESP","Spain","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,453,,

+"SWE","Sweden","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,454,,

+"CHE","Switzerland","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,460,,

+"TUR","Turkey","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,400,,

+"GBR","United Kingdom","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,466,,

+"USA","United States","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,450,,

+"BRA","Brazil","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,361,,

+"CHL","Chile","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,399,,

+"EST","Estonia","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,494,,

+"ISR","Israel","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,421,,

+"LVA","Latvia","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,456,,

+"RUS","Russia","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,464,,

+"SVN","Slovenia","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,472,,

+"OECD","OECD - Total","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,455,,

+"AUS","Australia","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,21,,

+"AUT","Austria","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,17,,

+"BEL","Belgium","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,19.3,,

+"CAN","Canada","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,17.3,,

+"CZE","Czech Republic","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,17.9,,

+"DNK","Denmark","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,19.5,,

+"FIN","Finland","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,19.8,,

+"FRA","France","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,16.5,,

+"DEU","Germany","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,18.1,,

+"GRC","Greece","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,19,,

+"HUN","Hungary","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,16.4,,

+"ISL","Iceland","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,19,,

+"IRL","Ireland","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,18.1,,

+"ITA","Italy","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,16.6,,

+"JPN","Japan","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,16.4,,

+"KOR","Korea","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,17.3,,

+"LUX","Luxembourg","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,15.1,,

+"MEX","Mexico","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,15.2,,

+"NLD","Netherlands","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,18.7,,

+"NZL","New Zealand","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,17.7,,

+"NOR","Norway","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,18.3,,

+"POL","Poland","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,17.6,,

+"PRT","Portugal","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,16.9,,

+"SVK","Slovak Republic","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,15.8,,

+"ESP","Spain","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,17.9,,

+"SWE","Sweden","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,19.3,,

+"CHE","Switzerland","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,17.5,,

+"TUR","Turkey","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,18.3,,

+"GBR","United Kingdom","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,17.5,,

+"USA","United States","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,17.2,,

+"BRA","Brazil","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,16.2,,

+"CHL","Chile","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,17.5,,

+"EST","Estonia","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,17.7,,

+"ISR","Israel","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,15.6,,

+"LVA","Latvia","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,18,,

+"RUS","Russia","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,16.2,,

+"SVN","Slovenia","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,18.3,,

+"OECD","OECD - Total","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,17.2,,

+"AUS","Australia","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,20.8,,

+"AUT","Austria","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,16.7,,

+"BEL","Belgium","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,18.6,,

+"CAN","Canada","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,16.9,,

+"CZE","Czech Republic","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,17.3,,

+"DNK","Denmark","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,19.1,,

+"FIN","Finland","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,19.4,,

+"FRA","France","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,16.2,,

+"DEU","Germany","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,18.1,,

+"GRC","Greece","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,19.2,,

+"HUN","Hungary","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,16.3,,

+"ISL","Iceland","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,18.3,,

+"IRL","Ireland","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,18.1,,

+"ITA","Italy","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,16.2,,

+"JPN","Japan","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,16.4,,

+"KOR","Korea","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,17.8,,

+"LUX","Luxembourg","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,15,,

+"MEX","Mexico","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,15,,

+"NLD","Netherlands","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,18.6,,

+"NZL","New Zealand","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,17.2,,

+"NOR","Norway","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,17.9,,

+"POL","Poland","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,17,,

+"PRT","Portugal","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,16.9,,

+"SVK","Slovak Republic","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,15.3,,

+"ESP","Spain","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,17.6,,

+"SWE","Sweden","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,18.5,,

+"CHE","Switzerland","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,17.6,,

+"TUR","Turkey","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,18.6,,

+"GBR","United Kingdom","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,17,,

+"USA","United States","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,16.7,,

+"BRA","Brazil","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,15.8,,

+"CHL","Chile","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,17.3,,

+"EST","Estonia","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,17.1,,

+"ISR","Israel","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,15.2,,

+"LVA","Latvia","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,17.4,,

+"RUS","Russia","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,16,,

+"SVN","Slovenia","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,17.7,,

+"OECD","OECD - Total","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,17,,

+"AUS","Australia","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,21.1,,

+"AUT","Austria","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,17.3,,

+"BEL","Belgium","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,20,,

+"CAN","Canada","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,17.8,,

+"CZE","Czech Republic","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,18.5,,

+"DNK","Denmark","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,19.9,,

+"FIN","Finland","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,20.3,,

+"FRA","France","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,16.8,,

+"DEU","Germany","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,18.1,,

+"GRC","Greece","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,18.8,,

+"HUN","Hungary","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,16.6,,

+"ISL","Iceland","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,19.8,,

+"IRL","Ireland","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,18.1,,

+"ITA","Italy","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,17,,

+"JPN","Japan","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,16.3,,

+"KOR","Korea","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,16.7,,

+"LUX","Luxembourg","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,15.2,,

+"MEX","Mexico","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,15.3,,

+"NLD","Netherlands","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,18.7,,

+"NZL","New Zealand","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,18.2,,

+"NOR","Norway","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,18.8,,

+"POL","Poland","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,18.4,,

+"PRT","Portugal","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,17,,

+"SVK","Slovak Republic","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,16.2,,

+"ESP","Spain","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,18.2,,

+"SWE","Sweden","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,20.1,,

+"CHE","Switzerland","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,17.4,,

+"TUR","Turkey","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,18,,

+"GBR","United Kingdom","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,18,,

+"USA","United States","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,17.7,,

+"BRA","Brazil","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,16.5,,

+"CHL","Chile","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,17.7,,

+"EST","Estonia","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,18.3,,

+"ISR","Israel","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,16.1,,

+"LVA","Latvia","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,18.7,,

+"RUS","Russia","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,16.4,,

+"SVN","Slovenia","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,19.1,,

+"OECD","OECD - Total","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,17.4,,

+"AUS","Australia","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,5,,

+"AUT","Austria","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,16,,

+"BEL","Belgium","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,15,,

+"CAN","Canada","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,7,,

+"CZE","Czech Republic","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,20,,

+"DNK","Denmark","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,9,,

+"FIN","Finland","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,6,,

+"FRA","France","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,13,,

+"DEU","Germany","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,14,,

+"GRC","Greece","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,18,,

+"HUN","Hungary","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,19,,

+"ISL","Iceland","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,3,,

+"IRL","Ireland","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,7,,

+"ITA","Italy","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,18,,

+"JPN","Japan","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,14,,

+"KOR","Korea","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,28,,

+"LUX","Luxembourg","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,12,,

+"MEX","Mexico","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,16,,

+"NLD","Netherlands","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,14,,

+"NZL","New Zealand","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,5,,

+"NOR","Norway","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,5,,

+"POL","Poland","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,22,,

+"PRT","Portugal","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,10,,

+"SVK","Slovak Republic","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,21,,

+"ESP","Spain","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,11,,

+"SWE","Sweden","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,6,,

+"CHE","Switzerland","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,15,,

+"TUR","Turkey","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,20,,

+"GBR","United Kingdom","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,11,,

+"USA","United States","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,10,,

+"BRA","Brazil","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,10,,

+"CHL","Chile","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,16,,

+"EST","Estonia","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,8,,

+"ISR","Israel","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,21,,

+"LVA","Latvia","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,11,,

+"RUS","Russia","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,15,,

+"SVN","Slovenia","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,16,,

+"ZAF","South Africa","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,22,,

+"OECD","OECD - Total","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,14,,

+"AUS","Australia","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,93,,

+"AUT","Austria","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,92,,

+"BEL","Belgium","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,84,,

+"CAN","Canada","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,91,,

+"CZE","Czech Republic","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,87,,

+"DNK","Denmark","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,95,,

+"FIN","Finland","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,95,,

+"FRA","France","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,81,,

+"DEU","Germany","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,91,,

+"GRC","Greece","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,69,,

+"HUN","Hungary","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,77,,

+"ISL","Iceland","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,99,,

+"IRL","Ireland","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,85,,

+"ITA","Italy","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,71,,

+"JPN","Japan","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,87,,

+"KOR","Korea","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,76,,

+"LUX","Luxembourg","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,84,,

+"MEX","Mexico","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,68,,

+"NLD","Netherlands","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,93,,

+"NZL","New Zealand","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,89,,

+"NOR","Norway","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,98,,

+"POL","Poland","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,82,,

+"PRT","Portugal","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,86,,

+"SVK","Slovak Republic","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,85,,

+"ESP","Spain","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,72,,

+"SWE","Sweden","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,96,,

+"CHE","Switzerland","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,95,,

+"TUR","Turkey","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,65,,

+"GBR","United Kingdom","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,84,,

+"USA","United States","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,83,,

+"BRA","Brazil","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,73,,

+"CHL","Chile","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,71,,

+"EST","Estonia","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,84,,

+"ISR","Israel","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,67,,

+"LVA","Latvia","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,79,,

+"RUS","Russia","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,55,,

+"SVN","Slovenia","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,90,,

+"ZAF","South Africa","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,67,,

+"OECD","OECD - Total","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,81,,

+"AUS","Australia","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,93,,

+"AUT","Austria","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,93,,

+"BEL","Belgium","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,86,,

+"CAN","Canada","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,92,,

+"CZE","Czech Republic","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,87,,

+"DNK","Denmark","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,96,,

+"FIN","Finland","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,96,,

+"FRA","France","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,80,,

+"DEU","Germany","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,91,,

+"GRC","Greece","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,71,,

+"HUN","Hungary","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,77,,

+"ISL","Iceland","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,99,,

+"IRL","Ireland","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,85,,

+"ITA","Italy","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,73,,

+"JPN","Japan","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,88,,

+"KOR","Korea","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,76,,

+"LUX","Luxembourg","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,85,,

+"MEX","Mexico","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,67,,

+"NLD","Netherlands","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,94,,

+"NZL","New Zealand","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,91,,

+"NOR","Norway","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,98,,

+"POL","Poland","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,83,,

+"PRT","Portugal","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,87,,

+"SVK","Slovak Republic","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,84,,

+"ESP","Spain","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,71,,

+"SWE","Sweden","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,96,,

+"CHE","Switzerland","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,95,,

+"TUR","Turkey","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,65,,

+"GBR","United Kingdom","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,82,,

+"USA","United States","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,82,,

+"BRA","Brazil","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,77,,

+"CHL","Chile","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,72,,

+"EST","Estonia","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,85,,

+"ISR","Israel","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,68,,

+"LVA","Latvia","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,79,,

+"RUS","Russia","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,58,,

+"SVN","Slovenia","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,90,,

+"ZAF","South Africa","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,70,,

+"OECD","OECD - Total","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,81,,

+"AUS","Australia","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,92,,

+"AUT","Austria","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,91,,

+"BEL","Belgium","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,81,,

+"CAN","Canada","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,90,,

+"CZE","Czech Republic","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,86,,

+"DNK","Denmark","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,94,,

+"FIN","Finland","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,94,,

+"FRA","France","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,82,,

+"DEU","Germany","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,92,,

+"GRC","Greece","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,68,,

+"HUN","Hungary","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,77,,

+"ISL","Iceland","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,98,,

+"IRL","Ireland","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,85,,

+"ITA","Italy","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,69,,

+"JPN","Japan","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,86,,

+"KOR","Korea","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,76,,

+"LUX","Luxembourg","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,83,,

+"MEX","Mexico","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,68,,

+"NLD","Netherlands","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,92,,

+"NZL","New Zealand","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,87,,

+"NOR","Norway","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,97,,

+"POL","Poland","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,81,,

+"PRT","Portugal","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,86,,

+"SVK","Slovak Republic","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,85,,

+"ESP","Spain","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,74,,

+"SWE","Sweden","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,96,,

+"CHE","Switzerland","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,96,,

+"TUR","Turkey","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,65,,

+"GBR","United Kingdom","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,85,,

+"USA","United States","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,83,,

+"BRA","Brazil","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,69,,

+"CHL","Chile","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,70,,

+"EST","Estonia","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,83,,

+"ISR","Israel","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,66,,

+"LVA","Latvia","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,80,,

+"RUS","Russia","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,53,,

+"SVN","Slovenia","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,89,,

+"ZAF","South Africa","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,64,,

+"OECD","OECD - Total","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,81,,

+"AUS","Australia","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,91,,

+"AUT","Austria","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,80,,

+"BEL","Belgium","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,89,,

+"CAN","Canada","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,68,,

+"CZE","Czech Republic","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,61,,

+"DNK","Denmark","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,86,,

+"FIN","Finland","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,67,,

+"FRA","France","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,75,,

+"DEU","Germany","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,76,,

+"GRC","Greece","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,64,,

+"HUN","Hungary","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,70,,

+"ISL","Iceland","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,79,,

+"IRL","Ireland","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,65,,

+"ITA","Italy","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,73,,

+"JPN","Japan","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,53,,

+"KOR","Korea","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,77,,

+"LUX","Luxembourg","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,91,,

+"MEX","Mexico","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,63,,

+"NLD","Netherlands","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,82,,

+"NZL","New Zealand","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,80,,

+"NOR","Norway","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,78,,

+"POL","Poland","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,55,,

+"PRT","Portugal","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,56,,

+"SVK","Slovak Republic","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,60,,

+"ESP","Spain","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,70,,

+"SWE","Sweden","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,86,,

+"CHE","Switzerland","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,49,,

+"TUR","Turkey","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,86,,

+"GBR","United Kingdom","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,69,,

+"USA","United States","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,65,,

+"BRA","Brazil","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,79,,

+"CHL","Chile","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,47,,

+"EST","Estonia","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,64,,

+"ISR","Israel","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,72,,

+"LVA","Latvia","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,59,,

+"RUS","Russia","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,68,,

+"SVN","Slovenia","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,53,,

+"ZAF","South Africa","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,73,,

+"OECD","OECD - Total","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,68,,

+"AUS","Australia","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,82.5,,

+"AUT","Austria","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,81.7,,

+"BEL","Belgium","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,81.5,,

+"CAN","Canada","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,81.9,,

+"CZE","Czech Republic","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,79.1,,

+"DNK","Denmark","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,80.9,,

+"FIN","Finland","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,81.5,,

+"FRA","France","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,82.4,,

+"DEU","Germany","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,81.1,,

+"GRC","Greece","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,81.5,,

+"HUN","Hungary","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,76.2,,

+"ISL","Iceland","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,82.3,,

+"IRL","Ireland","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,81.8,,

+"ITA","Italy","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,83.3,,

+"JPN","Japan","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,84.1,,

+"KOR","Korea","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,82.4,,

+"LUX","Luxembourg","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,82.8,,

+"MEX","Mexico","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,75.4,,

+"NLD","Netherlands","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,81.6,,

+"NZL","New Zealand","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,81.7,,

+"NOR","Norway","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,82.5,,

+"POL","Poland","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,78,,

+"PRT","Portugal","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,81.2,,

+"SVK","Slovak Republic","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,77.3,,

+"ESP","Spain","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,83.4,,

+"SWE","Sweden","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,82.4,,

+"CHE","Switzerland","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,83.7,,

+"TUR","Turkey","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,78,,

+"GBR","United Kingdom","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,81.2,,

+"USA","United States","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,78.6,,

+"BRA","Brazil","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,74.8,,

+"CHL","Chile","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,79.9,,

+"EST","Estonia","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,77.8,,

+"ISR","Israel","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,82.5,,

+"LVA","Latvia","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,74.7,,

+"RUS","Russia","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,71.8,,

+"SVN","Slovenia","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,81.3,,

+"ZAF","South Africa","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,57.5,,

+"OECD","OECD - Total","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,80.2,,

+"AUS","Australia","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,80.4,,

+"AUT","Austria","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,79.3,,

+"BEL","Belgium","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,79,,

+"CAN","Canada","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,79.8,,

+"CZE","Czech Republic","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,76.1,,

+"DNK","Denmark","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,79,,

+"FIN","Finland","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,78.6,,

+"FRA","France","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,79.2,,

+"DEU","Germany","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,78.6,,

+"GRC","Greece","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,78.9,,

+"HUN","Hungary","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,72.6,,

+"ISL","Iceland","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,80.4,,

+"IRL","Ireland","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,79.9,,

+"ITA","Italy","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,81,,

+"JPN","Japan","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,81,,

+"KOR","Korea","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,79.3,,

+"LUX","Luxembourg","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,80.1,,

+"MEX","Mexico","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,72.9,,

+"NLD","Netherlands","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,80,,

+"NZL","New Zealand","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,80,,

+"NOR","Norway","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,80.7,,

+"POL","Poland","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,73.9,,

+"PRT","Portugal","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,78.1,,

+"SVK","Slovak Republic","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,73.8,,

+"ESP","Spain","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,80.5,,

+"SWE","Sweden","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,80.6,,

+"CHE","Switzerland","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,81.7,,

+"TUR","Turkey","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,75.3,,

+"GBR","United Kingdom","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,79.4,,

+"USA","United States","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,76.1,,

+"BRA","Brazil","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,71,,

+"CHL","Chile","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,77.1,,

+"EST","Estonia","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,73.3,,

+"ISR","Israel","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,80.7,,

+"LVA","Latvia","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,69.8,,

+"RUS","Russia","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,66.5,,

+"SVN","Slovenia","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,78.2,,

+"ZAF","South Africa","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,55.5,,

+"OECD","OECD - Total","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,77.6,,

+"AUS","Australia","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,84.6,,

+"AUT","Austria","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,84.1,,

+"BEL","Belgium","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,84,,

+"CAN","Canada","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,83.9,,

+"CZE","Czech Republic","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,82.1,,

+"DNK","Denmark","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,82.8,,

+"FIN","Finland","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,84.4,,

+"FRA","France","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,85.5,,

+"DEU","Germany","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,83.5,,

+"GRC","Greece","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,84,,

+"HUN","Hungary","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,79.7,,

+"ISL","Iceland","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,84.1,,

+"IRL","Ireland","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,83.6,,

+"ITA","Italy","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,85.6,,

+"JPN","Japan","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,87.1,,

+"KOR","Korea","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,85.4,,

+"LUX","Luxembourg","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,85.4,,

+"MEX","Mexico","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,77.9,,

+"NLD","Netherlands","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,83.2,,

+"NZL","New Zealand","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,83.4,,

+"NOR","Norway","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,84.2,,

+"POL","Poland","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,82,,

+"PRT","Portugal","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,84.3,,

+"SVK","Slovak Republic","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,80.7,,

+"ESP","Spain","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,86.3,,

+"SWE","Sweden","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,84.1,,

+"CHE","Switzerland","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,85.6,,

+"TUR","Turkey","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,80.7,,

+"GBR","United Kingdom","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,83,,

+"USA","United States","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,81.1,,

+"BRA","Brazil","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,78.5,,

+"CHL","Chile","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,82.7,,

+"EST","Estonia","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,82.2,,

+"ISR","Israel","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,84.2,,

+"LVA","Latvia","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,79.6,,

+"RUS","Russia","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,77.1,,

+"SVN","Slovenia","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,84.3,,

+"ZAF","South Africa","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,59.5,,

+"OECD","OECD - Total","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,82.8,,

+"AUS","Australia","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,85,,

+"AUT","Austria","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,70,,

+"BEL","Belgium","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,74,,

+"CAN","Canada","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,88,,

+"CZE","Czech Republic","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,60,,

+"DNK","Denmark","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,71,,

+"FIN","Finland","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,70,,

+"FRA","France","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,66,,

+"DEU","Germany","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,65,,

+"GRC","Greece","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,74,,

+"HUN","Hungary","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,60,,

+"ISL","Iceland","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,76,,

+"IRL","Ireland","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,83,,

+"ITA","Italy","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,71,,

+"JPN","Japan","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,36,,

+"KOR","Korea","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,33,,

+"LUX","Luxembourg","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,69,,

+"MEX","Mexico","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,66,,

+"NLD","Netherlands","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,76,,

+"NZL","New Zealand","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,88,,

+"NOR","Norway","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,77,,

+"POL","Poland","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,58,,

+"PRT","Portugal","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,48,,

+"SVK","Slovak Republic","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,66,,

+"ESP","Spain","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,72,,

+"SWE","Sweden","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,75,,

+"CHE","Switzerland","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,78,,

+"TUR","Turkey","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,69,,

+"GBR","United Kingdom","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,69,,

+"USA","United States","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,88,,

+"CHL","Chile","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,57,,

+"EST","Estonia","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,53,,

+"ISR","Israel","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,84,,

+"LVA","Latvia","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,47,,

+"RUS","Russia","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,43,,

+"SVN","Slovenia","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,64,,

+"OECD","OECD - Total","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,69,,

+"AUS","Australia","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,85,,

+"AUT","Austria","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,72,,

+"BEL","Belgium","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,77,,

+"CAN","Canada","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,89,,

+"CZE","Czech Republic","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,62,,

+"DNK","Denmark","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,72,,

+"FIN","Finland","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,72,,

+"FRA","France","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,68,,

+"DEU","Germany","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,67,,

+"GRC","Greece","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,77,,

+"HUN","Hungary","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,63,,

+"ISL","Iceland","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,80,,

+"IRL","Ireland","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,83,,

+"ITA","Italy","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,74,,

+"JPN","Japan","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,37,,

+"KOR","Korea","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,36,,

+"LUX","Luxembourg","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,72,,

+"MEX","Mexico","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,67,,

+"NLD","Netherlands","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,79,,

+"NZL","New Zealand","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,88,,

+"NOR","Norway","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,79,,

+"POL","Poland","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,62,,

+"PRT","Portugal","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,53,,

+"SVK","Slovak Republic","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,70,,

+"ESP","Spain","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,75,,

+"SWE","Sweden","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,78,,

+"CHE","Switzerland","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,80,,

+"TUR","Turkey","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,74,,

+"GBR","United Kingdom","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,69,,

+"USA","United States","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,89,,

+"CHL","Chile","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,62,,

+"EST","Estonia","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,56,,

+"ISR","Israel","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,85,,

+"LVA","Latvia","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,53,,

+"RUS","Russia","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,49,,

+"SVN","Slovenia","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,68,,

+"OECD","OECD - Total","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,71,,

+"AUS","Australia","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,85,,

+"AUT","Austria","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,69,,

+"BEL","Belgium","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,71,,

+"CAN","Canada","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,88,,

+"CZE","Czech Republic","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,59,,

+"DNK","Denmark","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,70,,

+"FIN","Finland","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,69,,

+"FRA","France","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,65,,

+"DEU","Germany","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,64,,

+"GRC","Greece","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,71,,

+"HUN","Hungary","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,56,,

+"ISL","Iceland","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,73,,

+"IRL","Ireland","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,83,,

+"ITA","Italy","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,68,,

+"JPN","Japan","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,34,,

+"KOR","Korea","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,29,,

+"LUX","Luxembourg","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,67,,

+"MEX","Mexico","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,64,,

+"NLD","Netherlands","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,73,,

+"NZL","New Zealand","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,88,,

+"NOR","Norway","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,75,,

+"POL","Poland","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,56,,

+"PRT","Portugal","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,43,,

+"SVK","Slovak Republic","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,63,,

+"ESP","Spain","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,70,,

+"SWE","Sweden","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,72,,

+"CHE","Switzerland","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,76,,

+"TUR","Turkey","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,65,,

+"GBR","United Kingdom","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,69,,

+"USA","United States","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,87,,

+"CHL","Chile","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,53,,

+"EST","Estonia","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,51,,

+"ISR","Israel","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,82,,

+"LVA","Latvia","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,43,,

+"RUS","Russia","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,38,,

+"SVN","Slovenia","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,61,,

+"OECD","OECD - Total","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,67,,

+"AUS","Australia","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,94,,

+"AUT","Austria","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,80,,

+"BEL","Belgium","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,87,,

+"CAN","Canada","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,94,,

+"CZE","Czech Republic","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,79,,

+"DNK","Denmark","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,84,,

+"FIN","Finland","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,83,,

+"FRA","France","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,72,,

+"DEU","Germany","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,79,,

+"GRC","Greece","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,82,,

+"HUN","Hungary","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,74,,

+"ISL","Iceland","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,83,,

+"IRL","Ireland","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,93,,

+"ITA","Italy","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,77,,

+"JPN","Japan","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,39,,

+"KOR","Korea","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,38,,

+"LUX","Luxembourg","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,74,,

+"NLD","Netherlands","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,86,,

+"NZL","New Zealand","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,93,,

+"NOR","Norway","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,86,,

+"POL","Poland","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,72,,

+"PRT","Portugal","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,61,,

+"SVK","Slovak Republic","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,80,,

+"ESP","Spain","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,82,,

+"SWE","Sweden","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,86,,

+"CHE","Switzerland","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,86,,

+"TUR","Turkey","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,78,,

+"GBR","United Kingdom","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,83,,

+"USA","United States","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,96,,

+"CHL","Chile","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,67,,

+"EST","Estonia","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,79,,

+"ISR","Israel","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,96,,

+"LVA","Latvia","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,69,,

+"SVN","Slovenia","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,76,,

+"OECD","OECD - Total","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,78,,

+"AUT","Austria","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,62,,

+"BEL","Belgium","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,59,,

+"CAN","Canada","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,80,,

+"CZE","Czech Republic","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,44,,

+"DNK","Denmark","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,66,,

+"FIN","Finland","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,57,,

+"FRA","France","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,61,,

+"DEU","Germany","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,52,,

+"HUN","Hungary","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,52,,

+"ISL","Iceland","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,68,,

+"IRL","Ireland","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,72,,

+"ITA","Italy","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,69,,

+"JPN","Japan","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,28,,

+"KOR","Korea","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,29,,

+"LUX","Luxembourg","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,65,,

+"NLD","Netherlands","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,65,,

+"NOR","Norway","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,69,,

+"POL","Poland","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,52,,

+"PRT","Portugal","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,36,,

+"SVK","Slovak Republic","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,64,,

+"ESP","Spain","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,71,,

+"SWE","Sweden","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,64,,

+"CHE","Switzerland","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,67,,

+"TUR","Turkey","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,66,,

+"GBR","United Kingdom","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,60,,

+"USA","United States","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,74,,

+"CHL","Chile","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,46,,

+"EST","Estonia","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,33,,

+"LVA","Latvia","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,28,,

+"SVN","Slovenia","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,53,,

+"OECD","OECD - Total","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,61,,

+"AUS","Australia","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,7.3,,

+"AUT","Austria","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,7.1,,

+"BEL","Belgium","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,6.9,,

+"CAN","Canada","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,7.4,,

+"CZE","Czech Republic","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,6.7,,

+"DNK","Denmark","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,7.6,,

+"FIN","Finland","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,7.6,,

+"FRA","France","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,6.5,,

+"DEU","Germany","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,7,,

+"GRC","Greece","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,5.4,,

+"HUN","Hungary","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,5.6,,

+"ISL","Iceland","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,7.5,,

+"IRL","Ireland","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,7,,

+"ITA","Italy","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,6,,

+"JPN","Japan","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,5.9,,

+"KOR","Korea","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,5.9,,

+"LUX","Luxembourg","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,6.9,,

+"MEX","Mexico","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,6.5,,

+"NLD","Netherlands","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,7.4,,

+"NZL","New Zealand","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,7.3,,

+"NOR","Norway","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,7.6,,

+"POL","Poland","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,6.1,,

+"PRT","Portugal","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,5.4,,

+"SVK","Slovak Republic","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,6.2,,

+"ESP","Spain","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,6.3,,

+"SWE","Sweden","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,7.3,,

+"CHE","Switzerland","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,7.5,,

+"TUR","Turkey","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,5.5,,

+"GBR","United Kingdom","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,6.8,,

+"USA","United States","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,6.9,,

+"BRA","Brazil","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,6.4,,

+"CHL","Chile","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,6.5,,

+"EST","Estonia","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,5.7,,

+"ISR","Israel","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,7.2,,

+"LVA","Latvia","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,5.9,,

+"RUS","Russia","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,5.8,,

+"SVN","Slovenia","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,5.9,,

+"ZAF","South Africa","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,4.7,,

+"OECD","OECD - Total","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,6.5,,

+"AUS","Australia","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,7.2,,

+"AUT","Austria","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,7.1,,

+"BEL","Belgium","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,6.9,,

+"CAN","Canada","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,7.3,,

+"CZE","Czech Republic","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,6.7,,

+"DNK","Denmark","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,7.5,,

+"FIN","Finland","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,7.5,,

+"FRA","France","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,6.6,,

+"DEU","Germany","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,7,,

+"GRC","Greece","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,5.3,,

+"HUN","Hungary","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,5.6,,

+"ISL","Iceland","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,7.4,,

+"IRL","Ireland","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,7,,

+"ITA","Italy","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,6.2,,

+"JPN","Japan","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,5.8,,

+"KOR","Korea","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,5.6,,

+"LUX","Luxembourg","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,7,,

+"MEX","Mexico","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,6.4,,

+"NLD","Netherlands","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,7.4,,

+"NZL","New Zealand","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,7.2,,

+"NOR","Norway","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,7.6,,

+"POL","Poland","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,6,,

+"PRT","Portugal","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,5.4,,

+"SVK","Slovak Republic","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,6.3,,

+"ESP","Spain","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,6.2,,

+"SWE","Sweden","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,7.3,,

+"CHE","Switzerland","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,7.5,,

+"TUR","Turkey","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,5.4,,

+"GBR","United Kingdom","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,6.9,,

+"USA","United States","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,6.8,,

+"BRA","Brazil","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,6.2,,

+"CHL","Chile","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,6.5,,

+"EST","Estonia","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,5.8,,

+"ISR","Israel","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,7.2,,

+"LVA","Latvia","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,5.9,,

+"RUS","Russia","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,5.9,,

+"SVN","Slovenia","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,6,,

+"ZAF","South Africa","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,4.7,,

+"OECD","OECD - Total","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,6.5,,

+"AUS","Australia","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,7.4,,

+"AUT","Austria","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,7.1,,

+"BEL","Belgium","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6.9,,

+"CAN","Canada","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,7.4,,

+"CZE","Czech Republic","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6.7,,

+"DNK","Denmark","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,7.6,,

+"FIN","Finland","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,7.8,,

+"FRA","France","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6.4,,

+"DEU","Germany","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,7,,

+"GRC","Greece","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,5.4,,

+"HUN","Hungary","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,5.6,,

+"ISL","Iceland","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,7.6,,

+"IRL","Ireland","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,7,,

+"ITA","Italy","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,5.9,,

+"JPN","Japan","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6,,

+"KOR","Korea","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6.1,,

+"LUX","Luxembourg","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6.8,,

+"MEX","Mexico","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6.5,,

+"NLD","Netherlands","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,7.5,,

+"NZL","New Zealand","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,7.4,,

+"NOR","Norway","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,7.6,,

+"POL","Poland","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6.2,,

+"PRT","Portugal","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,5.5,,

+"SVK","Slovak Republic","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6.1,,

+"ESP","Spain","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6.4,,

+"SWE","Sweden","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,7.3,,

+"CHE","Switzerland","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,7.5,,

+"TUR","Turkey","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,5.5,,

+"GBR","United Kingdom","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6.7,,

+"USA","United States","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6.9,,

+"BRA","Brazil","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6.6,,

+"CHL","Chile","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6.5,,

+"EST","Estonia","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,5.7,,

+"ISR","Israel","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,7.1,,

+"LVA","Latvia","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6,,

+"RUS","Russia","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,5.8,,

+"SVN","Slovenia","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,5.9,,

+"ZAF","South Africa","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,4.7,,

+"OECD","OECD - Total","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6.6,,

+"AUS","Australia","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.5,,

+"AUT","Austria","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.6,,

+"BEL","Belgium","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.3,,

+"CAN","Canada","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.6,,

+"DNK","Denmark","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.9,,

+"FIN","Finland","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,8.2,,

+"FRA","France","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.3,,

+"DEU","Germany","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.4,,

+"GRC","Greece","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,6.2,,

+"HUN","Hungary","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,6.5,,

+"ISL","Iceland","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,8,,

+"IRL","Ireland","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.2,,

+"ITA","Italy","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,6.9,,

+"NLD","Netherlands","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.7,,

+"NOR","Norway","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.8,,

+"PRT","Portugal","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,6.4,,

+"SVK","Slovak Republic","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.1,,

+"SWE","Sweden","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.7,,

+"TUR","Turkey","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,6,,

+"GBR","United Kingdom","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.1,,

+"USA","United States","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.4,,

+"CHL","Chile","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.2,,

+"EST","Estonia","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,6.3,,

+"ISR","Israel","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7.4,,

+"LVA","Latvia","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,6.5,,

+"RUS","Russia","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,6.2,,

+"OECD","OECD - Total","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,7,,

+"FIN","Finland","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,7.2,,

+"FRA","France","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,5.9,,

+"GRC","Greece","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,4.4,,

+"HUN","Hungary","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,5.1,,

+"ISL","Iceland","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,7.4,,

+"ITA","Italy","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,5.7,,

+"NOR","Norway","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,7.5,,

+"PRT","Portugal","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,4.7,,

+"ESP","Spain","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,6,,

+"TUR","Turkey","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,5.2,,

+"GBR","United Kingdom","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,6.8,,

+"CHL","Chile","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,5.7,,

+"EST","Estonia","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,5.4,,

+"LVA","Latvia","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,5.4,,

+"RUS","Russia","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,5.7,,

+"OECD","OECD - Total","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,5.8,,

+"AUS","Australia","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.1,,

+"AUT","Austria","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.5,,

+"BEL","Belgium","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1,,

+"CAN","Canada","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.3,,

+"CZE","Czech Republic","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.5,,

+"DNK","Denmark","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.6,,

+"FIN","Finland","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.3,,

+"FRA","France","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.5,,

+"DEU","Germany","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.5,,

+"GRC","Greece","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.8,,

+"HUN","Hungary","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1,,

+"ISL","Iceland","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.5,,

+"IRL","Ireland","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.7,,

+"ITA","Italy","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.6,,

+"JPN","Japan","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.2,,

+"KOR","Korea","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1,,

+"LUX","Luxembourg","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.6,,

+"MEX","Mexico","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,18.1,,

+"NLD","Netherlands","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.6,,

+"NZL","New Zealand","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.3,,

+"NOR","Norway","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.4,,

+"POL","Poland","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.7,,

+"PRT","Portugal","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1,,

+"SVK","Slovak Republic","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.8,,

+"ESP","Spain","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.6,,

+"SWE","Sweden","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.9,,

+"CHE","Switzerland","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.6,,

+"TUR","Turkey","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.4,,

+"GBR","United Kingdom","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.2,,

+"USA","United States","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,5.5,,

+"BRA","Brazil","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,26.7,,

+"CHL","Chile","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,4.2,,

+"EST","Estonia","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,3.1,,

+"ISR","Israel","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.8,,

+"LVA","Latvia","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,4.8,,

+"RUS","Russia","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,9.6,,

+"SVN","Slovenia","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,0.6,,

+"ZAF","South Africa","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,13.7,,

+"OECD","OECD - Total","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,3.7,,

+"AUS","Australia","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,1.5,,

+"AUT","Austria","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,0.4,,

+"BEL","Belgium","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,1.2,,

+"CAN","Canada","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,1.8,,

+"CZE","Czech Republic","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,0.5,,

+"DNK","Denmark","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,0.8,,

+"FIN","Finland","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,1.9,,

+"FRA","France","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,0.6,,

+"DEU","Germany","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,0.5,,

+"GRC","Greece","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,1.2,,

+"HUN","Hungary","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,1.3,,

+"ISL","Iceland","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,0.6,,

+"IRL","Ireland","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,1.1,,

+"ITA","Italy","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,0.9,,

+"JPN","Japan","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,0.2,,

+"KOR","Korea","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,1,,

+"LUX","Luxembourg","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,0.6,,

+"MEX","Mexico","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,33.9,,

+"NLD","Netherlands","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,0.8,,

+"NZL","New Zealand","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,1.9,,

+"NOR","Norway","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,0.3,,

+"POL","Poland","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,1.1,,

+"PRT","Portugal","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,1,,

+"SVK","Slovak Republic","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,1,,

+"ESP","Spain","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,0.7,,

+"SWE","Sweden","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,1.3,,

+"CHE","Switzerland","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,0.7,,

+"TUR","Turkey","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,2.2,,

+"GBR","United Kingdom","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,0.2,,

+"USA","United States","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,8.8,,

+"BRA","Brazil","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,50,,

+"CHL","Chile","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,7.4,,

+"EST","Estonia","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,4.9,,

+"ISR","Israel","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,3,,

+"LVA","Latvia","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,7.2,,

+"RUS","Russia","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,16,,

+"SVN","Slovenia","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,0.5,,

+"ZAF","South Africa","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,24.7,,

+"OECD","OECD - Total","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,6.3,,

+"AUS","Australia","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.7,,

+"AUT","Austria","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.5,,

+"BEL","Belgium","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.9,,

+"CAN","Canada","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.7,,

+"CZE","Czech Republic","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.5,,

+"DNK","Denmark","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.4,,

+"FIN","Finland","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.6,,

+"FRA","France","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.3,,

+"DEU","Germany","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.5,,

+"GRC","Greece","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.5,,

+"HUN","Hungary","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.7,,

+"ISL","Iceland","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.4,,

+"IRL","Ireland","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.2,,

+"ITA","Italy","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.3,,

+"JPN","Japan","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.2,,

+"KOR","Korea","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,1,,

+"LUX","Luxembourg","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.5,,

+"MEX","Mexico","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,3.9,,

+"NLD","Netherlands","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.4,,

+"NZL","New Zealand","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.7,,

+"NOR","Norway","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.5,,

+"POL","Poland","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.4,,

+"PRT","Portugal","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,1,,

+"SVK","Slovak Republic","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.6,,

+"ESP","Spain","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.5,,

+"SWE","Sweden","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.5,,

+"CHE","Switzerland","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.5,,

+"TUR","Turkey","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.5,,

+"GBR","United Kingdom","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.1,,

+"USA","United States","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,2.2,,

+"BRA","Brazil","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,4.3,,

+"CHL","Chile","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,1,,

+"EST","Estonia","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,1.5,,

+"ISR","Israel","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.7,,

+"LVA","Latvia","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,2.8,,

+"RUS","Russia","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,4.2,,

+"SVN","Slovenia","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,0.7,,

+"ZAF","South Africa","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,3.6,,

+"OECD","OECD - Total","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,1.2,,

+"AUS","Australia","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,13.04,,

+"AUT","Austria","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,6.66,,

+"BEL","Belgium","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.75,,

+"CAN","Canada","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,3.69,,

+"CZE","Czech Republic","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,5.65,,

+"DNK","Denmark","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,2.34,,

+"FIN","Finland","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,3.81,,

+"FRA","France","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,7.67,,

+"DEU","Germany","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.26,,

+"GRC","Greece","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,6.42,,

+"HUN","Hungary","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,3.03,,

+"ISL","Iceland","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,15.06,,

+"IRL","Ireland","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,5.25,,

+"ITA","Italy","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.11,,

+"LUX","Luxembourg","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,3.82,,

+"MEX","Mexico","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,28.7,,

+"NLD","Netherlands","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.42,,

+"NZL","New Zealand","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,15.11,,

+"NOR","Norway","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,2.93,,

+"POL","Poland","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,5.95,,

+"PRT","Portugal","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,8.27,,

+"SVK","Slovak Republic","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.14,,

+"ESP","Spain","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.01,,

+"SWE","Sweden","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.07,,

+"CHE","Switzerland","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.37,,

+"TUR","Turkey","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,32.64,,

+"GBR","United Kingdom","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,12.15,,

+"USA","United States","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,11.09,,

+"BRA","Brazil","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,7.13,,

+"CHL","Chile","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,9.72,,

+"EST","Estonia","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,2.42,,

+"ISR","Israel","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,15.45,,

+"LVA","Latvia","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,1.27,,

+"RUS","Russia","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.14,,

+"SVN","Slovenia","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,4.39,,

+"ZAF","South Africa","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,18.12,,

+"OECD","OECD - Total","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,11.01,,

+"AUS","Australia","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,19.17,,

+"AUT","Austria","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,10.01,,

+"BEL","Belgium","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,6.81,,

+"CAN","Canada","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,5.91,,

+"CZE","Czech Republic","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,8.63,,

+"DNK","Denmark","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,3.72,,

+"FIN","Finland","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,5.72,,

+"FRA","France","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,10.38,,

+"DEU","Germany","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,6.41,,

+"GRC","Greece","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,8.31,,

+"HUN","Hungary","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,4.24,,

+"ISL","Iceland","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,23.94,,

+"IRL","Ireland","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,8.25,,

+"ITA","Italy","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,5.65,,

+"LUX","Luxembourg","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,5.52,,

+"MEX","Mexico","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,35.53,,

+"NLD","Netherlands","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,0.73,,

+"NZL","New Zealand","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,21.37,,

+"NOR","Norway","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,4.39,,

+"POL","Poland","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,9.24,,

+"PRT","Portugal","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,11.29,,

+"SVK","Slovak Republic","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,6.04,,

+"ESP","Spain","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,5.73,,

+"SWE","Sweden","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,1.54,,

+"CHE","Switzerland","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,0.45,,

+"TUR","Turkey","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,36.01,,

+"GBR","United Kingdom","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,17.66,,

+"USA","United States","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,15.21,,

+"BRA","Brazil","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,9.15,,

+"CHL","Chile","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,12.75,,

+"EST","Estonia","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,3.72,,

+"ISR","Israel","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,23.29,,

+"LVA","Latvia","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,1.7,,

+"RUS","Russia","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,0.23,,

+"SVN","Slovenia","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,6.2,,

+"ZAF","South Africa","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,22.08,,

+"OECD","OECD - Total","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,15.3,,

+"AUS","Australia","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,6.31,,

+"AUT","Austria","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,3.1,,

+"BEL","Belgium","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.55,,

+"CAN","Canada","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.42,,

+"CZE","Czech Republic","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.21,,

+"DNK","Denmark","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.89,,

+"FIN","Finland","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.92,,

+"FRA","France","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,4.92,,

+"DEU","Germany","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.92,,

+"GRC","Greece","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,4.11,,

+"HUN","Hungary","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.66,,

+"ISL","Iceland","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,5.6,,

+"IRL","Ireland","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.28,,

+"ITA","Italy","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.25,,

+"LUX","Luxembourg","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.88,,

+"MEX","Mexico","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,17.61,,

+"NLD","Netherlands","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.09,,

+"NZL","New Zealand","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,8.63,,

+"NOR","Norway","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.34,,

+"POL","Poland","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.28,,

+"PRT","Portugal","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,5.46,,

+"SVK","Slovak Republic","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.04,,

+"ESP","Spain","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.15,,

+"SWE","Sweden","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.59,,

+"CHE","Switzerland","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.29,,

+"TUR","Turkey","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,24.48,,

+"GBR","United Kingdom","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,6.56,,

+"USA","United States","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,6.67,,

+"BRA","Brazil","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,4.74,,

+"CHL","Chile","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,5.48,,

+"EST","Estonia","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.17,,

+"ISR","Israel","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,7.08,,

+"LVA","Latvia","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.87,,

+"RUS","Russia","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.05,,

+"SVN","Slovenia","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.41,,

+"ZAF","South Africa","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,13.32,,

+"OECD","OECD - Total","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,6.05,,

+"AUS","Australia","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,14.35,,

+"AUT","Austria","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,14.55,,

+"BEL","Belgium","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,15.7,,

+"CAN","Canada","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,14.56,,

+"DNK","Denmark","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,15.87,,

+"FIN","Finland","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,15.17,,

+"FRA","France","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,16.36,,

+"DEU","Germany","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,15.62,,

+"ITA","Italy","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,16.47,,

+"KOR","Korea","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,14.7,,

+"NZL","New Zealand","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,14.87,,

+"NOR","Norway","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,15.56,,

+"POL","Poland","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,14.42,,

+"ESP","Spain","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,15.93,,

+"SWE","Sweden","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,15.18,,

+"TUR","Turkey","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,14.79,,

+"GBR","United Kingdom","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,14.92,,

+"USA","United States","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,14.44,,

+"EST","Estonia","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,14.9,,

+"LVA","Latvia","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,13.83,,

+"SVN","Slovenia","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,14.75,,

+"ZAF","South Africa","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,14.92,,

+"OECD","OECD - Total","WL_TNOW","Time devoted to leisure and personal care","L","Value","TOT","Total","HOUR","Hours","0","Units",,,14.98,,

+"AUS","Australia","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,14.37,,

+"AUT","Austria","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,14.72,,

+"BEL","Belgium","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,15.78,,

+"CAN","Canada","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,14.64,,

+"DNK","Denmark","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,15.81,,

+"FIN","Finland","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,15.28,,

+"FRA","France","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,16.66,,

+"DEU","Germany","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,15.65,,

+"ITA","Italy","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,16.95,,

+"KOR","Korea","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,15.05,,

+"NZL","New Zealand","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,14.83,,

+"NOR","Norway","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,15.48,,

+"POL","Poland","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,14.67,,

+"ESP","Spain","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,16.17,,

+"SWE","Sweden","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,15.12,,

+"TUR","Turkey","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,14.97,,

+"GBR","United Kingdom","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,14.97,,

+"USA","United States","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,14.48,,

+"EST","Estonia","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,15.33,,

+"LVA","Latvia","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,14.2,,

+"SVN","Slovenia","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,15.27,,

+"ZAF","South Africa","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,15.02,,

+"OECD","OECD - Total","WL_TNOW","Time devoted to leisure and personal care","L","Value","MN","Men","HOUR","Hours","0","Units",,,15.04,,

+"AUS","Australia","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,14.33,,

+"AUT","Austria","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,14.32,,

+"BEL","Belgium","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,15.51,,

+"CAN","Canada","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,14.46,,

+"DNK","Denmark","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,15.94,,

+"FIN","Finland","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,15.04,,

+"FRA","France","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,15.99,,

+"DEU","Germany","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,15.57,,

+"ITA","Italy","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,15.52,,

+"KOR","Korea","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,14,,

+"NZL","New Zealand","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,14.95,,

+"NOR","Norway","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,15.65,,

+"POL","Poland","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,14.08,,

+"ESP","Spain","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,15.48,,

+"SWE","Sweden","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,15.23,,

+"TUR","Turkey","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,14.2,,

+"GBR","United Kingdom","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,14.86,,

+"USA","United States","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,14.4,,

+"EST","Estonia","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,14.43,,

+"LVA","Latvia","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,13.45,,

+"SVN","Slovenia","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,14.17,,

+"ZAF","South Africa","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,14.37,,

+"OECD","OECD - Total","WL_TNOW","Time devoted to leisure and personal care","L","Value","WMN","Women","HOUR","Hours","0","Units",,,14.67,,

+"COL","Colombia","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,1.4,,

+"LTU","Lithuania","CG_SENG","Stakeholder engagement for developing regulations","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,2.4,,

+"COL","Colombia","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,23.9,,

+"LTU","Lithuania","HO_BASE","Dwellings without basic facilities","L","Value","TOT","Total","PC","Percentage","0","Units",,,13.6,,

+"COL","Colombia","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,17,,

+"LTU","Lithuania","HO_HISH","Housing expenditure","L","Value","TOT","Total","PC","Percentage","0","Units",,,19,,

+"COL","Colombia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,44.4,,

+"LTU","Lithuania","PS_FSAFEN","Feeling safe walking alone at night","L","Value","TOT","Total","PC","Percentage","0","Units",,,55.9,,

+"COL","Colombia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,48.6,,

+"LTU","Lithuania","PS_FSAFEN","Feeling safe walking alone at night","L","Value","MN","Men","PC","Percentage","0","Units",,,61.6,,

+"COL","Colombia","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,40.7,,

+"LTU","Lithuania","PS_FSAFEN","Feeling safe walking alone at night","L","Value","WMN","Women","PC","Percentage","0","Units",,,51.2,,

+"COL","Colombia","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.2,,

+"LTU","Lithuania","HO_NUMR","Rooms per person","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,1.5,,

+"LTU","Lithuania","IW_HADI","Household net adjusted disposable income","L","Value","TOT","Total","USD","US Dollar","0","Units",,,21660,,

+"COL","Colombia","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,67,,

+"LTU","Lithuania","JE_EMPL","Employment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,70,,

+"COL","Colombia","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,79,,

+"LTU","Lithuania","JE_EMPL","Employment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,71,,

+"COL","Colombia","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,55,,

+"LTU","Lithuania","JE_EMPL","Employment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,70,,

+"COL","Colombia","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.79,,

+"LTU","Lithuania","JE_LTUR","Long-term unemployment rate","L","Value","TOT","Total","PC","Percentage","0","Units",,,2.69,,

+"COL","Colombia","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,0.51,,

+"LTU","Lithuania","JE_LTUR","Long-term unemployment rate","L","Value","MN","Men","PC","Percentage","0","Units",,,3.22,,

+"COL","Colombia","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,1.16,,

+"LTU","Lithuania","JE_LTUR","Long-term unemployment rate","L","Value","WMN","Women","PC","Percentage","0","Units",,,2.18,,

+"LTU","Lithuania","JE_PEARN","Personal earnings","L","Value","TOT","Total","USD","US Dollar","0","Units",,,24287,,

+"COL","Colombia","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,89,,

+"LTU","Lithuania","SC_SNTWS","Quality of support network","L","Value","TOT","Total","PC","Percentage","0","Units",,,88,,

+"COL","Colombia","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,89,,

+"LTU","Lithuania","SC_SNTWS","Quality of support network","L","Value","MN","Men","PC","Percentage","0","Units",,,87,,

+"COL","Colombia","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,89,,

+"LTU","Lithuania","SC_SNTWS","Quality of support network","L","Value","WMN","Women","PC","Percentage","0","Units",,,88,,

+"LTU","Lithuania","SC_SNTWS","Quality of support network","L","Value","HGH","High","PC","Percentage","0","Units",,,92,,

+"LTU","Lithuania","SC_SNTWS","Quality of support network","L","Value","LW","Low","PC","Percentage","0","Units",,,87,,

+"COL","Colombia","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,54,,

+"LTU","Lithuania","ES_EDUA","Educational attainment","L","Value","TOT","Total","PC","Percentage","0","Units",,,93,,

+"COL","Colombia","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,52,,

+"LTU","Lithuania","ES_EDUA","Educational attainment","L","Value","MN","Men","PC","Percentage","0","Units",,,90,,

+"COL","Colombia","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,56,,

+"LTU","Lithuania","ES_EDUA","Educational attainment","L","Value","WMN","Women","PC","Percentage","0","Units",,,95,,

+"COL","Colombia","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,410,,

+"LTU","Lithuania","ES_STCS","Student skills","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,475,,

+"COL","Colombia","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,411,,

+"LTU","Lithuania","ES_STCS","Student skills","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,468,,

+"COL","Colombia","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,409,,

+"LTU","Lithuania","ES_STCS","Student skills","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,483,,

+"COL","Colombia","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,456,,

+"LTU","Lithuania","ES_STCS","Student skills","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,519,,

+"COL","Colombia","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,378,,

+"LTU","Lithuania","ES_STCS","Student skills","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,438,,

+"COL","Colombia","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,14.1,,

+"LTU","Lithuania","ES_EDUEX","Years in education","L","Value","TOT","Total","YR","Years","0","Units",,,18.4,,

+"COL","Colombia","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,13.8,,

+"LTU","Lithuania","ES_EDUEX","Years in education","L","Value","MN","Men","YR","Years","0","Units",,,18,,

+"COL","Colombia","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,14.4,,

+"LTU","Lithuania","ES_EDUEX","Years in education","L","Value","WMN","Women","YR","Years","0","Units",,,18.8,,

+"COL","Colombia","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,10,,

+"LTU","Lithuania","EQ_AIRP","Air pollution","L","Value","TOT","Total","MICRO_M3","Micrograms per cubic metre","0","Units",,,14,,

+"COL","Colombia","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,75,,

+"LTU","Lithuania","EQ_WATER","Water quality","L","Value","TOT","Total","PC","Percentage","0","Units",,,81,,

+"COL","Colombia","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,76,,

+"LTU","Lithuania","EQ_WATER","Water quality","L","Value","MN","Men","PC","Percentage","0","Units",,,79,,

+"COL","Colombia","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,73,,

+"LTU","Lithuania","EQ_WATER","Water quality","L","Value","WMN","Women","PC","Percentage","0","Units",,,82,,

+"COL","Colombia","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,53,,

+"LTU","Lithuania","CG_VOTO","Voter turnout","L","Value","TOT","Total","PC","Percentage","0","Units",,,51,,

+"COL","Colombia","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,76.2,,

+"LTU","Lithuania","HS_LEB","Life expectancy","L","Value","TOT","Total","YR","Years","0","Units",,,74.8,,

+"COL","Colombia","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,73.1,,

+"LTU","Lithuania","HS_LEB","Life expectancy","L","Value","MN","Men","YR","Years","0","Units",,,69.5,,

+"COL","Colombia","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,79.4,,

+"LTU","Lithuania","HS_LEB","Life expectancy","L","Value","WMN","Women","YR","Years","0","Units",,,80.1,,

+"LTU","Lithuania","HS_SFRH","Self-reported health","L","Value","TOT","Total","PC","Percentage","0","Units",,,43,,

+"LTU","Lithuania","HS_SFRH","Self-reported health","L","Value","MN","Men","PC","Percentage","0","Units",,,49,,

+"LTU","Lithuania","HS_SFRH","Self-reported health","L","Value","WMN","Women","PC","Percentage","0","Units",,,40,,

+"LTU","Lithuania","HS_SFRH","Self-reported health","L","Value","HGH","High","PC","Percentage","0","Units",,,67,,

+"LTU","Lithuania","HS_SFRH","Self-reported health","L","Value","LW","Low","PC","Percentage","0","Units",,,29,,

+"COL","Colombia","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,6.3,,

+"LTU","Lithuania","SW_LIFS","Life satisfaction","L","Value","TOT","Total","AVSCORE","Average score","0","Units",,,5.9,,

+"COL","Colombia","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,6.2,,

+"LTU","Lithuania","SW_LIFS","Life satisfaction","L","Value","MN","Men","AVSCORE","Average score","0","Units",,,5.9,,

+"COL","Colombia","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6.3,,

+"LTU","Lithuania","SW_LIFS","Life satisfaction","L","Value","WMN","Women","AVSCORE","Average score","0","Units",,,6,,

+"LTU","Lithuania","SW_LIFS","Life satisfaction","L","Value","HGH","High","AVSCORE","Average score","0","Units",,,6.5,,

+"LTU","Lithuania","SW_LIFS","Life satisfaction","L","Value","LW","Low","AVSCORE","Average score","0","Units",,,5.6,,

+"COL","Colombia","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,24.5,,

+"LTU","Lithuania","PS_REPH","Homicide rate","L","Value","TOT","Total","RATIO","Ratio","0","Units",,,3.4,,

+"COL","Colombia","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,46.4,,

+"LTU","Lithuania","PS_REPH","Homicide rate","L","Value","MN","Men","RATIO","Ratio","0","Units",,,5.2,,

+"COL","Colombia","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,4.1,,

+"LTU","Lithuania","PS_REPH","Homicide rate","L","Value","WMN","Women","RATIO","Ratio","0","Units",,,1.7,,

+"COL","Colombia","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,26.56,,

+"LTU","Lithuania","WL_EWLH","Employees working very long hours","L","Value","TOT","Total","PC","Percentage","0","Units",,,0.54,,

+"COL","Colombia","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,32.09,,

+"LTU","Lithuania","WL_EWLH","Employees working very long hours","L","Value","MN","Men","PC","Percentage","0","Units",,,0.67,,

+"COL","Colombia","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,19.37,,

+"LTU","Lithuania","WL_EWLH","Employees working very long hours","L","Value","WMN","Women","PC","Percentage","0","Units",,,0.43,,

diff --git a/ML/01_intro/tools_matplotlib.ipynb b/ML/01_intro/tools_matplotlib.ipynb
new file mode 100644
index 0000000..69f4b9b
--- /dev/null
+++ b/ML/01_intro/tools_matplotlib.ipynb
@@ -0,0 +1,26425 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Tools - matplotlib**\n",
+    "\n",
+    "*This notebook demonstrates how to use the matplotlib library to plot beautiful graphs.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a href=\"https://colab.research.google.com/github/ageron/handson-ml3/blob/main/tools_matplotlib.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "  </td>\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml3/blob/main/tools_matplotlib.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "toc": "true"
+   },
+   "source": [
+    "# Table of Contents\n",
+    " <p><div class=\"lev1\"><a href=\"#Plotting-your-first-graph\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Plotting your first graph</a></div><div class=\"lev1\"><a href=\"#Line-style-and-color\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Line style and color</a></div><div class=\"lev1\"><a href=\"#Saving-a-figure\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Saving a figure</a></div><div class=\"lev1\"><a href=\"#Subplots\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Subplots</a></div><div class=\"lev1\"><a href=\"#Multiple-figures\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Multiple figures</a></div><div class=\"lev1\"><a href=\"#Pyplot's-state-machine:-implicit-vs-explicit\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Pyplot's state machine: implicit <em>vs</em> explicit</a></div><div class=\"lev1\"><a href=\"#Pylab-vs-Pyplot-vs-Matplotlib\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Pylab <em>vs</em> Pyplot <em>vs</em> Matplotlib</a></div><div class=\"lev1\"><a href=\"#Drawing-text\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Drawing text</a></div><div class=\"lev1\"><a href=\"#Legends\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>Legends</a></div><div class=\"lev1\"><a href=\"#Non-linear-scales\"><span class=\"toc-item-num\">10&nbsp;&nbsp;</span>Non-linear scales</a></div><div class=\"lev1\"><a href=\"#Ticks-and-tickers\"><span class=\"toc-item-num\">11&nbsp;&nbsp;</span>Ticks and tickers</a></div><div class=\"lev1\"><a href=\"#Polar-projection\"><span class=\"toc-item-num\">12&nbsp;&nbsp;</span>Polar projection</a></div><div class=\"lev1\"><a href=\"#3D-projection\"><span class=\"toc-item-num\">13&nbsp;&nbsp;</span>3D projection</a></div><div class=\"lev1\"><a href=\"#Scatter-plot\"><span class=\"toc-item-num\">14&nbsp;&nbsp;</span>Scatter plot</a></div><div class=\"lev1\"><a href=\"#Lines\"><span class=\"toc-item-num\">15&nbsp;&nbsp;</span>Lines</a></div><div class=\"lev1\"><a href=\"#Histograms\"><span class=\"toc-item-num\">16&nbsp;&nbsp;</span>Histograms</a></div><div class=\"lev1\"><a href=\"#Images\"><span class=\"toc-item-num\">17&nbsp;&nbsp;</span>Images</a></div><div class=\"lev1\"><a href=\"#Animations\"><span class=\"toc-item-num\">18&nbsp;&nbsp;</span>Animations</a></div><div class=\"lev1\"><a href=\"#Saving-animations-to-video-files\"><span class=\"toc-item-num\">19&nbsp;&nbsp;</span>Saving animations to video files</a></div><div class=\"lev1\"><a href=\"#What's-next?\"><span class=\"toc-item-num\">20&nbsp;&nbsp;</span>What's next?</a></div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting your first graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we need to import the `matplotlib` library."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's plot our first graph! :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6QHVDMzPG6rFpe3lkTArdO4XwlC5tspStZ308CswXkW+BQcDzDkuk1EWorm/ijbWFXNM3ln5xEVbH8RhtS5vSKayo5bWvdWmTVfxsuZMxZhuQ4dgoSl28t9cd4GR9sx6bdoAre8Vw04CuzF6Vz4SBcSRF6xuInE3fmajcXk1DM6+v3c/YtM70T9Bp2hF+M74vAb4+PPVJri5tsoAWtXJ7764voqquiZnjdJp2lNgOQfzXtb1Yk1fBP3cctjqO19GiVm6trrGZeWsKubJXDIO6RVodx6PdPTKJ9PgOPPPpLl3a5GRa1Mqtzd9QzLHaRp2mncDXR3huUn/Kaxr4y1e6tMmZtKiV2zrV2MJr3xQyOiWaId07Wh3HKwzsFsndI7rzzvoD7CjRpU3OokWt3Nb7G4upqGnQadrJHr+2N1Ghgfx6iS5tchYtauWW6ptaePXrAkYkRzGsR5TVcbxKRLA/T43vw7clJ1iQXWR1HK+gRa3c0sJNBymr1mnaKhMGxjE6JZo/fbmXsup6q+N4PC1q5XYamlt4ZXUBQ5M6MjK5k9VxvJKI8MzEfjQ0t/LsZ7qe3tG0qJXb+TCnhCMn65k5LhURsTqO10qOCePhq3qydPsh1ubp0iZH0qJWbqWxuZVXVhcwODGS0SnRVsfxeg9f1ZOkTiE89Uku9U26tMlRtKiVW1m0pYTSqlM6TbuIIH9f/ndSOvsrann16wKr43gsLWrlNppaWpmzOp+BCRFc2UsvTuEqLk+N4eaBcby8uoD9FbVWx/FIWtTKbSzeWsrBYzpNu6KnbupDoK8Pv9GlTQ6hRa3cQnNLK3NW5dMvrgNj0zpbHUd9R+cOQfzXdb1Zk1fBp9/q0iZ706JWbmHp9kMUVdbpNO3C7hrRnf7xEfzvZ7s4qUub7MqmohaRAyKyQ0S2iUiOo0MpdaaWVsPslfmkdQnnmj6xVsdR5+DrIzw/uT+VNQ385V97rY7jUS5koh5jjBlkjNErvSin+uzbQxRW1PLYuFR8fHSadmX9EyLaljZtKOLbkiqr43gMPfShXFprq2HWynx6xYZxXb8uVsdRNnj8ut5EhwXyxKIdegjETmwtagN8JSKbRWTa2e4gItNEJEdEcsrLy+2XUHm1L3KPkF9Ww6NjdZp2Fx2C/Hl+cn/2Hqlm0pwsCsprrI7k9mwt6tHGmMHADcB0Ebniu3cwxsw1xmQYYzJiYvQcV3Xp2qbpPHrGhHJj/65Wx1EX4Jq+sbw3dThVdU1MmpPFqr1lVkdyazYVtTGmtP3HMmAxMMyRoZQC+GrXEfYcqebRsan46jTtdkYkd2LpjEy6dQzh/r9v4pXVBXqO9UU6b1GLSKiIhJ/+OXAtkOvoYMq7GWN4cUU+PaJDGT9Ap2l3ldAxhI8eHsmN/bvyxy/3MPODbZxq1J0gF8rPhvvEAovbz131AxYYY750aCrl9ZbvLmP34ZP83w8G4uerr3m7s5AAP2bfcRl9u3bg/77aS2F5DXPvySA+MtjqaG7jvM8AY0yhMWZg+0c/Y8xzzgimvJcxhpdW5JEYFcKkQXFWx1F2ICJMH5PCG/dmUFxZx4RZa9m4/5jVsdyGjirK5azeW86O0hPMGJOi07SHGZsWy+LpmUQE+3PnvA28t0Ev5WULfRYol9J2bDqP+MhgJg+OtzqOcoCUzmEsnp7J6NRonlySy68W76CxudXqWC5Ni1q5lDV5FWw7WMX0MSn46zTtsSKC/Xnj3qE8fFVPFmQXM+X1DZRXN1gdy2XpM0G5jNPTdFxEELcO0Wna0/n6CL+8Po0Xbx/EjtITTJy9ltzSE1bHckla1MplrCuoZHPRcR6+qieBfr5Wx1FOMnFQPB89NAqAW19ZxyfbSi1O5Hq0qJXLeHFFHrEdAvlBRjeroygnS4+PYOmjoxmYEMljH2zj91/spqVV3xxzmha1cgkbCivZuP8YD13ZkyB/naa9UXRYIO9NHc6U4Ym89nUh9/99EydO6VIn0KJWLuKlFXnEhAdyx7BEq6MoCwX4+fDc5P48NzmdrPwKJs3JIr9MlzppUSvLbTpwjHUFlTx4RbJO0wqAKcO7s+CBEZw81cTkOVms2H3U6kiW0qJWlntpRR7RYQFMGd7d6ijKhQzrEcXSR0fTPTqEqe/kMGdVvtcuddKiVpbaUnycNXkVPHB5MsEBOk2rfxcfGcyHD47i5gFx/Plfe5nx/lbqGputjuV0WtTKUi+tyKNjiD93jdBpWp1dcIAvL94+iCduSOPzHYe59ZX1HDxWZ3Usp9KiVpbZfrCK1XvLmXp5MqGBtixyVN5KRHjwyp68ed9QSo7XMXFOFhsKK62O5TRa1Moys1bmERHszz0jdZpWthnTuzOfTM9s+1fY69m8u/6AVxy31qJWlsgtPcHy3WVMHd2D8CB/q+MoN5Ic07bU6YpeMTz1yU6eWLSDhmbPvhiBFrWyxKyVeYQH+XFvZpLVUZQb6hDkz7x7Mpg+picfbDrInfOyKauutzqWw9hc1CLiKyJbReQzRwZSnm/34ZP8a+dR7s/sQQedptVF8vURfnFdGrPvvIxdh04yYVYW35ZUWR3LIS5kon4M2O2oIMp7zFqZR1igH/dn9rA6ivIA4wfE8dHDI/H1EW57dT2Lt5ZYHcnubCpqEUkAbgJed2wc5emWbj/E5zuOcN+oJCJCdJpW9tEvLoKlMzK5rFskP1u4nec/96ylTrZO1C8A/w2c8zIMIjJNRHJEJKe8vNwe2ZQHaWk1bVehfn8rGd07Mu3KZKsjKQ/TqX2p0z0juzP3m0Lue2sjJ+o8Y6nTeYtaRMYDZcaYzd93P2PMXGNMhjEmIyYmxm4Blfs7Wd/E1Lc38crqAu4YlsiCB0bosWnlEP6+PjwzMZ0/3NKfDYWVTJyzlryj1VbHumS2TNSZwAQROQB8AIwVkfccmkp5jILyGibNyWJNXgXPTkrn97f0J8BPTzZSjnX7sETef2AENQ0tTJqTxbJd7r3U6bzPGGPME8aYBGNMEnA7sNIYc5fDkym3t2pPGZNmZ3Giron5U4fr28SVU2UkRfHpo5n07BzGA+/k8NKKPFrd9Li1jjbK7owxvLw6n/vf3kS3qBA+mZHJ8OROVsdSXqhrRDD/eHAkky+L56/L9jF9wRZqG9xvqdMFLVgwxqwGVjskifIIpxpb+MVH2/ns28OMH9CVP982ULfiKUsF+fvy1x8OpG/XDvz+i93sr6hl3j0ZdIsKsTqazXSiVnZTcryO215dxz93HOaX16cx647LtKSVSxARHrgimbd+PIxDVaeYMHst6/IrrI5lMy1qZRfZhZVMmJ1FcWUdb947lIev6omIWB1LqX9zZa8Yls4YTaewQO5+cyN/z9rvFkudtKjVJXt3QxFTXs8mMsSfJTMyGZPW2epISp1TUnQoix8ZxZjenfndp7v45cffuvxSJy1qddEam1t5YtEOnlqSy+Wp0SyZnknPmDCrYyl1XuFB/sy9ewgzx6bwj5wSbp+7gbKTrrvUSYtaXZTy6gbunLeB9zcW88hVPXn93qH6JhblVnx8hJ9f25uXpwxmz+Fqbp69lm0Hq6yOdVZa1OqC7Sg5wYTZa8k9dIKX7riM/74+DV8fPR6t3NON/buy6JFR+Pv68MPX1vPxZtdb6qRFrS7IJ9tKue3VdfiI8NFDo5gwMM7qSEpdsj5dO7B0xmiGJHbk8Q+388ynu2huOedqI6fTolY2aWk1/P7z3Tz2wTYGJkTyyYxM0uMjrI6llN1EhQbwzk+Gcd+oJN7M2s+9b23keG2j1bEALWplgxN1Tdz/90289k0hd41I5L2pw4kOC7Q6llJ25+/rw+8m9ONPtw5g0/7jTJyTxd4j1i910qJW3yu/rJpJL2exrqCC5yf359lJulRJeb4fDu3G+9NGcKqphckvZ/Fl7hFL8+gzTp3T8l1HmTRnHdX1TSx4YAR3Dk+0OpJSTjOke0c+nTGa1NhwHnpvMy8s32fZUictavUfjDHMWZXPA+/mkBQdwtIZoxmaFGV1LKWcrktEEAunjeCWwfG8sDyPh+dvpsaCpU5a1Orf1DU2M2PBVv78r71MGBjHRw+NIi4y2OpYSlkmyN+Xv/xgIE+N78uyXUe55eUsiiprnZpBi1r9fweP1XHrK+v5PPcwT9yQxgs/GkSQvy5VUkpE+MnoHrxz/3COnmxgwuws1uY5b6mTFrUCYH1BJRNmr6XkeB1v3TeUB6/UpUpKfdfo1GiWzsgktkMg97yZzetrCp2y1MmWayYGichGEdkuIjtF5GmHp1JOY4zhnfUHuOuNbKJCA/hkeiZX9dalSkqdS/dOoSx6JJOr+8Ty7D938/iH26lvcuxSJ1suHNAAjDXG1IiIP7BWRL4wxmxwaDLlcA3NLfxmyU4W5hzk6j6d+duPBhGu+zqUOq+wQD9evWsIL63M44XleRSU1/LaXUPoEhHkkO9nyzUTjTGmpv1T//YP11/gqr5XWXU9d8zdwMKcgzw6NoW5d2doSSt1AXx8hJ9e3YtX7xpC3tG2pU6bi4475nvZcicR8RWRbUAZsMwYk32W+0wTkRwRySkvL7dzTGVP2w9WMWFWFrsPVzPnzsE8fm1vfHSpklIX5fr0Lix6ZBTB/r5MfXuTQ67JKBdyIFxEIoHFwKPGmNxz3S8jI8Pk5ORcejpld4u2lPA/i3YQExbIvHsy6BvXwepISnmEqrpG9h6pvugLOYvIZmNMxtluu9CL21aJyCrgeuCcRa1cT3NLK3/8cg/z1uxnRHIUL08ZQlRogNWxlPIYkSEBF13S53PeohaRGKCpvaSDgWuAPzokjXKIE3VNzHh/C2vyKrh3ZHeeHN8Xf189M1Mpd2HLRN0VeFtEfGk7pv0PY8xnjo2l7GXf0WoeeCeHQ1Wn+OOt/fnRUN3XoZS7OW9RG2O+BS5zQhZlZ1/tPMLPFm4jOMCPD6aNYEh33dehlDu6oGPUyj20thpmr8rnr8v2MSAhgtfuHkLXCN3XoZS70qL2MLUNzfzXh9v5IvcIt1wWz/O39Nd9HUq5OS1qD1JcWce0d3PYd7SaJ2/qw09G99B9HUp5AC1qD7Euv4JHFmzBGHj7/mFcnhpjdSSllJ1oUbs5Ywx/X3eAZ/+5m+ToUObdk0FSdKjVsZRSdqRF7cYamlt4cnEuH24u4Zq+sfztR4MIC9Q/UqU8jT6r3VTZyXoefG8zW4urmDkulZ+OS9V9HUp5KC1qN7TtYBUPvptDdX0zr941mOvTu1odSSnlQFrUbuajzSX8avEOYjsEsuiRUaR10aVKSnk6LWo30dzSyvOf7+HNrP2M6tmJOXcOpqMuVVLKK2hRu4HjtY3MeH8LWfmV3J/Zg1/dmIafLlVSymtoUbu4vUfaliodOVHPn28bwA8yulkdSSnlZFrULuzL3CP8/B/bCAv044MHRzA4saPVkZRSFtCidkGtreb/XzRzULdIXrt7CLEdHHPRTKWU69OidjE1Dc38fOE2vtp1lFsHJ/Dc5HRdqqSUl9OidiFFlbU88E4OBeW1/GZ8X36cmaRLlZRSNl2KqxvwDhALGGCuMeZFRwfzNmvzKpi+YAsi8M79w8hMibY6klLKRdgyUTcDjxtjtohIOLBZRJYZY3Y5OJtXMMbwxtr9PP/5blI7hzPvngwSO4VYHUsp5UJsuRTXYeBw+8+rRWQ3EA9oUV+iospa/rZsH0u2HeL6fl34yw8HEqpLlZRS33FBrSAiSbRdPzH7LLdNA6YBJCbqBVTPpamllRW7jzI/u5g1eRX4+gg/u7oXj45N0aVKSqmzsrmoRSQM+Bj4qTHm5HdvN8bMBeYCZGRkGLsl9BClVadYuLGYDzYdpKy6ga4RQfz8ml78aGg3PfVOKfW9bCpqEfGnraTnG2MWOTaS52hpNXyzr5z52UWs3FOGAa7qFcPzw7tzVe8YfRu4Usomtpz1IcAbwG5jzF8dH8n9lVXX82FOCQuyiymtOkV0WCAPX9WT24cm0i1KXyhUSl0YWybqTOBuYIeIbGv/tV8ZYz53WCo31NpqWF9YyfzsIr7aeZTmVkNmSid+fVMfru4TS4CfTs9KqYtjy1kfawF9lescjtc28tHmEhZsLGZ/RS2RIf78ODOJO4YlkhwTZnU8pZQH0HPBLoIxhs1Fx5mfXcw/dxymsbmVjO4dmTkuhRvSu+pbvpVSdqVFfQFO1jexZGsp8zcUs/doNeGBftw+tBt3Dk/UK60opRxGi9oGO0pOMD+7iE+2HeJUUwsDEiL44639uXlgHCEB+p9QKeVY2jLnUNfYzNJth1iwsZhvS04Q7O/LxEFx3Dk8kQEJkVbHU0p5ES3q79hz5CQLsotZvKWU6oZmeseG88zEfky6LJ4OQf5Wx1NKeSEtaqC+qYUvcg8zf0MxOUXHCfDz4ab+XZkyPJEh3TvqqlGllKW8uqgLy2t4f2MxH24uoaquiR7Rofz6xj7cNiRBr/CtlHIZXlfUjc2tLNt1lAUbi8jKr8TPR7iuXxfuHJ7IyOROuhhJKeVyvKaoDx6r44NNxSzcVEJFTQPxkcH84rre/CAjgc7huhRJKeW6PLqoW1oNq/aUMT+7iNX7yhFgbFpnpgzvzhW9YvDV6Vkp5QY8sqiPnqxn4aaDfLCxmEMn6ukcHsijY1L40bBE4iODrY6nlFIXxGOKurXVsDa/ggXZxSzbfZSWVsPlqdH85ua+jOsTi7+uFFVKuSm3L+rKmgY+3Ny2UrT4WB1RoQFMvbwHdwxNJCk61Op4Sil1ydyyqI0xbNx/jPnZxXyZe4TGllaG9Yji8Wt7cX16FwL9dCmSUspzuFVRn6hrYtHWEuZnF5NfVkN4kB93Dk9kyvBEUmPDrY6nlFIO4fJFbYxhe8kJ5m8o4tNvD1Hf1MrAbpH86bYB3DwgjuAAnZ6VUp7NlktxvQmMB8qMMemOj9SmpqFtKdL87CJ2HjpJSIAvky9LYMrwRNLjI5wVQymlLGfLRP13YDbwjmOjtNl16CTzs4tYsrWU2sYW0rqE8+ykdCYOiiNclyIppbyQLZfi+kZEkhwdpKahmbvfyGZrcRWBfj6MHxDHlBGJXNYtUpciKaW8mt2OUYvINGAaQGJi4gX//rBAP7pHhTB+QBy3Do4nMkSXIimlFIAYY85/p7aJ+jNbj1FnZGSYnJycS4ymlFLeQ0Q2G2Myznabvl1PKaVcnBa1Ukq5uPMWtYi8D6wHeotIiYj8xPGxlFJKnWbLWR93OCOIUkqps9NDH0op5eK0qJVSysVpUSullIvTolZKKRdn0xteLviLipQDRRf526OBCjvGcQf6mD2ftz1e0Md8obobY2LOdoNDivpSiEjOud6d46n0MXs+b3u8oI/ZnvTQh1JKuTgtaqWUcnGuWNRzrQ5gAX3Mns/bHi/oY7YblztGrZRS6t+54kStlFLqDFrUSinl4lymqEXkehHZKyL5IvI/VudxBhF5U0TKRCTX6izOICLdRGSViOwSkZ0i8pjVmRxNRIJEZKOIbG9/zE9bnclZRMRXRLaKyGdWZ3EGETkgIjtEZJuI2PXKKS5xjFpEfIF9wDVACbAJuMMYs8vSYA4mIlcANcA7zrzCu1VEpCvQ1RizRUTCgc3AJE/+c5a2C36GGmNqRMQfWAs8ZozZYHE0hxORnwMZQAdjzHir8ziaiBwAMowxdn+Tj6tM1MOAfGNMoTGmEfgAmGhxJoczxnwDHLM6h7MYYw4bY7a0/7wa2A3EW5vKsUybmvZP/ds/rJ+OHExEEoCbgNetzuIJXKWo44GDZ3xegoc/gb1d+3U4LwOyLY7icO2HALYBZcAyY4zHP2bgBeC/gVaLcziTAb4Skc3tF/u2G1cpauVFRCQM+Bj4qTHmpNV5HM0Y02KMGQQkAMNExKMPc4nIeKDMGLPZ6ixONtoYMxi4AZjefmjTLlylqEuBbmd8ntD+a8rDtB+n/RiYb4xZZHUeZzLGVAGrgOstjuJomcCE9mO2HwBjReQ9ayM5njGmtP3HMmAxbYd07cJVinoTkCoiPUQkALgdWGpxJmVn7S+svQHsNsb81eo8ziAiMSIS2f7zYNpeMN9jaSgHM8Y8YYxJMMYk0fZcXmmMucviWA4lIqHtL5AjIqHAtYDdzuZyiaI2xjQDM4B/0fYC0z+MMTutTeV4Xnjh4EzgbtomrG3tHzdaHcrBugKrRORb2gaSZcYYrzhdzcvEAmtFZDuwEfinMeZLe31xlzg9Tyml1Lm5xEStlFLq3LSolVLKxWlRK6WUi9OiVkopF6dFrZRSLk6LWimlXJwWtVJKubj/B/SXuMjP5ve3AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot([1, 2, 4, 9, 5, 3])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Yep, it's as simple as calling the `plot` function with some data, and then calling the `show` function!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**:\n",
+    "\n",
+    "> Matplotlib can output graphs using various backend graphics libraries, such as Tk, wxPython, etc. When running Python using the command line, you may want to specify which backend to use right after importing matplotlib and before plotting anything. For example, to use the Tk backend, run `matplotlib.use(\"TKAgg\")`.\n",
+    "> However, in a Jupyter notebook, things are easier: importing `import matplotlib.pyplot` automatically registers Jupyter itself as a backend, so the graphs show up directly within the notebook. It used to require running `%matplotlib inline`, so you'll still see it in some notebooks, but it's not needed anymore."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the `plot` function is given one array of data, it will use it as the coordinates on the vertical axis, and it will just use each data point's index in the array as the horizontal coordinate.\n",
+    "You can also provide two arrays: one for the horizontal axis `x`, and the second for the vertical axis `y`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([-3, -2, 5, 0], [1, 6, 4, 3])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The axes automatically match the extent of the data. We would like to give the graph a bit more room, so let's call the `axis` function to change the extent of each axis `[xmin, xmax, ymin, ymax]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([-3, -2, 5, 0], [1, 6, 4, 3])\n",
+    "plt.axis([-4, 6, 0, 7])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's plot a mathematical function. We use NumPy's `linspace` function to create an array `x` containing 500 floats ranging from -2 to 2, then we create a second array `y` computed as the square of `x` (to learn about NumPy, read the [NumPy tutorial](tools_numpy.ipynb))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "x = np.linspace(-2, 2, 500)\n",
+    "y = x**2\n",
+    "\n",
+    "plt.plot(x, y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's a bit dry, let's add a title, and x and y labels, and draw a grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, y)\n",
+    "plt.title(\"Square function\")\n",
+    "plt.xlabel(\"x\")\n",
+    "plt.ylabel(\"y = x**2\")\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Line style and color"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, matplotlib draws a line between consecutive points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qghDCBhScuMC89Cw2ZlU0HPP3bDkza2423EuVUh0u65YpMx8vBsIue1yo+ViL4eTgQKS/B989N4QPthewYG02T7y3m5GdApiRGk9CsJe1myiEaMGOV15kfno2/9hViJOj4omhUUwfFs2fvzrUsIVgS3Cz4f4VMAl41fz3l5cdf1optRLTgGrltfrbrcXN2ZGpgyMZ3yeMd7bks3h9DmPmb+T2bh14ISVONu8VQvxMxblLLFyXw3vbjqK15qF+4fxmeAwBLbTw3zXDXSn1EZAM+CulioA/Ywr1T5RSU4GjwDjzw7/FNFMmG9NUyClN0OZG5eHqxG+Gx/Bw/468tSGX5Zvz+Pbgce7tFcqzI2Nb1bxWIUTjq7xY05ANVTV1rSYbrhnuWusJV7lr5BUeq4HfWNooa/Bu48xLo+OZPCii4bfzF/uKmdA3nKdb8G9nIUTTOH+ptuFT/Zmq2lb3qV6WWf6Cv6cr/3l7Ao8NieT1Ndl8uL2Aj3cWMmlgBNOHRePbgpciCyEsV1VT1zAed+J8NaM6B/BiSusbj5Nwv4oO3m34f3d3ZfrQaOamZ7J0Yy4fbi9g6uBIHhsSKfXYhbAxNXX1/GNXEa+vyeJ4ZRWDY/yZkRpHz/B21m7aTZFwv4ZwP3dmj+vBk8OimZ2Wybz0LFZszWf6sGgmDYigjUvrqB4nhLiyunrNV/uLmbs6i6MnLtAr3IdZ47ozMNrf2k2ziIT7dYoNbMvCh3vzQ3ElMw1GXv3uCMs25fH08BjG9w3D1UlCXojWRGvNqkMlzDJkklV2joQOXiyfnMTw+ACbKDsi4X6DuoR4886UvuzKP8lrq4z8+atDLNmQy3MjY7mnV4hV6ksIIa6f1pr1meXMMmRysLiS6PYevPlgL27tEmRTm+tIuN+kpAhfVk7rz6bsCmauMvLbzw6waH0Oz6fEcXvXDjb1QyKErdiee4KZBiM7808R2q4NM+/vzt09Q1rlLkzXIuFuAaUUQ2LbMzjGn7SMUmanZfLsR3tZsDabGanxjOpsGx/vhGjt9heeZqbByMasCgK9XPnLXV14ICnMpvc1lnBvBEopUhODGNU5kK8PHGPu6iwef3cX3cN8eDk1nkExfhLyQliBseQsswxGDBml+Hq48KcxnZk4oGOr2UbPEhLujcjBQTG2Rwi3de3AZ3uKmLc6i4eXbad/lC8vj46nd0dfazdRCLuQX3GeOasz+Wr/MTxdnHgxJY5HB0fa1Q5q9vNOm5GTowMP9Annrp4hfLS9gDfW5nDvwq0Mj2/PjNR4uoR4W7uJQtik4tMXeT09i3/sLsLF0YHpw6J5YmgUPu72t/hQwr0JuTo5MnlQJOP6hLFiy1EWrc/h9tc3MaZrEC+mxBET0NbaTRTCJpSfvcSba00rygEm9u/IU8Oj7Xrzegn3ZuDu4sSTydE81D+cpRvzWLYxl+9/KOGuniE8PzKOcL+WXYBIiJbq9IVqFm/I5Z3N+VTX1XN/71CeGRlLiE8bazfN6iTcm5GXmzMvpsQxeWAEi9bnsGJLPl/tO8YDfcJ4ZkQsQd72e5UhxI04d6mW5ZvyeGtDLueqa7mzezDPj4oj0t/D2k1rMSTcrcDXw4U/junM1MGRvLEmm5U7C/h0dxET+3fkyeRo/Dxdrd1EIVqkqpo63tt6lIXrczh5vprUhEBeTI2jU1DrKurVHCTcrSjQy42/3NWFaUOjmJeexfLNeXy0o4BHB0fy2JAovNtIcTIhAKpr6/l4VyFvrMmi9MwlhsT681JqPN3DfKzdtBZLwr0FCPN1Z+b93Zk+LJo5qzN5fU02K7bk88SwaKYMisDdRU6TsE919Zp/7i1mXnomhScv0ieiHfPH96RflJ+1m9biSWq0IDEBnrz5YC+eSq5ktiGT11YZeXtzHk8lx/Bgv3C7WHghBEB9vea7H0qYnWYkp/w8XUO8+cuULgyLay8LAq+ThHsLlBjszbLJfdh99BSzDEb+55sM3tqYy7MjY7mvdyjOUpxM2CitNWuNZcxclUnG8TPEBniy6OFejE4MklC/QRLuLVjvju348PH+bMmu4DWDkT98fpBF63N4YVQcd3QPtsliR8J+bckxFeHbU3CacF935jzQnTu722ZRr+Yg4d4KDIzx5/NoP9YcKWOmIZPnP97HgnXZvJgSz+jEQLmiEa3a3oJTzDQY2Zx9giAvN/7f3V25P0k+oVrKonBXSr0APAZo4CAwBegArAT8gN3ARK11tYXttHtKKUZ2DmR4fADf/nCc2WmZTH9/N91CvZmRGs/QWH8JedGqZBw7w+w0I6sPl+Hn4cJ/3p7AQzK21GhuOtyVUiHAs0CC1vqiUuoTYDwwBpijtV6plFoETAUWNkprBQ4Oitu7BXNLYpB5FkEWk5bvoG+ELy+NjqdvpBQnEy1bTvk55qRl8s2B43i5OfHy6HgmD4zAw46KejUHS/81nYA2SqkawB04DowAHjTfvwJ4BQn3Rufk6MD9SWGM7RHCxzsLeH1NNuMWb2VoXHteSo2jW6iPtZsoxM8UnbrAvNVZfLanCDdnR54eHsPjQ2U9R1O56XDXWhcrpWYCBcBFwICpG+a01rrW/LAiIORK36+UmgZMAwgPD7/ZZtg9FycHJg6I4L7eYby3LZ+F63K4843NjE4MZEZqPHGBUpxMWFfZmSreWJvNRzsKUEoxZVAkTyZH4y8rsZuUJd0y7YCxQCRwGvgHcMv1fr/WegmwBCApKUnfbDuESRsXR6YNjWZC33CWb8pn6cZcDBkbGGuuuREhNTdEMzt1vtpUQ2lrPrV1mnF9wnhmRAwdvKWoV3OwpFtmFJCntS4HUEp9DgwCfJRSTuar91Cg2PJmiuvV1s2Z50bF8siAjqZqeVvy+PrAccYlhfLMiFiCpVqeaGJnq2pM1U835XG+upa7e4Tw3KhYOvrJBUZzsiTcC4D+Sil3TN0yI4FdwFrgPkwzZiYBX1raSHHj2nm48PtbO/Ho4AgWrM3hw+0FfLanmIf6hfNUcgzt28pHYtG4LlbXsWJrPovW53D6Qg23djHtWxArXYNWYUmf+3al1KfAHqAW2Iupm+VfwEql1F/Nx5Y1RkPFzQlo68Yrdyby2JBIXk/P5t2tR1m5o5ApgyJ4Ymg03u4ymCUsc6m2jpU7CnljbTblZy+RHN+eGSnxdA2VHcesyaLZMlrrPwN//sXhXKCvJc8rGl9oO3f+fl83nhgWxdzVWSxcn8N7244ybUgUU+xsb0nROGrr6vl8j2k6bvHpi/SL9GXBQ73oEyHTcVsC+R9tZ6LaezJ/Qk+eGh7NLEMms9IyeXtLPk8lR/Nwf/vYFV5Ypr5e883B48xNyyS34jzdQ7159d6uDI6RhXQtiYS7neoU5MVbjySxr/A0swxG/vqvwyzdmMfTI2IYlxSGi5Ms/RY/p7Vm9eEyZhmMHCk5S3xgW5ZM7E1KgpTAaIkk3O1cjzAf3pvaj225J5i5ysh/fPEDizfk8PzIOO7qKUWbhCnUN2efYKbByL7C00T6ezBvfA/u6BaMg/x8tFgS7gKA/lF+/GP6ANZlljNzlZEZ/9jPwvU5vJgSxy2JQfKf2E7tPnqS11YZ2ZZ7kmBvN/5+b1fu7RWKkxT1avEk3EUDpRTD4wMYFtue7w+VMDstk6c+2ENisBcvpcaTHC8bJdiLH4ormWUwstZYjr+nK6/ckcCEfuG4OsmYTGsh4S7+jYODYkzXDoxODOLLfcXMXZ3FlHd2ktSxHTNS4xkQLVuc2arssrPMTsvk24MleLdx5ne3dGLSwI6y1WMrJGdMXJWjg+KeXqHc0T2YT3YVMj89iwlvbWNwjD8vjY6nh2xObDMKT15gzupMvthbTBtnR54dGctjQyLxcpN1EK2VhLu4JmdHBx7q15F7e4Xy/rajLFiXw11vbmZU50BmpMbRuYOXtZsoblJJZRWvr8ni452FODooHhsSxfRh0fh6uFi7acJCEu7iurk5O/LYkCjG9w3n7U15LNmYy5j5G7m9WzAvjIolqr2ntZsortOJc5dYuM60kK1eayb0DefpETEEerlZu2mikUi4ixvm6erEMyNjeWRABEs25vD25ny+PXice3uF8OzIWELbuVu7ieIqKi/WsHRjLss35XGxpo57eoXy3MhYwnzlnNkaCXdx07zdnXl5dCcmD4xkwbpsPthWwBd7jzGhbxi/GRFDQFu5CmwpLlTX8vbmfJZsyKXyYg23devAC6PiiAmQT1u2SsJdWKx9W1f+fEcijw+J4vU1Wby/vYCPdxUyeWAkTwyNop3031pNVU0dH24vYMG6bCrOVTOyUwAvpsaRGCxFvWydhLtoNME+bfjbPd14Ymg0c1dnsnhDDh9sO8rUIZFMHRxJW5l50Wxq6ur5dHcRr6dncayyigFRfiyeGE/vju2s3TTRTCTcRaOL8Pdg7viePJkcw+w0I3NXZ7FiSz7Th0XzyIAI2rjIQpimUl+v+frAMeakZZJ/4gI9w32YeX93Bsb4W7tpoplJuIsmEx/UlsUTkzhQdJqZhkz+9t0Rlm0yFScb3ydcipM1Iq01hoxSZhsyMZaepXMHL5ZNSmJEpwBZVWynJNxFk+sW6sO7j/ZlR95JZq4y8l9fHmLx+lyeGxXLPT1DpE6JBbTWbMiqYJbByIGiSqLae/DGgz0Z06WD1AOycxLuotn0jfTl4yf6N4TRbz89wKL1ObwwKo7bukoY3aid+aaiXjvyThLi04b/u6+b/LIUDSTcRbNSSjEsrj1DY/1ZdaiU2WlGnvloLwvW5fBSapx0I1yHg0WVzDQYWZ9ZTkBbV/4yNpEHpJtL/IKEu7AKpRS3dAkiJSGQr/cfY87qTKau2EXPcB9eTo2XAcAryCw9y2xDJt8fKsHH3Zk/junExP4yQC2uTMJdWJWjg+KuniHc1q0Dn+4uYn56Fg8u3c7AaD9mpMrUPYD8ivPMS8/ii33FeLg48fyoWJlaKq7JonBXSvkAS4EugAYeBYzAx0AEkA+M01qfsuR1hO1zdnRgQt9w7u4Z0rDo5t6FWxjRKYAZdrro5njlReanZ/PJrkKcHRXThkYxfWi0LAoT18XSK/d5wPda6/uUUi6AO/BHIF1r/apS6vfA74HfWfg6wk64OTvy6OBIHugTxjtb8lm8Pofb5m+yq+XyFecusWBtDu9vPwoaJvbvyFPDo6Wcg7ghNx3uSilvYCgwGUBrXQ1UK6XGAsnmh60A1iHhLm6Qh6sTvxkew8P9OzYUuvru4HGbLnRVeaGmoRDbpdp6KcQmLGLJlXskUA68rZTqDuwGngMCtdbHzY8pAQKv9M1KqWnANIDw8HALmiFsmXcbZ2akxjN5YAQL1+Xw7rajfLmvmPF9bKdE7flLtby9OY/FG3I5W1XLHd2lhLKwnCXh7gT0Ap7RWm9XSs3D1AXTQGutlVL6St+stV4CLAFISkq64mOE+JGfpyv/cXsCj5mLk320o4BPdhUyaWBEq91coqqmjve3HWXhuhxOnK+WzU9Eo7Ik3IuAIq31dvPtTzGFe6lSqoPW+rhSqgNQZmkjhfhRkLcb/3t3V1NxsvRMlm7M5cPtBTw6OLLVbAtXU1fPJ7sKeT09m5IzVQyO8WdGahw9w2VmkGg8Nx3uWusSpVShUipea20ERgIZ5j+TgFfNf3/ZKC0V4jLhfu7MHteDp5KjmZ2Wyfz0n4qTtdQNnevqdcOG4wUnL9C7YzvmPNBDNhwXTcLS/wHPAB+YZ8rkAlMAB+ATpdRU4CgwzsLXEOKqYgLasuCh3vxQXMksg5G/f28uTjY8mgn9wnF1sv4CH6013/9Qwuy0TLLKzpEY7MXbk/uQHN9eVuOKJmNRuGut9wFJV7hrpCXPK8SN6hLizdtT+rIr/yQzDUZe+TqDtzbm8ezIGO7tFWqVeitaa9ZlljPLYOSH4jPEBHiy4KFe3JIYJHV0RJNreZ9dhbBAUoQvHz3en83ZJ3jNYOR3nx1k0fpcnh8Vyx3dgpstVLflnmDmKiO7jp4izLcNs+7vzl09Q3CUUBfNRMJd2BylFINj/RkU48fqw2XMMhh5buU+Fq7L4cWUOFISApusO2R/4WlmGoxszKog0MuVv97VhXFJYVLUSzQ7CXdhs5RSpCQEMrJTAN8cPM6ctEymvbeb7mE+vJQax+AY/0YL+SMlZ5hlyCQtoxRfDxf+47bOPNy/I27O1u/zF/ZJwl3YPAcHxZ3dgxnTJYjP9xQzLz2Lict20C/Sl5dHx5MU4XvTz51XcZ45aZl8feAYnq5OzEiJY8rgSDxd5b+WsC75CRR2w8nRgXF9whjbM5iVOwp5fU029y3aSnJ8e15KjadLyPUXJys+fZH5q7P4dE8RLo4OPDksmmlDo/Bxb32LqYRtknAXdsfVyZFJAyMYlxTGiq35LFqfw+2vb+LWLkG8mBJHbGDbq35v2dkqFqzN4cPtBQA8MqAjTyXH0L6ta3M1X4jrIuEu7FYbF0emD4vmwX7hLNuYx7JNeXx/qIS7e4Tw/Kg4wv1+Kth1+kI1i9bnsmJLPtV19YxLCuWZEbEE+7Sx4jsQ4uok3IXd83Jz5oWUOCYNjGDx+hxWbM3nq/3HGNcnjEcHRfCvAyUs3ZjLuepaxnYP5vlRcUT4e1i72UL8Kgl3Icx8PVz4w5jOTB0cyUyDkQ+3FzR0v/SJaMdf7+pKfNDVu2yEaElk8q0Ql6murWfVoRLWGct/dvzQsTN8vf8YlRdrrNQyIW6MXLkLAdTW1fPPvaZpkkWnLtI3wpc3HuxF30hfcsrPMSctkzfWZvPu1nyeGBbN5IEReMh0R9GCyU+nsGv19ZpvfzjO7LRMcsvP0zXEm/+9uytDY39a4BTd3pM3HuzFU8lnmJ1m5LVVRt7enMeTyTE81C9cFiqJFknCXdglrTVrjpQx05DJ4eNniAv0ZNHDvRmdePXSBAnBXiyd1Ic9BaeYZTDyl28yWLoxl2dGxHJ/UijOVihOJsTVSLgLu7Mlu4KZBiN7Ck7T0c+duQ/04I7uwddd1KtXeDs+eKw/W3IqmLnKyB//eZDFG3J4flQsd3aX4mCiZZBwF3ZjT8EpZq4ysiXnBB283fjbPV25r/fNX3EPjPbnsyf9WGss47VVmbzw8f6G4mSjE4OkVruwKgl3YfMyjp1hlsFI+pEy/D1d+K/bE3iwkfrKlVKM6BRIclxAQ9/99Pf30DXEmxmpcQyLkw05hHVIuAublVN+jtlpmfzrwHG83Jx4eXR8k81ycXBQ3N4tmFsSgxpm3Ux+eyd9ItrxUmo8/aJkKz3RvCTchc0pPHmBeelZfL6nCDdnR54ZEcNjQ6LwbtP0m2c7OTpwf1IYY3uE8PGuQl5Pz+KBJdsYEuvPS6nxdA/zafI2CAES7sKGlJ6p4o012azcWYBSikcHRfJkcjR+ns1f1MvFyYGJ/Ttyf+9Q3tt6lAXrshn75mZSEwKZkRovK11Fk5NwF63eyfPVLFqfw4ot+dTVax7oE8YzI2IJ8nazdtNwc3bk8aFRjO8bxvJN+SzdmMst8zZwZ/dgXpAaNaIJWRzuSilHYBdQrLW+XSkVCawE/IDdwEStdbWlryPEL52pqmHpxjyWb8rjQnUtd/UM4fmRP6/m2FK0dXPmuVGxTBrYkcUbcnlncz7fHDjO/b1DeXakVJcUja8xrtyfAw4DXubbfwfmaK1XKqUWAVOBhY3wOkIAcKG6lhVbjrJ4Qw6nL9QwpmsQL4z69TrsLYWPuwu/u6UTUwZFNNSF/3xPMQ/2C+c3w6UuvGg8FoW7UioUuA34X+BFZZrzNQJ40PyQFcArSLiLRnCpto6PthfwxtocKs5dYnh8e2bc4A5KLUVAWzdeuTORx4dGMX91Fu9tO8rHOwuZPCiCJ2RHJ9EILL1ynwv8FvjxkskPOK21rjXfLgJCrvSNSqlpwDSA8PBwC5shbFltXT2f7Slifno2xacv0j/Kl0UP97Jo79OWIsSnDX+/rxvTk6OZk5bJovU5vL/1KI8PjeJR2YtVWOCmf3KUUrcDZVrr3Uqp5Bv9fq31EmAJQFJSkr7ZdgjbVV+v+frAMeauziKv4jzdw3z4+73dGBTjZ3MLgyL9PZg/oSdPDY9mtiGT2WmZvLMlnyeHRTNxQEcpTiZumCWXBYOAO5VSYwA3TH3u8wAfpZST+eo9FCi2vJnCnmitWX24jFkGI0dKztIpqC1vPZLEqM4BNhfqv9QpyIsljySxr/A0swxG/vfbwyzdlMvTI2J5ICkMFycpTiauz03/pGit/6C1DtVaRwDjgTVa64eAtcB95odNAr60uJXCLmit2ZRVwV0LtvD4u7u4VFvP/Ak9+fbZIaQkXL1aoy3qEebDe1P7sXJaf8LaufOfX/zAyNnr+HR3EXX18kFXXFtTdOj9DliplPorsBdY1gSvIWzMrvyTvLbKyPa8k4T4tOH/7u3GPb1CcLLzMrr9o/z4x/QBrMssZ5bByEv/2M/Cddm8mBLPrV2CcJAKlOIqGiXctdbrgHXmr3OBvo3xvML2/VBcyUyDkXXGcvw9XfnvOxMZ3zcMVyfpY/6RUorh8QEkx7Xn+x9KmJWWyW8+3ENisBczUuMYHm/73VXixslQvLCKrNKzzE7L5LsfSvBxd+b3t3Zi0oAI2rhIqF+NUopbu3YgNTGIL/cVM3d1Fo++s4veHdsxIzWOgdH+1m6iaEEk3EWzKjhxgbnpmXyxtxh3FyeeGxnL1CGReLk1fVEvW+HooLinVyh3dA/mk12FvJ6ezYNvbWdQjB8vpcbTM7ydtZsoWgAJd9EsSiqrmL8mi092FuLooHh8SBRPDIvG10MW69wsZ0cHHurXkXt7hfL+tqMsXJfD3Qu2MKpzADNS4+ncwevaTyJsloS7aFInzl1i4boc3t12FK11wzL7QC/rF/WyFW7Ojjw2JIoJfcN5e3Meizfkcuu8jdzerQMvpMQR3d7T2k0UViDhLppE5cUalm7MZfmmPC7W1HFvL1OBrDDfllfUy1Z4uDrx9IhYJvaPYMnGHN7enM+3B49zb69QnhsVS2g7+be3JxLuolGdv1TLO1vyWbw+hzNVtXL1aAXe7s68PLoTUwZFsmBtDu9vP8oX+4qZ0Decp4fHECCfmuyChLtoFFU1dXywvYCF67KpOFfNyE4BvJgaR2Jw6yvqZSv8PV35rzsSeHxoJPPTs/lwewGf7Cpk0oAIpg+Lpp2Md9g0CXdhkZq6ej7dXcT89CyOV1YxKMaPJanx9JIZGy1GB+82/O2erkwfFsXc1Vks2ZjLB9sLmDo4kseGRNJWZirZJAl3cVPq6jVf7z/GnNWZHD1xgV7hPswa113mWrdgHf08mPNAD55MNhUnm5eexYqtpuJkj8gaA5sj4S5uiNaaVYdKmZ1mJLP0HJ07eLF8cpKskmxF4gLbsmhibw4WmVYH/+27IyzdlMczI2J4oI+sDrYVEu7iumit2ZBVwSyDkQNFlUS19+CNB3sypksHqW/SSnUN9WbFo33ZkXeSmQYj//XlIRavz+W5kbFS18cGSLiLa9qRd5KZq4zsyD9JaLs2vHZfN+7uKf/5bUXfSF8+ntafjVkVzDQY+e1nB1i0PofnU+K4vav88m6tJNzFVR0oOs1MQyYbMssJaOvKX8Ym8kCfcKkpboOUUgyNa8+QWH8MGaXMNmTy7Ed7WbA2m5dS4xlpB7X0bY2Eu/g3xpKzzE4zsupQKe3cnfnjmE5M7C8DbvZAKcXoxCBGdQ7kmwPHmJOWyWPv7qJHmA8vj45nUIwMmLcWEu6iQX7FeeauzuTL/cfwdHHihVFxPDo4QqbK2SFHB8XYHiGM6dqBz8xTXR9aup0BUX68NDqO3h1b//61tk7CXXDs9EVeX5PFJ7uKcHF0YPqwaJ4YGoWPuyxysXfOjg6M7xvOXT1D+GhHAW+uzebehVsZ0SmAGbJIrUWTcLdj5WcvsWBdNh9sKwBgYv+OPDU8moC2sjxd/JybsyNTBkXyQJ8wc3mJXG6bv4nbuprKS8QESHmJlkbC3Q5VXqhh8QZTYanqunru6xXKs6NiCfFpY+2miRbO3cWJp5JjeKhfR5ZtzGXZpjy+++E4d/cM5flRUhiuJZFwtyPnLtXy9qY8lmzM5dylWu7oFswLKXFE+ntYu2milfFu48yLqfFMGhjBovU5vLv1KF/uK+aBPmE8MyKWIG/59GdtEu52oKqmjve3HWXBuhxOnq8mJSGQGalxdAqSzRyEZfw8XfnTbQlMHRzFG2uzWLmjkE93F/HIgI48mRwjm7FY0U2Hu1IqDHgXCAQ0sERrPU8p5Qt8DEQA+cA4rfUpy5sqblR1bb1pG7Y1WZSeucSQWH9mpMbTI8zH2k0TNibI242/3tWVJ4ZGM3d1Fss25fHhj8XJhkbJNopWYMmVey0wQ2u9RynVFtitlEoDJgPpWutXlVK/B34P/M7yporrVVev+WJvMXPTMyk8eZGkju2YN74n/aP8rN00YePCfN2ZNa47TyZHMScti/lrslmx9ShPDIti8sAI3F2ks6C53PS/tNb6OHDc/PVZpdRhIAQYCySbH7YCWIeEe7Oor9d8f6iE2WmZZJedo0uIF/8zpQvJce1ldaFoVjEBbXnzoV48WVzJ7LRM/u97I8s35fOb4dE82C9cipM1g0b5NaqUigB6AtuBQHPwA5Rg6ra50vdMA6YBhIeHN0Yz7JbWmnXGcmYajBw6doaYAE8WPtSLW7oESagLq+oS4s3yyX3YffQkr60y8t9fZ/DWhlyeHRnLvb1DcZb6RE3G4nBXSnkCnwHPa63PXB4mWmutlNJX+j6t9RJgCUBSUtIVHyOubWvOCWYajOw+eopwX3dmj+vO2B4hOEqxJ9GC9O7oy0eP92dLzgleW2Xk958fZNH6HF5IieOObsFSnKwJWBTuSilnTMH+gdb6c/PhUqVUB631caVUB6DM0kaKf7ev8DQzVxnZlF1BkJcb/3t3F8YlhcmVkGixlFIMivFnYLQf6YfLmGkw8tzKfSxYm8OLqXGkJgTKJ81GZMlsGQUsAw5rrWdfdtdXwCTgVfPfX1rUQvEzh4+fYZYhk9WHS/HzcOE/buvMw/074uYsfZiidVBKMSohkBGdAvjXwePMScvkifd20z3Umxmp8QyJ9ZeQbwSWXLkPAiYCB5VS+8zH/ogp1D9RSk0FjgLjLGqhACC3/BxzVmfxzYFjeLo68VJqHFMGReLhKrMPROvk4KC4o3swt3YJ4vM9xcxLz+KR5TvoG+nLy6Pj6RMhxcksYclsmU3A1X69jrzZ5xU/V3TqAvPTs/hsTzGuTg48lRzNtCHReLvLvGFhG5wcHRjXJ4yxPYP5eGchr6/J5v5FWxkW156XUuPpGirFyW6GXPa1UGVnq3hzTTYf7SgEBZMGRPDU8Gj8PV2t3TQhmoSrkyOPDIjg/t5hvLs1n4Xrc7jjjU3ckhjEi6lxxAW2tXYTWxUJ9xbm1PlqFm3IYcWWfGrrNPcnhfHsyBg6eEtRL2Ef2rg48sSwaCb0C2fZxjyWbcpjVUYJd/UI4flRsXT0k1pI10PCvYU4W1XDsk15LNuYx7nqWvlBFnbPy82ZF1LimGwuTrZiaz5f7z8mFzzXScLdyi5W1/Hu1nwWrc/h1IUa+QgqxC+083DhD2M6M3VwJG+uzebDHQV8tqeIh/t1lK7KXyHhbiXVtfWs3FnAG2uyKTt7SQaPhLiGAC83/ntsFx4bEsX89Cze2ZLHyp0FTBkUIZMMrkDCvZnV1tXz+d5i5q3Oovj0RfpG+vLGg73oGynTvoS4HmG+7rx2f3emJ0czJy2TN9fm8N7Wo0wbGiXTgy8j/wrNpL5emxZsrM4kt/w83UK9+ds9XWXBhhA3Kbq9J2882Iunks8wO83ITEMmb2/O58nkaFnYh4R7k9Nak364jFlpmRw+fob4wLYsnthblloL0UgSgr1YOqkPewpOMctg5K//OszSjXk8MzLGrktySLg3oc3ZFcw0GNlbcJoIP3fmje/B7d2CpaiXEE2gV3g7PnisP1tyKpi5ysif/vkDi9fn8vyoWLsspifh3gR2Hz3FzFVGtuaeINjbjVfv6SrlTYVoJgOj/fnsST/WGsuYuSqTFz/Zz8J1ObyYEmdXZbAl3BvRoWOVzDJksuZIGf6eLvz5jgQm9A23+74/IZqbUooRnQJJjgvgux9KmJ1m5MkP9tAlxIsZqfF2sYGNhHsjyC47x5y0TP518DjebZz57S3xsqWYEC2Ag4Pitm4dGJ0YyBf7jjF3dSZT3t5JUsd2vDQ63qa3npT0sUDhyQvMXZ3FP/cW0cbZkWdHxDB1SBTebWS+rRAtiZOjA/f1DuXO7sF8vKuQN9ZkMX7JNobE+vNSajzdbXDTeAn3m1B6poo31mSzcmcBDkoxdXAk04dF4ycr5YRo0VycHJjYvyP39w7lva1HWbg+h7FvbiYlIZAZqXF0CvKydhMbjYT7DTh5vpqF67J5d+tR6uo14/uG8fTwWIK83azdNCHEDXBzduTxoVFM6BfO8k15vLUhl1vnbeSObsG8kBJHpH/rr+kk4X4dzlTVsHRDLss25XGxpo67e4by/KhYwnzdrd00IYQFPF2deHZkLI8M6MjiDbm8szmffx08zn29Qnl2VCwhPq23OJmE+6+4UF3LO1vyWbw+l8qLNdzWtQMvpMQSEyBFvYSwJT7uLvzulk5MGRTBgrU5fLi9gH/uLebBfuE8NTyagLat79O5hPsVXKqt46PtBbyxNoeKc5cY0SmAF1Pi6BIiRb2EsGUBbd145c5EHh8axevpWby37Sgf7yxk0sAIpg+LwsfdxdpNvG4S7pepravn091FzE/P4lhlFf2jfFk8sRe9O0pRLyHsSYhPG169txtPDItm7upMFm/I4YNtR3lsSBRTh0Ti2QqKk7X8FjaD+nrN1weOMSctk/wTF+gR5sNr93dnYLSfzS90EEJcXaS/B/PG9+TJ5GhmGzKZszqTd7bk8WRyNI8MiGjRCxSbLNyVUrcA8wBHYKnW+tWmeq2bpbUmLaOU2WmZHCk5S6egtix9JImRnQMk1IUQDToFebHkkST2F55mpsHI//v2iKk42YgYHugTjotTyyst0iThrpRyBN4EUoAiYKdS6iutdUZTvN6N0lqzIbOcWQYj+4sqifL34PUJPbmtawcc7Ky4kBDi+nUP8+G9qf3YnnuCmQYj//nlIRZvyOW5kbHU1WtrN+9nmurKvS+QrbXOBVBKrQTGAlYP97XGMs5W1fLI8h0AxAV6ck+vUErPVLF8c56VWyeEaC1SE4Jwc3ZkY1YFL396oOF4fb1uEReJTRXuIUDhZbeLgH6XP0ApNQ2YBhAeHt5Ezfh3Z6tqf3Y7s/Qcr353pNleXwhh21rK9bvVBlS11kuAJQBJSUnN9u9x+H9uoba+vrleTghhJ+rrTQseW0rd+KYK92Ig7LLboeZjVtfGxRHTGK8QQjSulrRJd1MN8e4EYpVSkUopF2A88FUTvZYQQohfaJIrd611rVLqaWAVpsvk5VrrQ03xWkIIIf5dk/W5a62/Bb5tqucXQghxdS1v5r0QQgiLSbgLIYQNknAXQggbJOEuhBA2SMJdCCFskIS7EELYIAl3IYSwQRLuQghhgyTchRDCBimtrV+gUilVDhxtxpf0Byqa8fWam7y/1s2W358tvzdo/vfXUWvd/kp3tIhwb25KqV1a6yRrt6OpyPtr3Wz5/dnye4OW9f6kW0YIIWyQhLsQQtggew33JdZuQBOT99e62fL7s+X3Bi3o/dlln7sQQtg6e71yF0IImybhLoQQNsjuwl0pdYtSyqiUylZK/d7a7bGEUipMKbVWKZWhlDqklHrOfNxXKZWmlMoy/93O2m21hFLKUSm1Vyn1jfl2pFJqu/kcfmzep7dVUkr5KKU+VUodUUodVkoNsKXzp5R6wfyz+YNS6iOllFtrPn9KqeVKqTKl1A+XHbvi+VIm883v84BSqldzttWuwl0p5Qi8CdwKJAATlFIJ1m2VRWqBGVrrBKA/8Bvz+/k9kK61jgXSzbdbs+eAw5fd/jswR2sdA5wCplqlVY1jHvC91roT0B3T+7SJ86eUCgGeBZK01l0w7ac8ntZ9/t4BbvnFsaudr1uBWPOfacDCZmojYGfhDvQFsrXWuVrramAlMNbKbbppWuvjWus95q/PYgqGEEzvaYX5YSuAu6zSwEaglAoFbgOWmm8rYATwqfkhrfb9KaW8gaHAMgCtdbXW+jQ2dP4w7dPcRinlBLgDx2nF509rvQE4+YvDVztfY4F3tck2wEcp1aFZGor9hXsIUHjZ7SLzsVZPKRUB9AS2A4Fa6+Pmu0qAQGu1qxHMBX4L1Jtv+wGntda15tut+RxGAuXA2+Zup6VKKQ9s5PxprYuBmUABplCvBHZjO+fvR1c7X1bNG3sLd5uklPIEPgOe11qfufw+bZrr2irnuyqlbgfKtNa7rd2WJuIE9AIWaq17Auf5RRdMKz9/7TBdvUYCwYAH/96lYVNa0vmyt3AvBsIuux1qPtZqKaWcMQX7B1rrz82HS3/8+Gf+u8xa7bPQIOBOpVQ+pi60EZj6qH3MH/OhdZ/DIqBIa73dfPtTTGFvK+dvFJCntS7XWtcAn2M6p7Zy/n50tfNl1byxt3DfCcSaR+tdMA3ufGXlNt00c//zMuCw1nr2ZXd9BUwyfz0J+LK529YYtNZ/0FqHaq0jMJ2rNVrrh4C1wH3mh7Xm91cCFCql4s2HRgIZ2Mj5w9Qd018p5W7+Wf3x/dnE+bvM1c7XV8Aj5lkz/YHKy7pvmp7W2q7+AGOATCAH+JO122PhexmM6SPgAWCf+c8YTP3S6UAWsBrwtXZbG+G9JgPfmL+OAnYA2cA/AFdrt8+C99UD2GU+h18A7Wzp/AH/DRwBfgDeA1xb8/kDPsI0flCD6ZPX1KudL0Bhmp2XAxzENGuo2doq5QeEEMIG2Vu3jBBC2AUJdyGEsEES7kIIYYMk3IUQwgZJuAshhA2ScBdCCBsk4S6EEDbo/wOGPGth6EMh0wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([0, 100, 100, 0, 0, 100, 50, 0, 100],\n",
+    "         [0, 0, 100, 100, 0, 100, 130, 100, 0])\n",
+    "plt.axis([-10, 110, -10, 140])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can pass the 3rd argument to change the line's style and color.\n",
+    "For example `\"g--\"` means \"green dashed line\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8zL0n9/AP86eVaysAKi2sRHhcOF08uuDr5Usb1zZYm1trnPLfUcU9G+KS49gTvIetgVs58vcR/If7Y25qzi8XfiEpNYkuHl1wsnPSOqaiKFkgpWTT1U34Bfix/dp2ohKjsDG34bPmnzG+4Xit472SGgqZBafvnOaLP79gT/Ae4lPiKWpVlM4enYlMiMTB1oEB1QdoHVFRlGwSQvB65dd5vfLrJKcmc+jWIbYGbMWjuAcAwRHBDN42GF8vX7p5dsPF3kXjxP/eK4u7EGI50Bl4KKWskn5fMWA9UAG4CfSSUj4WaX0Wc4GOQBwwSEp5Lnei56wbj2+wNXArTcs3xaeUD4mpiZy9d5bBNdN2bNPyTdVc1IpiwMxNzWnt2prWrq31992Puc+j+Ee8v/t93t/9PtWdqtPNsxuj6o3K94vEv7JbRgjRFIgBfnmmuH8FREgpvxRCTALspZQThRAdgVGkFfd6wFwpZb1XhdCiW0ZKyV/3/9IPWbz44CIAX7T8go+bfMzT/y8F/Wy6oijZFxQRxNaArWwN3MrJOye5N/4exayLcejmIXRSR5PyTTRZbzjbfe5CiArA9meKeyDQXEp5TwhRCjgopfQUQixJ/3ntPx/3stfPy+J+4/ENNl3dhJWZFaN2jgKgSbkmtKjQgrjkONyKuT33+CblmuDt6E14XDgb/TdmeL2WLi3xKO7BvSf32Bq4NUN7O7d2uNi7cCvyFjuDdmZo7+zRGefCzgRFBLE3ZG+G9h6VeuBo68jVsKscunUoQ3tv797YW9tz8cFFjoUey9Dev1p/7CzsOHv3LKfvns7Q/laNt7A0s+R46HEuPLiQoX1YrWGYCBMO3zqMf5j/c21mJmYM8RkCwL6QfVyPuP5cu7WZNQNrDARgV9AubkbefK69sGVh+lbtC8Bvgb9x58md59qLWxenp3dPADb5byIsLuy59pJ2JfH18gVg3eV1RCZEPtderkg5Orp3BGDlhZXEJsc+1+5m70YbtzYALD+/nKTUpOfavUp40bxCcwCWnFmC5PnflaqOVWlUrhHJqcn8eP5H/smnlA91y9QlLjmOXy78kqG9vnN9apSsQVRCFGsvr83Qrj57+fOzF5UQhWcJT3y9fGm3qh17gvfo2+a1n8eoeqMyvJfckht97k7PFOz7wNMzimWA0Gcedzv9vgzFXQgxDBgGUK5c3l1YMP3QdFZcWKG/Pb7BeGa3nU1QRBDu8zOOdFnUcRHejt6ERoXy3u/vZWhf2X0lHsU9CIoIyrTdr7cfLvYuXAm7kmm7ezF3nAs7c/bu2UzbfUr54GjryNHQo5m2NyvfDHtre/aF7GPcnnEZ2jt7dMbOwo4d13cw9eDUDO19qvTB0sySLQFb+PrY1xnah/oMBQFrL63l+7PfP9dmbWat/wX76a+fWH1p9XPtjraO+l+wxWcWsy1w23PtbvZu+l+w705+x/4b+59rr1Gyhv4XbNbRWRkKRONyjfXFffqh6QSEBzzX3qFiB31x/2jfRxl+gXt599IX9/d3v090YvRz7YNrDtYX9+E7huvHTj81tt7YtOKuS85030xpMoW6ZeryJPFJpu2zWs+iRskahMWFZdquPnv5/7O3qdcmCs0spL9/9K7ReVrcXyarR+6RUsqiz7Q/llLaCyG2A19KKY+k378PmCilfOlheV4euffd1Je1l9diZmJGii6FemXq8V3776hdujbhceEZHl/YsjA25jYkpybzKP5RhvYilkWwNrcmKTWJiPiIDO1FrYpiZWZFYkoijxMeZ2i3t7LH0syS+OR4ohKjMrQXsy6GhakFcclxGYoPQAmbEpiZmBGbFMuTpCcZ2h1sHDA1MSUmKYaYpJgM7Y62jpgIE54kPslwZAvgZOuEEIKohCjiU+IztJe0KwmkXQKekJLwXJuJMMHR1hGAx/GPSUxNfK7dVJjiYOsAQER8RIYjZzMTM32/5qO4RyTrkp9rNzcxp7hNcQDCYsNIlanPtVuYWujXin0Y+zBDcbYys9JPnfwg5kGGI3NrM2uKWBUB0vpe/8nG3IbCloWRUvIg9kGGdltzWwpZFiJVl5rhWweAnYUddhZ2pOhS1GevAH72IhMiEUJQyKIQ/mH+NF/RHEdbRx58kPGzkFteduSOlPKV/0g7cXr5mduBQKn0n0sBgek/LwH6ZPa4l/2rVauWzCtrLq6RXx35SsYnx8s5x+dIh68cJJ8il51dlmcZFEUpuEKjQuWwbcOk2WdmsvevvfX3D/IbJKsvrp6nWYAz8gV1NatH7l8Dj+T/n1AtJqX8UAjRCRjJ/59QnSelfOXMPVqOc49JimHBqQUMrjkYB1sHzt87j425jVq8V1GU54TFhjHzyEwWnV6ETuoYVmsYk5tMplShUppletmR+yuvjRdCrAWOA55CiNtCiMHAl0AbIcR1oHX6bYAdQAgQBPwADM+B/DkqJimGqIT//wpqZ2HHpMaT9F/Rxu8ZT+VFlXl769sZTsIoimK8vj3+LXNPzqVv1b5cG3WNBR0XaFrYX8XorlDtu6kvZ+6e4dqoa5m2h8WG8eWRL1l4emG++eusKErei0mKYd7JedR3rk9Ll5ZExEcQFhv2wm/1C08tJORxCN+0+ybPMmbryN3YONg68E27bwgeHczgmoNZcnZJpsPQFEUxTAkpCXx34jtc57oyef9k/VDHYtbFXtpdezT0KL9d+y2vYr6Smn7gBcoULsPizouZ0GgCZQqlLcq76uIqrj+6zrgG4/SjKBRFMRzrLq9jwh8TuB19m1YurZjRcgb1netrHStL1JH7K7jau+oXpj595zSfHf4M13muzDoyi9ikjMO3FEUpWFJ1qaToUgAIjwvHubAz+wbsY++AvQW2sIMq7v/J3A5zOTP0DPXK1GPSvkm4zXNjw5UNWsdSFCULpJRs8t9Ete+r8dP5nwB4r/Z7HHv7GC1dWmqcLvuMrrj3rNyTsfXHZvn5tUrXYke/Hfz51p94lfDC0jTtqD4+OV7/119RlPxLSsnO6zup/UNtXv/1dXRSR5nCaV2vpiamWZ5PqqhVUf2ou/zA6EbL5KSn/++EEHyy/xPWX1nP9ObT6V2lt1qBSVHyqXd+e4el55ZSoWgFPm32Kf2q9dNk0q+coEbLPCM8Lpy7T+7myGsJIfR/5RuUbYCVmRV9N/elxvc12Bqwlfzwh1NRlLTzZU8nlnu98uss7LiQwJGBDKwxsMAW9lcxuuI+eudomv/cPMdft6N7R/569y/W9FhDQkoCvut9+WDPBzm+HUVR/r1LDy7hu86XusvqMu/kPADauLVheJ3hOb5g/exjs3l3+7s5+prZYZh/sjRiIkzoU7UPPb17suKvFdQunfZt6VbkLW5H36ZRuUYaJ1QU4xAUEcS0g9NYe2kthSwL8Vnzz7J1ru3fOHfvHGfu5p/uZVXcc4GZiRmDfQbrb3997GsWnl5IR/eOzGgxg5qlamqYTlEM3/g949kbspeJjSYyodEE/eygxsToumW0MKv1LL5s9SXHQ4/js9SHnr/25GrYVa1jKYrBeBDzgLG7xhIcEQzAd+2+I3h0MDNbzzTKwg6quOcJWwtbJjaeyI0xN5jadCq7gnbp+/8URcm6iPgIPtr7Ea7zXFlwaoF+xSgXexf9fO/Gyui6ZQZUH0A7t3aabLuIVRGmt5j+3EotR/8+ysqLK/mk6Sf6sbaKorzarCOz+N+R//Ek8QlvVHmD6c2n414842pqecXJ1onyRctrtv1/UuPcNbbg1ALG7R6HiTBheJ3hfNT4o3x1IYSi5CdJqUn6US4jd4zkdvRtPm/xOVWdqmqcTBtqnPszbkffJigiSOsYeiPrjiRwZCB9qvZh7sm5uMx14eujGdeTVBRjlpSaxOLTi6nwXQWO/H0EgLnt5+L3hp/RFvZXMbri/uEfH9JxdUetYzzHxd6Fn7r9xJXhV+jo3lG/1qdO6jJde1JRjEWKLoUVf63Ac4Enw3cMx62YG9Zm1kDaVAH5yYzDM3hzy5tax9Azuj73/MyrhBcbem7QX9m6/vJ6xu4ey0eNP+Ld2u9iZWalcUJFyTtSSpr/3JyjoUfxKeXD4k6LaefWLstzv+Q2/zD/fDXO3eiO3AuCpx9ej+IeVHGswvu738d9vjtLzy4lOTX5Fc9WlIJLSsnekL2k6lIRQjDUZygbe27kzNAztK/YPt8W9vxIFfd8rFbpWuwbsI99A/bhXNiZd7a/Q9d1XbWOpSi54sCNAzRa3og2K9uwJWALAANrDOS1yq+pop4FqlumAGjp0pJjbx/j9+u/6yc5ik2KZW/IXrp6dlUffKVAO3n7JJP3T2bfjX2UKVSGJZ2X0M2zm9axCrxsFXchxPvAEEACl4C3gFLAOqA4cBZ4U0qZlM2cOWZYrWH0qNRD6xj/mRCCzh6d9bdXXFjBiB0jqF26NjNazKCtW1tV5JUCRyd1DPAbwOP4x8xpN6dAn1uqULQCcclxWsfQy/I4dyFEGeAIUFlKGS+E2ADsADoCm6WU64QQ3wMXpJSLX/ZaxjzOPatSdCmsuriK6YemczPyJk3KNeGLll/QpHwTraMpyksFhgfyzfFvmNNuDrYWtlx5eIXyRctjZ2GndbQCJzfHuZsB1kIIM8AGuAe0BDamt68AfLO5jRwVFBHEhfsXtI6RbWYmZgyqMYjAkYEs7LiQoIggph6cqnUsRXmhW5G3eHvr21ReVJk1l9boR5Z4O3qrwp4LslzcpZR3gNnA36QV9SjSumEipZRP15u7DWR6Tb0QYpgQ4owQ4kxYWFhWY/xnUw9MpeevPfNse7nNwtSC4XWGEzQ6iBW+KwC4++QuvTf25srDKxqnU5S0C5BG7hiJ+3x31lxaw+i6owkZE0KzCs20jpajJu+bTI/1+afLN8vFXQhhD3QDXIDSgC3Q/t8+X0q5VEpZW0pZ28FBXW6fXTbmNpQrUg6Av+7/xa6gXVRdXJX+m/vnqytyFeORmJIIgLmJOdceXeOtGm8RNDqIOe3n4GjrqHG6nHcj8gaXH17WOoZedrplWgM3pJRhUspkYDPQCCia3k0D4AzcyWZG5T/q6N6RkNEhTGg4gc1XN+O1wIv3tr+nlv1T8kR0YjSfHvyUct+V43b0bYQQ7Oy3kyVdluBc2FnreEYjO8X9b6C+EMJGpA3TaAX4AweA19MfMxDYmr2ISlYUtynOrDazCB4dzHu13yNVpupH00QnRmucTjFEcclxfHX0K1zmujD90HQal2tMii6thza/TRVgDLI8FFJKeVIIsRE4B6QA54GlwO/AOiHEjPT7fsyJoErWlCpUivkd5+uP2s/fO0/Tn5syqu4oJjScgL21vcYJFUMQmxSL5wJP7jy5Q/uK7ZnRYga1StfSOpZRy9Y4dynlNGDaP+4OAepm53Vz0+h6o4mIj9A6Rp57etRe1KooXTy6MPPITBadXsQHDT9gTL0xFLIspHFCpaBJ0aVw8OZBWru2xtbClrH1x1LfuT6NyzXWOpomvEp45atrTdR87kbq4oOLfHLgE7YFbsPV3pXAkYH6q18V5WV0UseGKxuYemAq1yOuc+m9S1RxrKJ1LKP0snHuRvfbfOnBJZ4kPaFh2YZaR9FUNadqbH1jK6funOLao2uYmZghpeRX/1/x9fLVL4igKE9JKdl+bTtTDkzh4oOLVHGsgl9vP7wdvLWOpmTC6CYOm3lkJoP8BmkdI9+oW6Yu/av1B+DQrUP03tgbrwVe/HLhF1J1qRqnU/KTxwmP6bu5L3HJcazpsYYL716gm1e3fNUVoaXxu8fTbpU2S3hmxuiKu/Jizco3Y0ffHdhb2zPQbyBVF1dlo/9G/eIhivE5HnqckTtGIqWkmHUxDg06hP9wf/pU7YOJUOXjWfdi7nHj8Q2tY+ipvaPoCSHo4N6BM0PPsLFn2gwS43aPU3PIG6Hz987TaU0nGi5vyK/+v3Iz8iYAPqV8MDc11zac8q+o4q5kIITgtcqvcem9SxwYeABLM0sSUxLp9WsvDt48qHU8JReFxYbR89ee+Cz14XjocWa2mknI6BBc7F20jqb8R6q4Ky9kamKKWzE3AK5HXOdo6FFarGhBm5VtOHXnlMbplJz0dKqAwpaFuRp2lU+afkLImBAmNZ6ErYWtxumUrDC60TIfNvqQqIQorWMUOFUcqxA0KojFZxYz88hM6i2rR1fPrqzsvpLCloW1jqdk0Z3oO8w4PIM9IXu4MvwKVmZWXHj3grqiNAtqlqyZr2a3VOPclf/sSeIT5p6cy+Fbh9ndfzdCCKITo1WRL0DCYsP48siXLDqziBRdCkN9hvK/Vv+jqFVRraMp/4Ea5/6M03dOE5kQSRu3NlpHKbAKWRZiStMpSCkRQhAeF477fHd6ePVgarOplC9aXuuIyktce3SNWktrEZccx5vV3mRas2mqT90AGV2f+5wTcxixY4TWMQzC0/HNJsKEQdUHserSKtznuzNqxyjux9zXOJ3yrNikWA7fOgyAezF3RtUdxeX3LvOz78+qsOeQEb+PoPHy/DP1gtEVdyXnFbMuxpz2cwgaFcSgGoNYfGYxbvPceBDzQOtoRi8hJYG5J+biOs+VTms6EZUQhRCC/7X6H5UcKmkdz6A8TnjMw9iHWsfQU8VdyTFli5RlaZelBIwM4MtWX+Jk5wSAX4CfmmY4jyWnJrPs3DI85nswdvdYvB282dN/D0WsimgdTckjqrgrOa5isYqMqjcKSBuN8fqG13Gd68rsY7OJT47XOJ1x8A/zZ+hvQyldqDR739zL/oH7aVC2gdaxlDykiruSq8oULsPJISepU6YOE/6YgNs8NxadXkRSapLW0QyKlBK/AD+mH5wOQPWS1Tk15BTHBx+nlWsrjdMpWjC64j6t2TTWvLZG6xhGpVbpWuzst5PDgw5TsVhFxu0eR1hs3i2KbsiklOwO2k3dZXXpvr4766+sJyElAYA6ZeqoSb3yUAPnBnRy76R1DD01zl3JU1JKgiKCcC/uDsDQbUNp7dqant491URU/1FAeADvbH+Hw7cOU65IOaY1m8aA6gPUvPxG5GXj3I3ut+nPW3+y5eoWrWMYLSGEvrBHxEdw/PZx3tj0Bj5LfPgt8De1iPe/8HSqgEIWhQiNCmVBhwVcG3mNt2u+rQq7omd0xX3xmcVM3DtR6xgKaUMoL7x7gVXdVxGTFEPXdV1puLwhIY9DtI6WL115eIUe63vQfnV7pJSUKVyGoNFBjKg7AkszS63jGb3BWwdTc0lNrWPoGV1xV/IXUxNT+lXrx9URV1naeSkpuhScbNOGUKo5gNIERQTRf3N/qi6uyt6QvTQv35xUmbaQiurKyj/iU+KJTYrVOoZetr7DCSGKAsuAKoAE3gYCgfVABeAm0EtK+Tg721EMn7mpOUNrDWWIzxCEEKToUqi7rC7uxdyZ0XIGNUrW0DqiJnYF7aLzms5YmFowoeEEPmz0IcVtimsdSykAsvtnfy6wS0rpBVQHrgKTgH1SSndgX/ptRflXno7uSNWl8laNtzgaepSaS2rS69deBIQHaJwubzyIecCZu2kDDJqUa8L4BuMJHh3MrDazVGFX/rUsF3chRBGgKfAjgJQySUoZCXQDVqQ/bAXgm72IijGyNLNkUuNJ3BhzgylNprAzaCfei7w5Hnpc62i55nH8Yz7e9zGu81x5c8ubSCmxtbBlVptZlCpUSut4SgGTnW4ZFyAM+EkIUR04C4wBnKSU99Ifcx9wyuzJQohhwDCAcuXKZSPGfzOz1UzikuPybHtK9hS1KsrnLT9ndL3R/Hj+R+o51wPgj+A/8Hb0pnSh0honzL6YpBjmnpjL18e+JioxijeqvMH05tPVGPUCpkWFFrjau2odQy/L49yFELWBE0AjKeVJIcRcIBoYJaUs+szjHksp7V/2Wmqcu/JfJKUmUW5OOaISoxhZZyQTG0+khE0JrWNl2brL6+izqQ9dPbvyeYvPqeZUTetISgGRW+PcbwO3pZQn029vBHyAB0KIUukbLgXkn2nSSDviW31xtdYxlGywMLXg2OBj9PLuxbcnvsV1rivTDkwrMKNrklOTWXJmCUvPLgWgl3cvzg47y9Y3tqrCruSYLBd3KeV9IFQI4Zl+VyvAH9gGDEy/byCwNVsJc9hPf/3E9EPTtY6hZJOrvSsrfFdw+b3LtKvYjs8Of8alh5e0jvVSqbpUVl5YiddCL979/V22BW4D0oYz+pTy0Tidkl19N/XFY76H1jH0sns52yhgtRDCAggB3iLtD8YGIcRg4BbQK5vbUJQXquRQiV97/sr1R9f1V75OOzCN4jbFeafWO/nm4p5DNw8xfMdw/MP8qVGyBtv7bKeje0etYykGLFvFXUr5F5BZf4+ahk7JU08Lu07qOHHnBHuC9zD72GymNpvKoBqDNLksX0pJYmoiVmZWmJmYoZM6Nry+gdcqv6YuPlJynfqEKQbFRJiwq98u9vTfQ6lCpRj621AqLazEsdBjeZrj0M1DNPmpCe/veh+ARuUacWX4FTVBmpJn1KdMMThCCNq4teHE4BNsfWMrRSyLPDelQW5OTnbqzinarmxL8xXNuRF5g1qla+nbVFFX8pLRTSE3p90ctVCEkRBC0NWzK108uujHjPfb3I+HsQ+Z0XIGbVzb5OhY8nkn5zFm1xhK2JTgm7bf8F7t97A2t86x11fytw4VO1DVsarWMfSMrrg/XddTMR5PC7iUkh6VejD90HTarWpH0/JN+aLlFzQul/UV6689ugaAR3EPOnt0JjoxmjH1xlDIslCOZFcKjjerv6l1hOcY3ffEbYHb9OOLFeMihODtmm9zbeQ15neYT2B4IE1+asLPf/38n1/r76i/GbJtCJUXVmbS3rTpk1ztXZnSdIoq7EYqISUhX80KaXTFfd3ldcw+NlvrGIqGLM0sGVl3JCFjQpjdZja+Xr4AnLx9Ev8w/5c+937MfUbvHI37fHdWXlzJyLojWdxpcR6kVvK7t7e+reZzV5T8wMbchvENx1PUqigAH/zxAVUXV2XAlgEvXDBk8enFLDq9iIHVBxI0Kojv2n+nuvqUfEkVd0VJt6X3FsY3GM9G/414LvDk3e3vcjXsKp8d+oyd13cCMK7BOAJGBrC0y1LKFimrcWJFeTFV3BUlXQmbEnzV5iuCRwczoNoAlpxdQuVFlZl2cBoHbh4AoIhVESoWq6hxUkV5NVXcFeUfDtw8wM6gtCP1li4tOTXkFJ7FPZmyfwqRCZHahlOUf8nohkIu7rSYFF2K1jGUfCZFl4KUEnNTc2KTYnEr5sa619fRtHxTAFZeXMn8U/NZeHohExpOYHS90dhZ2GmcWslPelTqQX3n+lrH0MvyfO45Sc3nrmhFJ3Vs9N/I1ANTGVFnBKPqjUIndQhEhguc/rr/F58c+ITt17bjaOvI/A7z6eWt5sVTtJNb87kXSBuubGDO8Tlax1A0JqVk+7Xt+CzxoffG3piamOr70k2ESaZXrtYoWYPf+vzG8cHHqepYlSKWRQCITYolOTU5T/Mr+c/j+Mc8jM0/y1cYXXH3C/Bj8Rk1LtnYvff7e3RZ24WYpBhWdV/FxXcv0sG9w796bn3n+uwdsJe2bm0B+PTgp1RaWIlVF1eRqkvNzdhKPjZixwgaL8/61c45zeiKu2K8Ttw+QXhcOAB9qvRhaeelXB1xlX7V+mFqYvqfX+/p0X0r11bYWdjx5pY3qfZ9NTZf3Zyrk5Mpyr+hirti8P66/xdd1nahwY8N+O7EdwA0q9CMobWGYm5qnu3Xb1+xPefeOcf619eTqkvltQ2vMXHvxGy/rqJkh9GNllGMR2B4IFMPTmXDlQ0UtSrK/1r+j1H1RuXKtkyECb28e9GjUg9WX1xNnTJ1AAiKCOLuk7v6UTeKkldUcVcM1pQDU9h5fSdTmkx5bpqB3GRmYsbAGgP1t78++jVLzy2lrVtbZrSYoS/6ipLbjG4oZFJqEjqpw8rMKk+2p+Sdu0/u8r8//8eIOiOo5FCJ0KhQrMyscLB10CxTfHI8i04vYuaRmTyKf4Svly+ft/icKo5VNMuk5I7t17bzIOYBg30G59k2XzYU0uiO3C1MLbSOoOSw8LhwZh2ZxYLTC0jRpVCzZE0qOVTKF3O/WJtbM77heIbVGsZ3J75j9vHZVChSgTnt1XBcQ9PZo7PWEZ6T7SN3IYQpcAa4I6XsLIRwAdYBxYGzwJtSypcufZSXR+4r/lpBaHQoU5pOyZPtKblr5p8zmXlkJjFJMfSv1p9pzabhVsxN61gvFBEfgUBgb23P/hv7WXtpLVObTc0Xf4iU7Lkfc5+ElAQqFK2QZ9vM7YuYxgBXn7k9C5gjpawIPAby7jvKv7A7eDe/XPhF6xhKNsQnx+uHGobHhdPWrS2Xh1/ml+6/5OvCDlDMuhj21vYAXHl4hV8u/kLF+RUZs3MMD2IeaJxOyY5xu8fRdmVbrWPoZau4CyGcgU7AsvTbAmgJbEx/yArANzvbUJSnElMSmX9yPi5zXdh3Yx8AX7f9mo29NlLZobLG6f67UfVGcX3Udd6s9iYLTy/EdZ4rXx39SutYioHI7pH7d8CHgC79dnEgUkr5dGau20CZzJ4ohBgmhDgjhDgTFhaWzRiKIUvRpfDjuR/xWODB6F2j8SrhRTHrYkDaEMSCrFyRcizruoyrI67SzbOb/kR/ii6FJ4lPNE6nFGRZ/s0QQnQGHkopz2bl+VLKpVLK2lLK2g4O2o1mUPI3KSUtVrRgyG9DcLJ1Yk//PRwYeACfUj5aR8tR7sXdWfPaGkbXGw3AygsrcZ3nyjfHviE+OV7jdEpBlJ3DnkZAVyHETdJOoLYE5gJFhRBPR+E4A3eylTCHmQiTLF1qruQdKSU7r+8kRZeCEIIRdUbg19uPk0NO0satTaaTehma6iWrU7NkTT744wMqzq/I4tOLSUp96bgERXlOjoxzF0I0Bz5IHy3zK7BJSrlOCPE9cFFKuehlz1dT/iqQVtT3huxlyoEpnLpzitU9VtO3al+tY2nq0M1DTN4/maOhR+no3pHf+/6udSTlBfaF7CMsLow3qryRZ9vM63HuE4F1QogZwHngx1zYhmJgjv59lMn7J3Po1qG0fuguy9Rc6aTNgfPnW3+yO3i3/hqN6MRo9gTvoUelHgX+nIMhaeXaSusIzzG6K1SXnFnCzcibzGw9M0+2p7yaTuqo/n11wmLDmNJ0CkN9hmJpZql1rHxr7om5jN09lpola/J5i8/p6N7RKLqq8rsbj28QlxyHt6N3nm1TLdbxjEO3DrHp6iatYxg9/zB/BvkNIiohChNhwsaeGwkeHczIuiNVYX+FkXVHsrL7SqISo+i8tjONf2rMgRsHtI5l9Cbvn0z39d21jqFndMVd0VbI4xAGbBlA1cVV2XR1E+funQPAs4Qntha2GqcrGExNTOlfrT8BIwJY0nkJtyJvMevoLK1jKfmMKu5KnkhOTebd7e/iucCTX/1/ZVz9cdwYc4MWLi20jlZgmZuaM6zWMIJGB7G823IAbkbepMf6Hlx8cFHjdIrWVHFXctXTMdrmpubcfXKXYT7DCB4dzNdtv6aETQmN0xkGKzMrShcqDaRNabD/xn6qf1+dNza+QWB4oMbpFK0YXXG3NbelsGVhrWMYvMiESKbsn4LzHGduRt4EwO8NPxZ2WqgvRErO6+TRiRtjbvBx44/Zfm07lRdV5p3f3lHL/hkho5vy94euP2gdwaDFJMUw7+Q8vj72NZEJkfTy7oUgbSSHGraXN+yt7fmi1ReMqT+GmX/OJFWm6kfTPI5/rJ+4TMlZw+sMJyI+QusYekY3FFLJPfHJ8bjPd+fOkzt09ujM5y0+p0bJGlrHUtKduH2ClitaMrLuSCY2mkhxm+JaR1KySQ2FfMbcE3MZs3OM1jEMRnJqMjuu7wDSFqaY2Ggix94+xm99flOFPZ8paVeS1yq/xuxjs3GZ68KnBz8lOjFa61gG42rYVU7dOaV1DD2jK+4n75xkZ9BOrWMUeKm6VFZdXEWlhZXotKYT5++dB9KmsW1QtoHG6ZTMVChagZXdV3LpvUu0cWvD9EPT8VniQ6ouVetoBuHzw5/Tf3N/rWPoGV2fu5I9Ukq2BGxh6oGpXAm7QnWn6uoovYDxdvRmU69NnL17luDHwZiamKKTOlZfXE0v717qIjIDYXRH7kr2RCdGM3jbYFJ0Kax/fT3n3jlHZ4/O6vL3AqhW6Vr6+Xv2hexjgN8APBd4svz8clJ0Ka94tpLfqeKuvNKft/5k6Lah6KSOIlZFODzoMJeHX6aXdy81AsZAtHZtze7+u3G0dWTwtsF4L/Jm3eV16KTu1U9W8iWj+80sbl2cUoVKaR2jQDhz9wztVrWj6c9N2X59Ozce3wCgqlNVzExUj54hEULQ1q0tJ4ecxK+3HxamFkzeP1n1xxdgaiikkkF4XDjDfhvGloAtFLcuzqTGkxheZzg25jZaR1PyiE7qCI0KpXzR8sQlx9FnUx9G1x2d76a1zU/O3j1LZEJknv4/yuv53JUCKj45Hmtza4pYFuFW1C2mN5/O2Ppj1RW9RshEmFC+aHkAgiKCOH/vPK1XtqZFhRbMaDmDhmUbapww/6lVupbWEZ5jdN0ys47MYsi2IVrHyFdCo0IZum0oHgs8iEuOw9zUnNNDTzO12VRV2BWqOVXj2qhrzG0/lythV2i0vBGd1nRSC3j/w7l75/LV1MtGV9wvPLjA4VuHtY6RLzyIecCYnWOoOL8iv1z8he5e3fXrdKoTpcqzrMysGF1vNCGjQ5jZaiZmJmbYWdgB5KtL7rU0+9hs3tn+jtYx9FS3jJEKjgim2vfVSExJZFCNQXzS9BP913BFeRFbC1smNZ6kv33vyT08FnjQo1IPpjWbhqu9q4bplGepwzMj8iTxCXtD9gLgau/KxEYT8R/hz7Kuy1RhV7LE0sySd2q9w4YrG/Bc4Ml729/jTvQdrWMpqOJuFOKT4/nm2De4znOl69quPI5/jBCCqc2m4lHcQ+t4SgFWzLoYs9vOJmhUEEN9hrLs/DI8FngQFhumdTSjl+XiLoQoK4Q4IITwF0JcEUKMSb+/mBDiDyHE9fT/5qv5RcsUKoNbMTetY+SJpNQkvj/zPRXnV+SDPz6gZsmaHBx0UE35quS4MoXLsKjTIq6NvMacdnNwsHUAYMOVDUQlRGmczjhleZy7EKIUUEpKeU4IUQg4C/gCg4AIKeWXQohJgL2UcuLLXkuNc88d/mH+VFlUhYZlG/JFyy9oVqGZ1pEUI/J31N+4zHWhiGURPmz0IaPqjjLodXKvPLzCk6Qn1Heun2fbfNk49xy7iEkIsRVYkP6vuZTyXvofgINSSs+XPVcV95yhkzo2X93M2btnmdl6JgAX7l+gmlM1NfeLoonz987zyYFP+P367zjaOvJx4495p/Y7WJlZaR3NIOT6fO5CiApATeAk4CSlvJfedB9wesFzhgkhzgghzoSF5V3/3KcHP6XPpj55tr28IKVkx/Ud1F5am56/9mTbtW3EJccBUL1kdVXYFc3ULFWT7X23c+ztY3g7ePPx/o8NtpvmWOgxtl/brnUMvWwXdyGEHbAJGCulfG7mf5n2tSDTrwZSyqVSytpSytoODg7ZjfGvXXt0jbN3z+bZ9nJbQHgAjX9qTKc1nYhKjOIX31+4+O5FNVWAkq80KNuA/QP3c/m9yzjZOSGlpP/m/qy5tMZgJidbcGoB43aP0zqGXraKuxDCnLTCvlpKuTn97gfp3TFP++UfZi+ikpmnR+b2VvY8invE952+J2BEAG9WfxNTE1ON0ylK5lzsXQB4FP+ISw8v0W9zP6p/Xx2/AD+1iHcOy85oGQH8CFyVUn77TNM2YGD6zwOBrVmPp/zTxQcX6bq2K61/aY2UEic7J66OuMo7td/B3NRc63iK8q+UsCnB+XfOs+61dSSlJtF9fXfqLqurn3lUyb7sHLk3At4EWgoh/kr/1xH4EmgjhLgOtE6/rWRTYHggb2x8g+rfV+fwrcN0dO+oX1BB9akrBZGJMKF3ld5cGX6F5V2XY2FqQUm7koCa0iAnZHn6ASnlEeBFVSXfzgvqZu+mnz+loNgTvIcOqztgbWbNx40/5oOGH6ix6orBMDMx462ab/FWzbeAtOszai2thWdxT2a0nEHt0pkOBlFeQc3nnk/de3KPm5E3aVC2AQkpCcw4PIPR9UbjaOuodTRFyVWJKYnMOzmPL49+SUR8BN29uvN5i8/xdvTWOtpLhTwOITYplqpOVfNsm3kyzj07VHH/f4/iHvHV0a+Yf2o+ZQqXIXBkoJqhUTFK0YnRzDk+h2+Of0NMUgwnhpygbpm6WsfKV3J9nHtBMmnvJLqu7ap1jAyiE6OZfnA6rvNc+frY17xW+TV29dulCrtitApbFmZa82ncGHOD2W1nU6d0HQB+v/Y7t6Nva5wuo/039rP+8nqtY+gZXeX4O+pvAsIDtI6Rwd6QvXx66FNau7bm0nuXWNl9pdHMgaMoL1PcpjjjGoxDCJE2RfXWQVScV5H3d73Pw9j8M9J62bllfHLgE61j6Bldcc8vklKTWHhqIfNPzgegu1d3Lr57kU29NuX7vkVF0YqlmSVnhp6hX9V+zD81H9e5rkzeN5nH8Y+1jpbvqOKex1J0Kfx0/ic85nswcudI9oTsQUqJECJPT8QoSkFVvmh5fuz2I/4j/Oni2YWZR2ZyPeK61rHyHVXc89Chm4eosqgKb297GwdbB3b128W2N7apceqKkgUexT1Y+9paQsaE6E+0TvxjInOOzyEhJUHjdNozuuJexbEKDco2yLPtSSn1UwXYmNtgbmrO5l6bOTXkFO0qtlOFXVGyqULRCgCk6lK5+PAi4/aMo+K8iiw5s4Tk1GRtw2lIDYXMRftC9jF5/2QqO1RmebflAPouGEVRcseBGweYvH8yx28fx9XelZXdV9KwbMNc3+7dJ3dJSEnI03Vk1VDIPHYs9BgtV7Sk9crW3Hlyh8blGuvbVGFXlNzVwqUFR98+yvY+23G0dcS5sDOQNqVBbs5AWbpQ6Xy1QLjRFfexu8bSckXLXHv9+Sfn02h5I66EXeG7dt9xfdR13q75dq5tT1GUjIQQdPLoxPHBxylXpBwAvX7tRe2ltdlxfUeuzEC54/oOlp9fnuOvm1VGV9wfxj7M8QsgAsID8A/zB8DXy5f/tfwfIaNDGFN/jFpxRlHyASklA6oPIDIhkk5rOtHkpyYcvHkwR7ex6uIqvjySf+ZJNLrinpNuPL7BIL9BeC/yZtLeSQCULVKWj5p8ZNBrRSpKQSOEYED1AQSMDGBxp8XciLxBixUt+OXCL1pHyzVZnhXSmN19cpcZh2ew7NwyTE1Meb/++0xs9NI1wBVFyQcsTC14t/a7DKw+kB/O/UB3r+4AHPn7CEUsixjUtSbqyD0Lfjr/Ez+c+4EhPkMIGhXE7LazcbDNu6UCFUXJHmtza0bXG00hy0IATPhjAtW/r07fTX25/sgwLogyuuJeu3RtWru2/k/PiUqIYuqBqfgF+AEwpv4Yro28xqJOiyhTuEwupFQUJS/93vd3JjWexNbArVRaWIkh24bwd9TfWsfKFjXO/SVik2KZf2o+Xx39iscJj5nQcAJftflK61iKouSSBzEPmHlkJovPLOaHLj8woPqAf/3ciPgIklOTcbJzysWEz1PzuWfB6ourGb9nPA9iH9DRvSMzWsygZqmaWsdSFCUP3I6+TSm7UpiamLLw1EJCo0P5sNGHFLMupnW056iLmJ7x7vZ3qb+sfqZtyanJJKYkApAqU6nkUImjbx/l976/q8KuKEbEubAzpiamAAQ+CuSro1/hMteFzw59RnRidKbP2eS/ST/La35gdMU9OjE6w+K7OqljzaU1VF5Umfmn0nbOm9XeZP+A/Xly2bKiKPnXvA7zuPDuBVq6tGTawWm4znVlw5UNGR636eomff3ID3KtuAsh2gshAoUQQUKISbm1neyQUuIX4Ef176vTb3M/bMxtqOqYNhRKCKGmClAUBYCqTlXZ0nsLp4acolbpWvq1jKMTo0lKTdI4XeZyZZy7EMIUWAi0AW4Dp4UQ26SU/rmxvawa/vtwvj/7vX7q0F7evdSydoqivFCdMnXY3X+3/vaU/VPYFriNac2mkaJL0TBZRrl1EVNdIEhKGQIghFgHdAM0L+7xKfFcj7jOt8e/JTw+HG8HbwZUH0Bhy8L6wj7/5HySdc9PFVrVsSpt3NoA8O3xbzO8bq1StWhWoRlJqUksOLUgQ3sD5wY0KNuAmKQYlp5dmqG9afmm1C5dm8fxj/npr58ytLd2bU01p2o8iHnA6kurM7R3qNiBSg6VCI0K5Vf/XzO0d/PshlsxN4IjgtkauDVDe8/KPSlbpCxXw66yM2hnhvZ+VfvhZOfExQcX2RuyN0P7WzXewt7anjN3z3D41uEM7cNqDcPOwo7jocc5fvt4hvaRdUdiYWrBoZuHOHvvbIb2cQ3GAfBH8B9cenjpuTZzE3NG1RsFpM3v8c9lFG3NbXmn9jsA+AX4EfI45Ln2olZF9fP/bLiyIcP0FI62jvSv1h9Iu8T8n0u7ORd2ppd3LwCWn19OZELkc+2u9q74evkCsOTMEmKTY59r9yrhRUf3joD67BW0z96tqFuUsCnB29vy3/xRuVXcywChz9y+DdR79gFCiGHAMIBy5crlUoyMqjtVxy/Aj/F7xuvvm7h3In2q9NH/gn28/2NikmKee95Qn6H6X7Bnn/vUuPrjaFahGYkpiZm2T2s2jQZlGxCdGJ1p++w2s6ldujZhcWGZti/pvIRqTtW4HX070/aSdiWp5FCJ4MfBmba7F3PHrZgb/mH+mbb7lPKhbJGynL9/PtP2ZuWb4WTnxInbJzJt7+zRGXtrew7dPMQHf3yQof2NKm9gZ2HH7uDdTD80PUP7UJ+hWJhasC1wG9+eyFjAnv6C/er/Kz+c++G5NjsLO31xX3VxFWsvr32uvZRdKX1x//H8j2y/tv25dvdi7vrivuj0Ig7dOvRcu08pH31xn3NiDufunXuuvVn5Zvri/uWRLzOsCtTZo7O+uE8/NJ17Mfeea1efvYL92YueFI1fgB89NvTQL+KdH+TKUEghxOtAeynlkPTbbwL1pJQjM3t8Xg+FfJL4BMnz79vMxAwbcxuATM+Gm5uYY21u/cJ2C1MLrMyskFLyJOlJhnZLU0sszSzRSV2GX95n21N1qRmO7ACszKywMLV4Ybu1mTXmpuak6FL0i4Nk1p6cmkx8SnyGdhtzG8xMzF7Ybmtui6mJKUmpSZmucvO0PTElkcTUxAztdhZ2mAiTF7YXsiiEEIKElIRM+zALWxYGID45PsOR7bPtcclxGb4eC4T+SsTM2k2ECXYWdkDatQ2pMvWF7TFJMRmmjTUVpvq5hDJrf/azpT57hv3ZszS11I+yyQt5Ps5dCNEA+FRK2S799kcAUsqZmT0+P45zVxRFye+0GOd+GnAXQrgIISyAN4BtubQtRVEU5R9ypc9dSpkihBgJ7AZMgeVSyiu5sS1FURQlo1yb8ldKuQPYkVuvryiKoryYGtStKIpigFRxVxRFMUCquCuKohggVdwVRVEMkCruiqIoBkgVd0VRFAOkiruiKIoBUsVdURTFAKniriiKYoDyxQLZQogw4FYebrIEEJ6H28tr6v0VbIb8/gz5vUHev7/yUkqHzBryRXHPa0KIMy+aSc0QqPdXsBny+zPk9wb56/2pbhlFURQDpIq7oiiKATLW4p5xIUnDot5fwWbI78+Q3xvko/dnlH3uiqIohs5Yj9wVRVEMmiruiqIoBsjoirsQor0QIlAIESSEmKR1nuwQQpQVQhwQQvgLIa4IIcak319MCPGHEOJ6+n/ttc6aHUIIUyHEeSHE9vTbLkKIk+n7cH36Or0FkhCiqBBioxAiQAhxVQjRwJD2nxDi/fTP5mUhxFohhFVB3n9CiOVCiIdCiMvP3Jfp/hJp5qW/z4tCCJ+8zGpUxV0IYQosBDoAlYE+QojK2qbKlhRgvJSyMlAfGJH+fiYB+6SU7sC+9NsF2Rjg6jO3ZwFzpJQVgcfAYE1S5Yy5wC4ppRdQnbT3aRD7TwhRBhgN1JZSViFtPeU3KNj772eg/T/ue9H+6gC4p/8bBizOo4yAkRV3oC4QJKUMkVImAeuAbhpnyjIp5T0p5bn0n5+QVhjKkPaeVqQ/bAXgq0nAHCCEcAY6AcvSbwugJbAx/SEF9v0JIYoATYEfAaSUSVLKSAxo/5G2TrO1EMIMsAHuUYD3n5TyMBDxj7tftL+6Ab/INCeAokKIUnkSFOMr7mWA0Gdu306/r8ATQlQAagInAScp5b30pvuAk1a5csB3wIeALv12cSBSSpmSfrsg70MXIAz4Kb3baZkQwhYD2X9SyjvAbOBv0op6FHAWw9l/T71of2lab4ytuBskIYQdsAkYK6WMfrZNpo11LZDjXYUQnYGHUsqzWmfJJWaAD7BYSlkTiOUfXTAFfP/Zk3b06gKUBmzJ2KVhUPLT/jK24n4HKPvMbef0+wosIYQ5aYV9tZRyc/rdD55+/Uv/70Ot8mVTI6CrEOImaV1oLUnroy6a/jUfCvY+vA3cllKeTL+9kbRibyj7rzVwQ0oZJqVMBjaTtk8NZf899aL9pWm9MbbifhpwTz9bb0HayZ1tGmfKsvT+5x+Bq1LKb59p2gYMTP95ILA1r7PlBCnlR1JKZyllBdL21X4pZT/gAPB6+sMK8vu7D4QKITzT72oF+GMg+4+07pj6Qgib9M/q0/dnEPvvGS/aX9uAAemjZuoDUc903+Q+KaVR/QM6AteAYGCy1nmy+V4ak/YV8CLwV/q/jqT1S+8DrgN7gWJaZ82B99oc2J7+sytwCggCfgUstc6XjfdVAziTvg/9AHtD2n/AdCAAuAysBCwL8v4D1pJ2/iCZtG9eg1+0vwBB2ui8YOASaaOG8iyrmn5AURTFABlbt4yiKIpRUMVdURTFAKniriiKYoBUcVcURTFAqrgriqIYIFXcFUVRDJAq7oqiKAbo/wD7A61/gXBpowAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([0, 100, 100, 0, 0, 100, 50, 0, 100],\n",
+    "         [0, 0, 100, 100, 0, 100, 130, 100, 0],\n",
+    "         \"g--\")\n",
+    "plt.axis([-10, 110, -10, 140])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can plot multiple lines on one graph very simply: just pass `x1, y1, [style1], x2, y2, [style2], ...`\n",
+    "\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([0, 100, 100, 0, 0], [0, 0, 100, 100, 0], \"r-\",\n",
+    "         [0, 100, 50, 0, 100], [0, 100, 130, 100, 0], \"g--\")\n",
+    "plt.axis([-10, 110, -10, 140])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or simply call `plot` multiple times before calling `show`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([0, 100, 100, 0, 0], [0, 0, 100, 100, 0], \"r-\")\n",
+    "plt.plot([0, 100, 50, 0, 100], [0, 100, 130, 100, 0], \"g--\")\n",
+    "plt.axis([-10, 110, -10, 140])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also draw simple points instead of lines. Here's an example with green dashes, red dotted line and blue triangles.\n",
+    "Check out [the documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot) for the full list of style & color options."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-1.4, 1.4, 30)\n",
+    "plt.plot(x, x, 'g--', x, x**2, 'r:', x, x**3, 'b^')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plot function returns a list of `Line2D` objects (one for each line). You can set extra properties on these lines, such as the line width, the dash style or the alpha level. See the full list of properties in [the documentation](https://matplotlib.org/stable/tutorials/introductory/pyplot.html#controlling-line-properties)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-1.4, 1.4, 30)\n",
+    "line1, line2, line3 = plt.plot(x, x, 'g--', x, x**2, 'r:', x, x**3, 'b^')\n",
+    "line1.set_linewidth(3.0)\n",
+    "line1.set_dash_capstyle(\"round\")\n",
+    "line3.set_alpha(0.2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Saving a figure\n",
+    "Saving a figure to disk is as simple as calling [`savefig`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html) with the name of the file (or a file object). The available image formats depend on the graphics backend you use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-1.4, 1.4, 30)\n",
+    "plt.plot(x, x**2)\n",
+    "plt.savefig(\"my_square_function.png\", transparent=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Subplots\n",
+    "A matplotlib figure may contain multiple subplots. These subplots are organized in a grid. To create a subplot, just call the `subplot` function, and specify the number of rows and columns in the figure, and the index of the subplot you want to draw on (starting from 1, then left to right, and top to bottom). Note that pyplot keeps track of the currently active subplot (which you can get a reference to by calling `plt.gca()`), so when you call the `plot` function, it draws on the *active* subplot.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-1.4, 1.4, 30)\n",
+    "plt.subplot(2, 2, 1)  # 2 rows, 2 columns, 1st subplot = top left\n",
+    "plt.plot(x, x)\n",
+    "plt.subplot(2, 2, 2)  # 2 rows, 2 columns, 2nd subplot = top right\n",
+    "plt.plot(x, x**2)\n",
+    "plt.subplot(2, 2, 3)  # 2 rows, 2 columns, 3rd subplot = bottom left\n",
+    "plt.plot(x, x**3)\n",
+    "plt.subplot(2, 2, 4)  # 2 rows, 2 columns, 4th subplot = bottom right\n",
+    "plt.plot(x, x**4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Note that `subplot(223)` is a shorthand for `subplot(2, 2, 3)`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is easy to create subplots that span across multiple grid cells like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplot(2, 2, 1)  # 2 rows, 2 columns, 1st subplot = top left\n",
+    "plt.plot(x, x)\n",
+    "plt.subplot(2, 2, 2)  # 2 rows, 2 columns, 2nd subplot = top right\n",
+    "plt.plot(x, x**2)\n",
+    "plt.subplot(2, 1, 2)  # 2 rows, *1* column, 2nd subplot = bottom\n",
+    "plt.plot(x, x**3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you need more complex subplot positioning, you can use `subplot2grid` instead of `subplot`. You specify the number of rows and columns in the grid, then your subplot's position in that grid (top-left = (0,0)), and optionally how many rows and/or columns it spans. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplot2grid((3,3), (0, 0), rowspan=2, colspan=2)\n",
+    "plt.plot(x, x**2)\n",
+    "plt.subplot2grid((3,3), (0, 2))\n",
+    "plt.plot(x, x**3)\n",
+    "plt.subplot2grid((3,3), (1, 2), rowspan=2)\n",
+    "plt.plot(x, x**4)\n",
+    "plt.subplot2grid((3,3), (2, 0), colspan=2)\n",
+    "plt.plot(x, x**5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you need even more flexibility in subplot positioning, check out the [corresponding matplotlib tutorial](https://matplotlib.org/stable/tutorials/intermediate/arranging_axes.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiple figures\n",
+    "It is also possible to draw multiple figures. Each figure may contain one or more subplots. By default, matplotlib creates `figure(1)` automatically. When you switch figure, pyplot keeps track of the currently active figure (which you can get a reference to by calling `plt.gcf()`), and the active subplot of that figure becomes the current subplot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-1.4, 1.4, 30)\n",
+    "\n",
+    "plt.figure(1)\n",
+    "plt.subplot(211)\n",
+    "plt.plot(x, x**2)\n",
+    "plt.title(\"Square and Cube\")\n",
+    "plt.subplot(212)\n",
+    "plt.plot(x, x**3)\n",
+    "\n",
+    "plt.figure(2, figsize=(10, 5))\n",
+    "plt.subplot(121)\n",
+    "plt.plot(x, x**4)\n",
+    "plt.title(\"y = x**4\")\n",
+    "plt.subplot(122)\n",
+    "plt.plot(x, x**5)\n",
+    "plt.title(\"y = x**5\")\n",
+    "\n",
+    "plt.figure(1)      # back to figure 1, current subplot is 212 (bottom)\n",
+    "plt.plot(x, -x**3, \"r:\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pyplot's state machine: implicit *vs* explicit\n",
+    "So far we have used Pyplot's state machine which keeps track of the currently active subplot. Every time you call the `plot` function, pyplot just draws on the currently active subplot. It also does some more magic, such as automatically creating a figure and a subplot when you call `plot`, if they don't exist yet. This magic is convenient in an interactive environment (such as Jupyter).\n",
+    "\n",
+    "But when you are writing a program, *explicit is better than implicit*. Explicit code is usually easier to debug and maintain, and if you don't believe me just read the 2nd rule in the Zen of Python:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Zen of Python, by Tim Peters\n",
+      "\n",
+      "Beautiful is better than ugly.\n",
+      "Explicit is better than implicit.\n",
+      "Simple is better than complex.\n",
+      "Complex is better than complicated.\n",
+      "Flat is better than nested.\n",
+      "Sparse is better than dense.\n",
+      "Readability counts.\n",
+      "Special cases aren't special enough to break the rules.\n",
+      "Although practicality beats purity.\n",
+      "Errors should never pass silently.\n",
+      "Unless explicitly silenced.\n",
+      "In the face of ambiguity, refuse the temptation to guess.\n",
+      "There should be one-- and preferably only one --obvious way to do it.\n",
+      "Although that way may not be obvious at first unless you're Dutch.\n",
+      "Now is better than never.\n",
+      "Although never is often better than *right* now.\n",
+      "If the implementation is hard to explain, it's a bad idea.\n",
+      "If the implementation is easy to explain, it may be a good idea.\n",
+      "Namespaces are one honking great idea -- let's do more of those!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import this"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fortunately, Pyplot allows you to ignore the state machine entirely, so you can write beautifully explicit code. Simply call the `subplots` function and use the figure object and the list of axes objects that are returned. No more magic! For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-2, 2, 200)\n",
+    "fig1, (ax_top, ax_bottom) = plt.subplots(2, 1, sharex=True)\n",
+    "fig1.set_size_inches(10,5)\n",
+    "line1, line2 = ax_top.plot(x, np.sin(3*x**2), \"r-\", x, np.cos(5*x**2), \"b-\")\n",
+    "line3, = ax_bottom.plot(x, np.sin(3*x), \"r-\")\n",
+    "ax_top.grid(True)\n",
+    "\n",
+    "fig2, ax = plt.subplots(1, 1)\n",
+    "ax.plot(x, x**2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For consistency, we will continue to use pyplot's state machine in the rest of this tutorial, but we recommend using the object-oriented interface in your programs.\n",
+    "\n",
+    "# Pylab *vs* Pyplot *vs* Matplotlib\n",
+    "\n",
+    "There is some confusion around the relationship between pylab, pyplot and matplotlib. It's simple: matplotlib is the full library, it contains everything including pylab and pyplot.\n",
+    "\n",
+    "Pyplot provides a number of tools to plot graphs, including the state-machine interface to the underlying object-oriented plotting library.\n",
+    "\n",
+    "Pylab is a convenience module that imports matplotlib.pyplot and NumPy within a single namespace. You will find many examples using pylab, but it is now [strongly discouraged](https://matplotlib.org/stable/api/index.html#module-pylab) (because *explicit* imports are better than *implicit* ones)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Drawing text\n",
+    "You can call `text` to add text at any location in the graph. Just specify the horizontal and vertical coordinates and the text, and optionally some extra arguments. Any text in matplotlib may contain TeX equation expressions, see [the documentation](https://matplotlib.org/stable/tutorials/text/mathtext.html) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-1.5, 1.5, 30)\n",
+    "px = 0.8\n",
+    "py = px**2\n",
+    "\n",
+    "plt.plot(x, x**2, \"b-\", px, py, \"ro\")\n",
+    "\n",
+    "plt.text(0, 1.5, \"Square function\\n$y = x^2$\", fontsize=20, color='blue',\n",
+    "         horizontalalignment=\"center\")\n",
+    "plt.text(px - 0.08, py, \"Beautiful point\", ha=\"right\", weight=\"heavy\")\n",
+    "plt.text(px + 0.05, py - 0.4, \"x = %0.2f\\ny = %0.2f\"%(px, py), rotation=-30,\n",
+    "         color='gray')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Note: `ha` is an alias for `horizontalalignment`\n",
+    "\n",
+    "For more text properties, visit [the documentation](https://matplotlib.org/stable/tutorials/text/text_props.html).\n",
+    "\n",
+    "Every so often it is required to annotate elements of a graph, such as the beautiful point above. The `annotate` function makes it easy: just indicate the location of the point of interest, and the position of the text, plus optionally some extra arguments for the text and the arrow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, x**2, px, py, \"ro\")\n",
+    "plt.annotate(\"Beautiful point\", xy=(px, py), xytext=(px-1.3,py+0.5),\n",
+    "                           color=\"green\", weight=\"heavy\", fontsize=14,\n",
+    "                           arrowprops={\"facecolor\": \"lightgreen\"})\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also add a bounding box around your text by using the `bbox` argument:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, x**2, px, py, \"ro\")\n",
+    "\n",
+    "bbox_props = dict(boxstyle=\"rarrow,pad=0.3\", ec=\"b\", lw=2, fc=\"lightblue\")\n",
+    "plt.text(px-0.2, py, \"Beautiful point\", bbox=bbox_props, ha=\"right\")\n",
+    "\n",
+    "bbox_props = dict(boxstyle=\"round4,pad=1,rounding_size=0.2\", ec=\"black\",\n",
+    "                  fc=\"#EEEEFF\", lw=5)\n",
+    "plt.text(0, 1.5, \"Square function\\n$y = x^2$\", fontsize=20, color='black',\n",
+    "         ha=\"center\", bbox=bbox_props)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just for fun, if you want an [xkcd](https://xkcd.com)-style plot, just draw within a `with plt.xkcd()` section:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with plt.xkcd():\n",
+    "    plt.plot(x, x**2, px, py, \"ro\")\n",
+    "\n",
+    "    bbox_props = dict(boxstyle=\"rarrow,pad=0.3\", ec=\"b\", lw=2, fc=\"lightblue\")\n",
+    "    plt.text(px-0.2, py, \"Beautiful point\", bbox=bbox_props, ha=\"right\")\n",
+    "\n",
+    "    bbox_props = dict(boxstyle=\"round4,pad=1,rounding_size=0.2\", ec=\"black\",\n",
+    "                      fc=\"#EEEEFF\", lw=5)\n",
+    "    plt.text(0, 1.5, \"Square function\\n$y = x^2$\", fontsize=20, color='black',\n",
+    "             ha=\"center\", bbox=bbox_props)\n",
+    "\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Legends\n",
+    "The simplest way to add a legend is to set a label on all lines, then just call the `legend` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-1.4, 1.4, 50)\n",
+    "plt.plot(x, x**2, \"r--\", label=\"Square function\")\n",
+    "plt.plot(x, x**3, \"g-\", label=\"Cube function\")\n",
+    "plt.legend(loc=\"best\")\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Non-linear scales\n",
+    "Matplotlib supports non-linear scales, such as logarithmic or logit scales."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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bw4fEsM6mDg3piMjwfF/4xTBbZsrsiVXsOnyM3tiA11FEJA/5tvAj0QGCAaOsgJc3HGr2xCqc05w6IjK84mm7MYpEBwmXBjEzr6NkzezmxKmZGtYRkeH4tvC7+waoLi+Oq2xTZjSECQWMTft14FZETuTbwo9EB4rmKtuUkmCAmTpTR0RG4N/CL5LVroaa3Vylc/FFZFi+LfzuvoGiOQc/3RkTq9lxKEJfcjZQEZEU3xZ+JFqchT+nuYq4Q1Mli8gJfF34xTikc+akGgA27NWZOiJyPN8WfneR7uG3NlRSFgqwYV+X11FEJM/4svCdc0U7pBMKBpjTXM2GfdrDF5Hj+bLw+/rjxF1xzaOT7sxJ1azXkI6IDOHLwu+OJlaFKob1bIdz5uQaDvZEOdAd9TqKiOQRXxZ+JJo4ZbFY9/DPmpxYDGWjhnVEJI1PC7+45sIfKnWmzvq9OnArIr/jy8IvtuUNh5oQLqW5poz1OlNHRNL4s/D7im/xk6HOnFSjc/FF5Di+LPxIcoGQqiJa/GSoMydXs7mjh/7BuNdRRCRP+LLwi31IB+CsSTXEBuNsO6jFUEQkwZ+F74chneSZOjpwKyIpviz81Fk6lSXFeR4+QFtjFSVBY50KX0SSfFn4PdFBqspCBALFs7zhUKWhxBQLa3er8EUkwaeF3190q10N55yWWlbvPopzzusoIpIHfFn4kehgUY/fp5zdUsvRY/3sOnzM6ygikgd8Wfg90QGqfVD457TUArB691GPk4hIPvBt4fthD3/upGpCAVPhiwjg08Iv1tWuhiovCTKnuZo1KnwRwaeF31Oki58MRwduRSRFhV/kzp5ay5FeHbgVER8WvnOO7r4Bqot4Hp10qQO3GtYREd8Vfl9/nMG4o7q8xOsoOXGmDtyKSJLvCr+7L7m8oU/28MtLgsxurlbhi4j/Cr8rOXFajU8KH+CclhrW6MCtiO/5rvBTe/h+GcMHWDCtjsO9/ew41Ot1FBHxkA8LP7GH75cxfICF0+sBWNV+2OMkIuIlHxe+f/bw5zRXU1UW4rX2I15HEREP+a7we6KpIR3/7OEHA8aCabXawxfxOd8VfmoP3y8XXqUsnF7Phn3d9CbX8xUR//Fd4Xf5uPAH447Xd+r0TBG/8l3hd/f1U1UWIljEq10N57xpdYAO3Ir4mQ8L3z/TKqSrD5fS1hjmNRW+iG/5rvB7fFr4AOdPr2dV+xFdgCXiU74r/O5ov6/O0Em3cEYdnZEY7Z26AEvEj3JW+Gb2ATO738z+y8zelavtDtXd55+pkYdKXYC1coeGdUT8KKPCN7MHzazDzNYMeXyJmW00s81m9vnR3sM592Pn3MeBTwIfPvXIp8evY/iQuACrujzEy9s6vY4iIh7ItPkeAr4GfDf1gJkFga8D7wR2Aa+Y2WNAELh3yOtvdc51JD/+v8nXeaK7z79DOsGAsXjmBF5S4Yv4kmV6AM/MWoEnnHNnJ+9fDNzjnHt38v7dAM65oWWfer0BXwSeds79YpTtfAL4BEBzc/OiZcuWZfzFAPT09FBVVTXi87f/PMI7Z5Tw4bmlY3rfbDpZxvH01LZ+lm2M8ZUrK6gvH/kXPC8zZiLf84EyZosyjs1VV1210jl3wbBPOucyugGtwJq0+zcAD6Tdvxn42iiv/xSwEvgm8MlMtrlo0SI3VsuXLx/xub7+ATfjrifcV595c8zvm02jZRxvq3cdcTPuesL9+LVdo36elxkzke/5nFPGbFHGsQFedSN0as4Gs51z9wH35Wp7w+nx4UyZQ501uYbq8hAvbu3kuvNavI4jIjl0Omfp7Aampd2fmnwsb/l1Hp10wYBxYesEXtp6yOsoIpJjp1P4rwCzzWymmZUCHwEey06s8eHHqZGHc1FbA1sPRujo6vM6iojkUKanZS4FXgDmmtkuM7vNOTcA3AH8DFgPPOKcWzt+UU/f71a78u+QDsDitgkAvKizdUR8JaNdXefcjSM8/iTwZFYTjaPuqPbwAeZNrqG6LMSLWw/x/gVTvI4jIjniq6kVut9awNzfe/ihYIC3zZzAi1s0ji/iJz4rfP8tYD6Sy85oZOvBCDs1r46Ib/is8JNn6ajwuWJuEwDPbTrgcRIRyRWfFX4/5SUBSoK++rKH1dYYpqWugl9uVOGL+IWvmq8nOuD7M3RSzIwr5jbxmy2HiA3EvY4jIjngq8Lv8vFMmcO5Yk4TPdEBLXso4hO+KvzE1Mjaw0+5ZFYDoYDx3Jsa1hHxA18Vftexfmq0h/+W6vISFs6o55cqfBFf8FXhHz3WT22F9vDTXTGnibV7uujo1jQLIsVOhe9zV8xJnJ65YoP28kWKnW8K3znH0WP91FWq8NPNn1JDS10FP1u7z+soIjLOfFP4kdggg3GnPfwhzIx3zW/mV5sP0pOca0hEilPOCn8sC56Ph6PHEtMqqPBPtGT+JGIDcV2EJVLkclL4aQuevweYB9xoZvNyse2UI70xQIU/nAtaJ9AQLtWwjkiRy3gR89PayBgWPB+vRczXHxrkS6/0cdfbyjmrITj2LyKL8mnB45QH10R5ee8AX72mkpKA5WXGdPmeD5QxW5RxbLKyiPnp3BjjguepWzYXMf/p6j1uxl1PuDW7j4z5PbMtnxY8Tnlm/T43464n3LMb9jvn8jNjunzP55wyZosyjg2jLGLum4O2qTH8uspSj5Pkp0tmNVJVFuJnazSsI1KsclX4ni94roO2oysvCXLNWRP56Zp9mkxNpEjlqvA9X/D8SG8/wYARLvV2/D6ffeD8Fo4e62fFxg6vo4jIOMhJ4bs8WPA8dZWtmeVyswXl8jMaaawq5Uev5fSXLxHJkZyN4TvnnnTOzXHOzXLO/X2utpuiaRVOLhQMcO2CKTyzvoNI//ifvSUiueWrg7Yq/JO7/vwWYoNxXt2nq25Fio0KX45zTkstbU1hfrNHhS9SbFT4chwz4/rzWth4OM7Ozl6v44hIFqnw5QS/v2gqBix9ud3rKCKSRb4o/MG4o0tTI2dsSl0F500M8l+v7CQ6MOh1HBHJEl8U/tFj/cQdTAjrKttMXT0txKFIjKd05a1I0fBF4XdGooAKfyzmNwaZ0VDJ917c4XUUEckSXxT+oZ7E1MgN4TKPkxSOgBk3LZ7OK9sPs2Ffl9dxRCQLfFH4h5Nz4WsPf2w+uGgapaEA3/mN9vJFioEvCv9QRIV/KurDpfz+wqn8YOUuOrr6vI4jIqfJF4XfmRzSqQ/rLJ2x+t9vb2MgHudbz2/zOoqInCZ/FH5vjOqyEGUhzZQ5Vq2NYd57zmS+9+IOjvb2ex1HRE6DPwo/EqNewzmn7I+unEUkNsh/vrjd6ygichp8U/gavz9186fUcuXcJh789XYiUc2xI1KofFP4DSr80/Kpa2bTGYlx/6+2eh1FRE6RbwpfQzqnZ+H0epbMn8R/PLeVA91Rr+OIyCko+sJ3znFIe/hZ8bklc4kOxLnvmU1eRxGRU1D0hd8bGyQ2ENcYfhbMaqriI2+bxtKX29l2MOJ1HBEZo6Iv/M5I6hx8FX42fPodsykLBfjC42txTssgihSSoi/8Az2J8ebGKhV+NkysLucz75rL8o0H+MnqvV7HEZExKPrC3380MSVAc025x0mKx0cvnsHZLTX87ePrOHpMF2OJFIqiL/x9yTlgJqnwsyYUDHDv9edyqCfKl5/a4HUcEcmQLwq/NBjQQdssO2dqLR+7dCYPv9TO8g0dXscRkQwUfeF3dEWZWFOGmXkdpeh87t1zOXNSNZ/979c1m6ZIASj6wt93tE/DOeOkvCTIV288n0hsgM888jrxuM7aEclnRV/4+7v6aK5V4Y+X2c3V/PX75vP85oP8iy7IEslrRV34zjn2dfXRXK3CH083XjiNGxZN5b5nNvE/v93tdRwRGUFRF353dIDe2CCTarWW7XgyM/7h+nO4cOYEPvfoG6xqP+x1JBEZRlEXvs7Bz53SUIBv/uEiJtWUc9tDr2jhc5E8VNyF35W4ylYHbXNjQriU/7ztQkpDAW66/yU27e/2OpKIpCnqwn/roisdtM2ZGQ1hln78IgIB48b7X9KevkgeKerC39nZi5kKP9famqpY+vHFBAPwwW+8wPObDnodSUQo8sJv7+xlSm2FFi/3wBkTq/nRH19KS30Ft3z7ZZa+3K7ZNUU8VtSFv/1QhBkNlV7H8K0pdRU88smLuXhWA3f/cDWfeeR1rYkr4qGiLvz2Q70qfI/VlJfw0Mcu5M/eMYcf/3Y31371eVbu6PQ6logvFW3hHz3Wz6FIjBkNYa+j+F4wYHz6HbN5+PbF9PUPcsM3X+CvfryGrj5NrSySS0Vb+G8mTwmc21ztcRJJuWRWI09/5gpuuaSV7720gyv/cQXf/vU2YgNxr6OJ+ELRFv6GfcnCn6TCzyfhshB/c+18Hr/jMs6aXM3fPr6Oq/4pUfwa3xcZX0Vb+Bv3dVFdFmKyTsnMS2e31PK92xbznVsvZHJtOX/7+DouvvcZ7v3pejZ39HgdT6QohbwOMF5eaz/CudNqNQ9+HjMzrpjTxBVzmljVfpgHfrWV+5/byr//cisLptVx/XlTeMe8ZqbW68C7SDYUZeH3RAdYv7eLO66e7XUUydDC6fX8202L6Ojq47HX9/CDVbu55/F13PP4OuY0V3HV3IlcPruJ86bXUVVWlP9sRcZdUf7PeWHLIeIOLmyd4HUUGaOJNeXcfnkbt1/expYDPSzf0MHyjR08+Ott/PtzWwkYzJ1Uw6RQlD0V7cydVMXs5mpqyku8ji6S94qy8H/yxh5qK0q4cKYKv5DNaqpiVlMVt1/eRndfP6+1H2HljsOsaj/MC1u7WP6j1W997uTacmY2hmmpq2BqfSUt9RW01FXQXFNGQ7iMmoqQhvfE94qu8J1zrN/bzZL5kygNFe0xad+pLi/h7XOaePucJgCeXb6c2QsW8+b+bt7c38Ob+7vZfijCL988QEd39ITXhwLGhHApE8KlNFSVUltRQrg0RLgsRLgsSLgsRFVZiHBpiMrSIKWhQOIWDLz1cVkoQGkw8VxJ0AgFAwQDRtCMQAAClvpYP1gkPxVd4ZsZT915OZHYoNdRZBwFzJg2oZJpEyq55qzm457r6x9k79E+dh8+xoGePg71xOiMJG4He2J0RqLs74oSiQ7QEx0gEh0g28vxBgwMCP3ip4kfBAEjYBBI/oAwM1K/cKR+PPzuvg25n3r++B8kbz2f4eveenXa8729vVSuXHHqX2gOZJrRy9/geiO9VK76ZVbf88lPXZ71ndaiK3xI/MXrwJ5/lZcEmdkYZmZjZldZO+fo64+/Vf69sUFig3FiA3H6k39GB+JvPZa4DTIQd8SdYzAOceeIxx2DzhF3EI87tm3fwdTp0xKPpz7HOQbjLu0HjEtm4Pg/hz7O8M9zwvNuhM8f/vkDHX00TazJ6PvklYwyejwvX8eBY0xsyu41P+Pxi6JaUXzPzKgoDVJRGqSpOnvLYa5YsZcrrzwra+83HlasWMGVVy70OsaolDF7NMgtIuITKnwREZ9Q4YuI+IQKX0TEJ1T4IiI+ocIXEfEJFb6IiE+o8EVEfMJSV97lIzM7AOwY48sagYPjECeblPH05Xs+UMZsUcaxmeGcaxruibwu/FNhZq865y7wOsdolPH05Xs+UMZsUcbs0ZCOiIhPqPBFRHyiGAv/P7wOkAFlPH35ng+UMVuUMUuKbgxfRESGV4x7+CIiMgwVvoiITxRN4ZvZEjPbaGabzezzXucZysymmdlyM1tnZmvN7NNeZxqJmQXN7DUze8LrLMMxszoze9TMNpjZejO72OtMQ5nZnyX/nteY2VIzK8+DTA+aWYeZrUl7bIKZPW1mm5J/1udhxn9M/l2/YWY/MrM6DyMOmzHtuT83M2dmjV5kO5miKHwzCwJfB94DzANuNLN53qY6wQDw5865ecBFwJ/kYcaUTwPrvQ4xin8FnnLOnQksIM+ymlkL8CngAufc2UAQ+Ii3qQB4CFgy5LHPA88452YDzyTve+khTsz4NHC2c+5c4E3g7lyHGuIhTsyImU0D3gW05zpQpoqi8IELgc3Oua3OuRiwDLjO40zHcc7tdc6tSn7cTaKkWrxNdSIzmwr8HvCA11mGY2a1wNuBbwE452LOuSOehhpeCKgwsxBQCezxOA/OueeAziEPXwd8J/nxd4AP5DLTUMNldM793Dk3kLz7IjA158GOzzPc9xHgK8D/wfMVdkdWLIXfAuxMu7+LPCzTFDNrBc4HXvI4ynD+hcQ/2rjHOUYyEzgAfDs57PSAmWW2WnmOOOd2A/9EYk9vL3DUOfdzb1ONqNk5tzf58T6g2cswGbgV+KnXIYYys+uA3c65173OMppiKfyCYWZVwA+AO51zXV7nSWdm7wM6nHMrvc4yihCwEPiGc+58IIL3wxDHSY6DX0fih9MUIGxmf+htqpNziXO083bv1Mz+ksTQ6MNeZ0lnZpXAXwB/7XWWkymWwt8NTEu7PzX5WF4xsxISZf+wc+6HXucZxqXA+81sO4lhsavN7HveRjrBLmCXcy7129GjJH4A5JN3ANuccwecc/3AD4FLPM40kv1mNhkg+WeHx3mGZWa3AO8DbnL5d/HQLBI/3F9P/t+ZCqwys0mephpGsRT+K8BsM5tpZqUkDpA95nGm45iZkRh3Xu+c+2ev8wzHOXe3c26qc66VxPfwWedcXu2ZOuf2ATvNbG7yoWuAdR5GGk47cJGZVSb/3q8hzw4sp3kM+Gjy448C/+NhlmGZ2RISw4zvd871ep1nKOfcaufcROdca/L/zi5gYfLfal4pisJPHtC5A/gZif9Yjzjn1nqb6gSXAjeT2Gv+bfL2Xq9DFag/BR42szeA84B/8DbO8ZK/fTwKrAJWk/h/5vml92a2FHgBmGtmu8zsNuCLwDvNbBOJ30y+mIcZvwZUA08n/998Mw8zFgRNrSAi4hNFsYcvIiInp8IXEfEJFb6IiE+o8EVEfEKFLyLiEyp8ERGfUOGLiPjE/wdq2qSmPmmqigAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0.1, 15, 500)\n",
+    "y = x**3/np.exp(2*x)\n",
+    "\n",
+    "plt.figure(1)\n",
+    "plt.plot(x, y)\n",
+    "plt.yscale('linear')\n",
+    "plt.title('linear')\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.figure(2)\n",
+    "plt.plot(x, y)\n",
+    "plt.yscale('log')\n",
+    "plt.title('log')\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.figure(3)\n",
+    "plt.plot(x, y)\n",
+    "plt.yscale('logit')\n",
+    "plt.title('logit')\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.figure(4)\n",
+    "plt.plot(x, y - y.mean())\n",
+    "plt.yscale('symlog', linthresh=0.05)\n",
+    "plt.title('symlog')\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ticks and tickers\n",
+    "The axes have little marks called \"ticks\". To be precise, \"ticks\" are the *locations* of the marks (e.g. (-1, 0, 1)), \"tick lines\" are the small lines drawn at those locations, \"tick labels\" are the labels drawn next to the tick lines, and \"tickers\" are objects that are capable of deciding where to place ticks. The default tickers typically do a pretty good job at placing ~5 to 8 ticks at a reasonable distance from one another.\n",
+    "\n",
+    "But sometimes you need more control (e.g. there are too many tick labels on the logit graph above). Fortunately, matplotlib gives you full control over ticks. You can even activate minor ticks.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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e59wuRf/QjtoROJlz7pfOuZrYuX8hqVzSpRO8HimaINU5577inOt1znU6554Z8XiBoglcpaR3jNjDOiBppZnNds6FnHNPn+L8b5H0fefc8865PkmfkHSFnbiP74vOuTbnXJWkRxQduBqPHzjnymLLxe4c8ftvVXQZ2b2x1+1BSdskvXy0kzjn/ldShaRnFG3X/mm042LHVjjnHnTO9cXaw69qxHs9jnMNKLp8fkHs9R9zARgkjSJJnaP8/F9i/389qmhS8PqzOPeApHPNrCD2t/350xx72Dn3g9hn/BeKdtL/LRbDA5L6FU3YTvU8/xbrB9wrKSRpzYgBrU/E2pYjkr6iaLIwpMY591+xftDZLPl8t6QvO+eec1EVzrmjIx6/WtFE523OuXtiPwsr2t8618wynXNHYonIkE5FE8vRvEJSuXPux7GYfybpgKSbRxxzqjZprB6PtVthRf/uDP2duETSHOfcvznn+p1zhxTdQ3eqfuaPFG0Hh5LLGxVNwP9CKrZpJGmpYaGkFkVnk3IlbbfocoE2RTs3fzFqO8TMPhqbom+PHV+o6OjZVFssqcU513qKx4sUTUC/4JxrH/Hz/1D0Q/uARZdHfHxywwQC9WNFR0hv0yijkWZ2mZk9ElsG0i7pvfrLz3PdiH93SzrVyPTJ536bme0c0bacN8q5z8ZiRROwU7lc0RmuL540APMuRVcNHIgt97npFL+/QNHZM0mScy6k6OjuwhHHnNVrMobfXyrplqHXLPa6XaVoB+NU/lfR1/a/YkmlzOzqEcuR9sV+VmxmP48tl+pQdFb15PfjL841in9UdAT72diypneO9aKRNFol5Z/8s9hMy5Cjin6Wxuu1ig5KHI0tQbviNMfWj/h3jyQ5507+2ak+m83OucER3w99DmdLytSINiD275Gf/+rTXsGZnakNe6+kJ0fO+jjnKiR9SNGZoobYZ3nk65svaWRfZ6QT2rSYk68p3m1ajkW3pixVdHnkyDbtk4rOQo7mDkk3x5ZWvl7SY865Wtq0KJK0JGdmlyj6wXxcUpOijdh651xR7KvQOTfqh9Oi+8/+UdEPzgznXJGijcLQfowunViYYN5pQhnL7NXpjqlWdDaw6BSPtyo64v4DM3vR8AmjI2N/75xboej0/EfsxHXdQNKIjc4eVrTTc9coh/xU0RHbxc65Qkn/oxc+z2dyys+7mS1V9I/j30qaFWsr9o7x3GdqG6oVXYZ0Kg8ougTmYTMb7gg458qdc29SdDnPlyT9KtYROFmNoh0LScP7MGbppFnIMRrvLH21pB+PaI+LnHN5zrkvjnZwbCnX1xVdIvbp2MizYisEhpYjDS1b/3wsng0uuuTzrRrxfpzqXH9xQc7VOedud84tkPQeSd+22H4ipIzdig54jDTjpM/TEkU/Syc7od0wsxP6CbHZpVcq+jn9rU6xLHkSNemFmZUhS3Ti5/9Mn+uxtGHnnObx90paYmYnbOFwzv3UOXdVLDanaDs2ZJ2iKx1Gc0KbFnPyNY3V2bRph09q0/Kdc6daHXBc0lOSXqPo7OWPYz+nTRNJWtIys4LYyPHPJd3hYkucFO1Ifc1ipVvNbOFoa4Vj8iUNSmqUlBHb71Uw4vGdkl5u0YIe8xQd9TmVeknL7PRFO+p1is6Yc65W0Q2s3zazGRYtEnDNSceUKLp06S4zuzR2fTdZdJOoKZpghiVFThMDkOjeJeklJ41yD8lXdEa6N/YZefM4zrtTp/685yn6x7NRkiy6Kf+8MZ63UdHP5KkSsXskzTezD1m08FG+mV028gAX3cP6U0UTtdmxGN5qZnNi7V5b7NDRPvs/k/QOM7vQzLIV7Qg8E1v2NF5jaedGGhpFvtHM0s0sx6JFDhad4vhvSNrmnHu3osvL/uc0585XdElXu0X3vv3D2ZzLzG4ZEU+rou8zbWhquVd/uSxakj5j0SI9Vys6SPrLUY7ZJWl97POVoxGFN2K/+xYzK3TODUjq0BT/vxVbrnenpM/F2palkj6iv9zPezpn+tx/V9JHzWyjRa2MPc+QTkkvlXSNmX1RksxsjZm9JNYm9So6wD7ytXmxon2i0dwrabWZvdnMMszsDZLOVbQtHa96SbPM7FRLK0/2rKROixY9mRZr186LTRicyv8pOiGwQaMPLg5JuTaNJC353G1mnYqOZvyTomt2R26A/Ziiy/+ejk0XP6RT7zG7X9HlkGWKTpX36sRp/x8r2gAfUXQ0+3QVcoYa72YzO9Wa8y9I+ufYFPloFZ5uVXTE64CkBo2SFLrono53Kvo6XCxplaLXGFJ0tObbzrlHThMnkNCcc5XOuW2nePhvJP1brI34lMY3an3Kz7tzbr+i+zieUvSP+gZJT4wx3m5FN+M/EfvsX37S452K7rG7WdElNuWSrh3lPJ9VdCT+odgI6ksl7TOzkKJ/vN842n4S59xDkv5F0q8l1So64n3KfbpnMJZ2buRzV0t6paLLgRoVbV//QaP8bTazVyp6Te+L/egjki62U1TVU7R4y8WKDk79QSM6P+M81yWSnom9jr+X9HexfSZIHf+n6ADNtBE/q1O0g1sj6SeK7hc/cPIvumhRsn9T9O9wuaKreka6VdKRWH/kvYoOtE61Dyg643dI0fh+Kun74/j9037unXO/VLSN+6miCdlvFS0UNPKYNkXbuZeZ2WcV3Y/2RUVn+uoUnWn8hCTFkt2XK7qf6y+46D1ub1K0cFqzognQTc65pnFc09C5Dig6kHUo1j6fdklrLOm9SdE9bodj8X9Xp94/J0m/UXTm7zexvwenknJtmjlqKAAAAOAUzOzzkhqcc1+3aFnzO5xzp5rxxSQysw8oumT9H4OOJV7MrFLRKp8PBR2LTwK/KR8AAAD85Zz7i3sTIhjOOW9Lxp8NM3utoksOT3f7hZREkgYAAABgSplZiaL75W6N7R/GCCx3BAAAAACPUDgEAAAAADxCkgYAAAAAHglkT9rs2bPdsmXLxnRsV1eX8vJGu/9ocuD6EhvX94Lt27c3OefmTHJIk2o8bZPE+5/IkvnapOS/vvFKtfaJ9z+xJfv7x/W94HRtUyBJ2rJly7Rt26lu43OikpISbd68eXIDChDXl9i4vheY2dHJjWbyjadtknj/E1kyX5uU/Nc3XqnWPvH+J7Zkf/+4vhecrm1iuSMAAAAAeIQkDQAAAAA8QpIGAAAAAB4hSQMAAAAAj5CkAQAAeMrMbjazre3t7UGHAmAKkaQBAAB4yjl3t3NuS2FhYdChAJhCJGkAAAAA4BGSNAAAAADwCEkaAAAAAHiEJA0AAAAAPEKSBgAAAAAeIUkDAADwFCX4gdREkgYAAOApSvADqYkkDQAAAAA8QpIGAAAAAB4hSQMAAAAAj5CkAQAAAIBHSNIAAAA8RXVHIDWRpAEAAHiK6o5AaopLkmZmHzazfWa218x+ZmY58TgvAEwU7RMAAEg0E07SzGyhpA9K2uScO09SuqQ3TvS8ADBRtE8AACARxWu5Y4akaWaWISlXUk2czgsAE0X7BAAAEsqEkzTn3HFJ/09SlaRaSe3OuQcmel4Awbr9/7bpx/v7gg5jQmifgORz755a/dXXHlVte0/QoQDAMOecrv/qo3rgyEBczpcx0ROY2QxJr5S0XFKbpF+a2Vudc3ecdNwWSVskqbi4WCUlJWM6fygUGvOxiYjrS2zJfH1Pl3dpw0yX0Nc3lvbpbNsmKbnffym5ry+Zr01K7uu7v7xf5fUD2rv9aR1Ms6DDmXRmdrOkm1euXBl0KABOozHUp4qGkC6bmRWX8004SZN0vaTDzrlGSTKzuyRdKemEJM05t1XSVknatGmT27x585hOXlJSorEem4i4vsSWrNfX2tWvzvse1OKi7ES/vjO2T2fbNknJ+/4PSebrS+Zrk5L7+n5V87yWzGrXDS+5NuhQpoRz7m5Jd2/atOn2oGMBcGqHGrskSfOnx2fwKB570qokXW5muWZmkq6TVBqH8wIIyKGmkCRpfl7Cj1LTPgFJprKxSytm5wUdBgCcoLIx2nealxefkh/x2JP2jKRfSXpe0p7YObdO9LwAglM5NBoUp4YmKLRPQHKJRJwON4V0zpzpQYcCACc41NilnMw0zcyJzwB3PJY7yjn3r5L+NR7nAhC8ysaQMtNNs6cl/Ewa7ROQRGrae9Q7ENEKkjQAnqlsDGn57OlKs3BczpfYw+QAJsWhxi4tnZWn9BTYlA8gcQzt+Vgxh+WOAPxyqLErrm0TSRqAv3CoMaRz6AQB8Myh2J6PVFruaGY3m9nW9vb2oEMBcAq9A2Eda+2Oa9tEkgbgBAPhiI42d7OcCIB3Khu7lJ+TodnT41PiOhE45+52zm0pLCwMOhQAp3C0uVsRp7gOcJOkAThBdUu3BiMupUaqASSGQ7GiIdFirQDgh8mY5SdJA3CCSvZ8APBUZUN893wAQDwMld9fHsfbg5CkATjB8GjQbGbSAPgj1Deouo5eZvkBeOdQY5fmF+YoLzsuhfMlkaQBOEllY0izp2epMDcz6FAAYNjh2Cw/RY0A+KayMRT3WX6SNAAnONTYpRXMogHwzKGm6Cx/qhU1oroj4Dfn3KT0nUjSAJygsjGkc+YyUg3AL5UNIaWZtHRWbtChTCmqOwJ+a+zsU2ffYNxn+UnSAAxr7epXa/cAM2kAvFPZ1KXFM3OVnZEedCgAMOyFgmvMpAGYJBWxoiEr55KkAfBLZUNIK1NsqSMA/01W34kkDcCwigaSNAD+GQxHdKipi7YJgHcqG0LKy0rX/MKcuJ6XJA3AsIqGkHIy07SwaFrQoQDAsOrWHvUPRnQOSRoAz1Q0hHTO3Okys7ielyQNwLDyhpBWzJ6utLT4NjQAMBGpPMtPdUfAb+UNnZOyFJskDcCwyoZQSnaCAPgtlZM0qjsC/uroHVB9R9+kzPKTpAGQJHX1Dep4W09KdoIA+K2iIaS5+dkqyMkMOhQAGFY5iQNIJGkAJEVvYi1Jq0jSAHimojGkVcW0TQD8MjTLPxl9J5I0AJKkisZOSam5nAiAv5xzlN8H4KWKxpCy0tO0ZGZu3M9NkgZAUnQ0KD3NtHRWXtChAMCw+o4+hfoGGUAC4J3KhpCWzc5VRnr8UyqSNACSokna0lm5ysqgWQDgj/KG6Cw/5fcB+KZiEguu0RsDICnW0LCcCIBnUrmyo0QJfsBXvQNhVbV0T1rfiSQNgAbCER1t7k7ZThAAf1U0hFSQk6E507ODDiUQlOAH/HSkuUsRN3mz/CRpAHS0uUuDEUeSBsA7Q8uJzCzoUABg2GTP8pOkAUj55UQA/FXZOHl7PgDgbFU0hGQmncNyRwCTpayeJA2Af1q6+tUU6tfq4vygQwGAE5TXh7RkZq5yMtMn5fwkaQBUVt+pxTOnKTcrI+hQAGBYWX20suMqkjQAnimr79SquZPXNpGkAVB5fWhSGxoAOBvlQ0laCs/yU90R8E//YESHm7q0qnjy2iaSNCDFDYQjOtQUmtSGBgDORnlDSPnZGZpfmBN0KIGhuiPgnyOxgmurSdIATJajzV0aCDutZiYNgGfK6ju1spjKjgD8MrwUm+WOACbLUNEQNuYD8E15fYgBJADeKasPKc0mt+AaSRqQ4srroyVkqewIwCfNoT41d/WzFBuAdyoaOie1sqNEkgakvLKGTi2ekatpWZPX0ADAeDHLD8BXZfWhSa86S5IGpLjy+s5J3fgKAGejvCG65yPVkzSqOwJ+6R+M6EhT16T3nUjSgBQ2EI6WkF3Jng8Animvj1Z2LC7IDjqUQFHdEfDL4aZoZcfJvnURSRqQwo40xSo7MpMGwDNl9Z1aRWVHAJ4ZruzITBqAycKeDwC+Km8I0TYB8E55fafSTDpnDkkagElSVt8pm4KGBgDGoynUp5au/knfmA8A41VWH9LSWXmTWtlRIkkDUlp5rIQslR0B+KSsbqhoCANIAPxS1tCpVVNw2yKSNCCFHajr1BpGqgF45mBsz8eaebRPVHcE/NE7ENaRpi6tnYK2iSQNSFFT2dAAwHgcrOvUzLwszZme2pUdJao7Aj6paAgp4qQ18wom/blI0oAUNZUNDQCMx9AsP5UdAfjkQN3UzfKTpAEp6uBwQ8OeDwD+iEScyuo7WeoIwDtl9Z3KykjTslm5k/5cJGlAijo43NDkBR0KAAw73taj7v4wSRoA7xyo69TKOdOVkT75KRRJGpCiprKhAYCxmsrlRAAwHgfrOqZsLz+9MyBFldV1UjQEgHcO1nVIEjeyjqG6I+CHtu5+1Xf0TdkAEkkakILauwdU19Gr1SRpADxzoK5Ti2ZM0/TsjKBD8QLVHQE/DO3ln6q+E0kakIIOxEaqWU4EwDcHmeUH4KGh+zey3BHApCmb4oYGAMaibzCsw01dDCAB8M7Buk4V5GRoXkHOlDwfSRqQgg5McUMDAGNxqLFLgxHH/RsBeCc6y18wZfdvJEkDUtDBuug9iLhRLACfDN+/kaIhADzinNPB+k6tnsJ7y5KkASnGOTecpAGAT0rrOpSZbloxh/s3AvDH8bYedfYOTuksP0kakGKOtfaos29Q6+aznAiAXw7Udmrl3Hxlcv/GYZTgB4JXWhud5T93/tQNcNMKAimmtDZa2ZEkDYBvSms7tG4KO0GJgBL8QPAO1A5VxU6wmTQzKzKzX5nZATMrNbMr4nFeAPFXWtsps9Sp7Ej7BCSG5lCfGjr7dC4DSAA8U1rXoaWzcqf0/o3xeqZvSLrPOfc6M8uSlBun8wKIs9LaDi2blafcrJS5USztE5AAhpYTMcsPwDeltZ1aN8VVZyc8k2ZmhZKukfQ9SXLO9Tvn2iZ6XgCT40Bd6iwnon0CEseBuuhyolSZ5QeQGLr7B3WkuWvKB5DisdxxuaRGST8wsx1m9l0zoywT4KGuvkEdbenW2tS5BxHtE5Ag9td2aG5+tmZNzw46FAAYdqCuU85Ja6d4gDse650yJF0s6QPOuWfM7BuSPi7pX0YeZGZbJG2RpOLiYpWUlIzp5KFQaMzHJiKuL7El2vVVtIblnBRuPqqSkuNnPD7Rrm8UZ2yfzrZtkpLi9TmtZL6+ZL42KTGv77nyHhVnW8LFPdnM7GZJN69cuTLoUICUdGC4suPUDnDHI0k7JumYc+6Z2Pe/UrQTdALn3FZJWyVp06ZNbvPmzWM6eUlJicZ6bCLi+hJbol3f8WeOStqrW264UotmnHlrVqJd3yjO2D6dbdskJcXrc1rJfH3JfG1S4l3fQDiiugfv0ysuXq7Nm9cGHY5XnHN3S7p706ZNtwcdC5CKSms7lJ+doUUzpk3p8054uaNzrk5StZmtif3oOkn7J3peAPFXWtuh/JwMLSya2oYmKLRPQGKobAxpIOxSZr8sgMRRWtuhtfPzZWZT+rzxKu/2AUk/iVVOOyTpHXE6L4A4GqpONNUNTcBonwDPcf9GAD6KRJwO1HXq1RctnPLnjkuS5pzbKWlTPM4FYHJEIk4Hajv0uo2Lgg5lStE+Af4rre1UVkaaVsymrg8Afxxr7VGobzCQAaS43MwagP+qW7vV1R9mpBqAd0prO7S6eLoy0umWAPDH/uFZ/qlfik1rCKSIfTXRhubcBSRpAPzhnNO+mo4pr5yWKMzsZjPb2t7eHnQoQMrZX9OuNFMgty4iSQNSxL6adqWnmVYXszEfgD/qOnrV0tWv9QsKgw7FS865u51zWwoLeX2AqbavpkMr5kzXtKz0KX9ukjQgReyr6dCqudOVkzn1DQ0AnMr+2Cz/emb5AXhmf21HYG0TSRqQIvbVdLDUEYB39tV0yIzKjgD80tLVr9r2XpI0AJOnobNXjZ19LCcC4J19Ne1aPitPednxuisQAEzcvproPtCg+k4kaUAK2MdyIgCeYpYfgI+C7juRpAEpYD+VHQF4qL17QMdae5jlPw2qOwLB2FfToYVF01SUmxXI85OkASlgX027lszMVUFOZtChAMCwfbVDy4kYQDoVqjsCwdhX0x7o4DZJGpAC9tUEV50IAE6Fyo4AfNTVN6jDTV2Btk0kaUCS6+wd0NHmbjpBALyzr6ZD8wpyNGt6dtChAMCwA3Udci64oiESSRqQ9EprOyWxHw2Af/ZTNASAh3zYy0+SBiS5vceDLSELAKPpHQirojHELD8A7+w93qGi3EwtKMwJLAaSNCDJ7T3errn52SouCK6hAYCT7a/tUDjitGEhA0inQ3VHYOrtOd6uDQsLZWaBxUCSBiS5oYYGAHyy51g06diwiPbpdKjuCEyt3oGwyuo7A+87kaQBSayrb1AVjSGdR5IGwDN7jrdr9vQszWOWH4BHDtR1atCDWX6SNCCJ7a+NVicKuqEBgJPtPd6u8wJeTgQAJ9sT28sf9AA3SRqQxFhOBMBHPf1hlTeEGEAC4J29x9pVlJupRTOmBRoHSRqQxCgaAsBHFA0B4CsfioZIJGlAUqNoCAAfDd0ahFn+M6O6IzB1fCkaIpGkAUmrq29QlRQNAeAhioaMHdUdgalz0JOiIRJJGpC09td2KELREAAeomgIAB/t9qRoiESSBiQtioYA8FFPvz/LiQBgJF+KhkgkaUDS2nu8XXMoGgLAM0Oz/D6MVAPASL4UDZFI0oCktetYmy5YVBR0GABwgt3H2iRJFy4uCjQOABipdyCsg/Wd3vSdSNKAJNTRO6DKxi5duJiRagB+2VXdpnkFOczyA/DKvpp2hSNOF3gygESSBiShof1o53syGgQAQ3Yda9f57JUdM0rwA1NjZ3X0M3aBJ+0TSRqQhHZWt0kSHSEAXmnvHtDhpi5vRqoTASX4gamxq7pN8wtzNNeTWX6SNCAJ7T7WpuWz81SUmxV0KAAwbPfxNknsRwPgn92e7eUnSQOS0K5qlhMB8M+u2Cw/lR0B+KStu19Hmrt1vkd7+UnSgCRT196ruo5er0aDAECK7kdbMSdPhdMygw4FAIbtiu3lv9CjvhNJGpBkdsXKW7PnA4BPnHPaWd3mVScIAKToLL+ZdJ5Hq5BI0oAks/tYmzLSTOsXFAQdCgAMq+voVWNnH0uxx4nqjsDk232sTStm56kgx59ZfpI0IMnsqm7Xmnn5yslMDzoUABi2a6i8NbP840J1R2ByRWf5271rm0jSgCQSiTjtOtbG/dEAeGdndZsy003r5jPLD8Afx9t61BTq824vP0kakEQqG0Pq7B3UxUuKgg4FAE6wo6pV5y4oZJYfgFd2VLVJki5eMiPYQE5CkgYkkaGG5iLPGhoAqW0wHNHuY+26yLPlRACwo6pN2RlpWjs/P+hQTkCSBiSR56taVZCToRWz84IOBQCGHajrVM9AWBcxyw/AM89Xter8RYXKTPcrLfIrGgATsqOqTRctmaG0NAs6FAAYtiN2E2vflhMlAqo7ApOnbzCs/TUdXrZNJGlAkujsHVBZQycj1QC8s6OqVbOnZ2vRjGlBh5JwqO4ITJ59NR3qD0e87DuRpAFJYvexdjnHfjQA/tlZ1aaLlhTJjFl+AP7weS8/SRqQJJ4/2ipJupCN+QA80trVr0NNXV4uJwKQ2p6vatXComkqLsgJOpS/QJIGJIkd1W1aOXe6CqdlBh0KAAzbGduP5uNyIgCpbWdVmy70tG0iSQOSgHNOO6paKW8NwDs7qlqVnmY6fxF7qgD4o76jV8fberztO5GkAUngSHO3WrsHvFxTDSC1PV/VpjXF+crNygg6lIREdUdgcuyoim4T8bXvRJIGJIFtR1okSZuW+dnQAEhNg+GIdlS10jZNANUdgcmx7UirsjLSdN7CgqBDGRVJGpAEth+N3sR65ZzpQYcCAMMO1HWqqz+sjUtJ0gD4ZdvRVl2wqFDZGelBhzIqkjQgCWw72qqNS7mJNQC/vDDLPzPgSADgBb0DYe2radfGpf62TSRpQIJr7epXRUOIThAA72w72qr5hTlaWMRNrAH4Y1d1mwbCTpd4vBSbJA1IcNtj90djOREA32yPzfIDgE+2JUDfiSQNSHDbjrYqI810waKioEMBgGHH23pU296rTR53ghIB1R2B+Nt2pEUr505XUW5W0KGcEkkakOC2H23ReQsLNS3Lz42vAFIT+9Hig+qOQHxFIk7bj7Z6P4AUtyTNzNLNbIeZ3ROvcwI4vb7BsHYda/e+oQka7RMw9bYdaVVeVrrWzssPOhQAGFbRGFJH76D3A0jxnEn7O0mlcTwfgDPYe7xD/YMR7kF0ZrRPwBTbdrRVFy2ZoYx0Fu0A8Me2I9H9aL4PcMel5TSzRZJeIem78TgfgLEZWk50secNTZBon4Cp19E7oIN1HV5vyp8oM1tmZgfM7IdmVmZmPzGz683sCTMrN7NLY19PxWbynzSzNbHf/bCZfT/27w1mttfMcoO9IiA1bDvSotnTs7R0lt8fuXgNb31d0j9KisTpfADG4NnDLVoxO09z83OCDsVnXxftEzClth9pVcRJly33ezlRHKyU9BVJa2Nfb5Z0laSPSvqkpAOSrnbOXSTpU5I+H/u9b0haaWavlvQDSe9xznVPcexASnrmcIsuXT5TZn7fWzZjoicws5skNTjntpvZ5tMct0XSFkkqLi5WSUnJmM4fCoXGfGwi4voSW5DXF3FOT1Z069J5GZMWQ6K/f2Npn862bZIS//U5k2S+vmS+Nin46/vlwX6lm9R5dI9KjvndEZqgw865PZJkZvskPeycc2a2R9IySYWSfmRmqyQ5SZmS5JyLmNltknZL+o5z7onRTk7fKTUl+/sX5PU19UR0vK1H184P+993cs5N6EvSFyQdk3REUp2kbkl3nO53Nm7c6MbqkUceGfOxiYjrS2xBXt++4+1u6cfucXc9Xz1pzzGe65O0zU2wPYn313jbp/G0TeN9fRJRMl9fMl+bc8Ff36u/9bh7zbefCDSGkSajfVI0Cds74vsfSnrdyMdiP/vgiJ8dGXH8jbF26WdjeT76Tqkj2d+/IK/vruer3dKP3eP217RP2nPEq+804eWOzrlPOOcWOeeWSXqjpD8559460fMCOL1nDjdLki5dPivgSPxF+wRMve7+Qe0+1q5Lk3+p41gUSjoe+/dtQz80s0JJ/ynpGkmzzOx1Ux8akHqeOdSigpwMrSn2v+osJZeABPXs4RYtmjFNC4umBR0KAAzbUdWmwYgjSYv6sqQvmNkOnbjF5GuSvuWcK5P0LklfNLO5QQQIpJJnY/vR0tL8X4Y94T1pIznnSiSVxPOcAP6Sc07PHm7Ri9fMCTqUhEH7BEyNZw63KM38L289Uc65I5LOG/H9bad4bPWIX/vn2OPvHHFstaIFSABMoobOXh1q6tIbL10cdChjwkwakIAqG7vU3NWfCpXTACSYZw83a/2CQuXnZAYdCgAMe+5w9P5olyXINhGSNCABDe1HS5SGBkBq6BsMa0dVG0sdAXjnmcPNys1K1/oFBUGHMiYkaUACevZwi+bmZ3t/I0YAqWX3sXb1DUZ0yTKStHgxs5vNbGt7e3vQoQAJ7dnDLdq4dIYy0hMj/UmMKAEMc87pqcpmXb5ilvc3YgSQWp6qbJaZdPkKkrR4cc7d7ZzbUlhYGHQoQMJqDvXpQF2nLl+ROCuQSNKABFPZ2KWGzj5deU7iNDQAUsOTlU06d36BinKzgg4FAIY9fahFkhKq70SSBiSYpyqbJElXJFBDAyD59Q6E9fzRtoTqBAFIDU9WNml6doY2LEycGWmSNCDBPHWoWQuLpmnJTPajAfDH80db1R+OMIAEwDtPVTbr0uUzE2Y/mkSSBiSUSIT9aAD89GRls9LTjKIhALxS1x69P9oVCbQfTSJJAxLKwfpOtXYPsJwIgHeeOtSsDQu5P1q8Ud0RmJinDiXmNhGSNCCBPFkZvT9aojU0AJJbV9+gdlWzH20yUN0RmJgnK5pVOC1T585PjPujDSFJAxLIU5XNWjYrVwuKpgUdCgAMe+5IiwYjTleeMzvoUADgBE8datblK2YqLS2xtomQpAEJYjAc0TOHm5lFA+CdJyublZlu2rh0RtChAMCwquZuHWvtSbj9aBJJGpAwdh1rV2fvoF60kpFqAH55rLxJG5fO0LSs9KBDAYBhj1U0SpKuWpV4fSeSNCBBPF7eJDPpRSwnAuCRxs4+ldZ26OpVc4IOBQBO8Hh5k+YX5uicOdODDmXcSNKABPF4RaM2LCzUjLysoEMBgGFPVkYrp12dgCPViYDqjsDZCUecnqxs1lUrZyfkbYtI0oAEEOob1I6qNl3FUkcAnnmsvElFuZlav4Dqg5OB6o7A2dlzvF3tPQMJudRRIkkDEsLTlc0ajLiEbWgAJCfnnB4vb9KLzpmt9ASrnAYguT1eHt2Plqh7+UnSgATweEWTpmWmUzkNgFcqG0Oq6+hlAAmAdx4rb9L6BQWaPT076FDOCkkakAD+XN6oS5fPVHYGldMA+OPPZdH9aCzFBuCTrr5BPV/VmtADSCRpgOdq2np0qLGLTfkAvPN4RZOWzcrV4pm5QYcCAMOeOdysgbDT1SsTt+osSRrguUfLomuqr1mduA0NgOTTOxDWU5XNtE2TjOqOwPg9erBROZlp2rQscbeJkKQBnis52KAFhTlaNTfx7vEBIHk9d6RFPQNhbV5DkjaZqO4IjF9JWaOuPGe2cjITd5sISRrgsf7BiJ6oaNaL18xNyHt8AEheJQcblZWRpitWsBR7vMwsx8yeNbNdZrbPzD4TdExAsjjc1KWjzd0JP4CUEXQAAE5t+9FWhfoGE76hAZB8Sg426LLlMzUtK3FHqgPUJ+klzrmQmWVKetzM/uicezrowIBEV3KwQZK0efXcgCOZGGbSAI+VlDUoM90S9h4fAJJTdUu3Khu7tHlNYneCguKiQrFvM2NfLsCQgKRRcrBRK2bnacmsxC5oRJIGeOzRg43atHSmpmcz6Q3AHyWxgkbM8p89M0s3s52SGiQ96Jx7JuCQgITXOxDW04ea9eIkaJvo+QGeqm3v0YG6Tn3iZWuDDgUATvDowQYtnjlNK2bnBR1KwnLOhSVdaGZFkn5jZuc55/aOPMbMtkjaIknFxcUqKSkZ07lDodCYj4V/kv39m8zr2904qL7BiGb21qqkpHFSnuNM4nV9JGmApx49GG1crl3LciIA/ugbDOvJyma99uJFFDSKA+dcm5k9Iumlkvae9NhWSVsladOmTW7z5s1jOmdJSYnGeiz8k+zv32ReX8nv9ykns0q3v+rawCo7xuv6WO4IeOqh0gYtLJpG6X0AXnmqslnd/WFduzbxlxMFxczmxGbQZGbTJN0g6UCgQQEJzjmnh0rrE770/hCSNMBDvQNhPV7RqOvXUXofgF8eLm3QtMx0XXkOBY0mYL6kR8xst6TnFN2Tdk/AMQEJraw+pGOtPbp+XXHQocQFyx0BDz1R0aTegYiuS5KGBkBycM7p4dJ6XbUqOUaqg+Kc2y3poqDjAJLJQ6X1kqTr1iXHNhFm0gAPPVTaoLysdF22YmbQoQDAsP21Happ79UNDCAB8MzDpfU6f1Ghigtygg4lLkjSAM9EIk5/OlCva1bPUXYGI9UA/PFwaYPMKGgEwC9NoT7tqG7TdWuTZwCJJA3wzN6adtV39CXNmmoAyeOh0npdsKhIc/Kzgw4lZZjZzWa2tb29PehQAG/96UCDnEuepY4SSRrgnYdKG5TGSDUAz9R39Gr3sXbdcC4DSFPJOXe3c25LYWFh0KEA3nq4tF7zC3O0fkFB0KHEDUka4JkH99fr4iUzNDMvK+hQAGBYsm3KB5AcegfCeqy8SdclWUVskjTAI1XN3Sqt7dBLz5sXdCgAcIL799Vr2axcrSnODzoUABj257JGdfeH9dL184MOJa5I0gCP3L+vTpJ043qSNAD+aO8Z0JMVTbrxvHlJNVINIPHdv69ehdMyk64iNkka4JH79tVp/YICLZ6ZG3QoADDsTwfqNRhxeikDSAA8MhCO6KHSel23bq4y05MrrUmuqwESWENHr7YfbaUTBMA79+2t07yCHF2wqCjoUFIO1R2BU3vmUIvaewaSsu9EkgZ44v790U357EcD4JOe/rAeLWvUjeuLlZbGUsepRnVH4NTu21eraZnpumb1nKBDiTuSNMAT9++t04o5eVo5d3rQoQDAsEfLGtU7ENGNDCAB8Egk4nT/vnpdu3aOcjLTgw4n7kjSAA+0dPXrqUPNunE9m/IB+OWPe2s1IzdTly5Lrk35ABLb9qpWNXb2JW2xNZI0wAP37a1TOOL0ig3JVT4WQGLrHQjrof31eul585SRZJvyASS2e3bVKDsjTdetKw46lElBiwt44J7dNVo+O0/rFxQEHQoADHvkQIO6+sO66fwFQYcCAMPCEad799bp2jVzNT07I+hwJgVJGhCwxs4+PX2oWTedP5+ljgC8cs/uWs2enqXLlrPUMShUdwT+0jOHm9XY2aebLkjeFUgkaUDA/ri3VhEnRqoBeKWrb1APH6jXy86bz1LHAFHdEfhL9+yOVnV8ydq5QYcyaWh1gYDds6tWq+ZO15p5+UGHAgDDHj7QoN6BiG46P3lHqgEknsFwRPftrdN16+YqNys5lzpKJGlAoOrae/Xc0RZm0QB4555dNSouyNYlVHUE4JEnK5vV0tWf9H0nkjQgQHfvqpFz0s1JvKYaQOJp7x5QycFGvWLDAm5gDcArv915XPnZGdq8JvluYD0SSRoQoLt2HNcFi4u0Yg43sAbgjz/sqVV/OKLXXLww6FAAYFh3/6Du31unl2+Yn5Q3sB6JJA0IyIG6DpXWdug1F9EJAuCX3+w4plVzp3NbEA9Q3RF4wYP769XVH9arU2AAiSQNCMhvdhxXepqxKR+AV6pbuvXckVa96qKF3BbEA1R3BF7wmx3HtaAwR5emwF7ZCSdpZrbYzB4xs/1mts/M/i4egQHJLBJx+t2OGr149RzNmp4ddDhJi/YJGL/f7TwuSXrlhcm9KT9ItE3A+DV29umx8ia98qKFKbFXNh4zaYOS/t45d66kyyW938zOjcN5gaT19KFm1XX06tUsdZxstE/AODjndNeO47ps+UwtmpEbdDjJjLYJGKe7d9UoHHEps01kwkmac67WOfd87N+dkkolpcarB5ylXz1/TNOzM3T9uuKgQ0lqtE/A+OysbtOhxi4GkCYZbRMwfr9+/pjWLyjQquLUuK9sXPekmdkySRdJeiae5wWSSWfvgO7dU6ubL1igaVnJXZnIJ7RPwJndua1a0zLT9Qr2yk4Z2ibgzPYeb9e+mg69ftPioEOZMuaci8+JzKZLelTS55xzd43y+BZJWySpuLh4489//vMxnTcUCmn69OQtT871Jbazub6S6gH9cF+/PnV5jlYU+Z2kjef6rr322u3OuU2THNJZOV37dLZtk8T/34ksma9NOrvr6xt0+rtHurVpXobevSG59sr62j7Rd8Jokv39O5vr+/H+Pj16bFBf35yr6Vl+70eLW9/JOTfhL0mZku6X9JGxHL9x40Y3Vo888siYj01EXF9iO5vre+U3H3c3fLXERSKR+AcUZ+O5PknbXBzak3h/jad9Gk/bNN7XJxEl8/Ul87U5d3bX98tt1W7px+5xzxxqjn9AAfOxfaLvhFNJ9vdvvNfX0z/ozv/0/e5vf/r85AQUZ/HqO8WjuqNJ+p6kUufcVyd6PiCZldV3amd1m16/aTGlracA7RMwdnc+V63ls/N0ybIZQYeS9GibgLF7YH+92nsG9IYUWuooxWdP2osk3SrpJWa2M/b18jicF0g6dz5Xrcx0Y1P+1KF9AsbgcFOXnj3Sols2LWIAaWrQNgFj9Mtt1VpYNE1XnjMr6FCmVMZET+Cce1wSLTpwBr0DYf36+WO6fl0x90abIrRPwNj87NkqpaeZXnvxoqBDSQm0TcDYHG3u0mPlTfrQ9atS4t5oI8W1uiOAU7t3T61auwf01suXBh0KAAzrHQjrl9uq9VfnFqu4ICfocABg2E+fiQ4gvfGSJUGHMuVI0oApcsfTR7Vidl7KTdcD8BsDSH4zs5vNbGt7e3vQoQBTqncgrDu3VeuGdcWaV5h6A0gkacAU2FfTruer2vSWy5ey3wOAVxhA8ptz7m7n3JbCwsKgQwGm1B/3RgeQbr0iNQeQSNKAKXDH01XKyUzT69jvAcAjQwNIb75sCQNIALzy46dSewCJJA2YZB29A/rdzuO6+fwFKszNDDocABh2x9NHlZ2RptdtZAAJgD8YQCJJAybdL56tVnd/WG+/clnQoQDAsJauft31/HG9+qKFKsrNCjocABj2/cePKDcrXbek2L3RRiJJAybRYDiiHz55RJctn6nzFrKfAIA/fvZslfoGI3rnVcuDDgUAhjV09uruXTW6ZeMiFU5L3RVIJGnAJHpgf72Ot/XQCQLglf7BiH705BFdvWq2VhfnBx0OAAy74+kqDUQiuu1Fqd13IkkDJtH3Hj+sJTNzdf264qBDAYBh9+6pVUNnHwNICYAS/EglvQNh/eTpo7pu7Vwtn50XdDiBIkkDJsmOqlZtP9qq265cpvS01Nz0CsA/zjl97/HDOmdOnl68ak7Q4eAMKMGPVPLbHcfV3NXPAJJI0oBJ8z+PVqogJ0O3bKJqGgB/PF7RpD3H2/Xuq1cojQEkAJ4IR5y2/vmQ1i8o0BUrUrPs/kgkacAkqGjo1P376vX2K5cpPyd1N70C8M9/l1SquCBbr7l4YdChAMCw+/fV6VBTl/5m88qULbs/EkkaMAn+u+SQcjLTdBtl9wF4ZGd1m56sbNa7r1qh7Iz0oMMBAEnRZdjfLqnQ8tl5eul584IOxwskaUCcHW/r0e92HtcbL1miWdOzgw4HAIZ9+5EKFU7L1JsuWxJ0KAAw7PGKJu093qH3XLOCffwxJGlAnG19tFKSdPs1KwKOBABecLCuUw/sr9fbr1iq6dkZQYeDMaK6I1LBN/9UoeKCbL2aZdjDSNKAOKpt79HPnq3WLZsWaWHRtKDDAYBh33i4TNOzM6ialmCo7ohk92Rlk5453KL3vfgclmGPQJIGxNG3HqmQk9P7r10ZdCgAMGx/TYfu3VOnd75omYpys4IOBwAkRfeife3BMs0ryNEbL2UZ9kgkaUCcHGvt1i+eq9YbLlmsRTNygw4HAIZ9/aEy5edk6F1XsQwbgD8eK2/Sc0da9f6XrFROJrNoI5GkAXHyrUcqZDJm0QB4Ze/xdj2wv17vvmqFCnO5JQgAPzjn9NUHy7SwaJpezz1l/wJJGhAHFQ0h3bntmN582RLNL2QvGgB/fOm+AyrKzdQ7rloWdCgAMOz+ffXaWd2mv33JSvaijYIkDYiDL913QNMy0/WBlzCLBsAfj5Y16rHyJn3gJatUkMMsWiKiuiOS0UA4oi/fd0Ar507XLRuZRRsNSRowQc8ebtGD++v1vs3ncF80AN4IR5y+cG+plszM1a2XLw06HJwlqjsiGf38uWodaurSx1+6VhnppCOj4VUBJsA5p8/fW6rigmy980WUtQbgj9/sOK4DdZ36hxvXKCuDP/e+MbPvm1mDme0NOhZgKvUMOn3joTJdunymrls3N+hwvEWrDUzAU7Vh7axu09/fsEbTslhPDcAPPYNOX77vgC5YVKhXbJgfdDgY3Q8lvTToIICpdnflgJpC/frky9fJzIIOx1skacBZCvUN6s6D/bpgUaFex3pqAB75feWAGjr79Om/Xq+0NDpBPnLO/VlSS9BxAFOpsjGk+48M6HUbF+nCxUVBh+O1jKADABLVfz1crrY+px/QCQLgkYqGkB44MqBbNi7SRUtmBB0OAEiKbhH59O/3KStd+thL1wYdjvdI0oCzUNHQqe89flhXL8ygEwTAG845febuaCfoH+kEJTwz2yJpiyQVFxerpKRkTL8XCoXGfCz8k6zv3/b6QT1W3qfXLnfat/2poMOZNPF6/0jSgHGKRJw+cdce5WVn6JbVlLQG4I/f7azRY+VNesu6LM3Jp9psonPObZW0VZI2bdrkNm/ePKbfKykp0ViPhX+S8f3r6B3Qx776qNbOy9fLVoWT7vpGitf7x540YJx++myVnjvSqn9+xToVZLPMEYAfmkN9+szd+3TRkiJdt4QxWAD++PJ9B9TY2acvvvZ8ZbBFZExI0oBxqG3v0Rf/eEAvWjmLYiEAvPLZe/Yr1DeoL732fKVRMc17ZvYzSU9JWmNmx8zsXUHHBEyG54606I6nq/SOFy2nWMg4MNQGjJFz0WWOg5GIPv/qDZSNBeCNB/fX67c7a/TB61ZpdXG+akqDjghn4px7U9AxAJOtu39QH/vVbi0smqaP3LA66HASCjNpwBjd8fRRlRxs1Cdetk5LZ+UFHQ4ASJIaO/v08V/v1rnzC/S3164MOhwAGPa5P5TqcHOX/uOW85WXzdzQeJCkAWNQ2RjS5+4t1TWr5+htVywNOhwAkBSd4f/Yr3ers29QX3/jhcrK4M96sjGzm81sa3t7e9ChAOPypwP1+skzVXr3Vct15Tmzgw4n4dCaA2fQNxjWh36+UzmZ6fqP153PMkcA3rjj6aP604EGffyla7W6OD/ocDAJnHN3O+e2FBYWBh0KMGYNHb36x1/t1tp5+frojWuCDichMe8InMG/31OqPcfbtfXWjSouyAk6HACQJO0+1qbP3lOqzWvm6LYrlwUdDgBIkgbDEX3gZzsU6hvUT959ubIz0oMOKSGRpAGn8budx/Xjp49qyzUr9Ffr5wUdDgBIktq6+/W+O57XnPxsfe31FyqNktYAPPHVB8v0zOEWfeWWC7RmHjP8Z4skDTiF0toOfeKuPbpk2Qz9A1P1ADwRjjh96Bc71dDZqzvfc4Vm5GUFHRIASJLu31enb5dU6k2XLtZruVXRhJCkAaNo7OzTu3+0TQU5mfrmmy9WZjrbNwH44fP3lqrkYKM+9+rzdNGSGUGHAwCSpL3H2/Whn+/UBYuL9K83rw86nIRHzxM4Se9AWFt+vE3NXX367ts3sQ8NgDd+9myVvvf4Yd125TK95TIqzaYCqjsiETR09Or2/9umGbmZ+t+3bVROJvvQJookDRhhMBzRB3+2Qzuq2vS111+o8xZSTQuAHx7aX69//u1evXj1HP3zK9YFHQ6mCNUd4bv2ngG9/QfPqb1nQP/79k2am8/gdjyQpAExzjl98jd79MD+ev3rzefqZRvmBx0SAEiSnjnUrPf/9HmtX1Cgb73lYmWwBBuAB3r6w7r9R9tU0dCp/37rRq1fwGBCvLAnDVA0QfvsPaW6c9sxffC6VXrHi5YHHRIASJJ2Vrfp3T/apkUzpumH77hU07P50w0geL0DYf3NT7bruaMt+s83XqQXr54TdEhJhZYeKS8Scfr03fv0f08d1TtetEwfvn5V0CEBgCRp+9EWvf37z2lGXqZ+/K7LNJNKjgA80DsQ1u3/t02PlTfpC6/ZoJsvWBB0SEmHJA0pbTAc0b/8bq9+9my1br96uT758nUy435DAIL3ZGWTbv/RNs0tyNFPb79M8wunBR0SAKizd0Dv+fF2PXWoWV9+7fl6/SWLgw4pKZGkIWV19w/qAz/doYcPNOhvr12pv/+r1SRoALzwu53H9dFf7tKyWXm6492XUWUWgBfq2nv1jh8+p7L6Tn319Rfo1RdxL7TJQpKGlFTX3qv3/Hib9hxv12dfuV63XrEs6JAAQJGI07ceqdBXHizTZctnauutm1SYmxl0WAiQmd0s6eaVK1cGHQpS3L6adt3+o21q7xnQ92+7hD1ok4wkDSnnqcpmfeBnz6u7P6zv3LpJN5xbHHRIAKCO3gH9/Z279OD+er3qwgX60uvOV3YG9xpKdc65uyXdvWnTptuDjgWp61fbj+mffrNHM/OydOd7r6CK4xQgSUPKGAxH9N8llfr6w+VaNitXP99yuVbOzQ86LADQjqpWffgXO3WstUf/evO5uu3KZSy/BhC4UN+g/u3ufbpz2zFdsWKW/uvNF2n29Oygw0oJJGlICYcaQ/rInbu0s7pNr7xwgT736g2UsQYQuP7BiL75p3J9q6RS8wpy9LMtl+uSZTODDgsA9OzhFv39L3fqeGuP3n/tOfrw9au5R+MUopeKpNY7ENa3H6nQ/zx6SNOy0vVfb7qIMrEAvPBERZP+5Xd7daixS6+9eJH+9a/PVUEO+88ABKs51Kcv/vGAfrn9mJbMzNWd77lCmxg8mnIkaUhK4YjT73Ye11ceKNPxth696sIF+uQr1mluPhXSAASroqFT/3H/Qd2/r15LZ+XqB++4RNeumRt0WABSXO9AWD968oi+XVKprr5BvefFK/TBl6xSHiuPAsGrjqQyEI7ont01+u+SSpXVh3TewgL9xy3n68pzZgcdGoAUd7CuU9/5c6V+u+O4crMy9JEbVmvLNSuUk0lxEJwa1R0x2UJ9g/rFc9Xa+udK1Xf0afOaOfrky9dpdTH79oNEkoakUN/Rq19tP6afPH1UNe29WjV3ur755ov08vPmKy2NzfcAgjEQjujh0gb99Nkq/bmsUdMy0/XOFy3X31y7UjPzsoIODwmA6o6YLBUNnfrFc9X6xXPV6ugd1KXLZ+o/33iRLlsxK+jQoDglaWb2UknfkJQu6bvOuS/G47zA6bR09evB/XX6w546PV7eqIiTrjxnlv791edp8+q5JGeQRPuEqTcYjujZIy26b2+d7t1Tq6ZQv4oLsvXRv1qtt16+VEW5JGegbUIwqlu6df++Ov1hT612VLUpI8104/p5evfVy3XRkhlBh4cRJpykmVm6pG9JukHSMUnPmdnvnXP7J3puYKTmUJ92Vrdp29FWPV7epL017XJOWjxzmt774nP0+k2LtWx2XtBhwiO0T5gK/YMRldZ2aEdVq56sbNZTlc3q7BtUTmaarl0zV7dsWqRrVs2hKhqG0TZhKjjnVNveq+erWvXc4RY9VtGkQ41dkqRz5xfo4y9bq9devEhz8imp76N4zKRdKqnCOXdIkszs55JeKYmGBuPSNxhWe/eAGjr7VN/Rq2OtPapq6VZlY0gH6zpV294rScpIM120pEgfum61rls3V+sXFHA/IZwK7RMmLBJx6uwdVGOoTw2dvapt61VVS7eONHeprD6kyoaQ+sMRSdLComm66YL5umbVHL14zRzlZrGrAKOibcKEOefUOxBRS3e/Gjp6Vd/Rp2Ot3apq6VZ5fUhl9Z1q7uqXJOVkpumy5bP05kuX6IZzi7V0FoPavovHX4+FkqpHfH9M0mVxOC8C5pxTU6hfx1q71dDZp8bOPrX3DKi9Z0CdvYPq6R9U70BEA+GI+sMRRZxTJCJFnBs+R1tbj7598CnJSU5O4YhT2EWXAw2EI+odiKi7P6xQ34B6ByJ/EUNOZpqWzcrTZctnau38Al20uEjnLyrStCw22mNMaJ+SVE9/WNWt3apr71VDZ59au/pjbdOAuvrD6ukPq28w2s6EIy7aPjmnoebp5LYp4qJtVzji1D8YUf9gtG3q7h9UZ9+gRjRrkiQzaX5BjlbPy9c1q2br/EVFumhJkRYUTZv6FwOJiLYpSUUiTnUdvapp61FDZ5+aQn1q6472nbr6BtXdH1bfYFgDYaeBWN8pHHEntDEj26ehtivspIFYm9YzEG3jOnsHhweIRsrPztA5c6fr+nXFOndBgS5aUqR18wuUyWx+QpmyIT4z2yJpiyQVFxerpKRkTL8XCoXGfGwi8uX6+sNORzsiqmyLqLozomOhiGq7IuoP/+WxWWlSToYpO13KSo/ObKWblBb7GjmnFQ6H1d7WJov9PPq4KSNNykmTZmVL2XmmnPQ05Wama3qmqTA7+jV7mqkwy2QWkdQuuXb1VFXrmaopelHGwJf3b7Ik+/VJZ982Scn/+vhwfc45NfY4VbRFdKQ9rOOhiI6HnNr63F8ca5JyM6XsdFNWupSZFm1rhtqnoWOkv2ybzKQ0mdJMyk2TCjKk7OxoO5ebmam8TFN+lqko2zQj2zQ715SZZpK6o18t9Sprkcqm5mU5Ix/eO0wcfSe/dQ84HW6PqLI9rGOxvlNDl9PgXzZPykl/oe+UmXZi38lG6Tt1tLdJGtF3MlNWrH2aM82UM13KyUgf7jsVZUe/Zk9LU16mZDYgqUXqb1FLhfRExRS8IGOU7P9/xuv64pGkHZe0eMT3i2I/O4FzbqukrZK0adMmt3nz5jGdvKSkRGM9NhEFdX3OOZXVh/RQab0eK2/U80fbhkdjiguytbq4SC+ZO11LZ+Zq0YxcFRfkaE5+topyM8dVLpr3L7ElwfWdsX0627ZJSorX57SCur72ngE9cqBBfy5r1OMVTWro7JMUnVlfXZyv65bma/nsXC2emat5BTkqLsjRzOlZys/OGPPSZ947BIy+UwKKRJx2VLfq4dIGPV7RpD3H24dnwJbMzNW5i/N185w8LZmVq4VF0zQ3/4W+03hmsZL9/eP6xiYeSdpzklaZ2XJFG5g3SnpzHM6LSXCstVu/3HZMd++uGd48um5+gW570TJtWjpDFy4p4obPSCa0TwmibzCsB/bV667nj+nxiiYNhJ1m5mXpRStn6/IVM3XxkhlaXZyvdKq2IjnQNiWQ0toO3bmtWvfuqVV9R5/S00wXLS7SB1+ySpuWzdD5i4pUOC0z6DCRZCacpDnnBs3sbyXdr2gZ2e875/ZNODLEjXNOT1Y2638fO6RHyxolSZcvn6V3vGi5bjy3WHMLSMqQnGif/Fff0asfPHFEv3iuSq3dA1pQmKN3vGi5XnrePF24qIhbaSAp0Tb5bzAc0b176/S9xw9rV3WbstLTdO3aOXr5hvm6du1cFeSQlGFyxWVPmnPuXkn3xuNciB/nnP50oEFffbBM+2o6NHt6tj5w7UrdsmmxFs/MDTo8YErQPvmppq1H//Wncv1q+zGFI05/de48vfmyJXrRytnMliEl0Db5aTAc0S+3H9O3HqnQsdYerZidp0/ddK5efdFCzeAG9JhC1AZOUjuqWvW5P5Rq29FWLZuVqy+9doNeeeHCce0nA4B46+gd0H89XK4fPXVUctIbL1mi269eoSWzGDgCEBznnB7YX68v/fGADjV16cLFRfrUTefq+nXFzOgjECRpSaatu19fuu+gfv5cleZMz9bnX71Bt2xaRNlVAIFyzun3u2r0738oVVOoT6+9eJE+dP0qLZpBcgacjpndLOnmlStXBh1K0qpq7ta//n6vHjnYqFVzp+t/37ZJ16+byz1YESiStCTypwP1+sdf7VFrd7/efdVy/d31qzU9m7cYQLAaO/v0ibt266HSBp2/qFDfe/smnb+oKOiwgITgnLtb0t2bNm26PehYko1zTnc8fVSfu7dU6Wb651es021XLlMGA9vwAD34JNA7ENZn7t6vnz1bpbXz8vV/77xU5y4oCDosANDDpfX6h1/tVqhvUP9y07m67cpl7DkDELimUJ8+cucu/bmsUdesnqMvv/Z8zSukkBr8QZKW4KpbuvXeO7ZrX02H3vPiFfrIDauVncG+MwDBCkecvvZgmb75SIXWLyjQ199woVYV5wcdFgDo+apW/c0dz6u1u1+ffeV6vfXypSxthHdI0hLYk5VNet8dz8s5p++9fZOuW1ccdEgAoM7eAf3tT3fo0bJGvWHTYn3mlespWgTAC3duq9Y//WaP5hXm6K6/uVLrFxQGHRIwKpK0BPXbHcf1D7/apWWz8vTdt2/S0ll5QYcEAKpr79U7fvicyus79flXb9CbL1sSdEgAIOeis/v/+acKXb1qtv7rTRepKJeS+vAXSVoC+t8/H9Ln7i3V5Stm6jtv3aTCXG6oCCB4hxpDeut3n1F7z4C+f9slumb1nKBDAhIe1R0nLhxx+sRdu3XntmO6ZeMiff41G6h6De/xf2iC+eafyvW5e0v1ig3z9aN3XkqCBsAL5fWdesPWp9U3GNEv3nMFCRoQJ865u51zWwoLWZZ3NgbDEX3kzp26c9sxfeAlK/Xl151PgoaEwExaAvnGQ+X62kNlevVFC/UfrzufErEAvFBW36k3bX1aaWmmn2+5nAIhALwQjjh96Bc7dc/uWv3DjWv0/muZjUTiIElLEN9//LC+9lCZXnvxIn35dedTwhqAF6pbunXr955ReixBWzFnetAhAYCcc/rkXXt0z+5afeJla/WeF58TdEjAuJCkJYBfbz+mf7tnv25cX6wvvXYDCRoALzR29unW7z2j3oGI7nzPFSRoALzxxfsO6BfbqvWBl6wkQUNCYr2c556oaNI//nq3rjxnlr7xxotY4gjACz39Yb3rR8+pvqNP37/tEq2ZxxJHAH740ZNH9J1HD+mtly/RR25YHXQ4wFlhJs1jlY0hve+O7Vo5Z7q+c+tG7jMEwAuRiNPf/3Kn9hxv1//eukkbl84IOiQAkCT9uaxRn7l7n65fV6zP/PV53KQaCYtpGU+1dw/o3T/apsz0NH337ZuUn0MVRwB++PrD5bp3T50+8bK1uv7c4qDDAZKamd1sZlvb29uDDsV7FQ2dev9Pntfq4nx9440Xsj0ECY0kzUORiNNH7typY63d+p9bN2rxzNygQwIASdJD++v1nw+X65aNi3T71SuCDgdIepTgH5uuvkG9947nlZ2Zpu/ddonyslkshsRGkuah//lzpR4+0KB/fsW5umTZzKDDAQBJ0UqOH7lzp9YvKNBnX8UyIgB+cM7pE3ft0aHGkP7zTRdpYdG0oEMCJowkzTNPH2rW/7v/oG46f77edsXSoMMBAElS32BYf/OT5+Uk/fdb2CMLwB8/eaZKv99Vo4/csFpXnjM76HCAuGAu2CPt3QP68C92aumsPH3xteczSg3AG199oEx7jrfrO7du1JJZLMEG4Ify+k599p79evHqOfqbzdysGsmDJM0j//K7vWrs7NOv33elprOWGoAnnj7UrK2PHdKbLl2iG9fPCzocAJAk9Q9G9KFf7FRedob+45bzlUahECQRljt64nc7j+v3u2r0wetW6YLFRUGHAwCSpI7eAf39nbu0dGau/vkV64IOB0g5VHc8ta89VKZ9NR36wms2aG5+TtDhAHFFkuaBxs4+fep3+3TRkiL9zeZzgg4HAIZ97p5S1XX06mtvuJBqaUAAqO44up3VbfrOo5V6/aZFzPAjKZGkeeDTd+9TT39Y//G6C5SRzlsCwA/7m8P6xbZqvfvq5bpoCTesBuLBzNaa2VNm1mdmHw06nkQ0EI7o47/erTn52frnm84NOhxgUjAsGrAdDYP6w+5affSvVmvl3OlBhwMAkqTegbB+uK9PS2fl6sPXrw46HCCZtEj6oKRXBRxHwvrOo5U6UNep/33bJhXkZAYdDjApmLYJUKhvUP+3r19r5+VryzUscwTgj288XK6GbqcvvGYD5faBOHLONTjnnpM0EHQsiehQY0j/+XCFXrFhvm44tzjocIBJw0xagP7r4XK19jl97zUblJVBvgzADxUNIX33sUO6amEG9xwC4A3nnD59935lZ6TpX/+aZY5IbiRpAaloCOl7jx/W1QszdDF7PQB4wjmnz9y9TzmZ6bpldVbQ4QApzcy2SNoiScXFxSopKRnT74VCoTEfm0ierx/Un8v69Ka1Wdq//WntDzqgSZKs798Qrm9sSNICMNQJmpZFJwiAXx7YX6/Hypv0qZvOVeHg0aDDAZKCmb1f0u2xb1/unKsZy+8557ZK2ipJmzZtcps3bx7T85WUlGisxyaK3oGw/uVrj2p18XT9261XKzOJC60l4/s3Etc3Nsn7f7jHHipt0GPlTfrw9atVkM2NFwH4oW8wrH//w36tLp6uW69YGnQ4QNJwzn3LOXdh7GtMCRpO9N3HDqm6pUef/uv1SZ2gAUP4v3yKDYYj+sIfS7ViTh6dIABe+fFTR1Xd0qN/uelcOkHAJDGzeWZ2TNJHJP2zmR0zs4Kg4/JZU6hP/11SqRvXF7NPFimD5Y5T7BfbqnWosUtbb91IJwiAN9q7B/Rff6rQNavn6OpVc4IOB0hazrk6SYuCjiOR/OfD5eodjOhjL10bdCjAlCFLmEKhvkF97cFyXbJsBmVjAXjl2yUV6ugd0CdeRicIgD8ONYb002eq9OZLl2jFHO4ni9RBkjaFvvvYITWF+vTJl6+TGXvRAPihpq1HP3jyiF578SKtm8+qK8AnZnazmW1tb28POpRA/Mf9B5WdkaYPXrcq6FCAKUWSNkXauvv1vccO68b1xbqIkvsAPPLNRyokJ334htVBhwLgJM65u51zWwoLC4MOZcrtPd6uP+6t07uuXqE5+dlBhwNMKZK0KfLdxw6rs2+QThAAr1S3dOvO56r1xksXa2HRtKDDAYBhX3+oXAU5GXrXVcuDDgWYciRpU6Clq18/eOKwXnH+fK2dx1IiAP745p8qlJZm+pvNK4MOBQCG7T7WpodK63X71StUOC0z6HCAKUeSNgW2/vmQugfC+hDrqQF45Ghzl371/DG95bIlmleYE3Q4ADDsaw+WqSg3U7e9aFnQoQCBIEmbZG3d/fq/p47o5vMXaFVxftDhAMCwbz1SoYw00/tefE7QoQDAsD3H2vXIwUbdfvUK5ecwi4bURJI2yX705FF194f1/mtZSgTAH7XtPfrNjuN6wyWLNbeAWTTAV6lY3fG/H61Qfk6G3nbF0qBDAQJDkjaJuvsH9cMnD+v6dXO1Zh6zaAD88d3HDivipNuvXhF0KABOI9WqO1Y2hvTHvXV62xVLmUVDSiNJm0Q/e7Zard0Deh8b8gF4pLWrXz97tkp/fcECLZ6ZG3Q4ADDsO49WKis9Te94ERUdkdpI0iZJ/2BE333skC5dPlMbl3JfNAD++OGTR9TdH9Z72YsGwCMjl2HPns590ZDaSNImyR/21Ki2vVfv20wnCIA/egfC+vHTR3XdWpZhA/DLD588onDEsQwbEEnapHDO6XuPH9bKudO1efWcoMMBgGG/23lcLV39etfVLCUC4I+uvkH97Jkqvey8+SzDBkSSNimeO9Kqvcc79I4XLZOZBR0OAEh6YQBp3fwCXbFiVtDhABiDVKnueNfzx9TRO6h3XsUAEiCRpE2K7z1+SEW5mXrNRYuCDgUAhj1R0ayy+pDeyQASkDBSobpjJOL0/SeO6ILFRbp4SVHQ4QBeIEmLs6rmbj2wv15vvnSJpmWlBx0OAAz73uOHNHt6tv76wgVBhwIAwx452KDDTV1611XLGUACYkjS4uzHTx9RmpnedsWyoEMBgGFHmrr0yMFGvfXyJcrOYAAJgD9++OQRzSvI0cvOmxd0KIA3SNLiqHcgrF9uP6a/OrdY8wpzgg4HAIb99NkqpaeZ3nTpkqBDAYBhR5q69Fh5k9506RJlptMtBYbwaYijP+yuVVv3gG69fGnQoQDAsN6BsO7cVq0b1xeruIABJAD++OmzVcpIM73x0sVBhwJ4hSQtju545qhWzMnTFedQNQ2AP4YGkN56GQNIAPzROxDWL7dV668YQAL+AklanOyradeOqja95bKlbHoF4BUGkIDElcwl+O/dU6tWBpCAUZGkxclPnqlSTmaaXncxZfcB+GN/TQcDSEACS+YS/Hc8zQAScCoTStLM7D/M7ICZ7Taz35hZUZziSijd/YP6/c4avXzDfBXmZgYdDgDRPg35xXNVyspI02svXhh0KAAkmdlbYu3SHjN70swuCDqmIJTXd+r5qja96ZIlDCABo5joTNqDks5zzp0vqUzSJyYeUuL54546hfoG9YZNbHoFPJLy7VPvQFi/3VmjG9fPU1FuVtDhAIg6LOnFzrkNkj4raWvA8QTizm3VykgzvZoBJGBUE0rSnHMPOOcGY98+LSkl1/r9Ylu1ls/O06XLZwYdCoAY2ifpgf31au8ZYAAJ8Ihz7knnXGvs25RsmwbCEd31/HFdv65Ys6dnBx0O4KV47kl7p6Q/xvF8CeFwU5eePdyiWzYtYroe8FdKtk+/3FathUXTdCX7PQBfvUsp2DY9XNqg5q5+vf6SlMtPgTEz59zpDzB7SNJot4D/J+fc72LH/JOkTZJe405xQjPbImmLJBUXF2/8+c9/PqYAQ6GQpk+fPqZjg/Crsn794dCAvrp5mmbkjD/n9f36JorrS2zjub5rr712u3Nu0ySHdIJ4tE9n2zZJfr//TT0R/cOjPXrlyky9auXZLXX0+fomKpmvTUr+6xuvINqnMzGzayV9W9JVzrnmUxyTlH2nr23v1dGOiL7y4mlKT2OA+2S+v38TxfW94LRtk3NuQl+SbpP0lKTcsf7Oxo0b3Vg98sgjYz52qg2GI+7Szz3o3vmDZ8/6HD5fXzxwfYltPNcnaZubYHsS76/xtk/jaZvG+/pMta8/WOaWffweV93Sddbn8Pn6JiqZr8255L++8Qq6fZL0fkk7Y18LJJ0vqVLS6rGeI1n6TvXtPW75x+9xX/pjadCheMvn9y8euL4XnK5tmmh1x5dK+kdJf+2c657IuRLRU5XNqu/o02s3Ml0P+CaV2yfnnH6z45guXz5Li2bkBh0OkPKcc99yzl3onLtQUoakuyTd6pwrCzayqff7XTWKONF3As5gonvSvikpX9KDZrbTzP4nDjEljLt2HFN+ToZesnZu0KEA+Esp2z7tqG7TkeZuqqYBfvqUpFmSvh1rm7YFHdBUuuv547pgUaHOmZO8y92AeMiYyC8751bGK5BE090/qPv31unmCxYoJzM96HAAnCSV26ff7jiu7Iw0vey80bbrAQiSc+7dkt4ddBxBOFjXqf21HfrXm88NOhTAe/Gs7phSHtxfr67+sF51ESPVAPwxEI7o7l01uuHcYuXnZAYdDgAM++3O40pPM918wYKgQwG8R5J2ln6z47gWFk3Tpcu4NxoAfzx6sFGt3QN6NQNIQFIws5vNbGt7e3vQoUxIJOL0ux3Hdc2q2dwbDRgDkrSz0NjZp8fKm/TKCxcojdKxADzym53HNSsvS9esnhN0KADiwDl3t3NuS2FhYdChTMgzh1tU097LCiRgjEjSzsIf99YqHHF65YU0NAD80dU3qIdL6/XyDfOVmU7zDsAfd++uUW5Wum44tzjoUICEwF/xs3DPrlqtLp6uNfPygw4FAIY9VFqv3oGIbjp/ftChAMCwwXBE9+2t03XripWbNaGadUDKIEkbp7r2Xj13tEU3nc+mVwB+uWd3rYoLsnUJe2UBeOTJyma1dPUzgASMA0naOP1hT62cEw0NAK909A7o0YONevmG+eyVBeCVe3bXKD87Qy9mrywwZiRp43TP7hqdO79AK7gJIwCPPLivXv3hCLP8QJJJ9OqO/YPRpY43nFvMfWWBcSBJG4djrd3aUdWmmy5gFg2AX+7ZXaOFRdN08ZKioEMBEEeJXt3x8YpGdfQO0ncCxokkbRz+uKdOknTTBkaqAfijvWdAj1c06RXnz5cZSx0B+OMPu+tUkJOhq1ay1BEYD5K0cbhvX53OW1igJbNygw4FAIb96UC9BsJOLztvXtChAMCwgXBED5XW64Zz5ykrgy4nMB58YsaooaNX24+26sZz6QQB8Mt9e+s0ryBHFywqCjoUABj2zKEWtfcM6Mb13BsNGC+StDG6f3+9JOmljFQD8Eh3/6AeLWvUjeuLqeoIwCv37avVtMx0XUNVR2DcSNLG6P69dVoxJ08r51LVEYA//lzWqN6BiG5kAAlISola3TEScbp/X72uXTuHqo7AWSBJG4O27n49dahZL10/j035ALxy3946zcjN1KXcwBpISola3XFHdasaO/t043oGkICzQZI2Bg+VNigccSx1BOCV/sGIHi5t0PXripWRTnMOwB/37a1TVnqaXrJ2btChAAmJv+pjcP++Oi0ozNGGhYk1igUguT11qFmdfYOMVAPwinPRpY5Xrpyl/JzMoMMBEhJJ2hn0DoT1WHmjrj+3mKWOALzy0P56TctM11WrZgcdCgAMK28IqaqlWzecS1VH4GyRpJ3BExVN6h2I6Lp1NDQA/OGc08Ol9bpq1Ww25QPwyoOxitjXraXvBJwtkrQzeKi0QXlZ6bp8BZvyAfhjf22Hatp7dQMDSEBSS8Tqjg+X1mvDwkLNK8wJOhQgYZGknUYk4vSnA/W6ZvUcZWcwUg3AHw+XNshMupZN+UBSS7Tqjk2hPu2obtN162ibgIkgSTuNvTXtqu/oY6kjAO88XFqvCxYVaU5+dtChAMCwPx1okHPS9fSdgAkhSTuNh0oblGbStWvmBB0KAAxr6OjVrmPtbMoHEpCZvdLMdpvZTjPbZmZXBR1TPD1cWq95BTlav6Ag6FCAhJYRdAA+e7i0XhcvmaFZ0xmpBuCPPx1okCSWEwGJ6WFJv3fOOTM7X9KdktYGHFNcRCtiN+nVFy2kIjYwQcyknUJ9R6/21XToJXSCAHjmTwcatLBomtYU5wcdCoBxcs6FnHMu9m2eJHe64xPJs4db1N0fZgAJiAOStFN49GCjJOnaNTQ0APzRPxjRExVN2rxmDiPVQIIys1eb2QFJf5D0zqDjiZeSg43KykjTFSu4dyMwUSx3PIWSsgbNK8jR2nmMVAPwx7YjLerqD2szA0hAwnLO/UbSb8zsGkmflXT9aMeZ2RZJWySpuLhYJSUlYzp/KBQa87HxdO/Obq0uStMzTz425c+dTIJ6/6YK1zc2JGmjGAxH9Fh5k15+3nxGqgF4paSsUVnpabrynFlBhwJgjMzs/ZJuj337cudcjSQ55/5sZivMbLZzrunk33PObZW0VZI2bdrkNm/ePKbnKykp0ViPjZfqlm7V3feItly7RpuvWj6lz51sgnj/phLXNzYsdxzF81Vt6uwd1GaqOgLwTMnBBl2yfIbyshljAxKFc+5bzrkLnXMXSsq12AiwmV0sKVtSc5DxxUPJwWhBI/pOQHzwV34UJQcblJFmetEq1lQD8EdNW4/K6kO6ZePioEMBcPZeK+ltZjYgqUfSG0YUEklYJQcbtWRmrpbPzgs6FCApkKSNouRgoy5eOkMFOZlBhwIAw0piBY0YqQYSl3PuS5K+FHQc8dQ7ENaTlc26ZdMitokAccJyx5M0dPRqf20HnSAA3ik5GC29v3Lu9KBDAYBhzx1pUc9AmL4TEEckaSf5c3l03+6LV9PQAPDHQDiiJyubdc1qSu8DqcTMbjazre3t7UGHckp/jhU0unwFBY2AeCFJO8nj5Y2alZeldfMKgg4FAIbtqm5TqG9QV7NXFkgpzrm7nXNbCgsLgw7llB4rb9LGpTOUm8UuGiBeSNJGcM7p8YpmXbVqttLSGKkG4I/HypuUZqL0PgCvNHT26kBdp65ezQASEE8kaSMcqOtUU6hPV62koQHgl8crmrRhUZGKcrOCDgUAhj1REd0mcvVKtokA8USSNsLjsf1oV6+ioQHgj47eAe2sbtPVDCAB8Mxj5U2akZup9QvYJgLEE0naCI9VNGnl3OmaV5gTdCgAMOzpymaFI05XsR8NgEecc3q8vElXrmSbCBBvJGkxvQNhPXOomaWOALzzWHmTcrPSdfGSGUGHAmCK+VzdsbwhpIbOPmb5gUlAkhaz/Wir+gYjVE4D4J3HK5p02fKZysqgyQZSjc/VHf9c1ihJzPIDk4C/+DGPVzQpI810Gff4AOCR4209OtzUpavYKwvAM09UNGn57DwtmpEbdChA0iFJi3mqslkXLi7S9Gzu8QHAH09VNkuSXrSSASQA/hgIR/Ts4RZuCwJMEpI0RSun7T7WpitoaAB45snKJs3My9LquflBhwIAw/Ycb1dXf5i+EzBJSNIkPXe4RREnGhoAXnHO6enKZl2xYhaV0wB4ZWiW/3K2iQCTgiRN0YYmKyONymkAvHK0uVs17b26nAEkIGX5Wt3xqcpmrSnO1+zp2UGHAiQlkjRJT1Y2a+OSGcrJTA86FAAY9tSh6Eg1ez6A1OVjdce+wbC2HW1hBRIwiVI+SWvt6ldpXQcNDQDvPFnZrLn52VoxOy/oUABg2M6qNvUOROg7AZMo5ZO0Zw43yzlGqgH4xTmnpyqbdeU5s2TGfjQA/njqULPMpMuX03cCJkvKJ2lPVjYrNytd5y8qCjoUABhW0RBSU6hPV57DTWIB+OXJymadt6BQhbmZQYcCJK2UT9KePtSsjUtnKCsj5V8KAB55+hCV0wD4p3cgrJ1Vbbp8xcygQwGSWkpnJi1d/SqrD9EJAuCdZw63aH5hjhbPnBZ0KAAC5Ft1x53VbeoPR3QZSx2BSZXSSdpzR1okSZctZzQIgD+cc3rmcIsuWz6T/WhAivOtuuOzh1tkJl2yjL4TMJlSOkl75lCLsjPStGGRHw0fAEjSkeZuNXb26VJGqgF45pnDzVo7r4D9aMAki0uSZmZ/b2bOzBJqh/uzR5p10ZIiZWdwfzQgWSVi+/Ts4eh+tEuZ5QeSlpldYmaDZva6oGMZq/7BiLYfbWUFEjAFJpykmdliSX8lqWri4Uydjt4B7a/pYKQaSGKJ2j49c6hFs6dn6Zw53B8NSEZmli7pS5IeCDqW8dhb067egQgDSMAUiMdM2tck/aMkF4dzTZntR1sVcdLlNDRAMkvI9umZwy26lP1oQDL7gKRfS2oIOpDxePZwdC8/SRow+SaUpJnZKyUdd87tilM8U+bZwy3KSDNdtGRG0KEAmASJ2j4da+3W8bYeXcqmfCApmdlCSa+W9N9BxzJezx5u0Tlz8jR7enbQoQBJL+NMB5jZQ5LmjfLQP0n6pKJLic7IzLZI2iJJxcXFKikpGVOAoVBozMeOx4M7e7SswPTMk4/F/dzjMVnX5wuuL7H5fn3xaJ/Otm2SJuf1eeL4gCQprfmQSkqOxvXc4+X7+z8RyXxtUvJfX4L7uqSPOeciZ5ot96nvFHFOT1V067J5Gfy/NcmS/fPL9Y2Rc+6sviRtUHSa/kjsa1DRfR/zzvS7GzdudGP1yCOPjPnYseruG3QrP/kH9/l798f93OM1GdfnE64vsY3n+iRtc2fZnsT762zbp/G0TeN9fcbqY7/a5Tb8631uMByJ+7nHK5n//07ma3Mu+a9vvIJunyS9X9LO2NfhEW1TKNZWvepM5wi677TnWJtb+rF73F3PV8f93DhRsn9+ub4XnK5tOuNM2mmSuz2S5g59b2ZHJG1yzjWd7Tmnyq5jbRoIO5YTAUkqkdunbUdbtXHpDKWnsR8NSBbOuW9J+tbJPzezH0q6xzn326mOaby2H22VxP3RgKmSkvdJG2poNi5lPxoAf7R196uiIaRNdIIAeGbb0VbNK8jRwqJpQYcCpISznkk7mXNuWbzONdm2HWnRyrnTVZSbFXQoAKZAorRPDCABqcU5d1vQMYzV9iMt2rhsBlVngSmScjNpkYjT9qOtumQZnSAAfnnuSKsy0kwXLCoKOhQAGHa8rUc17b26hAEkYMqkXJJW3hBSR++gNi5lOREAv2w/2qL1Cws1LSs96FAAeMLMbjazre3t7YHFsO1I9P5oLMUGpk7KJWnbjsYaGkaDAHikbzCsXcfaGakGcALn3N3OuS2FhYWBxbD9aKtys9K1dl5+YDEAqSblkrTtR1o1e3qWls7KDToUABi293iH+gcj2sRSbACe2XakVRctKVJGesp1G4HApNynbdvRVm1aOpONrwC8sj02y89SbAA+CfUN6kBdhzbRNgFTKqWStIaOXlW1dDNSDcA72460aumsXM3Jzw46FAAYtqOqVREn+k7AFEupJO35KspbA/CPc07PV7XSNgHwzvajrUoz6cLFRUGHAqSUFEvS2pSVkab1C4LbfAsAJ6tu6VFTqF8XLyFJA3CioKs7Pl/VptXF+crPyQzk+YFUlVJJ2o6qVp23oEBZGSl12QA8t6M6Ost/0ZKiYAMB4J0gqztGIk47q1p1EQNIwJRLmWxlIBzR7mPtNDQAvLOjqk25WelaU0x5awD+ONTUpY7eQQaQgACkTJJWWtuhvsEIy4kAeOf5qladv6iQ8tYAvDK0l5++EzD1UqZHsKOqTRLLiQD4pXcgrP01HczyA/DOjqo2FeRkaMXsvKBDAVJOCiVprSouyNb8wpygQwGAYXuPt2sw4nQRldMAeGZHVasuXDJDaWncWxaYaimTpD1f1aaLFs/gJtYAvDK0nIiZNACjCaq6Y6hvUGX1nQwgAQFJiSStKdSnqpZuXby0KOhQAOAEO6ratHjmNG5iDWBUQVV33F3dpoiTLub+jUAgUiJJ2zm8H42GBoBfdsRm+QHAJzuq2yRJFy4qCjQOIFWlRJK2o7pVGWmm87iJNQCP1Lb3qK6jl4JGALyzo6pV58zJU2EuN7EGgpASSdqu6natnZ+vaVnpQYcCAMN2DY1Us+cDgEecc9pZ3a4LmeUHApP0SVok4rTrWJvOZ7oegGd2VrcrM920bn5B0KEAwLCa9l41hfp0wWJWIAFBSfok7Uhzlzp7B1lTDcA7u4+1ae28AuVkMssPYHRBVHccmuW/gL4TEJikT9J2HWuTJF3AciIAHolEnHYfa2ekGsBpBVHdcdexNmWlp2nt/Pwpe04AJ0r+JK26XblZ6Vo5d3rQoQDAsENNIYX6BhmpBlKEmf21mX086DjGYld1m9bNz1d2BrP8QFCSP0k71qbzFhYqPY2bWAPwx67q6NIlioYAqcE593vn3BeDjuNMwhGnPcfaWYEEBCypk7T+wYj21XTQCQLgnV3H2pSXla4Vc5jlBxKdmS0zswNm9kMzKzOzn5jZ9Wb2hJmVm9mlZnabmX0zdvwPzew/zexJMztkZq8L+hqGVDaG1NUfZpYfCFhSJ2kH6zrVPxjR+YvY8wHAL7uq27RhEbP8QBJZKekrktbGvt4s6SpJH5X0yVGOnx97/CZJ3sywDRcNYb8sEKiMoAOYTMNFQxgNAuCRvsGwSms79Y6rlgUdCoD4Oeyc2yNJZrZP0sPOOWdmeyQtG+X43zrnIpL2m1nxaCc0sy2StkhScXGxSkpKxhRIKBQa87En++O+PuWkS1X7tunYfgaRgjCR9y8RcH1jk9xJWnWbZuVladGMaUGHAgDDDtR2qj8c4dYgQHLpG/HvyIjvIxq9vzXy+FGzIefcVklbJWnTpk1u8+bNYwqkpKREYz32ZF/Z87guXpahl1x7+Vn9PiZuIu9fIuD6xiaplzvuPtauDYsKZcZIEAB/7I7N8m9gKTYAj/QNhnWgroO2CfBA0iZpPf1hlTd06vyFNDQA/LLneLtm5mVpYRGz/AD8cbCuUwNhp/MXFgUdCpDykna54/7adkWctIHlRAA8s/tYu85byCw/kCycc0cknTfi+9tO8dgPT3489r0XZV53H4veGoSCa0DwknYmbU+sodnATBoAj/QOhFXeEGKWH4B39h5vV+G0TPbyAx5I3iTteIdmT89WcUF20KEAwLDS2g6FI07nkaQB8Mye4+3awCw/4IWkTdL2Hm/XhoUFNDQAvLL3eGyWn+VEAMbAzG42s63t7e2T+jx9g2GV1XcygAR4IimTtKGiIexHA+CboaIhCwpzgg4FQAJwzt3tnNtSWDi5ydNw0RAGkAAvJGWSNlw0hNEgAJ6haAgAH+1mLz/glaRM0igaAsBHQ0VDNiwsCDoUADgBRUMAvyRnkkbREAAeGioasoF7EAHwDEVDAL8kZZK293i7zl9EQwPALxQNAeCjoaIhtE2AP5IuSRsqGkJ1IgC+oWgIgPGaiuqOQ0VD2CYC+CPpkrTSug5FnHTeAvZ8APDL3uMdWr+AW4MAGLupqO6493iHJOm8BSRpgC+SLknbXxNtaM4lSQPgkf7BiMobOrWeThAAz+yvbVd+dgZFQwCPJF2Stq+mQ4XTMrWwiIYGgD/KG6LLiRhAAuCbfTUdWregQGlpzPIDvki6JG1/TTvLiQB4Z19sln89SRoAj4QjTgdqO2mbAM8kVZI2GI7oQB0NDQD/7K/pUG5WupbPygs6FAAYdrgppJ6BMEuxAc8kVZJW2dilvsEIDQ0A7+yrade6+SwnAjA+k13dkVl+wE9JlaTtq4k2YDQ0AHwSiTjtr+mgbQIwbpNd3XFfTYeyMtK0cu70STk/gLOTZElah3Iy07RiDg0NAH8cbelWV3+YJA2Ad/bVtGtNcb4y05OqSwgkvKT6RO6radfaeQVKZzkRAI+8MMvPUmwA/nDOaR+z/ICXkiZJcy66nIjy1gB8s6+mQxlpplXFzPID8EdNe6/augfoOwEeSpok7Vhrjzp6BxkNAuCdfTUdWlWcr+yM9KBDAYBh+46zlx/wVdIkaS9UJ2I5EQC/UDQEwNmazOqO+2s7ZCatnUf7BPgmaZK00toOpZm0pjg/6FAAYFhjZ5+aQn1aN59OEIDxm8zqjqW1HVo2K0952RlxPzeAiZlwkmZmHzCzA2a2z8y+HI+gzkZpbYeWzc7TtCyWEwGI8qF9Kq2NzvKvm88AEgC/lNZ20jYBnprQ0ImZXSvplZIucM71mdnc+IQ1fgfqOrVhEUsdAUT50j4dqIsmaecykwbAI6G+QVW1dOuWjYuCDgXAKCY6k/Y+SV90zvVJknOuYeIhjV9n74CqWrrpBAEYyYv2qbS2U/MLc1SUmxXE0wPAqA7WDc3y03cCfDTRJG21pKvN7Bkze9TMLolHUON1sK5TkrR2HlP2AIZ50T6V1nbQNgHwzv7aaN9pHUWNAC+dcbmjmT0kad4oD/1T7PdnSrpc0iWS7jSzFc45N8p5tkjaIknFxcUqKSkZU4ChUOiMxz5cNSBJajuyTyX1pWM6ry/Gcn2JjOtLbL5fXzzap7Ntm6Qzvz4DEafy+m6tzO31+nU8Fd/f/4lI5muTkv/6UomZ3Szp5pUrV8b1vKW1HSrIydCCwpy4nhdAfJwxSXPOXX+qx8zsfZLuinV6njWziKTZkhpHOc9WSVsladOmTW7z5s1jCrCkpERnOvaB3+xR4bRaveal18rMxnReX4zl+hIZ15fYfL++eLRPZ9s2SWd+ffbXdCj8wGO68bLztPmCBWM+ry98f/8nIpmvTUr+60slzrm7Jd29adOm2+N53gO1HVo7vyDh+k1AqpjocsffSrpWksxstaQsSU0TPOe4DS0noqEBMMJvFXD7RGVHAD6KRJwO1HWylx/w2ESTtO9LWmFmeyX9XNLbR1vqOJkiEaeDdZ1sfAVwssDbp9LaDmVnpGnZrLypfFoAOK2qlm5194cZQAI8NqES/M65fklvjVMsZ+VorKFhNAjASD60T6V1HVozL18Z6RO+JSUAxM0Ls/z0nQBfJXzPgYYGgI+cc9Ebxc6jbQLgl9LaDqWZtLqYmTTAVwmfpB2INTSriqcHHQoADGvs7FNLV7/WspwIwASY2c1mtrW9vT1u5yyt69Ty2XnKyUyP2zkBxFfCJ2k0NAB8VDp8/0Zm0gCcPefc3c65LYWFhXE754G6aGVHAP5K+CStrL6TThAA75QNJ2nMpAHwR1ffoKpberSWpY6A1xI6SevuH1RVS7fW0AkC4JkDdZ2am5+tGXlZQYcCAMPK6qMDSPSdAL8ldJJWVh+Sc2x8BeCfg/UddIIAeOdgHUkakAgSOkk7WBet7MhyIgA+CUecyutDWsMAEgDPHKjrVG5WuhbPyA06FACnkeBJWkjTMtO1ZCYNDQB/HG3uUt9ghJFqAN4pq+/UquJ8paVZ0KEAOI3ETtLqO7S6eDoNDQCvsJwIQLzEuwT/wbpOreG2RYD3EjtJq+ukEwTAOwfqOmUmrZpL+wRgYuJZgr+xs0/NXf1aQ1VswHsJm6Q1hfrUFOqnaAgA7xys69SyWXmalsX9GwH44yC3BgESRsImaWXcKBaAp8rqO7Wa5UQAPHMwVn6fAW7AfwmbpB1gzwcAD/UOhHWkuYvlRAC8c7CuQ7PysjQnPzvoUACcQcImaQfrOmloAHinvD6kiGM5EQD/sJcfSByJm6TVdzJdD8A7LCcCEE/xqu4YiTiV1Ydom4AEkZBJmnNO5fWMBgHwT1l9p7LS07RsFvdvBDBx8arueKy1Rz0DYfpOQIJIyCTteFuPuvrDWsXGfACeKavv1Io5ecpIT8jmFUCSKhue5afvBCSChOxFlNeHJLGcCIB/yllOBMBD5Q3RvtNK7t8IJISETNKGRoNWzWU0CIA/Qn2DOt7Ww0g1AO+U13dqXkGOCqdlBh0KgDFIyCStvCGkOfnZKsrNCjoUABhWERupXsVMGgDPlDV0sk0ESCCJmaRxo1gAHiqjsiOAOItHdcdIxKmiIaRVLHUEEkbCJWmRiFM5DQ0AD5XXdyorI01LZlLZEUB8xKO6Y3Vrt3oHIgxwAwkk4ZK042096u4PM1INwDtl9SGdM2e60tMs6FAAYNhQwTWWYgOJI+GStPIGSsgC8BNLsQH4qCzWd2JPGpA4Ei5JKxsaDWK5IwCPdPYOqKa9l1l+AN4prw9pfmGOCnKo7AgkigRM0jo1Nz9bhbk0NAD8MXQPIm4NAsA3ZfWdWknbBCSUhEvSKhq4USwA/1TEZvlpnwDE00SrO4ZjlR1pm4DEklBJWiTiVF4fYk01AO+U1XcqOyNNi6nsCCCOJlrd8Vhrt/oGqewIJJqEStKOt/WoZyDMfjQA3ilvoLIjAP8MVXZcSd8JSCgJlaRVDO35YDQIgGcqGpjlB+CfcvpOQEJKyCRt5RwaGgD+6Oob1PG2HtomAN6paAhpbn42lR2BBJNwSdqsvCzNyMsKOhQAGHaosUuSqJ4GwDsVjSHaJiABJVaS1hjSOTQ0ADxT0Ri9USwdIQDxNpHqjs45VTaQpAGJKGGSNOeiJWS5BxEA31Q0hJSeZlo6Ky/oUAAkmYlUd6zv6FOob5C+E5CAEiZJawr1q71ngNEgAN6paAhp6axcZWUkTJMKIAUM7eVnFRKQeBKmRzFcNISGBoBnKhpCFA0B4J2KBpZiA4kqgZI0GhoA/ukfjOhIczdtEwDvlDeEVJCToTnTs4MOBcA4JVCSFtL07AzNK8gJOhQAGHa0uUvhiCNJA+CdiljREDMLOhQA45Q4SVpjSOfMyaOhAeAVlmID8FUl5feBhJU4SVoD5fcB+Gd4Yz570gBMgrMtwd/W3a+mUD9JGpCgEiJJ6+gdUH1Hn1bNzQ86FAA4QUVjSAuLpikvOyPoUAAkobMtwT80gETfCUhMCZGkVbKcCICnmOUH4COWYgOJLTGStMYuSdI5c7hRLAB/RJzTocYu2iYA3qlsDCk7I00Li6YFHQqAs5AgSVpImemmJTNzgw4FAIa19jr1DITZjwbAO5WNXVo+O09paRRcAxJRYiRpDSEtnZWnjPSECBdAiqjtikiiaAgA/1Q2shQbSGQJkfUcaurSitksJwLgl9ouJ4ml2AAmz9lUdxyIOFW3dOsc+k5AwvI+SQtHnI42dzEaBMA7dV0R5WdnaE5+dtChAEhSZ1PdsaHbKeJE3wlIYN4naY09TgNhx0waAO/UdkW0Yk6ezNjzAcAftaHoUuwVs0nSgETlfZJWN7Tng9EgAJ6p63LsRwPgnaG+0wqWYgMJy/skbXjPB6NBADzS1Teoll5HJwiAd2q7nOYV5CgvOyPoUACcpQRI0iKaPT1LhbmZQYcCAMMONw3dv5EBJAB+qe2K6Jy5DCABicz7JK2uK8KaagDeqWwMSZJWkKQBmETjre7onIvul6XvBCQ075O0oY35AOCTysYumaSls3KDDgVAEhtvdcemUL96BtmPBiS6CSVpZnahmT1tZjvNbJuZXRqvwCSprbtfnf0sJwIwfpPdPh1qDGn2NFNOZno8TwsAE3IoNstP3wlIbBOdSfuypM845y6U9KnY93FT2Rjd88FoEICzMOnt0/w87xcjAEgx9J2A5DDRHoaTVBD7d6Gkmgme7wSMBgGYgElrnyIRp8NNIc3P4/5oAPxyqDGkrDRpQeG0oEMBMAETrc36IUn3m9n/UzThu3LCEY1Q2dildJMWzaChATBuH9IktU817T3qHYhoXh7lrQH4pbIxpOK8NKWlMYgEJLIz9jDM7CFJ80Z56J8kXSfpw865X5vZ6yV9T9L1pzjPFklbJKm4uFglJSVnDO54db+W5zs9/tifz3hsogqFQmN6LRIV15fYfL++eLRPZ9M2He0Ia36eaUZ6n9evz0T5/v5PRDJfm5T815dKzOxmSTevXLlyTMfnZmdoRSFLsYFEZ865s/9ls3ZJRc45Z2Ymqd05V3Cm39u0aZPbtm3bmJ6jpKREmzdvPusYfcf1JTau7wVmtt05t2lyIxq7s2mfxtM2Sbz/iSyZr01K/usbL9/ap7NB3yl1JPv7x/W94HRt00SHWmokvTj275dIKp/g+QAgXmifAABAQprohorbJX3DzDIk9Sq2ZAgAPED7BAAAEtKEkjTn3OOSNsYpFgCIG9onAACQqNhZCgAAAAAeIUkDAADwlJndbGZb29vbgw4FwBQiSQMAAPCUc+5u59yWwsLCoEMBMIVI0gAAAADAIyRpAAAAAOARkjQAAAAA8AhJGgAAAAB4hCQNAADAU1R3BFITSRoAAICnqO4IpCaSNAAAAADwCEkaAAAAAHiEJA0AAAAAPEKSBgAAAAAeIUkDAAAAAI+QpAEAAHiKEvxAaiJJAwAA8BQl+IHURJIGAAAAAB4hSQMAAAAAj5CkAQAAAIBHSNIAAAAAwCPmnJv6JzVrlHR0jIfPltQ0ieEEjetLbFzfC5Y65+ZMZjCTbZxtk8T7n8iS+dqk5L++8UrY9snMbpZ0s6Q3SCof46/x/ie2ZH//uL4XnLJtCiRJGw8z2+ac2xR0HJOF60tsXF9qS/bXJ5mvL5mvTUr+68Pp8f4ntmR//7i+sWG5IwAAAAB4hCQNAAAAADySCEna1qADmGRcX2Lj+lJbsr8+yXx9yXxtUvJfH06P9z+xJfv7x/WNgfd70gAAAAAglSTCTBoAAAAApIyESNLM7D/M7ICZ7bb/3979vFw6xnEcf3/yY0VZqpkpFlJPUjYiOyweElGKhRJLilKi+R+sKBuymUghShqj1GyQ0ixmPI0mG4+UhQVlocnX4jmLSTJPuY7rvq77/Vqd+5yz+JzOOZ++3+7zI/kgyXW9M7WU5NEk55L8mWSaX7tJspvkfJILSV7qnaelJG8m+TnJ2d5ZWktyLMnnSb7dvC6f651pqeymMdlNWoPZ+2lGM3cTzN1P25idhljSgFPALVV1K/Ad8HLnPK2dBR4BTvcO0kqSK4DXgPuAHeDxJDt9UzX1FrDbO8SWXAReqKod4A7gmcmeu5bspsHYTVqR2ftpKivoJpi7n5rPTkMsaVX1aVVd3Bx+CRztmae1qtqrqvO9czR2O3Chqr6vqj+Ad4CHOmdqpqpOA7/0zrENVfVTVX2zufwbsAcc6ZtqmeymIdlNWoXZ+2lCU3cTzN1P25idhljS/uYp4JPeIXRZR4AfLjnex0F/OEluAG4DvuocZQR20xjsJq2R/bR8dtMkWs1OVzZJ00CSz4Dr/+Gm41X14eY+xzk4nXji/8zWwmEen7QkSa4B3gOer6pfe+fpxW6StFSz95M0mpaz02KWtKq6999uT/Ik8ABwTw34vwGXe3wT+hE4dsnx0c11GkCSqzgomRNV9X7vPD3ZTdOxmzSN2ftpZeymwbWenYb4uGOSXeBF4MGq+r13Hh3K18BNSW5McjXwGPBR50w6hCQB3gD2quqV3nmWzG4akt2kVbCfhmM3DWwbs9MQSxrwKnAtcCrJmSSv9w7UUpKHk+wDdwIfJznZO9N/tfmy8rPASQ6+PPluVZ3rm6qdJG8DXwA3J9lP8nTvTA3dBTwB3L15v51Jcn/vUAtlNw3GbtKKTN1Ps5m9m2D6fmo+O8Wz35IkSZK0HKOcSZMkSZKkVXBJkyRJkqQFcUmTJEmSpAVxSZMkSZKkBXFJkyRJkqQFcUmTJEmSpAVxSZMkSZKkBXFJkyRJkqQF+QvpJavhklAawwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-2, 2, 100)\n",
+    "\n",
+    "plt.figure(1, figsize=(15,10))\n",
+    "plt.subplot(131)\n",
+    "plt.plot(x, x**3)\n",
+    "plt.grid(True)\n",
+    "plt.title(\"Default ticks\")\n",
+    "\n",
+    "ax = plt.subplot(132)\n",
+    "plt.plot(x, x**3)\n",
+    "ax.xaxis.set_ticks(np.arange(-2, 2, 1))\n",
+    "plt.grid(True)\n",
+    "plt.title(\"Manual ticks on the x-axis\")\n",
+    "\n",
+    "ax = plt.subplot(133)\n",
+    "plt.plot(x, x**3)\n",
+    "plt.minorticks_on()\n",
+    "ax.tick_params(axis='x', which='minor', bottom=False)\n",
+    "ax.xaxis.set_ticks([-2, 0, 1, 2])\n",
+    "ax.yaxis.set_ticks(np.arange(-5, 5, 1))\n",
+    "ax.yaxis.set_ticklabels([\"min\", -4, -3, -2, -1, 0, 1, 2, 3, \"max\"])\n",
+    "plt.grid(True)\n",
+    "plt.title(\"Manual ticks and tick labels\\n(plus minor ticks) on the y-axis\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Polar projection\n",
+    "Drawing a polar graph is as easy as setting the `projection` argument to `\"polar\"` when creating the subplot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LzMzMZHBwMCsrK411OauQZZk1NTU8ffo0w8LCGBERwYyMDNbX1/c4rq6ljpbrLfnEwSf6dFxzFLU6XPrtpRz63tBeHdN05xMaGqo/n3/KZ6ewsJBBQUEsKjLuryPLMrdt29bi6emZB2AK+/b8/gxgWtvzub9t25cAUgEkAngXgHXbdgsAOwFEA5jQl+P90+2cL6CPN6ULUemw7zkA69p+O6FNew9gGYD0Mzim5Obmtm7WrFmV3VlsGhoaGBERwaSkpL/V10SWZVZWVjIhIYFBQUE8duwY8/PzzYoD2nN6DxEIBmUF9WkNfSEqH8d8TASCqRWpJo9pampidnY2o6OjGRISwpSUFNbW/r1WpJaWFh47doyxsbHdXtP09HQOHz68wtHR8Waa9xxdAeDjtt8diYo3BCdiDeArAC+bM+/51M75Avq06J6JyuAe9uUAcOvD8Ww9PDz233PPPbXGTJGyLDMrK+tv505aW1uZkZHB4OBgxsbGsrS0tM/u+w/sf4D2r9mzVdO3gMS+EJX0ynQiEPzw6Id9OqZarWZhYSGjo6MZHh7O3NxcqtXqPs1lCnrjWmpra7l48eIqd3f3twEoaNqztAlAQduzWAKgCcC3nfroic2/sZ3zBfRp0Z2ICjpEnQJYA+Dntt9eaFdGzwSQp/vfjGP5uru7n9q6dWsLjaCxsZGRkZF/K3dSVVXF2NhYhoSEMDMz86xEJg97fxiv+v6qPo/vC1GRZZn+7/nz6u+v7vNxdWhqauLp06f1HrO9iXt9RUtLC2NiYrrlWjQaDR977LE6Dw+PIJjp09KZU2n7KwF4D8Dr5sx1PrVzvgCzFwx8D1GUSt1G8VcB+KVNl5IIYB8A37a+DwNIadOpHAEw18xjzfL29i4ODQ01qgTIy8tjcHDw3+LAptVqWVBQwPDwcB49epQVFRVnzeO2qK6ICATfiXqnz3P0haiQ5Oq9q+m8yZla+ewESOo8ZiMiInjkyBGWlpb+LZ7JOq6lvLzc6P5vvvmm1cPDIwPAUPaNqAQBSGp7jr81l0CdT+2cL+B8bU5OTqtGjRpVkZOTw86QZZnJyck8evToWWe/ZVnWE6vk5GQ2Njae1flJ8tdTvxKBYGReZJ/n6CtR+fz452brVUxFTU0Njx8/ztDQUJaWlp71+ZuamhgWFsbs7Gyj+2NiYujr61tqaWm5hOfBM3yuWn+B9k6QJEnp4eGxdc6cOW/FxsYOHDJkiMF+tVqNo0ePQqlUYsaMGWanauwOJFFaWoqwsDDU1tZizpw5GDdunEkZzszF0cKjsFBYYIrXlLM+d2+Y4TMDAHCs8NhZn9vZ2RlTpkzBtGnTkJ+fj6ioKFRXV5+1+W1tbTF37lxUVlYiISEBsiwb7J8xYwaOHTvmMXr06J8HDBiw9qwd+F+GfqLSAZIkWbu7u/91zz333Pb777+7ODgYpkJsaGhAZGQkBg8ejNGjR581r9iqqipERUWhsLAQM2bMwPjx42FtbX1W5jaGmMIYTPKcBFvLs1sjyBSMcR8DWwtbxBbF/m3HsLe3x7Rp0zBu3DicPn0ax44dQ0PD2Umwr1QqMXXqVNjb2yM6OhqtrYalh7y9vRETEzNgyZIlgZ6entukcx2HcQ7wr3N++7sgSZKth4fHoeeff37Ko48+2uVtKysrw8mTJzFlyhSYmtm+N9TX1+PkyZMAgAkTJsDJyemszNsbksuScfnIy3vsQxIqlUrvaq9Wq0ESZWXAW285Q632h69vHmxthfu+lZWV3r3f0tKyW4JrobDAZK/JOF5y/O84NQM4Oztjzpw5qKiowIkTJ+Dk5ISAgIAzLrgmSRJGjBgBR0dHREdHY+rUqQb3zsbGBj/99JPzfffdd/PevXttJUm6i6Tcw5T/r9BPVCAKmLu7u4ds2LBhwr333mvAIpBEVlYWSkpKMGfOnLPCQWg0Gpw8eRJ1dXUYM2YMBg4ceMZzmorq5mqUNpZitNtoaLVavTdrXV2dnoDo2HorKyt9/I+FhQUkSYE1a3wQFWUHwBG2trV4+eVyyLKMmpoafbyPWq0GAH2Qoa2trT40wNHREWPcxuC39N/+sXN2c3PD/PnzUVxcjKNHj8LHxwcjRow443AAT09P2NnZIS4uDqNGjYK3d3tZEoVCgU8//dTJ1tb22p07d9pIknQLSe2Znsu/Af95oiJJkqO7u3vEW2+9Nfr22283qFouyzISEhIgSRLmzJlzVmJSysvLkZKSgqFDh2LChAn/WGChjoAEpwcDADQlGkREROjjbgYNGqQPHuwuPik6GoiKAt57Dzh8uAjff++Dt95yhoeH8WNqNBq0traiqakJdXV1yMjIQH19PSzrLFHaWIrk9GQM8hj0j8T7SJIEHx8feHl5IT09HZGRkZg0adIZc4eOjo6YO3cu4uLiUFdXh4CAAP09lSQJ77//vqOdnd2yL7/8co8kSdeQ1JyN8zmf8Z8mKpIk2Xt6ekY98MAD41asWGHwdmu1Whw7dgzu7u4YNmzYGb/8Go0GKSkpaGpqwqxZs856zWNjaG5u1tcDamlpgYuLC7LrRcD2tQuvxWj30WbN9+23gK0tcM89gJNTAfbt88GPPwIPP2y8v4WFBSwsLGBvbw93d3f99vLkcnyS9QkyqzPRWteqj/fx9PSEp6enQdzN2YZCocCoUaPg5eWF+Ph4eHl5nTHXYmVlhVmzZiEpKQlJSUkGHwtJkvD88887VFRUXLp///5fJUm6+v87x/KfVdRKkmTr7u4eunnz5oAHHnhAOnLkiF7pptFocPToUXh7e2P48OFnTFAqKioQEREBFxcXzJ49+28jKCSxb18DRo1qwUUXlSEkJBFarRYTJkzA4sWLMXnyZNQr6yFBwvABw82e/8AB4OKLAXt7YOjQJvj7AyEh5q9zjMcYAECzXTOmTZuGxYsXY+TIkWhqasIvvyRg9uxqTJ7cgtjYPsXrmQRdoCdJREZG6iO4+wqFQoGJEydCqVQiISEBpIhpraurQ1xcHN555x3lHXfcscjd3X1HWzqD/7841zbtc9EAWLu7u0d9+umnei/Z0tJShoSE6ON38vLyeKZQq9VMSEhgVFQUm5pMT9VoLhoaGpiSksKgoCAOHdpEJyctraxkTpxI1tUZ9r1nzz30esvL7GOUlpIA+dZb4v/g4GCuXEl6eJDm+prVttQSgeBbkW8ZbM/KIr28SBcXmVZWMpcsqWRISAjT0tLY0mLUofmsoKamhqGhoUxNTT3jqgWyLPPkyZOMi4tjdXU1g4KC9LFKsixzzZo1dR4eHl/BTM/uf1P7/00xjUCSJEt3d/eDL7744pTVq1frta4eHh4YOXIkgoOD4evri0GDBp3RcWpra/U6i7+DO5FlGYWFhYiKikJCQkKbj8ZCZGfb4tlnFdi7V0JyMnDffYbjCusLzS5HCgDH2txKZsxo37ZgAVBWBqSnmzeXo5UjbC1sUdxQrN+mUgE33gi0tACRkRJWrJCQljYAc+fOhaWlJY4ePYqYmBiUlZXpuYCzBR3XIssyoqKi0Nzc3Oe5JEnCmDFjYGFhgcjISEyZMkWvt9HpWK6++uprPDw8tp6t9Z9v+M8RFQ8Pj+0PPvjgjEceecSm43aNRoPs7GwMHz4cubm5XfwPzEFRURFOnDiB6dOnY8iQIWdVGatSqZCWlobQ0FDU1NRg0qRJmDt3Lnx9fVFbKxSsnp7AJZcAr7wCfP89cPBgh7XVF8HX0dfs4x47BigUwNSp7dvmzBF/Y810OZEkCV4OXihpKNFve+cdMc9nnwFjxwIeHkB1NWBpaQl/f38sXLgQAQEBKCoqQmhoKHJycqDRnD2dp0KhwOjRozF69GgcOXLkjJzm6urqUFlZiUGDBiEzM9OACEqShG3btjnNnz//pv+3DnLnmlX6J5urq+vDV111VXXn2BCNRsPIyEi9yKMThcxluWVZ5unTpxkdHd1jYuW+oKWlhYmJiQwODmZ2drbR8IDcXCGibN+uG0MGBJDDh5M66ctjswfv3Xuv2ce/5hpyzJj2/4ODg9ncTEoSGRho/vnM+WwOl361lCRZVETa25NXX92+/5FHSEdH42NbWlqYmprKoKAgpqamnvVQicbGRoaGhvZJBK6trTUQeU6ePMkTJ050iUdqbm7mhAkTKv8/uvT/ZzgVCwuLBT4+Put27tzp0pFz0Fl5Ooo8Hh4eGDNmDDoqb3uDRqNBbGwsNBoNZs2addYylqnVapw+fRrR0dFwdXXFokWL4O/vbzQ8YMAA8beqSvy1tgY+/hjIzAQ2bxbbaltq4WrravY6srKAESMMt9nYAIMHA2lpZk8HD3sPlDeVAwCef16IP2+91b6/vBzdmqqtra0REBCAhQsXQqlUIjw8HNnZ2V3c5vsKOzs7zJ07F8XFxUhJSTFZ3NIpZadNm6YXeUaPHg1LS0skJSUZzGNjY4M//vhjgIeHx05JkoaelYWfJ/hPEBVJkoZ4enr+/McffwzoqNsgifj4eHh4eKBzjI85hKWpqQlRUVHw9PTEuHHjzoq4I8sysrKyEBERAWtrayxcuBB+fn49zu3gALi4APn57duWLgWuvRZ4+22gpLwVrdpWOFmb55tBAtnZwFAjj35AgPk6FQBwtHZEg6oBx48DX34JPPaYIdEqK+ueqOigVCoxfPhwzJ8/H62trQgLC0NhYeFZ0blYWFhgxowZUCqVOHr0qN6hrzsYIyiAEHfGjh0LkkjrRH19fHywe/duDw8Pj78kSXI840WfJ/h/T1TavGX/+uWXXzz8/PwM9mVkZMDS0hLDhg0zOtYUwlJZWYmjR49i/PjxGDx48BmvlyTy8/MRFhYGtVqNBQsWYOjQoSb7Ufj5AQUFhtteeQWoqwPeeU+4R5hLVCorgfp6wNhlCgjoG6fiYOmABlUDXntNEMLnnzfcX1oKdHBt6RGWlpYYPXo05syZg8rKSoSHh6OsrMz8RXWCJEkYPXo0Bg8ejKioqG7jh7ojKB3nmTBhAqqqqlBcXGywb8aMGXj77bcHubu77/3/Ymr+f3ES3UGSJIWHh8evr7/++pDZs2cb7CspKUF5eTnGjx/f4xw9EZaSkhKkpKRg9uzZGKCTPc4AjY2NiIqKQk1NDebMmYNRo0aZHQXt5wfk5RlumzRJcCvbPrQGmp3NJipZWeKvv7+o+6PVavXy8+DBRG0tYG68noOVA+oK/LBrF7BmDdAxnEqrFdzPyJHmzWltbY2JEydi2rRpyM3NRVxcHFQqlXmTGIGPjw8mT56MY8eOoaamxmBfbwRFB4VCgWnTpiE1NbWLT8zKlSutVq5cOc3d3X3zGS/2PMD/a49aNze3TTfccMOsu+++28BFs76+HqdPn8bcuXNN4gA82vjwI0eOYPbs2bC2tkZRUREyMzMxe/bsM/YAJYmcnBzk5uZi4sSJZ0SgRo8GQkPFi9nR2/7ll4Hdu5XA0Udhe1tX87ZGo0FdXR3q6+u7FBQLDh4AYDzKy2MRHt4ESZLQ2NiIsLAw1NR4ABiDPXuOYsgQdZeaQY6OjnBycupynR2sHNAS/Cjs7IhHHjEU6TIzgdZWoBd63y3s7e0xY8YMFBUVITIyEqNHjzaIy+kLnJ2dMXPmTBw7dgyTJk2Cq6uryQRFBysrK0ybNg1xcXGYO3euwXOzefNmxxMnTtzl5OQUV1dXt+OMFnuO8f+WqDg6Ot44efLk1e+//76BrKpSqRAXF4epU6eaRQw6EpbBgwejqKgIs2fPPmOFbFNTE+Lj4+Hk5IQFCxaccV2gyZOB5mbxpR892nD74ktqERL+MFQtYaisrERNTY0+mFChUMDZ2RkODg5wdHSEm5ubnjCkpIg1XX75DOjezZCQECxatAgqFbBpEzBkyCzMnq3pUkysvLxcP78uqNDFxQV1JW5A0i1Y/agWbm6Gj2FKW7msvhIVHXx8fDBw4EAkJiaiqKioS+kNc2Fvb4+ZM2ciJiYGw4YNQ2ZmpskERQdHR0eMGTMGsbGxmD17tp7YKpVK7Nmzx3Xq1KlbJEk6RfJEnxd6rnGuzU9/RwMwZPDgweWd6xlrtVpGRkayuLiYfUVycjL3799/xiUjCgtlXn55PRcvLuWuXTVnNFdHnDghzMo7dxpub2pq4mtbowiQ1zywl/Hx8czJyWF1dXWvXqTr1ok5O1rJdZnfEhLEvp9+6nldarWalZWVzMzMZFxcHMdd9BehaGVYVNecu+vXC1P12arKodWSH39cyblzy3jbbY08U+fmsrIy7t27l7m5uX2eIz09nQkJCUa3t5X/sOd58C71pZ3zBZz1EwIkDw+PYyEhIV2cxxMTE5ma2vc0hgUFBYyIiGBRUVGf/Fh0UKlUvOmmIgKku7tMgLz5ZvJsVJ5obSUtLclnnpFZXV3NU6dOMSQkhJGRkdx99FfCI5H+o2rMcq1/+GHSxcVwm46o6Nz3P/jA9Pnq60lr+2ZiwrdMSElgWFgYw8PDmZ6ezvr6ei5fLnxrzgZyc8kFC8Qa3dzEtX755bw+u+Pr/FDKysoYHBzMqqqqPs0jyzJjY2ONpqbcunVri6en55c8D96nvrT/d4paFxeXNddcc03AokWLDAR1nZfsSHO1f20oLi5GdnY2Zs6cCW9vb7P9WHSor69HZGQkJMkJHh5Afr6E9euBH38E5s/vqmQ1HyqMGNGKoKBqZGRk6EPz586dCx9fb2DW+8hJdUZYmOkz9mTedXMTnralpabP9913QGujDTDjI4wYOQILFizA9OnTYWlpicTEJISEqDBxYiO02jML5j16VHgAnzgBfP45cOqUeCTUalujWdt6Q0cdiru7O2bOnImEhATU1tb2PrgTJEnC5MmTkZ+fj4qKCoN99913n/Xw4cOvtLCwWGT2xOcDzjVVO5sNgP+QIUPKO4smdXV1DAkJ6XMJjbKyMoaGhnbxkjXX87akpITBwcGsra3le++Jr6eOg/7zT9LZmfT0JI8eNX+NtbW1PH78OIODg3nTTTV0cpLZ+XSPFx0nnrelo3Mrr73W9LmXLCHnzTPc1jHxtYsLuWaNaXPJMjl+POkzsph4BWxUGSb2zs4W1+Wll4oZFBTU5+Tf+/eTtrbksGGkjjk9cEDM/ccf4t4FBwezpqbGpPk6e8rq0NDQwKCgoD6XCGlqamJQUFAXETA3N5eenp75+BeKQed8AWftRITYExsWFmbA2Gu1WoaFhZn88HSG7qFpbm42ut8UwiLLMtPT0xkREaHvl5oqrv47HapkpKSQQ4eSDg5kRIRp66uoqGBkZKRBeYpvvhFznzhh2Pdk2UkiELzq7pNUKMiCAtOOMWUKefnlhts6EpXBg8k77jBtrpAQsbarnhQVEjuX6vjuu/a1dyxTEhMTY3Jlwi+/JJVKcupUsqSkffuqVaSdXXvIQn19PYODg1lYWNjjfN0RFB1qamoYEhLS59CMgoICxsbGdtm+bdu2Fg8Pj694Hrxf5rRzvoCz1VxcXB697777utz1tLQ0njp1qssNMwUqlYohISHsrPDtjJ4Ii0ajYVxcHOPj47vI8dOni3iajpuLikS8jqMjGR3d/TFra2sZHR3NmJgY1nXKb5CTI+7sli2GY7KqsohA8PVffyFAvvZaj6elx8iR5E03GW7rSFTGj6fJnM+KFaSrK/nY3hdovcG6y/6HHhJEtXM4T2VlJSMiInj8+PEe00h8+61Q8l54oWHah8pKwbnc2ynsSaVSMTo6mqdPnzZaL6g3gqKDrnJiX2sOxcTEdDEgyLLMefPmVSoUisU8D94xU9s5X8BZOQlg6JAhQ8o7s8m1tbUMDQ3tk1JOlmUeOXKEBSZ+zo0RFpVKxYiICGZlZRkdo/sq79tnuL2ggBwxgnRy6ioKNTY2MjY2lpGRkT0qCQcNIm+80XBbYV0hEQhuO7aNixcLZagpl8bbm1y92nBbR6Iydy65dGnv81RWklZWQlRa8/saur7u2qXP5MndzyXLMouLixkSEsKkpKQuIsOuXYJDWbyYXSw8mzeLa23E4EJZlpmUlMTY2FiDZ8VUgqLDqVOnmJycbFLfzmhpaTEqBuXl5dHT07MA/6LiYud8AWd8AoDCw8MjLjw8/KyKPSkpKTx58qRZYzoSltbWVoaFhfVIlFQq8fIvWtR1X36+0Ac4O5OJicIkm5SUpC+U1dsX8ZZbBDHo2K2qqUpfmVAnIgWZUJ/d0ZF87DHDbR2JymWXCa6rN3z4oTjm8ePkqj2r6PO2j8H+igrBZaxb1/M8uoJrQUFBTEtLo1ar5YEDwuo1e3bXxFQqFenvTy5c2PO8qampjImJoVarNZug6NZ19OjRPif4KiwsNCoGffLJJ62enp4G9ZbP5/avt/64uro+et11142YP3++gbUnIyMD7u7ufSqnUVBQgPr6eowebV4OV51Lf1RUFKKjozFixAj4+nafu8TSUgTShYYKS0VH+PkBQUEideOll2qxe/cxODo6YsGCBfDw8Og1aHHhQqC42DAux8naCRIk1LTU4LrrhGv855/3fE6kcMHvVALJAI6OIraoN/zvfyJkYMoUoF5VD3tLe4P9wcHieBde2PM8kiRh0KBBWLhwIbRaLT79NBHLlxPjxgG//y7W0/m4OTnAk0/2PG9AQAAGDBiA6OhoxMbGmu3YJkkSpk6diqysrD7lY/Hx8YEsy13ig1avXm01cuTIZUql8gKzJz0H+FcTFUmSBtjb2z/31ltvGdz5uro6lJSUICAgwOw5a2pqkJmZialTp/Yp2tjFxQWyLEOtVpvkbn/PPSJw7plnxAvVEb6+Grz3Xhpqaoj162fBxcX0hE8XXST+/vVX+zalQgkXGxdUNVfB1ha49Vbg559FMqTu0NQk1tUTUbGxEakLekJiIhAXB9x9t/i/sqkSbnZuBn0OHRIEoWN2uZ6gVCphaTkaL700EQMGtGDr1iw4OxumP2huBtatEwmlrrii9znd3d3R0NAAa2trdC4mZwp00c3x8fF9yiA3ceJEnD592iBmSZIk7Nixw9Xd3f1zSZLOzOX6H8C/mqh4eHhs3LBhg0vH0qC6shqTJk0yO0O6SqXSZ2zri/u9riTquHHjMHHiRJP8WBwdgcBAwa38/nv7dl2y7JkzrfHrr0qcPq3A8uW9v7w6DBsGDB8O/Pmn4fYBtgNQ1SISrtx1l4ix2bXLsI8uDqisrAzp6SLkubq6CHFxcTh27BhiYmLQ2NiI2NhYxMXFoaGhCs3NGuTn56O8vBz19fVdfEz+9z/Bmd1yi/i/stk4UVm8WPQzBVVVwOWXA7KswKFD1nBxUSEyMhL19fX6Ph99BBQViVCC3uixzg9lzpw58PLyQlxcXJ9ytNjZ2WHChAl9Gm9tbY1Ro0YhKSnJYPugQYNw0003DXRwcLjb7AX90zjX8ldfGwC/4cOHl3f2PUlLS+Pp06e7yKWmIDY21mTFbGeo1WpGREQYmCdN9WNRqYSFZexYUqWSeerUKUZGRhr4Z3z9tdBH3Hab6Ymm779fWFI66v6mfzqdl357KUkxz8iRMhcsaGVGRgZjY2MZHBzM0NBQxsbGMjk5mVFR2QTIzZvrWVtby/r6ejY0NPDw4cOsrxfb7ryziQMGaJiens6kpCQeO3aMISEhDAkJ4fHjx5mWlkV3dy2vvbZ94X7v+PHOX+/U/6/zT3n/fdPOraVF6EisrMiwsPbt1dXVDAkJYW5uLmtqyAEDyEsu6X0+YzqU1NRUxsbG9tmic+rUqT57cB87dqyLNaiqqoqenp5FAGx4HryD3bVzvoC+Ni8vr5/37NljYLtobW1lUFBQn5zciouLGRMTY/Y4Uijojh07xpycnC77TCUsv/wi7sbzz2cxOTnZ6IO8fr3o8+abpq1r1y7RPzS0fdul317KKVunMDMzk5GRkbzjjmxKksyYmHzW1NR0sZTl5Yk5PvvMcO6OitpHHxWWqs7QaDSsqqri11+LkIR165J49OhR5ubm0vZVWz558El93+3bxXFMMZ7IMrlypej/3Xdd96vVasbExHDlynJKkszjx3ueryelbFJSUp8/UlqtlqGhoWYpe3VoampiSEhIl+dgw4YNja6urs/zPHgHu2vnfAF9WjQwZtKkSRWdL3hycnKfgrx0xKivsTypqalMTEzsdr8phKWhoZHjx9fQzU3D7izFsizMxJJE/vZb7+uqrhYm1hdeEE58p06d4uWfXM6BGwcyKyuLjY2NPH1aPAXvvmt8jsxMsf/LLw23dyQqTz0lfEC6w913CwtSY6PMuro6xiXHEYHgg98+yPT0dLa0tPDqq4UlzBSmQGceXr+++z4JCTKVSpnXXFPco1Nab1YeWZYZHR3dq4Ncd6ipqWFYWFif3BqSk5O7fKgaGxvp4+NTCsCZ58G7aKz9K3UqXl5eWz/66KOBHZWWzc3NqKio6FNpjaSkJAQEBPSpTnJxcTEqKysxbty4bvv0lkGuoqICMTFH8eGHEqqqlHjxRePzSBLwxRcijcHNNwOnT/e8NmdnYupUFX76qQHx8fFwcHDAlOFTUKWqgu9gX9jZ2WHUKBEfs6ObDB66hPU96TmsrLrX9eh0NtdcA9jZSXB0dISVu0g/MGfcHCiVSoSFxeDgQS0uvLAZQM+pIP/8Uyi1r78e3V4nElizRoKzs4QNG9hFz6KDKflQJEnCtGnTkJaW1qcYH2dnZ7i7uyMjI8PssSNHjkRWVpaBfsrOzg4vvfSSi7u7+zqzJ/yH8K8jKpIkzR49evT4efPmGWw/ffo0Ro0aZbbFpqSkBLIs92j67Q51dXVITU3FtGnTelUKd0dY8vPzcerUKcyePRuLFjlhzRpg69buy17Y2wO//iosLlddZdxyo9VqkZmZiZCQEMybV4m0NAcMHjwPfn5+GOI6BARRXN9utrzlFlGCw1iuWV1q1p4S0FlZiaRQxuL//vwTqKkRRFB/zrUiie6wgcMwdOhQqNUL0NKixNSphQgPD0d+fr6OIzVAZiZw003AuHFC8dvdrf7+eyAsDNi4ERg/3htTp05FXFwcKisr9X3MSbBkaWmJ6dOn48SJE30q3RIQEICSkhKzqyBaWVnBz88P2dnZBtvvueceK3t7+1slSTqzzFN/F841q2ROAyB5enrGJyUlGbCEtbW1jIiIMFuhdiZiT2trK4ODg7u4yPeGjqJQdnY2IyMjDUpM1NYKp7Vp09glILAjwsOFs9dll7V7xcqyzJycHL1TmEql4qlTQlT46CPR50D6ASIQjMhtDy7Kz+/e6Sw+Xozftctwe0fxZ+NG0cdYeNQttwhlaUcJ5JPYT4hAMK9GOIndf78o0dHcLEpXJCUlMSQkhMXFxfp7Wl8vwgFcXYVI1h26u346HUVZWVmfHNtIce8iIiL6JMr0VQzSaDQMCgrqIsL99NNPGisrq0wAZQCS2c37AmALgAwAiQCmdth3B4D0tnaHsfF9beecUJjTLCwsll133XVdNA5HjhxhZWWlCbfIELGxsX2SlUtLZa5dm8lXX61leLjhC2Pa+FIePHiQkZGRRpXK338v7syHH/Y8z8cfi34bNsj6HC/JyckGrt7CwtNuATlVfooIBL9J+MZgrsWLyVGjuuo0jh0Tx9i713B7R6Ly1luiT+d3tLFREIvO8TYvHH6BynVKqrVqyjLp59c1dqixsZHHjx9nREQEKyoqecMNpEIhorl7wv33CwJpLNK7paWFhw8f5sGDB80mKE1NIrr52WfL+fLLeexD4DRPnz7dJ2tQTk4OU1JSDLbJsswhQ4bUALiqB6KyDMCBNuIyG8DRtu0DAGS1/XVt++1qbI6+tHNOKExeqOBS0jorriorK3nkyBHT79AZjiPJ6dObKCR30Tw8yGefFbWATUF2djZDQ0MZHBxslEuSZfKii4RysyePb1kmb7hBTYVC5qefpncbaPfkk4Krqa0lWzWtVKxT8KWglwz6fPIJ9S70HREdLbb//rvh9o5E5e23RZ/OERE//ECjoQA3/nQjh78vsjDpMtV98YXxc6ytreXjj2e3BUD2XDQsNFTM1TmkoONchw4d0idZMgUnTogIbDs7GtzzlSvNpyparZbBwcFm19XWjescKX/o0CHZzc3tcA9E5RMAN3f4PxWAN4CbAXzSXb8zbf8mncr8OXPmDOxYn4ckTp06hTFjxpg1UV/HASLJUkkJMXcuUVwsPFLnzAHefFM4m11xRXvdYWPIz89HcXEx5s2bh7FjxxpV3koS8MkngCwD997b1dNWdw65uTm4665ojBihxYsvjkB1tfF6zVddJXQjBw8CVkorDHUZitTKVIM+114rki11dYQTf3vSqejW11mttHMn4O0tQgY64nTFaYx2EyEQ+/aJ8122zPjcp0874YMP/HHRRc2YNSscJSUlRvs1Nwvv5KFDgQ0buu7X6VBmzJiBuXPn4uTJkwY6ls7ns28fsGSJCCn4+WfhffzHH6LImZsbkZ3daHamfoVCgYCAgC71f0wdl5pqeM8uuOACycnJaTK6zzXtC6BDFSgUtG3rbvtZwb+GqPj4+Kx/8cUXDfzey8vLYWtra1Z8BgCUlZX1aZwsy4iPj8cttygQFSUhMxO47jqhOM3JAV56CThyBJg5U1g7OjlFoqysDLm5ufoiVT1ZhYYOBV5/XTzIX39tOE9TUxOio6NRX1+Piy6ag927LdDQIJShxsoLz50LDBwI7Nkj/g8YGIC0SsMH291dvPy7dxs/95703zqn0Y59GhqAAweElaZjLm+ZMtIq0wyIysyZov5zZ1RXi6LtPj7Azp22mD9/LvLz83H8+PEuL/T69ULR/OmnQpndEZ2VstbW1pg5cyaSkpK6WIVCQoDZswUhzsoSlR3z88W8l1wirmFFhYRbb1V28Xo1Bd7e3qirq+u2hlBv4xobG/XbJEnCAw884CRJklsPQ/95nC2W5+9sAIZMnDixC78aFRVltqJUlmWGhIT0KZuYLjdLfT05ZIiIfO3sU1JXJ/wnnJyEbH/zzSK/SX19fbdK4e78WLRacv58kVmtqEhs02WPq6ioMOirizp+5hnja7/jDjGPSkWuPbCWdq/ZdVFsb9ki5ujo6xUeLrb99ZfhfB3FnzfeEH06JtzTiT4dHe9IMrs6mwgEt8dtZ2Gh6PPqq13XK8vkVVcJsa2zlFpQUGCQtS0uTvjj3H1313l6UsrW1NQwODiYKpWK8fFC6Q0IHc8XX3TN6XLypBBJFy0S98ZYDhRTUF5e3q2j5YEDBxgQEMDhw4dz06ZNBvsKCwv5wQcfcNKkSZw0aRJHjhxJBwcHWlhYqAHYANACiG9re3GOxJ9zTjBMaZ6enlt/+OEHA7V5XV0do6Kierh1xpGXl8fO1iNT0Dk3S3S0eOCXLDFu9aisJJ9/XjiF2drKvOuubBYX13Q7f3eEJTWVtLEhr75aZmpqGiMjI7vk3NDhvvvEHd2zp+s+nXdtUBD5cczHRCBYUGsYkqDznu34LIeFiWTRv/7awLKyMhYVFbGwsJB//vkni4qKWF5eznXrWggY5jC54Qaha+qsh9ZZn8Jzw/nBB+J4xjJM6JS/3Tnl1dfXMyQkhJmZhRw7Vlh8OhN4U6w8SUnFvPLKEkqSTFdX4VhnTOVRXCw+JJ6e7SlAu8uBYgqioqK6JP/SaDQcNmwYMzNFhYGJEycaKGhlWTawBG3ZsoXXX389Bw4cqLW3t78HQAMNP8aXw1BRG9O2fQCAbAglrWvb7wE8S+/rOScYvS4QsPX19S1Vd/psxMfHs6RjrsAeIMsy61vrWVBTwK/2f8XonGjGFcUxqTSJqRWpzKnOYX1r9zlGu8vN8u234gpec03Xr5oOOTkyly6tICA4m127uvca7Y6wvPGGtu2LntujSbK5WaRQdHERsTQd0dAgiNOaNWRwdjARCP6R/odBH5VKxSlTVJw4sZnHjh1jcHAwt2w53maSTmVSUhJPnTrF06dP88CBAzx16hQTExP54IP5BMiDB0MZEhLCqKgTtLPT8s47m7tYt96NfpcIBMsayrhwITluXNfziIoiLSyERagnL4GOVQkOHDDs2BtB0WjIbduEidrCQuadd5axuwR/9fXCRG1nJ6xhHdFdDhQdZFlmZVMlc2tyebr8NE8Un2BMQQzD08L5w18/sKC2gM3q5rbzjuLFF1+sH7tx40Zu3LjRYL709HRmttnUBw4cyAEDBlCpVFKhUKgBtAC4H8D9pN6k/BGATABJAKaz/b26G8LUnAHgLp7Fd/a8LyZmY2Oz4vbbb7fvWP5TpVKhuroaEydO7NK/Sd2EowVHEZ4XjqSyJGRVZyGzKhO1rR28IbtxLLOztIOnvSc8HTwx3HU4Rg4YiYCBAbBrscMQ1yFdcrPcequoM/zoo8B99wGffdZV99DQcBIffqhEcfFAPPIIsHw5cPHFwsGtc21iY5UQW1tbMXNmDGbOnIE33hiMW24xXihdXCvgp5+EcvHWW0Xks+6yibwsQhEb+PpkAMDx4uOY7z0fJSUlKCkpgVarxcKFAXj/fS/Y2Y3CtGkOsLAQJxQQEGBQ3Ku4uFifb0bnN7h48UIolVrs3NmCpiYFZs0qQHh4AWxtbeHl5QVPT0/El8TDw94D6lo3hIeLOs8dUVkJrFgBDBokvId70uWEh1ti505v3HxzJQYMyIRWOw1KpbJXx7Zjx4AHHxQOhosXAx9+CDQ15aCurhkuLob1sBsbhX7sxAmhT5k+3XAuHx8fpOak4o+kP1Aml+FU+SmkVaWhsK4QxQ3FKK4vhlruobh7pPjjYe8BR9kRnES8G/0u5gyaA09fTxyPOW7QfciQIYiMjIRCoYClpSUKCgqgVCpx6aWX1hw8eNAVwD0ANJIklZD8FcBDxg5L8gsAX3S/sL7jvCcqrq6uT95///0Gqre8vDwMHjxY7z1b31qPH1J+wM7knQjLDYNaVkOChBEDRmDEgBGY4zcHfo5+KMsrw+Rxk+Fs4wxJkqDSqqDSqtCiaUFFUwVKG0pR2liK4oZiRORFYEfSDrDNbVwpKTEuYhyme0/HdJ/pmOU3C5M8J+GRR5SorBSKQp3VRqeYLC0tRUNDA2bOnInRo8WD+fHHwr18/HhhpXj0UUPLSkfCMmXKFJw4cQJjx47Bjz/aYOJE4LbbBLHorpDhsGHAtm3CS/bVV0VaBR10SuWTxx0w2HEwDiYdxBLLJfDy8sLUqVNha2sLT0/g/feBw4cdMXasafeoo6JWqVTijz/sMWAAsGrVCFhajkB9fT1KS0sRGxuLsIwwjB8wHr/8QpASbrihfR5S5FspKQGiokTh9u5QUwPceacoEP/ZZwNRWlqHmJgYjB49GvHx8UYJSkODKAT/4YeAl5cITbjpJqHw1GimIDIyEq6urnBsy/JUXy9SK0RGCg/eK64AWjQtOFF8AjGFMThaeBQxhTHIrM7UH8NCYYHhrsMxyHkQAgYGwMfRB14OXnCwcoCthS1sLW1hqbBEq7YV1Q3VSM1MhbOPM/Jq83As7RgyrDPw+J+PAwCsJCv4Ofnhx5Qfcc3oa2CltIKlpSVcXV2xf/9+XH/99fqKlk888YTb8ePH95WVlV0lSdIwAEGSJCWRzMQ/jbPJ9pztBmD03LlzDRS0sizrlWvVzdV85q9n6LTJiQgER2wZwaf/fJq/pf3G6uZqA7YxMzOT6enpxjjUbtGkauKe6D38MPhDvnD4BV767aV0e9ONCAQRCLq87sKrvr+K70S9y3sfKyYgAv5aW9u9dY1l4c/LI6+4QohO06cLr9XOyM/P5759+wyc83TK2E4csVHcfrtwFuuYFiA/v54WFlquWJHLy764jP7v+hsdO3ascIYjTVPU6qKn1WqRksDZmbzrrq7zNrQ2ULFOwQd+fICTJlVx2LAWA1FPl26yOz1KR6xcKZSzHfWdp0+f5r59+4zm7v3rLyF+SpIojmZMKqqurtZ7vVZXi9SUSqXMDR+l8bWw17j0q6W0edVGf//93vHj8h+W87Ww1/jegfcYmhxKlaarJ2RPytfjx4/zm2++oZubG0eMGEEHBwdu3raZu07u4qz1s2jzgjie8hklb3r/Jjarm1lTU8ONGzcyMjJSP49Wq+WgQYPK0JbLFsCXAK7nuXhvz8VBTW2enp6f7tq1y0CJUFFRwbi4OP6Q/APd3nSjFChxxU8rGJ3ffSbzjoTIHLS0tDA4ONhAjyHLMnOqc/hd4ne8Z889HP7+cP1D5nzVekEoFpYyKKznpNmyLCwkHh7i5XjuOfFC6o4bEhLCtLQ0Ax2LLkrZwoLsQYwnKaxQw4aJyN+iombGx8czLCyMF1zQwqFDZW4M20QEglVNXV/AF18UBKm83DSioiuLqtWK6GlA1N3pjIjcCCIQ/Cr8ICVJ5qOPCuvLqVOnGBurorU1uWxZ75HKP/4ojhEY2L5Np0M5ffo0o6Ki9Lqc6mphFQJElYLw8J7nPnXqFP8KTeTQsZVUWKhpt/JW/f2duHUiH/vjMe4+tZuFdYae2M3NzV2eFbJ35WtNTQ1//PFHPvTQQ1Sr1Rw6dCizsrLY2trKcePG0cfXhzvjdnLR54uIQND/HX9+G/Yt3333XQMnuqqqKr744ouNdnZ29wJwg3C/H8t+otJhYYDSy8urpDMhiDkWw/t3308EgrO2z+KJ4hPsDcXFxYw3xg70gsTERJOSNuXV5PGL419w+Q/LaXXNQwS0VAyJ4JWf3clfTv6iV8QZQ2Uleeed4k5MnEjGxan1ya3JrsrbykrS11e41PdmFY+MVNPCQubixaUsKCikLMv87DNxrI/2RBGB4OGsw13GxcVRn0PFFKLyyiuijyyLF9jJqZ1AdsR70e8RgeDGd6oJkElJ4gubkpLNIUMa6O6uZnFxz7ExhYUilmjmzPbwiM5K2aysLB49epT79sn09hZE+9lnjVt1dKhurub2uO2c98YqwiWLsGik01038a5f7+LOpJ0sbSjtcV2kIEidy5iaonz96aef+OSTIrfMb7/9xpEjR3LYsGG88cYbee+99/Kll17inj17uOzhZXR/zZ3SKxKv23Sdgct/ZGQkR40aRaVS2QKhlF3Fc/XunqsD97owYO6KFSsMnDFUKhWv3X4tEQiu+X2NUVbTGPriz9LQ0MDQ0FCzgxRrG2t5y+O/UmmpotItg1gzgk6bnHjH7jt4MOMgNVrjUYL795OenjItLLR89tkqA1NsZ8Jy6JC4cw8+2P06ysvLGRwczCefrDRwgy8vFy/Z4083EoHga2Fdi//IsjCfXn65aUTl5ZepF38GDiRvvdX4mlbuWknvt7y5ZAk5enQ7R3LvvaQkyfzssxyGhYV1e6+0WvLii4WZXvc+GbPyNDSQN9wgznvChO65umZ1M385+Quv3XktrTZYEXcuoMKumrYutXzq7R+pUpvH2apUKgYFBRkEiP70009ctWqV/v+vv/6aDz30kMG4b7/9lmvWrOGECRN43XXX6bPxb968mRs2bND3W79+PTe8uYFX7LiCCAQf++6xLs/nuHHjygC481y+u+fy4D01T0/Pj/Z0crh4P+h9IhBce2CtyS97fX29gexpKmJjY/Xcgjk4fvw4CwoKGBkpCoI7urTystc26fU+g98dzMDgQH2EbkdER6fzootqCAint47RuJ0Jy2OP0aiYoVarmZiYqE9HqdEIXxp7+/YX8YILxEs99qOx+tSSnfHYYyJVo06cMYWo6Ihd54hmHfzf8+eyT++kQkG+1BZ69PPPNHDa06WDTEtL63KPdU5227aJ/40RlCNHRM0kSZJ5662FzMrqGjCaXJrMNb+vocvrLkQg6LnZk5c+8yUtrbQcNUpmVpbgOtLS0oyeR086koyMDH7++eccM2YMJ0yYwPHjx3PFihX6/ZIkceDAgZw0aRKvvPJKkuIDcPjwYarVam7bto1LliwhaZyobN68mWqtmsu+W0blOiUPnzLkNDds2NBkZWV1Vk3E5rZzTjy6a15eXrkdvV4bWhs4cNNATt82nWptz4FlHZGcnGx2JHJDQ0OfUinU1NQYELCMDCGmWFqSX3zZyh+Tf+TF31xMKVCiYp2Cy75bxn2p+6iVtSwsLGzTBWj5zTdC2engINIs6pbRkbA0Nwtxyd293du2oaGBISEhzM7ONlh7fr4QGaZNE0rkjz4Sd/7GjwLpuNHR6PWMiBB9Hnqod6Ly0kuizwMPCF8OY2JZbk0uEQje+MxhAqKoV26u8KnpKMqQQiQ6efKkgaNfdLTQJd1wg7genQmKSiXEMKVSlGENDm6vMFlTU8NmdTO/jv+a8z6fRwSCVhusePPPN/NA2kG+EqghIHLe6oLddSkHOju29aYj0Wg03Lt3r57bevLJJ+np6anfb2lp2UX8IYWSOTc3lxqNhk5tuTl37NjBezuEeN97773csWMHSbKmuYYeb3hw4paJBvc6JSWFfn5+oewnKp0WBYyYP3++gdXni7gviEAwOCu4yw3pDlqtlkFBQWbnsEhMTGSR7k01A9HR0V0sD1VVglPQfY01GuGq/uLhF+nztg8RCA57bxgf+fYRVjW0j83LExwFICxFOm/wjoQlJUWIAhdeSJaUlDEoKKjbqoU6j9qnnhJESJLI6x9KIALB2MKu8oFWS3p5CU/V3ojKiy+KPl5e5PXXG782X8d/LfRgC+sYECBEpfnzhdt7RobxMUVFRQwODmZeXp0+LKKmpitBSU0lZ8wQa7jtNsNo6azSLN7xvzvo/qY7EQiO3DKSmyM3s7yxnPX15PLlYtztt3fVA+Xl5XWpOGiKjiQ1NVWvW4mJiaG1tbVe+apQKLrMWVRUxKamJoaHh3PXrl2cNWsWSRFJ7+/vz6qqKlZVVdHf398gxccH0R8QgeCR/PY4BlmW6efnVwbAmv1Epb05Ozs/u23bNoPP5+VfXU7vN73N4h4KCwvNLkOpk4vN5VJ6iudobRV5PgCR10T3XKg0Kn6f9D3HvTNOWI82OfPJg0+yqE4QNK1WZJe3sREcyYEDYlxHwqJLWfDAA1ndFpHXQefG/9df5Lx55NgJrUQg+G70u0b769ZsKlEByJ07jR971Z5VdH5pOJVKmc8/367c/fbbHpfMmppaLlxYRgsLmUeOGBIUWSa3bhXckaursArpcLLsJO/Zcw+tN1gTgeCCrQt4KPOQ/r5mZoqkTwqFMGEbu926OLGOVhZTdCQ6650sy3zooYd422236ZWvkiRx2rRp9PX15XPPPUeSfPbZZzl27Fi+8cYbXL58uUHt788//5zDhw/n8OHD+UWn/BA1zTVUBCr43MHnDLbfe++91QAuYT9RaW8+Pj6JnTmFwZsH88pvrqQ5OHLkiNkK2r74s8iyzPDwcNbXd+/qTwpRxspKmHp1NX3T0tJ48uRJRudHc8VPK6hcp6T1Bms+/NvDer1LSopQOALkE08IIqUjLCkpJ7lkSSUtLGT2VgygsVEUhPf2FnFJAOn34iJes/Mao/11StreiIouETXQteSoDiO2jOCke7cQIN97T7zMt9/e83pJQTQE0cxkRkaGnqCUl4uAQ0DkntEZ6RJLEnnDjzdQCpRo+6ot7993P0+VnTII/jt0SIiDrq5dz6sziouLebxDkhlTiAopdGs7duzgrFmzDHxxdNbEzMxMDhkyhBkd2LTCwsIuyZh6w8h3R/LCzy402BYUFEQfH58d7CcqbQsCXEePHt3F4c31NVfeu7dTCrEeoNFoDB58U6DzZzE3QKyoqIgnTpwwqW90NOnjI76u//ufSHHYUTzLqMzgqj2raLHegpbrLXnv3nuZXZ3NpiZh7dE5zKWniwf3t99+Y2FhEwcNEgXXe6Oh8fGCsE2aJOaaccdPdNrk1MWSptVqWVVVTU9PNQFy+/Y4BgcH69vevXv1v19++TQBctGiJtbV1XXh8vJq8ohAcPTsLLq4iCjgESN6X2tCAmltLbi78vJK7tu3jxkZGfzrL0EYrawEl6HVkvHF8Vz+w3IiEHTc6MgXDr/A8sZy/VwtLS08dCiI69erqVCImKOOYld3yldZltuyz1XwxhtvpK+vL52cnPTizcaNG3nxxRdz+PDhDAgI4B9/iHiqP/74g+vXr+9R2X/HHXfwp59+0v+vVqsZEhLS80XphPmfzefk9yYbbFOpVPT09CwGILGfqBCWlpa3vvzyywaqvurqavq+6ctbf+nGVmkExcXFZos+lZWVPQaHGYMxFrn3tZHz5ono3/vuazHq05FTncMH9j9Aqw1WtFhvwXv23MPCukLu2iW+sPb2Wq5fn8GSkhKGhITw8OFWKhRCp9Ab3nuPes5i5IRKIhAMzQllU1MTU1NT9Vnp4uLi+McfuXz77XqqVIbK3I4Eu7a2levX1zMqKpMxMTEMDg5mRESEXo/wSewnxHMOtLAQ52xp2bvzXkODsFB5eZEZGXUMCgpicXElb7pJBC+OGSOysiWWJPKandcQgaDTJie+FPQSK5u6phatqCCXLGkmQN50kyFB6035WlFRwR07dvC+++6jWq2mh4cHly1bxtbWVgYEBDAgIIAtLS3MysrisGHDeOzYMQ4bNowHDx404FKqqqr0/5eXl3PEiBFdOJPo6Gg2dMwh0QumfjKVs7fM7uLYefnll5cDmMx+okL4+voeStDJBm04deoU5306j1M/mWryxY6Pj2d5eXnvHTsgLi6uS56S3lBaWmoyl9IRaWk5vP32csEtzOg+FWVBbQHX/L6GlustafuqLV88/CLDjuRw4sQavYIxK6uMISEhfPFFwVV8/XXPx5Zl8sor2wmL8gl/3vbVbQwPD2dOTo5Jnse9cYHNzc1MT09nSEgIF21dRLfVt+qP99ZbvU7Pu+8WyuQ9exoYFBTE2Nh6Tp0qxl91VQHjM9K4as8qKtYp6LTJiS8HvWzUO5gUUc+DBgnO5qmnslhRUdlpf+/K148++ojhbe64e/fupUKh4LBhw3jRRRdx48aNege1iy++mNOnT6eHhwdXrVrFBx98UG86joyM5Pjx4zlx4kSOHz+en3Wu0EbhuJfRnea6EzRaDe1fs+fK71Z2cdLcsWOHPHDgwNf5XycqAJS+vr6lndnn0NBQvnDoBSrWKVjR2PtLr8s7YY7VR61WMzg42GwFbXR0tNlJlDtmSN+9W5iPnZ279+8gycyqTN70800i5ug1F74V+h5feFFDhUK4nx8+XMlDh0I4b56WDg5CPOoJtbWkm5swpY56/E5O+GiCWedgqmjZrGqm/Wv2nHL7xwTI2bNV7O227NjBNv1RCw8fDuKWLU20sxN6kB0/NvGZA8/QZr0NLddb8rE/Huv2mZBlIR5ZWJBDhwruyFjlBVP0JCtWrGB0dLT+/2HDhrG8vJwPPfQQv/mmPYn43XffrRdpdCZtc9DU1GSyX1VUnvCK3h69vQuHXV1dTW9v7zSeg/f4fEsnOWrixIlS5yJhSqUSN4y7ATJl7EjqpupVB9TU1MDJycmsAu3799fi+eenw8tLgq2tSK84apTIP3vddSJV5PffA/HxIh8qADQ2NkKr1ZqdljI7Oxt+fn6wtLTUh9UHBIi0CGvXGi/MNcx1GL6+6mtsm7oN4z3H48ngtfjRYww2fR2DhgZg2bIBOHZsKh55JAYWFsQtt3Rf4EulUiEtLRbff5+I779X4Y6rRyOpPAlF9UVmnYcpCM8LR6O6Ec8/5o9vv23Ga68dw6lTKV0KuOuQlSXSSMyapcHcucfw/vvz8cgjtpg9m3juu+/xeN4wvHH0DVzkfxG+m/sd3r74bQy0G9hlntpakcryscdEpHFcHDBtGuDk5ARbW1uUlZWZdR4nT55EXV2dft2ybIvjxy1w8uQcfPvtNCxfLp6VH398HStXXgN7e2DoUEsEBo5HbGxjL7O3w9bWFhqNBmp1D+kS2vBt4rewVlrjhsk3oK6uTvdhBgC4uLjA1tbWRZIk8yvknSnOBSXrrkmSdNumTZsMNAyFhYX6WrYzPp3BYe8P69U9/9SpU2YVWtdqSQcHDV1ctFy1SmSff+ABcsUKYVkYNUo4VenYd4VCOJ7dcEMV3367iqdOsdevrw46k3Xn5EWtraImcU/iUFxcHPPy8ijLMven7mfABwFEIHj59ju55GKR4X/ZsmY+9ZRQnD79dNc5ioqKGBQUZOAQmFSaRASCW49tNfmamcqpPHrgUVpvsGajSqjJZFlmZmYmg4ODu5RVUamEI5yzs8xnn02hr6+WFhbk2pcKOOvTOUQgOPfzuYzKExn/Tp48adTrNTZWWNgsLESm/87MZ2FhIbdt28aRI0fywgsv5B9//NFF/Hn44Yc5e/Zsjh07luPHT+aIETfywQdP8corG+noWExAq38eLCxaOHYsuXQp6e39F2+6qZBPPCH0W5aWMidN6tnU3xmmPL9lDWW0fdWWd+y+g6Qo6N6ZY77uuuvK0SEx0z/Vzjkh6dh8fX2/CupUzyElJUWf4W1f6j4iEPzwaM8FcUJCQsyKSNZqZXp6NnHkSJndKetbWkQA3I8/Cg/Siy+W6eCg0j9Yrq4iVubtt4WFpTsic/LkSaOF3HXYtatdHPrll/btxcXFPHr0qAHb3qJu4cawjbR91Za2G+y57KFDtLSU6een4fDh9QTa6+SoVCrGxsby2LFjXTLLybLMUR+M4tKvlppwtQRMISpaWUu/d/x45Y6urgA6r+Xk5GS9mPr00+JaDh7cQEmSOXyEhjdt2UzFOgXd33Tn1/FfG5y/VqtlSEiI3m1AqxWu/BYWwsLUnRTx1FNP8fPPP2d+fj43bdrEJ5980iA6eMKEqXz99UiuWVPBCy8USnHdffbwaOHAgaH089vOXbvI339P5YQJk/SK2qFDh+o/GCkppIuLzFmzjOt6ukN1dXWvBoMH9j9AxToFT5ULn5b09HR9zJAOW7ZsUVtaWt7P/zJR8fb2PtU5b2dkZKTeqUuWZV749YV03OjI/Np8oxfbHJlUh6qqKm7fnkpbWxFIZ8rw4uJiJiQk8eRJEay3erXQbegePjc3kaZg27b21I5qtdokXU9WVruH6H33kTU13edmIYWlSGcBGfLU9fQZ1Kxfx4ABMnNzmxkSEtLj109X4KujGbYnmEJUIvMijRYu00GWZaalpTE6OprR0Wr9mgHyguuy6LVxBKVAiffvu79bJawuB0p+vqz3QL7++nYHQ2MICAhgTk4OQ0NDWVhYyJEjA7h1awjd3dfTzu4Qraxa2tah5dChNXzwQdLP72kuWnQbN23axDFjxvDDDpXeXn31VQ4bNowBAQH8va1A0s8/iw+Dtzf53XdHzXJT6E0nGJkXSSlQ4iO/P6LfVlZWxsTERIN+0dHR9PPz+4n/VaICQDlo0CADPkF3cTsiozKDdq/ZccEXC4zGrBQUFOjFJVNx8uRJFhYW8tgx4QquUAifkJ7qTR0/ftxoVcT8fPLLL4VVxte3/SUZN468//5q7tiR22M5Ux1aW4VLPUAOH97Ev/7qPWv7b2m/cci7Q4hnnTlqcZz+2Fdfnd+rJex40XEiEPwsrqtFwhhMISprD6yl1QYr1jTX9NgvNzeXEybUtImWMmc/9hYRCE79ZCqPFhgpNdgJW7bk0cVFQzs7ka6hN127s7Mzm5rI995L4x13NFGS8vXXasQIIfru2kV9ztqjR49y9OjR1Gq1zMnJ4YYNGxgQEMAJEyZw7dq1BpxferrgWHX+RNnZIq4nP9/4R7A7xMbGGjUAlDeW0+8dPw57f5jBdVWpVHrrlA6NjY308vLK5H+YqIy5+OKLDYhKY2Oj0SqC3yR8QwSCD//2cBdrTXJysskJsXXoKC7V1YkgOqVS5AV5882u2fJlWebhw4d7tRTJMnnqFPnOO+QFF8hUKgUbPXCgyFy2cye7Tbasw6+/NnHAgFba2MjcurX3F6aupY4P7H+ACAS97r2bvkNL+MADqb3Wi5ZlmcPeH8ZLvrmk5wO0oTei0pPo0xm1tbW88spcDp+UTtfnJtFqgxU3hW/qNXC0sbE99GDkyDomJbWLvEuXLuW4ceMM2qhRC/nww8dpYbFPX3HQzk5DS8tf+emnXZOFk0IHFRAQoLf8NDc36+99S0sLb7/9dq5bt45VVcJL2cpKxDO9/XZ7kGRNTQ2Pdc6Y3QvS09OZq0vb3wa1Vs1LvrmEVhusGFcU12WMMeulv79/GQAr/heJiiRJK1977TWD17ewsNAgDqIjnjj4BBEIbgjdYLA9IiLCrILrjY2NRkt9nDzZ/sUZPJj84IP26NvKykoD121TUFlZyZCQ4/zxR6HAGziQbUo+EXD4/vuCy+mMmJgYpqRU8JJLRP/ly3tm7XU4mHaQ3q97UwqUeO+ue3nw8MFer8sLh4XZXhd71BN6IyphOWE9ij461NbWcvfB3bx2h8iTM/qd0UwsTuxxDCkc30aPFtdkzZomrlnzBB988EFeeOGF+qBKWSaTk0X6zTlz2EG5msdBg37lgQMyf/vtEMePH8+ZM2dy+PDhvPHGG/WiSm1tLadMmWLg9UqSYWFh+j6//hrB4cN30smJ+oDGzrGofXFxKC8vZ0d/La2s5cpdK4lA8NPYT42OiYmJ6RKWcu2115ajQ2H2f6Kdc2Kiaz4+Pv87dOiQwQVJSUnptliTVtbytl23EYHg+0feJ2lcXOoN+fn5PRbN1gXf6fQkGzaQUVGnzI5iPn78uEH9Xo1G6G6ee04EtunY79mzhXNYdrbQ9ei+kFqt2G5pKRy5Ouae7QxZlnnkyBFm5Gbw4d8eJgLBcR+M41e/fdUjYUmtSCUCwTcj3uz1fHojKnf9ehcdNjqwobV779Da2lq+vettem/2puV6S74W9hpPnj7Zoye0Vis4Pysroa/46y+heN20aRODg4P5+utvcuXKD/jMM6Iwve66Tp9OWlquZ3w8+cQTT+pd8bdt28Z77rmH33//PUnyvvvu48cff8zW1lZecMEFfNdIwtzY2FjGxBTyiSdkWlq2ENDyhhuM5xruOKZziZeeoFKpGNZ2k2VZ5v377u82qZYOaWlpXZS17733nsrS0vI+/heJire398nOYfsdlbTGoNKoePX3VxOB4IuHX2R9fb3ZRdeTkpJMSsYUHt7OudjaqvnQQ1p2w0R1gSniUmqq+KLqvEYBcvToBr78cqOBI9uxY0LuVyhEblhj+pm8vDwDL9/9qfvp9qYbbV+15eM7Hu/xms79fC7HfjTWYK1qtZoVFRXMzMxkYmIiT5w4wd9++41JSUnMzs5mdXW1wVe4vrWe9q/Zc9WeVcYOQZKsrK7kyi9WUgqUGPBBAI8XHddfq4iICKP6qpIS8tJL2eZVK7LYkeTIkWP4448VvP32Onp4tOo5wIsvFgGJOuu5vb09SeF2f8EFF3DEiBG84oor+Prrr1OtVvPYsWO88sorefHFF/Obb76hhYWFvhrgpEmTePz4CYaHk35+kVQoNAQ09PeP4LFjvbvVZ2Rk9Gj1M4bg4GA2q5p56y8iT+4zf3VTfrINxpS1kZGR9PX1/ZH/NaICQOHn59cliNAUrkOtVfPuX+8mAsGbv7+ZiSm9s84dERERYZZm/siRRl56aRmtrMTVu+ACoenvyYJdUVFhlit/Zib56qutHDOmTk9gJk0iX3uNTEsTep+VK8X2hQtF7hUdmpqaDKrY6VBUV8SLvr6ICAQXfriQZbXGtdCfxn5KBIJRuVHMyspiWFgYQ0NDGR8fz5ycHFZUVLC6upp//fUXy8rKmJmZybg4EWwYFRXFgoICfn78cyIQjMiNMHqM5Pxkjn1nLBEI3v3r3V0KudXX1zM4ONjAl+fgQVEd0NpaJJlqaiL37hX5fSWpso3Yy5w/v5QODvd3qVZIkkqlktOmTeOsWbO4e/dukkLMePvtt/UcXF5eHsd1qnDW1ER+/jk5ebK45i4uMm+4IZfp6aaLMxUVFWbnSQ6KCuKCzxeIvL5hG3vV4bW2tnanrM3gf5Co+M2dO9dAu9rY2GjgFt0TZFnm84eeJwLB6Vund2tuNjbOXHEpLy+PaWlpLC0V5UGHDBFX0cdHpFU05rSWnJxsds3dlJQU5ufnMzdXsPtCJ2BIYF56SWSHc3Uld+9uF3u647y0spabIzdTuU7JQW8M4omCE136VDZU0nqDNa/65CqmpaV1y9UYE3/q6+uZkpLCCe9M4NB3hhrVIexJ2kOnV53ouNGROxJ3dHv+iYmJfOmllzh8+CgOGbKzLRCxkG5u2+jkdIAKRSMBUpJqCXxDf/+1tLJy4e7du3nZZZfxjjvuoL+/v57LOHHihNG0A+Xl5bznnnv0SlEdUZFl4UT34IMiOx0gxNRPPhHBjnFxcV3KlvYEtVqtF2dMQXxxPIe+PZQW6yz4dXwvwVwdYCwXkJ+fXyn/g0Rlxs0332xg8ywvL+/CyvWG9b+sp8NGB7q96cY/M/7stX99fT2PHu3dZNkRiYmJXXQj+/YJtlyS2JYCQJiVdelVgoOD2blsa0/QZazr7HWbl9eVwNjbt/9eubKex4+f6HX+kOwQur3hRpv1NtwR3/5i6/LDXv3l1XTc6Mjalu5jmrrTqZwuP00Ego/89Ig+Ty4pCN76w+upCFRw9JbRTK3oXo9FCj3J//73PadNM/RfAQTHct99gntpbRV+JykpKXR1deXJkye5efPmLmkFOkO3X5Zljh8/Xi82HzgQw1GjPtKnhrCxEYm8g4MNLW+ZmZldMuf3huDgriU8OkOWZX4S+wmtN1jT801PfhH0RY/9OyMyMrKL3mzs2LGlAOz5D73P50vsj/eQIUNsOm5oaWmBjY1Nd/2NYr7rfBxbfQye9p64+NuL8dBvD6FB1dBt/5qaGrj0VAbPCGpraw3KnyqVonLdgQNAbi7w2mtAUZGonuflBaxcqUZysgckyfRikFVVVXB1ddVXn9Nh0CARyxIVBeTlAe++C3Ss/Prttw6wth7X6/yL/Bch/v54jHMfh1t+vQVP/PEETqedRnJyMqZOnYrnL3we9ap6fBX/lclr1uHDmA9hpbTCC5e9gFGjRiEmJgbpuem4bud1eDn8ZVw58krE3BuDgIEBPc6zZ88eNDQsQVycuG6Wlvl4/HEgIgIoLBRVGC++GLCyAq666iq8+OKLuOyyy7Bnzx54eHjA2tow5KW6uhqtra0AgIqKCkRGRmLs2LGQJAkjRkzETz9JuOYa4vLLJyM19UFYWopqksXFwLffivKoHUuwuri4oLa2FubA0dER9fX13e4vaSjB9T9dj/v234dF/osQtzoOIy1HmnUMGxsbtLS0GGzzFXVpvc2a6EzwT1GvnpqFhcX9W7duNSDhGRkZXTTZPaEje9moauTaA2spBUoc8u4Q/pVpPL1XcnKyWRnzTRWXZFlYdu65h3RwEGZMLy9RGS8iovc4oaSkJLPEpYoK8pNPyrlxY0Gvfiwd0app5V0/3UUEgvM+mseaphr9vlnbZzHggwBqZeOLNcap1DTX0GGjgz4ehSSzKrI4fPNwKgIVDDwUaHIUuLOzM5ubyRdeyGRoaC2dnJy77VtRUUEXFxd6e3tz6dKlTEhI4Msvv8zBgwfT1dWVa9euZXBwsEHagW3bPuf+/YIL0bnhW1mVc8SIfTx2rHcdm1ot6jOZg+6UtbIs8/Pjn9PldRdab7DmGxFv6K+7uYnGUlJSujzTq1atqgSwgP8lTmXAgAEjfXx8DNbS0tLS5WvTE1pbW/X97Szt8O6l7yL8rnBYW1jjom8uwoqfVyC3JtdgTG1trVkRxg0NDXBwcOi1nyQBc+cC27cDhw6l4PPP6zB/vijgPn8+MGQI8OSTolA42XV8RUUF3N3dTV7XgAHE6NEn8dhjA3ssaN4ZVkorPDfpOTw15ilEl0dj4ZcLUVBXAAB4ZNYjSKtMw5+Zf5o835fxX6JB1YA1M9cAABJKEjDvf/NQ3FKM1ye+jtVjVqNjBPqFF16I8ePHd2l79uwBIArOP/aYM+zt06FQdH9iKpUKFhYWyM3NxaFDhzB06FAsXboUOTk5KC4uRlVVFcLCwpCQkIT330/A3LlJeP75u3HFFcDvvwO33KLAd98VIyWlHunpV2D6dKtez9XCwgKyLEPWFZI2AU5OTqirqzPYllCSgKVfL8Wqvasw0XMiEh9IxNPznoZCan8daOwh6QbW1tZo1oXRt8Hf398WgI/Jk5whzguiYm1t7e/jY3jO5oo/xvrPGzwP8ffF45VFr2Bf6j6M/mg0Xgp6SS8SqVQqswhXX8QllaoGt91mi59+AsrKBCs9ZQqwZQswcyYwYoQoGn7ihCAwDQ0NsLW17SL69ITa2lrY2tqaLS5WVlaivLwcr1//OnZcvgMZlRmYtX0WThSfwPVjr4eXgxfeP/q+SXNpZA22xGzBvEHzMM1nGv7M/BML/rcAarUaf938F9ZesRZHjx7FvHnzoFAoEBsbi0OHDiE5OblLs7a2RlNTE/z9/bF9+3ZUV1dj8ODByM7OxqxZszBixAisWLECqrbcDj/++COuvfZaWFpaAhBihu6FVyisMXHiY/jii6kYNAhYsgT47jvg0kuBfftEMfhPPwUuvdQaDQ3miTPGiERPsLW11YsmxfXFuGfvPZjyyRQklCZg6+VbEXxHcBex0NLS0qQ0CDrY2NjoxTwdBg8ebGtrazvI5EnOEOcFUSHp5+1tKPKdDaICALaWtghcHIjUh1OxfMxyvBr+KoZvGY63o95Gi7bF4MvZG+rr683ibEhCq9V2eNiBW28F9u4FSkuBL74QROXNN4GpU8XvJ5/UorDQ1ygH0x3y8/MxePBg0wcA0Gg0SEpKwuTJk6FQKLBi+gr8dt1v0Kg1WPjlQkTlR+GhGQ/hj4w/cKL4RK/z/ZjyI7Kqs/DEnCfwvxP/w7LvlsHd0h1ht4dh7vC5sLS0xODBg7Fu3TosXLiw23m0Wi0eeugh3HHHHVi9ejW+//57JCQk4Pbbb8czzzyDxx57DBkZGXB1dcXnn38OAPj+++9x88036+dobgaOHvXEypUaeHoSTz45GYWFF2PWLGDnTnHtv/tO6MKs2pgSZ2dns3UkTk5OPepIOsPGxgYldSV4MehFjPxgJL5O+BqPz3kcGWsycP/0+w24k45jOutIejtG5/7e3t5wcXEZYfIkZ4p/Ss7qqXl5eWV3to6Ym4UtIyOjS6yEMUTnR+v9NVxfc+XbUW/36PXZEXFxcWZ5RdbW1poU81FeLgLhLr2U+jyugwaRa9f2roPROdb1pbaRMetFQlYC/d/0p82rNvw24Vs6bXLi8h+Wd+nXUdbXylqO+2gcx300jm9HvS1M++9PZ35ZV9N+YmIiH3744W6viy61o85BbeDAgZw8eTKDg4M5cOBARkdHc9WqVfp+2dnZ9PHxYWWllt9+S153HfVxPVZWDXR23sOFC99haWnPlQ5IEQPW2eLWE/Lz87utYtgZJfUlfOrPp2iz3oZSoMQbf7qRGZW9p400V+/X0NDQxaKZlJTEwYMHH+B/SaeiVCqtLSy6WkfM4SJM5Wxm+83Gn7f9ib9u/gsjnEbgiT+fwKB3B+HZQ88ivzb/rBxDh4aGBjg6Ovbaz80NWLVKWJD27DmCzz/XYPJkYOtWoYPx8wMefhgIDgY0GsOxzc3NsLOzMyvLXUtLC6qqqjBkyJAu+yYOnYjfb/wdg20G4849d8LD3gO7Tu1Ccllyt/PtTd2LlPIUEMQTfz6BhW4LceC2A/Bz9+vSd8yYMZg0aVK3eoLCwkIMGjQIAwcOxOHDh/Huu+9i3rx50Gg0cHV1xezZs/HZZ5/Bz88PublN+Osvf0yYUAgvLwVWrhSWsTvvBHbtqkd4eBpqaq5CaOhj8PDoXRdmb2+PpqamXvvpYAoXkVKWgvv334+h7w/F29FvY57bPCQ9kIQfrv8BwwcMPyvH6K2/t7c3tFqtr8mTnCHOOVGRJElpZWVlQFG0Wq1ZLwkgFLXmvPBT3aZi+4LtiLw7EkuHLcXmqM0Y+v5Q3PTzTQjPDTf60KtUKlhZ9a7E6+uaZFmGnV0r7r7bAnv3Ch3Mjh1C6fvFF8AFFwA+PsC99wJ//gmo1X3T8+Tk5GDo0KHdEu0xQ8bg9xW/Y5zjOGRUZQAAXgt/zWhfkvp9J8tPwrXAFWUfl2HxgsVGla8WFhbIzs7uokzsDTY2NnBzc0NREfDBB8CNN7ojNTUE994LZGSINJzR0UBBAfDRR8BVV9mhudl0fYfuGJ31EX3pr5W12Ju6Fxd+fSHGbx2PL+O/xM3jb8bJB09i3cR1CHDt2Zx+JmtSKpVdlMcDBgyARqNxNXmSM4TpzhN/H9zc3d0NrkJffFTMtRbpjjFp0CTMHTQXOTU5+ODoB/jsxGf4IeUHjBwwEndPuRu3T7odPo7tSmRzuSdX1/Z7SQpC0NoK6O67bjpJAlpaWkHao7FR/G9lJfLjXn+90BMcPAjs2iVy5W7fLvpcc40d3nnH9DWRRHFxcY96DQAY7jccv97wK2785UYcqz6Gnck7sX7xegx2GIyGhgZ9HtU/sv5AbFEsAGCZ1zJ8++i3cHXp+flNTk7GggULjO7z9fVFfn47x1hQUABfX19IkjcyMj6Cb9v31srKCt7ewfj446WYNAlQKMT1KCzU/VaistISpaViu26/QgFYWra3jt8ua2trs7iCzv1PV5zGV/Ff4evEr1FUXwQ/Jz9svGAjVk9bDTc7NwBAQ24DWlpa9Ho2U45RVVVl8pp0IKl/ViVJgqWpBzwLOB+Iip2Dg4PBW6HRaEy+6DqYy0V0Jlz+Lv54+5K3sW7JOvx88md8ceILPHf4ObwQ9AIuHnYJLh98M5RZfpBl4RBVXAzU1IhWWytaTQ1QVycIgEoFNDSMgCwroVJB33qGLYAZJp8DCeze7YJ77rGFv79pY3RmcVOsS/6+/vj5+p9x0fcXIa0hDXf9cBc2TdgEZ2dnqNVqREZG4qrQqwAAV/tcjS9v/BIuzi69zltbW9vBOmPIkc6YMQPp6enIzs6Gr68vdu7ciR07dmDXLg9UVLSLCyqVAsXFS3HttT0daW6va+lIZJTKYbCwIGxtAXt7wMlJKNcdHdt/u7kBHh6Apyfg4WGJ+LRGHFFtxy/ZX+BIwREoJSUuG3kZtly6BVePvhoWCsNXTEeITBGLAfM5FXFOCgOiAgBKpdIMZ4MzQ69ERZKkLwBcAaCM5Pi2bZMBbANgA0AD4EGSMW1p8N8HsAxAE4A7SR5vG/MYgNsAvEHyh45r6ExAZFk2iyPosFaT+6rVatja2hps02iA1CQHKE/eiQUZd8IuqR5xSU04WOSIP1R2XeZQKABnZ8DFRfx1dhY+KLa2gsuoqamCj89A2NkpYGUFWFuLZmUlPHF1Epbub21tHZqbW+Dh4QFZFttlGV1+y7Lu2ERDQzIuuWS8yedtrrg02Gcw9i/fj5t+vQkXDLtAz2FUVVVh0aJFuLnwZsiNMj5a/lGvBGX37t1Ys2YNysvLERoaiu+++w7fffcdioqKcM899+D333+HhYUFPvzwQ1xyySXQarW4++67MW7cOAwapIJWm4BvvvkJDQ1NGDLEH/ff/wCUSkuQ7den4+/s7Bx4enrBysrG4Pqp1cZbXZ0KdXXNcHAYgIYGoL5etPJy8beuDqiu7uxbdAkAQGl/AwYNacb0cS4Yr7FFK4Fca2DYMENPXCsrK7NMxAqFotvKA91BkqQuBFsy4+WQJOlSiPdYCeAzkq9LkjQOwGcA0gDcRbJbBx1TOJUvAXwI4OsO294EsI7kAUmSlrX9vxjAZQBGtrVZALYCmCVJkgPEJ3gmgF8A9EhUOlPZvwM6wpWVBfzyCxASAoSHi4cHEC/9iBGOmDvREcOuIVqsM5BU+ycyFOEoVRwHHEoxfdhoXB6wDJeNuAzTfKZ1MQmGhp7GwoULTXZIy86uhCRJJnMdGo0W0dE1MOcjVFtbC09PT5P7A0BZURl+vOpHFBQUGDgZ1tfXY7XPavj6+qKgoAADB3YtldER1157La5tYy2ysrL03JKPjw9+//13fb9ly5Zh2bJlBmOdnKyweHE1AgNfNXndSUn18PZ2gJubaaJ0XZ0a6enZmDZtgMH2Vk0rIvMj8UfGHziQ9ieSc4qBRg8MtZyDQS3TMNP7GjSWeSIjwwXxscCeX9rFWw8PYPZs4MILgRtuEETCHIc5SZLMcn4D2jmVrlNJEnuZTJIkJYCPAFwEoADAMUmS9gJ4HMBVAG4CcDGAP7qbo1eiQjJMkiT/zpsB6Bw2nAHoCsZcDeDrtoUfkSTJRZIkbwANHcZ1WYOlpaXBW0HSbEWtuaisVOCZZ1ywb5/4f/Ro4UOyeDEwaZL4wrRLUxLq6jyQkTEbU6Y8gLjiOPye/jt+T/8dgSGBeCXkFQy0HYiFQxZisf9iLBqyCBM8J5hNHDUaTRfuqSdotVqznOQAQQhGjDDdZaGurg4WFhYYPnw4HB0dceTIEcyePRuyLCMuLg7Tpk2Do6MjQkNDoVarTRZbHR0dUVpaatbazYWlpSU0nc1lPUD3Mqq0KsQWxSI8NxxheWEIzQlFo7oRlgpLLBiyAG9ccwuuHX0tRg4cidDQUCxc6GHw4VCpgNOnheI4OlpYpPbuBZ54Arj5Zje8+KLpvi3dEIgeYYwQtT0nCgC9sT0zAWSQzGqbayfEe62EeH9lAD0+1H3VqawFcFCSpLfaFqoTXn0BdLTLFgDwJRkrSVISgFgAmzuvwcLCogtR+bs5lRde8EFUlDVefFFYUwb14m+oW5MkSZjuMx3Tfabj5UUvo7yxHAczD+Jw9mGE5oRi9+ndAABXG1eMtB2JS3gJZvjMwAzfGfBy8Or1GOYQ074QFY1GA2Pm++5QXV2tDxnw8PAAAISEhKClpQVLlizROwNaWFigoqICnZ0YuwNJVFZWmrV2c53TGhsbe9WzkUROTQ5ii2Lx/OHnkVGdAZuDNmjRCAXsGLcxuGPSHbh0xKVYMnQJHKwMTdM1NTXQarUG19TKSgR6TpwoCqMBwMmTwov6k09c8PXXLiY7N5JERUWF6ScNcc80Go3BuWdmZrpBqCt6q2xm7B2eBSEO/QYgHUL10S0kU6hgG6eyv4NOZQuAUJK/SJJ0I4B7SV4oSdJ+AK+TjGjrdxjAMyRje5h76uWXXx755JNP6nlUnWXBnK+2qXE5Ojz00ERkZTnh8cfTsHhxOSwte74OWq0Wra2tsLPrqlvpiNKWUiTUJiCxJhHJNcnIb8mHDMHuulu7Y6jdUAyxH4IhdkPgb+ePIfZD4GAh1t3S0gKlUmny116WZTQ3N8Pe3t6k/oC4Tvb29iYRbZKobKqEpWQJZzsRma3VatHQIBhPR0dHPRHMrcqFrbUt3O3cTZpbrVajubnZLA/lzhHivaGxsRFKpRI2NjYgiWp1NfKa8pDXlIfcplzkNuYivSEddRpD0/Mlnpdgnts8THCaABcrl17X5OTk1Os5Nzcr8dtv3vjoI8ElBgeHmHQOsiyjvr7erPOura3tooxfsmQJADiT7NHOLknS9QAuJXlP2/+3AZhF8mFTj99XTuUOAI+2/f4JQoEDAIUAOn7z/dq29QSNtbV1/eLFi/VEpbKyEkVFRZgwYYLJCwoJCcHixYtN7v/GGxl45hlHbNw4Ftu2iViQBQuEu/yECULZ2hF1dXXIyMjA1KlTe517BVbo1zRj7gycKDmBY4XHEFcch5PlJ7GvZJ/+SwgAPo4+GOY6DK6SK4a4DME4v3EY4jwEg50Hw8PeAwNsB0Cp6MqRtLS04MSJE5gzZ47J5x0VFYVp06bB2toaMmVUNVehpKEEJQ0lyKrOQkZVBtKr0pFemY6U8hQAgK+dLwqeKkBdXR3i4uJwwQUX4OjRo1AqlZg9ezZUUGHJ60v0x5jiNQUjBozQt2Guw+Dj6AMvBy84WjlCkqSzfo81sgZVzVXIr81Hfl0+8mvzEV8ajyp1FUpaS5BakYrqlmp9f3tLe4x2G40bh96I6T7TMc1nGobaDUV2RjamT59u8pqE+LOwC1FpbARiY9vFn0OHhFVw7twmfPRRNSZPNn4endHa2orjx4+bfY+nT59uwKmMGDGiPCMjwxTPvr68wwboK1EpArAIQAiACyBYIgDYC+DhNjlsFoBaksW9zKVRdbK16rTXfyeGD1fjr7+qkJDgiZ07hTfrjrYyzUolMHasqKXs7y8sOh4eFiCtMXgw4Ooq+pgCO0s7zB88H/MHz9dv08pa5Nbm4mT5SX3LqclBfGU8DuQdgCbBUA8gQYKLjQsG2g2Em50bBtgOgJ2lHawV1qivqsfQ2qGwsbCBUlKCaHOXbvvbomlBg6oBjepGNKgaUFJVAlW8CpUtlShrLINGNjyWtdIarVpDE+ZNfjehtLQUJ0+exPTp0+Ho6AgLCwuMGTMGR44cwciRI/HYmMfw7ql3AQAnSk4gviQeSoWyy/y2FrbwcvCCg9IBdhZ28Ej2gKO1IxytHGFvaQ+lQgmFpIBCUujPp1XTihZNC7ILsvFF9Rdo0bSgpqUGVc1V+lbb2lU0slJYwcfBB0MHDMWKcSswxn0MRruNxhi3MfB18u2iWK+rqzNZ7FaphHNiaqo9GhokZGYKJ7yMDCAzUzSd0SYgALjrLqGz8/AoMssHqy+qAGNjtFqthN71KQBwDMBISZKGQhCTmwDcYs7xTTEpfw9h2XGTJKkAwCsAVgN4XxKZh1oA3NvW/XcIc3IGhEn5LhPWoFGr1QayR1803uZC6EdkXHYZcNllwkyYmwscP97ekpJEJKtwE7ADMK5tLDBgADBwoPBbGDhQ+DA4OAj/Bt3fsrLBOHlSA1tbSyiVgIWFrinbfCKGYZLFFZhiD8AeqLSpRGNjA+zd7FHeVIrSxjLUqmpQ11KHutZa1FbVoq64Fhmt9WjRNkOlVaHJogQhlSFoVjdDprBoSZD0f20sbOBg5QAHKwfYW9nDwsICbhZueh2Pp4On+GvvCZVWhS0xW7A/bT+8Hbzx4sIXsWrKKpSXlCMmJgazZs0y8K/w8PBAfX094uLi8PLFLyPw6kC8FfUW3ol+B63aVtw56U7cOflONGua9dyQruWW5UKr1CK/Lh/1rfV6wqeVtZApQ6YMLcU7YK20hrXSBiyaAOtmL1ho3GFnNQoOVg7wtXLAKEt7OFg5wMnWHgPtBsLdfiDcHQaioqAcg319YW9vJxzfqgG5Gog/CRwzYlKurlagstIbhw/DwKRcV9f+u6JCBCTW1OiuQjtX4+gogkInTQJuvFFYfWbPFs+HDmlp5rlL9IWoGHPJkGUZvVl+2o6nkSTpYQAHIZSzX5BMMef4plh/bu5m1zQjfQngIXMWAEDd2W7fF4132/FNvgGdQ8olSXAl/v7A8uXt/WRZfJEyM7X46680DBw4BhUVMGi5ueIhbGxs/yswzMwzGNjWAMANOiLWGw4dUmPpUtP0MDU1NcjKyjIQ48oby7EudB22xW6DnaUdXrvgNaydvRZ2lnaoq6tDeno6pkyZglOnTqGgoAC7indBrpFhe9QWGo0GkyZNQkJCAmbPno31S9bjoRkPYUPYBnwS9wl2JO/AM/OewdPznoaNRfsXOiwsDHPnzjVZafzOOw144gPTdWYCLmb2d2hrAjrHt47Ob5MmdXR+A8rKknDhhRMwfLj4wPT2+JljIQP6FrJi7D0whaB06Ps7BIPQJ5wPHrWtzc3NBiesVCrNMgUC7eZDU2+YjY0NGht7U4QLJzMvL8DLSwm1uhSLF4/pdYwsC/k5Lu40nJ3d4OLiBo1GONdptTD4rVa3O1M1NTUjOzsbY8aMBdDVOa6kBNizB9i9G9B5h48Z04whQxoAmJbUycnJSe/RShDbj2/Hc4efQ31rPe6ddi8CFwfCw15YeXQ6FJ3I4+vri7KKMry2T8T6PHvls3pFq42Njd7c7OngiQ+XfYjHZj+G5w4/h1dCXsFXCV9hy6VbcHnA5WhpESknzLFCjRtXBScnW9TVCbnT11eEMFxzjbg/nR3gZBmIiYnF1KnTQEoGjnEd3fQ7tsLCHNjaKjF06CDY2Rm68BuDVqtFVFQNZs82+TTMjgczN+cPAKOeyj05q51tnA9Epby8vNzgClhbW5vtmqyLzvwnYyq6g0IhxB9fX0tYWjbB1FQnWq0VIiLK0TEsJitLEJHdu4XCjxQ+NMuXi2hcZ+fKtsha04iKQqGAm5sbDqUcwktHX0JMYQyW+C/Bh8s+xFj3sfp+nQkKIETGCrabNxU27bdNZ27WERZra2sMHzAcP97wIw5nHcbDBx7GFd9fgSsDrsTa0WsxbJB5XJybWzlyc52RleWMX34RDotbtojgwrlzBYFZvlzovwDxYtXUNGD6dNNFh5qaRri7u8NUI6K58WZ9GdOXYwCG3uUkoVarzftKnwHOeZQySXVzc7OBAslcpyXA/GAwc0PKdesyNwuXOcdQKpUggfh4LQIDBas9fLhIPdnQALzyCpCQIJSBmzcD48aJ5EI17QJ+r2hUNWJr9lZctusy5Nbk4rvl3+Hw7Yd7JSg6dEyBEF8Sb7DPw8NDr7zt+FFYOmwpEu5PwJsXvomg7CBctv8y/FjwI7Sy6e7nIkGWI6ZOFcnFT50CkpOBwEBxbR5/XIiu06cDmzYB8fFNJsfX6HC2EoP1hDONUesNxvQpdXV1UCgU5oVsnwHOOVEBAK1WqzI3vqEzzkbeibM9xt7eXu/T0RNIka/22WeB22+fhSlTlFi/Xsjxb78tuJX4eEFUJk40lNsdHBzQ0NBgkg4qPDcck7ZNwtYTW3HdkOsQekMobplwi8FD2BNBAQyJSlxRXJf93REWK6UVnpr3FA5ccQALvBfgmcPPYP7/5uN0xele161Wq6FQKDrFsgii+vLL4tqkpwNvvCGscs8/D0yb5oBbbhmPV14RxMcUjUJjY2Ovfkgd0ReiItZ+9vME9dS/uLgYFhYWRd0MOes4L4iKUqms6Ow1aGFh8bdyBRYWFmYHapl7jJ5KMmi1ItZo7VrBss+cKQiIv7+MV1+tQHGx2P/448DQod0fQ5IkuLi4oLq6uts+zepmPH7wcSz6chEIIuSOEHx909coyipCR3N+bwQFAJLKkjDGbQwGWA1AXHFXogJ0T1jq6+shV8s4eNdBfLf8O6RVpmHytsl4K+qtHrmWoqKiXj11R4wAnn4aOHpUlC95+ulCDByowIYNwu9o3DjB1Zw8aXy8LmraHD2PuaJJX5SuZ4uoaDSa3G6GnHWcF0RFoVAUFhcburOY+wL3RQ8DmJep3MHBwaycpDqPRh3xIoVD1GOPiWxuCxeK+jVTpgBffilMlXv3qnDppbkwJ+Zv8ODByMvLM7ovpjAGkz+ZjHePvIsHpj+AhPsTsMh/EWxsbBAQEICkpCSQNImgAIJTGe8xHgEOAd0SFaArYZFlGQkJCZg0aRKUSiVumXALUh5MwaUjLsVTfz2FhV8uRE5NjtG58vPz4efXNYtcdxg0CLj88kyEhooUFR9/LCw169cL4jJhArBhA5Ca2j6mvr7ebHHJ3DEdgzH/rjHdEZX6+vpMsw58BjgviIparc4pKjLkzswlEv+EjsTFxcUs/QUgdB7Hj9dj3ToRtDhjhnjI58wRSZjLy4VF5447hO+Ls7Mz6urqzHL+GzhwIGpqagzORaaMzZGbMe+LeWjRtODQbYfw0eUfGcSu+Pr6QqlUIikpySSCUttSi6zqLEz0nIgAxwCcrjiNRlX3FjQdYYmOjkZcXJwuAbN+v5eDF3av2I3vln+H5LJkTN42GT+m/Ggwhy6g0ZyQjebmZlhYWMDS0hKensADD4go9MJCodh1dRWi5OjRQm/12msiotncDHp1dXVmhRk0NzebLS6ZS1SM9c/Pz2+tr6//b3EqVVVV6UVFRQYswz+hI3F0dDSrxEJvFeY6oqQEeP994I47xmDmTBesWyfMoJ99JjiSXbuAFSuE70NHSJKEAQMGmGWZEh7IQ7FvXwkAoKyxDJfvuBxPH3oaV4+6Ggn3J2DpsKVGx/r7+yM/Px9ubm69xk4dLTwKgpjtNxsBDgGQKXdR1naGm5sbrKysUFlZaZTbkCQJt0y4BfH3xWOM+xis+HkFVu9djUZVI0jg559LoFCYlwi+pKTEaHoHb2+R6zcsDMjPF/fH0RF48UVgwQI/3H77YPzvf8LZrTf0RVwylwjpcKY6mJycnCa0ZxL423FeEBWVSlWYl5dnEJfQFx2JOVwHYD7noVMWdmeZUqmEqfOyywQBWbsWACzw6KP5yM8HgoJEguvePoienp4oKSnpsQ8pPH7XrRPK26VLh+C66wbhm+AoTNo2CcHZwdh6+Vb8dMNPcLExfsC6ujqcOHEC8+fPh1arxfHjx9E5ZKIjovOjIUHCTN+ZCHAUeVZ7EoGamppw5MgRuLu7Y+rUqV10LB0x1HUowu4Mw3Pzn8PnJz7HjO0z8OE3WVi1KgCzZrlh5EjgkUcEx9GbKqykpAReXj1HhPv6ivkiIoCcHGDVqmyUl1vg7ruF38utt4r0nd0xjH0hEOYmyNJoNGdFB5Obm6sG0Fu4zFnDeUFUABTn5uYaUBBziUpbHk6zxRlzw+mN1YdJSxNKwkGDRD7Z5GTgueeEUjA+XoGrr86At7fp4oy7uzvKy8u76Ht0VqLnnhNxSRMnCuVjUpLY7z0yH3eELIGztTNiVsfg/un3d/uV66hDcXZ2xuTJk+Hl5YXIyMhuCVpUQRTGe4yHk7UT3Kzc4GnviePFx7v0I4mcnBzExMRg1KhRGDlyZLfK246wVFpi49KN+PO2P1HRVIFnM+bAwVk8AxkZQv+0ZIngOO67rz35d0e0trZCrVabFbnt7t6EVatKkJoq4cgR4f9z4IAIMh0+XJioO1+SviQcN5cQ9YVwGSMqhYWFwH+UqBh8f2xtbc0qlwCY77NhruIVaOduNBrg559FUqdRo4B33hFOWPv3iy/fq68CY8a0izPm5A5RKpUYMGAAysrKjFqJNm8WPhmPP96eByZg2UEU3zQM8z1n4dCKQ5joObHb+btTyvr6+mLu3LkoKCjQExedbkemjKMFRzHHT0TLSpKEaT7TDDgVjUaDvLw8hIWFob6+HvPnzzfIBmcKYQGAC4ddiN+u/g3+A9zQ8IAHxl8oipmNHy8sZBdcIII/L7lEuMvfdZe47q2t5it1gXZxSZKAWbPaC7Pv3Cksb88/L67zDTeIaGNZFukFzCEqOmW9Oflv+kK4jOlU2t6Jf8xP5R8pLtRbA2A5ZMiQMnaAqcXQOyIvL4/p6elmjQkLC6NKpTK5f25uLR95JI+DBwun76FDyY0byaKi7seUlJQwMTHR5GOoVOTu3Q289toSenqK41hbk1deSf7vf2RlJfn556S9PensoqH/vU9QCpT4WthrrK6pZmhoaLfFxWpraxkUFMS6uroe11BfX8/ExEQGBQUxNjaW+4/tJwLB7ce2U61WMygoiM/9+RwV6xSMPxnPmJgYBgUF8dSpU2xubu5x7tLSUoaEhLClpcXo/paWFgYFBbG2sZb37LmHCAQn3P8GHRxkuriQP/1ENjeTe/aQt91GOjuLa+ToKHPp0hL+8IOajY2mXGmBqKgoNvYw4PRp8vHHyQEDxHHGjiWffvo0GxrU3Y7pjKqqKh4/ftz0RZE8fvw4q6qqTO5vrGh8a2srvby8cvlPvs//5MF6al5eXhkNDYaVAsPDw9na2mryRTW1ImBHJCQksLy8vNd+OTnkmjWkvb2oILhokczdu0lTCtppNJpeKy42NJC7d5N33EG6uoo7Y2ur4bXXqrhzJ6mjAZWVogofQE6dU02X58bTeZMzf0v7TT9XamoqT58+3eUYphKUjtBqtaypqeEL+18gAsHdh3czLCyM+/bt4zt73yECwa+jv2ZdXZ1ZFSV7IizHjh1jUQcq/Wnsp7TaYMVhr1zMiVObCZCrV4trRpKtreSBA+QttzTSxUVNQFQpvP568vvv26+dMahUKoaEhJi05uZm8uuvyYkTNQRIT0/ytdfEPekNWVlZzMrKMuk4OphbMbGiooLx8fEG2+Li4ujn57ef/0Wi4uvr+0NERITBBUlKSjKr5KMsywblOE1BTk4OMzK6Lz+Zn08+8ABpaSna7beTO3emmrUukoyJiWF1dbXBttJSUe70yitJGxtxN1xcxNf311/J7OwSxsXF6fsHB5N+fqSFBXnLYwm0WmfLkVtGMq3CsPSmVqtlWFiYwfH6QlA64uJvLubYj8Z2WEswG1WNtN5gzbUH1vZpTmOEpaCggLGxsV36huaEcsAbA+i20YcrHyigJJGjR5O6d0iW5bZzruehQ+KeeXm1c3lXXUV+9RXZ+cOfl5fHU6dOmbXu9PQMfvNNES+9lHoC9uyzZKfba4ATJ06w0hTq0waNRmMysdMhIyODOTk5Bts++eQTjZ2d3WP8LxIVCwuL1e+8846BHJKXl2dyrVodzBVnamtru9SeJcULv2YNaWUliMkDD5B5ebp9pWaJM6QQgeLj45maSr75Jjl3LilJ4g4MGUI+8gh5+LAQfXSQZZkRERGsqKjlc8+J/iNHylzz2VdEIDjv83msaKwweryGhgYGBQWxvr7+jAlKk6qJNq/aGBAPHfG+6OuLOObDMX2alzQkLGVlZQwNDe32/qVWpHLElhG03mDNlz4Lobe3uD/vv08WFBR2+UprNGRYGPnoo4IYA4IgX3IJ+emnZEEBGRERwc4ccm+IjIxkU1MTSTIxkbz5ZjG3qyv5xhtk2y4DBAcHm8V1VFZW8sSJE2atKy4ursuH69Zbb60AMJf/RaICYMo111xjoFepq6tjTEyMWRfWVHFGB53uRnfDZVnoLQYMEA/g6tVC9OkIrVbLoKAgk9j9hgZy/37y4Ydl+vk1UheAP2UKGRhInjghjtkd4uKqOXZsPQHyrru1XPXjo0QgeONPN7JZ3bPuIj6+jhdeWMQXX0zsM0EhyT8z/iQCYSBi6YjKW5FvEYFgXk1en+cvLS3lgw+m8vLLC5mR0fM5lTeWc97n84hA8OV9H/CKK8T1nD27gnl53Y/VaskjR8inniKHDaP+PowY0cBnniFDQgwJendQqVRd9BakuI+XXSbm9PUlf/mlfV9DQwOjo6N7n7wD+iIuBQcHd9GlBQQElAKw43+UqFj5+/ufsbI2NzfXbGVtYmIiS0tLmZ9PLl0qrsq8eWRKSvdjYmJiWFtb22W7RkMePy6+WBdcIL6kQj9CLlzYwMDACubmmrau774jHR1JBwc1t35awWt2XkMEgs/89Qy1snFFrA7fftv+4lxySSHr6+tNO6gRPHnwSVqut2RDa/sXXUdUkkuThQI3bnuf5y8vL+fEiVVtooTM//2vZ0LbrG7mip9WEIHg038+w1deKaeVlZZeXuRff/V+PFkmExLItWuLOGdOCy0sxHVyciKvvZb84AMyOdn4GgoKCnoUl0JCyEmTxHw33ST0ORkZGczOzu59YR1wNpS0KpXqH1fS8nwiKiTh7e2d0VkLb66ytq6uzqg40xPKysq4Z89penuTDg7k1q3iy9YTdLJ4aysZFUW+/jq5bJl4MHUv8/jx5BNPkH/+KZR8ra2tDAkJ6ZXDqasTCltAiElxCRWc/v50IhDccmRLr2Nvu619DQB55Egdg4KCurDGpkCWZY7YMoKXfHOJwXYdUZFlmYPeGcSrv7/a7LlJsqioiCEhIfz551aDNV93XVf9R0doZS3v33c/EQhe9clVjI1VccwYISK+8AKp7sUwo1Kp9Mrz2lpy1y7BleqsegDp7k7ecAP50UeCyGg0Qonc23VUqchXXyWVSnLiRHL//iN6cclU9EVc6iz+nThxgoMGDfqN/2Wi4ufn91NUVJTBhUlMTGRZmQED0yN0ylpzbohGo+WIEXV0d5eZlNR9P5VKfOH+9z/yoYe0HD++mnZ2sv4hHDOGvPdewSUUFBifIz4+3sCy0RmxseSIEaRCQb78MllRX8P5X8ynYp2C6/au6/E8YmLI4cNJhUKmr68QtTZsEPsaGhoYFhbGU6dOdWtuNobEkkQiEPwk9hOD7R0V4mt+X0ObV21Y32o6N6RSqXjixAkeOXJE/9F44AFxHadOraKFhcxBg4ROpDtotVqu/HIlEQje8sstrK5V8a67xBwLFggle3dIS0szqqCXZTIrS5jsb7utXRcDkA4OMidNquYTT8j84QcyM7Nn69/Bg6S1tcwLLzRPqV9fX88jR46YNSYzM7MLN7R9+3atvb39k/wvExVLS8v73n///TNW1iYlJbGkpMTk/hoNaWWl5ZAhWr71FvnFF+Qnnwj/k/vuIy+9VBAMa+v2B8zenpwypZ6rVzfyl1+EYtcUNDU1GeVWZJl87z2hFPbzI0NDhf5g2ifTaLHegj8k/8CoqCij+iKtVnBKFhakn5+WTz11kg4OMhcsMPxia7VapqamMjQ0lDU1NSatd13IOkqBEkvqDa9nR6ISkh1CBII/Jv9o0pylpaUMCgpiXl6ewXVoahLcnZublps2JXLECC0VCvKll4xzHtnZ2UxMTOSm8E1EIHjFjivYrG7mN9+I+zNwIPnbb13Htba2GujReoIsC+Lx5ZfkypU1nDSpWS/S6ixLY8aQV19NPvmkeGbefZfctk38dnTUctSonvVEnZGent7FitMbjClpb7vttgoA8/lfJioApi5fvtzgrekL1S4vL+/CCvaG996rpJeXyoAFB8SDOXUquXy5UPLt2CGcoTQasrq62mxRiySTk5OZ20GxUlsr2GxAmD4rK8myhjKO+2gcbV610StIm5qaGBQUZMBKFxa264GuvlrFn3+O4MSJGg4c2P2Xura2lmFhYUxKOs2kpJ65lklbJ3H+F/O7bO9IVDRaDd3fdOeKn1Z0O49GQyYmqnj8uOBOunOQS04W+qdFi1r522/hvP124RMyZw7Z8UNcVVXFkJAQqtuozdZjW4lAcNl3y9iibuHp00L0AMR966iE7Xz9TYGOA25tbWVrq+AoP/1UzH3NNeS4cYYfHV0bOrSJe/aYp8+KiIjo1YHQ2No6E8lRo0aVAXDgf5yoWHX2rCXNly/Nsc7oIPQdoayqEg9vQQFpim4zPDy8R2/M7o6l+1ImJpIBAUL+fvNN8WWsaqri5G2TafOqDQ9nHTYYW1FRwbCwMGo0Gu7dK4ienR25ZUsTDx8O4r33Ct3E/v09r0Gt1nLOHCEi7dmTa9SMm16ZTgSCb0e93WVfZ3+g1XtX02Gjg1GLVFNTE995J6+N8DVQq+35vnzyiXgyX3qpniEhIfz6axWdnIS+6vvvyebmZgYFBXUxBX8S+wkRCF79/dVUaVRsaiLvv59t1iFhxeuOU+wNpaWlvZp4ZVnozqqrhYd1TY2mT89hWE8ynxHU1dV1+fA2NTXRy8srh+fiPT4XB+2p+fr6Hu0s6yYnJ7O4uNiMy0zGxsaarZg8cuSIyWKBDgUFBUxOTjZrDCm8XjdvLqGtrXDS0inua1tqOXP7TFptsOIf6X8YHXv6dA7/r73rjo+izN/fSW9AQtomlCgQBKRIsx2KHsiJ4s+u2Pt5KodgPVEhYEUBUfHwRGwHnICIVBtk03snpJdN3930stnNlnl+f7yZzZbZZDdEEu72+XzeT7Iz77zzzu7MM9/+3nOPAkTAZZcB6emdiI6Oxt693SBiIeUDYetWGN+mv/xSDalUiuzsbDQ1NRkfgg3SDeCiONS2WxuILElFcDsfPHsQACP2hoYGpKWlITY2Fl980Wg83+ef9z83nmeSm5sbcPJkM2JiYlBUpMHVV7Pjly+Xo7JSPD7n09RPQVGEuw/cDZ2BSTH79zNCCggAPv20rF+bli0kJyeLevv6Q21tLc7250IUQXV1tWg0dH8oKSmxUpeOHTuG0NDQz+EkFZCPj8+zW7ZsMYvdbmpqcjgQaCDXnxjkcjlyc3MdOkaQihwJuFOrgaeeMvQaFPUQ+LKrpwuLvloEt01uOFJ0RPTY/Hxg1iz2yz30UBMUijZER0fjzJlO+PsDCxeysPX+kJHBbDdEQFgYe4h5nkdjYyMyMzNx+vRppKSkYOKWibjqo1vw22/WA5qSisHA4/BP3Qh9JwLXf3E9kpOTcfr0aeTl5RlJuqWFGZ+J2LkHyqZobWVBgRdfDJSWKhETE4OOjm488UQtXFx4TJlie4ytSVuNxlu9gUm4ZWXAzJk6cByPTZv4Ab17pujo6EBiYqL9B/QiMTHR4cA6W6EK/SE+Pt4q3eG+++5rJqJr4CQVEBGFz5s3z8zsORh1xlaQUn+wDISzF5WVlSgoKLCrb0UFs9EQAatXdyM2NhE8z0OtU2PJt0vgstEF+/P3i8yNuTa9vJir8/hxHqmpqTh58iSamtpx1VXsbVxe3v/5OzqYdyk4mM3hsces+/A8j1PFp0BRhIhLq0AEfPNNCuLj45GUlISUlBQcP34ciYmJiIuLwwcfZIMIuOiKdLhtdEOlslL0t7rxRpb8N348m8NA8XiJiUwtvO8+oL6+ASdOnEBhYSHi4oAJE5gks3mzuPv/3bh3QVGEJ488CZ7nodPp8PPPsbj3XpYbdMstgL1CaWpqKpqaxCUjW+jq6nKYiAZzn2s0Git1yWAwICwsTEFEbnCSCmthYWGllqpLRkaGQ8FAgHk4tb0oLCx02IhnMBgglUoHNK6dPs0idceMYRm2AHOZl1eU4879d4KiCN/mfGt1nFwO3Hwz+7WWLQMaGpix9fTp00hNTcVTTylBxMT8gfDgg0xiePllNt7Jk+L9njn+DLxevcioshw/zkOr1UKj0UClUuH06dPQaDTQ6XTYvLnPrU7/GIWPUz4WHfPLL1mf7dvZHB54oP8gN4DFexABb7xRjqysLGNIf0tLX2Ll0qXiWeKvn2ZJkG9Gv4m8vDxUVlaC54FPPmGEFBmJfkMIABb/4aijAGAqe62tuAIbUCgUDkvKVVVVKC4uNtuWlpaG8ePHn/f4FKENO4GItcDAwPf27dtndrvV1tbaLQ0IkMlkVl/4QFCr1YiNjXXYkFdXZ517YorPPmNv3RkzANOAX61Wizt33WnTIHrsGJMqPD1ZjovBYJ4ceOoUD47jcdttygElrG+/Zb94VBSLwQgIEFeV1Do1xm4eiwVP7TaShSVhmao/778PY7+JD0dh3r/miZ6/sZF9B6+9BmzciF4JqN8pQ6XSYN68Vnh7G1BYaJ4rxPPArl3MUB0UBBw9an4sz/N4/KfHWeTt/lfMftP4eGbL8vOzPs70+ISEBIfVEaE0hCPxQAB7cTqSdAgwKcpyfq+88kqni4vLXXCSismkiC676aabzLxAjqSoCxB+XEcJIiMjw6H8IaAvS9ZSh9Zq+4K6Vqxg7mNTCPr/PbvvMbsJVao+z8Xs2cyWApgTilLJbCLTpgFnz8oQFxdnUzIrLmaxG9dey8YePVpc9QGA73K+A0UR5l7VDFdXNofvvjPvY0oqmzfDaJ+ZdqUMFEXIqhevHbJ0KZMQ9Hpg8WI2J1u839bWBqlUitzcRgQFsfB3jcY6u7mwkBmsiYBVq8wT+rq6u3DlJ1fCZaMLDhceNhu/rg6YP59F4W7ZYi01KRQKsyxxe1FRUeHwy8zeaGtT6PXi3qUpU6YoiWgUnKRiRiqcRCKptzR+JiYmOuy+zc7OdrhMwWDjT5RKpVk9l6Ym4Prr2bf8yivW0ZcHzx40eipKSkuQnZ0NnueRkQFccgk77qWX2IMEmBMKzzOVyNOzL/2/qakJ0dHRVoSo0bAExrFjWezK0aNs7J9/Fr+Oq768CpPevQIuLjxWrmR9v7XQysRI5bnnADc3Hl7rwvHU0adEx965k/XNy2Nu+8BARgimdkae51FdzTxSQs7S8eMw82xZEotGA6xdC2N6RH4+U0sTEhJQXlOOK3ZdAe+3vZFZb04SKhWru0IEPPFEn+TG8zxiY2Mdvt9M41kcQVlZWb8lOMQgli0vk8kQHh6eieF8fofz5P218PDwvZbJhOXl5SgfyBJpgZaWFocznQFWDWwwuTLJycloampCZSUjBk9P4N//tu6XVZ8F77e9cfXuq6HWqcHzPNLSsvDii81wc2OZrqdNQlQsyxds385+vU8/NR+3u7sbSUlJyM3NNQaGrVmD3ngU1ufBB5nqI+awymnIYUT36m8gYjkxYmqKKal88AHrI5Wyv9eu/go+7/igVd1qNb5cziSD9evZZ4HgVq9mn9VqNVJTU5GVlWXlUXvuOdZXSBoUq8fy889ASAgzaK9bV4OSEqZryjvlmLBtAsZvG4/6DnMDjMEAvPEGG3vxYvYykMlkODOQwUUENTU1DpfF4HkeMTExDhNRTk6OVQrLxx9/rPX19V0DJ6mITIzoL3/9619bTb+w7u5uxMfH2/mVMwhvHEcNtq2trbDMQ7IHKpUKu3alQyJhpQ/Fpivc4BO2TTCGv585AyxYwPcGiKnNqolZEkpWFst+vuUWcUMnz/OorKyEVCrFnj1tIGK1YQDmzh49Gnj8cfH5/+3Y3+D1theuX6rFlCnMm2QvqXR0sIS8RUvbQFFk02C7eDGzLQlYvZod/9VXjYiOjraZYtHdzULiw8LYgw+IE4tcDixerOqNUOaNLvvshmz4vOODK3ZdIRqkt2cP+16nTTNg//4kIynbC8Fgb6tMpi00NjaKFqYa6FxidpuFCxcqiWginKQiSiqe48ePV1rqiykpKQ4bzqqrqx028gIsI9WRZEYAOHUK8PMzQCLpES2doNFpcPXuq+H9tjey6rOMGa3u7swgu2cPi7YVrtGSULq7mQ0lPJwZPvtDaWk3xozRIjKyCwoFG+/IEdhUfdo17fB71w/3fvssXF1ZNbOKCtb/66/N+5qSyocf9pHKCy+wa1nwyRJc8uklojaCTz9l/YWfpKZGicjITvj761BZ2X+8T3Y2G//22/sI1ZJY5HI5YmLisHWrAZ6eTCr75hvW/8eCH0FRhAd/fFB0btHRPHx8dBg3Tg8Hw5xQXl7ucOAawIytjkrFYkGXra2tkEgkFRjuZ3e4J9BfCwsL+9EycrOhocFh8VIwaDkaf9LZ2Ym4uDi7jWf797MbftYsHj/9lC5q7H3m+DOgKML+/P3Ize2LWbn3XkDgr44OVqqgoaHBqmLb88/DTAWwBb0euO465hlJSWlFQkICMjMzsXKl1qbqIxRcenNrBYhYkFxlJXqlCPO+pr/Lli19pJKUxP5/+p14UBTh17Jfrc5TV8f6rFvHVLW0tDRkZXXB15dJMQP9TML5vvyyb5tALHV1dWYEU1wMLFrE+v/lL0BVFfBW7FugKMI/0/5pNXZlZSW+/74YISHM3mOvaU1wCjgq3ahUKoelb4DZFy1r5HzyySc9/v7+6+AklX4mR3TFihUrzKKOBLHPUYIoKipyOPMTYHprXV3dgP2+/57FXlxzDYsGValUkEqlZjfZvrx9oCjC80fW4dVXWaxESAjwww/W49XV1eHo0aNm6QmnT8NMlekPmzaZkwHP86iqUsDHR4dbblFALpebkWWPvgfjt43Hdd9ch+XLgYsuYm92mYyNs3u3+fhipNLezuwT48YBt/yfHuFbw7Hk2yVmx+n1etTU1GDWrDZERnaZvaG/+YaNs7H/Cg8wGFgSpY+PueeopKQEx44ds5JkDQYmHfn6Mhfyjh0GLP/3zfB4ywNptX32NtPfrLSURfP6+valUPSHoqIih+19wODiWbq6umBZz5nneURGRiqJKAjD/dwO9wT6nRwRFxoaWmEZzVhQUIBqoWCsndBoNJCKlNsbCGq1esDjBEK59lrzJMTKykpj7EpRYxH83vXD9NUvIyKC2U4efVRchRFUnvr6eqMq1NbG7BVTp2LA5SdiYsSDywSj6KFDXcjNze0N7z+DxsZGfJ31NZOg0n+DuzvzOgHszT4QqQh5REKE6urVzEC96bePQFGElOoUNDQ0IDs7G9HR0SgoKMDmzSzx0bSqBc+zObu49F9HBWCeo4AAYMECJnUJkkpNTY3NKv2VlcANN7C5zp2vg2TtCkR8FIEmFct3SkxMNJMu6+uZDcfPj5WitIXB3luDPe7s2bOosUhBT09PR3h4+O8YCc/tcE9goDZq1KgXP/zwQ7M7pLu72+FMToCRkaNl/YTjbNUL3b9fnFAA9Hp00lBYVohL3l4Kj5nHQMSMlLbefpY2FEEVWrlSDReX/m9ugNV1CQtj5GMZBv/YYyyaV3Ay6PV6yOVyZGdn4+IPLsbkrZPx4YcsUTExkUmCAqmYqhqAOals22ZOKtHRLBT+vS2V8HvbD4t3LEZ+fj6ampqMD5Aw7nvvmY8rpBFMmDDw0hc//MDGeP75VsTFxRmJpL/lP3ieGWQlEoDjeLgs/BzLvliJM2fOiOaK1dWxwlf+/sxALobc3Fyrh9we5OXlOXycLVX+/vvvbyGiJRgBz+ywT2DACRIFXHzxxVYG27S0NIejD7Va7aD0Xp1OB6lUauVBSkxk3oJFi2yXSWhr02HaLftA7l3w9NLjvfdsJ/zZqnp/4AB7qz/7bEu/9h2DgYXxe3mxCnXm18BsBA88YH3cyZKToCjCzqSdWLKkCxJJD6TSGMTExODIkSwQAW+91QCZTIba2lrU1dXht99+Q01NDSorK/Hqq3IQAb/9lgapVAqpNA5BQVosX67CCydfABfFWS0jAjAp44orrOcjJDzedlv/YfwGgwErVrTC1dWA9HTz33SgBcva2pir3cXVAPJR4sa/HYROJ34ymYxJiYGBrJaOKZqampCYmOhwgKVKpRpU5LZMJrMiv46ODoSGhtYSkQtGwjM73BOwp4WFhR363cIy2dzc7PDCYQALMnI02hFgbr+kpCTjTVBdzRaTmjxZ/I2q17O3+9gQtvjVuPlJKCiwnRtki1AUCuYVuuwyHqmp2cjOzrYpLr/zDvtF//Uv631CDIml/YbneSz6ahHGbxuPxpYeeHiwhw1gD21JCSun8MEHrSgvL0dJSQmKiorw888/o6SkBJWVldi4kbmt6+q6jXN79llWbKlcLofnW56iwXBCXo9Y3o6gUu3YIf599fT09CY3lkAi4TF7tjVZD0QsABAT2wK/SYw4p07X4NgxcSIrLWW/w9Spfev7CC8bRzORARa17Uh1QqAvnsUyx2zr1q0af3//1zACnlVcKKRCRLOuvfZaM+vDYONPBPHR0UAjgIm5MpkMOh17y44aZV1x32BgbluhPIF7RAYuevF+1CnqEBsbKyol2SIUnmeuUw8PFsfC8zzKysqQkJBgFekZF8fUsJUrxR+K1auZBGN5//9S+ovREyJU4Dd1RtTWsm1ffGF+nKn6IwTimeZ7RkezbQcPMo+X+yZ3VLZWmo2Rnw+b9VUMBrbkhWnEsIDW1lZIpVJjXRTBViQE1JmiP2IRKumVysvg98Aj8Ayp6i1HwbxYloiL613I7X72+cyZMw4vowGw9IOEhASHpRShNIUpDAYDJk6cqCQif4yAZxUXCqkAoLCwsBzLpTdqamoGVSCpqqpqUMcJb6atW5k6csCkJKtWy0LZZ8xg3+qkScDVL3wE141uxtBwmUyGtLQ0s5upv4W+9u9nY23ebL69qakJUqm0N+uWR2Mj87hERlrnFgGMZCZOZKUqzbfzWPjFQkR8FIEefQ/+7//YOKaCkEAqltLPQKSi07E3+733AjXtNfB8yxOP/vSo1bymTGElEcSgUDDbx7RpjAwNBgMKCgoQHx9v9X099BB74MXsHmLEotPpEBcXZyxpcPDsQdCbbrhl7XHjyoZ//jMjLNPvQ0iE/PHHtkGpPQCLunZUdReOs4xnOXr0KC+RSP6NEfCMCm3YJ2Bvc3Nzu+2pp55qM/1CBXHQUWllsMcB7G0xZUoXrrySB8+zt+2GDcywSMQklL17gcP5x0BRhE0xm8yOLygoMOb49EcoLS1MvZo/X7zos06nQ15eHuLjE7FsmR6eniwwTAwZGRANYDtSdAQURdidtRutrTBTfQQIMSWW0oSY98eS0P76V+aS7e4GXvjlBbhsdEGB0jwI8aWXmP3EVjzjqVMsrP/BBzWIiYlBaWmp6IPc3MwIaM4c8e/LlFj0ej0SExOtPIj3HrwXHm95IFNWhA8+6KukHxnJjNEyGYtIDg7mcf31CofzggD2QhhMXpmt4y6//HIlEV2CEfCMCm3YJ2D3RIlcQ0NDayzdy/X19Q5XhROOy7Jlzh8AU6cyO4O/P/sGOY5l3544wd6+nT2dmPjRRMz850xo9eZRZjzP48yZM0hPT8fp06dtrhz4xBOsTMBAU3zxRVVvbIfCZuzO66+zsUy/OgNvwOydszHlkynQGXTGGBHLhfTq69n2nTvNt4slFFqqVr/9xrYfPswKefu964e7D9xt1ichgfX5/nvx6+vp6THWi9m9u/+XwKFDbKwtW8T3KxQKSKVSJCYmisYsyTvlCHg/ANd8dQ0MvAFaLZvXFVfAWNohJIT9vflmx+0oPM+LSlmDPS4nJwcSiSQFI+D5NG3DPgFHmq+v7xOrVq0ye6edyw+VmJjocEUvAMjN1eHWW2vx+OMa7NplXbX+hV9eAEUREqvFK3+1tbXh5MmTyMjIEH3rCkbVl1/ufx4//cT6PfKIAcXFJYiOjkZVVZXVmDNmsGxpU+zP3w+KIuzJ3QOA2S8iIqztMQ0NA5PKu++yPpY1qrRalhkteJzejH7TqiyCXs/UpJUrzY/V6/UoKWHXVFoqw9VX8/D1ZdnNtsDzLB/Kx8d6qVphzJiYGPz66682jbe7s3aLrnNUXMzUvPvv78J999Whvt5xtUcmkzlchAmw/eK85pprmojocoyAZ9O0DfsEHJoskWtISEiFpdg6WJGyq6vLKurVXnR2smLTlpb47IZsuGx0wdPHnhY9TlB52tvbkZ+fb1SFBKjVzMNw8cX9B7kVFTFD8YIFfQ9zT08P8vPzERMTg/r6evA8b8zd+eijvmM1Og0u3n4xZv5zJvQGPZqamD1CjMQEUvmnRUS7Kam89RbrIxb6//jjbJ4aDdCmbkPA+wFWqx0+8QRLcuzpYXYTmUzWSyalRumrro7F30ya1H/8ikzGSGXFCnOC1Ol0Rgml/zgWHtd/cz383/dHo8o8MlGo7+JIPWIB3d3dg7rXbKnqsbGxkEgk0RgBz6VlG/YJONrc3Nz+79577zUxCTKkpKQMyvhVUVHhcC6RAKVSifj4eJPF3Xks/noxgj4IQku31RStbCg8z6OgoABpaWnGm00wBP5qnTJjREcHi/QMCoLouszd3d3IyclBTEwMNm1qApF5OPvmhM2gKMJvZb8B6CvzKJYoK5ezfZ99Zr7dlFSiolgfMU/3yZNs37Fj7LNQlMp0sXfBe/P11zXGiFuxBzc5mdl9brih/2VNhQRHYZF0tVqN+Ph4MxtKf8SSr8iHy0YXPHfiOeM2Ifp1MGtS8zxvcyG4gSDmVOB5HrNmzWoaabYUoQ37BByeMAvdP2OZddze3j4oN925qEEAy0zNysoCz/P4qfAnm4lq/RllZTIZYmNjUVKihrc3q71qCwYDCwpzdWVu2/7Q09OD667rRHi4CtnZOWhtbYW8U45R747Cin0rjP1uuIHF24h9dQoFRONFTEnlzTeZXUl8DiyK95FHej/rezD106mY+ulUaHQaKJVKxMVlwt3dgKeeahvwTb5rFwZUDbVaZrAdNw6orWXuZ7Fs8/6I5dnjz8J1oyvyFfnGYk+OFvsSMFi1x1b4w+HDhw2hoaH7MQKeR7E27BMY1KSJ/rRkyRIrsSQzM9PhgCJAPPnPXvA8j+zsbBQUFyDyk0hM3zHduN6MgP4IRUBzczOuv14OLy8e/WUSvPIK+9W2bx94bmo1C0BbtYqHQqFAWloabvnXLXDb6Iak4iQYDAYolYyg1q0TH0OpHJhU1q1j6pMtPPwwM2r39DA15LsUVq7yme+eQXZ2Npqbm7FkCY+ZMwe+JoAF1hGxcHtbiI9Hb35VZb/BabaIpVHVCP/3/bHsu2XIzs52uCqbgHO5t0pLS62W/NXr9Zg8eXIjEY3DCHgWxZoLXYAAkHj27NmClJQUs+3Tpk2j4uJigXjsho+PD0VERFBhYaHDc+E4jmbPnk27snZRaUspfXjDh+Tm4mbc39HRQZmZmbRgwQIaNWqUzXHOnh1LUmko3XNPNXFclWif3buJPviA6G9/I1q9euC5xcYSqdVEN93EUUhICFE40fGG4/TYzMfIR+1DsbGx9OGHVWQwEN14Yzvp9XqbY/X3lRoMRK6u4vt0Oh0tW9ZObW1En31WSAkJCTTXdy4tmbiE9tXto7ApYTR27Fi68UaO8vOJamsHvq6PPiJavJjo8ceJ4uPF5goKDCykP/+5mQ4ciKC2Nl+bY4WEhND06dMpJSWFenp6jNuDfIJo/bXr6beK3yhVmUqTJk0aeGIi88jNzaWZM2eSm5vbwAeYQKfTUU1NDV188cVm27/55htdZ2fnAQB1Dk/ofGG4WW2wjYhmzps3r8lS3Tlz5sygShycixqk1qkRtiUMcz6aY3a8PRIKwDwgc+eyuIi2Nh3S0tKQk5Nj9naLjmbSwA03iBtExSBE0XZ3AzqDDnM/n4uwLWFo17Qbr3nuXB2mT+9BdnY2YmNjIZVKkZmZieLiYshkMpw9yxIMt23TmqmWgqTC8zyef14HX18eDQ0NqKysRFFREdLT0yGVShEXF4eMjDPw8zPgoYf6Jl7UWASPtzxw/yEWnnrmDEQTF22huZmV6xw71txepFarkZKSgrNnz6KsjIeHR5/q1R/EJJYyWRmC3gvCot2LBhXkVllZOSi1BxC/j9VqNcaPH68kogCMgGfQVhv2CZxLk0gkP508edLMPGgr+c8eCKKqoyH8O1J3gKIIJwtPQiqVorW11W5CAdj6wEQsaA4wLwfZ2NiI4mKW5j99el/eiT249FJGQgDwccrHoCjCgfy+MOCzZ9FLGH3HGAwGtLa2oq6uDuXl5UhMLAIRsGZNeW+yIGtHjx6FVCpFTEwM7rijAQEBWmM2d319Pdrb280exEcfZSUETDWRDdINxkJOPM9sIHfdZf/1lZUxY/XkyYBCwaOmpsaqHKWgLtpTrdGUWOrr6xEfH4/tSdtBUYRT5afsnxhYkt9g1Z7m5mZR++DmzZvVgYGBGzACnr3+2rBP4JwmTxQxderURsugL8vkP0fQ0NCAxMREu2tcaHQajN82Hou+Ym+zrq4unDp1Cr///rtdhKLXszD0Sy+19p6oVCr89FM6wsN7EBTED7j6oCkEr8377wO17bUY9e4o3LjnRrPv5JVXmPTTn/2xo4ON8+GH5ttNbSqPPMJiXPpDXBwbx7Qqv1qnxtRPp2LSx5PQre3GY48x24sj9beSkgBPTx6zZnUgMdG6WHZ7O8suvvlm+8ZTKBT4/fffERMTA61WC7VOjXFbx+FPu/9k95y0Wi2kUqnD63IDzGYilqTYWyqygYh8MAKevf7aBWlTEQCgqrW1de+WLVvUptuDgoLI19eXqqurHR5TIpFQUFAQnT171q7+/877N9V21NL6a9cTx3FkMBgIgPH/gfD990RFRURRUUQuFr+GWu1D69bNp/Z2N3r77SwaM6bZ7uuQStnf664DPX38adLzetqxfAdxHEdEzA6yZw/R8uVEISG2x/HtNUd0ddnuo1L19bOFRYuIpkwh+uqrvm1ebl70+c2fU0VrBW2M3UjXX0/U1kZ05szA10fEXogTJ9bRP/5xlvLz/ejdd+cS4G7WZ/RoorVriU6cIMrOHnhMg8FAHMcRz/PE8zx5uXnRq396lRJrEimlNmXA43mep4yMDJo6dSqNGTPGvgsxQWFhIUVERJCvxRf67LPPtqlUqtcAdDs86PnGcLPauTYi8gwJCSm3LDh8LmoQz/NIT08f0DbD8zxm75yNOTvnWOXyCIF1/cXO6PUs0G32bGsppbOThYd7ejJ7ikqlQnJyMtLT0+2KlfjrX1lA2Zfp34CiCNuTt5vt/+UXiJZCEIOvL/Dii+bbTCWV5ctZEN5AEEodWEpcj//0OFvsKyXTbs9WS0sLEhMTkZWVhZ6eHuN6QnffbS3ptLay72Ig1aqurg5xcXHQarVmqlCHpgOj3xuNlT+s7H8AMFvIYIpfA7Zrs5w8edIQGhoaQ0QcRsAzN1Ab9gkMyUUQzZ89e3bzUKpBer3eLItVDHGyOFAU4cvML0VtKIKNxlZ8g7BAlmm2M8AMq0uXMlfvTz+Z72NxHXHIycnpd+3mKVOApcu7Mfq90cZcFlPceSdTC+xZTSI0FHjaIkDYlFQWL2ZtIFRXs3iWN980396uacfEjyZi6qdTEXGRAbffbnuMjo4OpKamIjk52Uq9EGrlPvqoNUmvW8fObWtRBZlMhoSEBDP1yZRY1v6yFm6b3FDbbruebFVVlVUWur2wVZulra0N48ePVxCRBCPgWbOnDfsEhqoFBwdvf/fdd63EEqEGymAg1NuwlY16z8F7EPB+ABqarKveC9BoNIiPj0dZWZnVzXbbbSxBzdQM0N7OHlCOs73OMM/zqKurg1QqFY0+FcoVXHL/5/B5xwdlzeYxFnV1jLAGyi0SMHlyXw0RAaaksmABk1bswV/+wrxclvbL0xWnQVGE6UuTERhoTQrd3d3Izs5GfHx8v0QvRPc+9ZS5xKJUsmhcYdEyAQaDAXl5eUhPTxdNyBSIpVBeCJeNLnjj9Bui521ubrZZL8ce2KrNsnLlylY/P7+HMQKeMXvbsE9gyC6kVw2yLLUnvAEGk6YO2L5ZmlRNcNvkhlVHVw3o5TEYDMjKykJWVpbxxpXLmZFUKDANsCLYCxaw7fv2DTw3g8GAyspKREdHIz8/33iNBw+yX5aeXIjP0j6zOk5IBbAoT2MTc+ZY12IxJZVJk6xJxxYOH0ZvPRLrfX8/+XfQrY+AqC9xsK2tDVlZWWb5TP2B59kC8ESswJWp9nvPPcwFLfyUPT09SExMRHFxcb/jCsSy7LtliPgowkrqG+jlMxBsqT0nTpwwhIaGSi8UtUdowz6BIb0YovmzZs0SVYMGW1AHYGJtSkqKmUfo8/TPQVGEXUd32eXl4Xke5eXliI+Ph1qtxhdfsG9fWFmzvJxlE3t69uXJ2AuDwYDa2lrEx8cjNTUVdz1aCXLV4Obv7rC6Zp2OSQrLltk//rXXsmYKU1Lx97dv2RDh/BMnsgJIllBpVZiy4UYQAa+/xdSRlJQUKJVKh3+77duZtLdoUV/xKIFs4+NZDJFUKjVbAqU/KBQKrPt+HSiKECfrK7re09OD2NjYQad52Hrptba2Yty4cReU2iO0YZ/AULfg4OCt77zzjtUro6CgAGfFlgy0E2VlZUhLSzMSy6IvF2Hi5okOr5aoUCgQHR2Nxx7TICCAvVkPHGBxKAEBrOzBuaBaUQ3PSclwm5COpJwktLW1mT2QQrmEw4ftH/POO1mMjCkEUtHp2HgbNtg/nlAqwdS+wfM8mpub8WPCYZBXC4IXHUJ7h2PfrSW+/56pPBddxOq2CG72t99mhOLob1dRWwGvTV548qcnATDXcVxcnN3EZAme55GamipaUf/uu+9u9fPzexAj4JlytA37BIb8gog8QkJCyiwTDnmeR0pKyqCWUhBQXFyMjIwMFNYWgovi8Nqvrw1qnM7OTtx6ay2IgCuuYGsAzZ9v7RVxFDzPY+WBB0HuXbjjkRpUV1cjLS0N0dHRyM3NhUKhwJIlPMaN6z/L1xJ/+xsLMjOFQCpCbtAnn9g/nmDfeOYZA+rq6pCZmYnTp08jKysLDQ0NmH55LSgsHVHSKPsHtYHkZBZDQwRcfTVvDOQbTI1iALh9z+0Y/fZotHewBFZ7FpqzhcLCQtGypseOHTOEhoaevtDUHqEN+wT+kIsimjtz5sxmyxtHq9UiJibG4XVrTZGTk4M136wBRRHy5IMrmQCwQk9Tp3YjMrILW7f2OPSQ28K2pG2gv822SrYzGAxQKBT4/vtiEAGrVlWjrKwMTU1NdhkWhSxkU61SIJXCQphFA/eHnp4eKBQKlJSUYPlyBby9dUhJKURzc7OZNPXyyzxc3HSgNzzxc6nIos8OoqMDeOUVNSZMUGHhwm4olYNTgwFgb95eUBRh2/5tgyp6LaC2thbJyclWal1TU5Og9oRiBDxLg2kXdPCbLQDIbmho2Prkk092AH2ZcO7u7rRgwQLKzs4mjUbj8LgdHR3U3NxMxfpiCvQMpBlBMwY9x9mz3ai42JuSktQ0b14S1dbKyHSujuJUxSl66feXaIHb40RENHdu3z4XFxcKCQmhI0em0qhRoFdeGUNubm5UW1tLSUlJFBMTQ5mZmVReXk5KpZI6OjpIq9Ua5xMSwhIKm0Vi75qa2N/AQPYXAGk0GmpvbyeFQkElJSWUnp5OUqmU0tLSSKlUkpeXF73xxihSq93o9OlpNHbsWGNQHhHRwoUc8Xo3itTfTit/WEnFTcWD/l54nqe6uiK65ZYMOnvWQGlp3hQczA18oA1cN+E64oijSpdKqq6uNktCtBdtbW1UVlZG8+fPN7tunU5HN910U2tLS8sTABSDnuQwgzuXG3kkg+M4LiQk5NDrr7++fPXq1V6m+xobG6m4uJiuuuoqcrWVXmsBIdt4/vz5FPlFJF0RdAW9eembNH/+fHKxDIV1EHq9ngoKCkilUtGcOXPIx8fHoeMrWytpwa4FFOYXRjfVZNK2Dz2pu5vIw8OkTyVRZCTRCy+wTGdT8DxPnZ2d1NbWRl1dXaTRaEij0ZBOpyMiopiYUNq4cTr95z/5FBnZQy4uLtTQ0EChoaH066/+9Prrk+nrr9PpootURETk6elJnp6e5OXlRaNHj6YxY8aQn5+f2QNERHTTTUTp6URVVUSml1xaSjR1KtGHnzXRZtV0CvQOpNQnU2mMl2MRqu3t7ZSbm0thYWE0ZcoUq/M7Cq1WS6mpqfT3vL/TKJ9RdOjmQ1RYWEhXXnkleXp62jWGRqOh5ORkWrhwIfn5+Znte+KJJzqOHj26rbGxceM5TXS4Mdyi0h/ZiMgzODg4/9SpU1aJPBUVFcjMzLTLq2Aa2FbcVGwMeCsrK0NKSsqgYxMs0djYCKlUioqKCrtzj1rVrbj0s0vh/74/SptLcdddLPDNEqtWsar1Dq4FDgCIjdX3VlJTob29Ha2trTh16hTa29uxaZMGREBbm2PrAQN9Ra8tI2h7etgaRm++CcTKYuG2yQ3L9yy3qlNjCzqdDgUFBYiLi3O4drEtqNVqxMbGoqGhAWt+XgOfd3ygN+jtWrBMgMFgQHx8vGgw5I4dO9QhISFH6QK1o5i2YZ/AH36BRBKJRFJfbmEF5XkeOTk5AxbfsYyUPXj2ICiKkFHH0l6rq6sRGxs76BgFSwgPhFQqRV1dXb+kp9FpsPjrxXDf5I7TFacBsCVCVqww71dVxVzVjz8+uDkJy3SYlpQUbCrPPce8VoPFtdey7GTLZzIioq9g9r8y/gWKIjx19KkBln01oKKiAtHR0SgvL3d44XNbaGlpQXR0tLEc5DfZLPVBWG7EHmLheR5ZWVmi91tMTAwfHBxcQEReGAHPzLm2YZ/AeblIovmRkZFNlm8toUygLZegWOj9+uj1cNnogm5tX1RVc3MzoqOjBx2rIAa1Wo2cnBzExcWJlkI08Abce/BeUBRhb16fldTX13rtnkceYaQiVs/WHhgMrC6LaaCeQCq33MKC4wYLYRkP0/ILAKv+f9VVfZ9fP/06KIqwQbrBagyeZ2UPpFIpCgsLB1WY2hZqamoQExNjFj6fK8+1+t4HIpaysjJj2VFTVFZWIiwsrJ6IwjECnpWhaMM+gfPVRo8e/dCSJUtaLN9eQvCS5YNrqx7K3QfuxpRPrPWL7u5uxMXFDTolwBY6OzuRnp6OpKQko9eK53k8//PzoCjC5oS+5QuFmJFNJuuX5eQwz80rr5zbPKZPB+64o++zQCqzZjFiORfceCOrY2v6Ezz2GJNgBPA8j8d/ehwURfg8/XPjNoVCgdjYWOTl5dmlgtgLnudx9uxZpKSkWJGURqcRXSjOFrFUVlYiOTnZSnLq7OzE1KlTR+QyG+fShn0C57MFBwd/8tJLL1kp2RoNW/1OkDT6K7C0+OvFuOara6y2AywJMT09HXl5eUMmegtobW1FUlISkpOTserIKlAUYc3Pa8zefC0tMFuOg+dZYuLYsY4VdxLDzTebSySsmBWLN3n11XMbu6CApSaYJi2uXcuW9jCFzqDDin0rwEVx2C7djoSEBGRkZAyZ6ilAq9UiJSUFBQUFNtWtgPcD8OzxZ622WxJLVVUVkpKSrPKKDAYDli1b1jpmzJjHMAKejaFs/5UuZVtobGxc8+233+bs27dPZ7rd09OTLr/8cjpz5gzV1NT0W1O2Rd1CgT6BouO7urrS/PnzydPTk1JSUkir1Q7Z3P39/enKK6+kgy0HaUf2Drpt3G30/LTnied5Y5/OTvZXcCp89x3RqVNEGzcS+fuf2/lnziQqKCAy9aAWFRFptURz5pzb2NOnEz33HNEXXxAlJ7Ntrq5EliVzeT1P7819j+b5z6O1sWupwL2A5s+f77C3rD+oVCpKSkqi8ePH0/Tp0216jCR+EpKr5FbbTWveymQyqqmpoYULF1p5GdetW9eVnZ39n7a2tq+HbPIjBP9TpAKAb2xsXLFmzZqKkydPmlVQ8vb2phkzZlBOTg5FRkbaLFLdpmkjfy9/m+fgOI6mTp1KkyZNoqSkJGoWC+4Y3NzpTembtC19Gz017yna+8Be6unpobi4OMrLy6P29nYSptzeTpSfzx7Ua64hevbZcz//woVEOh1RXl7fttxc9vdcSYWIEV9EBNF99xEpFEStrazAEgBqamqizMxMSk5OJh8PH4p+KpqWTFpCT//8NH2X+925n5zYeerq6igtLY3mzJlD48aN67f/KM9R1KUVr1wVEhJCwcHBlJ+fT5dddplV0evt27erv/rqq/TGxsa/D8nkRxqGW1QajkZEgcHBwaW///67UUcRVB6lUgmpVGrT6BrxUQQeOfyI6D5LqFQqJCYmIi8v75zcznqDHk8fexoURXjyyJNmWbI8z0MulyM1NRUxMbEIC9MiIsKAoCAeEgnz3AwFqqpgtlSHVCrF2rXMADxEHnWkprIlRSIjefj7G3D11Z2QSqXIysqyirpVaVVY+t1SpgpZFKByFBqNBqmpqcjMzLQ7fH/hFwutVloUUF1djYSEBNTX11vZWHbu3KkJDg5OICJPjIBn4Y9owz6BYbtwopCQkJDK2NhY3tKG0t3djZiYGFGvy7Qd03Dn/n5W+7KAaRHrwVbqv3P/naAowmunXuvXpdrT04OPPmqGh4cBU6Z04MiRAigUthdudwQ8z4o1CSUOpFIp5s61rziTPdBqtairq8OuXcWQSLoxapQOhw839Tt3tU6NO/bfAYoivPTrS1YlCexBbW0toqOjHU4KvOTTS3DXAetScjKZDImJicaXiKmN5euvv9YEBwen0X+J69hWG/YJDOvFE4WHhobW7tixw8ooq9FojMFOpljy7RJcsesKOIrBSC3yTjkWfbUIFEX4KPkju8+l16N3oTAl8vLyEB0djdTUVJSWlqKxsXHQLteHH2YxKVotsH9/EoiAd94Z1FDQaDRQKBQoLi5GUlISYmJiUFBQgJaWFhgMvOgSqmLQG/R47sRzoCjCfT/cZ+bqH+j8aWlpDkknAniex5j3xmDViVVm28vLy5GcnGxFhAqFAhs2bOBDQkJyiMgXI+De/yPbsE9guBsRTZBIJDWxsbFWIkBPTw8SEhJQWlpqlBCeOf4MRr07CnqD429/tmB6hV1SS1ptGsZtHQfvt73x/ZnvHT6X5Xk7OjpQVVWF3NxcxMXFQSqVIj093Ug0PT09A0YXnzgBY/LgQw9VitabFTu3Wq2GXC5HUVERUlNTER0djYSEBOTn56OmpuacvTc8z+O9+PdAUYS5n89FRUv/iX6DlU4EVLdVg6LIWADLYDAgNzcXGRkZol6/b7/9tic0NDSPiEZhBNzzf3T7r839cQQcx4WHhIQk7t27d+LSpUvNjNc8z9OZM2fIYDDQnDlzaM+ZPfTokUcp++lsukxy2aDO193dTTk5OeTt7U3Tpk0jb29v4z4AtDt7N606uYokfhL6aeVPgz5Pf+B5nrq6uqitrY3a2tqos7PTmOvj6upKXl5exvwdLy8vcnd3J4CjpUuDqKrKlXp6QMuXa2nXrjbieZ50Op0xZ0ij0VBPT4/RM+Xh4UFjxowhf39/GjNmDPn6+p5zHo4YTpScoAcPP0gunAvtu2Mf/WXKX8z2d3R0UEFBAbm7u9OsWbPIwzQ5ygHsO7OPHvjxAUp7Mo3mBM+hjIwMCg4OFs0v+te//tWzfv36PKVSeT0A1aAv7kLCcLPaSGlEFBIcHFx64sQJURGkoqIC8fHxkDXJ4LLRBa+ffl2sm93gebaiX0xMDPLz81lZgC4Fbv3PraAowpJvl6BR1XhO5xgsdDodOjs70dTUhNraWpSVlaGoqAgFBQU4erQU8+Z1YeFCBRISilBYWIiioiKUl5ejrq4Ozc3NUKlUQ2LHGQzKmssw65+zQFGE1SdXQ6VVQaVSITMzEwkJCf2ubmAv7th/B0I/DEVrG1v83db63du3b+8ODg5OJCJvjIB7/Hy1YZ/ASGrEvEJFe/fuFTU6KJVKREdHY9k3yxD0QRC6emwv/G0veJ6HTCZD1IEoBL4XCI+3PLA1aeugjI7nE6blJEcaVFoVq3cbRYjYEoEdR3ZALpcPupyoKcpbyuGy0QXP/PgMpFKpaIAkz/PYsGGDKjg4WEr/xV4eW+1/Kk5lIABobmxsvHzNmjXJL730UpdpYBkRUXBwMF1++eV0a8Ct1NTdRFExUed8zoLGAnoi9gmKKoiiEJ8Q2nnZTroj7A4ip1Y6aHhwHvTcpOdo6+ytZCADrcpeRS8nvUx1nee2pjkA+vvJv5OXqxfd6H8jXX311VbxTN3d3XTrrbe27dy5c39jY+MNABwvuHKhY7hZbSQ2InIJDg7+eMmSJS1ibyKtVovbd98OLorDv3P/bbXfHhQ1FuGJI0/AdaMrAt4PwKepn0Jn0EGr1RqzlGUy2bCpEQNhJEoqPT09KC0tNctS7tB04B+//wOeb3nC+21vvHbqNTR0Om6g5Xke635nha9f/fFVUYNsTU0NZsyY0ezv7/8sRsB9PFxt2Cfg8ISJJhCRlIgKiOgsET3fu/1DIioiojwiOkxE/r3bLyIiNRHl9LbPTca6jogyiOgDsXONHj364cjIyCaxdHVVjwpX/PMKY61atc72wl4CdAYdfin9Bbd/zwjJ620v/P3k39GksvYEaTQaFBUVITo6GgUFBYNaafGPxEgilY6ODuTk5EAqlaK8vFzUZV/ZWon7frgPXBQHj7c88MjhR5BSk2KXStTc3Yz7998PiiLcvedu0WMSExP58PBwuaur6yJY37NeRJRGRLm99+zG3u2riKiMmFwaBPP7st3knl1vsm8lEWUR0RrL84yUNuwTcHjCRGFENK/3/1FEVEJEM4hoGRG59W7fTESb0Ucq+TbG2k9E3kS0lYim2egzXyKR1IsVelJpVbjnP/eAoghhH4Rh3al1OFV+CtVt1WjpbkFtey1SalKwK3MXHvzxQYR8GAKKIozdPBZvnH4Diq5+VkbvhcFgQHV1NeLi4pCWlgaFQjEktoFzxXCTirAsSWJiIpKSkuy2mZQ0lWDViVXweceH2Vw+isDTx57Gntw9yKzPRENnA1q6W1DeUo7jxcex6sQqjH5nNFyiXPDyzy+L2rq+/PLLnuDg4GIimgjxe4gjIr/e/92JKJWIriSiub33p0yEVI7bGOsnInIlou+FMUdau+BdyhzHHSGiHQB+N9l2OxHdBeABjuMuIvYDzRQ59iARPUREbxHRVwAKbZxDEhwc/Pvrr78+ZfXq1V6WbsPfy36nDb9voFRlKvHEiw1BwT7BtHTSUrp7xt10U+RN5OlmX/lBU7S1tVFVVRW1tLRQeHg4TZw40cwdfT4RExND11133Xk9JwDq7OykmpoaUiqVFBoaShMnTrQqy2gP2jRtdKToCP1Q+APFymKpU9sp2s+dc6cbJtxAb//lbZobPtdsn16vp9WrV3ceOnQoRalU3g47XMYcx/kQUQIRPQMgtXebjIgWAGjq/XwdEb0EYIXI8UeI6A4i2kNEfwUgPvFhxAVNKr2EEUdEMwF0mGw/RkT7Aezp7XOWmETTQURvAIjv7fcXInqPiKQAXhzgXJ4hISH/WbZs2Z937949RizGobS2lI6kHSH9KD15+HnQKI9RFOoXSjOCZ9CkgEnkwg2NXVyv11NdXR3V1dWRXq+nkJAQkkgkNGbMmD8k/kMM54tUeJ6nlpYWksvl1NTURN7e3jRhwgSSSCTnXBtYgIE3UGFTIZU0l5C8S04anYY0bRoapR9Fd//pbpIESqyOaW1tpVtuuaW1pKTkX42NjeswwIPEcZwrEWUS0RQi+gzAqyb7ZGRNKoeIqJaI6okRzNnefY8Q0Roi2gNg6zlf/B+B4RaVBtuIyI/Yj3SHxfbXidlUBML0JKLA3v/nE1ENEY0e5Dm5wMDAdTNnzmy2XFdIgE6nQ25uLpKSks6LHUSr1aK2thYZGRmIjo5GTk4O5HL5H27g/SPVn+G6JoAtsxobG4vi4mKbNXGkUik/ceJEpZ+f391w/B7yJ2YTnGmyTUbm6s9o6lOXbiKiUkfPM5ztgpRUOI5zJ6LjRPQrgG0m2x8loqeJaAmAbhvHxhBj/oxzOP/ckJCQg6tXrw77xz/+4SNWkb+xsZHOnj1LF198MU2cOPG8SBBib/XAwEBjNKu7u/uQnWsoJZWenh5jZG9zc/OwSF8Gg4FKS0upsbGR5syZQ6NHj7bqo1KpaM2aNR1Hjx4tViqVdwGoHsy5OI5bT0TdALb0fpaRiaQi0r/f/SMNFxypcOwO+5aIWgCsMdl+IxFtI6LFABpNtgf39jVwHDeJiOKJaBaAlnOch0dwcPB7Eonk0QMHDoydNm2aVR+9Xk9FRUXU0tJCl1xyCYWEhJw39QQAqVQqamlpofb2dmpvbye9Xk9+fn5GkjkXohksqQhrAgkk0t3dTZ6ensY5jR079rzaiQBQTU0NlZeX0/jx42ny5MmialVsbCweeeSR5vb29k1tbW074MCD03sP6gC0cRznTUS/EXMkHO/dLyNz9UdCRAoA4DjuciL6gYgiHDnncOJCJJVFxIjhDJHRKrqOiD4hpuoIVZFSAPyN47g7iWgTEel6+28AcGwI5zMvJCTk4PPPPy959dVXRaWW7u5uKioqIrVaTdOnT6exY8cO1ekdAsAMncJD3d7eTjqdjtzd3c3yfIQmbHN3d7ciQ0tSAUBardaY96NWq6mnp8csH8hgMJCnp6eR0MaMGUM+Pj7njWhNAYAUCgUVFxdTUFAQRUZGiuYCqVQqWrt2bceRI0dKeqWTKkfPxXHcbGIvQldihdEOANjEcdxqInqFiCREpCSikwCe5DhuFRE9Q0R6YuEQLwBIGvTFnmdccKQyEsFxnEdQUND74eHhDx84cCDwkksuEe3X0dFBhYWFxHEcTZ8+3WZ1ufMNnU5nRQCmn4VEQ1N0dXVZeV08PDysCMn0s2UFtOFCc3MzFRYWkq+vr1VCpyni4uLw8MMPN7e3t7/V1tb26YUiKQw3nKQyhOA4bn5ISMiBtWvXhr388svetlY/tPemHskYDpfyucJeUu/u7qYXXnih4/Dhw6W90ons/M70woaTVIYYHMd5BgcHb/b3939g+/btY5cvX+4iJt5bit+TJk26oMjlQiKVzs5OKi0tHVD9NBgM9N133+nWr1/f1tXV9W5bW9vHTunEcThJ5Q8Cx3EREonk4/Dw8D999tlnQVdeeaVoP4AVXK6srCRvb2+aNGnSsNlcHMFIJxUApFQqqaKigoiIJk+eTMHBwaL2GwB09OhR/sUXX2zp7Ow8qFQq3zhXQ/7/Mpyk8geD47iZEolk54wZM2bs2LFj7PTp00X7AaDW1laqqKggtVpNF110EYWHh9u9gPz5xkglFZ1ORzU1NVRdXU0BAQE0adKkfm1XCQkJ9NxzzzUplcoYuVy+FkDteZzufyWcpHKewHHcn0JDQz9fvHjx+C1btvhPmDDBZl+1Wk0ymYzkcjkFBQVRRESEaNzEcGIkkQoAamlpIZlMRp2dnTRhwgSaMGFCv5Xdzpw5Q6tWrWouKSk5I5fLnwFQdB6n/F8NJ6mcR3Acx7m5ua0IDAz8+K677grcuHHj6MBA8YXJiFgwm1KppKqqKtJqtTRu3DiSSCRDunjWYDHcpCK4x+VyOdXX19Po0aMpIiKCxo4d26+LWiaT0QsvvNCalJRUrVAonkZv/o0TQwcnqQwDOI5z9fX1fcTPz2/jihUrRr366qtjIiMj+z1GrVZTQ0MDKRQK0mq1xohTf3//YYnzGA5S4XmempubjRHDvr6+JJFIKCwsbMAgvvT0dHrnnXdaUlNTm5RK5fM8z//qNML+MXCSyjCC4zhXNze3FcHBwVFTpkwZv379+qAlS5YMSBI6nY4aGxupoaGBOjo6KCAggCQSCQUHB583G8z5IhWdTkcKhYLkcjl1dnbS2LFjKSwsjAIDAwe8Vp1ORz/++KPh7bffbm1ubj7T0NCwnogSnWTyx8JJKiMEHMfNCgsLW+/l5bX42WefHf3YY4959qcaCeB5nlpbW0kul1NjYyN5e3vT2LFjjRGrg60YPxD+KFLRaDTGaN+mpiYyGAwUGhpKEomERo8ebZdUVl1dTTt37lR99913Kr1ef0ipVL4/2DwdJxyHk1RGGDiOCxg9evRjPj4+q+bPnz/qxRdfDLruuuvsVnG6urqotbXVLAzfMt9nKIhmKEhFrVab5QGp1WqzMP6AgAC7Y3d0Oh0dPXqU37p1a7NMJlO0trZ+qNFoDgJQn9MknXAYTlIZoehNnFwYFhb2qouLyzU33XST+8qVK/2vueYah5IAAVBXV5dZvo9WqyVfX1/y8fERDat3c3MbkMQGIhUAomsBaTQaUqlUpFarycvLy4zsvL29HbIPqVQqOnXqFO3bt685Li5OD+CIQqHYBqDY7kGcGHI4SeUCAMdxnkS0ODw8/GGDwfDnefPmuT/44IOBy5cv5wICAhweT8hgVqvVVnk+Go2G9Ho9EfUtKiaQDMdx5OLiQhzHUU1NDY0bN86sjoZAIsIqBO7u7qJJir6+vuTl5TUoA3N9fT0dPXpUv2fPntby8vJunuePK5XKvUSUBsDg8IBODDmcpHKBoVeCmR0YGHivh4fH3RKJZMwDDzww+rbbbvOcPHnykJ5LSDQ0GAzE87yRPHiep9zcXJo7d64Z0bi5uZGXl9eQGosBUF5eHh06dKj7wIEDqs7Ozoaurq69HR0dhwCUD9mJnBgyOEnlAgfHceE+Pj63BQQEPOzm5jZp3rx53OLFi8csXLjQ/bLLLvvDYlr+KENta2srZWdnU2pqqiY2NrYzPz8fRJQnl8u/NhgMPwNoHfKTOjGkcJLKfxE4jvMgoks9PDwWhoSELDEYDPM8PT3HXHbZZXTNNdf4X3755e6XXXbZoApFW2IoSKWlpYWysrIEAukoLCzkDAZDE8dxqQ0NDacBZBJRiVOtubDgJJX/cvSW3rzUzc1tQWho6FKe5+d7eHiMiYiIQEREhGtERITXxIkTfcLDw7nw8HAKCwuzK95lIFIR4kvq6+upoaGB6uvr+aqqKlVVVVVPdXU1X11dTQaDoYmIUuVyuSmBiC9H4MQFAyep/A+il2jCia2hFObh4TEuICAg0sPD4yIA4/R6fZCrq6unl5eXa3BwMO/t7U3u7u6cu7u78Jfr6ury8/Dw6NTpdNDr9aTT6dDV1cU1NjZyOp1ObzAYNG5ubkqO42o1Go2subm5lOf5eiJqEJqTQP474SQVJ2yil3yCiJXpdCcit97mTqw0p96iqYmoyamu/G/DSSpOOOHEkGJoVmNywgknnOiFk1SccMKJIYWTVJxwwokhhZNUnHDCiSGFk1SccMKJIYWTVJxwwokhhZNUnHDCiSGFk1SccMKJIYWTVJwwguO4CRzHSTmOK+A47izHcc/3bt/PcVxOb5NxHJdjcsxrHMeVcRxXzHHcX0y2r+Q4LovjuDXn/0qcGE6MjBWznRgp0BPRiwCyOI4bRUSZHMf9DuBeoQPHcVuJqL33/xlEtJKILiWWS3SK47ipvWH6K4loIRHt5TjOD0DX+b4YJ4YHTknFCSMANADI6v2/k4gKiWicsL+3QNQ9RPSf3k23EtH3AHoAVBJRGRFdLnQXhjX534n/AThJxQlRcBx3ERHNJSLTxbauISIFgNLez+OIqMZkfy31kdCPRJRBRBm9BOXE/wic6o8TVuA4zo+IDhHRGgAdJrvuoz4ppV8A+JaIvv0DpufECIeTVJwwQ2+5g0NEtBfAjybb3YjoDiKab9K9johMF4Ue37vNif9hONUfJ4zotZnsJqJCANssdi8loiIAtSbbjhLRSo7jPDmOu5iIIoko7fzM1omRCqek4oQp/kREDxHRGRO38ToAJ4l5c8xUHwBnOY47QEQFxDxHzzkLNDnhLNLkhBNODCmc6o8TTjgxpHCSihNOODGkcJKKE044MaRwkooTTjgxpHCSihNOODGkcJKKE044MaRwkooTTjgxpPh/63QXAYFZkf8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "radius = 1\n",
+    "theta = np.linspace(0, 2*np.pi*radius, 1000)\n",
+    "\n",
+    "plt.subplot(111, projection='polar')\n",
+    "plt.plot(theta, np.sin(5*theta), \"g-\")\n",
+    "plt.plot(theta, 0.5*np.cos(20*theta), \"b-\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3D projection\n",
+    "\n",
+    "Plotting 3D graphs is quite straightforward: when creating a subplot, set the `projection` to `\"3d\"`. It returns a 3D axes object, which you can use to call `plot_surface`, providing x, y, and z coordinates, plus other optional arguments. For more information on generating 3D plots, check out the [matplotlib tutorial](https://matplotlib.org/stable/tutorials/toolkits/mplot3d.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-5, 5, 50)\n",
+    "y = np.linspace(-5, 5, 50)\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "R = np.sqrt(X**2 + Y**2)\n",
+    "Z = np.sin(R)\n",
+    "\n",
+    "figure = plt.figure(1, figsize = (12, 4))\n",
+    "subplot3d = plt.subplot(111, projection='3d')\n",
+    "surface = subplot3d.plot_surface(X, Y, Z, rstride=1, cstride=1,\n",
+    "                                 cmap=matplotlib.cm.coolwarm, linewidth=0.1)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another way to display this same data is *via* a contour plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.contourf(X, Y, Z, cmap=matplotlib.cm.coolwarm)\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Scatter plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To draw a scatter plot, simply provide the x and y coordinates of the points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(42)  # to make this notebook's output reproducible\n",
+    "\n",
+    "x, y = np.random.rand(2, 100)\n",
+    "plt.scatter(x, y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You may also optionally provide the scale of each point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x, y, scale = np.random.rand(3, 100)\n",
+    "scale = 500 * scale ** 5\n",
+    "plt.scatter(x, y, s=scale)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And as usual there are a number of other arguments you can provide, such as the fill and edge colors and the alpha level."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for color in ['red', 'green', 'blue']:\n",
+    "    n = 100\n",
+    "    x, y = np.random.rand(2, n)\n",
+    "    scale = 500.0 * np.random.rand(n) ** 5\n",
+    "    plt.scatter(x, y, s=scale, c=color, alpha=0.3, edgecolors='blue')\n",
+    "\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lines\n",
+    "You can draw lines simply using the `plot` function, as we have done so far. However, it is often convenient to create a utility function that plots a (seemingly) infinite line across the graph, given a slope and an intercept. You can also use the `hlines` and `vlines` functions that plot horizontal and vertical line segments.\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_line(axis, slope, intercept, **kwargs):\n",
+    "    xmin, xmax = axis.get_xlim()\n",
+    "    plt.plot([xmin, xmax],\n",
+    "             [xmin*slope+intercept, xmax*slope+intercept],\n",
+    "             **kwargs)\n",
+    "\n",
+    "x = np.random.randn(1000)\n",
+    "y = 0.5*x + 5 + np.random.randn(1000) * 2\n",
+    "plt.axis([-2.5, 2.5, -5, 15])\n",
+    "plt.scatter(x, y, alpha=0.2)\n",
+    "plt.plot(1, 0, \"ro\")\n",
+    "plt.vlines(1, -5, 0, color=\"red\")\n",
+    "plt.hlines(0, -2.5, 1, color=\"red\")\n",
+    "plot_line(axis=plt.gca(), slope=0.5, intercept=5, color=\"magenta\")\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Histograms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEGCAYAAABvtY4XAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUNklEQVR4nO3da7BlZX3n8e+P7lZAUCR9ogzQHpyyTNrES0+DJORiTFISW2UyYZK2ImNZSTqJOgM1mUlaJqWJU1PFm1FjKhnDKDXgBTCihuGSiCUkk1g2diMKdEvs0TYBqcFLQoMyMk3+82Ktlt2Hc/qsc1l7717n+6na1Wuvtc5+/v3w9I91nrX3s1NVSJKG57hJFyBJ6ocBL0kDZcBL0kAZ8JI0UAa8JA3U+kkXMGrjxo01Ozs76TIk6ZixZ8+eb1TVzHzHpirgZ2dn2b1796TLkKRjRpKvLnTMKRpJGigDXpIGyoCXpIGaqjl4SZo2sztv7L2NA5dt6+V1vYKXpIEy4CVpoAx4SRooA16SBsqAl6SBMuAlaaAMeEkaKANekgbKgJekgTLgJWmgDHhJGigDXpIGyoCXpIEy4CVpoAx4SRqo3gM+ybokn0tyQ99tSZKeMI4r+IuBfWNoR5I0oteAT3IGsA14b5/tSJKerO8r+HcBvw3800InJNmRZHeS3V//+td7LkeS1o7eAj7Jq4AHq2rP0c6rqsuramtVbZ2ZmemrHElac/q8gj8PeE2SA8A1wMuTfKDH9iRJI3oL+Kp6S1WdUVWzwHbgU1X1ur7akyQdyffBS9JArR9HI1V1G3DbONqSJDW8gpekgTLgJWmgDHhJGigDXpIGyoCXpIEy4CVpoAx4SRooA16SBsqAl6SBMuAlaaAMeEkaKANekgbKgJekgTLgJWmgDHhJGigDXpIGyoCXpIEy4CVpoAx4SRooA16SBsqAl6SBMuAlaaAMeEkaKANekgbKgJekgTLgJWmgDHhJGigDXpIGqreAT3JmkluT7E1yT5KL+2pLkvRk63t87UPAb1XVHUlOBvYkuaWq9vbYpiSp1dsVfFU9UFV3tNsPA/uA0/tqT5J0pLHMwSeZBV4C7BpHe5KkfqdoAEhyEnAdcElVHZzn+A5gB8CmTZuW3c7szhuX/bNdHbhsW+9tSNJq6fUKPskGmnD/YFV9dL5zquryqtpaVVtnZmb6LEeS1pQ+30UT4H3Avqp6R1/tSJLm1+cV/HnARcDLk9zZPl7ZY3uSpBG9zcFX1V8D6ev1JUlH5ydZJWmgDHhJGigDXpIGyoCXpIEy4CVpoAx4SRooA16SBsqAl6SBMuAlaaAMeEkaKANekgbKgJekgTLgJWmgev9GJw3XJL9Fa622LS2FV/CSNFAGvCQNlAEvSQNlwEvSQBnwkjRQBrwkDZQBL0kDZcBL0kAZ8JI0UAa8JA2UAS9JA2XAS9JAGfCSNFAGvCQNlAEvSQPVa8AnOT/JvUn2J9nZZ1uSpCP1FvBJ1gF/BPwcsBl4bZLNfbUnSTpSn1fw5wD7q+rLVfUYcA1wQY/tSZJGpKr6eeHkQuD8qvrV9vlFwEur6s1zztsB7GifPh+4dwnNbAS+sQrlDo39sjD7Zn72y8KmvW+eU1Uz8x2Y+HeyVtXlwOXL+dkku6tq6yqXdMyzXxZm38zPflnYsdw3fU7R3A+cOfL8jHafJGkM+gz4zwLPS3JWkqcA24Hre2xPkjSitymaqjqU5M3AXwDrgCuq6p5VbmZZUztrgP2yMPtmfvbLwo7ZvuntJqskabL8JKskDZQBL0kDNfUBn+SKJA8muXuB40ny7nY5hC8k2TLuGiehQ7+8LMlDSe5sH28dd42TkuTMJLcm2ZvkniQXz3POmhs3HftlTY6bJMcnuT3J59u++f15znlqkmvbMbMryewESl2aqprqB/ATwBbg7gWOvxK4GQhwLrBr0jVPSb+8DLhh0nVOqG9OA7a02ycDfwtsXuvjpmO/rMlx046Dk9rtDcAu4Nw557wReE+7vR24dtJ1L/aY+iv4qvor4FtHOeUC4KpqfAY4Jclp46lucjr0y5pVVQ9U1R3t9sPAPuD0OaetuXHTsV/WpHYcPNI+3dA+5r4D5QLgynb7I8BPJ8mYSlyWqQ/4Dk4H/n7k+X04aA/7kfZXzpuTvGDSxUxC+2v0S2iuyEat6XFzlH6BNTpukqxLcifwIHBLVS04ZqrqEPAQ8H1jLXKJhhDwmt8dNGtUvAj4Q+Djky1n/JKcBFwHXFJVByddz7RYpF/W7Lipqser6sU0n7o/J8kPTbikFRtCwLskwjyq6uDhXzmr6iZgQ5KNEy5rbJJsoAmxD1bVR+c5ZU2Om8X6Za2PG4Cq+kfgVuD8OYe+N2aSrAeeAXxzrMUt0RAC/nrg37TvijgXeKiqHph0UZOW5NmH5weTnEPz33qqB+Nqaf/e7wP2VdU7FjhtzY2bLv2yVsdNkpkkp7TbJwA/C3xxzmnXA69vty8EPlXtHddpNfHVJBeT5GqaO/sbk9wHvI3mBghV9R7gJpp3ROwHvgO8YTKVjleHfrkQ+M0kh4BHge3TPhhX0XnARcBd7ZwqwKXAJljT46ZLv6zVcXMacGX7RUXHAR+uqhuSvB3YXVXX0/zP8f1J9tO8wWH75MrtxqUKJGmghjBFI0mahwEvSQNlwEvSQE3VTdaNGzfW7OzspMuQpGPGnj17vlHT+p2so2ZnZ9m9e/eky5CkY0aSry50rPcpmvbjv59LckPfbUmSnjCOOfiLaRY1kiSNUa8Bn+QMYBvw3j7bkSQ9Wd9z8O8Cfptm7el5JdkB7ADYtGlTz+VIGrfZnTdOuoSpd+Cybb28bm9X8EleBTxYVXuOdl5VXV5VW6tq68zMvDeCJUnL0OcUzXnAa5IcAK4BXp7kAz22J0ka0VvAV9VbquqMqpqlWZTnU1X1ur7akyQdyU+yStJAjeWDTlV1G3DbONqSJDW8gpekgTLgJWmgDHhJGigDXpIGyoCXpIEy4CVpoAx4SRooA16SBsqAl6SBMuAlaaAMeEkaKANekgbKgJekgTLgJWmgDHhJGqhOAZ/kh/suRJK0urpewf9xktuTvDHJM3qtSJK0KjoFfFX9OPDLwJnAniQfSvKzvVYmSVqRznPwVfUl4HeB3wF+Enh3ki8m+Vd9FSdJWr6uc/AvTPJOYB/wcuDVVfWD7fY7e6xPkrRMXb90+w+B9wKXVtWjh3dW1deS/G4vlUmSVqRrwG8DHq2qxwGSHAccX1Xfqar391adJGnZus7BfxI4YeT5ie0+SdKU6hrwx1fVI4eftNsn9lOSJGk1dA34byfZcvhJkn8BPHqU8yVJE9Z1Dv4S4E+TfA0I8Gzgl/oqSpK0cp0Cvqo+m+QHgOe3u+6tqv/XX1mSpJXqegUPcDYw2/7MliRU1VW9VCVJWrFOAZ/k/cA/B+4EHm93F2DAS9KU6noFvxXYXFXVZzGSpNXT9V00d9PcWJUkHSO6XsFvBPYmuR347uGdVfWaXqqSJK1Y14D/vT6LkCStvq5vk/zLJM8BnldVn0xyIrCu39IkSSvRdbngXwM+AvxJu+t04OM91SRJWgVdb7K+CTgPOAjf+/KP7++rKEnSynUN+O9W1WOHnyRZT/M+eEnSlOoa8H+Z5FLghPa7WP8U+J/9lSVJWqmuAb8T+DpwF/DrwE0038+6oCRnJrk1yd4k9yS5eGWlSpKWouu7aP4J+O/to6tDwG9V1R1JTgb2JLmlqvYuo05J0hJ1XYvmK8wz515Vz13oZ6rqAeCBdvvhJPto3n1jwEvSGCxlLZrDjgf+NXBq10aSzAIvAXbNc2wHsANg06ZNXV/ySWZ33rjsn10rDly2bdIlHPMcZ4tznE2PTnPwVfXNkcf9VfUumi/iXlSSk4DrgEuq6uA8r315VW2tqq0zMzNLqV2SdBRdp2i2jDw9juaKftGfTbKBJtw/WFUfXVaFkqRl6TpF819Htg8BB4BfPNoPJAnwPmBfVb1jWdVJkpat67tofmoZr30ecBFwV5I7232XVtVNy3gtSdISdZ2i+fdHOz7fFXpV/TXNF3RLkiZgKe+iORu4vn3+auB24Et9FCVJWrmuAX8GsKWqHgZI8nvAjVX1ur4KkyStTNelCp4FPDby/LF2nyRpSnW9gr8KuD3Jx9rn/xK4speKJEmrouu7aP5LkpuBH293vaGqPtdfWZKkleo6RQNwInCwqv4AuC/JWT3VJElaBV2/su9twO8Ab2l3bQA+0FdRkqSV63oF//PAa4BvA1TV14CT+ypKkrRyXQP+saoq2iWDkzytv5IkSauha8B/OMmfAKck+TXgkyztyz8kSWPWZUXIANcCPwAcBJ4PvLWqbum5NknSCiwa8FVVSW6qqh8GDHVJOkZ0naK5I8nZvVYiSVpVXT/J+lLgdUkO0LyTJjQX9y/sqzBJ0socNeCTbKqqvwNeMaZ6JEmrZLEr+I/TrCL51STXVdUvjKEmSdIqWGwOfvQLO57bZyGSpNW12BV8LbCtAZjdeeOkS5h6By7bNukSpGVbLOBflOQgzZX8Ce02PHGT9em9VidJWrajBnxVrRtXIZKk1bWU5YIlSccQA16SBsqAl6SBMuAlaaAMeEkaKANekgbKgJekgTLgJWmgDHhJGigDXpIGyoCXpIEy4CVpoAx4SRooA16SBsqAl6SBMuAlaaAMeEkaKANekgaq14BPcn6Se5PsT7Kzz7YkSUfqLeCTrAP+CPg5YDPw2iSb+2pPknSkPq/gzwH2V9WXq+ox4Brggh7bkySNSFX188LJhcD5VfWr7fOLgJdW1ZvnnLcD2NE+fT5w7xKb2gh8Y4Xl9mFa64LprW1a6wJrW45prQumt7bl1PWcqpqZ78D6ldezMlV1OXD5cn8+ye6q2rqKJa2Kaa0Lpre2aa0LrG05prUumN7aVruuPqdo7gfOHHl+RrtPkjQGfQb8Z4HnJTkryVOA7cD1PbYnSRrR2xRNVR1K8mbgL4B1wBVVdU8PTS17eqdn01oXTG9t01oXWNtyTGtdML21rWpdvd1klSRNlp9klaSBMuAlaaCmNuCTXJHkwSR3L3A8Sd7dLoPwhSRbRo69PsmX2sfrx1zXL7f13JXk00leNHLsQLv/ziS7V7OujrW9LMlDbft3JnnryLHelpXoUNd/HKnp7iSPJzm1PdZ3n52Z5NYke5Pck+Tiec4Z+1jrWNdExlrH2sY+1jrWNZGxluT4JLcn+Xxb2+/Pc85Tk1zb9suuJLMjx97S7r83ySs6N1xVU/kAfgLYAty9wPFXAjcDAc4FdrX7TwW+3P75zHb7mWOs60cPt0ezTMOukWMHgI0T7LOXATfMs38d8L+B5wJPAT4PbB5XXXPOfTXwqTH22WnAlnb7ZOBv5/7dJzHWOtY1kbHWsbaxj7UudU1qrLVj56R2ewOwCzh3zjlvBN7Tbm8Hrm23N7f99FTgrLb/1nVpd2qv4Kvqr4BvHeWUC4CrqvEZ4JQkpwGvAG6pqm9V1T8AtwDnj6uuqvp02y7AZ2je/z8WHfpsIb0uK7HEul4LXL1abS+mqh6oqjva7YeBfcDpc04b+1jrUtekxlrHPltIb2NtGXWNbay1Y+eR9umG9jH3HS4XAFe22x8BfjpJ2v3XVNV3q+orwH6aflzU1AZ8B6cDfz/y/L5230L7J+FXaK78DivgE0n2pFmiYRJ+pP018eYkL2j3TUWfJTmRJiCvG9k9tj5rfyV+Cc3V1aiJjrWj1DVqImNtkdomNtYW67NJjLUk65LcCTxIc2Gw4DirqkPAQ8D3sYI+m/hSBUOV5Kdo/tH92MjuH6uq+5N8P3BLki+2V7fjcgfNuhWPJHkl8HHgeWNsfzGvBv6mqkav9sfSZ0lOovnHfklVHVzt11+uLnVNaqwtUtvExlrH/5ZjH2tV9Tjw4iSnAB9L8kNVNe99qdVyLF/BL7QUwsSXSEjyQuC9wAVV9c3D+6vq/vbPB4GP0fHXrNVSVQcP/5pYVTcBG5JsZAr6rLWdOb8yj6PPkmygCYQPVtVH5zllImOtQ10TG2uL1Tapsdalz1oTGWvt6/8jcCtPns77Xt8kWQ88A/gmK+mzPm4orNYDmGXhG4bbOPLG1+3t/lOBr9Dc9Hpmu33qGOvaRDNH9qNz9j8NOHlk+9M0q22Os8+ezRMfbjsH+Lu2/9bT3CA8iydufL1gXHW1x59BM0//tHH2Wfv3vwp411HOGftY61jXRMZax9rGPta61DWpsQbMAKe02ycA/wt41Zxz3sSRN1k/3G6/gCNvsn6ZjjdZp3aKJsnVNHfiNya5D3gbzY0Jquo9wE00727YD3wHeEN77FtJ/jPNWjgAb68jfw3ru6630syb/XFzf4RD1awO9yyaX8ugGeQfqqo/X626OtZ2IfCbSQ4BjwLbqxlBvS4r0aEugJ8HPlFV3x750d77DDgPuAi4q50fBbiUJjwnOda61DWpsdaltkmMtS51wWTG2mnAlWm+COk4mvC+Icnbgd1VdT3wPuD9SfbT/A9oe1v3PUk+DOwFDgFvqma6Z1EuVSBJA3Usz8FLko7CgJekgTLgJWmgDHhJGigDXpIGyoDXmtKuNviKOfsuSfLfFjj/tiRT9+XMUhcGvNaaq2nfXzziSZ9qlIbAgNda8xFgW5ovgj+8KNU/A16bZPdCa3W35z4ysn1hkv/Rbs8kuS7JZ9vHeb3/LaQODHitKe0nTW+nWT8d2o+EA/+p/RToC4GfbNd46eoPgHdW1dnAL9CsDSNN3NQuVSD16PA0zZ+1f/4K8IvtErHraT5Wvhn4QsfX+xlgc/sxd4CnJzmpnlj/W5oIA15r0Z8B70zz1Xsn0qz78R+As6vqH9qpl+Pn+bnRdT1Gjx9H8+08/7eneqVlcYpGa057ZX0rcAXN1fzTgW8DDyV5Fk9M38z1f5L8YJLjaBasOuwTwL89/CTJi/uoW1oqA15r1dXAi4Crq+rzwOeALwIfAv5mgZ/ZCdxAs5TsAyP7/x2wNc0XYO8FfqO3qqUlcDVJSRoor+AlaaAMeEkaKANekgbKgJekgTLgJWmgDHhJGigDXpIG6v8DfvBLSvfYgT4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = [1, 1.1, 1.8, 2, 2.1, 3.2, 3, 3, 3, 3]\n",
+    "plt.subplot(211)\n",
+    "plt.hist(data, bins = 10, rwidth=0.8)\n",
+    "\n",
+    "plt.subplot(212)\n",
+    "plt.hist(data, bins = [1, 1.5, 2, 2.5, 3], rwidth=0.95)\n",
+    "plt.xlabel(\"Value\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data1 = np.random.randn(400)\n",
+    "data2 = np.random.randn(500) + 3\n",
+    "data3 = np.random.randn(450) + 6\n",
+    "data4a = np.random.randn(200) + 9\n",
+    "data4b = np.random.randn(100) + 10\n",
+    "\n",
+    "plt.hist(data1, bins=5, color='g', alpha=0.75, histtype='bar',  # default type\n",
+    "         label='bar hist')\n",
+    "plt.hist(data2, color='b', alpha=0.65, histtype='stepfilled',\n",
+    "         label='stepfilled hist')\n",
+    "plt.hist(data3, color='r', histtype='step', label='step hist')\n",
+    "plt.hist((data4a, data4b), color=('r','m'), alpha=0.55, histtype='barstacked',\n",
+    "         label=('barstacked a', 'barstacked b'))\n",
+    "\n",
+    "plt.xlabel(\"Value\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Images\n",
+    "Reading, generating and plotting images in matplotlib is quite straightforward.\n",
+    "\n",
+    "To read an image, just import the `matplotlib.image` module, and call its `imread` function, passing it the file name (or file object). It returns the image data, as a NumPy array. Let's try it with the `my_square_function.png` image we saved earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(288, 432, 4) float32\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.image as mpimg\n",
+    "\n",
+    "img = mpimg.imread(\"my_square_function.png\")\n",
+    "print(img.shape, img.dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have loaded a 288x432 image. Each pixel is represented by a 4-element array: red, green, blue, and alpha levels, stored as 32-bit floats between 0 and 1. Now all we need to do is to call `imshow`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(img)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tadaaa! You may want to hide the axes when you are displaying an image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(img)\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Under the hood, `imread()` uses the Python Image Library (PIL), and Matplotlib's documentation now recommends using PIL directly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(288, 432, 4) uint8\n"
+     ]
+    }
+   ],
+   "source": [
+    "import PIL\n",
+    "\n",
+    "img = np.asarray(PIL.Image.open(\"my_square_function.png\"))\n",
+    "print(img.shape, img.dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the array now contains unsigned 8-bit integers (from 0 to 255), but that's fine as `plt.imshow()` supports this format as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(img)\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also generate an image from scratch:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[   0    1    2 ...   97   98   99]\n",
+      " [ 100  101  102 ...  197  198  199]\n",
+      " [ 200  201  202 ...  297  298  299]\n",
+      " ...\n",
+      " [9700 9701 9702 ... 9797 9798 9799]\n",
+      " [9800 9801 9802 ... 9897 9898 9899]\n",
+      " [9900 9901 9902 ... 9997 9998 9999]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "img = np.arange(100*100).reshape(100, 100)\n",
+    "print(img)\n",
+    "plt.imshow(img)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we did not provide RGB levels, the `imshow` function automatically maps values to a color gradient. By default, the color gradient goes from blue (for low values) to yellow (for high values), but you can select another color map. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(img, cmap=\"hot\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also generate an RGB image directly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "img = np.empty((20,30,3))\n",
+    "img[:, :10] = [0, 0, 0.6]\n",
+    "img[:, 10:20] = [1, 1, 1]\n",
+    "img[:, 20:] = [0.6, 0, 0]\n",
+    "plt.imshow(img)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the `img` array is just quite small (20x30), when the `imshow` function displays it, it grows the image to the figure's size. Imagine stretching the original image, leaving blanks between the original pixels. How does imshow fill the blanks? Well, by default, it just colors each blank pixel using the color of the nearest non-blank pixel. This technique can lead to pixelated images. If you prefer, you can use a different interpolation method, such as [bilinear interpolation](https://en.wikipedia.org/wiki/Bilinear_interpolation) to fill the blank pixels. This leads to blurry edges, which may be nicer in some cases:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(img, interpolation=\"bilinear\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Animations\n",
+    "Although matplotlib is mostly used to generate images, it is also capable of displaying animations. First, you need to import `matplotlib.animation`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.animation as animation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following example, we start by creating data points, then we create an empty plot, we define the update function that will be called at every iteration of the animation, and finally we add an animation to the plot by creating a `FuncAnimation` instance.\n",
+    "\n",
+    "The `FuncAnimation` constructor takes a figure, an update function and optional arguments. We specify that we want a 50-frame long animation, with 100ms between each frame. At each iteration, `FuncAnimation` calls our update function and passes it the frame number `num` (from 0 to 49 in our case) followed by the extra arguments that we specified with `fargs`.\n",
+    "\n",
+    "Our update function simply sets the line data to be the first `num` data points (so the data gets drawn gradually), and just for fun we also add a small random number to each data point so that the line appears to wiggle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.linspace(-1, 1, 100)\n",
+    "y = np.sin(x**2*25)\n",
+    "data = np.array([x, y])\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "line, = plt.plot([], [], \"r-\")  # start with an empty plot\n",
+    "plt.axis([-1.1, 1.1, -1.1, 1.1])\n",
+    "plt.plot([-0.5, 0.5], [0, 0], \"b-\", [0, 0], [-0.5, 0.5], \"b-\", 0, 0, \"ro\")\n",
+    "plt.grid(True)\n",
+    "plt.title(\"Marvelous animation\")\n",
+    "\n",
+    "# this function will be called at every iteration\n",
+    "def update_line(num, data, line):\n",
+    "    # we only plot the first `num` data points.\n",
+    "    line.set_data(data[..., :num] + np.random.rand(2, num) / 25)\n",
+    "    return line,\n",
+    "\n",
+    "line_ani = animation.FuncAnimation(fig, update_line, frames=50,\n",
+    "                                   fargs=(data, line), interval=100)\n",
+    "plt.close()  # call close() to avoid displaying the static plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, let's display the animation. One option is to convert it to HTML5 code (using a `<video>` tag), and render this code using `IPython.display.HTML`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<video width=\"432\" height=\"288\" controls autoplay loop>\n",
+       "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAIGZ0eXBNNFYgAAACAE00ViBpc29taXNvMmF2YzEAAAAIZnJlZQABF3FtZGF0AAACrgYF//+q\n",
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+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "HTML(line_ani.to_html5_video())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, we can display the animation using a nice little HTML/Javascript interactive widget:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\"\n",
+       "href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css\">\n",
+       "<script language=\"javascript\">\n",
+       "  function isInternetExplorer() {\n",
+       "    ua = navigator.userAgent;\n",
+       "    /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
+       "    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
+       "  }\n",
+       "\n",
+       "  /* Define the Animation class */\n",
+       "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
+       "    this.img_id = img_id;\n",
+       "    this.slider_id = slider_id;\n",
+       "    this.loop_select_id = loop_select_id;\n",
+       "    this.interval = interval;\n",
+       "    this.current_frame = 0;\n",
+       "    this.direction = 0;\n",
+       "    this.timer = null;\n",
+       "    this.frames = new Array(frames.length);\n",
+       "\n",
+       "    for (var i=0; i<frames.length; i++)\n",
+       "    {\n",
+       "     this.frames[i] = new Image();\n",
+       "     this.frames[i].src = frames[i];\n",
+       "    }\n",
+       "    var slider = document.getElementById(this.slider_id);\n",
+       "    slider.max = this.frames.length - 1;\n",
+       "    if (isInternetExplorer()) {\n",
+       "        // switch from oninput to onchange because IE <= 11 does not conform\n",
+       "        // with W3C specification. It ignores oninput and onchange behaves\n",
+       "        // like oninput. In contrast, Mircosoft Edge behaves correctly.\n",
+       "        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
+       "        slider.setAttribute('oninput', null);\n",
+       "    }\n",
+       "    this.set_frame(this.current_frame);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.get_loop_state = function(){\n",
+       "    var button_group = document[this.loop_select_id].state;\n",
+       "    for (var i = 0; i < button_group.length; i++) {\n",
+       "        var button = button_group[i];\n",
+       "        if (button.checked) {\n",
+       "            return button.value;\n",
+       "        }\n",
+       "    }\n",
+       "    return undefined;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.set_frame = function(frame){\n",
+       "    this.current_frame = frame;\n",
+       "    document.getElementById(this.img_id).src =\n",
+       "            this.frames[this.current_frame].src;\n",
+       "    document.getElementById(this.slider_id).value = this.current_frame;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.next_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.previous_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.first_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(0);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.last_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(this.frames.length - 1);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.slower = function()\n",
+       "  {\n",
+       "    this.interval /= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.faster = function()\n",
+       "  {\n",
+       "    this.interval *= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_forward = function()\n",
+       "  {\n",
+       "    this.current_frame += 1;\n",
+       "    if(this.current_frame < this.frames.length){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.first_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.last_frame();\n",
+       "        this.reverse_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.last_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_reverse = function()\n",
+       "  {\n",
+       "    this.current_frame -= 1;\n",
+       "    if(this.current_frame >= 0){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.last_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.first_frame();\n",
+       "        this.play_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.first_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.pause_animation = function()\n",
+       "  {\n",
+       "    this.direction = 0;\n",
+       "    if (this.timer){\n",
+       "      clearInterval(this.timer);\n",
+       "      this.timer = null;\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.play_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = 1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_forward();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.reverse_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = -1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_reverse();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "</script>\n",
+       "\n",
+       "<style>\n",
+       ".animation {\n",
+       "    display: inline-block;\n",
+       "    text-align: center;\n",
+       "}\n",
+       "input[type=range].anim-slider {\n",
+       "    width: 374px;\n",
+       "    margin-left: auto;\n",
+       "    margin-right: auto;\n",
+       "}\n",
+       ".anim-buttons {\n",
+       "    margin: 8px 0px;\n",
+       "}\n",
+       ".anim-buttons button {\n",
+       "    padding: 0;\n",
+       "    width: 36px;\n",
+       "}\n",
+       ".anim-state label {\n",
+       "    margin-right: 8px;\n",
+       "}\n",
+       ".anim-state input {\n",
+       "    margin: 0;\n",
+       "    vertical-align: middle;\n",
+       "}\n",
+       "</style>\n",
+       "\n",
+       "<div class=\"animation\">\n",
+       "  <img id=\"_anim_img799ea21e2ff840028c1eeba7d7b24d47\">\n",
+       "  <div class=\"anim-controls\">\n",
+       "    <input id=\"_anim_slider799ea21e2ff840028c1eeba7d7b24d47\" type=\"range\" class=\"anim-slider\"\n",
+       "           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
+       "           oninput=\"anim799ea21e2ff840028c1eeba7d7b24d47.set_frame(parseInt(this.value));\"></input>\n",
+       "    <div class=\"anim-buttons\">\n",
+       "      <button title=\"Decrease speed\" onclick=\"anim799ea21e2ff840028c1eeba7d7b24d47.slower()\">\n",
+       "          <i class=\"fa fa-minus\"></i></button>\n",
+       "      <button title=\"First frame\" onclick=\"anim799ea21e2ff840028c1eeba7d7b24d47.first_frame()\">\n",
+       "        <i class=\"fa fa-fast-backward\"></i></button>\n",
+       "      <button title=\"Previous frame\" onclick=\"anim799ea21e2ff840028c1eeba7d7b24d47.previous_frame()\">\n",
+       "          <i class=\"fa fa-step-backward\"></i></button>\n",
+       "      <button title=\"Play backwards\" onclick=\"anim799ea21e2ff840028c1eeba7d7b24d47.reverse_animation()\">\n",
+       "          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
+       "      <button title=\"Pause\" onclick=\"anim799ea21e2ff840028c1eeba7d7b24d47.pause_animation()\">\n",
+       "          <i class=\"fa fa-pause\"></i></button>\n",
+       "      <button title=\"Play\" onclick=\"anim799ea21e2ff840028c1eeba7d7b24d47.play_animation()\">\n",
+       "          <i class=\"fa fa-play\"></i></button>\n",
+       "      <button title=\"Next frame\" onclick=\"anim799ea21e2ff840028c1eeba7d7b24d47.next_frame()\">\n",
+       "          <i class=\"fa fa-step-forward\"></i></button>\n",
+       "      <button title=\"Last frame\" onclick=\"anim799ea21e2ff840028c1eeba7d7b24d47.last_frame()\">\n",
+       "          <i class=\"fa fa-fast-forward\"></i></button>\n",
+       "      <button title=\"Increase speed\" onclick=\"anim799ea21e2ff840028c1eeba7d7b24d47.faster()\">\n",
+       "          <i class=\"fa fa-plus\"></i></button>\n",
+       "    </div>\n",
+       "    <form title=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_select799ea21e2ff840028c1eeba7d7b24d47\"\n",
+       "          class=\"anim-state\">\n",
+       "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_799ea21e2ff840028c1eeba7d7b24d47\"\n",
+       "             >\n",
+       "      <label for=\"_anim_radio1_799ea21e2ff840028c1eeba7d7b24d47\">Once</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_799ea21e2ff840028c1eeba7d7b24d47\"\n",
+       "             checked>\n",
+       "      <label for=\"_anim_radio2_799ea21e2ff840028c1eeba7d7b24d47\">Loop</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_799ea21e2ff840028c1eeba7d7b24d47\"\n",
+       "             >\n",
+       "      <label for=\"_anim_radio3_799ea21e2ff840028c1eeba7d7b24d47\">Reflect</label>\n",
+       "    </form>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "<script language=\"javascript\">\n",
+       "  /* Instantiate the Animation class. */\n",
+       "  /* The IDs given should match those used in the template above. */\n",
+       "  (function() {\n",
+       "    var img_id = \"_anim_img799ea21e2ff840028c1eeba7d7b24d47\";\n",
+       "    var slider_id = \"_anim_slider799ea21e2ff840028c1eeba7d7b24d47\";\n",
+       "    var loop_select_id = \"_anim_loop_select799ea21e2ff840028c1eeba7d7b24d47\";\n",
+       "    var frames = new Array(50);\n",
+       "    \n",
+       "  frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAbAAAAEgCAYAAADVKCZpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n",
+       "bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsT\\\n",
+       "AAALEwEAmpwYAAAemElEQVR4nO3df5RfdX3n8efLpAnFVBKIDiGwSVjSFSxLdCJKPVsIP6PdQ2KL\\\n",
+       "mhRr0GDUilv0rGty2KqL0mJ7dump4oo/UoJSBqXlONVwUn7M6GklSNDIj7AxAwgEiPxIQh2QAOG9\\\n",
+       "f9zPeC6T70zm+53v9879kNfjnHty7+d+Pve+5jOTec+93zvzVURgZmaWm1dNdAAzM7NWuICZmVmW\\\n",
+       "XMDMzCxLLmBmZpYlFzAzM8uSC5iZmWXJBczMzLLkAmZmZllyATMzsyy5gJmZWZZcwMzMLEsuYGZm\\\n",
+       "liUXMDMzy5ILmJmZZckFzMzMsuQCZmZmWXIBMzOzLLmAmZlZllzAzEokXSnp8xOdY6wkDUo6uqJz\\\n",
+       "fUXSX1RxLrOxcAGzCSPpF5KelzRzWPtPJYWkuRMULRsRMS0i7m/3cSWdJ+lfh53rwxHxuXafy6xV\\\n",
+       "LmA20R4Alg9tSDoeOLjVg0ma3I5QZlZ/LmA20b4JvK+0vQK4qtxB0h+mq7J/l/SwpM+W9s1NV2sr\\\n",
+       "JT0E3CLpBkkXDDvGzyT9UVp/vaQbJe2UtFXSu0cKJ+mDkgZS315JRww77+RS335J56f1YyT9QNLT\\\n",
+       "kp6UdO0o5/iOpB2p7w8lvaG070pJl0v6vqRfSbpN0n8s7Q9Jx5T6fjl9/IOS/k3S4ZL+VtIuSf9P\\\n",
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+       "JU0ClgHfGtbnGYoiNx34Q+AjkpYO63MycCxwFnANL7+qOw6YA3xf0quBG4F/AF6Xzvfl1OdlJJ0K\\\n",
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+       "8WHXEBF5QEQWB8fBJep8UETmRl7bReSCoOwmEVkWKZuYl11BvZ7Is6dHrtezvyaKyF+Cv/c8Eflk\\\n",
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+       "sinB332xiEzJ2a7rIjYtEpGNkbIs++tGEVkjIs+XKRcR+a/A7nki8q5IWWb9VTfqvSFZvV7AMcBR\\\n",
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+       "uvUXMAEYH5wPB14DBvnur0r/XiJ1/h64PjifDNwWnB8b1O8HjA3aacrRrg9G/g19MbSr0t80J7su\\\n",
+       "AX5Y4t4hwNLgODg4H5yXXb3qfxm4Mev+Cto+BXgX8HyZ8o8A9+M2An4v8GTW/VXP11vWA1PVBaq6\\\n",
+       "sEq1k4AlqrpUVXcCtwLni4gApwF3BPVuBi7wZNr5QXtx270QuF9Vt3p6fjmS2vUm9e4vVV2kqouD\\\n",
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+       "tiSyS0ROAH4CnKeqayLXS/5Nc7SrLfL2PGBBcD4DODOwbzBwJntHIjK1K7DtaFxCxF8i17LsrzhM\\\n",
+       "Bz4TZCO+F9gU/EjLsr/qR72zSOr1Aj6KiwPvAFYDM4Lrw4H7IvU+AizC/YL6RuT6kbgvmCXA74B+\\\n",
+       "nuwaCjwELAYeBIYE108EfhapNwb3q6pPr/sfBp7DfRH/GjgoL7uA9wXPfjY4XlqE/gI+DewC5kZe\\\n",
+       "E7Por1L/XnAhyfOC8wODz78k6I8jI/d+I7hvIfBhz//eq9n1YPD/IOyf6dX+pjnZdS3wQvD8duDo\\\n",
+       "yL1/F/TjEuCzedoVvL8S+G6v+7Lur1twWbS7cN9flwJfAL4QlAvwo8Du54hkWGfZX/V62UochmEY\\\n",
+       "RkNiIUTDMAyjITEBMwzDMBoSEzDDMAyjITEBMwzDMBoSEzDDMAyjITEBMwzDMBoSEzDDMAyjITEB\\\n",
+       "MwzDMBoSEzDDMAyjITEBMwzDMBoSEzDDMAyjITEBMwzDMBoSEzDDMAyjITEBMwzDMBoSEzDDMAyj\\\n",
+       "ITEBMwzDMBoSEzDDMAyjITEBMwzDMBoSEzDDMAyjITEBMwzDMBqS/weY4iCvxhPiIQAAAABJRU5E\\\n",
+       "rkJggg==\\\n",
+       "\"\n",
+       "\n",
+       "\n",
+       "    /* set a timeout to make sure all the above elements are created before\n",
+       "       the object is initialized. */\n",
+       "    setTimeout(function() {\n",
+       "        anim799ea21e2ff840028c1eeba7d7b24d47 = new Animation(frames, img_id, slider_id, 100.0,\n",
+       "                                 loop_select_id);\n",
+       "    }, 0);\n",
+       "  })()\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(line_ani.to_jshtml())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can configure Matplotlib to use this widget by default when rendering animations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "matplotlib.rc('animation', html='jshtml')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After that, you don't even need to use `IPython.display.HTML` anymore:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\"\n",
+       "href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css\">\n",
+       "<script language=\"javascript\">\n",
+       "  function isInternetExplorer() {\n",
+       "    ua = navigator.userAgent;\n",
+       "    /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
+       "    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
+       "  }\n",
+       "\n",
+       "  /* Define the Animation class */\n",
+       "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
+       "    this.img_id = img_id;\n",
+       "    this.slider_id = slider_id;\n",
+       "    this.loop_select_id = loop_select_id;\n",
+       "    this.interval = interval;\n",
+       "    this.current_frame = 0;\n",
+       "    this.direction = 0;\n",
+       "    this.timer = null;\n",
+       "    this.frames = new Array(frames.length);\n",
+       "\n",
+       "    for (var i=0; i<frames.length; i++)\n",
+       "    {\n",
+       "     this.frames[i] = new Image();\n",
+       "     this.frames[i].src = frames[i];\n",
+       "    }\n",
+       "    var slider = document.getElementById(this.slider_id);\n",
+       "    slider.max = this.frames.length - 1;\n",
+       "    if (isInternetExplorer()) {\n",
+       "        // switch from oninput to onchange because IE <= 11 does not conform\n",
+       "        // with W3C specification. It ignores oninput and onchange behaves\n",
+       "        // like oninput. In contrast, Mircosoft Edge behaves correctly.\n",
+       "        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
+       "        slider.setAttribute('oninput', null);\n",
+       "    }\n",
+       "    this.set_frame(this.current_frame);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.get_loop_state = function(){\n",
+       "    var button_group = document[this.loop_select_id].state;\n",
+       "    for (var i = 0; i < button_group.length; i++) {\n",
+       "        var button = button_group[i];\n",
+       "        if (button.checked) {\n",
+       "            return button.value;\n",
+       "        }\n",
+       "    }\n",
+       "    return undefined;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.set_frame = function(frame){\n",
+       "    this.current_frame = frame;\n",
+       "    document.getElementById(this.img_id).src =\n",
+       "            this.frames[this.current_frame].src;\n",
+       "    document.getElementById(this.slider_id).value = this.current_frame;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.next_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.previous_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.first_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(0);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.last_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(this.frames.length - 1);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.slower = function()\n",
+       "  {\n",
+       "    this.interval /= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.faster = function()\n",
+       "  {\n",
+       "    this.interval *= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_forward = function()\n",
+       "  {\n",
+       "    this.current_frame += 1;\n",
+       "    if(this.current_frame < this.frames.length){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.first_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.last_frame();\n",
+       "        this.reverse_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.last_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_reverse = function()\n",
+       "  {\n",
+       "    this.current_frame -= 1;\n",
+       "    if(this.current_frame >= 0){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.last_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.first_frame();\n",
+       "        this.play_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.first_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.pause_animation = function()\n",
+       "  {\n",
+       "    this.direction = 0;\n",
+       "    if (this.timer){\n",
+       "      clearInterval(this.timer);\n",
+       "      this.timer = null;\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.play_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = 1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_forward();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.reverse_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = -1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_reverse();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "</script>\n",
+       "\n",
+       "<style>\n",
+       ".animation {\n",
+       "    display: inline-block;\n",
+       "    text-align: center;\n",
+       "}\n",
+       "input[type=range].anim-slider {\n",
+       "    width: 374px;\n",
+       "    margin-left: auto;\n",
+       "    margin-right: auto;\n",
+       "}\n",
+       ".anim-buttons {\n",
+       "    margin: 8px 0px;\n",
+       "}\n",
+       ".anim-buttons button {\n",
+       "    padding: 0;\n",
+       "    width: 36px;\n",
+       "}\n",
+       ".anim-state label {\n",
+       "    margin-right: 8px;\n",
+       "}\n",
+       ".anim-state input {\n",
+       "    margin: 0;\n",
+       "    vertical-align: middle;\n",
+       "}\n",
+       "</style>\n",
+       "\n",
+       "<div class=\"animation\">\n",
+       "  <img id=\"_anim_img58b7cd9e988546fa824e47eeb5d051a4\">\n",
+       "  <div class=\"anim-controls\">\n",
+       "    <input id=\"_anim_slider58b7cd9e988546fa824e47eeb5d051a4\" type=\"range\" class=\"anim-slider\"\n",
+       "           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
+       "           oninput=\"anim58b7cd9e988546fa824e47eeb5d051a4.set_frame(parseInt(this.value));\"></input>\n",
+       "    <div class=\"anim-buttons\">\n",
+       "      <button title=\"Decrease speed\" onclick=\"anim58b7cd9e988546fa824e47eeb5d051a4.slower()\">\n",
+       "          <i class=\"fa fa-minus\"></i></button>\n",
+       "      <button title=\"First frame\" onclick=\"anim58b7cd9e988546fa824e47eeb5d051a4.first_frame()\">\n",
+       "        <i class=\"fa fa-fast-backward\"></i></button>\n",
+       "      <button title=\"Previous frame\" onclick=\"anim58b7cd9e988546fa824e47eeb5d051a4.previous_frame()\">\n",
+       "          <i class=\"fa fa-step-backward\"></i></button>\n",
+       "      <button title=\"Play backwards\" onclick=\"anim58b7cd9e988546fa824e47eeb5d051a4.reverse_animation()\">\n",
+       "          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
+       "      <button title=\"Pause\" onclick=\"anim58b7cd9e988546fa824e47eeb5d051a4.pause_animation()\">\n",
+       "          <i class=\"fa fa-pause\"></i></button>\n",
+       "      <button title=\"Play\" onclick=\"anim58b7cd9e988546fa824e47eeb5d051a4.play_animation()\">\n",
+       "          <i class=\"fa fa-play\"></i></button>\n",
+       "      <button title=\"Next frame\" onclick=\"anim58b7cd9e988546fa824e47eeb5d051a4.next_frame()\">\n",
+       "          <i class=\"fa fa-step-forward\"></i></button>\n",
+       "      <button title=\"Last frame\" onclick=\"anim58b7cd9e988546fa824e47eeb5d051a4.last_frame()\">\n",
+       "          <i class=\"fa fa-fast-forward\"></i></button>\n",
+       "      <button title=\"Increase speed\" onclick=\"anim58b7cd9e988546fa824e47eeb5d051a4.faster()\">\n",
+       "          <i class=\"fa fa-plus\"></i></button>\n",
+       "    </div>\n",
+       "    <form title=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_select58b7cd9e988546fa824e47eeb5d051a4\"\n",
+       "          class=\"anim-state\">\n",
+       "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_58b7cd9e988546fa824e47eeb5d051a4\"\n",
+       "             >\n",
+       "      <label for=\"_anim_radio1_58b7cd9e988546fa824e47eeb5d051a4\">Once</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_58b7cd9e988546fa824e47eeb5d051a4\"\n",
+       "             checked>\n",
+       "      <label for=\"_anim_radio2_58b7cd9e988546fa824e47eeb5d051a4\">Loop</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_58b7cd9e988546fa824e47eeb5d051a4\"\n",
+       "             >\n",
+       "      <label for=\"_anim_radio3_58b7cd9e988546fa824e47eeb5d051a4\">Reflect</label>\n",
+       "    </form>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "<script language=\"javascript\">\n",
+       "  /* Instantiate the Animation class. */\n",
+       "  /* The IDs given should match those used in the template above. */\n",
+       "  (function() {\n",
+       "    var img_id = \"_anim_img58b7cd9e988546fa824e47eeb5d051a4\";\n",
+       "    var slider_id = \"_anim_slider58b7cd9e988546fa824e47eeb5d051a4\";\n",
+       "    var loop_select_id = \"_anim_loop_select58b7cd9e988546fa824e47eeb5d051a4\";\n",
+       "    var frames = new Array(50);\n",
+       "    \n",
+       "  frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAbAAAAEgCAYAAADVKCZpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n",
+       "bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsT\\\n",
+       "AAALEwEAmpwYAAAemElEQVR4nO3df5RfdX3n8efLpAnFVBKIDiGwSVjSFSxLdCJKPVsIP6PdQ2KL\\\n",
+       "mhRr0GDUilv0rGty2KqL0mJ7dump4oo/UoJSBqXlONVwUn7M6GklSNDIj7AxAwgEiPxIQh2QAOG9\\\n",
+       "f9zPeC6T70zm+53v9879kNfjnHty7+d+Pve+5jOTec+93zvzVURgZmaWm1dNdAAzM7NWuICZmVmW\\\n",
+       "XMDMzCxLLmBmZpYlFzAzM8uSC5iZmWXJBczMzLLkAmZmZllyATMzsyy5gJmZWZZcwMzMLEsuYGZm\\\n",
+       "liUXMDMzy5ILmJmZZckFzMzMsuQCZmZmWXIBMzOzLLmAmZlZllzAzEokXSnp8xOdY6wkDUo6uqJz\\\n",
+       "fUXSX1RxLrOxcAGzCSPpF5KelzRzWPtPJYWkuRMULRsRMS0i7m/3cSWdJ+lfh53rwxHxuXafy6xV\\\n",
+       "LmA20R4Alg9tSDoeOLjVg0ma3I5QZlZ/LmA20b4JvK+0vQK4qtxB0h+mq7J/l/SwpM+W9s1NV2sr\\\n",
+       "JT0E3CLpBkkXDDvGzyT9UVp/vaQbJe2UtFXSu0cKJ+mDkgZS315JRww77+RS335J56f1YyT9QNLT\\\n",
+       "kp6UdO0o5/iOpB2p7w8lvaG070pJl0v6vqRfSbpN0n8s7Q9Jx5T6fjl9/IOS/k3S4ZL+VtIuSf9P\\\n",
+       "0htLY1dLui8dd4ukd6b2Y4GvACel4+wuHf/zpfEN56aU68OStknanT4GjTQHZq1wAbOJthF4jaRj\\\n",
+       "JU0ClgHfGtbnGYoiNx34Q+AjkpYO63MycCxwFnANL7+qOw6YA3xf0quBG4F/AF6Xzvfl1OdlJJ0K\\\n",
+       "/BXwbmAW8CDQM8aP63PAvwAzgCOBL47S9wZgfsrzE+DqYfuXAf8rHWsAuGSUY70b+J/ATGAPcGs6\\\n",
+       "5kzgOuD/lPreB/wX4JB0/G9JmhUR9wIfBm5NtyinDz/JGOfmvwJvBv5z6nfWKLnNmuYCZnUwdBV2\\\n",
+       "BnAv8Eh5Z0T0R8RdEfFSRNxJUaBOHnaMz0bEMxHxa+B6YIGkOWnfucA/RcQeim+qv4iIv4+IFyPi\\\n",
+       "p8A/Au9qkOtcYG1E/CSNXUNxVTJ3DB/TCxRF84iIeC4i/nWkjhGxNiJ+lc7xWeAESYeUulwfET+O\\\n",
+       "iBcpituCUc57fUTcERHPUczDcxFxVUTsBa4FfnMFFhHfiYhH07xeC2wDThzDxwZjm5tLI2J3RDwE\\\n",
+       "9O0nt1nTXMCsDr4J/AlwHsNuHwJIeoukPklPSHqa4upg5rBuDw+tRMSvgO9TXLlAcTU2dFUzB3hL\\\n",
+       "uq21O90eOxc4vEGuIyiuLIaOOwg8Bcwew8f0PwABP5Z0j6QPNOokaZKkS9OtvH8HfpF2lT++HaX1\\\n",
+       "Z4Fpo5z3l6X1XzfY/s1YSe+TtLk0D7/HvvM6krHMTTO5zZrmAmYTLiIepHiY4x3APzXo8g9AL3BU\\\n",
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+       "TWICZhiGYdQkJmCGYRhGTWICZhiGYdQkJmCGYRhGTWICZhiGYdQkJmCGYRhGTWICZhiGYdQkJmCG\\\n",
+       "YRhGTWICZhiGYdQkJmCGYRhGTWICZhiGYdQkJmCGYRhGTWICZhiGYdQkJmCGYRhGTWICZhiGYdQk\\\n",
+       "JmCGYRhGTWICZhiGYdQk/x8I8NOIq4NPHQAAAABJRU5ErkJggg==\\\n",
+       "\"\n",
+       "\n",
+       "\n",
+       "    /* set a timeout to make sure all the above elements are created before\n",
+       "       the object is initialized. */\n",
+       "    setTimeout(function() {\n",
+       "        anim58b7cd9e988546fa824e47eeb5d051a4 = new Animation(frames, img_id, slider_id, 100.0,\n",
+       "                                 loop_select_id);\n",
+       "    }, 0);\n",
+       "  })()\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<matplotlib.animation.FuncAnimation at 0x12861a670>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "animation.FuncAnimation(fig, update_line, frames=50, fargs=(data, line),\n",
+    "                        interval=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Warning:** if you save the notebook along with its outputs, then the animations will take up a lot of space."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Saving animations to video files\n",
+    "Matplotlib relies on 3rd-party libraries to write videos such as [FFMPEG](https://www.ffmpeg.org/) or [ImageMagick](https://imagemagick.org/). In the following example we will be using FFMPEG so be sure to install it first. To save the animation to the GIF format, you would need ImageMagick."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Writer = animation.writers['ffmpeg']\n",
+    "writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)\n",
+    "line_ani.save('my_wiggly_animation.mp4', writer=writer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# What's next?\n",
+    "Now you know all the basics of matplotlib, but there are many more options available. The best way to learn more, is to visit the [gallery](https://matplotlib.org/stable/gallery/index.html), look at the images, choose a plot that you are interested in, then just copy the code in a Jupyter notebook and play around with it."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "toc": {
+   "toc_cell": true,
+   "toc_number_sections": true,
+   "toc_section_display": "block",
+   "toc_threshold": 6,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/ML/01_intro/tools_numpy.ipynb b/ML/01_intro/tools_numpy.ipynb
new file mode 100644
index 0000000..4beaa3d
--- /dev/null
+++ b/ML/01_intro/tools_numpy.ipynb
@@ -0,0 +1,5073 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Tools - NumPy**\n",
+    "\n",
+    "*NumPy is the fundamental library for scientific computing with Python. NumPy is centered around a powerful N-dimensional array object, and it also contains useful linear algebra, Fourier transform, and random number functions.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a href=\"https://colab.research.google.com/github/ageron/handson-ml3/blob/main/tools_numpy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "  </td>\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml3/blob/main/tools_numpy.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating Arrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's import `numpy`. Most people import it as `np`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `np.zeros`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `zeros` function creates an array containing any number of zeros:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 0.])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.zeros(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's just as easy to create a 2D array (i.e. a matrix) by providing a tuple with the desired number of rows and columns. For example, here's a 3x4 matrix:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.zeros((3,4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Some vocabulary\n",
+    "\n",
+    "* In NumPy, each dimension is called an **axis**.\n",
+    "* The number of axes is called the **rank**.\n",
+    "    * For example, the above 3x4 matrix is an array of rank 2 (it is 2-dimensional).\n",
+    "    * The first axis has length 3, the second has length 4.\n",
+    "* An array's list of axis lengths is called the **shape** of the array.\n",
+    "    * For example, the above matrix's shape is `(3, 4)`.\n",
+    "    * The rank is equal to the shape's length.\n",
+    "* The **size** of an array is the total number of elements, which is the product of all axis lengths (e.g. 3*4=12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.zeros((3,4))\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 4)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.ndim  # equal to len(a.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## N-dimensional arrays\n",
+    "You can also create an N-dimensional array of arbitrary rank. For example, here's a 3D array (rank=3), with shape `(2,3,4)`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.zeros((2,3,4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Array type\n",
+    "NumPy arrays have the type `ndarray`s:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(np.zeros((3,4)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `np.ones`\n",
+    "Many other NumPy functions create `ndarray`s.\n",
+    "\n",
+    "Here's a 3x4 matrix full of ones:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.ones((3,4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `np.full`\n",
+    "Creates an array of the given shape initialized with the given value. Here's a 3x4 matrix full of `π`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3.14159265, 3.14159265, 3.14159265, 3.14159265],\n",
+       "       [3.14159265, 3.14159265, 3.14159265, 3.14159265],\n",
+       "       [3.14159265, 3.14159265, 3.14159265, 3.14159265]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.full((3,4), np.pi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `np.empty`\n",
+    "An uninitialized 2x3 array (its content is not predictable, as it is whatever is in memory at that point):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.00e+000, 3.06e-322, 0.00e+000],\n",
+       "       [0.00e+000, 0.00e+000, 0.00e+000]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.empty((2,3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## np.array\n",
+    "Of course, you can initialize an `ndarray` using a regular python array. Just call the `array` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1,  2,  3,  4],\n",
+       "       [10, 20, 30, 40]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array([[1,2,3,4], [10, 20, 30, 40]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `np.arange`\n",
+    "You can create an `ndarray` using NumPy's `arange` function, which is similar to python's built-in `range` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arange(1, 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It also works with floats:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 2., 3., 4.])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arange(1.0, 5.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, you can provide a step parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arange(1, 5, 0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, when dealing with floats, the exact number of elements in the array is not always predictable. For example, consider this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.         0.33333333 0.66666667 1.         1.33333333 1.66666667]\n",
+      "[0.         0.33333333 0.66666667 1.         1.33333333 1.66666667]\n",
+      "[0.         0.33333333 0.66666667 1.         1.33333334]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.arange(0, 5/3, 1/3)) # depending on floating point errors, the max value is 4/3 or 5/3.\n",
+    "print(np.arange(0, 5/3, 0.333333333))\n",
+    "print(np.arange(0, 5/3, 0.333333334))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `np.linspace`\n",
+    "For this reason, it is generally preferable to use the `linspace` function instead of `arange` when working with floats. The `linspace` function returns an array containing a specific number of points evenly distributed between two values (note that the maximum value is *included*, contrary to `arange`):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.         0.33333333 0.66666667 1.         1.33333333 1.66666667]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.linspace(0, 5/3, 6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `np.rand` and `np.randn`\n",
+    "A number of functions are available in NumPy's `random` module to create `ndarray`s initialized with random values.\n",
+    "For example, here is a 3x4 matrix initialized with random floats between 0 and 1 (uniform distribution):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.47698476, 0.39453995, 0.09641682, 0.67521148],\n",
+       "       [0.41412975, 0.44694094, 0.62320442, 0.90437753],\n",
+       "       [0.31266721, 0.50163731, 0.96093721, 0.38303191]])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.rand(3,4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a 3x4 matrix containing random floats sampled from a univariate [normal distribution](https://en.wikipedia.org/wiki/Normal_distribution) (Gaussian distribution) of mean 0 and variance 1:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.18260514e-01,  1.09540729e-01,  7.98591536e-04,\n",
+       "         8.32733930e-01],\n",
+       "       [ 5.89942056e-01,  1.06164114e+00,  9.26427739e-01,\n",
+       "         3.50822290e-02],\n",
+       "       [-1.18654745e+00,  3.13723026e-01, -2.01279322e-01,\n",
+       "        -6.71803289e-01]])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.randn(3,4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To give you a feel of what these distributions look like, let's use matplotlib (see the [matplotlib tutorial](tools_matplotlib.ipynb) for more details):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcm0lEQVR4nO3dfZgcZZnv8e+PEAnvIImoeZnJSgQCCIEREDjKOSAkgHBQjkvcCNFgziq4q6J7RUUg7F7nHBXxsLu4iiuL4AIGWCVosggrgkdezAASmUAkCxOYiBAC8h5I5D5/VDWpTLpnema6prq7fp/rmmu66/XuTqbueu6n6ilFBGZmVl5bFR2AmZkVy4nAzKzknAjMzErOicDMrOScCMzMSs6JwMys5JwIrGVJOl/SDwrad0jaI339bUlfadB2p0h6UdKY9P0vJJ3RiG2n21sq6fRGbc/aw9ZFB2DtRVIvsDvwJ+BF4N+BsyLixSLjylNE/GU9y6XfzRkRccsA23oM2KERcUk6H9gjIuZktj+rEdu29uIWgeXhAxGxA3AAMAP4YrHhtAZJPjGzQjgRWG4i4g/ATSQJAQBJCyT9p6QXJK2QdHJm3lxJ/0/ShZKelfSopFmZ+VMl3ZauezMwPrs/SSdK6pH0x7SksndmXq+kL0haLuklSd+TtHtaKnlB0i2Sdq31WdJ1n5D0e0kf7zfvckl/l74eL+knaQzPSPqlpK0kXQlMAW5MSz9/I6kzLTHNk/QY8PPMtGxSeIekX0t6XtINkt6c7utISX39YumVdLSkmcCXgD9P93d/Ov+NUlMa1zmSVkt6StIVknZO51XiOF3SY5KelvTlzH4OltSdxvSkpItqfXfW/JwILDeSJgGzgFWZyf8J/BdgZ2Ah8ANJb8vMPwRYSXKQ/xrwPUlK510F3JPO+1vgjVq3pHcCVwOfASYAS0gOum/KbPtDwPuBdwIfAJaSHCwnkPwt/FWNzzET+Hy67jTg6AE+9tlAX7rN3dPtR0R8FHiMtLUUEV/LrPM+YG/g2BrbPA34OPA2YCPw9wPsH5Id/jvwv4Afpvvbv8pic9Of/wr8GUlJ6h/7LXMEsCdwFHBuJrleDFwcETsB7wAWDRaTNS8nAsvDjyW9ADwOPAWcV5kREddGxO8j4vWI+CHwMHBwZt3VEfHdiPgT8H2Sg9/ukqYA7wa+EhGvRsTtwI2Z9f4c+GlE3BwRG4ALgW2BwzLL/ENEPBkRa4BfAndHxH0RsR74EUkZq5oPA/8SEQ9ExEvA+QN89g1pzB0RsSEifhmDD+h1fkS8FBGv1Jh/ZWbfXwE+XOlMHqG/AC6KiEfSPpwvAqf2a40sjIhXIuJ+4H6gklA2AHtIGh8RL0bEXQ2IxwriRGB5+O8RsSNwJLAXmRKOpNMk/SYtnfwR2JfNSzx/qLyIiJfTlzsAbweeTQ+GFaszr9+efR8Rr5MkoomZZZ7MvH6lyvtanbRvT7dVbb/9fZ2kBfQzSY9IWjDAshWPD2H+amAs/cpiw7TZd5a+3pqkJVPxh8zrl9n0Hc0jaVk9JGmZpBMaEI8VxInAchMRtwGXk5ydI6kD+C5wFrBbROwCPACoxiayngB2lbR9ZtqUzOvfAx2VN2k5aTKwZvifYLN9T66x381ExAsRcXZE/BlwIvA5SUdVZtdabZD999/3BuBp4CVgu8qMtJUwYQjb3ew7S7e9kc0TZFUR8XBEzAbeAnwVuK7fv421ECcCy9v/Bd4vaX9ge5KD01oASR8jaREMKiJWA93AQklvknQESZ2/YhFwvKSjJI0lqdW/CtzRgM+wCJgrabqk7ciUuvqTdIKkPdJE9BzJZbSvp7OfJKnFD9WczL4vAK5LS2e/A8ZJOj79zOcA22TWexLolFTr7/xq4LNpJ/wObOpT2DhYQJLmSJqQtrz+mE5+fYBVrIk5EViuImItcAVwbkSsAL4B3ElykNoP+NUQNvcRks7kZ0gOxldk9rMSmAP8A8nZ8gdIOmZfa8BnWEqS0H5OUvb5+QCLTwNuIbmH4k7gWxFxazrvfwPnpGWxzw8hhCtJWlZ/AMaRdmpHxHPAp4B/Jmn5vETSUV1xbfp7naR7q2z3snTbtwOPAuuBT9cZ00ygR9KLJB3Hpw7Qx2FNTn4wjZlZublFYGZWck4EZmYl50RgZlZyTgRmZiXXcoNcjR8/Pjo7O4sOw8yspdxzzz1PR8SEavNaLhF0dnbS3d1ddBhmZi1FUs074l0aMjMrOScCs5Lp7Ex+zCparjRkZiOzeqAh86yU2iIRbNiwgb6+PtavX190KIUYN24ckyZNYuzYsUWHYmYtqC0SQV9fHzvuuCOdnZ1seoZJOUQE69ato6+vj6lTpxYdjpm1oLboI1i/fj277bZb6ZIAgCR222230raGbHMl/BOwBmiLRACUMglUlPmzm9nItU0iMGs2Q8nPgy3rXG95astE0NmZ/OE06qeIS+06Ozt5+umnR3/HZlY6bdFZ3N/q1dDIxywM9WwsIogIttqqLfOsNYA09P+jnZ3Q25tHNFsaTnzWunykapDe3l723HNPTjvtNPbdd1/mzZtHV1cX++yzD+edt+nJhp2dnZx33nkceOCB7Lfffjz00EMArFu3jmOOOYZ99tmHM844Az8wqP0N58Yu3whmeXAiaKCHH36YT33qU/T09PCNb3yD7u5uli9fzm233cby5cvfWG78+PHce++9fPKTn+TCCy8EYOHChRxxxBH09PRw8skn89hjjxX1MWyUrF49tJu7entb52Yw92m0FieCBuro6ODQQw8FYNGiRRx44IHMmDGDnp4eVqxY8cZyH/zgBwE46KCD6E3b+rfffjtz5swB4Pjjj2fXXXcd3eCtEB0dzXGWP9zWSTPEbiPXln0ERdl+++0BePTRR7nwwgtZtmwZu+66K3Pnzt3sOv9tttkGgDFjxrBx48ZCYrV81XuA7O3N52AqJUmmso/BVGtpDNYn0SqtExucWwQ5eP7559l+++3ZeeedefLJJ1m6dOmg67z3ve/lqquuAmDp0qU8++yzeYdpOal2YB+oVDKcDuB6z8YHO1j33072/UDrVvs8biG0rtxaBJIuA04AnoqIfavMF3AxcBzwMjA3Iu5txL47Ohpbo6ycWdVr//33Z8aMGey1115MnjyZww8/fNB1zjvvPGbPns0+++zDYYcdxpQpU4YZrRWtctVa5cBYOdD3P8Bm/19VSkTZFkJvb+2rd+o5G6+sX0tn55bbGc5ZfiVutxBal/K6OkXSe4EXgStqJILjgE+TJIJDgIsj4pDBttvV1RX9H0zz4IMPsvfeezck7lbl76B5ZA/eldf1lGqyy8Km17V+V5aptY3s7/7L1Kva4aFaAqkVtzUPSfdERFe1ebmVhiLiduCZARY5iSRJRETcBewi6W15xWNWtN7e/O4DqHVwz6Mz2mf+7afIzuKJwOOZ933ptCf6LyhpPjAfcMnESqMRB/DBykNDiaNWEmuWK59s+FriqqGIuBS4FJLSUMHhmNU00gNi9qBd68x7OPsY7GCeVSlhrV69ZRKpllQakWysWEVeNbQGmJx5PymdZtay8iibdHZu3rGc7WyuZyysjo5N62SXrXURRK0SVmXdoV48Yc2vyBbBYuAsSdeQdBY/FxFblIXMWtlwyybZztdaY2dVzsSrHeSrLdf/zH2oZ/KV/bgF0H7yvHz0auBIYLykPuA8YCxARHwbWEJyxdAqkstHP5ZXLGajKXvQHslBM3smX6RmicPyk1siiIjZg8wP4Mxcdl7t+raR6OgYvWEfU52dnXR3dzN+/PhR3a8VL3uZaa0kUquFUO9NXZX1sy2PWuqJI9uvYK2nJTqLh6zgcag9DLWNxFDOOfofyCvJII86fv8b4LIqMRf1/A4bmfZMBAXo7e3l2GOP5ZBDDuGee+7h4IMP5re//S2vvPIKp5xyCgsXLgSSM/3TTz+dG2+8kQ0bNnDttdey1157sW7dOmbPns2aNWt4z3ve88Yw1L29vcyaNYsjjjiCO+64g4kTJ3LDDTew7bbbFvlxrYq8DsBD1ajGa62DfSP3Yc3Bp6wNlNcw1A8//DBnnnkmPT097LLLLlx//fWj/tlscKtXN+8BcqAGckdH9QQ23BvgmvU7sNqcCBoor2Gop06dygEHHLDFOmZQ+0BerzzveLbW4NJQA+U1DHVl+co6r7zySoMjt1ZR68x9JOubuUWQAw9DbXkZ6dn7cNf3AHLtrT1bBAWPQ+1hqK1RRvMM3q2F8sptGOq8eBjq6vwdFK/W0Mv1jvMzlPGAmp2HoW4+Aw1D3Z4tArMmUu+BvR0SgLUm9xGYmZVc2ySCVitxNVKZP7uZjVxbJIJx48axbt26Uh4QI4J169Yxbty4okMxsxbVFn0EkyZNoq+vj7Vr1xYdSiHGjRvHpEmTig7DzFpUWySCsWPHMnXq1KLDMDNrSW1RGjIzs+FzIjAzKzknAjOzknMiMDMrOScCM7OScyIwMys5JwIzs5JzIjAzKzknAjOzknMiMDMrOScCM7OScyIwMys5JwIzs5JzIjAzKzknAjOzknMiMDMrOScCM7OSyzURSJopaaWkVZIWVJk/RdKtku6TtFzScXnGY2ZmW8otEUgaA1wCzAKmA7MlTe+32DnAooiYAZwKfCuveMzMrLo8WwQHA6si4pGIeA24Bjip3zIB7JS+3hn4fY7xmJlZFXkmgonA45n3fem0rPOBOZL6gCXAp6ttSNJ8Sd2SuteuXZtHrGZmpVV0Z/Fs4PKImAQcB1wpaYuYIuLSiOiKiK4JEyaMepBmZu0sz0SwBpiceT8pnZY1D1gEEBF3AuOA8TnGZGZm/eSZCJYB0yRNlfQmks7gxf2WeQw4CkDS3iSJwLUfM7NRlFsiiIiNwFnATcCDJFcH9Ui6QNKJ6WJnA5+QdD9wNTA3IiKvmMzMbEtb57nxiFhC0gmcnXZu5vUK4PA8YzAzs4EV3VlsZmYFcyIwMys5JwIzs5JzIjAzKzknAjOzknMiMDMrOScCM7OScyIwMys5JwIzs5JzIjAzKzknAjOzknMiMDMrOScCM7OScyIwMys5JwIzs5JzIjAzKzknAjOzknMiMDMrOScCM7OScyIwMys5JwIzs5JzIjAzKzknAjOzknMiMDMrOScCM7OScyIwMys5JwIzs5KrKxFI+jdJx0ty4jAzazP1Hti/BXwEeFjS/5G0Z44xmZnZKKorEUTELRHxF8CBQC9wi6Q7JH1M0tg8AzQzs3zVXeqRtBswFzgDuA+4mCQx3DzAOjMlrZS0StKCGst8WNIKST2SrhpS9GZmNmJb17OQpB8BewJXAh+IiCfSWT+U1F1jnTHAJcD7gT5gmaTFEbEis8w04IvA4RHxrKS3DP+jmJnZcNSVCIDvRsSS7ARJ20TEqxHRVWOdg4FVEfFIuvw1wEnAiswynwAuiYhnASLiqSFFb2ZmI1Zvaejvqky7c5B1JgKPZ973pdOy3gm8U9KvJN0laWa1DUmaL6lbUvfatWvrDNnMzOoxYItA0ltJDt7bSpoBKJ21E7Bdg/Y/DTgSmATcLmm/iPhjdqGIuBS4FKCrqysasF8zM0sNVho6lqSDeBJwUWb6C8CXBll3DTA5835SOi2rD7g7IjYAj0r6HUliWDbIts3MrEEGTAQR8X3g+5I+FBHXD3Hby4BpkqaSJIBTSe5FyPoxMBv4F0njSUpFjwxxP2ZmNgKDlYbmRMQPgE5Jn+s/PyIuqrJaZd5GSWcBNwFjgMsiokfSBUB3RCxO5x0jaQXwJ+ALEbFuBJ/HzMyGaLDS0Pbp7x2Gs/H0SqMl/aadm3kdwOfSHzMzK8BgpaHvpL8Xjk44ZmY22uoddO5rknaSNFbSf0haK2lO3sGZmVn+6r2P4JiIeB44gWSsoT2AL+QVlJmZjZ56E0GlhHQ8cG1EPJdTPGZmNsrqHWLiJ5IeAl4BPilpArA+v7DMzGy01DsM9QLgMKArvfnrJZJxg8zMrMXV2yIA2IvkfoLsOlc0OB4zMxtl9Q5DfSXwDuA3JDd+AQROBGZmLa/eFkEXMD29AczMzNpIvVcNPQC8Nc9AzMysGPW2CMYDKyT9Gni1MjEiTswlKjMzGzX1JoLz8wzCzMyKU1ciiIjbJHUA0yLiFknbkYwoamZmLa7esYY+AVwHfCedNJHkWQJmZtbi6u0sPhM4HHgeICIeBt6SV1BmZjZ66k0Er0bEa5U36U1lvpTUzKwN1JsIbpP0JZKH2L8fuBa4Mb+wzMxstNSbCBYAa4HfAv+T5Klj5+QVlJmZjZ56rxp6XdKPgR9HxNp8QzIzs9E0YItAifMlPQ2sBFamTyc7d6D1zMysdQxWGvosydVC746IN0fEm4FDgMMlfTb36MzMLHeDJYKPArMj4tHKhIh4BJgDnJZnYGZmNjoGSwRjI+Lp/hPTfoKx+YRkZmajabBE8Now55mZWYsY7Kqh/SU9X2W6gHE5xGNmZqNswEQQER5YzsyszdV7Q5mZmbUpJwIzs5JzIjAzKzknAjOzknMiMDMruVwTgaSZklZKWiVpwQDLfUhSSOrKMx4zM9tSbolA0hjgEmAWMB2YLWl6leV2BP4auDuvWMxGpLMTpC1/OjuLjsysIfJsERwMrIqIR9Knm10DnFRlub8FvgqszzEWs+FbvRoitvxZvbr+bTiZWBPLMxFMBB7PvO9Lp71B0oHA5Ij46UAbkjRfUrek7rVr/TgEaxIdHZsd1IMBDuyNSCZmOSmss1jSVsBFwNmDLRsRl0ZEV0R0TZgwIf/gzOrR27vZQV34wG6tKc9EsAaYnHk/KZ1WsSOwL/ALSb3AocBidxhboaqVcDo66l+/XythWNswG2V1PapymJYB0yRNJUkApwIfqcyMiOeA8ZX3kn4BfD4iunOMySzR2Vn97L2jIznDH67e3uGva1aQ3FoEEbEROAu4CXgQWBQRPZIukHRiXvs1q0utmv1oH8irtSBq9TO4w9lykmeLgIhYAizpN63q844j4sg8YzFrStUST+WA31+t1kq1Zc2GINdEYFa4gUpAzcrlJRtlHmLC2ke10gk0RwkoT0MpL5lV4RaBtY9K3b9sqiU1l4tsCNwiMDMrOScCM7OScyKw1lPrMspm7gA2a2JOBNa8ah3wof07gEeq1h3O7kS2KtxZbM2rrJ2/jVArKboT2apwi8DMrOScCMzKxCUjq8KlIbMyccnIqnCLwJrDSId/NrNhcyKw0TWUK4F8FZDZqHBpyEaXrwQyazpuEZiZB64rObcIzMwD15WcWwRmZiXnRGBmVnJOBGZWnW8+Kw33EVg+WvERkbY533xWGk4Elg9fJmrWMlwaMrOhccmo7bhFYCNXrQzkElD7csmo7TgR2Mi5DGTW0lwaMjMrOScCM2uMTN9B4H6DVuLSkNXPl4TaQDJ9B1JaLXS/QUtwIrD6uS/ArC25NGRm+fGlpi3BLQIzy48vNW0JubYIJM2UtFLSKkkLqsz/nKQVkpZL+g9JLjabmY2y3BKBpDHAJcAsYDowW9L0fovdB3RFxLuA64Cv5RWPDUGtx0m6U9isLeXZIjgYWBURj0TEa8A1wEnZBSLi1oh4OX17FzApx3isXpVOYT9D2PLiJ6I1lTz7CCYCj2fe9wGHDLD8PGBptRmS5gPzAaZMmdKo+MysKH4iWlNpiquGJM0BuoCvV5sfEZdGRFdEdE2YMGF0gzMza3N5tgjWAJMz7yel0zYj6Wjgy8D7IuLVHOMxM7Mq8mwRLAOmSZoq6U3AqcDi7AKSZgDfAU6MiKdyjMVqqdYx7E5hK4LvOShMbi2CiNgo6SzgJmAMcFlE9Ei6AOiOiMUkpaAdgGuV1Acfi4gT84rJqvDdwtYsfM9BYXK9oSwilgBL+k07N/P66Dz3bxkeJ8jMavCdxWXhM38zq6EprhoyM6vJfQe5c4vAzJqb+w5y5xaBmVnJORG0G48TZGZD5NJQu3GnsJkNkVsErcw3g1mZeeC6hnEiaGXVRgn1CKFWFr29W/7/B19hNAwuDZlZ+/AVRsPiFoGZWck5EbQCXwlkZjlyaagV+EogM8uRWwRm1v48TMWA3CJoNtVGCXUJyGxk3Ik8ICeCZuMykNnoqbQU+k8r2WXYTgRF8fMBzIpX7YBfwlaCE0FRfOZv1pyqtRIq09u0peBEYGaWVcL+BF81lDffA2BmTc4tgry5BGTWHtq4ZOQWgZlZPaoNcldroLsWuz/BLYJG8j0AZuXTBlceORE0kstAZtaCXBoaDncAm9lAWmxIC7cIhsNn/mY2kBa7BNUtgoH4zN/MGqlJWwpOBBXVDvpQ/SqBFr9UzMwK0qRXHpWvNDTQGD8u95hZEQq+8qh9WwS1yjrgs3wza36jWEZqj0Tgso6ZtZtRLCO1XiK45576Dvo+4JtZO6qWIKB666H/cbKGXBOBpJmSVkpaJWlBlfnbSPphOv9uSZ2DbvSgg3zQNzPLqtV66J8sasgtEUgaA1wCzAKmA7MlTe+32Dzg2YjYA/gm8NW84jEzs+rybBEcDKyKiEci4jXgGuCkfsucBHw/fX0dcJTUpHdcmJm1qTwvH50IPJ553wccUmuZiNgo6TlgN+Dp7EKS5gPz07cvSlqZS8RDM55+cZaYv4vEeMnfQ8rfxSbN8vdR807YlriPICIuBS4tOo4sSd0R0VV0HM3A30XC38Mm/i42aYXvIs/S0Bpgcub9pHRa1WUkbQ3sDKzLMSYzM+snz0SwDJgmaaqkNwGnAov7LbMYOD19fQrw8wjf3mtmNppyKw2lNf+zgJuAMcBlEdEj6QKgOyIWA98DrpS0CniGJFm0iqYqVRXM30XC38Mm/i42afrvQj4BNzMrt9a7s9jMzBrKicDMrOScCIZJ0tclPSRpuaQfSdql6JiKIul/SOqR9Lqkpr5MLi+DDadSFpIuk/SUpAeKjqVokiZLulXSivTv46+LjqkWJ4LhuxnYNyLeBfwO+GLB8RTpAeCDwO1FB1KEOodTKYvLgZlFB9EkNgJnR8R04FDgzGb9f+FEMEwR8bOI2Ji+vYvkPolSiogHI6IZ7vYuSj3DqZRCRNxOcgVg6UXEExFxb/r6BeBBktEUmo4TQWN8HFhadBBWmGrDqTTlH7wVIx1ZeQZwd8GhVNUSQ0wURdItwFurzPpyRNyQLvNlkibgv45mbKOtnu/CzLYkaQfgeuAzEfF80fFU40QwgIg4eqD5kuYCJwBHtfsd0YN9FyVXz3AqVkKSxpIkgX+NiH8rOp5aXBoaJkkzgb8BToyIl4uOxwpVz3AqVjLpkPrfAx6MiIuKjmcgTgTD94/AjsDNkn4j6dtFB1QUSSdL6gPeA/xU0k1FxzSa0osGKsOpPAgsioieYqMqhqSrgTuBPSX1SZpXdEwFOhz4KPDf0mPEbyQdV3RQ1XiICTOzknOLwMys5JwIzMxKzonAzKzknAjMzErOicDMrOScCMyqSEeNPLbftM9I+qcay/+irCOvWutzIjCr7mq2fHTqqel0s7biRGBW3XXA8emdwpVBw95OMsR0dzq+/MJqK0p6MfP6FEmXp68nSLpe0rL05/DcP4VZHZwIzKqIiGeAX5M8YwCS1sAikkH2uoB3Ae+T9K4hbPZi4JsR8W7gQ8A/NzBks2HzoHNmtVXKQzekv+cBH5Y0n+Rv520kD6JZXuf2jgamJ0PQALCTpB0i4sUB1jHLnROBWW03AN+UdCCwHckDVz4PvDsink1LPuOqrJcdtyU7fyvg0IhYn1O8ZsPi0pBZDemZ+q3AZSStg52Al4DnJO3OprJRf09K2lvSVsDJmek/Az5deSPpgDziNhsqJwKzgV0N7A9cHRH3A/cBDwFXAb+qsc4C4CfAHcATmel/BXRJWi5pBfCXuUVtNgQefdTMrOTcIjAzKzknAjOzknMiMDMrOScCM7OScyIwMys5JwIzs5JzIjAzK7n/D0qEckkeGdr5AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(np.random.rand(100000), density=True, bins=100, histtype=\"step\", color=\"blue\", label=\"rand\")\n",
+    "plt.hist(np.random.randn(100000), density=True, bins=100, histtype=\"step\", color=\"red\", label=\"randn\")\n",
+    "plt.axis([-2.5, 2.5, 0, 1.1])\n",
+    "plt.legend(loc = \"upper left\")\n",
+    "plt.title(\"Random distributions\")\n",
+    "plt.xlabel(\"Value\")\n",
+    "plt.ylabel(\"Density\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## np.fromfunction\n",
+    "You can also initialize an `ndarray` using a function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],\n",
+       "        [ 10.,  11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.]],\n",
+       "\n",
+       "       [[100., 101., 102., 103., 104., 105., 106., 107., 108., 109.],\n",
+       "        [110., 111., 112., 113., 114., 115., 116., 117., 118., 119.]],\n",
+       "\n",
+       "       [[200., 201., 202., 203., 204., 205., 206., 207., 208., 209.],\n",
+       "        [210., 211., 212., 213., 214., 215., 216., 217., 218., 219.]]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def my_function(z, y, x):\n",
+    "    return x + 10 * y + 100 * z\n",
+    "\n",
+    "np.fromfunction(my_function, (3, 2, 10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NumPy first creates three `ndarray`s (one per dimension), each of shape `(3, 2, 10)`. Each array has values equal to the coordinate along a specific axis. For example, all elements in the `z` array are equal to their z-coordinate:\n",
+    "\n",
+    "    [[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
+    "      [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]\n",
+    "    \n",
+    "     [[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
+    "      [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]\n",
+    "    \n",
+    "     [[ 2.  2.  2.  2.  2.  2.  2.  2.  2.  2.]\n",
+    "      [ 2.  2.  2.  2.  2.  2.  2.  2.  2.  2.]]]\n",
+    "\n",
+    "So the terms `x`, `y` and `z` in the expression `x + 10 * y + 100 * z` above are in fact `ndarray`s (we will discuss arithmetic operations on arrays below).  The point is that the function `my_function` is only called *once*, instead of once per element. This makes initialization very efficient."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Array data\n",
+    "## `dtype`\n",
+    "NumPy's `ndarray`s are also efficient in part because all their elements must have the same type (usually numbers).\n",
+    "You can check what the data type is by looking at the `dtype` attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "int64 [1 2 3 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "c = np.arange(1, 5)\n",
+    "print(c.dtype, c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "float64 [1. 2. 3. 4.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "c = np.arange(1.0, 5.0)\n",
+    "print(c.dtype, c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of letting NumPy guess what data type to use, you can set it explicitly when creating an array by setting the `dtype` parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "complex64 [1.+0.j 2.+0.j 3.+0.j 4.+0.j]\n"
+     ]
+    }
+   ],
+   "source": [
+    "d = np.arange(1, 5, dtype=np.complex64)\n",
+    "print(d.dtype, d)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Available data types include signed `int8`, `int16`, `int32`, `int64`, unsigned `uint8`|`16`|`32`|`64`, `float16`|`32`|`64` and `complex64`|`128`. Check out the documentation for the [basic types](https://numpy.org/doc/stable/user/basics.types.html) and [sized aliases](https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases) for the full list.\n",
+    "\n",
+    "## `itemsize`\n",
+    "The `itemsize` attribute returns the size (in bytes) of each item:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "e = np.arange(1, 5, dtype=np.complex64)\n",
+    "e.itemsize"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `data` buffer\n",
+    "An array's data is actually stored in memory as a flat (one dimensional) byte buffer. It is available *via* the `data` attribute (you will rarely need it, though)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<memory at 0x127dc16c0>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f = np.array([[1,2],[1000, 2000]], dtype=np.int32)\n",
+    "f.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In python 2, `f.data` is a buffer. In python 3, it is a memoryview."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b'\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\xe8\\x03\\x00\\x00\\xd0\\x07\\x00\\x00'"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "if hasattr(f.data, \"tobytes\"):\n",
+    "    data_bytes = f.data.tobytes() # python 3\n",
+    "else:\n",
+    "    data_bytes = memoryview(f.data).tobytes() # python 2\n",
+    "\n",
+    "data_bytes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Several `ndarray`s can share the same data buffer, meaning that modifying one will also modify the others. We will see an example in a minute."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reshaping an array\n",
+    "## In place\n",
+    "Changing the shape of an `ndarray` is as simple as setting its `shape` attribute. However, the array's size must remain the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n",
+      "Rank: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "g = np.arange(24)\n",
+    "print(g)\n",
+    "print(\"Rank:\", g.ndim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0  1  2  3]\n",
+      " [ 4  5  6  7]\n",
+      " [ 8  9 10 11]\n",
+      " [12 13 14 15]\n",
+      " [16 17 18 19]\n",
+      " [20 21 22 23]]\n",
+      "Rank: 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "g.shape = (6, 4)\n",
+    "print(g)\n",
+    "print(\"Rank:\", g.ndim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[ 0  1  2  3]\n",
+      "  [ 4  5  6  7]\n",
+      "  [ 8  9 10 11]]\n",
+      "\n",
+      " [[12 13 14 15]\n",
+      "  [16 17 18 19]\n",
+      "  [20 21 22 23]]]\n",
+      "Rank: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "g.shape = (2, 3, 4)\n",
+    "print(g)\n",
+    "print(\"Rank:\", g.ndim)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `reshape`\n",
+    "The `reshape` function returns a new `ndarray` object pointing at the *same* data. This means that modifying one array will also modify the other."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0  1  2  3  4  5]\n",
+      " [ 6  7  8  9 10 11]\n",
+      " [12 13 14 15 16 17]\n",
+      " [18 19 20 21 22 23]]\n",
+      "Rank: 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "g2 = g.reshape(4,6)\n",
+    "print(g2)\n",
+    "print(\"Rank:\", g2.ndim)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set item at row 1, col 2 to 999 (more about indexing below)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  0,   1,   2,   3,   4,   5],\n",
+       "       [  6,   7, 999,   9,  10,  11],\n",
+       "       [ 12,  13,  14,  15,  16,  17],\n",
+       "       [ 18,  19,  20,  21,  22,  23]])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2[1, 2] = 999\n",
+    "g2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The corresponding element in `g` has been modified."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[  0,   1,   2,   3],\n",
+       "        [  4,   5,   6,   7],\n",
+       "        [999,   9,  10,  11]],\n",
+       "\n",
+       "       [[ 12,  13,  14,  15],\n",
+       "        [ 16,  17,  18,  19],\n",
+       "        [ 20,  21,  22,  23]]])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `ravel`\n",
+    "Finally, the `ravel` function returns a new one-dimensional `ndarray` that also points to the same data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  0,   1,   2,   3,   4,   5,   6,   7, 999,   9,  10,  11,  12,\n",
+       "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23])"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Arithmetic operations\n",
+    "All the usual arithmetic operators (`+`, `-`, `*`, `/`, `//`, `**`, etc.) can be used with `ndarray`s. They apply *elementwise*:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a + b  = [19 27 35 43]\n",
+      "a - b  = [ 9 19 29 39]\n",
+      "a * b  = [70 92 96 82]\n",
+      "a / b  = [ 2.8         5.75       10.66666667 20.5       ]\n",
+      "a // b  = [ 2  5 10 20]\n",
+      "a % b  = [4 3 2 1]\n",
+      "a ** b = [537824 279841  32768   1681]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.array([14, 23, 32, 41])\n",
+    "b = np.array([5,  4,  3,  2])\n",
+    "print(\"a + b  =\", a + b)\n",
+    "print(\"a - b  =\", a - b)\n",
+    "print(\"a * b  =\", a * b)\n",
+    "print(\"a / b  =\", a / b)\n",
+    "print(\"a // b  =\", a // b)\n",
+    "print(\"a % b  =\", a % b)\n",
+    "print(\"a ** b =\", a ** b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the multiplication is *not* a matrix multiplication. We will discuss matrix operations below.\n",
+    "\n",
+    "The arrays must have the same shape. If they do not, NumPy will apply the *broadcasting rules*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Broadcasting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, when NumPy expects arrays of the same shape but finds that this is not the case, it applies the so-called *broadcasting* rules:\n",
+    "\n",
+    "## First rule\n",
+    "*If the arrays do not have the same rank, then a 1 will be prepended to the smaller ranking arrays until their ranks match.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[0, 1, 2, 3, 4]]])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h = np.arange(5).reshape(1, 1, 5)\n",
+    "h"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's try to add a 1D array of shape `(5,)` to this 3D array of shape `(1,1,5)`. Applying the first rule of broadcasting!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[10, 21, 32, 43, 54]]])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h + [10, 20, 30, 40, 50]  # same as: h + [[[10, 20, 30, 40, 50]]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Second rule\n",
+    "*Arrays with a 1 along a particular dimension act as if they had the size of the array with the largest shape along that dimension. The value of the array element is repeated along that dimension.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2],\n",
+       "       [3, 4, 5]])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "k = np.arange(6).reshape(2, 3)\n",
+    "k"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's try to add a 2D array of shape `(2,1)` to this 2D `ndarray` of shape `(2, 3)`. NumPy will apply the second rule of broadcasting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[100, 101, 102],\n",
+       "       [203, 204, 205]])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "k + [[100], [200]]  # same as: k + [[100, 100, 100], [200, 200, 200]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Combining rules 1 & 2, we can do this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[100, 201, 302],\n",
+       "       [103, 204, 305]])"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "k + [100, 200, 300]  # after rule 1: [[100, 200, 300]], and after rule 2: [[100, 200, 300], [100, 200, 300]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And also, very simply:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1000, 1001, 1002],\n",
+       "       [1003, 1004, 1005]])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "k + 1000  # same as: k + [[1000, 1000, 1000], [1000, 1000, 1000]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Third rule\n",
+    "*After rules 1 & 2, the sizes of all arrays must match.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "operands could not be broadcast together with shapes (2,3) (2,) \n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    k + [33, 44]\n",
+    "except ValueError as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Broadcasting rules are used in many NumPy operations, not just arithmetic operations, as we will see below.\n",
+    "For more details about broadcasting, check out [the documentation](https://numpy.org/doc/stable/user/basics.broadcasting.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upcasting\n",
+    "When trying to combine arrays with different `dtype`s, NumPy will *upcast* to a type capable of handling all possible values (regardless of what the *actual* values are)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "uint8 [0 1 2 3 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "k1 = np.arange(0, 5, dtype=np.uint8)\n",
+    "print(k1.dtype, k1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "int16 [ 5  7  9 11 13]\n"
+     ]
+    }
+   ],
+   "source": [
+    "k2 = k1 + np.array([5, 6, 7, 8, 9], dtype=np.int8)\n",
+    "print(k2.dtype, k2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that `int16` is required to represent all *possible* `int8` and `uint8` values (from -128 to 255), even though in this case a `uint8` would have sufficed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "float64 [1.5 2.5 3.5 4.5 5.5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "k3 = k1 + 1.5\n",
+    "print(k3.dtype, k3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conditional operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The conditional operators also apply elementwise:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False,  True,  True, False])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m = np.array([20, -5, 30, 40])\n",
+    "m < [15, 16, 35, 36]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And using broadcasting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True,  True, False, False])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m < 25  # equivalent to m < [25, 25, 25, 25]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is most useful in conjunction with boolean indexing (discussed below)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([20, -5])"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m[m < 25]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mathematical and statistical functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Many mathematical and statistical functions are available for `ndarray`s.\n",
+    "\n",
+    "## `ndarray` methods\n",
+    "Some functions are simply `ndarray` methods, for example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-2.5  3.1  7. ]\n",
+      " [10.  11.  12. ]]\n",
+      "mean = 6.766666666666667\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.array([[-2.5, 3.1, 7], [10, 11, 12]])\n",
+    "print(a)\n",
+    "print(\"mean =\", a.mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that this computes the mean of all elements in the `ndarray`, regardless of its shape.\n",
+    "\n",
+    "Here are a few more useful `ndarray` methods:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "min = -2.5\n",
+      "max = 12.0\n",
+      "sum = 40.6\n",
+      "prod = -71610.0\n",
+      "std = 5.084835843520964\n",
+      "var = 25.855555555555554\n"
+     ]
+    }
+   ],
+   "source": [
+    "for func in (a.min, a.max, a.sum, a.prod, a.std, a.var):\n",
+    "    print(func.__name__, \"=\", func())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These functions accept an optional argument `axis` which lets you ask for the operation to be performed on elements along the given axis. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0,  1,  2,  3],\n",
+       "        [ 4,  5,  6,  7],\n",
+       "        [ 8,  9, 10, 11]],\n",
+       "\n",
+       "       [[12, 13, 14, 15],\n",
+       "        [16, 17, 18, 19],\n",
+       "        [20, 21, 22, 23]]])"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c=np.arange(24).reshape(2,3,4)\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[12, 14, 16, 18],\n",
+       "       [20, 22, 24, 26],\n",
+       "       [28, 30, 32, 34]])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.sum(axis=0)  # sum across matrices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[12, 15, 18, 21],\n",
+       "       [48, 51, 54, 57]])"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.sum(axis=1)  # sum across rows"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also sum over multiple axes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 60,  92, 124])"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.sum(axis=(0,2))  # sum across matrices and columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(60, 92, 124)"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0+1+2+3 + 12+13+14+15, 4+5+6+7 + 16+17+18+19, 8+9+10+11 + 20+21+22+23"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Universal functions\n",
+    "NumPy also provides fast elementwise functions called *universal functions*, or **ufunc**. They are vectorized wrappers of simple functions. For example `square` returns a new `ndarray` which is a copy of the original `ndarray` except that each element is squared:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  6.25,   9.61,  49.  ],\n",
+       "       [100.  , 121.  , 144.  ]])"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([[-2.5, 3.1, 7], [10, 11, 12]])\n",
+    "np.square(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are a few more useful unary ufuncs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original ndarray\n",
+      "[[-2.5  3.1  7. ]\n",
+      " [10.  11.  12. ]]\n",
+      "\n",
+      " absolute\n",
+      "[[ 2.5  3.1  7. ]\n",
+      " [10.  11.  12. ]]\n",
+      "\n",
+      " sqrt\n",
+      "[[       nan 1.76068169 2.64575131]\n",
+      " [3.16227766 3.31662479 3.46410162]]\n",
+      "\n",
+      " exp\n",
+      "[[8.20849986e-02 2.21979513e+01 1.09663316e+03]\n",
+      " [2.20264658e+04 5.98741417e+04 1.62754791e+05]]\n",
+      "\n",
+      " log\n",
+      "[[       nan 1.13140211 1.94591015]\n",
+      " [2.30258509 2.39789527 2.48490665]]\n",
+      "\n",
+      " sign\n",
+      "[[-1.  1.  1.]\n",
+      " [ 1.  1.  1.]]\n",
+      "\n",
+      " ceil\n",
+      "[[-2.  4.  7.]\n",
+      " [10. 11. 12.]]\n",
+      "\n",
+      " modf\n",
+      "(array([[-0.5,  0.1,  0. ],\n",
+      "       [ 0. ,  0. ,  0. ]]), array([[-2.,  3.,  7.],\n",
+      "       [10., 11., 12.]]))\n",
+      "\n",
+      " isnan\n",
+      "[[False False False]\n",
+      " [False False False]]\n",
+      "\n",
+      " cos\n",
+      "[[-0.80114362 -0.99913515  0.75390225]\n",
+      " [-0.83907153  0.0044257   0.84385396]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/lf/d71zm48s6y7c391ynbnyp85w0000gp/T/ipykernel_81957/2634842825.py:5: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  print(func(a))\n",
+      "/var/folders/lf/d71zm48s6y7c391ynbnyp85w0000gp/T/ipykernel_81957/2634842825.py:5: RuntimeWarning: invalid value encountered in log\n",
+      "  print(func(a))\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Original ndarray\")\n",
+    "print(a)\n",
+    "for func in (np.abs, np.sqrt, np.exp, np.log, np.sign, np.ceil, np.modf, np.isnan, np.cos):\n",
+    "    print(\"\\n\", func.__name__)\n",
+    "    print(func(a))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The two warnings are due to the fact that `sqrt()` and `log()` are undefined for negative numbers, which is why there is a `np.nan` value in the first cell of the output of these two functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Binary ufuncs\n",
+    "There are also many binary ufuncs, that apply elementwise on two `ndarray`s.  Broadcasting rules are applied if the arrays do not have the same shape:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 3,  6,  2, 11])"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([1, -2, 3, 4])\n",
+    "b = np.array([2, 8, -1, 7])\n",
+    "np.add(a, b)  # equivalent to a + b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False,  True, False])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.greater(a, b)  # equivalent to a > b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 8, 3, 7])"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.maximum(a, b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.,  2., -3.,  4.])"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.copysign(a, b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Array indexing\n",
+    "## One-dimensional arrays\n",
+    "One-dimensional NumPy arrays can be accessed more or less like regular python arrays:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([1, 5, 3, 19, 13, 7, 3])\n",
+    "a[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 3, 19, 13])"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[2:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 3, 19, 13,  7])"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[2:-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 5])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 3, 13,  3])"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[2::2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 3,  7, 13, 19,  3,  5,  1])"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[::-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, you can modify elements:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  1,   5,   3, 999,  13,   7,   3])"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[3]=999\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also modify an `ndarray` slice:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  1,   5, 997, 998, 999,   7,   3])"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[2:5] = [997, 998, 999]\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Differences with regular python arrays\n",
+    "Contrary to regular python arrays, if you assign a single value to an `ndarray` slice, it is copied across the whole slice, thanks to broadcasting rules discussed above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1,  5, -1, -1, -1,  7,  3])"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[2:5] = -1\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Also, you cannot grow or shrink `ndarray`s this way:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "could not broadcast input array from shape (6,) into shape (3,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    a[2:5] = [1,2,3,4,5,6]  # too long\n",
+    "except ValueError as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You cannot delete elements either:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cannot delete array elements\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    del a[2:5]\n",
+    "except ValueError as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Last but not least, `ndarray` **slices are actually *views*** on the same data buffer. This means that if you create a slice and modify it, you are actually going to modify the original `ndarray` as well!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([   1,    5,   -1, 1000,   -1,    7,    3])"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_slice = a[2:6]\n",
+    "a_slice[1] = 1000\n",
+    "a  # the original array was modified!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  -1, 2000,   -1,    7])"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[3] = 2000\n",
+    "a_slice  # similarly, modifying the original array modifies the slice!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want a copy of the data, you need to use the `copy` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([   1,    5,   -1, 2000,   -1,    7,    3])"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "another_slice = a[2:6].copy()\n",
+    "another_slice[1] = 3000\n",
+    "a  # the original array is untouched"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  -1, 3000,   -1,    7])"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[3] = 4000\n",
+    "another_slice  # similarly, modifying the original array does not affect the slice copy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multidimensional arrays\n",
+    "Multidimensional arrays can be accessed in a similar way by providing an index or slice for each axis, separated by commas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11],\n",
+       "       [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],\n",
+       "       [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35],\n",
+       "       [36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]])"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = np.arange(48).reshape(4, 12)\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b[1, 2]  # row 1, col 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b[1, :]  # row 1, all columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1, 13, 25, 37])"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b[:, 1]  # all rows, column 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Caution**: note the subtle difference between these two expressions: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b[1, :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]])"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b[1:2, :]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first expression returns row 1 as a 1D array of shape `(12,)`, while the second returns that same row as a 2D array of shape `(1, 12)`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fancy indexing\n",
+    "You may also specify a list of indices that you are interested in. This is referred to as *fancy indexing*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  3,  4],\n",
+       "       [26, 27, 28]])"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b[(0,2), 2:5]  # rows 0 and 2, columns 2 to 4 (5-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[11,  2, 11],\n",
+       "       [23, 14, 23],\n",
+       "       [35, 26, 35],\n",
+       "       [47, 38, 47]])"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b[:, (-1, 2, -1)]  # all rows, columns -1 (last), 2 and -1 (again, and in this order)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you provide multiple index arrays, you get a 1D `ndarray` containing the values of the elements at the specified coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([41, 33, 37, 33])"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b[(-1, 2, -1, 2), (5, 9, 1, 9)]  # returns a 1D array with b[-1, 5], b[2, 9], b[-1, 1] and b[2, 9] (again)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Higher dimensions\n",
+    "Everything works just as well with higher dimensional arrays, but it's useful to look at a few examples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0,  1,  2,  3,  4,  5],\n",
+       "        [ 6,  7,  8,  9, 10, 11]],\n",
+       "\n",
+       "       [[12, 13, 14, 15, 16, 17],\n",
+       "        [18, 19, 20, 21, 22, 23]],\n",
+       "\n",
+       "       [[24, 25, 26, 27, 28, 29],\n",
+       "        [30, 31, 32, 33, 34, 35]],\n",
+       "\n",
+       "       [[36, 37, 38, 39, 40, 41],\n",
+       "        [42, 43, 44, 45, 46, 47]]])"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c = b.reshape(4,2,6)\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "34"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c[2, 1, 4]  # matrix 2, row 1, col 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([27, 33])"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c[2, :, 3]  # matrix 2, all rows, col 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you omit coordinates for some axes, then all elements in these axes are returned:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([30, 31, 32, 33, 34, 35])"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c[2, 1]  # Return matrix 2, row 1, all columns.  This is equivalent to c[2, 1, :]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ellipsis (`...`)\n",
+    "You may also write an ellipsis (`...`) to ask that all non-specified axes be entirely included."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[24, 25, 26, 27, 28, 29],\n",
+       "       [30, 31, 32, 33, 34, 35]])"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c[2, ...]  #  matrix 2, all rows, all columns.  This is equivalent to c[2, :, :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([30, 31, 32, 33, 34, 35])"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c[2, 1, ...]  # matrix 2, row 1, all columns.  This is equivalent to c[2, 1, :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([27, 33])"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c[2, ..., 3]  # matrix 2, all rows, column 3.  This is equivalent to c[2, :, 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 3,  9],\n",
+       "       [15, 21],\n",
+       "       [27, 33],\n",
+       "       [39, 45]])"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c[..., 3]  # all matrices, all rows, column 3.  This is equivalent to c[:, :, 3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Boolean indexing\n",
+    "You can also provide an `ndarray` of boolean values on one axis to specify the indices that you want to access."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11],\n",
+       "       [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],\n",
+       "       [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35],\n",
+       "       [36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]])"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = np.arange(48).reshape(4, 12)\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11],\n",
+       "       [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]])"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rows_on = np.array([True, False, True, False])\n",
+    "b[rows_on, :]  # Rows 0 and 2, all columns. Equivalent to b[(0, 2), :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1,  4,  7, 10],\n",
+       "       [13, 16, 19, 22],\n",
+       "       [25, 28, 31, 34],\n",
+       "       [37, 40, 43, 46]])"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols_on = np.array([False, True, False] * 4)\n",
+    "b[:, cols_on]  # All rows, columns 1, 4, 7 and 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `np.ix_`\n",
+    "You cannot use boolean indexing this way on multiple axes, but you can work around this by using the `ix_` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1,  4,  7, 10],\n",
+       "       [25, 28, 31, 34]])"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b[np.ix_(rows_on, cols_on)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[0],\n",
+       "        [2]]),\n",
+       " array([[ 1,  4,  7, 10]]))"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.ix_(rows_on, cols_on)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you use a boolean array that has the same shape as the `ndarray`, then you get in return a 1D array containing all the values that have `True` at their coordinate. This is generally used along with conditional operators:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1,  4,  7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46])"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b[b % 3 == 1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Iterating\n",
+    "Iterating over `ndarray`s is very similar to iterating over regular python arrays. Note that iterating over multidimensional arrays is done with respect to the first axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0,  1,  2,  3],\n",
+       "        [ 4,  5,  6,  7],\n",
+       "        [ 8,  9, 10, 11]],\n",
+       "\n",
+       "       [[12, 13, 14, 15],\n",
+       "        [16, 17, 18, 19],\n",
+       "        [20, 21, 22, 23]]])"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c = np.arange(24).reshape(2, 3, 4)  # A 3D array (composed of two 3x4 matrices)\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Item:\n",
+      "[[ 0  1  2  3]\n",
+      " [ 4  5  6  7]\n",
+      " [ 8  9 10 11]]\n",
+      "Item:\n",
+      "[[12 13 14 15]\n",
+      " [16 17 18 19]\n",
+      " [20 21 22 23]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for m in c:\n",
+    "    print(\"Item:\")\n",
+    "    print(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Item:\n",
+      "[[ 0  1  2  3]\n",
+      " [ 4  5  6  7]\n",
+      " [ 8  9 10 11]]\n",
+      "Item:\n",
+      "[[12 13 14 15]\n",
+      " [16 17 18 19]\n",
+      " [20 21 22 23]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(len(c)):  # Note that len(c) == c.shape[0]\n",
+    "    print(\"Item:\")\n",
+    "    print(c[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to iterate on *all* elements in the `ndarray`, simply iterate over the `flat` attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Item: 0\n",
+      "Item: 1\n",
+      "Item: 2\n",
+      "Item: 3\n",
+      "Item: 4\n",
+      "Item: 5\n",
+      "Item: 6\n",
+      "Item: 7\n",
+      "Item: 8\n",
+      "Item: 9\n",
+      "Item: 10\n",
+      "Item: 11\n",
+      "Item: 12\n",
+      "Item: 13\n",
+      "Item: 14\n",
+      "Item: 15\n",
+      "Item: 16\n",
+      "Item: 17\n",
+      "Item: 18\n",
+      "Item: 19\n",
+      "Item: 20\n",
+      "Item: 21\n",
+      "Item: 22\n",
+      "Item: 23\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in c.flat:\n",
+    "    print(\"Item:\", i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Stacking arrays\n",
+    "It is often useful to stack together different arrays. NumPy offers several functions to do just that. Let's start by creating a few arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.]])"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q1 = np.full((3,4), 1.0)\n",
+    "q1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2., 2., 2., 2.],\n",
+       "       [2., 2., 2., 2.],\n",
+       "       [2., 2., 2., 2.],\n",
+       "       [2., 2., 2., 2.]])"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q2 = np.full((4,4), 2.0)\n",
+    "q2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3., 3., 3., 3.],\n",
+       "       [3., 3., 3., 3.],\n",
+       "       [3., 3., 3., 3.]])"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q3 = np.full((3,4), 3.0)\n",
+    "q3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `vstack`\n",
+    "Now let's stack them vertically using `vstack`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.],\n",
+       "       [2., 2., 2., 2.],\n",
+       "       [2., 2., 2., 2.],\n",
+       "       [2., 2., 2., 2.],\n",
+       "       [2., 2., 2., 2.],\n",
+       "       [3., 3., 3., 3.],\n",
+       "       [3., 3., 3., 3.],\n",
+       "       [3., 3., 3., 3.]])"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q4 = np.vstack((q1, q2, q3))\n",
+    "q4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 4)"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q4.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It was possible because q1, q2 and q3 all have the same shape (except for the vertical axis, but that's ok since we are stacking on that axis).\n",
+    "\n",
+    "## `hstack`\n",
+    "We can also stack arrays horizontally using `hstack`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 1., 1., 1., 3., 3., 3., 3.],\n",
+       "       [1., 1., 1., 1., 3., 3., 3., 3.],\n",
+       "       [1., 1., 1., 1., 3., 3., 3., 3.]])"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q5 = np.hstack((q1, q3))\n",
+    "q5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 8)"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q5.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is possible because q1 and q3 both have 3 rows. But since q2 has 4 rows, it cannot be stacked horizontally with q1 and q3:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 3 and the array at index 1 has size 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    q5 = np.hstack((q1, q2, q3))\n",
+    "except ValueError as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `concatenate`\n",
+    "The `concatenate` function stacks arrays along any given existing axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.],\n",
+       "       [2., 2., 2., 2.],\n",
+       "       [2., 2., 2., 2.],\n",
+       "       [2., 2., 2., 2.],\n",
+       "       [2., 2., 2., 2.],\n",
+       "       [3., 3., 3., 3.],\n",
+       "       [3., 3., 3., 3.],\n",
+       "       [3., 3., 3., 3.]])"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q7 = np.concatenate((q1, q2, q3), axis=0)  # Equivalent to vstack\n",
+    "q7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 4)"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q7.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you might guess, `hstack` is equivalent to calling `concatenate` with `axis=1`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `stack`\n",
+    "The `stack` function stacks arrays along a new axis. All arrays have to have the same shape."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.]],\n",
+       "\n",
+       "       [[3., 3., 3., 3.],\n",
+       "        [3., 3., 3., 3.],\n",
+       "        [3., 3., 3., 3.]]])"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q8 = np.stack((q1, q3))\n",
+    "q8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3, 4)"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q8.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Splitting arrays\n",
+    "Splitting is the opposite of stacking. For example, let's use the `vsplit` function to split a matrix vertically.\n",
+    "\n",
+    "First let's create a 6x4 matrix:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3],\n",
+       "       [ 4,  5,  6,  7],\n",
+       "       [ 8,  9, 10, 11],\n",
+       "       [12, 13, 14, 15],\n",
+       "       [16, 17, 18, 19],\n",
+       "       [20, 21, 22, 23]])"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r = np.arange(24).reshape(6,4)\n",
+    "r"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's split it in three equal parts, vertically:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2, 3],\n",
+       "       [4, 5, 6, 7]])"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r1, r2, r3 = np.vsplit(r, 3)\n",
+    "r1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 8,  9, 10, 11],\n",
+       "       [12, 13, 14, 15]])"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[16, 17, 18, 19],\n",
+       "       [20, 21, 22, 23]])"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is also a `split` function which splits an array along any given axis. Calling `vsplit` is equivalent to calling `split` with `axis=0`. There is also an `hsplit` function, equivalent to calling `split` with `axis=1`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1],\n",
+       "       [ 4,  5],\n",
+       "       [ 8,  9],\n",
+       "       [12, 13],\n",
+       "       [16, 17],\n",
+       "       [20, 21]])"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r4, r5 = np.hsplit(r, 2)\n",
+    "r4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  3],\n",
+       "       [ 6,  7],\n",
+       "       [10, 11],\n",
+       "       [14, 15],\n",
+       "       [18, 19],\n",
+       "       [22, 23]])"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Transposing arrays\n",
+    "The `transpose` method creates a new view on an `ndarray`'s data, with axes permuted in the given order.\n",
+    "\n",
+    "For example, let's create a 3D array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0,  1,  2],\n",
+       "        [ 3,  4,  5]],\n",
+       "\n",
+       "       [[ 6,  7,  8],\n",
+       "        [ 9, 10, 11]],\n",
+       "\n",
+       "       [[12, 13, 14],\n",
+       "        [15, 16, 17]],\n",
+       "\n",
+       "       [[18, 19, 20],\n",
+       "        [21, 22, 23]]])"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t = np.arange(24).reshape(4,2,3)\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's create an `ndarray` such that the axes `0, 1, 2` (depth, height, width) are re-ordered to `1, 2, 0` (depth→width, height→depth, width→height):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0,  6, 12, 18],\n",
+       "        [ 1,  7, 13, 19],\n",
+       "        [ 2,  8, 14, 20]],\n",
+       "\n",
+       "       [[ 3,  9, 15, 21],\n",
+       "        [ 4, 10, 16, 22],\n",
+       "        [ 5, 11, 17, 23]]])"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t1 = t.transpose((1,2,0))\n",
+    "t1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3, 4)"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t1.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, `transpose` reverses the order of the dimensions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0,  6, 12, 18],\n",
+       "        [ 3,  9, 15, 21]],\n",
+       "\n",
+       "       [[ 1,  7, 13, 19],\n",
+       "        [ 4, 10, 16, 22]],\n",
+       "\n",
+       "       [[ 2,  8, 14, 20],\n",
+       "        [ 5, 11, 17, 23]]])"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t2 = t.transpose()  # equivalent to t.transpose((2, 1, 0))\n",
+    "t2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 2, 4)"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t2.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NumPy provides a convenience function `swapaxes` to swap two axes. For example, let's create a new view of `t` with depth and height swapped:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0,  1,  2],\n",
+       "        [ 6,  7,  8],\n",
+       "        [12, 13, 14],\n",
+       "        [18, 19, 20]],\n",
+       "\n",
+       "       [[ 3,  4,  5],\n",
+       "        [ 9, 10, 11],\n",
+       "        [15, 16, 17],\n",
+       "        [21, 22, 23]]])"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t3 = t.swapaxes(0,1)  # equivalent to t.transpose((1, 0, 2))\n",
+    "t3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 4, 3)"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t3.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear algebra\n",
+    "NumPy 2D arrays can be used to represent matrices efficiently in python. We will just quickly go through some of the main matrix operations available. For more details about Linear Algebra, vectors and matrices, go through the [Linear Algebra tutorial](math_linear_algebra.ipynb).\n",
+    "\n",
+    "## Matrix transpose\n",
+    "The `T` attribute is equivalent to calling `transpose()` when the rank is ≥2:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2, 3, 4],\n",
+       "       [5, 6, 7, 8, 9]])"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m1 = np.arange(10).reshape(2,5)\n",
+    "m1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 5],\n",
+       "       [1, 6],\n",
+       "       [2, 7],\n",
+       "       [3, 8],\n",
+       "       [4, 9]])"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m1.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `T` attribute has no effect on rank 0 (empty) or rank 1 arrays:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3, 4])"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m2 = np.arange(5)\n",
+    "m2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3, 4])"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m2.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can get the desired transposition by first reshaping the 1D array to a single-row matrix (2D):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2, 3, 4]])"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m2r = m2.reshape(1,5)\n",
+    "m2r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0],\n",
+       "       [1],\n",
+       "       [2],\n",
+       "       [3],\n",
+       "       [4]])"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m2r.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matrix multiplication\n",
+    "Let's create two matrices and execute a [matrix multiplication](https://en.wikipedia.org/wiki/Matrix_multiplication) using the `dot()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2, 3, 4],\n",
+       "       [5, 6, 7, 8, 9]])"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n1 = np.arange(10).reshape(2, 5)\n",
+    "n1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2],\n",
+       "       [ 3,  4,  5],\n",
+       "       [ 6,  7,  8],\n",
+       "       [ 9, 10, 11],\n",
+       "       [12, 13, 14]])"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n2 = np.arange(15).reshape(5,3)\n",
+    "n2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 90, 100, 110],\n",
+       "       [240, 275, 310]])"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n1.dot(n2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Caution**: as mentioned previously, `n1*n2` is *not* a matrix multiplication, it is an elementwise product (also called a [Hadamard product](https://en.wikipedia.org/wiki/Hadamard_product_(matrices)))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matrix inverse and pseudo-inverse\n",
+    "Many of the linear algebra functions are available in the `numpy.linalg` module, in particular the `inv` function to compute a square matrix's inverse:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1,  2,  3],\n",
+       "       [ 5,  7, 11],\n",
+       "       [21, 29, 31]])"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy.linalg as linalg\n",
+    "\n",
+    "m3 = np.array([[1,2,3],[5,7,11],[21,29,31]])\n",
+    "m3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-2.31818182,  0.56818182,  0.02272727],\n",
+       "       [ 1.72727273, -0.72727273,  0.09090909],\n",
+       "       [-0.04545455,  0.29545455, -0.06818182]])"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linalg.inv(m3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also compute the [pseudoinverse](https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_pseudoinverse) using `pinv`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-2.31818182,  0.56818182,  0.02272727],\n",
+       "       [ 1.72727273, -0.72727273,  0.09090909],\n",
+       "       [-0.04545455,  0.29545455, -0.06818182]])"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linalg.pinv(m3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Identity matrix\n",
+    "The product of a matrix by its inverse returns the identity matrix (with small floating point errors):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.00000000e+00, -1.66533454e-16,  0.00000000e+00],\n",
+       "       [ 6.31439345e-16,  1.00000000e+00, -1.38777878e-16],\n",
+       "       [ 5.21110932e-15, -2.38697950e-15,  1.00000000e+00]])"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m3.dot(linalg.inv(m3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can create an identity matrix of size NxN by calling `eye(N)` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 0., 0.],\n",
+       "       [0., 1., 0.],\n",
+       "       [0., 0., 1.]])"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.eye(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## QR decomposition\n",
+    "The `qr` function computes the [QR decomposition](https://en.wikipedia.org/wiki/QR_decomposition) of a matrix:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.04627448,  0.98786672,  0.14824986],\n",
+       "       [-0.23137241,  0.13377362, -0.96362411],\n",
+       "       [-0.97176411, -0.07889213,  0.22237479]])"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q, r = linalg.qr(m3)\n",
+    "q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-21.61018278, -29.89331494, -32.80860727],\n",
+       "       [  0.        ,   0.62427688,   1.9894538 ],\n",
+       "       [  0.        ,   0.        ,  -3.26149699]])"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.,  2.,  3.],\n",
+       "       [ 5.,  7., 11.],\n",
+       "       [21., 29., 31.]])"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q.dot(r)  # q.r equals m3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Determinant\n",
+    "The `det` function computes the [matrix determinant](https://en.wikipedia.org/wiki/Determinant):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "43.99999999999997"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linalg.det(m3)  # Computes the matrix determinant"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Eigenvalues and eigenvectors\n",
+    "The `eig` function computes the [eigenvalues and eigenvectors](https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors) of a square matrix:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([42.26600592, -0.35798416, -2.90802176])"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eigenvalues, eigenvectors = linalg.eig(m3)\n",
+    "eigenvalues # λ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.08381182, -0.76283526, -0.18913107],\n",
+       "       [-0.3075286 ,  0.64133975, -0.6853186 ],\n",
+       "       [-0.94784057, -0.08225377,  0.70325518]])"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eigenvectors # v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 6.66133815e-15,  1.66533454e-15, -3.10862447e-15],\n",
+       "       [ 7.10542736e-15,  5.16253706e-15, -5.32907052e-15],\n",
+       "       [ 3.55271368e-14,  4.94743135e-15, -9.76996262e-15]])"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m3.dot(eigenvectors) - eigenvalues * eigenvectors  # m3.v - λ*v = 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Singular Value Decomposition\n",
+    "The `svd` function takes a matrix and returns its [singular value decomposition](https://en.wikipedia.org/wiki/Singular_value_decomposition):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0, 0, 0, 2],\n",
+       "       [0, 0, 3, 0, 0],\n",
+       "       [0, 0, 0, 0, 0],\n",
+       "       [0, 2, 0, 0, 0]])"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m4 = np.array([[1,0,0,0,2], [0,0,3,0,0], [0,0,0,0,0], [0,2,0,0,0]])\n",
+    "m4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.,  1.,  0.,  0.],\n",
+       "       [ 1.,  0.,  0.,  0.],\n",
+       "       [ 0.,  0.,  0., -1.],\n",
+       "       [ 0.,  0.,  1.,  0.]])"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "U, S_diag, V = linalg.svd(m4)\n",
+    "U"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3.        , 2.23606798, 2.        , 0.        ])"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "S_diag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `svd` function just returns the values in the diagonal of Σ, but we want the full Σ matrix, so let's create it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3.        , 0.        , 0.        , 0.        , 0.        ],\n",
+       "       [0.        , 2.23606798, 0.        , 0.        , 0.        ],\n",
+       "       [0.        , 0.        , 2.        , 0.        , 0.        ],\n",
+       "       [0.        , 0.        , 0.        , 0.        , 0.        ]])"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "S = np.zeros((4, 5))\n",
+    "S[np.diag_indices(4)] = S_diag\n",
+    "S  # Σ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.        ,  0.        ,  1.        , -0.        ,  0.        ],\n",
+       "       [ 0.4472136 ,  0.        ,  0.        ,  0.        ,  0.89442719],\n",
+       "       [-0.        ,  1.        ,  0.        , -0.        ,  0.        ],\n",
+       "       [ 0.        ,  0.        ,  0.        ,  1.        ,  0.        ],\n",
+       "       [-0.89442719,  0.        ,  0.        ,  0.        ,  0.4472136 ]])"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "V"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 0., 0., 0., 2.],\n",
+       "       [0., 0., 3., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0.],\n",
+       "       [0., 2., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "U.dot(S).dot(V) # U.Σ.V == m4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagonal and trace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1,  7, 31])"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.diag(m3)  # the values in the diagonal of m3 (top left to bottom right)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m3.trace()  # equivalent to np.diag(m3).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solving a system of linear scalar equations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `solve` function solves a system of linear scalar equations, such as:\n",
+    "\n",
+    "* $2x + 6y = 6$\n",
+    "* $5x + 3y = -9$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-3.,  2.])"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coeffs  = np.array([[2, 6], [5, 3]])\n",
+    "depvars = np.array([6, -9])\n",
+    "solution = linalg.solve(coeffs, depvars)\n",
+    "solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's check the solution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 6., -9.]), array([ 6, -9]))"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coeffs.dot(solution), depvars  # yep, it's the same"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks good! Another way to check the solution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.allclose(coeffs.dot(solution), depvars)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Vectorization\n",
+    "Instead of executing operations on individual array items, one at a time, your code is much more efficient if you try to stick to array operations. This is called *vectorization*. This way, you can benefit from NumPy's many optimizations.\n",
+    "\n",
+    "For example, let's say we want to generate a 768x1024 array based on the formula $sin(xy/40.5)$. A **bad** option would be to do the math in python using nested loops:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "\n",
+    "data = np.empty((768, 1024))\n",
+    "for y in range(768):\n",
+    "    for x in range(1024):\n",
+    "        data[y, x] = math.sin(x * y / 40.5)  # BAD! Very inefficient."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sure, this works, but it's terribly inefficient since the loops are taking place in pure python. Let's vectorize this algorithm. First, we will use NumPy's `meshgrid` function which generates coordinate matrices from coordinate vectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[   0,    1,    2, ..., 1021, 1022, 1023],\n",
+       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
+       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
+       "       ...,\n",
+       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
+       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
+       "       [   0,    1,    2, ..., 1021, 1022, 1023]])"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_coords = np.arange(0, 1024)  # [0, 1, 2, ..., 1023]\n",
+    "y_coords = np.arange(0, 768)   # [0, 1, 2, ..., 767]\n",
+    "X, Y = np.meshgrid(x_coords, y_coords)\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  0,   0,   0, ...,   0,   0,   0],\n",
+       "       [  1,   1,   1, ...,   1,   1,   1],\n",
+       "       [  2,   2,   2, ...,   2,   2,   2],\n",
+       "       ...,\n",
+       "       [765, 765, 765, ..., 765, 765, 765],\n",
+       "       [766, 766, 766, ..., 766, 766, 766],\n",
+       "       [767, 767, 767, ..., 767, 767, 767]])"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, both `X` and `Y` are 768x1024 arrays, and all values in `X` correspond to the horizontal coordinate, while all values in `Y` correspond to the vertical coordinate.\n",
+    "\n",
+    "Now we can simply compute the result using array operations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.sin(X * Y / 40.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can plot this data using matplotlib's `imshow` function (see the [matplotlib tutorial](tools_matplotlib.ipynb))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig = plt.figure(1, figsize=(7, 6))\n",
+    "plt.imshow(data, cmap=\"hot\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Saving and loading\n",
+    "NumPy makes it easy to save and load `ndarray`s in binary or text format.\n",
+    "\n",
+    "## Binary `.npy` format\n",
+    "Let's create a random array and save it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.29589295, 0.74421264, 0.01108453],\n",
+       "       [0.00981294, 0.07941431, 0.10770867]])"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.random.rand(2,3)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.save(\"my_array\", a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Done! Since the file name contains no file extension, NumPy automatically added `.npy`. Let's take a peek at the file content:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f8', 'fortran_order': False, 'shape': (2, 3), }                                                          \\n\\\\\\x9cd\\xfd\\xe8\\xef\\xd2? \\xda^\\x07\\x97\\xd0\\xe7?@\\x7f\\xb4\\xf8|\\xb3\\x86?\\x00m\\x1d\\x18\\xce\\x18\\x84?\\xa0\\xf7\\xa6\\x1c\\x7fT\\xb4?\\xf0\\xe7\\xf5\\x90\\xcb\\x92\\xbb?\""
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with open(\"my_array.npy\", \"rb\") as f:\n",
+    "    content = f.read()\n",
+    "\n",
+    "content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To load this file into a NumPy array, simply call `load`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.29589295, 0.74421264, 0.01108453],\n",
+       "       [0.00981294, 0.07941431, 0.10770867]])"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_loaded = np.load(\"my_array.npy\")\n",
+    "a_loaded"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Text format\n",
+    "Let's try saving the array in text format:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt(\"my_array.csv\", a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's look at the file content:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.958929514447328213e-01 7.442126411395442176e-01 1.108453401553755047e-02\n",
+      "9.812936888627721288e-03 7.941431474225035814e-02 1.077086666972772999e-01\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"my_array.csv\", \"rt\") as f:\n",
+    "    print(f.read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a CSV file with tabs as delimiters. You can set a different delimiter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt(\"my_array.csv\", a, delimiter=\",\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To load this file, just use `loadtxt`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.29589295, 0.74421264, 0.01108453],\n",
+       "       [0.00981294, 0.07941431, 0.10770867]])"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_loaded = np.loadtxt(\"my_array.csv\", delimiter=\",\")\n",
+    "a_loaded"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Zipped `.npz` format\n",
+    "It is also possible to save multiple arrays in one zipped file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0,  1,  2,  3],\n",
+       "        [ 4,  5,  6,  7],\n",
+       "        [ 8,  9, 10, 11]],\n",
+       "\n",
+       "       [[12, 13, 14, 15],\n",
+       "        [16, 17, 18, 19],\n",
+       "        [20, 21, 22, 23]]], dtype=uint8)"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = np.arange(24, dtype=np.uint8).reshape(2, 3, 4)\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savez(\"my_arrays\", my_a=a, my_b=b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, let's take a peek at the file content. Note that the `.npz` file extension was automatically added."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'b\"PK\\\\x03\\\\x04\\\\x14\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00!\\\\x00\\\\xf1\\\\xe2\\\\x90\\\\xa8\\\\xb0\\\\x00\\\\x00\\\\x00\\\\xb0\\\\x00\\\\x00\\\\x00\\\\x08\\\\x00\\\\x14\\\\x00my_a.npy\\\\x01\\\\x00\\\\x10\\\\x00\\\\xb0\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\xb0\\\\x00\\\\x0[...]'"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with open(\"my_arrays.npz\", \"rb\") as f:\n",
+    "    content = f.read()\n",
+    "\n",
+    "repr(content)[:180] + \"[...]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You then load this file like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<numpy.lib.npyio.NpzFile at 0x127ebe1f0>"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_arrays = np.load(\"my_arrays.npz\")\n",
+    "my_arrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a dict-like object which loads the arrays lazily:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KeysView(<numpy.lib.npyio.NpzFile object at 0x127ebe1f0>)"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_arrays.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.29589295, 0.74421264, 0.01108453],\n",
+       "       [0.00981294, 0.07941431, 0.10770867]])"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_arrays[\"my_a\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# What's next?\n",
+    "Now you know all the fundamentals of NumPy, but there are many more options available. The best way to learn more is to experiment with NumPy, and go through the excellent [reference documentation](https://numpy.org/doc/stable/reference/index.html) to find more functions and features you may be interested in."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "toc": {
+   "toc_cell": false,
+   "toc_number_sections": true,
+   "toc_section_display": "block",
+   "toc_threshold": 6,
+   "toc_window_display": false
+  },
+  "toc_position": {
+   "height": "677px",
+   "left": "1195.02px",
+   "right": "20px",
+   "top": "78px",
+   "width": "238px"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/ML/01_intro/tools_pandas.ipynb b/ML/01_intro/tools_pandas.ipynb
new file mode 100644
index 0000000..25597d3
--- /dev/null
+++ b/ML/01_intro/tools_pandas.ipynb
@@ -0,0 +1,10698 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Tools - pandas**\n",
+    "\n",
+    "*The `pandas` library provides high-performance, easy-to-use data structures and data analysis tools. The main data structure is the `DataFrame`, which you can think of as an in-memory 2D table (like a spreadsheet, with column names and row labels). Many features available in Excel are available programmatically, such as creating pivot tables, computing columns based on other columns, plotting graphs, etc. You can also group rows by column value, or join tables much like in SQL. Pandas is also great at handling time series.*\n",
+    "\n",
+    "Prerequisites:\n",
+    "* NumPy – if you are not familiar with NumPy, we recommend that you go through the [NumPy tutorial](tools_numpy.ipynb) now."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a href=\"https://colab.research.google.com/github/ageron/handson-ml3/blob/main/tools_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "  </td>\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml3/blob/main/tools_pandas.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, let's import `pandas`. People usually import it as `pd`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# `Series` objects\n",
+    "The `pandas` library contains the following useful data structures:\n",
+    "* `Series` objects, that we will discuss now. A `Series` object is 1D array, similar to a column in a spreadsheet (with a column name and row labels).\n",
+    "* `DataFrame` objects. This is a 2D table, similar to a spreadsheet (with column names and row labels).\n",
+    "* `Panel` objects. You can see a `Panel` as a dictionary of `DataFrame`s. These are less used, so we will not discuss them here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a `Series`\n",
+    "Let's start by creating our first `Series` object!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    2\n",
+       "1   -1\n",
+       "2    3\n",
+       "3    5\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = pd.Series([2,-1,3,5])\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Similar to a 1D `ndarray`\n",
+    "`Series` objects behave much like one-dimensional NumPy `ndarray`s, and you can often pass them as parameters to NumPy functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      7.389056\n",
+       "1      0.367879\n",
+       "2     20.085537\n",
+       "3    148.413159\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.exp(s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Arithmetic operations on `Series` are also possible, and they apply *elementwise*, just like for `ndarray`s:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1002\n",
+       "1    1999\n",
+       "2    3003\n",
+       "3    4005\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s + [1000,2000,3000,4000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar to NumPy, if you add a single number to a `Series`, that number is added to all items in the `Series`. This is called * broadcasting*:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1002\n",
+       "1     999\n",
+       "2    1003\n",
+       "3    1005\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s + 1000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The same is true for all binary operations such as `*` or `/`, and even conditional operations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    False\n",
+       "1     True\n",
+       "2    False\n",
+       "3    False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s < 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Index labels\n",
+    "Each item in a `Series` object has a unique identifier called the *index label*. By default, it is simply the rank of the item in the `Series` (starting from `0`) but you can also set the index labels manually:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "alice       68\n",
+       "bob         83\n",
+       "charles    112\n",
+       "darwin      68\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s2 = pd.Series([68, 83, 112, 68], index=[\"alice\", \"bob\", \"charles\", \"darwin\"])\n",
+    "s2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can then use the `Series` just like a `dict`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "83"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s2[\"bob\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can still access the items by integer location, like in a regular array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "83"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s2[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make it clear when you are accessing by label or by integer location, it is recommended to always use the `loc` attribute when accessing by label, and the `iloc` attribute when accessing by integer location:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "83"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s2.loc[\"bob\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "83"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s2.iloc[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Slicing a `Series` also slices the index labels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bob         83\n",
+       "charles    112\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s2.iloc[1:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This can lead to unexpected results when using the default numeric labels, so be careful:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1000\n",
+       "1    1001\n",
+       "2    1002\n",
+       "3    1003\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "surprise = pd.Series([1000, 1001, 1002, 1003])\n",
+    "surprise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2    1002\n",
+       "3    1003\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "surprise_slice = surprise[2:]\n",
+    "surprise_slice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Oh, look! The first element has index label `2`. The element with index label `0` is absent from the slice:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Key error: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    surprise_slice[0]\n",
+    "except KeyError as e:\n",
+    "    print(\"Key error:\", e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But remember that you can access elements by integer location using the `iloc` attribute. This illustrates another reason why it's always better to use `loc` and `iloc` to access `Series` objects:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1002"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "surprise_slice.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Init from `dict`\n",
+    "You can create a `Series` object from a `dict`. The keys will be used as index labels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "alice     68\n",
+       "bob       83\n",
+       "colin     86\n",
+       "darwin    68\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights = {\"alice\": 68, \"bob\": 83, \"colin\": 86, \"darwin\": 68}\n",
+    "s3 = pd.Series(weights)\n",
+    "s3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can control which elements you want to include in the `Series` and in what order by explicitly specifying the desired `index`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "colin    86\n",
+       "alice    68\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s4 = pd.Series(weights, index = [\"colin\", \"alice\"])\n",
+    "s4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Automatic alignment\n",
+    "When an operation involves multiple `Series` objects, `pandas` automatically aligns items by matching index labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['alice', 'bob', 'charles', 'darwin'], dtype='object')\n",
+      "Index(['alice', 'bob', 'colin', 'darwin'], dtype='object')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "alice      136.0\n",
+       "bob        166.0\n",
+       "charles      NaN\n",
+       "colin        NaN\n",
+       "darwin     136.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(s2.keys())\n",
+    "print(s3.keys())\n",
+    "\n",
+    "s2 + s3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The resulting `Series` contains the union of index labels from `s2` and `s3`. Since `\"colin\"` is missing from `s2` and `\"charles\"` is missing from `s3`, these items have a `NaN` result value (i.e. Not-a-Number means *missing*).\n",
+    "\n",
+    "Automatic alignment is very handy when working with data that may come from various sources with varying structure and missing items. But if you forget to set the right index labels, you can have surprising results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "s2 = [ 68  83 112  68]\n",
+      "s5 = [1000 1000 1000 1000]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "alice     NaN\n",
+       "bob       NaN\n",
+       "charles   NaN\n",
+       "darwin    NaN\n",
+       "0         NaN\n",
+       "1         NaN\n",
+       "2         NaN\n",
+       "3         NaN\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s5 = pd.Series([1000,1000,1000,1000])\n",
+    "print(\"s2 =\", s2.values)\n",
+    "print(\"s5 =\", s5.values)\n",
+    "\n",
+    "s2 + s5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas could not align the `Series`, since their labels do not match at all, hence the full `NaN` result."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Init with a scalar\n",
+    "You can also initialize a `Series` object using a scalar and a list of index labels: all items will be set to the scalar."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "life          42\n",
+       "universe      42\n",
+       "everything    42\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meaning = pd.Series(42, [\"life\", \"universe\", \"everything\"])\n",
+    "meaning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `Series` name\n",
+    "A `Series` can have a `name`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bob      83\n",
+       "alice    68\n",
+       "Name: weights, dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s6 = pd.Series([83, 68], index=[\"bob\", \"alice\"], name=\"weights\")\n",
+    "s6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting a `Series`\n",
+    "Pandas makes it easy to plot `Series` data using matplotlib (for more details on matplotlib, check out the [matplotlib tutorial](tools_matplotlib.ipynb)). Just import matplotlib and call the `plot()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "temperatures = [4.4,5.1,6.1,6.2,6.1,6.1,5.7,5.2,4.7,4.1,3.9,3.5]\n",
+    "s7 = pd.Series(temperatures, name=\"Temperature\")\n",
+    "s7.plot()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are *many* options for plotting your data. It is not necessary to list them all here: if you need a particular type of plot (histograms, pie charts, etc.), just look for it in the excellent [Visualization](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html) section of pandas' documentation, and look at the example code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Handling time\n",
+    "Many datasets have timestamps, and pandas is awesome at manipulating such data:\n",
+    "* it can represent periods (such as 2016Q3) and frequencies (such as \"monthly\"),\n",
+    "* it can convert periods to actual timestamps, and *vice versa*,\n",
+    "* it can resample data and aggregate values any way you like,\n",
+    "* it can handle timezones.\n",
+    "\n",
+    "## Time range\n",
+    "Let's start by creating a time series using `pd.date_range()`. It returns a `DatetimeIndex` containing one datetime per hour for 12 hours starting on October 29th 2016 at 5:30pm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2016-10-29 17:30:00', '2016-10-29 18:30:00',\n",
+       "               '2016-10-29 19:30:00', '2016-10-29 20:30:00',\n",
+       "               '2016-10-29 21:30:00', '2016-10-29 22:30:00',\n",
+       "               '2016-10-29 23:30:00', '2016-10-30 00:30:00',\n",
+       "               '2016-10-30 01:30:00', '2016-10-30 02:30:00',\n",
+       "               '2016-10-30 03:30:00', '2016-10-30 04:30:00'],\n",
+       "              dtype='datetime64[ns]', freq='H')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dates = pd.date_range('2016/10/29 5:30pm', periods=12, freq='H')\n",
+    "dates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This `DatetimeIndex` may be used as an index in a `Series`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016-10-29 17:30:00    4.4\n",
+       "2016-10-29 18:30:00    5.1\n",
+       "2016-10-29 19:30:00    6.1\n",
+       "2016-10-29 20:30:00    6.2\n",
+       "2016-10-29 21:30:00    6.1\n",
+       "2016-10-29 22:30:00    6.1\n",
+       "2016-10-29 23:30:00    5.7\n",
+       "2016-10-30 00:30:00    5.2\n",
+       "2016-10-30 01:30:00    4.7\n",
+       "2016-10-30 02:30:00    4.1\n",
+       "2016-10-30 03:30:00    3.9\n",
+       "2016-10-30 04:30:00    3.5\n",
+       "Freq: H, dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp_series = pd.Series(temperatures, dates)\n",
+    "temp_series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot this series:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "temp_series.plot(kind=\"bar\")\n",
+    "\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Resampling\n",
+    "Pandas lets us resample a time series very simply. Just call the `resample()` method and specify a new frequency:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pandas.core.resample.DatetimeIndexResampler object at 0x122870ac0>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp_series_freq_2H = temp_series.resample(\"2H\")\n",
+    "temp_series_freq_2H"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The resampling operation is actually a deferred operation, which is why we did not get a `Series` object, but a `DatetimeIndexResampler` object instead. To actually perform the resampling operation, we can simply call the `mean()` method. Pandas will compute the mean of every pair of consecutive hours:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp_series_freq_2H = temp_series_freq_2H.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "temp_series_freq_2H.plot(kind=\"bar\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note how the values have automatically been aggregated into 2-hour periods. If we look at the 6-8pm period, for example, we had a value of `5.1` at 6:30pm, and `6.1` at 7:30pm. After resampling, we just have one value of `5.6`, which is the mean of `5.1` and `6.1`. Rather than computing the mean, we could have used any other aggregation function, for example we can decide to keep the minimum value of each period:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016-10-29 16:00:00    4.4\n",
+       "2016-10-29 18:00:00    5.1\n",
+       "2016-10-29 20:00:00    6.1\n",
+       "2016-10-29 22:00:00    5.7\n",
+       "2016-10-30 00:00:00    4.7\n",
+       "2016-10-30 02:00:00    3.9\n",
+       "2016-10-30 04:00:00    3.5\n",
+       "Freq: 2H, dtype: float64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp_series_freq_2H = temp_series.resample(\"2H\").min()\n",
+    "temp_series_freq_2H"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or, equivalently, we could use the `apply()` method instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016-10-29 16:00:00    4.4\n",
+       "2016-10-29 18:00:00    5.1\n",
+       "2016-10-29 20:00:00    6.1\n",
+       "2016-10-29 22:00:00    5.7\n",
+       "2016-10-30 00:00:00    4.7\n",
+       "2016-10-30 02:00:00    3.9\n",
+       "2016-10-30 04:00:00    3.5\n",
+       "Freq: 2H, dtype: float64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp_series_freq_2H = temp_series.resample(\"2H\").apply(np.min)\n",
+    "temp_series_freq_2H"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upsampling and interpolation\n",
+    "It was an example of downsampling. We can also upsample (i.e. increase the frequency), but it will create holes in our data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016-10-29 17:30:00    4.4\n",
+       "2016-10-29 17:45:00    NaN\n",
+       "2016-10-29 18:00:00    NaN\n",
+       "2016-10-29 18:15:00    NaN\n",
+       "2016-10-29 18:30:00    5.1\n",
+       "2016-10-29 18:45:00    NaN\n",
+       "2016-10-29 19:00:00    NaN\n",
+       "2016-10-29 19:15:00    NaN\n",
+       "2016-10-29 19:30:00    6.1\n",
+       "2016-10-29 19:45:00    NaN\n",
+       "Freq: 15T, dtype: float64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp_series_freq_15min = temp_series.resample(\"15Min\").mean()\n",
+    "temp_series_freq_15min.head(n=10) # `head` displays the top n values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One solution is to fill the gaps by interpolating. We just call the `interpolate()` method. The default is to use linear interpolation, but we can also select another method, such as cubic interpolation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016-10-29 17:30:00    4.400000\n",
+       "2016-10-29 17:45:00    4.452911\n",
+       "2016-10-29 18:00:00    4.605113\n",
+       "2016-10-29 18:15:00    4.829758\n",
+       "2016-10-29 18:30:00    5.100000\n",
+       "2016-10-29 18:45:00    5.388992\n",
+       "2016-10-29 19:00:00    5.669887\n",
+       "2016-10-29 19:15:00    5.915839\n",
+       "2016-10-29 19:30:00    6.100000\n",
+       "2016-10-29 19:45:00    6.203621\n",
+       "Freq: 15T, dtype: float64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp_series_freq_15min = temp_series.resample(\"15Min\").interpolate(method=\"cubic\")\n",
+    "temp_series_freq_15min.head(n=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "temp_series.plot(label=\"Period: 1 hour\")\n",
+    "temp_series_freq_15min.plot(label=\"Period: 15 minutes\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Timezones\n",
+    "By default, datetimes are *naive*: they are not aware of timezones, so 2016-10-30 02:30 might mean October 30th 2016 at 2:30am in Paris or in New York. We can make datetimes timezone *aware* by calling the `tz_localize()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016-10-29 17:30:00-04:00    4.4\n",
+       "2016-10-29 18:30:00-04:00    5.1\n",
+       "2016-10-29 19:30:00-04:00    6.1\n",
+       "2016-10-29 20:30:00-04:00    6.2\n",
+       "2016-10-29 21:30:00-04:00    6.1\n",
+       "2016-10-29 22:30:00-04:00    6.1\n",
+       "2016-10-29 23:30:00-04:00    5.7\n",
+       "2016-10-30 00:30:00-04:00    5.2\n",
+       "2016-10-30 01:30:00-04:00    4.7\n",
+       "2016-10-30 02:30:00-04:00    4.1\n",
+       "2016-10-30 03:30:00-04:00    3.9\n",
+       "2016-10-30 04:30:00-04:00    3.5\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp_series_ny = temp_series.tz_localize(\"America/New_York\")\n",
+    "temp_series_ny"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that `-04:00` is now appended to all the datetimes. It means that these datetimes refer to [UTC](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) - 4 hours.\n",
+    "\n",
+    "We can convert these datetimes to Paris time like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016-10-29 23:30:00+02:00    4.4\n",
+       "2016-10-30 00:30:00+02:00    5.1\n",
+       "2016-10-30 01:30:00+02:00    6.1\n",
+       "2016-10-30 02:30:00+02:00    6.2\n",
+       "2016-10-30 02:30:00+01:00    6.1\n",
+       "2016-10-30 03:30:00+01:00    6.1\n",
+       "2016-10-30 04:30:00+01:00    5.7\n",
+       "2016-10-30 05:30:00+01:00    5.2\n",
+       "2016-10-30 06:30:00+01:00    4.7\n",
+       "2016-10-30 07:30:00+01:00    4.1\n",
+       "2016-10-30 08:30:00+01:00    3.9\n",
+       "2016-10-30 09:30:00+01:00    3.5\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp_series_paris = temp_series_ny.tz_convert(\"Europe/Paris\")\n",
+    "temp_series_paris"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You may have noticed that the UTC offset changes from `+02:00` to `+01:00`: this is because France switches to winter time at 3am that particular night (time goes back to 2am). Notice that 2:30am occurs twice! Let's go back to a naive representation (if you log some data hourly using local time, without storing the timezone, you might get something like this):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016-10-29 23:30:00    4.4\n",
+       "2016-10-30 00:30:00    5.1\n",
+       "2016-10-30 01:30:00    6.1\n",
+       "2016-10-30 02:30:00    6.2\n",
+       "2016-10-30 02:30:00    6.1\n",
+       "2016-10-30 03:30:00    6.1\n",
+       "2016-10-30 04:30:00    5.7\n",
+       "2016-10-30 05:30:00    5.2\n",
+       "2016-10-30 06:30:00    4.7\n",
+       "2016-10-30 07:30:00    4.1\n",
+       "2016-10-30 08:30:00    3.9\n",
+       "2016-10-30 09:30:00    3.5\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp_series_paris_naive = temp_series_paris.tz_localize(None)\n",
+    "temp_series_paris_naive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now `02:30` is really ambiguous. If we try to localize these naive datetimes to the Paris timezone, we get an error:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pytz.exceptions.AmbiguousTimeError'>\n",
+      "Cannot infer dst time from 2016-10-30 02:30:00, try using the 'ambiguous' argument\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    temp_series_paris_naive.tz_localize(\"Europe/Paris\")\n",
+    "except Exception as e:\n",
+    "    print(type(e))\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fortunately, by using the `ambiguous` argument we can tell pandas to infer the right DST (Daylight Saving Time) based on the order of the ambiguous timestamps:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016-10-29 23:30:00+02:00    4.4\n",
+       "2016-10-30 00:30:00+02:00    5.1\n",
+       "2016-10-30 01:30:00+02:00    6.1\n",
+       "2016-10-30 02:30:00+02:00    6.2\n",
+       "2016-10-30 02:30:00+01:00    6.1\n",
+       "2016-10-30 03:30:00+01:00    6.1\n",
+       "2016-10-30 04:30:00+01:00    5.7\n",
+       "2016-10-30 05:30:00+01:00    5.2\n",
+       "2016-10-30 06:30:00+01:00    4.7\n",
+       "2016-10-30 07:30:00+01:00    4.1\n",
+       "2016-10-30 08:30:00+01:00    3.9\n",
+       "2016-10-30 09:30:00+01:00    3.5\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp_series_paris_naive.tz_localize(\"Europe/Paris\", ambiguous=\"infer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Periods\n",
+    "The `pd.period_range()` function returns a `PeriodIndex` instead of a `DatetimeIndex`. For example, let's get all quarters in 2016 and 2017:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PeriodIndex(['2016Q1', '2016Q2', '2016Q3', '2016Q4', '2017Q1', '2017Q2',\n",
+       "             '2017Q3', '2017Q4'],\n",
+       "            dtype='period[Q-DEC]', freq='Q-DEC')"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quarters = pd.period_range('2016Q1', periods=8, freq='Q')\n",
+    "quarters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Adding a number `N` to a `PeriodIndex` shifts the periods by `N` times the `PeriodIndex`'s frequency:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PeriodIndex(['2016Q4', '2017Q1', '2017Q2', '2017Q3', '2017Q4', '2018Q1',\n",
+       "             '2018Q2', '2018Q3'],\n",
+       "            dtype='period[Q-DEC]', freq='Q-DEC')"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quarters + 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `asfreq()` method lets us change the frequency of the `PeriodIndex`. All periods are lengthened or shortened accordingly. For example, let's convert all the quarterly periods to monthly periods (zooming in):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PeriodIndex(['2016-03', '2016-06', '2016-09', '2016-12', '2017-03', '2017-06',\n",
+       "             '2017-09', '2017-12'],\n",
+       "            dtype='period[M]', freq='M')"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quarters.asfreq(\"M\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, the `asfreq` zooms on the end of each period. We can tell it to zoom on the start of each period instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PeriodIndex(['2016-01', '2016-04', '2016-07', '2016-10', '2017-01', '2017-04',\n",
+       "             '2017-07', '2017-10'],\n",
+       "            dtype='period[M]', freq='M')"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quarters.asfreq(\"M\", how=\"start\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can zoom out:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PeriodIndex(['2016', '2016', '2016', '2016', '2017', '2017', '2017', '2017'], dtype='period[A-DEC]', freq='A-DEC')"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quarters.asfreq(\"A\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, we can create a `Series` with a `PeriodIndex`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016Q1    300\n",
+       "2016Q2    320\n",
+       "2016Q3    290\n",
+       "2016Q4    390\n",
+       "2017Q1    320\n",
+       "2017Q2    360\n",
+       "2017Q3    310\n",
+       "2017Q4    410\n",
+       "Freq: Q-DEC, dtype: int64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quarterly_revenue = pd.Series([300, 320, 290, 390, 320, 360, 310, 410], index=quarters)\n",
+    "quarterly_revenue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RjYAL+JcxZg1wGnCtiJSJyJsiUuRbPQeoPGbzKt8ypU7I7ui+ZrD6BtglU0aTlhSnUzP20fIdbn762jbtcHoSpQ4Xo9ISmTAi1epQPqFPid8Y02WMmQbkAnNEZDKQALQZY2YBfwEe68+BReR23y+NMrfb3c+wVSSxO92MzUyhIDPF0jiS4m1cPTOXf245hKuxzdJYQt2+mha++tR6Hlm5mxf1vsgndHi8rCyvZuGE4Fep9UW/qnqMMfWAHVhC95X8i763XgKm+F7vp3vsv0eub9nx+3rYGDPLGDMrKyv0xsBUcLR2dLFqV43lV/s9bizJx+M1PLuusveVo1Rnl5c7n90AAqePGsp9bzloavdYHVZIKdtbS1O7JySHeaBvVT1ZIpLue50EnAc4gH8A5/pWWwDs8L1+BbjZV91TAjQYYw76OW4VIT6sqKbD4w14N86+GpuZwjlFmTy9Zh+eLu3fcyL3/2sHGyvr+cUVU/j5FWfgPtLOg6U7rQ4rpNh7qtROs6ZKrTd9ueIfBdhFZDOwju4x/teAXwBXisjHwM+B23zrvwHsAsrpHgL6it+jVhHD7nSRHG9jztjhVody1M3zCjjU2MY72w9bHUrI+bC8mj8ur+C62Xl8esoopuWlc+WMXB5buZvd1c1Whxcy7E43c8cNJyXBmiq13vSlqmezMWa6MWaKMWayMebHvuX1xphPG2POMMbMM8Zs8i03xpj/MMac5nuvLNA/hApPxhjsDjdnFWaSEGuzOpyjFhVnk5OepDd5j1Pb3MHXn9vIuMwUfnDJxKPL714ygXhbDD97fZuF0YWOytoWyl1NITN8eSL65K6yzE5XE/vrW0PuA2KLEW6YO4YPymsodzX1vkEUMMbwn3/bRH1LJ7+/fvq/PW+RPTSROxYX8c52F8t3aKFGT7PBUOrGeTxN/MoyPWWcCyeExvj+sa6ZlUecTXhqjV71Azz+4R7edbj4zkXFTBr9yS6TnzurgIKMZH786lY6o/zeiN3hoiAjmbEWV6mdiiZ+ZRm700XxyFRGpydZHconZKUmcOHkUbzwURUtHdFdsbLtQCP//YaDRcXZ3HJmwQnXSYi18b1PT6TC3cwTUdziuq2ziw8rakL6ah808SuLNLZ1UranLqQ/IDfPy+dIm4dXNh6wOhTLtHR4uOOZ9aQnx/Grq6acsiZ98enZzB+fxQPv7KCmqT2IUYaOVRU1tHu8ITd8eTxN/MoSK3dW4/GakP6AzMwfRvHIVJ5YFb39e37y2jZ2VTdz/7XTyBhy6vliRYQfXHw6rR1d/PrtHadcN1LZnS6S4kKrSu1ENPErS9gdLoYmxjJjTLrVoZyUiLB0Xj7bDjayfl+91eEE3eubD/LM2kq+tOA0zirM7NM2hdmp3DyvgGfX7WPL/oYARxhajDGUOlycVZhJYlzoVKmdiCZ+FXRer8HudDN/fBaxttA+BS+blsOQhNiom6Slqq6Fe17czNS8dO46b3y/tv3ap4oYlhzPva9ujaq/lCrcTVTVtYbMw4inEtqfOhWRth5opLqpPaSHeXqkJMRy5YwcXt98MGrGrT1dXr727EaMgQevm05cP385pyXF8a3zJ7BuTx2vbY6eh/btju5S1oVhcF5r4ldBZ3e6EIEFIVjGeSI3leTT0eXl+bIqq0MJit+/u5OP9tbxs8snMyYjeUD7uHZ2HpNGD+Xnb2yntaPLzxGGpp4qtZwQrFI7niZ+FXSlDhdTctPJ7OVmYagoGpHKvHEZLFu9l64Ib0G8elcND9nLuWpmLpdOG3g3dVuM8MNLJnGgoY0/La/wY4Sh6UhbJ2t314bF1T5o4ldBVtPUzqaqes4Nk6v9Hkvn5bO/vpX3nK7eVw5Tdc0dfOO5jeRnpHDvZyYNen9zxg7n4imj+NPyCqrqWvwQYej6oLynSi08zmtN/CqoVux0Y0xozkp0KudNHEF2akLE9u8xxnD33zdT3dTO76+b7rfmYt+96HRE4OdvOPyyv1Bld7hJTYxlZv4wq0PpE038KqhKHW4yh8Qz+QSP/YeyOFsM188Zw/IdbvbWRF4XymVr9vH2tsPcvaSYM3L99/9mdHoSX15QyOsfH2T1rhq/7TeUGGOwO11hUaXWIzyiVBHB0+VlxQ43C8ZnExMTerMS9eb6OWOIEeHpNfusDsWvnIeO8NPXtrFgfBafP2us3/d/+/xx5KQnce+r2yLyHsnWA424joRHlVoPTfwqaDZW1tPQ2hl2wzw9RqYlcsGkETxXVklbZ2RUqrR1dnHHM+tJTYzj11dPDcgv5KR4G9+96HS2H2zkmbWR9UsTOHrfJxSbDZ6MJn4VNKUOF7YY4eyivj0FGopuKsmnvqUzYurTf/r6NnYcbuK310wlKzVwVVYXnTGSuWOH85u3nTS0dAbsOFYodbiYmpsWNlVqoIlfBZHd6WZm/jDSkuKsDmXA5o3L4LSslIi4yfvWlkMsW72P2+ePY/74wF6tinSXdza0dnL/O5HTx6e2uYMNlfVhU8bZQxO/CopDDW1sP9gYtsM8PUSEpSX5bKqsZ3NVvdXhDNiB+lbu/vtmzshJ41vnTwjKMSeOHsr1c8bw5Oq97Dh8JCjHDLT3w7RKTRO/CoqjsxKF2ZXRiVwxM5ekOFvY9u/p8hq+/txGPF1efn/9dOJjg5cGvnn+BFLibfzktW0R0cen1OEiIyWeM3LCq0pNE78KCrvDxei0RMaPGGJ1KIM2NDGOy6bn8PLGA2E5Xv0Hezlrd9fyk8smB32WqOEp8XzjvPG8v7Oaf20L78nsu7yG5TvcLJiQFXZVapr4VcC1e7r4oLyac4uzTzmRRzhZWpJPu8fL3z6qtDqUfinbU8sD7+zg8uk5XDEj15IYbirJpyh7CD99fXtYV0dtrKynviU8q9Q08auAW7e7juaOrogY5ukxcfRQZuUPY9nqvXjDpDa9oaWTrz27kdxhyfz40sG3ZBioOFsMP7xkEvtqW3jsg92WxTFYdl+V2jmF4VPG2UMTvwo4u9NFfGwMZxZmWB2KXy2dl8+emhZWlldbHUqvjDHc8+JmDje28fvrp5OaaG1l1dlFmZw3cQQPlZZzuLHN0lgGyu50MXPMMNKSw69KTRO/Cji700XJuAyS4/3T/yVULJk8koyU+LAo7Xx2XSVvbjnEty6YwLS8dKvDAeB7nz4dT5fhvjfDr4/P4cY2th5oDOk5o0+l18QvIokislZENonIVhG597j3fy8iTcd8nyAiz4lIuYisEZGCAMStwsTemmZ2uZvDpmthfyTE2rh2dh7vbj/M/vpWq8M5qZ2Hj3Dvq1s5uzCT288ZZ3U4R+VnpHDbOWN5ccN+1u+rszqcful5WjccZts6kb5c8bcDi4wxU4FpwBIRKQEQkVnA8e3obgXqjDGFwP3Aff4LV4UbuyNyyjhP5Ia5YwB4ek1oXvV3t2TYQEp8LL+9JjAtGQbjK+cWkp2awL2vbA2beyXQ3Y1zVFoiE0akWh3KgPSa+E23niv6ON+XEREb8Cvg28dtcinwuO/1C8BiiZRSDtVvdqebcZkpFAS5bDBYcocls6h4BM+tq6TdE3oVKr9404Hj0BF+ffVUsocmWh3OJwxJiOWeC4vZVNXA39eHxwxnHR4vK8O8Sq1PY/wiYhORjYAL+JcxZg3wVeAVY8zxTUtygEoAY4wHaAAi666e6pOWDg+rdtWE3ePs/bV0Xj7VTR28teWQ1aH8m3e2HeZ/P9zD588aG9Jj0ZdNy2H6mHTue8vJkbbQfy6ibG8tTe2esP4rtk+J3xjTZYyZBuQCc0RkPnA18OBADywit4tImYiUud3uge5GhbBVFTV0eLxhOw7aV+cUZpKfkRxST/IeamjjP1/YxMRRQ7n7wuC0ZBioGN80jdVN7TxkL7c6nF7ZHS7ibTGceVr4Xs/2q6rHGFMP2IFzgUKgXET2AMki0vN/bD+QByAisUAa8IkZGIwxDxtjZhljZmVlRXZiiFZ2p4vkeBtzxg63OpSAiokRbpqbz7o9dWw/2Gh1OHR5Dd94biNtnV4evGE6CbE2q0Pq1bS8dK6amctjK3ezuzq0J7qxO93MHTfcb7OUWaEvVT1ZIpLue50EnAd8ZIwZaYwpMMYUAC2+m7kArwCf9b2+Cig1kdCUQ/WLMQa7w81ZhZlhkXgG6+pZuSTExoTEVf+fllewalcN9146idOywqdFxreXTCDeFsPPXt9mdSgnVVnbQrmrKayHeaBvV/yjALuIbAbW0T3G/9op1n8UyPD9BXAXcM/gw1ThZqerif31rWH/Aemr9OR4PjN1NC9t2E+jhePU6/fV8dt/7eCSqaO5eqY1LRkGKjs1kTsWF/HOdhfLd4Tm8O/RZoMhfM+kL/pS1bPZGDPdGDPFGDPZGPPjE6wz5JjXbcaYq40xhcaYOcaYXf4OWoW+o2WcET6+f6yl8/Jp6ejipfX7LTl+Y1sndz6zgVFpifzs8slhWXHyubMKKMhI5sevbqWzy2t1OJ9gd7gYm5kS9OZ2/qZP7qqAKHW4KB6Zyqi0JKtDCZopuelMzU3jydV7g95y2BjDd1/8mIMNbfzuuukMtbglw0AlxNr4/sUTqXA388Qq64fNjtXW2cWHFTVhNcXiyWjiV37X2NZJ2d66sP9zeCBuKsmn3NXE6l21QT3u3z6q4rXNB7nrvPHMzD/+mcrwsqg4m/njs3jgnR3UNLVbHc5RqypqaPd4I2L4UhO/8ruVO6vp8pqwbFc7WJdMHU16clxQb/JWuJv40StbmTcugy8tOC1oxw0UEeEHF0+ktaOLX7/ttDqco+xOF0lxNuaOC/8qNU38yu9KHS6GJsYyPUSagQVTYpyNa2bl8c+th4LSdbLd08Wdz2wgITaG+6+dhi3EWjIMVGH2ED57ZgHPrqtky/4Gq8PBGEOpwxUxVWqa+JVfeb2G95xu5o/PItYWnafXjXPH4PEanlm7L+DH+uVbTrYeaORXV01lZFrotWQYjDsXFzE8OZ57X91q+TSNFe4mqupaI6ZYITo/mSpgth5opLqpPSqHeXrkZ6SwYHwWz6zdF9DKFLvDxaMrd/PZefl8auKIgB3HKmlJcXzrggms21PHa5uP7wwTXHZHd3lpJIzvgyZ+5WelDhciMH98ZFwZDdTSknwON7bzToDmlXU1tvGtv22ieGQq37no9IAcIxRcMyuPSaOH8vM3ttPaYV0TPLuzu0ptdHpkVKlp4ld+ZXe6mJKbTuaQBKtDsdS5xdnkpCcFZJIWr9fwzb9tornDw4PXTycxLvzHnE/G5uvjc6ChjT8ur7AkhiNtnazdXRtRzQY18Su/qWlqZ1NVPYsi6AMyULYY4Ya5Y/iwooZy1xG/7vsv7+/i/Z3V/PCSSRSFaT/4/pgzdjiXTB3Nn5dXUFXXEvTjf1BejSfCqtQ08Su/Wb7DjTHR9bTuqVw7O494WwzLVvvvJu+mynp+9U8nF50xkutm5/ltv6HuOxcWIwI/fyP40zTaHW5SE2OZMSY96McOFE38ym/sTjeZQxKYPDrN6lBCQuaQBC46YyR//6iK5nbPoPd3pK2TO5/dwIihifz88ilh2ZJhoEanJ/HlBYW8/vFBVlV8otlvwBhjsDtdEVelFjk/ibKUp8vLih1uFk7ICrnp/ay0dF4+R9o9vLzxwKD39YOXt1JZ28ID100jLTk8WzIMxhcXjCMnPYl7X91KV5Cmadx6oBHXkfaIG77UxK/8YkNlPQ2tnRFT7uYvM8YM4/RRQ3li1Z5B1aK/uL6Klzbs52uLxzO7IPyfHB2IxDgb373odByHjgTlGQn4/0nVF0RAf55jaeJXfmF3uLDFCGcXZVodSkgREZaW5OM4dIT1++oGtI891c18/x9bmDN2OF9dVNj7BhHsojNGMnfscH7ztpOGlsC3vy51uJiamxZxVWqa+JVf2J1uZuUPIy0p+oYgenPptNGkJsQOqNtkh8fLnc9uINYWwwMR1JJhoES6yzsbWju5/50dAT1WbXMHGyrrI7LZoCZ+NWgHG1rZfrAxIj8g/pCSEMuVM3N54+ODVPez2+Rv3nayuaqB+66cEjEPDw3WxNFDuX7OGJ5cvZcdh/1bKnus93f6qtQicPhSE78atPeckfU4eyDcVJJPZ5fhuXWVfd5mxQ43f16xixvnjmHJ5JEBjC78fPP8CaTE2/jxq9sC1sen1OEic0g8Z+REXpWaJn41aHaHi9FpiYwfET7zuwZbYfYQzjwtg6fX7OtTRUp1Uzt3Pb+J8SOG8P2LJwYhwvAyPCWeu84bz8ryav4VgLYYXV7D8h1uFozPjsgqNU38alDaPV2sLK/m3OLsqKorH4ilJfnsr289Oi3lyXi9hm8+v4kjbZ08eP2MiG7JMBg3luRTlD2En76+nbZO//bx2VhZT31LZ8Q+jKiJXw3Kut11tHR06TBPH3xq4ghGDE3giV769zz2wW6W73DzvYsnMmFk5LdkGKg4Www/vGQS+2pbeHTlbr/uu6dK7ZwiTfxKfYLd6SI+NoYzCzOsDiXkxdliuGFOPit2uNlT3XzCdbbsb+C+txycP3EEN80dE+QIw8/ZRZmcP3EEf7CX+3XiG7vTxcwIrlLTxK8Gxe5wUTIug+T4WKtDCQvXzckjNkZ4as0nr/qb2z3c8cwGMlISuO/K6GrJMBj/9enT8XQZ7nvTP318Dje2sfVAY0T/FauJXw3YnupmdlU3c26EPdUYSCOGJnLBpJE8X1b1iXHpH76ylT01zTxw3TSGpcRbFGH4yc9I4bZzxvLihv0DfkjuWD1P60bq+D5o4leDcPQDEsFXRoFwU0k+Da2dvLrp//v3vLxxPy98VMUd5xZSMk6HzfrrP84tJDs1gXtf2Yp3kH187A43o9MSmRDBLa97Tfwikigia0Vkk4hsFZF7fcufEhGniGwRkcdEJM63XETk9yJSLiKbRWRGoH8IZY1Sp5txmSkUZKZYHUpYKRk3nMLsISzz3eTdV9PC917awsz8Ydy5uMji6MJTSkIs91xYzKaqBv6+vmrA++nweFlZXs3CCK9S68sVfzuwyBgzFZgGLBGREuApoBg4A0gCbvOtfyFQ5Pu6Hfijn2NWIaClw8PqXTURNStRsPT079lU1cBHe2u589kNIPC766ZFVOvfYLtsWg7Tx6Rz31tOjrQNrI9P2Z5amto9Ef9XbK9nmenW5Ps2zvdljDFv+N4zwFog17fOpcATvrdWA+kiMioQwSvrrKqoocPjjehx0EC6YkYOyfE2bnu8jI2V9fziiinkDku2OqywFhMj/OiSSVQ3tfOQvXxA+7A7XcTbYjgrwqvU+nR5ISI2EdkIuIB/GWPWHPNeHLAUeMu3KAc49rn0Kt+yiHOgvpUvPlnGb952Buyx8VBV6nCRHG9jztjobBE8WKmJcVw+PYe6lk6um53Hp6fotZE/TM1L56qZuTy2cje7T1Iyeyp2p5u544ZHfJVanxK/MabLGDON7qv6OSIy+Zi3/wdYYYx5vz8HFpHbRaRMRMrcbnd/NrWcMYYX11dxwQMreGe7iwdLy7nvrehJ/sYY3nO6Oaswk4RYfap0oO5cXMSdi4v4wSXaksGfvr1kAgmxNn762rZ+bVdZ20K5qynih3mgn1U9xph6wA4sARCRHwJZwF3HrLYfOHYy0FzfsuP39bAxZpYxZlZWVvgMF9Q2d/CVp9Zz1/ObKB6ZSuk3F3Dj3DH8aXkFv3t3p9XhBcVOVxP761uj4gMSSCOGJnLXeeMj/uoy2LJTE7ljUSHvOlxHK8/6wu5bN5ImVT+ZvlT1ZIlIuu91EnAe4BCR24ALgOuNMd5jNnkFuNlX3VMCNBhjDvo/9OB7d/thzr9/Be9ud3HPhcU8e/s88jNS+Mmlk7lqZi4PvLOT/3lvYGOL4aTUEfl1ziq83XJWAQUZyfzktW10dnl734DuhxHHRkmVWl8uNUYBj4uIje5fFM8bY14TEQ+wF1jlK3t60RjzY+AN4CKgHGgBPheQyIOoqd3DT1/bxrPrKikemcqTt87h9FFDj74fEyPcd+UUOjxefvmWk4RYG7eePdbCiAPL7nBRPDKVUWnaH16FpoRYG9+/eCK3Pl7G4x/u4bZzxp1y/daOLj6sqOGGKGmT0WviN8ZsBqafYPkJt/VV+fzH4EMLDWt31/LNv21kf10rX1l4Gl/7VNEJx7VtMcJvr5lKh8fLT17bRkJsDDeV5FsQcWA1tnVStreO2+ef+oOklNUWFWezYHwWv3t3J5dNzznl9Imrd9XQ7vFGxTAP6JO7J9Xu6eLnb2zn2odXIQjPf3Ee315SfMqbmbG2GH5//XQWFWfzvX9s4fmyvk+6ES7e31FNl9dEzQdEhS8R4fsXT6S1o4vfvO085bp2p4ukuOipUtPEfwJbDzTwmQc/4M8rdnH9nDG8+bVzmFXQtxMiPjaG/7lxBucUZXL33zfz8sZP3NcOa3ani6GJsUzPS7c6FKV6VZg9hM+eWcCz6yrZsr/hhOsYYyh1uKKqSk0T/zE8XV7+YC/nsj98QG1LB3/93Gz++/IzSEnoX9VFYpyNh5fOYk7BcO56fhNvfhwR97bxervLOOePz9InTFXYuHNxEcOT47n31a0nLLmucDdRVdcaVX/F6qfXZ091M9f8eRW/+qeT8yeO5O2vzx9UuWJSvI3HbpnN1Nw07nx2A+9u9//0cMG25UAD1U3tUfUBUeEvLSmOb10wgXV76nh18ycvwuyO7ueIFkZRl9moT/zGGJat3suFv3ufclcTv7tuGg/dMN0vbXFTEmL53893VwB9edl6VuwIrwfVjmd3uBGB+eOj5wOiIsM1s/KYNHooP39jO60d/94O2+7srlIbnR49VWpRnfgPN7Zxy1/X8b1/bGFWwTDe/sYCLp2W49eufEMT43ji83MYl5XC7U+Wsaqixm/7Dja708WU3PRTVkcoFYpsMcKPPjOJgw1t/HF5xdHlR9o6Wbu7lnOj7K/YqE38r246wPn3r2DN7hp+cukknvj8HEamJQbkWOnJ8Tx121zyhiVz6+Pr+GhvbUCOE0g1Te1sqqpnkT6tq8LU7ILhXDJ1NH9eXkFVXQsAH5RX4/GaqHsKPeoSf31LB3c+s4E7ntnA2MwU3rjzHJbOKwh47+2MIQk8ddtcslMTuOWxdWyuqg/o8fxt+Q43xujTuiq8fefCYkTg5290T9Nod7gZmhjLjDHp1gYWZFGV+JfvcHPBAyt44+ODfOv88bzwpXmMyxoStONnD03k6S+UkJYcx9JH17LtQGPQjj1YdqebzCEJTB6dZnUoSg3Y6PQkvrKwkNc/PsiHFdXYna6orFKLip+2pcPD9/7xMZ99bC1DE+P4x3+cxVcXFVnyP3t0ehLPfKGE5HgbNz26hp2HjwQ9hv7ydHlZscPNwglZxMRE7qxEKjrcPn8cOelJfO3ZjbiOtEfdMA9EQeL/aG8dF/3ufZ5as48vnDOWV+84m8k51l615g1P5ukvlGCLEW54ZM2A+oYH04bKehpaO6PyA6IiT2Kcjf/69Om4j7QjAguiqIyzR8Qm/g6Pl1/908HVf/qQzi7DM18o4b8+PZHEuNB4Mm9sZgpP3zaXLq/hhr+sprK2xeqQTsrucGGLEc4uyrQ6FKX84sLJIzmnKJOSsRlRWaUWkYnfeegIl/3hA/5gr+Cqmbm89fVzKBkXelOpFY1IZdmtc2np6OL6v6zmQH2r1SGdkN3pZlb+MNKS4qwORSm/EBEeu2U2j39+jtWhWCKiEn+X1/DwigoueXAlhxvbeHjpTH551VRSE0M3YU0cPZQnb51DQ0snN/xlNa7GNqtD+jcHG1rZfrAx6uqcVeSLs8UQHxtRKbDPIuanrqxt4fq/rOa/33CwcEIW//zGfM6fNNLqsPpkSm46//v5ObiOtHPDI2uobmq3OqSj3nN2P22s4/tKRY6wT/zGGJ5bt48lD6xg+4FGfn31VP68dGbYjdvNzB/GY7fMpqquhZseWUN9S4fVIQHd4/s56UmMHxG8slelVGCFdeJ3H2nnC0+UcfffP2ZKbjpvfv0crpqZG/CHsQKlZFwGf7l5Fruqm1n66Foa2zotjafd08XK8moWTsgK239TpdQnhW3if2vLQS54YAUrdlbz/Ysn8tRtc8kdlmx1WIN2TlEWf7xxBo5Djdzy2Fqa2j2WxbJudx0tHV06zKNUhAm7xN/Y1sldz2/kS8vWk5OexOt3nM2tZ4+NqAeLFp8+ggevn86mqgY+/7/rPtFNMFjsThfxsTGcWRh6FVFKqYELq8T/QXk1S+5fwcsbD3Dn4iJe/MqZFI1ItTqsgFgyeRS/vWYq6/bUcvuTZbR1Bj/52x0uSsZlkBzfv4lolFKhLSwSf1tnF/e+upUbH1lDYpyNv3/5TO46bzxxEd5f49JpOfzyyim8v7Oarzy1ng6PN2jH3lPdzK7qZs6NwqcalYp0IX8pt7mqnm88t5EKdzO3nFnA3UuKSYoPjadvg+HqWXl0dHn5r5e2cMcz63nohhlB+YX3ntMFaBmnUpEoZBN/p2/+2wdLy8lOTWDZrXOjtmXAjXPzae/08uPXtnHX85t44Npp2AJ8T6PU6WZcZgoFmSkBPY5SKvhCMvGXu5q46/mNbK5q4PLpOfzoM5Oivl3A588eS7vHy31vOUiIjeGXV04J2A3tlg4Pq3fVcNPc/IDsXyllrV4Tv4gkAiuABN/6LxhjfigiY4FngQzgI2CpMaZDRBKAJ4CZQA1wrTFmT1+C8XoNj6/awy/edJAcb+N/bpzBRWeMGtAPFom+vPA02j1dPPDOTuJjY/jZZZMDUl+/qqKGDo9XJ1VXKkL15Yq/HVhkjGkSkThgpYi8CdwF3G+MeVZE/gTcCvzR9986Y0yhiFwH3Adc29tB9te38p9/28SHFTUsKs7mF1eeQXZqYKZCDGdfW1xEu8fLH9+rICE2hh9cPNHvyb/U4SI53sbsscP8ul+lVGjoNfEbYwzQ5Ps2zvdlgEXADb7ljwM/ojvxX+p7DfAC8JCIiG8/J1Tf0sGS+1fgNYZfXHEG187O0ydFT0JE+PYFE2jv9PLYB7tJiLVx95IJfvv3MsbwntPNWYWZJMRGz010paJJn8b4RcRG93BOIfAHoAKoN8b0PFZaBeT4XucAlQDGGI+INNA9HFR9sv1X1rXymVGp/ObqaYzJCP+nbwNNRPj+xafT7uniT8srSIyL4eufGu+Xfe90NbG/vpWvLir0y/6UUqGnT4nfGNMFTBORdOAloHiwBxaR24HbATJyxvLs7fMCXqkSSUSEn1w6mXaP9+iY/1cWDj5Zlzq6yzgXav2+UhGrX1U9xph6EbED84B0EYn1XfXnAvt9q+0H8oAqEYkF0ui+yXv8vh4GHgaYNWuW0aTffzExwn1XTqHD4+WXbzlJiLVx69ljB7VPu8NF8chURqUl+SlKpVSo6fVJIBHJ8l3pIyJJwHnAdsAOXOVb7bPAy77Xr/i+x/d+6anG99Xg2GKE314zlQsnj+Qnr21j2eq9A95XY1snZXvrtJpHqQjXlyv+UcDjvnH+GOB5Y8xrIrINeFZEfgpsAB71rf8o8KSIlAO1wHUBiFsdI9YWw++um07Hso/43j+2EB8bwzWz8vq9n/d3VNPlNTrbllIRri9VPZuB6SdYvgv4xISVxpg24Gq/RKf6LD42hj/cOMM3P8FmEmJjuHRaTu8bHsPudJGWFMf0vPTABKmUCgmR3eUsyiTG2Xh46SzmFAznruc38ebHB/u8rdfbXcY5f3wWsRHe/E6paKef8AiTFG/jsVtmMy0vnTue2cC72w/3abstBxqobmrXbpxKRQFN/BEoJSGWv35uNhNHD+XLy9azYoe7123sDjcisGC8Jn6lIp0m/gg1NDGOJz4/h3FZKdz+ZBmrKj5RUftv7E4XU3PTyQizSeqVUv2niT+CpSfH89Rtc8kblsytj6/jo721J1yvpqmdTVX12ntfqSihiT/CZQxJ4Knb5jJiaCK3PLaOzVX1n1hn+Q43xsC5xTrMo1Q00MQfBbKHJvLUbXNJS45j6aNr2Xag8d/etzvdZA5JYPLoNIsiVEoFkyb+KDE6PYlnvlBCcryNmx5dw87DRwDwdHlZ7nSxcEJWwCZ2UUqFFk38USRveDJPf6EEW4xwwyNr2OVuYkNlPY1tHh3fVyqKaOKPMmMzU3j6trl4vYYb/rKGZav3YosRzhkfnfMZKxWNNPFHoaIRqTx561xaO7t4eeMBZuUPY2hidM9prFQ00cQfpSaOHsqTt84hc0gCV87MtTocpVQQ9asfv4osU3LTWfvdxXpTV6koo1f8UU6TvlLRRxO/UkpFGU38SikVZTTxK6VUlNHEr5RSUUYTv1JKRRlN/EopFWU08SulVJQRY4zVMSAiRwCn1XEMQhrQYHUQg6DxWycTqLY6iEEI5397CP/4i4wx/e6nHipP7jqNMbOsDmKgRORhY8ztVscxUBq/dUSkTM9960RC/APZTod6/ONVqwMYJI1fDVS4/9tHZfyhMtQT1lc9Sg2UnvvKCqFyxT+gP1eUigB67qugC4krfqWUUsETKlf8YUFEckXkZRHZKSK7ROQhEUkQkfNE5CMR+dj330VWx3oip4h/johs9H1tEpHLrY71RE4W/zHvjxGRJhH5lpVxRio9/63j73NfE38fiYgALwL/MMYUAUVAEvBLusvxLjHGnAF8FnjSskBPopf4twCzjDHTgCXAn0UkVCq+gF7j7/Fb4E0Lwot4ev5bJyDnvjEmqF9ALvAysBPYBTwEJAAZgB1oAh4Kdlx9iHsxsOK4ZUOBOmDIMcsEqAUSrI55gPGPBQ4DsVbH3J/4gcuAXwE/Ar5ldbwn+RnC8tzv5/mj53+QYx/IuR/UK/5efnO1Ad8HQvXP9EnAR8cuMMY0AnuAwmMWXwmsN8a0By+0Pjll/CIyV0S2Ah8DXzLGeIIf4imdKv5pwN3AvUGPqo/C/NwHPf+t5PdzP9hDPYuANmPMXwGMMV3AN4Cb6b7RvJLuD0FYEpFJwH3AF62Opb+MMWuMMZOA2cB3RCTR6pj64UfA/caYJqsDOYWIPvdBz3+L/IgBnPvBTvx9vWoIRduAmccuEJGhwEjAKSK5wEvAzcaYCgvi680p4+9ZZozZTveQw+SgRte7U8WfBvxSRPYAXwe+KyJfDXaAvQjncx/0/LeS3899vbnbd+8CySJyM4CI2IDf8P/jtK8D9xhjPrAuxFM6Vfwje25miUg+UEx3QgolJ43fGDPbGFNgjCkAHgD+2xjzkGWRRiY9/63j93M/2Im/T791Q5HpvqNyOXCViOwEagCvMeZnwFfpvmr7wTFlYdkWhvsJvcR/NrBJRDbSfdX2FWNMSDUO6yX+cBC25z7o+W+lgJz7Qb47LUAZ3X8OAtiAvwD/dcw6txCilQ3H/SxnAnuBGVbHovFbH08f4o2Ycz8c//0jKX5/xB70J3dFJA/4A3A6kAU8Z4z5ou+9PXSXKcUD9cD5xphtQQ1QqQDRc1+FCktbNojImcAzwOXGmPWWBaJUkOm5r6ykvXqUUirKaFWPUkpFmYAkfhHJExG7iGwTka0i8jXf8uEi8i9fo6F/icgw3/JiEVklIu3HNxkSkXQReUFEHCKyXUTmBSJmpfzBX+e+iEw4pkJmo4g0isjXLfqxVIQJyFCPiIwCRhlj1otIKt0PrlxGd9VCrTHmFyJyDzDMGHO3r/Qr37dOnTHm18fs63HgfWPMIyISDyQbY+r9HrRSfuDPc/+YfdqA/cBcY8ze4PwkKpIF5IrfGHOw54aVMeYIsB3IAS4FHvet9jjdJzvGGJcxZh3Qeex+RCQNmA886luvQ5O+CmX+OvePsxio0KSv/CXgY/wiUgBMB9YAI4wxB31vHQJG9LL5WMAN/FVENojIIyKSErBglfKjQZ77x7qO7gogpfwioIlfRIYAfwe+brr7khxluseYehtnigVmAH80xkwHmoF7AhGrUv7kh3O/Zz/xwGeAv/k9SBW1Apb4RSSO7hP/KWPMi77Fh31joD1joa5edlMFVBlj1vi+f4HuXwRKhSw/nfs9LqS7zfFh/0eqolWgqnqE7nH57caY3x7z1it0z9CD778vn2o/xphDQKWITPAtWkx3zxOlQpK/zv1jXI8O8yg/C1RVz9nA+3RPauD1Lf4u3WOdzwNj6O41cY0xplZERtLdx2Sob/0mYKIxplFEpgGP0P0o+y7gc8aYOr8HrZQf+PncTwH2AeOMMQ3B/UlUJNMnd5VSKsrok7tKKRVlNPErpVSU0cSvlFJRRhO/UkpFGU38SikVZTTxK6VUlNHEr5RSUUYTv1JKRZn/Az/HWLEmqwY8AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "quarterly_revenue.plot(kind=\"line\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can convert periods to timestamps by calling `to_timestamp`. By default, it will give us the first day of each period, but by setting `how` and `freq`, we can get the last hour of each period:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016-03-31 23:59:59.999999999    300\n",
+       "2016-06-30 23:59:59.999999999    320\n",
+       "2016-09-30 23:59:59.999999999    290\n",
+       "2016-12-31 23:59:59.999999999    390\n",
+       "2017-03-31 23:59:59.999999999    320\n",
+       "2017-06-30 23:59:59.999999999    360\n",
+       "2017-09-30 23:59:59.999999999    310\n",
+       "2017-12-31 23:59:59.999999999    410\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "last_hours = quarterly_revenue.to_timestamp(how=\"end\", freq=\"H\")\n",
+    "last_hours"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And back to periods by calling `to_period`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016Q1    300\n",
+       "2016Q2    320\n",
+       "2016Q3    290\n",
+       "2016Q4    390\n",
+       "2017Q1    320\n",
+       "2017Q2    360\n",
+       "2017Q3    310\n",
+       "2017Q4    410\n",
+       "Freq: Q-DEC, dtype: int64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "last_hours.to_period()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas also provides many other time-related functions that we recommend you check out in the [documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html). To whet your appetite, here is one way to get the last business day of each month in 2016, at 9am:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PeriodIndex(['2016-01-29 09:00', '2016-02-29 09:00', '2016-03-31 09:00',\n",
+       "             '2016-04-29 09:00', '2016-05-31 09:00', '2016-06-30 09:00',\n",
+       "             '2016-07-29 09:00', '2016-08-31 09:00', '2016-09-30 09:00',\n",
+       "             '2016-10-31 09:00', '2016-11-30 09:00', '2016-12-30 09:00'],\n",
+       "            dtype='period[H]', freq='H')"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "months_2016 = pd.period_range(\"2016\", periods=12, freq=\"M\")\n",
+    "one_day_after_last_days = months_2016.asfreq(\"D\") + 1\n",
+    "last_bdays = one_day_after_last_days.to_timestamp() - pd.tseries.offsets.BDay()\n",
+    "last_bdays.to_period(\"H\") + 9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# `DataFrame` objects\n",
+    "A DataFrame object represents a spreadsheet, with cell values, column names and row index labels. You can define expressions to compute columns based on other columns, create pivot-tables, group rows, draw graphs, etc. You can see `DataFrame`s as dictionaries of `Series`.\n",
+    "\n",
+    "## Creating a `DataFrame`\n",
+    "You can create a DataFrame by passing a dictionary of `Series` objects:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>weight</th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>children</th>\n",
+       "      <th>hobby</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>68</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Biking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>83</td>\n",
+       "      <td>1984</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Dancing</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>112</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         weight  birthyear  children    hobby\n",
+       "alice        68       1985       NaN   Biking\n",
+       "bob          83       1984       3.0  Dancing\n",
+       "charles     112       1992       0.0      NaN"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people_dict = {\n",
+    "    \"weight\": pd.Series([68, 83, 112], index=[\"alice\", \"bob\", \"charles\"]),\n",
+    "    \"birthyear\": pd.Series([1984, 1985, 1992], index=[\"bob\", \"alice\", \"charles\"], name=\"year\"),\n",
+    "    \"children\": pd.Series([0, 3], index=[\"charles\", \"bob\"]),\n",
+    "    \"hobby\": pd.Series([\"Biking\", \"Dancing\"], index=[\"alice\", \"bob\"]),\n",
+    "}\n",
+    "people = pd.DataFrame(people_dict)\n",
+    "people"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A few things to note:\n",
+    "* the `Series` were automatically aligned based on their index,\n",
+    "* missing values are represented as `NaN`,\n",
+    "* `Series` names are ignored (the name `\"year\"` was dropped),\n",
+    "* `DataFrame`s are displayed nicely in Jupyter notebooks, woohoo!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can access columns pretty much as you would expect. They are returned as `Series` objects:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "alice      1985\n",
+       "bob        1984\n",
+       "charles    1992\n",
+       "Name: birthyear, dtype: int64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"birthyear\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also get multiple columns at once:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>hobby</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>1985</td>\n",
+       "      <td>Biking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>1984</td>\n",
+       "      <td>Dancing</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         birthyear    hobby\n",
+       "alice         1985   Biking\n",
+       "bob           1984  Dancing\n",
+       "charles       1992      NaN"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[[\"birthyear\", \"hobby\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you pass a list of columns and/or index row labels to the `DataFrame` constructor, it will guarantee that these columns and/or rows will exist, in that order, and no other column/row will exist. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>height</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>1984.0</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>1985.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>eugene</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        birthyear  weight height\n",
+       "bob        1984.0    83.0    NaN\n",
+       "alice      1985.0    68.0    NaN\n",
+       "eugene        NaN     NaN    NaN"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d2 = pd.DataFrame(\n",
+    "        people_dict,\n",
+    "        columns=[\"birthyear\", \"weight\", \"height\"],\n",
+    "        index=[\"bob\", \"alice\", \"eugene\"]\n",
+    "     )\n",
+    "d2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another convenient way to create a `DataFrame` is to pass all the values to the constructor as an `ndarray`, or a list of lists, and specify the column names and row index labels separately:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>children</th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>weight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>1985</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Biking</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>1984</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         birthyear  children    hobby  weight\n",
+       "alice         1985       NaN   Biking      68\n",
+       "bob           1984       3.0  Dancing      83\n",
+       "charles       1992       0.0      NaN     112"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "values = [\n",
+    "            [1985, np.nan, \"Biking\",   68],\n",
+    "            [1984, 3,      \"Dancing\",  83],\n",
+    "            [1992, 0,      np.nan,    112]\n",
+    "         ]\n",
+    "d3 = pd.DataFrame(\n",
+    "        values,\n",
+    "        columns=[\"birthyear\", \"children\", \"hobby\", \"weight\"],\n",
+    "        index=[\"alice\", \"bob\", \"charles\"]\n",
+    "     )\n",
+    "d3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To specify missing values, you can use either `np.nan` or NumPy's masked arrays:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>children</th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>weight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>1985</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Biking</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>1984</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        birthyear children    hobby weight\n",
+       "alice        1985      NaN   Biking     68\n",
+       "bob          1984        3  Dancing     83\n",
+       "charles      1992        0      NaN    112"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "masked_array = np.ma.asarray(values, dtype=object)\n",
+    "masked_array[(0, 2), (1, 2)] = np.ma.masked\n",
+    "d3 = pd.DataFrame(\n",
+    "        masked_array,\n",
+    "        columns=[\"birthyear\", \"children\", \"hobby\", \"weight\"],\n",
+    "        index=[\"alice\", \"bob\", \"charles\"]\n",
+    "     )\n",
+    "d3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of an `ndarray`, you can also pass a `DataFrame` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>children</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>Biking</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hobby children\n",
+       "alice   Biking      NaN\n",
+       "bob    Dancing        3"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d4 = pd.DataFrame(\n",
+    "         d3,\n",
+    "         columns=[\"hobby\", \"children\"],\n",
+    "         index=[\"alice\", \"bob\"]\n",
+    "     )\n",
+    "d4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also possible to create a `DataFrame` with a dictionary (or list) of dictionaries (or lists):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>children</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>1985</td>\n",
+       "      <td>Biking</td>\n",
+       "      <td>68</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>1984</td>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>83</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         birthyear    hobby  weight  children\n",
+       "alice         1985   Biking      68       NaN\n",
+       "bob           1984  Dancing      83       3.0\n",
+       "charles       1992      NaN     112       0.0"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people = pd.DataFrame({\n",
+    "    \"birthyear\": {\"alice\": 1985, \"bob\": 1984, \"charles\": 1992},\n",
+    "    \"hobby\": {\"alice\": \"Biking\", \"bob\": \"Dancing\"},\n",
+    "    \"weight\": {\"alice\": 68, \"bob\": 83, \"charles\": 112},\n",
+    "    \"children\": {\"bob\": 3, \"charles\": 0}\n",
+    "})\n",
+    "people"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multi-indexing\n",
+    "If all columns are tuples of the same size, then they are understood as a multi-index. The same goes for row index labels. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">public</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">private</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>children</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
+       "      <th>alice</th>\n",
+       "      <td>1985</td>\n",
+       "      <td>Biking</td>\n",
+       "      <td>68</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>1984</td>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>83</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>London</th>\n",
+       "      <th>charles</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  public          private         \n",
+       "               birthyear    hobby  weight children\n",
+       "Paris  alice        1985   Biking      68      NaN\n",
+       "       bob          1984  Dancing      83      3.0\n",
+       "London charles      1992      NaN     112      0.0"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d5 = pd.DataFrame(\n",
+    "  {\n",
+    "    (\"public\", \"birthyear\"):\n",
+    "        {(\"Paris\",\"alice\"): 1985, (\"Paris\",\"bob\"): 1984, (\"London\",\"charles\"): 1992},\n",
+    "    (\"public\", \"hobby\"):\n",
+    "        {(\"Paris\",\"alice\"): \"Biking\", (\"Paris\",\"bob\"): \"Dancing\"},\n",
+    "    (\"private\", \"weight\"):\n",
+    "        {(\"Paris\",\"alice\"): 68, (\"Paris\",\"bob\"): 83, (\"London\",\"charles\"): 112},\n",
+    "    (\"private\", \"children\"):\n",
+    "        {(\"Paris\", \"alice\"): np.nan, (\"Paris\",\"bob\"): 3, (\"London\",\"charles\"): 0}\n",
+    "  }\n",
+    ")\n",
+    "d5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can now get a `DataFrame` containing all the `\"public\"` columns very simply:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>hobby</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
+       "      <th>alice</th>\n",
+       "      <td>1985</td>\n",
+       "      <td>Biking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>1984</td>\n",
+       "      <td>Dancing</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>London</th>\n",
+       "      <th>charles</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                birthyear    hobby\n",
+       "Paris  alice         1985   Biking\n",
+       "       bob           1984  Dancing\n",
+       "London charles       1992      NaN"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d5[\"public\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Paris   alice       Biking\n",
+       "        bob        Dancing\n",
+       "London  charles        NaN\n",
+       "Name: (public, hobby), dtype: object"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d5[\"public\", \"hobby\"]  # Same result as d5[\"public\"][\"hobby\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dropping a level\n",
+    "Let's look at `d5` again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">public</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">private</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>children</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
+       "      <th>alice</th>\n",
+       "      <td>1985</td>\n",
+       "      <td>Biking</td>\n",
+       "      <td>68</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>1984</td>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>83</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>London</th>\n",
+       "      <th>charles</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  public          private         \n",
+       "               birthyear    hobby  weight children\n",
+       "Paris  alice        1985   Biking      68      NaN\n",
+       "       bob          1984  Dancing      83      3.0\n",
+       "London charles      1992      NaN     112      0.0"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are two levels of columns, and two levels of indices. We can drop a column level by calling `droplevel()` (the same goes for indices):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>children</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
+       "      <th>alice</th>\n",
+       "      <td>1985</td>\n",
+       "      <td>Biking</td>\n",
+       "      <td>68</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>1984</td>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>83</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>London</th>\n",
+       "      <th>charles</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                birthyear    hobby  weight  children\n",
+       "Paris  alice         1985   Biking      68       NaN\n",
+       "       bob           1984  Dancing      83       3.0\n",
+       "London charles       1992      NaN     112       0.0"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d5.columns = d5.columns.droplevel(level = 0)\n",
+    "d5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transposing\n",
+    "You can swap columns and indices using the `T` attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Paris</th>\n",
+       "      <th>London</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>alice</th>\n",
+       "      <th>bob</th>\n",
+       "      <th>charles</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>birthyear</th>\n",
+       "      <td>1985</td>\n",
+       "      <td>1984</td>\n",
+       "      <td>1992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hobby</th>\n",
+       "      <td>Biking</td>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>weight</th>\n",
+       "      <td>68</td>\n",
+       "      <td>83</td>\n",
+       "      <td>112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>children</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Paris           London\n",
+       "            alice      bob charles\n",
+       "birthyear    1985     1984    1992\n",
+       "hobby      Biking  Dancing     NaN\n",
+       "weight         68       83     112\n",
+       "children      NaN      3.0     0.0"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d6 = d5.T\n",
+    "d6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stacking and unstacking levels\n",
+    "Calling the `stack()` method will push the lowest column level after the lowest index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>London</th>\n",
+       "      <th>Paris</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">birthyear</th>\n",
+       "      <th>alice</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1984</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">hobby</th>\n",
+       "      <th>alice</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Biking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Dancing</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">weight</th>\n",
+       "      <th>alice</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>112</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">children</th>\n",
+       "      <th>bob</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  London    Paris\n",
+       "birthyear alice      NaN     1985\n",
+       "          bob        NaN     1984\n",
+       "          charles   1992      NaN\n",
+       "hobby     alice      NaN   Biking\n",
+       "          bob        NaN  Dancing\n",
+       "weight    alice      NaN       68\n",
+       "          bob        NaN       83\n",
+       "          charles    112      NaN\n",
+       "children  bob        NaN      3.0\n",
+       "          charles    0.0      NaN"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d7 = d6.stack()\n",
+    "d7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that many `NaN` values appeared. This makes sense because many new combinations did not exist before (e.g. there was no `bob` in `London`).\n",
+    "\n",
+    "Calling `unstack()` will do the reverse, once again creating many `NaN` values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">London</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">Paris</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>alice</th>\n",
+       "      <th>bob</th>\n",
+       "      <th>charles</th>\n",
+       "      <th>alice</th>\n",
+       "      <th>bob</th>\n",
+       "      <th>charles</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>birthyear</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>1984</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>children</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hobby</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Biking</td>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>weight</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112</td>\n",
+       "      <td>68</td>\n",
+       "      <td>83</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          London                Paris                 \n",
+       "           alice  bob charles   alice      bob charles\n",
+       "birthyear    NaN  NaN    1992    1985     1984     NaN\n",
+       "children     NaN  NaN     0.0     NaN      3.0     NaN\n",
+       "hobby        NaN  NaN     NaN  Biking  Dancing     NaN\n",
+       "weight       NaN  NaN     112      68       83     NaN"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d8 = d7.unstack()\n",
+    "d8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we call `unstack` again, we end up with a `Series` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "London  alice    birthyear        NaN\n",
+       "                 children         NaN\n",
+       "                 hobby            NaN\n",
+       "                 weight           NaN\n",
+       "        bob      birthyear        NaN\n",
+       "                 children         NaN\n",
+       "                 hobby            NaN\n",
+       "                 weight           NaN\n",
+       "        charles  birthyear       1992\n",
+       "                 children         0.0\n",
+       "                 hobby            NaN\n",
+       "                 weight           112\n",
+       "Paris   alice    birthyear       1985\n",
+       "                 children         NaN\n",
+       "                 hobby         Biking\n",
+       "                 weight            68\n",
+       "        bob      birthyear       1984\n",
+       "                 children         3.0\n",
+       "                 hobby        Dancing\n",
+       "                 weight            83\n",
+       "        charles  birthyear        NaN\n",
+       "                 children         NaN\n",
+       "                 hobby            NaN\n",
+       "                 weight           NaN\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d9 = d8.unstack()\n",
+    "d9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `stack()` and `unstack()` methods let you select the `level` to stack/unstack. You can even stack/unstack multiple levels at once:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">London</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">Paris</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>alice</th>\n",
+       "      <th>bob</th>\n",
+       "      <th>charles</th>\n",
+       "      <th>alice</th>\n",
+       "      <th>bob</th>\n",
+       "      <th>charles</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>birthyear</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>1984</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>children</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hobby</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Biking</td>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>weight</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112</td>\n",
+       "      <td>68</td>\n",
+       "      <td>83</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          London                Paris                 \n",
+       "           alice  bob charles   alice      bob charles\n",
+       "birthyear    NaN  NaN    1992    1985     1984     NaN\n",
+       "children     NaN  NaN     0.0     NaN      3.0     NaN\n",
+       "hobby        NaN  NaN     NaN  Biking  Dancing     NaN\n",
+       "weight       NaN  NaN     112      68       83     NaN"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d10 = d9.unstack(level = (0,1))\n",
+    "d10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Most methods return modified copies\n",
+    "As you may have noticed, the `stack()` and `unstack()` methods do not modify the object they are called on. Instead, they work on a copy and return that copy. This is true of most methods in pandas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accessing rows\n",
+    "Let's go back to the `people` `DataFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>children</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>1985</td>\n",
+       "      <td>Biking</td>\n",
+       "      <td>68</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>1984</td>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>83</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         birthyear    hobby  weight  children\n",
+       "alice         1985   Biking      68       NaN\n",
+       "bob           1984  Dancing      83       3.0\n",
+       "charles       1992      NaN     112       0.0"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `loc` attribute lets you access rows instead of columns. The result is a `Series` object in which the `DataFrame`'s column names are mapped to row index labels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "birthyear    1992\n",
+       "hobby         NaN\n",
+       "weight        112\n",
+       "children      0.0\n",
+       "Name: charles, dtype: object"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.loc[\"charles\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also access rows by integer location using the `iloc` attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "birthyear    1992\n",
+       "hobby         NaN\n",
+       "weight        112\n",
+       "children      0.0\n",
+       "Name: charles, dtype: object"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.iloc[2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also get a slice of rows, and this returns a `DataFrame` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>children</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>1984</td>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>83</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         birthyear    hobby  weight  children\n",
+       "bob           1984  Dancing      83       3.0\n",
+       "charles       1992      NaN     112       0.0"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.iloc[1:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, you can pass a boolean array to get the matching rows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>children</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>1985</td>\n",
+       "      <td>Biking</td>\n",
+       "      <td>68</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         birthyear   hobby  weight  children\n",
+       "alice         1985  Biking      68       NaN\n",
+       "charles       1992     NaN     112       0.0"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[np.array([True, False, True])]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is most useful when combined with boolean expressions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>children</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>1985</td>\n",
+       "      <td>Biking</td>\n",
+       "      <td>68</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>1984</td>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>83</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       birthyear    hobby  weight  children\n",
+       "alice       1985   Biking      68       NaN\n",
+       "bob         1984  Dancing      83       3.0"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[people[\"birthyear\"] < 1990]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding and removing columns\n",
+    "You can generally treat `DataFrame` objects like dictionaries of `Series`, so the following works fine:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>children</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>1985</td>\n",
+       "      <td>Biking</td>\n",
+       "      <td>68</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>1984</td>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>83</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         birthyear    hobby  weight  children\n",
+       "alice         1985   Biking      68       NaN\n",
+       "bob           1984  Dancing      83       3.0\n",
+       "charles       1992      NaN     112       0.0"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>age</th>\n",
+       "      <th>over 30</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>Biking</td>\n",
+       "      <td>68</td>\n",
+       "      <td>33</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>83</td>\n",
+       "      <td>34</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112</td>\n",
+       "      <td>26</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           hobby  weight  age  over 30\n",
+       "alice     Biking      68   33     True\n",
+       "bob      Dancing      83   34     True\n",
+       "charles      NaN     112   26    False"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"age\"] = 2018 - people[\"birthyear\"]  # adds a new column \"age\"\n",
+    "people[\"over 30\"] = people[\"age\"] > 30      # adds another column \"over 30\"\n",
+    "birthyears = people.pop(\"birthyear\")\n",
+    "del people[\"children\"]\n",
+    "\n",
+    "people"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "alice      1985\n",
+       "bob        1984\n",
+       "charles    1992\n",
+       "Name: birthyear, dtype: int64"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "birthyears"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you add a new column, it must have the same number of rows. Missing rows are filled with NaN, and extra rows are ignored:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>age</th>\n",
+       "      <th>over 30</th>\n",
+       "      <th>pets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>Biking</td>\n",
+       "      <td>68</td>\n",
+       "      <td>33</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>83</td>\n",
+       "      <td>34</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112</td>\n",
+       "      <td>26</td>\n",
+       "      <td>False</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           hobby  weight  age  over 30  pets\n",
+       "alice     Biking      68   33     True   NaN\n",
+       "bob      Dancing      83   34     True   0.0\n",
+       "charles      NaN     112   26    False   5.0"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"pets\"] = pd.Series({\"bob\": 0, \"charles\": 5, \"eugene\": 1})  # alice is missing, eugene is ignored\n",
+    "people"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When adding a new column, it is added at the end (on the right) by default. You can also insert a column anywhere else using the `insert()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>height</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>age</th>\n",
+       "      <th>over 30</th>\n",
+       "      <th>pets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>Biking</td>\n",
+       "      <td>172</td>\n",
+       "      <td>68</td>\n",
+       "      <td>33</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>181</td>\n",
+       "      <td>83</td>\n",
+       "      <td>34</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>185</td>\n",
+       "      <td>112</td>\n",
+       "      <td>26</td>\n",
+       "      <td>False</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           hobby  height  weight  age  over 30  pets\n",
+       "alice     Biking     172      68   33     True   NaN\n",
+       "bob      Dancing     181      83   34     True   0.0\n",
+       "charles      NaN     185     112   26    False   5.0"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.insert(1, \"height\", [172, 181, 185])\n",
+    "people"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assigning new columns\n",
+    "You can also create new columns by calling the `assign()` method. Note that this returns a new `DataFrame` object, the original is not modified:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>height</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>age</th>\n",
+       "      <th>over 30</th>\n",
+       "      <th>pets</th>\n",
+       "      <th>body_mass_index</th>\n",
+       "      <th>has_pets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>Biking</td>\n",
+       "      <td>172</td>\n",
+       "      <td>68</td>\n",
+       "      <td>33</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.985398</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>181</td>\n",
+       "      <td>83</td>\n",
+       "      <td>34</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.335002</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>185</td>\n",
+       "      <td>112</td>\n",
+       "      <td>26</td>\n",
+       "      <td>False</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>32.724617</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
+       "alice     Biking     172      68   33     True   NaN        22.985398   \n",
+       "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
+       "charles      NaN     185     112   26    False   5.0        32.724617   \n",
+       "\n",
+       "         has_pets  \n",
+       "alice       False  \n",
+       "bob         False  \n",
+       "charles      True  "
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.assign(\n",
+    "    body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2,\n",
+    "    has_pets = people[\"pets\"] > 0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that you cannot access columns created within the same assignment:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Key error: 'body_mass_index'\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    people.assign(\n",
+    "        body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2,\n",
+    "        overweight = people[\"body_mass_index\"] > 25\n",
+    "    )\n",
+    "except KeyError as e:\n",
+    "    print(\"Key error:\", e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The solution is to split this assignment in two consecutive assignments:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>height</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>age</th>\n",
+       "      <th>over 30</th>\n",
+       "      <th>pets</th>\n",
+       "      <th>body_mass_index</th>\n",
+       "      <th>overweight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>Biking</td>\n",
+       "      <td>172</td>\n",
+       "      <td>68</td>\n",
+       "      <td>33</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.985398</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>181</td>\n",
+       "      <td>83</td>\n",
+       "      <td>34</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.335002</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>185</td>\n",
+       "      <td>112</td>\n",
+       "      <td>26</td>\n",
+       "      <td>False</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>32.724617</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
+       "alice     Biking     172      68   33     True   NaN        22.985398   \n",
+       "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
+       "charles      NaN     185     112   26    False   5.0        32.724617   \n",
+       "\n",
+       "         overweight  \n",
+       "alice         False  \n",
+       "bob            True  \n",
+       "charles        True  "
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d6 = people.assign(body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2)\n",
+    "d6.assign(overweight = d6[\"body_mass_index\"] > 25)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Having to create a temporary variable `d6` is not very convenient. You may want to just chain the assignment calls, but it does not work because the `people` object is not actually modified by the first assignment:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Key error: 'body_mass_index'\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    (people\n",
+    "         .assign(body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2)\n",
+    "         .assign(overweight = people[\"body_mass_index\"] > 25)\n",
+    "    )\n",
+    "except KeyError as e:\n",
+    "    print(\"Key error:\", e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But fear not, there is a simple solution. You can pass a function to the `assign()` method (typically a `lambda` function), and this function will be called with the `DataFrame` as a parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>height</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>age</th>\n",
+       "      <th>over 30</th>\n",
+       "      <th>pets</th>\n",
+       "      <th>body_mass_index</th>\n",
+       "      <th>overweight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>Biking</td>\n",
+       "      <td>172</td>\n",
+       "      <td>68</td>\n",
+       "      <td>33</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.985398</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>181</td>\n",
+       "      <td>83</td>\n",
+       "      <td>34</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.335002</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>185</td>\n",
+       "      <td>112</td>\n",
+       "      <td>26</td>\n",
+       "      <td>False</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>32.724617</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
+       "alice     Biking     172      68   33     True   NaN        22.985398   \n",
+       "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
+       "charles      NaN     185     112   26    False   5.0        32.724617   \n",
+       "\n",
+       "         overweight  \n",
+       "alice         False  \n",
+       "bob            True  \n",
+       "charles        True  "
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(people\n",
+    "     .assign(body_mass_index = lambda df: df[\"weight\"] / (df[\"height\"] / 100) ** 2)\n",
+    "     .assign(overweight = lambda df: df[\"body_mass_index\"] > 25)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Problem solved!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluating an expression\n",
+    "A great feature supported by pandas is expression evaluation. It relies on the `numexpr` library which must be installed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "alice      False\n",
+       "bob         True\n",
+       "charles     True\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.eval(\"weight / (height/100) ** 2 > 25\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assignment expressions are also supported. Let's set `inplace=True` to directly modify the `DataFrame` rather than getting a modified copy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>height</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>age</th>\n",
+       "      <th>over 30</th>\n",
+       "      <th>pets</th>\n",
+       "      <th>body_mass_index</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>Biking</td>\n",
+       "      <td>172</td>\n",
+       "      <td>68</td>\n",
+       "      <td>33</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.985398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>181</td>\n",
+       "      <td>83</td>\n",
+       "      <td>34</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.335002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>185</td>\n",
+       "      <td>112</td>\n",
+       "      <td>26</td>\n",
+       "      <td>False</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>32.724617</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           hobby  height  weight  age  over 30  pets  body_mass_index\n",
+       "alice     Biking     172      68   33     True   NaN        22.985398\n",
+       "bob      Dancing     181      83   34     True   0.0        25.335002\n",
+       "charles      NaN     185     112   26    False   5.0        32.724617"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.eval(\"body_mass_index = weight / (height/100) ** 2\", inplace=True)\n",
+    "people"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use a local or global variable in an expression by prefixing it with `'@'`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>height</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>age</th>\n",
+       "      <th>over 30</th>\n",
+       "      <th>pets</th>\n",
+       "      <th>body_mass_index</th>\n",
+       "      <th>overweight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>Biking</td>\n",
+       "      <td>172</td>\n",
+       "      <td>68</td>\n",
+       "      <td>33</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.985398</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>181</td>\n",
+       "      <td>83</td>\n",
+       "      <td>34</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.335002</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>185</td>\n",
+       "      <td>112</td>\n",
+       "      <td>26</td>\n",
+       "      <td>False</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>32.724617</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
+       "alice     Biking     172      68   33     True   NaN        22.985398   \n",
+       "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
+       "charles      NaN     185     112   26    False   5.0        32.724617   \n",
+       "\n",
+       "         overweight  \n",
+       "alice         False  \n",
+       "bob           False  \n",
+       "charles        True  "
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "overweight_threshold = 30\n",
+    "people.eval(\"overweight = body_mass_index > @overweight_threshold\", inplace=True)\n",
+    "people"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Querying a `DataFrame`\n",
+    "The `query()` method lets you filter a `DataFrame` based on a query expression:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>height</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>age</th>\n",
+       "      <th>over 30</th>\n",
+       "      <th>pets</th>\n",
+       "      <th>body_mass_index</th>\n",
+       "      <th>overweight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>181</td>\n",
+       "      <td>83</td>\n",
+       "      <td>34</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.335002</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       hobby  height  weight  age  over 30  pets  body_mass_index  overweight\n",
+       "bob  Dancing     181      83   34     True   0.0        25.335002       False"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.query(\"age > 30 and pets == 0\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sorting a `DataFrame`\n",
+    "You can sort a `DataFrame` by calling its `sort_index` method. By default, it sorts the rows by their index label, in ascending order, but let's reverse the order:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>height</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>age</th>\n",
+       "      <th>over 30</th>\n",
+       "      <th>pets</th>\n",
+       "      <th>body_mass_index</th>\n",
+       "      <th>overweight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>185</td>\n",
+       "      <td>112</td>\n",
+       "      <td>26</td>\n",
+       "      <td>False</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>32.724617</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>181</td>\n",
+       "      <td>83</td>\n",
+       "      <td>34</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.335002</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>Biking</td>\n",
+       "      <td>172</td>\n",
+       "      <td>68</td>\n",
+       "      <td>33</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.985398</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
+       "charles      NaN     185     112   26    False   5.0        32.724617   \n",
+       "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
+       "alice     Biking     172      68   33     True   NaN        22.985398   \n",
+       "\n",
+       "         overweight  \n",
+       "charles        True  \n",
+       "bob           False  \n",
+       "alice         False  "
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.sort_index(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that `sort_index` returned a sorted *copy* of the `DataFrame`. To modify `people` directly, we can set the `inplace` argument to `True`. Also, we can sort the columns instead of the rows by setting `axis=1`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>body_mass_index</th>\n",
+       "      <th>height</th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>over 30</th>\n",
+       "      <th>overweight</th>\n",
+       "      <th>pets</th>\n",
+       "      <th>weight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>33</td>\n",
+       "      <td>22.985398</td>\n",
+       "      <td>172</td>\n",
+       "      <td>Biking</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>34</td>\n",
+       "      <td>25.335002</td>\n",
+       "      <td>181</td>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>26</td>\n",
+       "      <td>32.724617</td>\n",
+       "      <td>185</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>112</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         age  body_mass_index  height    hobby  over 30  overweight  pets  \\\n",
+       "alice     33        22.985398     172   Biking     True       False   NaN   \n",
+       "bob       34        25.335002     181  Dancing     True       False   0.0   \n",
+       "charles   26        32.724617     185      NaN    False        True   5.0   \n",
+       "\n",
+       "         weight  \n",
+       "alice        68  \n",
+       "bob          83  \n",
+       "charles     112  "
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.sort_index(axis=1, inplace=True)\n",
+    "people"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To sort the `DataFrame` by the values instead of the labels, we can use `sort_values` and specify the column to sort by:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>body_mass_index</th>\n",
+       "      <th>height</th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>over 30</th>\n",
+       "      <th>overweight</th>\n",
+       "      <th>pets</th>\n",
+       "      <th>weight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>26</td>\n",
+       "      <td>32.724617</td>\n",
+       "      <td>185</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>33</td>\n",
+       "      <td>22.985398</td>\n",
+       "      <td>172</td>\n",
+       "      <td>Biking</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>34</td>\n",
+       "      <td>25.335002</td>\n",
+       "      <td>181</td>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>83</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         age  body_mass_index  height    hobby  over 30  overweight  pets  \\\n",
+       "charles   26        32.724617     185      NaN    False        True   5.0   \n",
+       "alice     33        22.985398     172   Biking     True       False   NaN   \n",
+       "bob       34        25.335002     181  Dancing     True       False   0.0   \n",
+       "\n",
+       "         weight  \n",
+       "charles     112  \n",
+       "alice        68  \n",
+       "bob          83  "
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.sort_values(by=\"age\", inplace=True)\n",
+    "people"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting a `DataFrame`\n",
+    "Just like for `Series`, pandas makes it easy to draw nice graphs based on a `DataFrame`.\n",
+    "\n",
+    "For example, it is trivial to create a line plot from a `DataFrame`'s data by calling its `plot` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "people.sort_values(by=\"body_mass_index\", inplace=True)\n",
+    "people.plot(kind=\"line\", x=\"body_mass_index\", y=[\"height\", \"weight\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can pass extra arguments supported by matplotlib's functions. For example, we can create scatterplot and pass it a list of sizes using the `s` argument of matplotlib's `scatter()` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "people.plot(kind=\"scatter\", x=\"height\", y=\"weight\", s=[40, 120, 200])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, there are way too many options to list here: the best option is to scroll through the [Visualization](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html) page in pandas' documentation, find the plot you are interested in and look at the example code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Operations on `DataFrame`s\n",
+    "Although `DataFrame`s do not try to mimic NumPy arrays, there are a few similarities. Let's create a `DataFrame` to demonstrate this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         sep  oct  nov\n",
+       "alice      8    8    9\n",
+       "bob       10    9    9\n",
+       "charles    4    8    2\n",
+       "darwin     9   10   10"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grades_array = np.array([[8, 8, 9], [10, 9, 9], [4, 8, 2], [9, 10, 10]])\n",
+    "grades = pd.DataFrame(grades_array, columns=[\"sep\", \"oct\", \"nov\"], index=[\"alice\", \"bob\", \"charles\", \"darwin\"])\n",
+    "grades"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can apply NumPy mathematical functions on a `DataFrame`: the function is applied to all values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>2.828427</td>\n",
+       "      <td>2.828427</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>3.162278</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.828427</td>\n",
+       "      <td>1.414214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.162278</td>\n",
+       "      <td>3.162278</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              sep       oct       nov\n",
+       "alice    2.828427  2.828427  3.000000\n",
+       "bob      3.162278  3.000000  3.000000\n",
+       "charles  2.000000  2.828427  1.414214\n",
+       "darwin   3.000000  3.162278  3.162278"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sqrt(grades)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, adding a single value to a `DataFrame` will add that value to all elements in the `DataFrame`. This is called *broadcasting*:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>11</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>10</td>\n",
+       "      <td>11</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         sep  oct  nov\n",
+       "alice      9    9   10\n",
+       "bob       11   10   10\n",
+       "charles    5    9    3\n",
+       "darwin    10   11   11"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grades + 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, the same is true for all other binary operations, including arithmetic (`*`,`/`,`**`...) and conditional (`>`, `==`...) operations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           sep   oct    nov\n",
+       "alice     True  True   True\n",
+       "bob       True  True   True\n",
+       "charles  False  True  False\n",
+       "darwin    True  True   True"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grades >= 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Aggregation operations, such as computing the `max`, the `sum` or the `mean` of a `DataFrame`, apply to each column, and you get back a `Series` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sep    7.75\n",
+       "oct    8.75\n",
+       "nov    7.50\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grades.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `all` method is also an aggregation operation: it checks whether all values are `True` or not. Let's see during which months all students got a grade greater than `5`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sep    False\n",
+       "oct     True\n",
+       "nov    False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(grades > 5).all()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most of these functions take an optional `axis` parameter which lets you specify along which axis of the `DataFrame` you want the operation executed. The default is `axis=0`, meaning that the operation is executed vertically (on each column). You can set `axis=1` to execute the operation horizontally (on each row). For example, let's find out which students had all grades greater than `5`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "alice       True\n",
+       "bob         True\n",
+       "charles    False\n",
+       "darwin      True\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(grades > 5).all(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `any` method returns `True` if any value is True. Let's see who got at least one grade 10:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "alice      False\n",
+       "bob         True\n",
+       "charles    False\n",
+       "darwin      True\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(grades == 10).any(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you add a `Series` object to a `DataFrame` (or execute any other binary operation), pandas attempts to broadcast the operation to all *rows* in the `DataFrame`. This only works if the `Series` has the same size as the `DataFrame`s rows. For example, let's subtract the `mean` of the `DataFrame` (a `Series` object) from the `DataFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>0.25</td>\n",
+       "      <td>-0.75</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>2.25</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>-3.75</td>\n",
+       "      <td>-0.75</td>\n",
+       "      <td>-5.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>1.25</td>\n",
+       "      <td>1.25</td>\n",
+       "      <td>2.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          sep   oct  nov\n",
+       "alice    0.25 -0.75  1.5\n",
+       "bob      2.25  0.25  1.5\n",
+       "charles -3.75 -0.75 -5.5\n",
+       "darwin   1.25  1.25  2.5"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grades - grades.mean()  # equivalent to: grades - [7.75, 8.75, 7.50]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We subtracted `7.75` from all September grades, `8.75` from October grades and `7.50` from November grades. It is equivalent to subtracting this `DataFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>7.75</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>7.75</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>7.75</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>7.75</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          sep   oct  nov\n",
+       "alice    7.75  8.75  7.5\n",
+       "bob      7.75  8.75  7.5\n",
+       "charles  7.75  8.75  7.5\n",
+       "darwin   7.75  8.75  7.5"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame([[7.75, 8.75, 7.50]]*4, index=grades.index, columns=grades.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to subtract the global mean from every grade, here is one way to do it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>-4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         sep  oct  nov\n",
+       "alice    0.0  0.0  1.0\n",
+       "bob      2.0  1.0  1.0\n",
+       "charles -4.0  0.0 -6.0\n",
+       "darwin   1.0  2.0  2.0"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grades - grades.values.mean() # subtracts the global mean (8.00) from all grades"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Automatic alignment\n",
+    "Similar to `Series`, when operating on multiple `DataFrame`s, pandas automatically aligns them by row index label, but also by column names. Let's create a `DataFrame` with bonus points for each person from October to December:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "      <th>dec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>colin</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         oct  nov  dec\n",
+       "bob      0.0  NaN  2.0\n",
+       "colin    NaN  1.0  0.0\n",
+       "darwin   0.0  1.0  0.0\n",
+       "charles  3.0  3.0  0.0"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bonus_array = np.array([[0, np.nan, 2], [np.nan, 1, 0], [0, 1, 0], [3, 3, 0]])\n",
+    "bonus_points = pd.DataFrame(bonus_array, columns=[\"oct\", \"nov\", \"dec\"], index=[\"bob\", \"colin\", \"darwin\", \"charles\"])\n",
+    "bonus_points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dec</th>\n",
+       "      <th>nov</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>sep</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>colin</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         dec   nov   oct  sep\n",
+       "alice    NaN   NaN   NaN  NaN\n",
+       "bob      NaN   NaN   9.0  NaN\n",
+       "charles  NaN   5.0  11.0  NaN\n",
+       "colin    NaN   NaN   NaN  NaN\n",
+       "darwin   NaN  11.0  10.0  NaN"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grades + bonus_points"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks like the addition worked in some cases but way too many elements are now empty. That's because when aligning the `DataFrame`s, some columns and rows were only present on one side, and thus they were considered missing on the other side (`NaN`). Then adding `NaN` to a number results in `NaN`, hence the result.\n",
+    "\n",
+    "## Handling missing data\n",
+    "Dealing with missing data is a frequent task when working with real life data. Pandas offers a few tools to handle missing data.\n",
+    " \n",
+    "Let's try to fix the problem above. For example, we can decide that missing data should result in a zero, instead of `NaN`. We can replace all `NaN` values by any value using the `fillna()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dec</th>\n",
+       "      <th>nov</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>sep</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>colin</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         dec   nov   oct  sep\n",
+       "alice    0.0   0.0   0.0  0.0\n",
+       "bob      0.0   0.0   9.0  0.0\n",
+       "charles  0.0   5.0  11.0  0.0\n",
+       "colin    0.0   0.0   0.0  0.0\n",
+       "darwin   0.0  11.0  10.0  0.0"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(grades + bonus_points).fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's a bit unfair that we're setting grades to zero in September, though. Perhaps we should decide that missing grades are missing grades, but missing bonus points should be replaced by zeros:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dec</th>\n",
+       "      <th>nov</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>sep</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>colin</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         dec   nov   oct   sep\n",
+       "alice    NaN   9.0   8.0   8.0\n",
+       "bob      NaN   9.0   9.0  10.0\n",
+       "charles  NaN   5.0  11.0   4.0\n",
+       "colin    NaN   NaN   NaN   NaN\n",
+       "darwin   NaN  11.0  10.0   9.0"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fixed_bonus_points = bonus_points.fillna(0)\n",
+    "fixed_bonus_points.insert(0, \"sep\", 0)\n",
+    "fixed_bonus_points.loc[\"alice\"] = 0\n",
+    "grades + fixed_bonus_points"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's much better: although we made up some data, we have not been too unfair.\n",
+    "\n",
+    "Another way to handle missing data is to interpolate. Let's look at the `bonus_points` `DataFrame` again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "      <th>dec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>colin</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         oct  nov  dec\n",
+       "bob      0.0  NaN  2.0\n",
+       "colin    NaN  1.0  0.0\n",
+       "darwin   0.0  1.0  0.0\n",
+       "charles  3.0  3.0  0.0"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bonus_points"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's call the `interpolate` method. By default, it interpolates vertically (`axis=0`), so let's tell it to interpolate horizontally (`axis=1`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "      <th>dec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>colin</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         oct  nov  dec\n",
+       "bob      0.0  1.0  2.0\n",
+       "colin    NaN  1.0  0.0\n",
+       "darwin   0.0  1.0  0.0\n",
+       "charles  3.0  3.0  0.0"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bonus_points.interpolate(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bob had 0 bonus points in October, and 2 in December. When we interpolate for November, we get the mean: 1 bonus point. Colin had 1 bonus point in November, but we do not know how many bonus points he had in September, so we cannot interpolate, this is why there is still a missing value in October after interpolation. To fix this, we can set the September bonus points to 0 before interpolation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "      <th>dec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>colin</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         sep  oct  nov  dec\n",
+       "bob      0.0  0.0  1.0  2.0\n",
+       "colin    0.0  0.5  1.0  0.0\n",
+       "darwin   0.0  0.0  1.0  0.0\n",
+       "charles  0.0  3.0  3.0  0.0\n",
+       "alice    0.0  0.0  0.0  0.0"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "better_bonus_points = bonus_points.copy()\n",
+    "better_bonus_points.insert(0, \"sep\", 0)\n",
+    "better_bonus_points.loc[\"alice\"] = 0\n",
+    "better_bonus_points = better_bonus_points.interpolate(axis=1)\n",
+    "better_bonus_points"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great, now we have reasonable bonus points everywhere. Let's find out the final grades:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dec</th>\n",
+       "      <th>nov</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>sep</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>colin</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         dec   nov   oct   sep\n",
+       "alice    NaN   9.0   8.0   8.0\n",
+       "bob      NaN  10.0   9.0  10.0\n",
+       "charles  NaN   5.0  11.0   4.0\n",
+       "colin    NaN   NaN   NaN   NaN\n",
+       "darwin   NaN  11.0  10.0   9.0"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grades + better_bonus_points"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is slightly annoying that the September column ends up on the right. This is because the `DataFrame`s we are adding do not have the exact same columns (the `grades` `DataFrame` is missing the `\"dec\"` column), so to make things predictable, pandas orders the final columns alphabetically. To fix this, we can simply add the missing column before adding:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "      <th>dec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>colin</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          sep   oct   nov  dec\n",
+       "alice     8.0   8.0   9.0  NaN\n",
+       "bob      10.0   9.0  10.0  NaN\n",
+       "charles   4.0  11.0   5.0  NaN\n",
+       "colin     NaN   NaN   NaN  NaN\n",
+       "darwin    9.0  10.0  11.0  NaN"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grades[\"dec\"] = np.nan\n",
+    "final_grades = grades + better_bonus_points\n",
+    "final_grades"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's not much we can do about December and Colin: it's bad enough that we are making up bonus points, but we can't reasonably make up grades (well, I guess some teachers probably do). So let's call the `dropna()` method to get rid of rows that are full of `NaN`s:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "      <th>dec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          sep   oct   nov  dec\n",
+       "alice     8.0   8.0   9.0  NaN\n",
+       "bob      10.0   9.0  10.0  NaN\n",
+       "charles   4.0  11.0   5.0  NaN\n",
+       "darwin    9.0  10.0  11.0  NaN"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_grades_clean = final_grades.dropna(how=\"all\")\n",
+    "final_grades_clean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's remove columns that are full of `NaN`s by setting the `axis` argument to `1`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          sep   oct   nov\n",
+       "alice     8.0   8.0   9.0\n",
+       "bob      10.0   9.0  10.0\n",
+       "charles   4.0  11.0   5.0\n",
+       "darwin    9.0  10.0  11.0"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_grades_clean = final_grades_clean.dropna(axis=1, how=\"all\")\n",
+    "final_grades_clean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Aggregating with `groupby`\n",
+    "Similar to the SQL language, pandas allows grouping your data into groups to run calculations over each group.\n",
+    "\n",
+    "First, let's add some extra data about each person so we can group them, and let's go back to the `final_grades` `DataFrame` so we can see how `NaN` values are handled:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>hobby</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Biking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Dancing</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>colin</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Dancing</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Biking</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          sep   oct   nov  dec    hobby\n",
+       "alice     8.0   8.0   9.0  NaN   Biking\n",
+       "bob      10.0   9.0  10.0  NaN  Dancing\n",
+       "charles   4.0  11.0   5.0  NaN      NaN\n",
+       "colin     NaN   NaN   NaN  NaN  Dancing\n",
+       "darwin    9.0  10.0  11.0  NaN   Biking"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_grades[\"hobby\"] = [\"Biking\", \"Dancing\", np.nan, \"Dancing\", \"Biking\"]\n",
+    "final_grades"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's group data in this `DataFrame` by hobby:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x1228f4040>"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grouped_grades = final_grades.groupby(\"hobby\")\n",
+    "grouped_grades"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are ready to compute the average grade per hobby:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "      <th>dec</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hobby</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Biking</th>\n",
+       "      <td>8.5</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dancing</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          sep  oct   nov  dec\n",
+       "hobby                        \n",
+       "Biking    8.5  9.0  10.0  NaN\n",
+       "Dancing  10.0  9.0  10.0  NaN"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grouped_grades.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That was easy! Note that the `NaN` values have simply been skipped when computing the means."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pivot tables\n",
+    "Pandas supports spreadsheet-like [pivot tables](https://en.wikipedia.org/wiki/Pivot_table) that allow quick data summarization. To illustrate this, let's create a simple `DataFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>oct</th>\n",
+       "      <th>nov</th>\n",
+       "      <th>dec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>colin</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         oct  nov  dec\n",
+       "bob      0.0  NaN  2.0\n",
+       "colin    NaN  1.0  0.0\n",
+       "darwin   0.0  1.0  0.0\n",
+       "charles  3.0  3.0  0.0"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bonus_points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>month</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>bonus</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>alice</td>\n",
+       "      <td>sep</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>alice</td>\n",
+       "      <td>oct</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>alice</td>\n",
+       "      <td>nov</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>bob</td>\n",
+       "      <td>sep</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>bob</td>\n",
+       "      <td>oct</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>bob</td>\n",
+       "      <td>nov</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>charles</td>\n",
+       "      <td>sep</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>charles</td>\n",
+       "      <td>oct</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>charles</td>\n",
+       "      <td>nov</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>darwin</td>\n",
+       "      <td>sep</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>darwin</td>\n",
+       "      <td>oct</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>darwin</td>\n",
+       "      <td>nov</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       name month  grade  bonus\n",
+       "0     alice   sep    8.0    NaN\n",
+       "1     alice   oct    8.0    NaN\n",
+       "2     alice   nov    9.0    NaN\n",
+       "3       bob   sep   10.0    0.0\n",
+       "4       bob   oct    9.0    NaN\n",
+       "5       bob   nov   10.0    2.0\n",
+       "6   charles   sep    4.0    3.0\n",
+       "7   charles   oct   11.0    3.0\n",
+       "8   charles   nov    5.0    0.0\n",
+       "9    darwin   sep    9.0    0.0\n",
+       "10   darwin   oct   10.0    1.0\n",
+       "11   darwin   nov   11.0    0.0"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "more_grades = final_grades_clean.stack().reset_index()\n",
+    "more_grades.columns = [\"name\", \"month\", \"grade\"]\n",
+    "more_grades[\"bonus\"] = [np.nan, np.nan, np.nan, 0, np.nan, 2, 3, 3, 0, 0, 1, 0]\n",
+    "more_grades"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can call the `pd.pivot_table()` function for this `DataFrame`, asking to group by the `name` column. By default, `pivot_table()` computes the mean of each numeric column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>bonus</th>\n",
+       "      <th>grade</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>9.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>6.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>10.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            bonus      grade\n",
+       "name                        \n",
+       "alice         NaN   8.333333\n",
+       "bob      1.000000   9.666667\n",
+       "charles  2.000000   6.666667\n",
+       "darwin   0.333333  10.000000"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.pivot_table(more_grades, index=\"name\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can change the aggregation function by setting the `aggfunc` argument, and we can also specify the list of columns whose values will be aggregated:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>bonus</th>\n",
+       "      <th>grade</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         bonus  grade\n",
+       "name                 \n",
+       "alice      NaN    9.0\n",
+       "bob        2.0   10.0\n",
+       "charles    3.0   11.0\n",
+       "darwin     1.0   11.0"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.pivot_table(more_grades, index=\"name\", values=[\"grade\", \"bonus\"], aggfunc=np.max)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also specify the `columns` to aggregate over horizontally, and request the grand totals for each row and column by setting `margins=True`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>month</th>\n",
+       "      <th>nov</th>\n",
+       "      <th>oct</th>\n",
+       "      <th>sep</th>\n",
+       "      <th>All</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>9.00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>8.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>10.00</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>9.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charles</th>\n",
+       "      <td>5.00</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>6.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>darwin</th>\n",
+       "      <td>11.00</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>10.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>All</th>\n",
+       "      <td>8.75</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>7.75</td>\n",
+       "      <td>8.666667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "month      nov   oct    sep        All\n",
+       "name                                  \n",
+       "alice     9.00   8.0   8.00   8.333333\n",
+       "bob      10.00   9.0  10.00   9.666667\n",
+       "charles   5.00  11.0   4.00   6.666667\n",
+       "darwin   11.00  10.0   9.00  10.000000\n",
+       "All       8.75   9.5   7.75   8.666667"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.pivot_table(more_grades, index=\"name\", values=\"grade\", columns=\"month\", margins=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can specify multiple index or column names, and pandas will create multi-level indices:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>bonus</th>\n",
+       "      <th>grade</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th>month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">alice</th>\n",
+       "      <th>nov</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oct</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sep</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">bob</th>\n",
+       "      <th>nov</th>\n",
+       "      <td>2.000</td>\n",
+       "      <td>10.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oct</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sep</th>\n",
+       "      <td>0.000</td>\n",
+       "      <td>10.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">charles</th>\n",
+       "      <th>nov</th>\n",
+       "      <td>0.000</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oct</th>\n",
+       "      <td>3.000</td>\n",
+       "      <td>11.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sep</th>\n",
+       "      <td>3.000</td>\n",
+       "      <td>4.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">darwin</th>\n",
+       "      <th>nov</th>\n",
+       "      <td>0.000</td>\n",
+       "      <td>11.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oct</th>\n",
+       "      <td>1.000</td>\n",
+       "      <td>10.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sep</th>\n",
+       "      <td>0.000</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>All</th>\n",
+       "      <th></th>\n",
+       "      <td>1.125</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               bonus  grade\n",
+       "name    month              \n",
+       "alice   nov      NaN   9.00\n",
+       "        oct      NaN   8.00\n",
+       "        sep      NaN   8.00\n",
+       "bob     nov    2.000  10.00\n",
+       "        oct      NaN   9.00\n",
+       "        sep    0.000  10.00\n",
+       "charles nov    0.000   5.00\n",
+       "        oct    3.000  11.00\n",
+       "        sep    3.000   4.00\n",
+       "darwin  nov    0.000  11.00\n",
+       "        oct    1.000  10.00\n",
+       "        sep    0.000   9.00\n",
+       "All            1.125   8.75"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.pivot_table(more_grades, index=(\"name\", \"month\"), margins=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview functions\n",
+    "When dealing with large `DataFrames`, it is useful to get a quick overview of its content. Pandas offers a few functions for this. First, let's create a large `DataFrame` with a mix of numeric values, missing values and text values. Notice how Jupyter displays only the corners of the `DataFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>some_text</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "      <th>F</th>\n",
+       "      <th>G</th>\n",
+       "      <th>H</th>\n",
+       "      <th>I</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Q</th>\n",
+       "      <th>R</th>\n",
+       "      <th>S</th>\n",
+       "      <th>T</th>\n",
+       "      <th>U</th>\n",
+       "      <th>V</th>\n",
+       "      <th>W</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Y</th>\n",
+       "      <th>Z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>143.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>154.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>154.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>154.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>154.0</td>\n",
+       "      <td>132.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>143.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>154.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>154.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         A     B     C some_text      D      E      F     G      H      I  \\\n",
+       "0      NaN  11.0  44.0    Blabla   99.0    NaN   88.0  22.0  165.0  143.0   \n",
+       "1     11.0  22.0  55.0    Blabla  110.0    NaN   99.0  33.0    NaN  154.0   \n",
+       "2     22.0  33.0  66.0    Blabla  121.0   11.0  110.0  44.0    NaN  165.0   \n",
+       "3     33.0  44.0  77.0    Blabla  132.0   22.0  121.0  55.0   11.0    NaN   \n",
+       "4     44.0  55.0  88.0    Blabla  143.0   33.0  132.0  66.0   22.0    NaN   \n",
+       "...    ...   ...   ...       ...    ...    ...    ...   ...    ...    ...   \n",
+       "9995   NaN   NaN  33.0    Blabla   88.0  165.0   77.0  11.0  154.0  132.0   \n",
+       "9996   NaN  11.0  44.0    Blabla   99.0    NaN   88.0  22.0  165.0  143.0   \n",
+       "9997  11.0  22.0  55.0    Blabla  110.0    NaN   99.0  33.0    NaN  154.0   \n",
+       "9998  22.0  33.0  66.0    Blabla  121.0   11.0  110.0  44.0    NaN  165.0   \n",
+       "9999  33.0  44.0  77.0    Blabla  132.0   22.0  121.0  55.0   11.0    NaN   \n",
+       "\n",
+       "      ...     Q     R     S     T      U      V      W     X      Y      Z  \n",
+       "0     ...  11.0   NaN  11.0  44.0   99.0    NaN   88.0  22.0  165.0  143.0  \n",
+       "1     ...  22.0  11.0  22.0  55.0  110.0    NaN   99.0  33.0    NaN  154.0  \n",
+       "2     ...  33.0  22.0  33.0  66.0  121.0   11.0  110.0  44.0    NaN  165.0  \n",
+       "3     ...  44.0  33.0  44.0  77.0  132.0   22.0  121.0  55.0   11.0    NaN  \n",
+       "4     ...  55.0  44.0  55.0  88.0  143.0   33.0  132.0  66.0   22.0    NaN  \n",
+       "...   ...   ...   ...   ...   ...    ...    ...    ...   ...    ...    ...  \n",
+       "9995  ...   NaN   NaN   NaN  33.0   88.0  165.0   77.0  11.0  154.0  132.0  \n",
+       "9996  ...  11.0   NaN  11.0  44.0   99.0    NaN   88.0  22.0  165.0  143.0  \n",
+       "9997  ...  22.0  11.0  22.0  55.0  110.0    NaN   99.0  33.0    NaN  154.0  \n",
+       "9998  ...  33.0  22.0  33.0  66.0  121.0   11.0  110.0  44.0    NaN  165.0  \n",
+       "9999  ...  44.0  33.0  44.0  77.0  132.0   22.0  121.0  55.0   11.0    NaN  \n",
+       "\n",
+       "[10000 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "much_data = np.fromfunction(lambda x,y: (x+y*y)%17*11, (10000, 26))\n",
+    "large_df = pd.DataFrame(much_data, columns=list(\"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"))\n",
+    "large_df[large_df % 16 == 0] = np.nan\n",
+    "large_df.insert(3, \"some_text\", \"Blabla\")\n",
+    "large_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `head()` method returns the top 5 rows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>some_text</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "      <th>F</th>\n",
+       "      <th>G</th>\n",
+       "      <th>H</th>\n",
+       "      <th>I</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Q</th>\n",
+       "      <th>R</th>\n",
+       "      <th>S</th>\n",
+       "      <th>T</th>\n",
+       "      <th>U</th>\n",
+       "      <th>V</th>\n",
+       "      <th>W</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Y</th>\n",
+       "      <th>Z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>143.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>154.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>154.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      A     B     C some_text      D     E      F     G      H      I  ...  \\\n",
+       "0   NaN  11.0  44.0    Blabla   99.0   NaN   88.0  22.0  165.0  143.0  ...   \n",
+       "1  11.0  22.0  55.0    Blabla  110.0   NaN   99.0  33.0    NaN  154.0  ...   \n",
+       "2  22.0  33.0  66.0    Blabla  121.0  11.0  110.0  44.0    NaN  165.0  ...   \n",
+       "3  33.0  44.0  77.0    Blabla  132.0  22.0  121.0  55.0   11.0    NaN  ...   \n",
+       "4  44.0  55.0  88.0    Blabla  143.0  33.0  132.0  66.0   22.0    NaN  ...   \n",
+       "\n",
+       "      Q     R     S     T      U     V      W     X      Y      Z  \n",
+       "0  11.0   NaN  11.0  44.0   99.0   NaN   88.0  22.0  165.0  143.0  \n",
+       "1  22.0  11.0  22.0  55.0  110.0   NaN   99.0  33.0    NaN  154.0  \n",
+       "2  33.0  22.0  33.0  66.0  121.0  11.0  110.0  44.0    NaN  165.0  \n",
+       "3  44.0  33.0  44.0  77.0  132.0  22.0  121.0  55.0   11.0    NaN  \n",
+       "4  55.0  44.0  55.0  88.0  143.0  33.0  132.0  66.0   22.0    NaN  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "large_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, there's also a `tail()` function to view the bottom 5 rows. You can pass the number of rows you want:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>some_text</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "      <th>F</th>\n",
+       "      <th>G</th>\n",
+       "      <th>H</th>\n",
+       "      <th>I</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Q</th>\n",
+       "      <th>R</th>\n",
+       "      <th>S</th>\n",
+       "      <th>T</th>\n",
+       "      <th>U</th>\n",
+       "      <th>V</th>\n",
+       "      <th>W</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Y</th>\n",
+       "      <th>Z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>Blabla</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         A     B     C some_text      D     E      F     G     H      I  ...  \\\n",
+       "9998  22.0  33.0  66.0    Blabla  121.0  11.0  110.0  44.0   NaN  165.0  ...   \n",
+       "9999  33.0  44.0  77.0    Blabla  132.0  22.0  121.0  55.0  11.0    NaN  ...   \n",
+       "\n",
+       "         Q     R     S     T      U     V      W     X     Y      Z  \n",
+       "9998  33.0  22.0  33.0  66.0  121.0  11.0  110.0  44.0   NaN  165.0  \n",
+       "9999  44.0  33.0  44.0  77.0  132.0  22.0  121.0  55.0  11.0    NaN  \n",
+       "\n",
+       "[2 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "large_df.tail(n=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `info()` method prints out a summary of each column's contents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10000 entries, 0 to 9999\n",
+      "Data columns (total 27 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   A          8823 non-null   float64\n",
+      " 1   B          8824 non-null   float64\n",
+      " 2   C          8824 non-null   float64\n",
+      " 3   some_text  10000 non-null  object \n",
+      " 4   D          8824 non-null   float64\n",
+      " 5   E          8822 non-null   float64\n",
+      " 6   F          8824 non-null   float64\n",
+      " 7   G          8824 non-null   float64\n",
+      " 8   H          8822 non-null   float64\n",
+      " 9   I          8823 non-null   float64\n",
+      " 10  J          8823 non-null   float64\n",
+      " 11  K          8822 non-null   float64\n",
+      " 12  L          8824 non-null   float64\n",
+      " 13  M          8824 non-null   float64\n",
+      " 14  N          8822 non-null   float64\n",
+      " 15  O          8824 non-null   float64\n",
+      " 16  P          8824 non-null   float64\n",
+      " 17  Q          8824 non-null   float64\n",
+      " 18  R          8823 non-null   float64\n",
+      " 19  S          8824 non-null   float64\n",
+      " 20  T          8824 non-null   float64\n",
+      " 21  U          8824 non-null   float64\n",
+      " 22  V          8822 non-null   float64\n",
+      " 23  W          8824 non-null   float64\n",
+      " 24  X          8824 non-null   float64\n",
+      " 25  Y          8822 non-null   float64\n",
+      " 26  Z          8823 non-null   float64\n",
+      "dtypes: float64(26), object(1)\n",
+      "memory usage: 2.1+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "large_df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, the `describe()` method gives a nice overview of the main aggregated values over each column:\n",
+    "* `count`: number of non-null (not NaN) values\n",
+    "* `mean`: mean of non-null values\n",
+    "* `std`: [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of non-null values\n",
+    "* `min`: minimum of non-null values\n",
+    "* `25%`, `50%`, `75%`: 25th, 50th and 75th [percentile](https://en.wikipedia.org/wiki/Percentile) of non-null values\n",
+    "* `max`: maximum of non-null values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "      <th>F</th>\n",
+       "      <th>G</th>\n",
+       "      <th>H</th>\n",
+       "      <th>I</th>\n",
+       "      <th>J</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Q</th>\n",
+       "      <th>R</th>\n",
+       "      <th>S</th>\n",
+       "      <th>T</th>\n",
+       "      <th>U</th>\n",
+       "      <th>V</th>\n",
+       "      <th>W</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Y</th>\n",
+       "      <th>Z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>8823.000000</td>\n",
+       "      <td>8824.000000</td>\n",
+       "      <td>8824.000000</td>\n",
+       "      <td>8824.000000</td>\n",
+       "      <td>8822.000000</td>\n",
+       "      <td>8824.000000</td>\n",
+       "      <td>8824.000000</td>\n",
+       "      <td>8822.000000</td>\n",
+       "      <td>8823.000000</td>\n",
+       "      <td>8823.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8824.000000</td>\n",
+       "      <td>8823.000000</td>\n",
+       "      <td>8824.000000</td>\n",
+       "      <td>8824.000000</td>\n",
+       "      <td>8824.000000</td>\n",
+       "      <td>8822.000000</td>\n",
+       "      <td>8824.000000</td>\n",
+       "      <td>8824.000000</td>\n",
+       "      <td>8822.000000</td>\n",
+       "      <td>8823.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>87.977559</td>\n",
+       "      <td>87.972575</td>\n",
+       "      <td>87.987534</td>\n",
+       "      <td>88.012466</td>\n",
+       "      <td>87.983791</td>\n",
+       "      <td>88.007480</td>\n",
+       "      <td>87.977561</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.022441</td>\n",
+       "      <td>88.022441</td>\n",
+       "      <td>...</td>\n",
+       "      <td>87.972575</td>\n",
+       "      <td>87.977559</td>\n",
+       "      <td>87.972575</td>\n",
+       "      <td>87.987534</td>\n",
+       "      <td>88.012466</td>\n",
+       "      <td>87.983791</td>\n",
+       "      <td>88.007480</td>\n",
+       "      <td>87.977561</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.022441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>47.535911</td>\n",
+       "      <td>47.535523</td>\n",
+       "      <td>47.521679</td>\n",
+       "      <td>47.521679</td>\n",
+       "      <td>47.535001</td>\n",
+       "      <td>47.519371</td>\n",
+       "      <td>47.529755</td>\n",
+       "      <td>47.536879</td>\n",
+       "      <td>47.535911</td>\n",
+       "      <td>47.535911</td>\n",
+       "      <td>...</td>\n",
+       "      <td>47.535523</td>\n",
+       "      <td>47.535911</td>\n",
+       "      <td>47.535523</td>\n",
+       "      <td>47.521679</td>\n",
+       "      <td>47.521679</td>\n",
+       "      <td>47.535001</td>\n",
+       "      <td>47.519371</td>\n",
+       "      <td>47.529755</td>\n",
+       "      <td>47.536879</td>\n",
+       "      <td>47.535911</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "      <td>132.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 A            B            C            D            E  \\\n",
+       "count  8823.000000  8824.000000  8824.000000  8824.000000  8822.000000   \n",
+       "mean     87.977559    87.972575    87.987534    88.012466    87.983791   \n",
+       "std      47.535911    47.535523    47.521679    47.521679    47.535001   \n",
+       "min      11.000000    11.000000    11.000000    11.000000    11.000000   \n",
+       "25%      44.000000    44.000000    44.000000    44.000000    44.000000   \n",
+       "50%      88.000000    88.000000    88.000000    88.000000    88.000000   \n",
+       "75%     132.000000   132.000000   132.000000   132.000000   132.000000   \n",
+       "max     165.000000   165.000000   165.000000   165.000000   165.000000   \n",
+       "\n",
+       "                 F            G            H            I            J  ...  \\\n",
+       "count  8824.000000  8824.000000  8822.000000  8823.000000  8823.000000  ...   \n",
+       "mean     88.007480    87.977561    88.000000    88.022441    88.022441  ...   \n",
+       "std      47.519371    47.529755    47.536879    47.535911    47.535911  ...   \n",
+       "min      11.000000    11.000000    11.000000    11.000000    11.000000  ...   \n",
+       "25%      44.000000    44.000000    44.000000    44.000000    44.000000  ...   \n",
+       "50%      88.000000    88.000000    88.000000    88.000000    88.000000  ...   \n",
+       "75%     132.000000   132.000000   132.000000   132.000000   132.000000  ...   \n",
+       "max     165.000000   165.000000   165.000000   165.000000   165.000000  ...   \n",
+       "\n",
+       "                 Q            R            S            T            U  \\\n",
+       "count  8824.000000  8823.000000  8824.000000  8824.000000  8824.000000   \n",
+       "mean     87.972575    87.977559    87.972575    87.987534    88.012466   \n",
+       "std      47.535523    47.535911    47.535523    47.521679    47.521679   \n",
+       "min      11.000000    11.000000    11.000000    11.000000    11.000000   \n",
+       "25%      44.000000    44.000000    44.000000    44.000000    44.000000   \n",
+       "50%      88.000000    88.000000    88.000000    88.000000    88.000000   \n",
+       "75%     132.000000   132.000000   132.000000   132.000000   132.000000   \n",
+       "max     165.000000   165.000000   165.000000   165.000000   165.000000   \n",
+       "\n",
+       "                 V            W            X            Y            Z  \n",
+       "count  8822.000000  8824.000000  8824.000000  8822.000000  8823.000000  \n",
+       "mean     87.983791    88.007480    87.977561    88.000000    88.022441  \n",
+       "std      47.535001    47.519371    47.529755    47.536879    47.535911  \n",
+       "min      11.000000    11.000000    11.000000    11.000000    11.000000  \n",
+       "25%      44.000000    44.000000    44.000000    44.000000    44.000000  \n",
+       "50%      88.000000    88.000000    88.000000    88.000000    88.000000  \n",
+       "75%     132.000000   132.000000   132.000000   132.000000   132.000000  \n",
+       "max     165.000000   165.000000   165.000000   165.000000   165.000000  \n",
+       "\n",
+       "[8 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "large_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Saving & loading\n",
+    "Pandas can save `DataFrame`s to various backends, including file formats such as CSV, Excel, JSON, HTML and HDF5, or to a SQL database. Let's create a `DataFrame` to demonstrate this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>children</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>Biking</td>\n",
+       "      <td>68.5</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>83.1</td>\n",
+       "      <td>1984</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hobby  weight  birthyear  children\n",
+       "alice   Biking    68.5       1985       NaN\n",
+       "bob    Dancing    83.1       1984       3.0"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_df = pd.DataFrame(\n",
+    "    [[\"Biking\", 68.5, 1985, np.nan], [\"Dancing\", 83.1, 1984, 3]], \n",
+    "    columns=[\"hobby\", \"weight\", \"birthyear\", \"children\"],\n",
+    "    index=[\"alice\", \"bob\"]\n",
+    ")\n",
+    "my_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving\n",
+    "Let's save it to CSV, HTML and JSON:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_df.to_csv(\"my_df.csv\")\n",
+    "my_df.to_html(\"my_df.html\")\n",
+    "my_df.to_json(\"my_df.json\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Done! Let's take a peek at what was saved:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# my_df.csv\n",
+      ",hobby,weight,birthyear,children\n",
+      "alice,Biking,68.5,1985,\n",
+      "bob,Dancing,83.1,1984,3.0\n",
+      "\n",
+      "\n",
+      "# my_df.html\n",
+      "<table border=\"1\" class=\"dataframe\">\n",
+      "  <thead>\n",
+      "    <tr style=\"text-align: right;\">\n",
+      "      <th></th>\n",
+      "      <th>hobby</th>\n",
+      "      <th>weight</th>\n",
+      "      <th>birthyear</th>\n",
+      "      <th>children</th>\n",
+      "    </tr>\n",
+      "  </thead>\n",
+      "  <tbody>\n",
+      "    <tr>\n",
+      "      <th>alice</th>\n",
+      "      <td>Biking</td>\n",
+      "      <td>68.5</td>\n",
+      "      <td>1985</td>\n",
+      "      <td>NaN</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <th>bob</th>\n",
+      "      <td>Dancing</td>\n",
+      "      <td>83.1</td>\n",
+      "      <td>1984</td>\n",
+      "      <td>3.0</td>\n",
+      "    </tr>\n",
+      "  </tbody>\n",
+      "</table>\n",
+      "\n",
+      "# my_df.json\n",
+      "{\"hobby\":{\"alice\":\"Biking\",\"bob\":\"Dancing\"},\"weight\":{\"alice\":68.5,\"bob\":83.1},\"birthyear\":{\"alice\":1985,\"bob\":1984},\"children\":{\"alice\":null,\"bob\":3.0}}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for filename in (\"my_df.csv\", \"my_df.html\", \"my_df.json\"):\n",
+    "    print(\"#\", filename)\n",
+    "    with open(filename, \"rt\") as f:\n",
+    "        print(f.read())\n",
+    "        print()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the index is saved as the first column (with no name) in a CSV file, as `<th>` tags in HTML and as keys in JSON.\n",
+    "\n",
+    "Saving to other formats works very similarly, but some formats require extra libraries to be installed. For example, saving to Excel requires the openpyxl library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No module named 'openpyxl'\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    my_df.to_excel(\"my_df.xlsx\", sheet_name='People')\n",
+    "except ImportError as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading\n",
+    "Now let's load our CSV file back into a `DataFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hobby</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>birthyear</th>\n",
+       "      <th>children</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>alice</th>\n",
+       "      <td>Biking</td>\n",
+       "      <td>68.5</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bob</th>\n",
+       "      <td>Dancing</td>\n",
+       "      <td>83.1</td>\n",
+       "      <td>1984</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hobby  weight  birthyear  children\n",
+       "alice   Biking    68.5       1985       NaN\n",
+       "bob    Dancing    83.1       1984       3.0"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_df_loaded = pd.read_csv(\"my_df.csv\", index_col=0)\n",
+    "my_df_loaded"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you might guess, there are similar `read_json`, `read_html`, `read_excel` functions as well.  We can also read data straight from the Internet. For example, let's load the top 1,000 U.S. cities from GitHub:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>State</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>City</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Marysville</th>\n",
+       "      <td>Washington</td>\n",
+       "      <td>63269</td>\n",
+       "      <td>48.051764</td>\n",
+       "      <td>-122.177082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Perris</th>\n",
+       "      <td>California</td>\n",
+       "      <td>72326</td>\n",
+       "      <td>33.782519</td>\n",
+       "      <td>-117.228648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cleveland</th>\n",
+       "      <td>Ohio</td>\n",
+       "      <td>390113</td>\n",
+       "      <td>41.499320</td>\n",
+       "      <td>-81.694361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Worcester</th>\n",
+       "      <td>Massachusetts</td>\n",
+       "      <td>182544</td>\n",
+       "      <td>42.262593</td>\n",
+       "      <td>-71.802293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Columbia</th>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>133358</td>\n",
+       "      <td>34.000710</td>\n",
+       "      <td>-81.034814</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     State  Population        lat         lon\n",
+       "City                                                         \n",
+       "Marysville      Washington       63269  48.051764 -122.177082\n",
+       "Perris          California       72326  33.782519 -117.228648\n",
+       "Cleveland             Ohio      390113  41.499320  -81.694361\n",
+       "Worcester    Massachusetts      182544  42.262593  -71.802293\n",
+       "Columbia    South Carolina      133358  34.000710  -81.034814"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "us_cities = None\n",
+    "try:\n",
+    "    csv_url = \"https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv\"\n",
+    "    us_cities = pd.read_csv(csv_url, index_col=0)\n",
+    "    us_cities = us_cities.head()\n",
+    "except IOError as e:\n",
+    "    print(e)\n",
+    "us_cities"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are more options available, in particular regarding datetime format. Check out the [documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html) for more details."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Combining `DataFrame`s\n",
+    "\n",
+    "## SQL-like joins\n",
+    "One powerful feature of pandas is its ability to perform SQL-like joins on `DataFrame`s. Various types of joins are supported: inner joins, left/right outer joins and full joins. To illustrate this, let's start by creating a couple of simple `DataFrame`s:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>city</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CA</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>37.781334</td>\n",
+       "      <td>-122.416728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NY</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>40.705649</td>\n",
+       "      <td>-74.008344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>FL</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>25.791100</td>\n",
+       "      <td>-80.320733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>Cleveland</td>\n",
+       "      <td>41.473508</td>\n",
+       "      <td>-81.739791</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>UT</td>\n",
+       "      <td>Salt Lake City</td>\n",
+       "      <td>40.755851</td>\n",
+       "      <td>-111.896657</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  state            city        lat         lng\n",
+       "0    CA   San Francisco  37.781334 -122.416728\n",
+       "1    NY        New York  40.705649  -74.008344\n",
+       "2    FL           Miami  25.791100  -80.320733\n",
+       "3    OH       Cleveland  41.473508  -81.739791\n",
+       "4    UT  Salt Lake City  40.755851 -111.896657"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_loc = pd.DataFrame(\n",
+    "    [\n",
+    "        [\"CA\", \"San Francisco\", 37.781334, -122.416728],\n",
+    "        [\"NY\", \"New York\", 40.705649, -74.008344],\n",
+    "        [\"FL\", \"Miami\", 25.791100, -80.320733],\n",
+    "        [\"OH\", \"Cleveland\", 41.473508, -81.739791],\n",
+    "        [\"UT\", \"Salt Lake City\", 40.755851, -111.896657]\n",
+    "    ], columns=[\"state\", \"city\", \"lat\", \"lng\"])\n",
+    "city_loc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>population</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>808976</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>California</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8363710</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New-York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>413201</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>Florida</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2242193</td>\n",
+       "      <td>Houston</td>\n",
+       "      <td>Texas</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   population           city       state\n",
+       "3      808976  San Francisco  California\n",
+       "4     8363710       New York    New-York\n",
+       "5      413201          Miami     Florida\n",
+       "6     2242193        Houston       Texas"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_pop = pd.DataFrame(\n",
+    "    [\n",
+    "        [808976, \"San Francisco\", \"California\"],\n",
+    "        [8363710, \"New York\", \"New-York\"],\n",
+    "        [413201, \"Miami\", \"Florida\"],\n",
+    "        [2242193, \"Houston\", \"Texas\"]\n",
+    "    ], index=[3,4,5,6], columns=[\"population\", \"city\", \"state\"])\n",
+    "city_pop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's join these `DataFrame`s using the `merge()` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state_x</th>\n",
+       "      <th>city</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>population</th>\n",
+       "      <th>state_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CA</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>37.781334</td>\n",
+       "      <td>-122.416728</td>\n",
+       "      <td>808976</td>\n",
+       "      <td>California</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NY</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>40.705649</td>\n",
+       "      <td>-74.008344</td>\n",
+       "      <td>8363710</td>\n",
+       "      <td>New-York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>FL</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>25.791100</td>\n",
+       "      <td>-80.320733</td>\n",
+       "      <td>413201</td>\n",
+       "      <td>Florida</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  state_x           city        lat         lng  population     state_y\n",
+       "0      CA  San Francisco  37.781334 -122.416728      808976  California\n",
+       "1      NY       New York  40.705649  -74.008344     8363710    New-York\n",
+       "2      FL          Miami  25.791100  -80.320733      413201     Florida"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(left=city_loc, right=city_pop, on=\"city\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that both `DataFrame`s have a column named `state`, so in the result they got renamed to `state_x` and `state_y`.\n",
+    "\n",
+    "Also, note that Cleveland, Salt Lake City and Houston were dropped because they don't exist in *both* `DataFrame`s. This is the equivalent of a SQL `INNER JOIN`. If you want a `FULL OUTER JOIN`, where no city gets dropped and `NaN` values are added, you must specify `how=\"outer\"`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state_x</th>\n",
+       "      <th>city</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>population</th>\n",
+       "      <th>state_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CA</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>37.781334</td>\n",
+       "      <td>-122.416728</td>\n",
+       "      <td>808976.0</td>\n",
+       "      <td>California</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NY</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>40.705649</td>\n",
+       "      <td>-74.008344</td>\n",
+       "      <td>8363710.0</td>\n",
+       "      <td>New-York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>FL</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>25.791100</td>\n",
+       "      <td>-80.320733</td>\n",
+       "      <td>413201.0</td>\n",
+       "      <td>Florida</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>Cleveland</td>\n",
+       "      <td>41.473508</td>\n",
+       "      <td>-81.739791</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>UT</td>\n",
+       "      <td>Salt Lake City</td>\n",
+       "      <td>40.755851</td>\n",
+       "      <td>-111.896657</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Houston</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2242193.0</td>\n",
+       "      <td>Texas</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  state_x            city        lat         lng  population     state_y\n",
+       "0      CA   San Francisco  37.781334 -122.416728    808976.0  California\n",
+       "1      NY        New York  40.705649  -74.008344   8363710.0    New-York\n",
+       "2      FL           Miami  25.791100  -80.320733    413201.0     Florida\n",
+       "3      OH       Cleveland  41.473508  -81.739791         NaN         NaN\n",
+       "4      UT  Salt Lake City  40.755851 -111.896657         NaN         NaN\n",
+       "5     NaN         Houston        NaN         NaN   2242193.0       Texas"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_cities = pd.merge(left=city_loc, right=city_pop, on=\"city\", how=\"outer\")\n",
+    "all_cities"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, `LEFT OUTER JOIN` is also available by setting `how=\"left\"`: only the cities present in the left `DataFrame` end up in the result. Similarly, with `how=\"right\"` only cities in the right `DataFrame` appear in the result. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state_x</th>\n",
+       "      <th>city</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>population</th>\n",
+       "      <th>state_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CA</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>37.781334</td>\n",
+       "      <td>-122.416728</td>\n",
+       "      <td>808976</td>\n",
+       "      <td>California</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NY</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>40.705649</td>\n",
+       "      <td>-74.008344</td>\n",
+       "      <td>8363710</td>\n",
+       "      <td>New-York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>FL</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>25.791100</td>\n",
+       "      <td>-80.320733</td>\n",
+       "      <td>413201</td>\n",
+       "      <td>Florida</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Houston</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2242193</td>\n",
+       "      <td>Texas</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  state_x           city        lat         lng  population     state_y\n",
+       "0      CA  San Francisco  37.781334 -122.416728      808976  California\n",
+       "1      NY       New York  40.705649  -74.008344     8363710    New-York\n",
+       "2      FL          Miami  25.791100  -80.320733      413201     Florida\n",
+       "3     NaN        Houston        NaN         NaN     2242193       Texas"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(left=city_loc, right=city_pop, on=\"city\", how=\"right\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the key to join on is actually in one (or both) `DataFrame`'s index, you must use `left_index=True` and/or `right_index=True`. If the key column names differ, you must use `left_on` and `right_on`. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state_x</th>\n",
+       "      <th>city</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>population</th>\n",
+       "      <th>name</th>\n",
+       "      <th>state_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CA</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>37.781334</td>\n",
+       "      <td>-122.416728</td>\n",
+       "      <td>808976</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>California</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NY</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>40.705649</td>\n",
+       "      <td>-74.008344</td>\n",
+       "      <td>8363710</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New-York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>FL</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>25.791100</td>\n",
+       "      <td>-80.320733</td>\n",
+       "      <td>413201</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>Florida</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  state_x           city        lat         lng  population           name  \\\n",
+       "0      CA  San Francisco  37.781334 -122.416728      808976  San Francisco   \n",
+       "1      NY       New York  40.705649  -74.008344     8363710       New York   \n",
+       "2      FL          Miami  25.791100  -80.320733      413201          Miami   \n",
+       "\n",
+       "      state_y  \n",
+       "0  California  \n",
+       "1    New-York  \n",
+       "2     Florida  "
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_pop2 = city_pop.copy()\n",
+    "city_pop2.columns = [\"population\", \"name\", \"state\"]\n",
+    "pd.merge(left=city_loc, right=city_pop2, left_on=\"city\", right_on=\"name\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Concatenation\n",
+    "Rather than joining `DataFrame`s, we may just want to concatenate them. That's what `concat()` is for:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>city</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CA</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>37.781334</td>\n",
+       "      <td>-122.416728</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NY</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>40.705649</td>\n",
+       "      <td>-74.008344</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>FL</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>25.791100</td>\n",
+       "      <td>-80.320733</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>Cleveland</td>\n",
+       "      <td>41.473508</td>\n",
+       "      <td>-81.739791</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>UT</td>\n",
+       "      <td>Salt Lake City</td>\n",
+       "      <td>40.755851</td>\n",
+       "      <td>-111.896657</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>California</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>808976.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>New-York</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8363710.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Florida</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>413201.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Texas</td>\n",
+       "      <td>Houston</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2242193.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        state            city        lat         lng  population\n",
+       "0          CA   San Francisco  37.781334 -122.416728         NaN\n",
+       "1          NY        New York  40.705649  -74.008344         NaN\n",
+       "2          FL           Miami  25.791100  -80.320733         NaN\n",
+       "3          OH       Cleveland  41.473508  -81.739791         NaN\n",
+       "4          UT  Salt Lake City  40.755851 -111.896657         NaN\n",
+       "3  California   San Francisco        NaN         NaN    808976.0\n",
+       "4    New-York        New York        NaN         NaN   8363710.0\n",
+       "5     Florida           Miami        NaN         NaN    413201.0\n",
+       "6       Texas         Houston        NaN         NaN   2242193.0"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_concat = pd.concat([city_loc, city_pop])\n",
+    "result_concat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that this operation aligned the data horizontally (by columns) but not vertically (by rows). In this example, we end up with multiple rows having the same index (e.g. 3). Pandas handles this rather gracefully:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>city</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>Cleveland</td>\n",
+       "      <td>41.473508</td>\n",
+       "      <td>-81.739791</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>California</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>808976.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        state           city        lat        lng  population\n",
+       "3          OH      Cleveland  41.473508 -81.739791         NaN\n",
+       "3  California  San Francisco        NaN        NaN    808976.0"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_concat.loc[3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or you can tell pandas to just ignore the index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>city</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CA</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>37.781334</td>\n",
+       "      <td>-122.416728</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NY</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>40.705649</td>\n",
+       "      <td>-74.008344</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>FL</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>25.791100</td>\n",
+       "      <td>-80.320733</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>Cleveland</td>\n",
+       "      <td>41.473508</td>\n",
+       "      <td>-81.739791</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>UT</td>\n",
+       "      <td>Salt Lake City</td>\n",
+       "      <td>40.755851</td>\n",
+       "      <td>-111.896657</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>California</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>808976.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>New-York</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8363710.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Florida</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>413201.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Texas</td>\n",
+       "      <td>Houston</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2242193.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        state            city        lat         lng  population\n",
+       "0          CA   San Francisco  37.781334 -122.416728         NaN\n",
+       "1          NY        New York  40.705649  -74.008344         NaN\n",
+       "2          FL           Miami  25.791100  -80.320733         NaN\n",
+       "3          OH       Cleveland  41.473508  -81.739791         NaN\n",
+       "4          UT  Salt Lake City  40.755851 -111.896657         NaN\n",
+       "5  California   San Francisco        NaN         NaN    808976.0\n",
+       "6    New-York        New York        NaN         NaN   8363710.0\n",
+       "7     Florida           Miami        NaN         NaN    413201.0\n",
+       "8       Texas         Houston        NaN         NaN   2242193.0"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([city_loc, city_pop], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that when a column does not exist in a `DataFrame`, it acts as if it was filled with `NaN` values. If we set `join=\"inner\"`, then only columns that exist in *both* `DataFrame`s are returned:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>city</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CA</td>\n",
+       "      <td>San Francisco</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NY</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>FL</td>\n",
+       "      <td>Miami</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>Cleveland</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>UT</td>\n",
+       "      <td>Salt Lake City</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>California</td>\n",
+       "      <td>San Francisco</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>New-York</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Florida</td>\n",
+       "      <td>Miami</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Texas</td>\n",
+       "      <td>Houston</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        state            city\n",
+       "0          CA   San Francisco\n",
+       "1          NY        New York\n",
+       "2          FL           Miami\n",
+       "3          OH       Cleveland\n",
+       "4          UT  Salt Lake City\n",
+       "3  California   San Francisco\n",
+       "4    New-York        New York\n",
+       "5     Florida           Miami\n",
+       "6       Texas         Houston"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([city_loc, city_pop], join=\"inner\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can concatenate `DataFrame`s horizontally instead of vertically by setting `axis=1`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>city</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>population</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CA</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>37.781334</td>\n",
+       "      <td>-122.416728</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NY</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>40.705649</td>\n",
+       "      <td>-74.008344</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>FL</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>25.791100</td>\n",
+       "      <td>-80.320733</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>Cleveland</td>\n",
+       "      <td>41.473508</td>\n",
+       "      <td>-81.739791</td>\n",
+       "      <td>808976.0</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>California</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>UT</td>\n",
+       "      <td>Salt Lake City</td>\n",
+       "      <td>40.755851</td>\n",
+       "      <td>-111.896657</td>\n",
+       "      <td>8363710.0</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New-York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>413201.0</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>Florida</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2242193.0</td>\n",
+       "      <td>Houston</td>\n",
+       "      <td>Texas</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  state            city        lat         lng  population           city  \\\n",
+       "0    CA   San Francisco  37.781334 -122.416728         NaN            NaN   \n",
+       "1    NY        New York  40.705649  -74.008344         NaN            NaN   \n",
+       "2    FL           Miami  25.791100  -80.320733         NaN            NaN   \n",
+       "3    OH       Cleveland  41.473508  -81.739791    808976.0  San Francisco   \n",
+       "4    UT  Salt Lake City  40.755851 -111.896657   8363710.0       New York   \n",
+       "5   NaN             NaN        NaN         NaN    413201.0          Miami   \n",
+       "6   NaN             NaN        NaN         NaN   2242193.0        Houston   \n",
+       "\n",
+       "        state  \n",
+       "0         NaN  \n",
+       "1         NaN  \n",
+       "2         NaN  \n",
+       "3  California  \n",
+       "4    New-York  \n",
+       "5     Florida  \n",
+       "6       Texas  "
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([city_loc, city_pop], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case it really does not make much sense because the indices do not align well (e.g. Cleveland and San Francisco end up on the same row, because they shared the index label `3`). So let's reindex the `DataFrame`s by city name before concatenating:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>population</th>\n",
+       "      <th>state</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>city</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>San Francisco</th>\n",
+       "      <td>CA</td>\n",
+       "      <td>37.781334</td>\n",
+       "      <td>-122.416728</td>\n",
+       "      <td>808976.0</td>\n",
+       "      <td>California</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New York</th>\n",
+       "      <td>NY</td>\n",
+       "      <td>40.705649</td>\n",
+       "      <td>-74.008344</td>\n",
+       "      <td>8363710.0</td>\n",
+       "      <td>New-York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Miami</th>\n",
+       "      <td>FL</td>\n",
+       "      <td>25.791100</td>\n",
+       "      <td>-80.320733</td>\n",
+       "      <td>413201.0</td>\n",
+       "      <td>Florida</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cleveland</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>41.473508</td>\n",
+       "      <td>-81.739791</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Salt Lake City</th>\n",
+       "      <td>UT</td>\n",
+       "      <td>40.755851</td>\n",
+       "      <td>-111.896657</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Houston</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2242193.0</td>\n",
+       "      <td>Texas</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               state        lat         lng  population       state\n",
+       "city                                                               \n",
+       "San Francisco     CA  37.781334 -122.416728    808976.0  California\n",
+       "New York          NY  40.705649  -74.008344   8363710.0    New-York\n",
+       "Miami             FL  25.791100  -80.320733    413201.0     Florida\n",
+       "Cleveland         OH  41.473508  -81.739791         NaN         NaN\n",
+       "Salt Lake City    UT  40.755851 -111.896657         NaN         NaN\n",
+       "Houston          NaN        NaN         NaN   2242193.0       Texas"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([city_loc.set_index(\"city\"), city_pop.set_index(\"city\")], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This looks a lot like a `FULL OUTER JOIN`, except that the `state` columns were not renamed to `state_x` and `state_y`, and the `city` column is now the index."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Categories\n",
+    "It is quite frequent to have values that represent categories, for example `1` for female and `2` for male, or `\"A\"` for Good, `\"B\"` for Average, `\"C\"` for Bad. These categorical values can be hard to read and cumbersome to handle, but fortunately pandas makes it easy. To illustrate this, let's take the `city_pop` `DataFrame` we created earlier, and add a column that represents a category:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>population</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state</th>\n",
+       "      <th>eco_code</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>808976</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>California</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8363710</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New-York</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>413201</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>Florida</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2242193</td>\n",
+       "      <td>Houston</td>\n",
+       "      <td>Texas</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   population           city       state  eco_code\n",
+       "3      808976  San Francisco  California        17\n",
+       "4     8363710       New York    New-York        17\n",
+       "5      413201          Miami     Florida        34\n",
+       "6     2242193        Houston       Texas        20"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_eco = city_pop.copy()\n",
+    "city_eco[\"eco_code\"] = [17, 17, 34, 20]\n",
+    "city_eco"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Right now the `eco_code` column is full of apparently meaningless codes. Let's fix that. First, we will create a new categorical column based on the `eco_code`s:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Int64Index([17, 20, 34], dtype='int64')"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_eco[\"economy\"] = city_eco[\"eco_code\"].astype('category')\n",
+    "city_eco[\"economy\"].cat.categories"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can give each category a meaningful name:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>population</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state</th>\n",
+       "      <th>eco_code</th>\n",
+       "      <th>economy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>808976</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>California</td>\n",
+       "      <td>17</td>\n",
+       "      <td>Finance</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8363710</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New-York</td>\n",
+       "      <td>17</td>\n",
+       "      <td>Finance</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>413201</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>Florida</td>\n",
+       "      <td>34</td>\n",
+       "      <td>Tourism</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2242193</td>\n",
+       "      <td>Houston</td>\n",
+       "      <td>Texas</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Energy</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   population           city       state  eco_code  economy\n",
+       "3      808976  San Francisco  California        17  Finance\n",
+       "4     8363710       New York    New-York        17  Finance\n",
+       "5      413201          Miami     Florida        34  Tourism\n",
+       "6     2242193        Houston       Texas        20   Energy"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_eco[\"economy\"].cat.categories = [\"Finance\", \"Energy\", \"Tourism\"]\n",
+    "city_eco"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that categorical values are sorted according to their categorical order, *not* their alphabetical order:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>population</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state</th>\n",
+       "      <th>eco_code</th>\n",
+       "      <th>economy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>413201</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>Florida</td>\n",
+       "      <td>34</td>\n",
+       "      <td>Tourism</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2242193</td>\n",
+       "      <td>Houston</td>\n",
+       "      <td>Texas</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Energy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>808976</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>California</td>\n",
+       "      <td>17</td>\n",
+       "      <td>Finance</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8363710</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New-York</td>\n",
+       "      <td>17</td>\n",
+       "      <td>Finance</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   population           city       state  eco_code  economy\n",
+       "5      413201          Miami     Florida        34  Tourism\n",
+       "6     2242193        Houston       Texas        20   Energy\n",
+       "3      808976  San Francisco  California        17  Finance\n",
+       "4     8363710       New York    New-York        17  Finance"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_eco.sort_values(by=\"economy\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# What's next?\n",
+    "As you probably noticed by now, pandas is quite a large library with *many* features. Although we went through the most important features, there is still a lot to discover. Probably the best way to learn more is to get your hands dirty with some real-life data. It is also a good idea to go through pandas' excellent [documentation](https://pandas.pydata.org/pandas-docs/stable/index.html), in particular the [Cookbook](https://pandas.pydata.org/pandas-docs/stable/user_guide/cookbook.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "toc": {
+   "toc_cell": false,
+   "toc_number_sections": true,
+   "toc_section_display": "none",
+   "toc_threshold": 6,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}