{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TP 01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function just merges the OECD's life satisfaction data and the IMF's GDP per capita data. It's a bit too long and boring and it's not specific to Machine Learning, which is why I left it out of the book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_country_stats(oecd_bli, gdp_per_capita):\n",
    "    oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
    "    oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
    "    gdp_per_capita.rename(columns={\"2015\": \"GDP per capita\"}, inplace=True)\n",
    "    gdp_per_capita.set_index(\"Country\", inplace=True)\n",
    "    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=\"GDP per capita\", inplace=True)\n",
    "    remove_indices = [0, 1, 6, 8, 33, 34, 35]\n",
    "    keep_indices = list(set(range(36)) - set(remove_indices))\n",
    "    return full_country_stats[[\"GDP per capita\", 'Life satisfaction']].iloc[keep_indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code in the book expects the data files to be located in the current directory. I just tweaked it here to fetch the files in `datasets/lifesat`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "datapath = os.path.join(\"datasets\", \"lifesat\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To plot pretty figures directly within Jupyter\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.96242338]]\n"
     ]
    }
   ],
   "source": [
    "# Code example\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn.linear_model\n",
    "\n",
    "# Load the data\n",
    "oecd_bli = pd.read_csv(datapath + \"oecd_bli_2015.csv\", thousands=',')\n",
    "gdp_per_capita = pd.read_csv(datapath + \"gdp_per_capita.csv\",thousands=',',delimiter='\\t',\n",
    "                             encoding='latin1', na_values=\"n/a\")\n",
    "\n",
    "# Prepare the data\n",
    "country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)\n",
    "X = np.c_[country_stats[\"GDP per capita\"]]\n",
    "y = np.c_[country_stats[\"Life satisfaction\"]]\n",
    "\n",
    "# Visualize the data\n",
    "country_stats.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction')\n",
    "plt.show()\n",
    "\n",
    "# Select a linear model\n",
    "model = sklearn.linear_model.LinearRegression()\n",
    "\n",
    "# Train the model\n",
    "model.fit(X, y)\n",
    "\n",
    "# Make a prediction for Cyprus\n",
    "X_new = [[22587]]  # Cyprus' GDP per capita\n",
    "print(model.predict(X_new)) # outputs [[ 5.96242338]]"
   ]
  },
  {
   "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",
    "import sklearn.linear_model\n",
    "model = sklearn.linear_model.LinearRegression()\n",
    "```\n",
    "\n",
    "with these two:\n",
    "\n",
    "```python\n",
    "import sklearn.neighbors\n",
    "model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.76666667]]\n"
     ]
    }
   ],
   "source": [
    "# Select a 3-Nearest Neighbors regression model\n",
    "import sklearn.neighbors\n",
    "model1 = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)\n",
    "\n",
    "# Train the model\n",
    "model1.fit(X,y)\n",
    "\n",
    "# Make a prediction for Cyprus\n",
    "print(model1.predict(X_new)) # outputs [[5.76666667]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a function to save the figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"fundamentals\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make this notebook's output stable across runs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and prepare Life satisfaction data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want, you can get fresh data from the OECD's website.\n",
    "Download the CSV from http://stats.oecd.org/index.aspx?DataSetCode=BLI\n",
    "and save it to `datasets/lifesat/`."
   ]
  },
  {
   "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>Indicator</th>\n",
       "      <th>Air pollution</th>\n",
       "      <th>Assault rate</th>\n",
       "      <th>Consultation on rule-making</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>Homicide rate</th>\n",
       "      <th>Household net adjusted disposable income</th>\n",
       "      <th>Household net financial wealth</th>\n",
       "      <th>...</th>\n",
       "      <th>Long-term unemployment rate</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>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>13.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>14.02</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>31588.0</td>\n",
       "      <td>47657.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.08</td>\n",
       "      <td>50449.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>85.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>14.41</td>\n",
       "      <td>93.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>27.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>7.61</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>31173.0</td>\n",
       "      <td>49887.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.19</td>\n",
       "      <td>45199.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>69.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>14.46</td>\n",
       "      <td>75.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Indicator  Air pollution  Assault rate  Consultation on rule-making  \\\n",
       "Country                                                               \n",
       "Australia           13.0           2.1                         10.5   \n",
       "Austria             27.0           3.4                          7.1   \n",
       "\n",
       "Indicator  Dwellings without basic facilities  Educational attainment  \\\n",
       "Country                                                                 \n",
       "Australia                                 1.1                    76.0   \n",
       "Austria                                   1.0                    83.0   \n",
       "\n",
       "Indicator  Employees working very long hours  Employment rate  Homicide rate  \\\n",
       "Country                                                                        \n",
       "Australia                              14.02             72.0            0.8   \n",
       "Austria                                 7.61             72.0            0.4   \n",
       "\n",
       "Indicator  Household net adjusted disposable income  \\\n",
       "Country                                               \n",
       "Australia                                   31588.0   \n",
       "Austria                                     31173.0   \n",
       "\n",
       "Indicator  Household net financial wealth  ...  Long-term unemployment rate  \\\n",
       "Country                                    ...                                \n",
       "Australia                         47657.0  ...                         1.08   \n",
       "Austria                           49887.0  ...                         1.19   \n",
       "\n",
       "Indicator  Personal earnings  Quality of support network  Rooms per person  \\\n",
       "Country                                                                      \n",
       "Australia            50449.0                        92.0               2.3   \n",
       "Austria              45199.0                        89.0               1.6   \n",
       "\n",
       "Indicator  Self-reported health  Student skills  \\\n",
       "Country                                           \n",
       "Australia                  85.0           512.0   \n",
       "Austria                    69.0           500.0   \n",
       "\n",
       "Indicator  Time devoted to leisure and personal care  Voter turnout  \\\n",
       "Country                                                               \n",
       "Australia                                      14.41           93.0   \n",
       "Austria                                        14.46           75.0   \n",
       "\n",
       "Indicator  Water quality  Years in education  \n",
       "Country                                       \n",
       "Australia           91.0                19.4  \n",
       "Austria             94.0                17.0  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oecd_bli = pd.read_csv(datapath + \"oecd_bli_2015.csv\", thousands=',')\n",
    "oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
    "oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
    "oecd_bli.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "Australia    7.3\n",
       "Austria      6.9\n",
       "Belgium      6.9\n",
       "Brazil       7.0\n",
       "Canada       7.3\n",
       "Name: Life satisfaction, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oecd_bli[\"Life satisfaction\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and prepare GDP per capita data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like above, you can update the GDP per capita data if you want. Just download data from http://goo.gl/j1MSKe (=> imf.org) and save it to `datasets/lifesat/`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Subject Descriptor</th>\n",
       "      <th>Units</th>\n",
       "      <th>Scale</th>\n",
       "      <th>Country/Series-specific Notes</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Estimates Start After</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>599.994</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>3995.383</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Subject Descriptor         Units  \\\n",
       "Country                                                                        \n",
       "Afghanistan  Gross domestic product per capita, current prices  U.S. dollars   \n",
       "Albania      Gross domestic product per capita, current prices  U.S. dollars   \n",
       "\n",
       "             Scale                      Country/Series-specific Notes  \\\n",
       "Country                                                                 \n",
       "Afghanistan  Units  See notes for:  Gross domestic product, curren...   \n",
       "Albania      Units  See notes for:  Gross domestic product, curren...   \n",
       "\n",
       "             GDP per capita  Estimates Start After  \n",
       "Country                                             \n",
       "Afghanistan         599.994                 2013.0  \n",
       "Albania            3995.383                 2010.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_per_capita = pd.read_csv(datapath+\"gdp_per_capita.csv\", thousands=',', delimiter='\\t',\n",
    "                             encoding='latin1', na_values=\"n/a\")\n",
    "gdp_per_capita.rename(columns={\"2015\": \"GDP per capita\"}, inplace=True)\n",
    "gdp_per_capita.set_index(\"Country\", inplace=True)\n",
    "gdp_per_capita.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Air pollution</th>\n",
       "      <th>Assault rate</th>\n",
       "      <th>Consultation on rule-making</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>Homicide rate</th>\n",
       "      <th>Household net adjusted disposable income</th>\n",
       "      <th>Household net financial wealth</th>\n",
       "      <th>...</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",
       "      <th>Subject Descriptor</th>\n",
       "      <th>Units</th>\n",
       "      <th>Scale</th>\n",
       "      <th>Country/Series-specific Notes</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Estimates Start After</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>Brazil</th>\n",
       "      <td>18.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10.41</td>\n",
       "      <td>67.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>11664.0</td>\n",
       "      <td>6844.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.97</td>\n",
       "      <td>79.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>8669.998</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>30.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>37.0</td>\n",
       "      <td>28.83</td>\n",
       "      <td>61.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>9056.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.89</td>\n",
       "      <td>63.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>14.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>9009.280</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.16</td>\n",
       "      <td>69.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>19292.0</td>\n",
       "      <td>3412.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.97</td>\n",
       "      <td>65.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>9054.914</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>35.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>12.7</td>\n",
       "      <td>34.0</td>\n",
       "      <td>40.86</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>14095.0</td>\n",
       "      <td>3251.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.42</td>\n",
       "      <td>88.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>9437.372</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>82.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>15442.0</td>\n",
       "      <td>13277.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.04</td>\n",
       "      <td>62.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>12239.894</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>33.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.41</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>17852.0</td>\n",
       "      <td>10919.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.20</td>\n",
       "      <td>55.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>12495.334</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>46.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>57.0</td>\n",
       "      <td>15.42</td>\n",
       "      <td>62.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>14533.0</td>\n",
       "      <td>17733.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.41</td>\n",
       "      <td>49.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>13340.905</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovak Republic</th>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>92.0</td>\n",
       "      <td>7.02</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>17503.0</td>\n",
       "      <td>8663.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.99</td>\n",
       "      <td>59.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>15991.736</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>16.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.98</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>18404.0</td>\n",
       "      <td>17299.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.98</td>\n",
       "      <td>59.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>17256.918</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>9.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>68.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>15167.0</td>\n",
       "      <td>7680.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.90</td>\n",
       "      <td>64.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>17288.083</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>27.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>68.0</td>\n",
       "      <td>6.16</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>18575.0</td>\n",
       "      <td>14579.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>64.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>18064.288</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>18.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>38.0</td>\n",
       "      <td>9.62</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>20086.0</td>\n",
       "      <td>31245.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.95</td>\n",
       "      <td>58.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>19121.592</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>26.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.63</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>19326.0</td>\n",
       "      <td>18465.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.62</td>\n",
       "      <td>52.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>20732.482</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.89</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>22477.0</td>\n",
       "      <td>24774.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.06</td>\n",
       "      <td>69.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>25864.721</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea</th>\n",
       "      <td>30.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>10.4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>82.0</td>\n",
       "      <td>18.72</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>19510.0</td>\n",
       "      <td>29091.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.63</td>\n",
       "      <td>76.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>27195.197</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>21.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>3.66</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>25166.0</td>\n",
       "      <td>54987.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.98</td>\n",
       "      <td>75.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>29866.581</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>24.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>94.0</td>\n",
       "      <td>22.26</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>26111.0</td>\n",
       "      <td>86764.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.93</td>\n",
       "      <td>53.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>32485.545</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.7</td>\n",
       "      <td>85.0</td>\n",
       "      <td>16.03</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>22104.0</td>\n",
       "      <td>52933.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.48</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>35343.336</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>11.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13.87</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>23815.0</td>\n",
       "      <td>28290.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.87</td>\n",
       "      <td>77.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>37044.891</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>8.15</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>28799.0</td>\n",
       "      <td>48741.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.33</td>\n",
       "      <td>80.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>37675.006</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>4.57</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>28307.0</td>\n",
       "      <td>83876.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.71</td>\n",
       "      <td>89.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>40106.632</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>16.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>86.0</td>\n",
       "      <td>5.25</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>31252.0</td>\n",
       "      <td>50394.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.31</td>\n",
       "      <td>72.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>40996.511</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>15.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.58</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>27927.0</td>\n",
       "      <td>18761.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.89</td>\n",
       "      <td>69.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>41973.988</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>89.0</td>\n",
       "      <td>3.94</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>29365.0</td>\n",
       "      <td>67913.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.25</td>\n",
       "      <td>61.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>43331.961</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>30.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>27888.0</td>\n",
       "      <td>77961.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.44</td>\n",
       "      <td>75.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>43603.115</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>27.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>7.61</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>31173.0</td>\n",
       "      <td>49887.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.46</td>\n",
       "      <td>75.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>43724.031</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>13.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>78.0</td>\n",
       "      <td>12.70</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27029.0</td>\n",
       "      <td>60778.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.83</td>\n",
       "      <td>66.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>43770.688</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.13</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>29185.0</td>\n",
       "      <td>60328.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.11</td>\n",
       "      <td>86.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>19.3</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>49866.266</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>18.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>71.0</td>\n",
       "      <td>12.25</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>23965.0</td>\n",
       "      <td>43045.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.61</td>\n",
       "      <td>81.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>50854.583</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>13.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>14.02</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>31588.0</td>\n",
       "      <td>47657.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.41</td>\n",
       "      <td>93.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>50961.865</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>13.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>75.0</td>\n",
       "      <td>4.20</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>23917.0</td>\n",
       "      <td>31580.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.19</td>\n",
       "      <td>70.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>51350.744</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.03</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>26491.0</td>\n",
       "      <td>44488.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.06</td>\n",
       "      <td>88.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>52114.165</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>18.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>11.30</td>\n",
       "      <td>67.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>41355.0</td>\n",
       "      <td>145769.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.27</td>\n",
       "      <td>68.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>55805.204</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>16.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2.82</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>33492.0</td>\n",
       "      <td>8797.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.56</td>\n",
       "      <td>78.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>74822.106</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>20.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>6.72</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>33491.0</td>\n",
       "      <td>108823.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.98</td>\n",
       "      <td>49.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>80675.308</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>12.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>78.0</td>\n",
       "      <td>3.47</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>38951.0</td>\n",
       "      <td>61765.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.12</td>\n",
       "      <td>91.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>101994.093</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Air pollution  Assault rate  Consultation on rule-making  \\\n",
       "Country                                                                     \n",
       "Brazil                    18.0           7.9                          4.0   \n",
       "Mexico                    30.0          12.8                          9.0   \n",
       "Russia                    15.0           3.8                          2.5   \n",
       "Turkey                    35.0           5.0                          5.5   \n",
       "Hungary                   15.0           3.6                          7.9   \n",
       "Poland                    33.0           1.4                         10.8   \n",
       "Chile                     46.0           6.9                          2.0   \n",
       "Slovak Republic           13.0           3.0                          6.6   \n",
       "Czech Republic            16.0           2.8                          6.8   \n",
       "Estonia                    9.0           5.5                          3.3   \n",
       "Greece                    27.0           3.7                          6.5   \n",
       "Portugal                  18.0           5.7                          6.5   \n",
       "Slovenia                  26.0           3.9                         10.3   \n",
       "Spain                     24.0           4.2                          7.3   \n",
       "Korea                     30.0           2.1                         10.4   \n",
       "Italy                     21.0           4.7                          5.0   \n",
       "Japan                     24.0           1.4                          7.3   \n",
       "Israel                    21.0           6.4                          2.5   \n",
       "New Zealand               11.0           2.2                         10.3   \n",
       "France                    12.0           5.0                          3.5   \n",
       "Belgium                   21.0           6.6                          4.5   \n",
       "Germany                   16.0           3.6                          4.5   \n",
       "Finland                   15.0           2.4                          9.0   \n",
       "Canada                    15.0           1.3                         10.5   \n",
       "Netherlands               30.0           4.9                          6.1   \n",
       "Austria                   27.0           3.4                          7.1   \n",
       "United Kingdom            13.0           1.9                         11.5   \n",
       "Sweden                    10.0           5.1                         10.9   \n",
       "Iceland                   18.0           2.7                          5.1   \n",
       "Australia                 13.0           2.1                         10.5   \n",
       "Ireland                   13.0           2.6                          9.0   \n",
       "Denmark                   15.0           3.9                          7.0   \n",
       "United States             18.0           1.5                          8.3   \n",
       "Norway                    16.0           3.3                          8.1   \n",
       "Switzerland               20.0           4.2                          8.4   \n",
       "Luxembourg                12.0           4.3                          6.0   \n",
       "\n",
       "                 Dwellings without basic facilities  Educational attainment  \\\n",
       "Country                                                                       \n",
       "Brazil                                          6.7                    45.0   \n",
       "Mexico                                          4.2                    37.0   \n",
       "Russia                                         15.1                    94.0   \n",
       "Turkey                                         12.7                    34.0   \n",
       "Hungary                                         4.8                    82.0   \n",
       "Poland                                          3.2                    90.0   \n",
       "Chile                                           9.4                    57.0   \n",
       "Slovak Republic                                 0.6                    92.0   \n",
       "Czech Republic                                  0.9                    92.0   \n",
       "Estonia                                         8.1                    90.0   \n",
       "Greece                                          0.7                    68.0   \n",
       "Portugal                                        0.9                    38.0   \n",
       "Slovenia                                        0.5                    85.0   \n",
       "Spain                                           0.1                    55.0   \n",
       "Korea                                           4.2                    82.0   \n",
       "Italy                                           1.1                    57.0   \n",
       "Japan                                           6.4                    94.0   \n",
       "Israel                                          3.7                    85.0   \n",
       "New Zealand                                     0.2                    74.0   \n",
       "France                                          0.5                    73.0   \n",
       "Belgium                                         2.0                    72.0   \n",
       "Germany                                         0.1                    86.0   \n",
       "Finland                                         0.6                    85.0   \n",
       "Canada                                          0.2                    89.0   \n",
       "Netherlands                                     0.0                    73.0   \n",
       "Austria                                         1.0                    83.0   \n",
       "United Kingdom                                  0.2                    78.0   \n",
       "Sweden                                          0.0                    88.0   \n",
       "Iceland                                         0.4                    71.0   \n",
       "Australia                                       1.1                    76.0   \n",
       "Ireland                                         0.2                    75.0   \n",
       "Denmark                                         0.9                    78.0   \n",
       "United States                                   0.1                    89.0   \n",
       "Norway                                          0.3                    82.0   \n",
       "Switzerland                                     0.0                    86.0   \n",
       "Luxembourg                                      0.1                    78.0   \n",
       "\n",
       "                 Employees working very long hours  Employment rate  \\\n",
       "Country                                                               \n",
       "Brazil                                       10.41             67.0   \n",
       "Mexico                                       28.83             61.0   \n",
       "Russia                                        0.16             69.0   \n",
       "Turkey                                       40.86             50.0   \n",
       "Hungary                                       3.19             58.0   \n",
       "Poland                                        7.41             60.0   \n",
       "Chile                                        15.42             62.0   \n",
       "Slovak Republic                               7.02             60.0   \n",
       "Czech Republic                                6.98             68.0   \n",
       "Estonia                                       3.30             68.0   \n",
       "Greece                                        6.16             49.0   \n",
       "Portugal                                      9.62             61.0   \n",
       "Slovenia                                      5.63             63.0   \n",
       "Spain                                         5.89             56.0   \n",
       "Korea                                        18.72             64.0   \n",
       "Italy                                         3.66             56.0   \n",
       "Japan                                        22.26             72.0   \n",
       "Israel                                       16.03             67.0   \n",
       "New Zealand                                  13.87             73.0   \n",
       "France                                        8.15             64.0   \n",
       "Belgium                                       4.57             62.0   \n",
       "Germany                                       5.25             73.0   \n",
       "Finland                                       3.58             69.0   \n",
       "Canada                                        3.94             72.0   \n",
       "Netherlands                                   0.45             74.0   \n",
       "Austria                                       7.61             72.0   \n",
       "United Kingdom                               12.70             71.0   \n",
       "Sweden                                        1.13             74.0   \n",
       "Iceland                                      12.25             82.0   \n",
       "Australia                                    14.02             72.0   \n",
       "Ireland                                       4.20             60.0   \n",
       "Denmark                                       2.03             73.0   \n",
       "United States                                11.30             67.0   \n",
       "Norway                                        2.82             75.0   \n",
       "Switzerland                                   6.72             80.0   \n",
       "Luxembourg                                    3.47             66.0   \n",
       "\n",
       "                 Homicide rate  Household net adjusted disposable income  \\\n",
       "Country                                                                    \n",
       "Brazil                    25.5                                   11664.0   \n",
       "Mexico                    23.4                                   13085.0   \n",
       "Russia                    12.8                                   19292.0   \n",
       "Turkey                     1.2                                   14095.0   \n",
       "Hungary                    1.3                                   15442.0   \n",
       "Poland                     0.9                                   17852.0   \n",
       "Chile                      4.4                                   14533.0   \n",
       "Slovak Republic            1.2                                   17503.0   \n",
       "Czech Republic             0.8                                   18404.0   \n",
       "Estonia                    4.8                                   15167.0   \n",
       "Greece                     1.6                                   18575.0   \n",
       "Portugal                   1.1                                   20086.0   \n",
       "Slovenia                   0.4                                   19326.0   \n",
       "Spain                      0.6                                   22477.0   \n",
       "Korea                      1.1                                   19510.0   \n",
       "Italy                      0.7                                   25166.0   \n",
       "Japan                      0.3                                   26111.0   \n",
       "Israel                     2.3                                   22104.0   \n",
       "New Zealand                1.2                                   23815.0   \n",
       "France                     0.6                                   28799.0   \n",
       "Belgium                    1.1                                   28307.0   \n",
       "Germany                    0.5                                   31252.0   \n",
       "Finland                    1.4                                   27927.0   \n",
       "Canada                     1.5                                   29365.0   \n",
       "Netherlands                0.9                                   27888.0   \n",
       "Austria                    0.4                                   31173.0   \n",
       "United Kingdom             0.3                                   27029.0   \n",
       "Sweden                     0.7                                   29185.0   \n",
       "Iceland                    0.3                                   23965.0   \n",
       "Australia                  0.8                                   31588.0   \n",
       "Ireland                    0.8                                   23917.0   \n",
       "Denmark                    0.3                                   26491.0   \n",
       "United States              5.2                                   41355.0   \n",
       "Norway                     0.6                                   33492.0   \n",
       "Switzerland                0.5                                   33491.0   \n",
       "Luxembourg                 0.4                                   38951.0   \n",
       "\n",
       "                 Household net financial wealth  ...  \\\n",
       "Country                                          ...   \n",
       "Brazil                                   6844.0  ...   \n",
       "Mexico                                   9056.0  ...   \n",
       "Russia                                   3412.0  ...   \n",
       "Turkey                                   3251.0  ...   \n",
       "Hungary                                 13277.0  ...   \n",
       "Poland                                  10919.0  ...   \n",
       "Chile                                   17733.0  ...   \n",
       "Slovak Republic                          8663.0  ...   \n",
       "Czech Republic                          17299.0  ...   \n",
       "Estonia                                  7680.0  ...   \n",
       "Greece                                  14579.0  ...   \n",
       "Portugal                                31245.0  ...   \n",
       "Slovenia                                18465.0  ...   \n",
       "Spain                                   24774.0  ...   \n",
       "Korea                                   29091.0  ...   \n",
       "Italy                                   54987.0  ...   \n",
       "Japan                                   86764.0  ...   \n",
       "Israel                                  52933.0  ...   \n",
       "New Zealand                             28290.0  ...   \n",
       "France                                  48741.0  ...   \n",
       "Belgium                                 83876.0  ...   \n",
       "Germany                                 50394.0  ...   \n",
       "Finland                                 18761.0  ...   \n",
       "Canada                                  67913.0  ...   \n",
       "Netherlands                             77961.0  ...   \n",
       "Austria                                 49887.0  ...   \n",
       "United Kingdom                          60778.0  ...   \n",
       "Sweden                                  60328.0  ...   \n",
       "Iceland                                 43045.0  ...   \n",
       "Australia                               47657.0  ...   \n",
       "Ireland                                 31580.0  ...   \n",
       "Denmark                                 44488.0  ...   \n",
       "United States                          145769.0  ...   \n",
       "Norway                                   8797.0  ...   \n",
       "Switzerland                            108823.0  ...   \n",
       "Luxembourg                              61765.0  ...   \n",
       "\n",
       "                 Time devoted to leisure and personal care  Voter turnout  \\\n",
       "Country                                                                     \n",
       "Brazil                                               14.97           79.0   \n",
       "Mexico                                               13.89           63.0   \n",
       "Russia                                               14.97           65.0   \n",
       "Turkey                                               13.42           88.0   \n",
       "Hungary                                              15.04           62.0   \n",
       "Poland                                               14.20           55.0   \n",
       "Chile                                                14.41           49.0   \n",
       "Slovak Republic                                      14.99           59.0   \n",
       "Czech Republic                                       14.98           59.0   \n",
       "Estonia                                              14.90           64.0   \n",
       "Greece                                               14.91           64.0   \n",
       "Portugal                                             14.95           58.0   \n",
       "Slovenia                                             14.62           52.0   \n",
       "Spain                                                16.06           69.0   \n",
       "Korea                                                14.63           76.0   \n",
       "Italy                                                14.98           75.0   \n",
       "Japan                                                14.93           53.0   \n",
       "Israel                                               14.48           68.0   \n",
       "New Zealand                                          14.87           77.0   \n",
       "France                                               15.33           80.0   \n",
       "Belgium                                              15.71           89.0   \n",
       "Germany                                              15.31           72.0   \n",
       "Finland                                              14.89           69.0   \n",
       "Canada                                               14.25           61.0   \n",
       "Netherlands                                          15.44           75.0   \n",
       "Austria                                              14.46           75.0   \n",
       "United Kingdom                                       14.83           66.0   \n",
       "Sweden                                               15.11           86.0   \n",
       "Iceland                                              14.61           81.0   \n",
       "Australia                                            14.41           93.0   \n",
       "Ireland                                              15.19           70.0   \n",
       "Denmark                                              16.06           88.0   \n",
       "United States                                        14.27           68.0   \n",
       "Norway                                               15.56           78.0   \n",
       "Switzerland                                          14.98           49.0   \n",
       "Luxembourg                                           15.12           91.0   \n",
       "\n",
       "                 Water quality  Years in education  \\\n",
       "Country                                              \n",
       "Brazil                    72.0                16.3   \n",
       "Mexico                    67.0                14.4   \n",
       "Russia                    56.0                16.0   \n",
       "Turkey                    62.0                16.4   \n",
       "Hungary                   77.0                17.6   \n",
       "Poland                    79.0                18.4   \n",
       "Chile                     73.0                16.5   \n",
       "Slovak Republic           81.0                16.3   \n",
       "Czech Republic            85.0                18.1   \n",
       "Estonia                   79.0                17.5   \n",
       "Greece                    69.0                18.6   \n",
       "Portugal                  86.0                17.6   \n",
       "Slovenia                  88.0                18.4   \n",
       "Spain                     71.0                17.6   \n",
       "Korea                     78.0                17.5   \n",
       "Italy                     71.0                16.8   \n",
       "Japan                     85.0                16.3   \n",
       "Israel                    68.0                15.8   \n",
       "New Zealand               89.0                18.1   \n",
       "France                    82.0                16.4   \n",
       "Belgium                   87.0                18.9   \n",
       "Germany                   95.0                18.2   \n",
       "Finland                   94.0                19.7   \n",
       "Canada                    91.0                17.2   \n",
       "Netherlands               92.0                18.7   \n",
       "Austria                   94.0                17.0   \n",
       "United Kingdom            88.0                16.4   \n",
       "Sweden                    95.0                19.3   \n",
       "Iceland                   97.0                19.8   \n",
       "Australia                 91.0                19.4   \n",
       "Ireland                   80.0                17.6   \n",
       "Denmark                   94.0                19.4   \n",
       "United States             85.0                17.2   \n",
       "Norway                    94.0                17.9   \n",
       "Switzerland               96.0                17.3   \n",
       "Luxembourg                86.0                15.1   \n",
       "\n",
       "                                                Subject Descriptor  \\\n",
       "Country                                                              \n",
       "Brazil           Gross domestic product per capita, current prices   \n",
       "Mexico           Gross domestic product per capita, current prices   \n",
       "Russia           Gross domestic product per capita, current prices   \n",
       "Turkey           Gross domestic product per capita, current prices   \n",
       "Hungary          Gross domestic product per capita, current prices   \n",
       "Poland           Gross domestic product per capita, current prices   \n",
       "Chile            Gross domestic product per capita, current prices   \n",
       "Slovak Republic  Gross domestic product per capita, current prices   \n",
       "Czech Republic   Gross domestic product per capita, current prices   \n",
       "Estonia          Gross domestic product per capita, current prices   \n",
       "Greece           Gross domestic product per capita, current prices   \n",
       "Portugal         Gross domestic product per capita, current prices   \n",
       "Slovenia         Gross domestic product per capita, current prices   \n",
       "Spain            Gross domestic product per capita, current prices   \n",
       "Korea            Gross domestic product per capita, current prices   \n",
       "Italy            Gross domestic product per capita, current prices   \n",
       "Japan            Gross domestic product per capita, current prices   \n",
       "Israel           Gross domestic product per capita, current prices   \n",
       "New Zealand      Gross domestic product per capita, current prices   \n",
       "France           Gross domestic product per capita, current prices   \n",
       "Belgium          Gross domestic product per capita, current prices   \n",
       "Germany          Gross domestic product per capita, current prices   \n",
       "Finland          Gross domestic product per capita, current prices   \n",
       "Canada           Gross domestic product per capita, current prices   \n",
       "Netherlands      Gross domestic product per capita, current prices   \n",
       "Austria          Gross domestic product per capita, current prices   \n",
       "United Kingdom   Gross domestic product per capita, current prices   \n",
       "Sweden           Gross domestic product per capita, current prices   \n",
       "Iceland          Gross domestic product per capita, current prices   \n",
       "Australia        Gross domestic product per capita, current prices   \n",
       "Ireland          Gross domestic product per capita, current prices   \n",
       "Denmark          Gross domestic product per capita, current prices   \n",
       "United States    Gross domestic product per capita, current prices   \n",
       "Norway           Gross domestic product per capita, current prices   \n",
       "Switzerland      Gross domestic product per capita, current prices   \n",
       "Luxembourg       Gross domestic product per capita, current prices   \n",
       "\n",
       "                        Units  Scale  \\\n",
       "Country                                \n",
       "Brazil           U.S. dollars  Units   \n",
       "Mexico           U.S. dollars  Units   \n",
       "Russia           U.S. dollars  Units   \n",
       "Turkey           U.S. dollars  Units   \n",
       "Hungary          U.S. dollars  Units   \n",
       "Poland           U.S. dollars  Units   \n",
       "Chile            U.S. dollars  Units   \n",
       "Slovak Republic  U.S. dollars  Units   \n",
       "Czech Republic   U.S. dollars  Units   \n",
       "Estonia          U.S. dollars  Units   \n",
       "Greece           U.S. dollars  Units   \n",
       "Portugal         U.S. dollars  Units   \n",
       "Slovenia         U.S. dollars  Units   \n",
       "Spain            U.S. dollars  Units   \n",
       "Korea            U.S. dollars  Units   \n",
       "Italy            U.S. dollars  Units   \n",
       "Japan            U.S. dollars  Units   \n",
       "Israel           U.S. dollars  Units   \n",
       "New Zealand      U.S. dollars  Units   \n",
       "France           U.S. dollars  Units   \n",
       "Belgium          U.S. dollars  Units   \n",
       "Germany          U.S. dollars  Units   \n",
       "Finland          U.S. dollars  Units   \n",
       "Canada           U.S. dollars  Units   \n",
       "Netherlands      U.S. dollars  Units   \n",
       "Austria          U.S. dollars  Units   \n",
       "United Kingdom   U.S. dollars  Units   \n",
       "Sweden           U.S. dollars  Units   \n",
       "Iceland          U.S. dollars  Units   \n",
       "Australia        U.S. dollars  Units   \n",
       "Ireland          U.S. dollars  Units   \n",
       "Denmark          U.S. dollars  Units   \n",
       "United States    U.S. dollars  Units   \n",
       "Norway           U.S. dollars  Units   \n",
       "Switzerland      U.S. dollars  Units   \n",
       "Luxembourg       U.S. dollars  Units   \n",
       "\n",
       "                                     Country/Series-specific Notes  \\\n",
       "Country                                                              \n",
       "Brazil           See notes for:  Gross domestic product, curren...   \n",
       "Mexico           See notes for:  Gross domestic product, curren...   \n",
       "Russia           See notes for:  Gross domestic product, curren...   \n",
       "Turkey           See notes for:  Gross domestic product, curren...   \n",
       "Hungary          See notes for:  Gross domestic product, curren...   \n",
       "Poland           See notes for:  Gross domestic product, curren...   \n",
       "Chile            See notes for:  Gross domestic product, curren...   \n",
       "Slovak Republic  See notes for:  Gross domestic product, curren...   \n",
       "Czech Republic   See notes for:  Gross domestic product, curren...   \n",
       "Estonia          See notes for:  Gross domestic product, curren...   \n",
       "Greece           See notes for:  Gross domestic product, curren...   \n",
       "Portugal         See notes for:  Gross domestic product, curren...   \n",
       "Slovenia         See notes for:  Gross domestic product, curren...   \n",
       "Spain            See notes for:  Gross domestic product, curren...   \n",
       "Korea            See notes for:  Gross domestic product, curren...   \n",
       "Italy            See notes for:  Gross domestic product, curren...   \n",
       "Japan            See notes for:  Gross domestic product, curren...   \n",
       "Israel           See notes for:  Gross domestic product, curren...   \n",
       "New Zealand      See notes for:  Gross domestic product, curren...   \n",
       "France           See notes for:  Gross domestic product, curren...   \n",
       "Belgium          See notes for:  Gross domestic product, curren...   \n",
       "Germany          See notes for:  Gross domestic product, curren...   \n",
       "Finland          See notes for:  Gross domestic product, curren...   \n",
       "Canada           See notes for:  Gross domestic product, curren...   \n",
       "Netherlands      See notes for:  Gross domestic product, curren...   \n",
       "Austria          See notes for:  Gross domestic product, curren...   \n",
       "United Kingdom   See notes for:  Gross domestic product, curren...   \n",
       "Sweden           See notes for:  Gross domestic product, curren...   \n",
       "Iceland          See notes for:  Gross domestic product, curren...   \n",
       "Australia        See notes for:  Gross domestic product, curren...   \n",
       "Ireland          See notes for:  Gross domestic product, curren...   \n",
       "Denmark          See notes for:  Gross domestic product, curren...   \n",
       "United States    See notes for:  Gross domestic product, curren...   \n",
       "Norway           See notes for:  Gross domestic product, curren...   \n",
       "Switzerland      See notes for:  Gross domestic product, curren...   \n",
       "Luxembourg       See notes for:  Gross domestic product, curren...   \n",
       "\n",
       "                 GDP per capita  Estimates Start After  \n",
       "Country                                                 \n",
       "Brazil                 8669.998                 2014.0  \n",
       "Mexico                 9009.280                 2015.0  \n",
       "Russia                 9054.914                 2015.0  \n",
       "Turkey                 9437.372                 2013.0  \n",
       "Hungary               12239.894                 2015.0  \n",
       "Poland                12495.334                 2014.0  \n",
       "Chile                 13340.905                 2014.0  \n",
       "Slovak Republic       15991.736                 2015.0  \n",
       "Czech Republic        17256.918                 2015.0  \n",
       "Estonia               17288.083                 2014.0  \n",
       "Greece                18064.288                 2014.0  \n",
       "Portugal              19121.592                 2014.0  \n",
       "Slovenia              20732.482                 2015.0  \n",
       "Spain                 25864.721                 2014.0  \n",
       "Korea                 27195.197                 2014.0  \n",
       "Italy                 29866.581                 2015.0  \n",
       "Japan                 32485.545                 2015.0  \n",
       "Israel                35343.336                 2015.0  \n",
       "New Zealand           37044.891                 2015.0  \n",
       "France                37675.006                 2015.0  \n",
       "Belgium               40106.632                 2014.0  \n",
       "Germany               40996.511                 2014.0  \n",
       "Finland               41973.988                 2014.0  \n",
       "Canada                43331.961                 2015.0  \n",
       "Netherlands           43603.115                 2014.0  \n",
       "Austria               43724.031                 2015.0  \n",
       "United Kingdom        43770.688                 2015.0  \n",
       "Sweden                49866.266                 2014.0  \n",
       "Iceland               50854.583                 2014.0  \n",
       "Australia             50961.865                 2014.0  \n",
       "Ireland               51350.744                 2014.0  \n",
       "Denmark               52114.165                 2015.0  \n",
       "United States         55805.204                 2015.0  \n",
       "Norway                74822.106                 2015.0  \n",
       "Switzerland           80675.308                 2015.0  \n",
       "Luxembourg           101994.093                 2014.0  \n",
       "\n",
       "[36 rows x 30 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita, left_index=True, right_index=True)\n",
    "full_country_stats.sort_values(by=\"GDP per capita\", inplace=True)\n",
    "full_country_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GDP per capita       55805.204\n",
       "Life satisfaction        7.200\n",
       "Name: United States, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_country_stats[[\"GDP per capita\", 'Life satisfaction']].loc[\"United States\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_indices = [0, 1, 6, 8, 33, 34, 35]\n",
    "keep_indices = list(set(range(36)) - set(remove_indices))\n",
    "\n",
    "sample_data = full_country_stats[[\"GDP per capita\", 'Life satisfaction']].iloc[keep_indices]\n",
    "missing_data = full_country_stats[[\"GDP per capita\", 'Life satisfaction']].iloc[remove_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure money_happy_scatterplot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3))\n",
    "plt.axis([0, 60000, 0, 10])\n",
    "position_text = {\n",
    "    \"Hungary\": (5000, 1),\n",
    "    \"Korea\": (18000, 1.7),\n",
    "    \"France\": (29000, 2.4),\n",
    "    \"Australia\": (40000, 3.0),\n",
    "    \"United States\": (52000, 3.8),\n",
    "}\n",
    "for country, pos_text in position_text.items():\n",
    "    pos_data_x, pos_data_y = sample_data.loc[country]\n",
    "    country = \"U.S.\" if country == \"United States\" else country\n",
    "    plt.annotate(country, xy=(pos_data_x, pos_data_y), xytext=pos_text,\n",
    "            arrowprops=dict(facecolor='black', width=0.5, shrink=0.1, headwidth=5))\n",
    "    plt.plot(pos_data_x, pos_data_y, \"ro\")\n",
    "plt.xlabel(\"GDP per capita (USD)\")\n",
    "save_fig('money_happy_scatterplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data.to_csv(os.path.join(\"datasets\", \"lifesat\", \"lifesat.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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</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>Hungary</th>\n",
       "      <td>12239.894</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea</th>\n",
       "      <td>27195.197</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>37675.006</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>50961.865</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>55805.204</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               GDP per capita  Life satisfaction\n",
       "Country                                         \n",
       "Hungary             12239.894                4.9\n",
       "Korea               27195.197                5.8\n",
       "France              37675.006                6.5\n",
       "Australia           50961.865                7.3\n",
       "United States       55805.204                7.2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data.loc[list(position_text.keys())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure tweaking_model_params_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3))\n",
    "plt.xlabel(\"GDP per capita (USD)\")\n",
    "plt.axis([0, 60000, 0, 10])\n",
    "X=np.linspace(0, 60000, 1000)\n",
    "plt.plot(X, 2*X/100000, \"r\")\n",
    "plt.text(40000, 2.7, r\"$\\theta_0 = 0$\", fontsize=14, color=\"r\")\n",
    "plt.text(40000, 1.8, r\"$\\theta_1 = 2 \\times 10^{-5}$\", fontsize=14, color=\"r\")\n",
    "plt.plot(X, 8 - 5*X/100000, \"g\")\n",
    "plt.text(5000, 9.1, r\"$\\theta_0 = 8$\", fontsize=14, color=\"g\")\n",
    "plt.text(5000, 8.2, r\"$\\theta_1 = -5 \\times 10^{-5}$\", fontsize=14, color=\"g\")\n",
    "plt.plot(X, 4 + 5*X/100000, \"b\")\n",
    "plt.text(5000, 3.5, r\"$\\theta_0 = 4$\", fontsize=14, color=\"b\")\n",
    "plt.text(5000, 2.6, r\"$\\theta_1 = 5 \\times 10^{-5}$\", fontsize=14, color=\"b\")\n",
    "save_fig('tweaking_model_params_plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.853052800266435, 4.911544589158485e-05)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "lin1 = linear_model.LinearRegression()\n",
    "Xsample = np.c_[sample_data[\"GDP per capita\"]]\n",
    "ysample = np.c_[sample_data[\"Life satisfaction\"]]\n",
    "lin1.fit(Xsample, ysample)\n",
    "t0, t1 = lin1.intercept_[0], lin1.coef_[0][0]\n",
    "t0, t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure best_fit_model_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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u0NoYZHWo0zegNtqM1e2zYAzUTtSLFi2Sex8bG6v0uJKSEjx48AA7duxAx44dqxUcIYTUBGVDgW48lC9BMgZI8ixkU2Wuf1IXi1J4Un78WPW1rewLwRxyeM9q5+d8YhCnXHRvbY8t7wpnmFFFtRO12Wasi17l+kbtNmpxmToXkUhUadW2u7s79u7di/bt21cvwhog1HYJQojuJafnoueKU7L3jAElzy3/nTKzDoqf2qLw3+kzJfnmKq/j5aXYw9rKJRdDfjql8pwDU7uilYd+rMtYG23GNU2ouUDtEnVMTAwA3lbRs2dPhISEYPz48QrHmZiYwNHREU2bNpVL7oQQIiQlJcDdu8DPx4uRfa6hLDEXPbUFK1T1XyNDHZcCdG1vjtYtxXLLLipr6Yu5VXGV8dy913BoWrdq30tNoDbj2qN2og4ICJB9HxYWhsDAQLlthBAiREVFQFKS4qQgN28C+fkAnyqzXKlWLIFZ3TyYOeVi4kAnNPKTwMbtBbq2s0AzT/WrYSurMr6elqM3k3dQm3Ht0agzWVhYmLbjIISQaiko4OONy3fqun2bJ2tlLCz4GOQcy6fItngKE8d/F5iomwdT039LjJPq/Xt01RdGbuhiixbudrj+UMU0YdC/yTuozbjmaZSoN23ahP/97384ePAg3N3dFfY/fPgQQUFB+PTTTzF69OhqB0kIIVJ5ebw0XL6EnJTEp9RUxsZG+RjkBg34DF7ZL+wwbXtSuWpdF61U6y4Z0hLB351Vud/YJu8gVadRot64cSPMzc2VJmmAdySzsrLCTz/9RImakGow5iX+srMVZ+hKSODtyqrUsWPwbFiMFs2Bju3MZInZ07PiMci6rNZt7emA7o1dcCYxXelkIcb2cyVVp1GivnHjBoYNG1bhMW3atMGePXs0CooQY2dMS/xlZCiOP75xg6/+pIqLi3zp2KthEbbcvI4LTx4iTwScA2Bh74JJAVV7Xrqq1qWOWKQ6NErU2dnZqFu3boXH2NnZ4dmzZxoFRYixM7Ql/hgDHj1SPilIuuplm+HurjhDV7NmPFGXNe6ny7ickSE3UYiQnhd1xCLVoVGidnd3x5UrVyo85urVq3Bzc9MoKEKMmT4v8SeRAKmpiqXjGzd4VbYqPj6K7cdNmwIOagwx1qfnRR2xiCY0StR9+vTBunXrcPz4cfTu3Vth/7Fjx3D06FFMmjSp2gESYmz0YYm/khIgJUWxhJyQwDt7KSMWA76+ipOCNG3KO3tpSh+eFyHVoVGinjNnDnbs2IH+/ftj7Nix6N27N+rXr4+0tDQcO3YMUVFRsLOzQ2hoqLbjJcTgCWmJv8JCvqJT+RLyrVt8OJQyZmZ8icXyC0s0aQJYVn2EU6WE9LwI0QWNErWPjw+OHDmCESNGYOPGjdi0aZNsH2MMHh4e2LlzJxo0aKC1QAkxFg1dbNG9sYvK6Rp1UTp8+VJ+DLI0Kd+5AxQXKz/H0pKXhsuXkH19ebKuKbXxvAipSRqtRy1VWFiIAwcO4Pz588jKyoKDgwM6dOiAQYMGwdxc9Zy4QiPU+V2J8cp+UaTQS1gbvb5zc0urqMtWWycnQ+Wyi7a2iu3HzZrxdmUTE41D0SpdPS9iXISaC6qVqA2FUH84hGjaS/jZM/lkLP3+/n3V5zg4AM2bKyZlD4+aXXaxOqhXNakOoeYCna1HXVPOnDmDpUuXIi4uDvn5+fDw8MC4ceOwYMGC2g6NaIExT/gBVNxLmDE+tElZD+tHj1Rf09VVsXTs7w+4uelPQlaFelUTQ1StRP3gwQPExMTg4cOHKFDSs0QkEuk0YW7btg1jx47F22+/jc2bN8PW1hZJSUl4WNFMCUQvGNOEH5VhjE/+oWwM8tOnqs/z8FBsP27WDHByqrnYCSHVp3HV96xZs7By5UqUlJTItjHGIPr3T3Lp92X3a1NaWhr8/Pwwbtw4rF69ulrXEmp1h7bpU+nUGNa+LU8iAe7dUz5tZo6KNR1EIt5WXL6E3KwZUJVfZX363SBEV4SaCzQqUa9duxYrVqxA7969MXnyZAwbNgwhISHo27cvTp8+jXXr1iE4OBhTpkzRdrwy69atQ15eHmbPnq2zzzAU+lY61acJLDRRXMw7b5UvHd+8CbxQMSTYxARo1Eix/djPD7CueHRShfTtd4MQY6RRol6zZo1siJb435nufXx8MGLECIwYMQJvv/02evfujbffflurwZZ1+vRpODo64ubNmwgODsb169fh6OiIoUOH4r///a+g/hqqbfo2HaWhTGBRUMCHN5UvId++zccnK2NmxpNv+fbjxo35kozaps3fDSqVE6IbGiXqmzdvYuzYsbIkDQDFZQZbBgQEYMCAAVi+fDneeuut6kepRFpaGl68eIHhw4cjNDQU33zzDS5cuICwsDBcv34dv//+u6wavryCggK5NvUcVfWKBkAfS6f6NoHFixd8ApDynboSE/kMXspYWSlfdrFhQ8C0hrp4aut3g0rlhOiWxv8lOJSZhNfGxgZPy/Vq8fPzw4kTJzSPrBISiQT5+fkICwvDnDlzAACBgYEwNzfH9OnTcfLkSfTq1UvpuZGRkYiIiNBZbEJSE6VTbZekhDqBRU5O6TrIZautU1JUj0GuU0d5D2tv74qXXVRXdZ69tn439K3GhhB9o1Girl+/Ph48eCB77+vri3Pnzskdc/36ddhUZwLfSjg5OeHOnTvo27ev3PY333wT06dPx6VLl1Qm6tDQUHzyySey9zk5OfD09NRZrLVJl6VTXZakanNZwMxM5csulvmVV+DoqDgGuVkzoH593Qx50saz18bvhj7W2BCibzRK1F27dsXvv/8uex8cHIzFixdj8uTJCAoKwpkzZ3DkyJFK16yujlatWiE+Pl5hu7QTu7iC4oqFhQUsdNHgJ0C6LJ3qsiSl62UBGQMeP1bew/rxY9XnvfKKYunY358vu1iTY5C18ey18bthKP0JCBEyjRL12LFj8fDhQ9y7dw/e3t6YNWsWDh06hDVr1mDt2rVgjMHHxwdffvmltuOVGTZsGNasWYMjR46gbdvSUtbhw4cBAJ06ddLZZ+sbXZROa6okVd0JLBjjJWFlJeSKlku3cSxAiX02zJxyYeaci/ZtTPHj1Mbwrl/7ba7afPbV/d3Qt/4EhOgjjRJ1YGAgAgMDZe9tbW0RHx+P/fv3IykpCd7e3ggKCtJp1XefPn0QFBSERYsWQSKRoFOnTvjzzz8RERGBgQMH4vXXX9fZZ+sbZaVTxhgupT7TuKQqlJKUtI3W08EG4jwbpZOC5OYqP1ck4p23ypeQvzr/J84/eCJXyrwDERYczRVEm6s2n311ay6E2p+AEEOiVqLu2bMnQkJCMG7cOAB8aJSPjw+8vLxkx5iZmemsh7cqP//8MyIiIrBmzRpERETA3d0dM2bMQFhYWI3GoS8aONugrrWZVtqVa6skVVQEJCUBFy4X4audj3D7thhFGbYozrQEU7HKk6kpH95Uvod1kya893VZyem5iNujWPctpDZXXTz76tRc1GZ/AkKMgVqJOjY2Vq4E3aNHD4SFhWHhwoW6ikstVlZWWLZsGZYtW1arcegTbbUr67okVVAgv+yitIR8+zZP1oAZgHIdAE1KULdeAfq+bi2XlBs1AtRdzE0oNQUVEVopVtf9CQgxdmolakdHR7nhV7Tgln7SdruyNkpSeXl8yFP5Tl1JSXxKTWWsrBlK7Erbj82cnsPMKRemDi8hEjMsnRmocaLQlzZXIZZiaUEMQnRDrUTdqlUrbNmyBR4eHnBzcwMAXLlyBZs3b670XGl1Oal92i4tVqUklZ2tfNnFu3cruL698h7WiS/SMWnTBa3dR1lCK62qQqVYQoyHWotynDt3DkFBQcjIyIBIJJJbfEMVXS/KoU1CnYhd25LTc9FzxSmV+2OqURKVyshQvuxiRQuaubgoth83awbUq6d8yJOu7yP7RZFCaZVm2iLE8Ak1F6hVou7YsSMSExNx4cIFpKWlISQkBMHBwQgODtZ1fESLtFVaZIyvd6ysh3W6Ys26jLu7Ygm5WTOeqGvjPlSh0iohREg0WuZSLBYjPDy81juTaYtQ/4rShaqUFiUSIDVVeQk5O1v1Z3h7K192scysszV6H4QQog6h5gKN16M2JEL94ehS2dKiV10bpKQolo4TEnhnL2XEYsDXV7H92M8PsLWtnfugUi8hpDqEmgu0uk5PXFwcDh06BCsrK0ycOBHu7u7avDyppsJCvqITT8Q2/774yk9lFhOTY2bGxyCXLyE3aQJYWtZs/MpQT2NCiKHTKFHPnDkT3377LR4+fAhHR0cAwO7duzFy5EhI/h1T8+233+LixYuoX7++9qIlasnPL112sWwJ+c4doFjFpCCWlkDTpoqdunx9ebImhBBSOzRK1DExMejRo4csSQPAggULYG9vj5UrV+LRo0cIDQ3FihUr8NVXX2ktWCIvN5cn4fLtxykpqscg29oqrvDk7w/4+AAmJjUafq3Q9pKchBCiaxol6vv376Nr166y93fu3MGtW7cQFhaGMWPGAAB+//13HD58mBK1Fjx7pnwM8v37qs9xcFBcdtHfH/DwqNlVnoRCl0tyEkKILmmUqHNzc2FbpsfQmTNnIBKJ8Oabb8q2+fv74+TJk9WP0Egwxoc2lS8d37jBh0Kp4uqq2H7s7w+4uRlnQlZFl0tyEkKILmmUqOvVq4dbt27J3h89ehS2trZ49dVXZdtycnKMZs3nqmCMT/6hbNnFMrO0KvDwUD4piJNTzcWur2pqSU5CCNEFjRJ1QEAAtm/fju+++w6WlpbYt28fBg0aBJMyjZyJiYnw8PDQWqD6RiIB7t1TLCEnJAA5OcrPEYl4W7GySUEENFJA7+jDQhuEEKKKRol63rx52LdvHz766CMwxmBtbS23tGR6ejpiY2MxadIkrQUqVMXFQHKyYgn55k3ghYr8YGLCV3QqX0L28wOsK14TQnD0oXOWviy0QQghymiUqBs1aoQbN25gz549AICBAwfCx8dHtv/evXv48MMPMXr0aK0EKQSFhXx4U/nS8a1bfJ8yZmY8+ZYvITduDOh7q4A+dc7Sl4U2CCFEGZqZDPKz0Zia2snGIJettk5MBFStL2Jlxccgly0d+/sDDRsCplqdUkY4xv10XmXiq+3OWcpK+TTlKCGkMkYxM5m+a92atyur+tOlTh3lyy56e/MpNY2FUDtnVVbKp4U2CCH6SK1EvWjRIohEIkyZMgWOjo5YtGiRWhcXiURYsGBBtQKsSdK1kR0dFUvHzZoB9evTkCdAuJ2z1BmCRVOOEkL0jVpV32KxGCKRCAkJCWjSpAnEahYf9W096kOHstG+vR1cXCghV6Qm1rWuKiHGRAjRL3pd9R0TEwMA8PLykntvaLp1o2FQ6hBi5yyhlvIJIaS61ErUAQEBFb4nxmfVqLYKnbO6NnLGqlFtayUeGoJFCDFUGnUm27x5M9q0aYNWrVqpPObvv//GxYsXMW7cOI2DI8IltM5ZQizlE0KINmjUVzkkJAT79u2r8JhDhw5hwoQJGgVF9EcDZxv08HMVRCJcNaotujZylttWm6V8QgjRBp0NzyopKVG70xkh2iC0Uj4hhGiDzhL15cuX5darJqSm0BAsQoghUTtR9+zZU+79xo0bERsbq3BcSUkJHjx4gLt37+Ltt9+udoCEEEKIMVN7CtGy1dgikQiqThOLxXB0dETPnj2xcuVKuLm5aSdSHRLq2DlCCCE1R6i5QO0StUQikX0vFosRHh6OhQsX6iQoQgghhHAatVHHxMTIrZZFCCGEEN3QKFHThCeEEEJIzahWr+8HDx4gJiYGDx8+REFBgcL+ml6UY926dXjvvfdgY2OD3NzcGvtcQgghRFc0Xo961qxZWLlypdyiG4wxiP5dzUL6fU0typGWlobmzZvDxsYG2dnZVUrUQu1AQAghpOYINRdoNCPJ2rVrsWLFCvTo0QO7d+8GYwzjx4/H9u3bMXnyZJiamuKtt95CdHS0tuNVafLkyejevTt69+5dY59JCCGE6JpGiXrNmjXw8fHBkSNHMGTIEACAj48PRowYge+++w7Hjh3Dvn37kJ6eXsmVtCMqKgqnTp3C6tWra+TzCCGEkJqiUWO13gEAACAASURBVKK+efMm+vXrJze2uri4WPZ9QEAABgwYgOXLl1c/wko8efIE06dPx7Jly+Dh4aHzzyOEEEJqksadyRwcHGTf29jY4OnTp3L7/fz8cOLECc0jU9OHH34IPz8/fPDBB2qfU1BQINf5LScnRxehEUIIIdWmUYm6fv36ePDggey9r68vzp07J3fM9evXYWOj2/mW9+zZg4MHD2Lt2rWyTmzqiIyMhL29vezl6empwygJIYQQzWmUqLt27Yr4+HjZ++DgYFy+fBmTJ0/Gr7/+itDQUBw5cgTdu3fXWqDl5ebmYsqUKZg2bRrc3d2RlZWFrKwsFBYWAgCysrKQl5en9NzQ0FBkZ2fLXqmpqTqLkxBCCKkOjYZnxcbG4osvvsAPP/wAb29v5ObmIiAgAJcvX5bNA+7j44OYmBh4e3vrIm7cvXsXDRo0qPCY4ODgStfNBoTbJZ8QQkjNEWou0HgcdXlFRUXYv38/kpKS4O3tjaCgIJ1Wfefn58uV6qWWLVuGU6dO4ciRI3B2dkaLFi0qvZZQfziZmcCiRcDevcDjx0CjRsC8ecCoUbUdGSGEGB6h5gKtJWqhCAkJwe7du/V+wpPbt4GePYHsbODttwE7OyAqCsjIAA4eBAYOrO0IVfvvf4HZs/n3cXFAp07qn8sY/8Nk1Srg5k1+/56eQGAgv2bDhvLH+/gA9+4pv9b77wM//KDJHRBCjJEQcwFQzSlEy0tJScGJEydgZWWFIUOG6LwzmaHKzQUGDABKSoBLl4DGjfn28eOBdu2ApUuFm6gTEoCFCwEbG0BFF4EKzZwJfPUVUK8eMHgw/wPl6lVg7Vpg+3bgjz+A8pUk9vbA9OmK13rtNc3ugRBCBIVpYNmyZaxRo0YsMzNTti0mJobZ2NgwsVjMxGIx8/Pzk9svZNnZ2QwAy87Oru1QGGOMzZrFGMDYgQOK+5o3Z0wkYqygoObjqkxxMWPt2zPWoQNjY8bwe4iLU//8f/5hTCxmzMeHsfI/iq+/5tebMEF+u7c3fxFCSHUJLRdIadTre//+/ahfvz7q1q0r2zZr1ixIJBJERETggw8+wO3bt7Fy5Uot/TlhPLKygG+/BVq1AoKCFPc7OfHq4Rqa9K1KvviCl37XrwdMTKp+/t27gEQCdO3KS9JlDRjAvz55Uu0wCSFEr2hU9Z2cnIxhw4bJ3qempuLixYv49NNPMX/+fADArVu3sGfPHoSHh2slUGOxaxfw8iWv5lYmP59/NTevuZjUcf06EBEBzJ8PNG+u2TUaN+b3dfYs8Pw5UKdO6b7Dh/nXnj0VzysoADZtAtLSgLp1gS5dgNatNYuBEEKERqNEnZWVJTcz2ZkzZyASiRBUpgjYrl07/EA9earsyBH+NSEBUPY3TnIyYGnJS9aaqurfTtOnA2V+3AqKi4GQEKBZM2DOHM3jcnICliwBZs3i1xo0iCfra9eAEyeA//wHmDZN8bxHj/jnl9WvH7BlC+DsrHk8hBAiBBolajc3N9wr09X2+PHjsLCwQMeOHWXb8vPzqzRbGOHOnuVf161TfUzr1oBYo0YLLiKiaseHhFScqJcu5VXe584BZmaaxwXwzmTu7rzH9vffl27v0gUYM0bx+hMnAgEBvBRvYQHcuMHv78gRnujPngXo15AQos80+u++ffv22L9/P3799VecPHkSO3fuRGBgICwsLGTHJCcnw93dXWuBGoPMTN4GGxDA26HLv379lR/XtWvpOV9/zYcvWVnxauHbtyv/HGXXrujl46P6WlevAosX8wTbrl21bh8Av1ZICBAaCqSm8h7wZ87wUnuPHsAvv8gfv3Ahf17Ozrz03bEjcOgQ8PrrfGiYtMqcEEL0lUaJeu7cuSguLsagQYPQp08f5OfnIzQ0VLb/+fPniImJkSthk8qlpfGv9eop33/0KP/65pv867ZtwNy5vBPXhQu8fbZfP95mW1PGjwd8fatena5MdDSwYAEwdSq/Lw8PPsyra1eefK2sgBkzKr+OWAxMmMC/l9ZQEEKIvtKo6rtdu3aIj4/Hli1bAABvvfUWOpWZ1eLq1avo3bs3Ro8erZ0ojURREf9apmJCprAQ2LkTcHMD+vbl277+GvjwQ0D6mDduBFxdgf37+SQpqmizjfrqVf7V0lL5/s6d+de9e/m46IpIawx69FDc5+ICtGzJS8kZGZW3PUv3v3hR8XGEECJ0Gk940rp1a7RW0bX29ddfx+uvv65xUMbKzY1/ffxYcd+aNXz7V1/xdtrCQuDyZfmkK636jY+vOFFrs4160iTl20+fBu7c4e3ELi4VV59L/bueisqhZ9Ltyv6QKU+6mJs6n0sIIUKm1ZnJSPXUr8+rkU+d4u2z0tU34+N5b+oOHUp7PWdk8JnLXF3lr+HqqjzRl6XNSWNVdXoLCeGJOjRU+RSiSUm8BsHXt7SDWNeufAz5V18Bw4bxGcekNm0CEhOBV18tHbZ14wbveFb+j4gzZ/g1LCyAoUOrfYuEEFKrKFELTGgo8O67vJfzyJF86NHOnTyh7d8PmBrIT+yNN/gc3SkppaXe4cOBH38EYmP5mOpBg3i7+9WrwPHjPPF+803pNXbu5POKv/EGv4aFBR/PfewYb6f+4QfAy6sWbo4QQrTIQP7bNxyTJvEq4K++Av73P96hatYsnsDLTp3u7Mxn/yo/U9eTJ9rpfV0bTEx4h7mVK4Gff+ZzexcW8iaB0aP5Myg7z3ePHny8+aVLvBYiP58fO2IE73TWoUPt3QshhGiLwa2epQmhrphSmfbt+dCk5cv5+9xc3h68aVPFbdSEEEIUCTUXUIlaj02fDrz3Hl8lqkUL3kmsXj1eZUwIIcQwVGN+K1Lb3nmHT7k5cyZP1hkZfEYuVUOliPEJD+czs5V90fKfhOgXrZSoMzMzkZeXB09pN2VSY2bMUG8SEGK8WrcunSwHqP40r4SQmqVxiTo7Oxsff/wx3Nzc4OLiggYNGsj2nTt3Dv3798fFixe1EqSxyszk1dve3ryU3KIF72BFSFWYmgKvvFL6qs6CLoSQmqdRos7MzETHjh2xatUqeHp6olmzZijbJ61Vq1Y4e/Ystm7dqrVAjc3t23xN6p9+Anr1Aj74gI+PHj2aT6cpZP/9b2k1a3x81c6VSPhY6nbtAGtrvi51QABw4IDy46Oi+AIer73Gh2eJRHyGttqkSUwXLgD9+/PhaDY2vMf6tm3aiSchgfddaNSIL2Ly6JF2rksIqSFMA9OmTWMikYjt2LGDMcZYeHg4E4vFcscEBQWx1q1ba3L5Gpednc0AsOzs7NoOhTHG2PPnjDVqxNgrrzB2+3bp9suXGROJGOvcufZiq8yNG4xZWDBmY8OX9IiLU/9ciYSxYcP4eb6+jE2dyth77zHm6sq3rVqleI63N9/n7Fz6/YYNWroZDVU1ppgYxszNGbO1Zezddxn79FPGGjTg5y1ZUr1YDh9mbOdOxv76i3/fvj1jzZoxlp9fvesSYoiElgukNErU3t7eLCgoSPZeWaKeOnUqc3Jyql50NURoP5xZs/h/0gcOKO5r3pwn64KCmo+rMsXFPBF06MDYmDFVT9S7dvFzunZl7MWL0u3p6TzhWVgwlpIif87x44zdvcu/j4zUPFFv3lx6HWUKCxlbvpyxoqLKr1WVmIqK+B8lFhaMXbpUuj0nh/+sTU3l/1hjjLF58ypf80yV9HTGrK0Z27278vsgxNgILRdIaVT1/c8//8Df37/CYywtLZGXl6fJ5Y1aVhav+m3VCggKUtzv5MT/K1Y1H3Zt+uILPovY+vV88pKq2rePf507l6+UJeXszDvMFRQAGzbIn9OrF2/Dr44HD/gwtx49+NSt5RUX81niZs7kY9QrU5WYoqP5dKqjRwNt25Zur1OHryRWXKx4zzNn8hndKnqp4uwMNGxY8TGEEGHRKFE7OTkhVdn/aGXcvHkT9VSt10hU2rULePmSLx+pTH4+/2puXnMxqeP6dT6Oe/58oHlzza4hnaO8TL9EGem26GjNrl0RDw/eHpyaypP1gwel+4qLgVGj+DrY773H23i1KTaWf+3TR3GfdNupU/LbHRz4lKkVvVTJzgbu3qXFSgjRJxoNz+revTsOHDiAtLQ01K9fX2H/jRs3cPToUUyQLgpM1HbkCP+akKB8OcrkZN4DvDo9d7W5zCXAk1lICNCsGV88RFMuLvxrSgq/VlnSEuDt25pfvyJDh/JkPWoUT9axsaVTl+7ezde3/vFH3jFMm+7c4V8bN1bcV7cuLwFLj9HErFl8AhxPT/4HyIIF/L7699f8moSQmqVRop43bx7279+Prl27YunSpcjIyAAAJCQk4I8//sC8efNgYWGBWbNmaTVYY3D2LP+qalUqgI+LFVdjqhptLnMJAEuX8irvc+eqN0b3zTf58LNly4CePUsnbnn6tHQxjqwsza9fmeHD+R8dY8fyz2/Rgpekx4/nPw9tJ2mAl3AB+ZXCyrKzky/hV1VqKp/7PCODJ+iAAN4D3dpa82sSQmqWRom6ZcuW+PnnnzFu3DiMHTsWAMAYQ4sWLcAYQ506dbBz5040VlZMICplZvJFNQICSqtEyzp8GBgwgC8HWR3anN396lVg8WLeblrdxUBGjeLtsTExQMuWQL9+fCnMfftK1+rWpO27qjEUFPAS9O3bfM709eur94dRbdqxo7YjIIRUl8Yzkw0aNAjJycnYtGkTzp07h8zMTNjZ2aFjx46YMGECnJ2dtRmnUUhL419VNe1LZ5d6803+9ZdfgO+/By5eBJ49k18ysqaMH8+X4Kxqdboypqa86n/ZMl4NvWYNL2kOGcL/EGjSpLR6XFckEvk/kq5d4x33pH8oaJu0JC0tWZeXk6O6tE0IMQ5qJepFixYhMDAQ3bt3l9vu6OiIGTR/pdYUFfGvFhaK+woL+frLbm5A3758W14e0K0bb1/98EP1P0ebbdRXr/KvquYX79yZf927Fxg8uPLPsrAAwsL4qyxp8tTlPNWM8Q5jmzbx6u/AQP6+Z09eynd11f5nSiud7twBXn1Vft+zZ7zKuksX7X8uIUR/qJWow8PDER4eLkvUJiYmCA8Px4IFC3QanLGRltqkvZ/LWrOGb//qq9J24H9bHXDzZtU+R5tt1JMmKd9++jRPPoMG8VJwdUv60knuRo6s3nVUYQz4z394Nffo0bwdVyzm7dKTJvFkHR2t/WQdEABERgLHjine27FjpccQQoyXWonaxsYGL1++lL1nfKIUnQVlrOrX59XIp07xTkDSNU7i43lv6g4dgGnTqv852vzRqer0FhLCE3VoKNCpk+L+pCReg+DrK98BLSeHd6Aqa/dunkDbt+e1B9rGGJ+idd06niw3by5tk54wge9/913gjTd4stZm9fsbb/Bxzdu2AR99BLRpw7c/fw58/jlvDggJ0d7nEUL0j1qJulGjRti7dy+GDh0Kt3+LfVlZWbh//36l53p5eVUvQiMTGsqTQpcuPGk8esSrvH19gf37+X/chuCNN4B79xTb1Tt25H+gNGvGq9PPn+fV3g0b8jHm5TuTrVsHnDnDv792rXSbtKp88ODKq9wfPuRV82+/zefpLv8ZEyfytuvJk/kfUW+9VfH1qhKTqSnf17cvb8YYNYr/ofLLL/zZLF7M2+YJIUZMnenLtmzZwkQiEROLxUwsFst9X9HLxMREl7OqaY3Qpo1bvZrP9W1uzljDhnzKyNxc1ccnJPBpI8tPr1mbxo+veApR6RzY5WMOC2OsZUvG6tRhzNKSz0s9fz5jqn400s9R9QoLUy/epKTKpwe9dUu9a2kS07lzjPXrx5i9PWNWVoy99hpjUVHqfR4hRDuElgukRIypVxEaFxeHw4cPIy0tDRs3bkTr1q3RRlpPV4EN5ec/1JLo6GhERUXhjz/+QGpqKhwcHPDaa69h4cKFeLV8r5xK5OTkwN7eHtnZ2bArX++qB27e5CXQ2uj1TQghhkKouUDtitTOnTuj879deDdu3IghQ4Zg4cKFOgusMt9//z2ePn2Kjz/+GP7+/khPT8eKFSvQqVMn/Pbbb+jZs2etxUYIIYRoi0YtnjExMfCp5aLbd999B9dyXXD79euHRo0aYenSpUaRqDMzgfv3+dzNAHDjBp+5y8sLcHSs1dAIIYRoiUaJOkAA40XKJ2kAsLW1hb+/f6ULhhiKAwd4r2SpAQP41w0bqKcwIYQYCrUS9ebNmwEAQ4YMQZ06dWTv1TFu3DjNItNAdnY2Ll26ZBSlaYAnY0rIhBBi2NTqTCYWiyESiZCQkIAmTZrI3leEMQaRSISSkhKtBVuZMWPG4Oeff0Z8fHyFHcoKCgpQUFAge5+TkwNPT0/BdSAghBBSc/S6M9n69eshEolk60vrqid3dSxYsABbt27FqlWrKu31HRkZiYiqTs9FCCGE1AK1h2cJWUREBMLDw7FkyRLMnTu30uOpRE0IIaQ8oZaodbZ436pVqzBs2DBdXV5GmqTDw8PVStIAYGFhATs7O7kXIYQQIkQ6S9SXLl3Cvn37dHV5AMDnn3+O8PBwzJ8/H2Hll1sihBBCDIDezhy9YsUKLFy4EP369cOAAQMQHx8vt7+TspUgCCGEED2jt4n64MGDAICjR4/i6NGjCvsNoOmdEEII0d9EHStdiogQQggxYDproyaEEEJI9VGiJoQQQgRM7arv/v37V+nC165dq3IwhBBCCJGndqJW1mGrMpVNM0oIIYSQiqmdqFNSUnQZByGEEEKUUDtRe3t76zIOQgghhChBnckIIYQQAaNETQghhAgYJWpCCCFEwChRE0IIIQJGiZoQQggRMErUhBBCiIBRoiaEEEIEjBI1IYQQImCUqAkhhBABo0RNCCGECBglakIIIUTAKFETQgghAkaJmhBCCBEwStSEEEKIgFGiJoQQQgSMEjUhhBAiYJSoCSGEEAGjRE0IIYQIGCVqQgghRMAoURNCCCECRomaEEIIETBK1IQQQoiAUaImhBBCBIwSNSGEECJgep2oc3NzMX36dLi7u8PS0hJt2rTBjh07ajssQgghRGtMazuA6hg6dCguXLiAZcuWoUmTJti2bRtGjRoFiUSC0aNH13Z4hBBCSLWJGGOstoPQxOHDhzFgwABZcpbq06cP/v77b9y/fx8mJiZqXSsnJwf29vbIzs6GnZ2drkImhBAiYELNBXpb9b13717Y2tpi+PDhctsnTJiAhw8f4ty5c7UUGSGEEKI9epuor1+/jmbNmsHUVL72vlWrVrL9hBBCiL7T2zbqp0+fomHDhgrbHR0dZftVKSgoQEFBgex9dnY2AF7tQQghxDhJc4DQWoT1NlEDgEgk0mhfZGQkIiIiFLZ7enpqJS5CCCH66+nTp7C3t6/tMGT0NlE7OTkpLTVnZmYCKC1ZKxMaGopPPvlE9j4rKwve3t64f/++oH44NSUnJweenp5ITU0VVAeKmmTsz8DY7x+gZwDQM8jOzoaXl1eF+aM26G2ibtmyJbZv347i4mK5dupr164BAFq0aKHyXAsLC1hYWChst7e3N8pfTik7Ozujvn+AnoGx3z9AzwCgZyAWC6v7lrCiqYIhQ4YgNzcXe/bskdu+adMmuLu7o2PHjrUUGSGEEKI9eluifvPNN9G7d2988MEHyMnJQaNGjbB9+3YcPXoUUVFRao+hJoQQQoTMJDw8PLy2g9DUkCFDkJGRgW+//RZr165FQUEBvvnmG41mJTMxMUFgYKDCcC9jYez3D9AzMPb7B+gZAPQMhHj/ejszGSGEEGIM9LaNmhBCCDEGlKgJIYQQATPaRK1PS2Q+f/4cn332Gfr06QMXFxeIRCKo6lpw6dIl9OrVC7a2tnBwcMDQoUORnJys9NhVq1ahadOmsLCwQIMGDRAREYGioiKF4548eYKQkBA4OzvD2toanTt3xsmTJ5Ve88SJE+jcuTOsra3h7OyMkJAQPHnyRON7l4qOjsbEiRPRtGlT2NjYoH79+ggODsbFixcVjjXEZ3DlyhUMGDAAXl5esLKygqOjIzp37oyoqCijuH9l1q1bB5FIBFtbW4V9hvgMYmNjIRKJlL7i4+MN/v7LOnPmDPr374+6devCysoKjRs3xueffy53jEE9A2akevfuzRwcHNgPP/zAoqOj2bvvvssAsK1bt9Z2aApSUlKYvb096969uyzOsLAwheMSEhJYnTp1WLdu3divv/7K9uzZw5o3b87c3d3ZkydP5I5dvHgxE4lELDQ0lMXExLD//ve/zNzcnL333ntyx+Xn57MWLVowDw8PFhUVxY4dO8aCg4OZqakpi42NlTs2NjaWmZqasuDgYHbs2DEWFRXF6tevz1q0aMHy8/Or9Qzeeust1qNHD7Z69WoWGxvLdu3axTp16sRMTU3ZyZMnDf4ZxMTEsPfff59t2bKFRUdHs4MHD7KRI0cyAOzzzz83+Psv78GDB8ze3p65u7szGxsbuX2G+gxiYmIYALZ06VIWFxcn93r+/LnB37/U1q1bmVgsZiNHjmQHDhxg0dHRbO3atSwiIsJgn4FRJupff/2VAWDbtm2T2967d2/m7u7OiouLayky5SQSCZNIJIwxxtLT01Um6uHDhzNnZ2eWnZ0t23b37l1mZmbGPvvsM9m2jIwMZmlpyf7zn//Inb9kyRImEonY33//Ldv23XffMQDsjz/+kG0rKipi/v7+rEOHDnLnt2/fnvn7+7OioiLZtrNnzzIAbPXq1Zrd/L8eP36ssO358+fMzc2NvfHGG7JthvwMlOnYsSPz9PSUvTeW+x84cCALCgpi48ePV0jUhvoMpIl6165dFR5nqPfPGP8DzcbGhn3wwQcVHmdoz8AoE/W7777LbG1t5R4iY4xt27aNAWBnz56tpcgqpypRFxUVMSsrK/b+++8rnNOnTx/WuHFj2fuoqCgGgMXFxckd9/DhQwaALVmyRLatV69ezM/PT+GaS5cuZQDYgwcPGGP8HxAAFhkZqXBskyZNWO/evat0n+rq0aMHa9KkCWPMOJ/BgAEDWIMGDRhjxnP/W7ZsYXXq1GGpqakKidqQn4E6idqQ758xxsLDwxkAdvfuXZXHGOIzMMo2akNcIjMpKQkvX76U3UNZrVq1QmJiIvLz8wGU3l/Lli3ljqtXrx6cnZ3l7v/69esqrwkAf//9t9w1VR2ri2eanZ2NS5cuoXnz5gCM4xlIJBIUFxcjPT0dq1evxm+//YbZs2cDMI77f/LkCaZPn45ly5bBw8NDYb8xPIMpU6bA1NQUdnZ26Nu3L86cOSPbZ+j3f/r0aTg6OuLmzZto06YNTE1N4erqismTJ8tWvjLEZ2CUifrp06dKJ11XZ4lMoZLGrOq+GGN49uyZ7FgLCwvY2NgoPbbs/av7rCr7fF080ylTpiAvLw/z5s1TKwZDeAYffvghzMzM4OrqihkzZuB///sf3n//fbU+31Du38/PDx988IHS/Yb8DOzt7fHxxx/jxx9/RExMDFauXInU1FQEBgbit99+U+vz9fn+ASAtLQ0vXrzA8OHDMWLECJw4cQKzZs3C5s2b0b9/fzDGDPIZCGfqlRqm6RKZQqfufVXl/rVxrLaf6YIFC7B161asWrUKr776qtqfpe/PYO7cuXj33Xfx5MkTHDx4EFOnTkVeXh5mzpxZ5Vj17f737NmDgwcP4vLly5VeyxCfQdu2bdG2bVvZ+27dumHIkCFo2bIlPvvsM/Tt27fKserT/QO8Rik/Px9hYWGYM2cOACAwMBDm5uaYPn06Tp48CWtr6yrFqw/PwChL1NVZIlOonJycACivDcjMzIRIJIKDg4Ps2Pz8fLx48ULpsWXvX91nVdnna/OZRkREYPHixViyZAmmTp0qF2tFMRjCM/Dy8sJrr72G/v374/vvv8d//vMfhIaGIj093aDvPzc3F1OmTMG0adPg7u6OrKwsZGVlobCwEABfqjYvL8+gn4EyDg4OGDhwIP766y+8fPnS4O9fev2yf5QAfO0HgA/JMsRnYJSJumXLlkhISEBxcbHcdnWWyBQqX19fWFlZye6hrGvXrqFRo0awtLQEUNoeU/7YR48eISMjQ+7+W7ZsqfKaQOmzkn5Vday2nmlERATCw8MRHh6OuXPnyu0zlmdQVocOHVBcXIzk5GSDvv+MjAw8fvwYK1asQN26dWWv7du3Iy8vD3Xr1sU777xj0M9AFfbvLNAikcjg719Zuy9Q+gzEYrFhPoMqdz8zAIcPH2YA2I4dO+S29+vXT5DDs8qqaHjW22+/zVxdXVlOTo5s271795i5uTmbPXu2bNvTp0+ZpaUlmzx5stz5kZGRCkMSVq9ezQCw+Ph42baioiLWvHlz1rFjR7nzO3TowFq0aCH3/OLi4hgA9v3332t8z1KLFi1iANj8+fNVHmPoz6C8sWPHMrFYLBsbaqj3//LlSxYTE6Pw6tu3L7O0tGQxMTHs2rVrBv0MlMnMzGT169dnbdq0kW0z5Pv/7bffFHpjM8bYV199xQCw33//nTFmeM/AKBM1Y3zMdN26ddmaNWtYdHQ0e++99xgAFhUVVduhKXX48GG2a9cutn79egaADR8+nO3atYvt2rWL5eXlMcb4IH9bW1vWvXt3dvjwYfbLL7+wFi1aVDjIf+7cuSw2NpZ9+eWXzMLCQukg/+bNmzNPT0+2detWdvz4cTZkyBClg/xjYmKYqakpGzJkCDt+/DjbunUr8/T01MpEB8uXL2cAWL9+/RQmeyg7tMJQn8F7773HPv30U/bzzz+z2NhYtnv3bjZixAgGgM2aNcvg718VZeOoDfUZjBo1is2ePZvt2rWLxcTEsDVr1jA/Pz9mamrKjh8/bvD3LxUUFMQsLCzY559/zo4fP84iIyOZpaUlGzhwoME+A6NN1M+fP2cfffQRe+WVV5i5uTlr1aoV2759e22HpZK3tzcDoPSVkpIiO+7PP/9kb7zxBrO2tmZ2dnZs8ODBLDExUek1V65cuYAllgAAEPxJREFUyZo0acLMzc2Zl5cXCwsLY4WFhQrHPXr0iI0bN445OjoyS0tL1qlTJ7n/GMo6duwY69SpE7O0tGSOjo5s3LhxSicrqaqAgACV91++YsgQn8H69etZt27dmLOzMzM1NWUODg4sICCAbdmyReFYQ7x/VZQlasYM8xlERkayNm3aMHt7e2ZiYsJcXFzYkCFD2Pnz543i/qVevHjBZs+ezTw9PZmpqSnz8vJioaGhCgnQkJ4BLXNJCCGECJhRdiYjhBBC9AUlakIIIUTAKFETQgghAkaJmhBCCBEwStSEEEKIgFGiJoQQQgSMEjUhhBAiYJSoCSGEEAGjRE0I0brAwMBaXy529+7dEIlEOHfuXK3GoYmwsDDUqVMHjx8/ru1QiABQoia15sqVK5g8eTL8/f1hZ2cHc3Nz1KtXD3369ME333yjdJk4kUgk97KyssIrr7yC119/HTNnzsTVq1eVftbdu3cVzjU3N4enpydGjx6Nv/76S9e3a/Q2btwIkUiEjRs36vyzioqKEBoaiv79+6Njx46y7bGxsRCJRAgJCVF5bkXH/PrrrxgwYABcXV1hZmYGZ2dntGjRAhMnTsT+/fvljpXer/QlFothZ2eHBg0aIDg4GKtWrZItkVjejBkzYGJigoULF2p0/8SwmNZ2AMT4SCQSfPbZZ1ixYgVMTU3RvXt39OnTB9bW1njy5An++OMPzJgxAwsXLkRycjKcnZ3lzndycpKtQ11UVISMjAxcunQJK1aswIoVKzBx4kSsXr0aFhYWCp/t6+uLMWPGAOBrHMfHx2P79u345ZdfEB0djS5duuj+ARiBzZs3K13jt6Zs3LgRiYmJWLt2rdauKV1i1draGgMHDoSPjw+ys7ORlJSEn3/+Gbdv30ZwcLDCeW+88QZef/11APx37sGDB/j9999x4MABhIWF4ccff8Tw4cPlznFwcMC7776Lb775BnPnzoW3t7fW7oPoIY1mCCekGubMmcMAsNdee03lJPnnz59ngYGBLDU1VW47AObn56f0nL/++ou1adOGAWBjxoyR25eSksIAsL59+yqcN2/ePAaABQYGanhHRB0bNmxgANiGDRt0/lnt2rVjXl5eTCKRyG2PiYlhANj48eNVnqvsmJSUFCYWi5mnpydLS0tTOOfFixcsJiZGbpv0fiMjIxWOLy4uZuvWrWNWVlbMxMSE/fbbbwrHXL58mQFg8+bNq/hmicGjqm9So+7cuYMvv/wSrq6uOHLkCHx9fZUe1759e0RHR6NevXpqX7tly5Y4duwYXF1dERUVhfPnz6t13rRp0wAAFy5cqPTY8PBwiEQixMbGYu3atWjevDksLS3h5eWF0NBQ5OfnKz3vr7/+wsiRI1GvXj2Ym5vD29sb06ZNU6jel1bRh4SE4ObNmxg6dCicnZ0hEolw9+7dSuN78uQJZs6cCT8/P1haWsLR0RGdOnXCihUr5I5bv349goOD4ePjIzuub9++iImJUbimtCo4PDwcp0+fRkBAAGxtbeHo6IjRo0fjwYMHCueUb6MOCQnBhAkTAAATJkyQqxKWunjxIqZOnYoWLVrA3t4eVlZWaNmyJZYtW4aioqJK713q2rVruHTpEoYNG6a1dvLz589DIpFg6NChcHd3V9hvZWWFwMBAta9nYmKCSZMm4YcffkBJSQk++eQTsHLrI7Vp0waNGzeukaYCImyUqEmN2rhxI0pKSvD+++8rVGmXJxKJYGJiUqXru7i4YPLkyQCAn3/+Wa1zNPnPfMWKFfjkk0/QuXNnfPzxx7C3t8eyZcswePBghf9wDxw4gA4dOuDgwYMIDAzE9OnT0bJlS3z77bfo3Lkznj17pnD9xMREdOrUCY8fP8b48eMREhICc3PzCmO6c+cO2rVrhxUrVsDV1RUff/wxRo8eDUtLSyxZskTu2ClTpuDx48fo1asXZsyYgYEDByIuLg69evVSaGuVio+PR+/eveHk5ISPPvoIHTp0wPbt29GlS5dKOz0NHjxYVi0cHByMsLAw2Utq7dq12Lt3L1q2bIn3338fkyZNAmMMoaGhGDlyZIXXL+vkyZMAgE6dOql9TmUcHR0B8J+LNo0ZMwY+Pj74+++/cf36dYX9nTt3RlpaGm7duqXVzyV6ppZL9MTI9OjRgwFg0dHRGp2PCqq+pU6ePMkAsG7dusm2aavqOywsjAFglpaW7Pr167LtRUVFrHfv3gwA27x5s2x7RkYGs7OzYx4eHuzevXty19q2bRsDwKZOnaoQJwC2YMGCSuMpq0OHDgwAW7NmjcK+8k0IycnJCsc8fPiQubu7s8aNG8ttl1YFA2Dr1q2T2xcREcEAsIkTJ8ptl64fXlZlVd93795lxcXFctskEgmbOHEiA8DOnDmj9Lzyhg8fzgCwO3fuKOzTtOr7+fPnzMPDgwFgwcHBbPv27SwxMVGhar2siqq+yxo7diwDwH766SeFfStXrmQA2Pr16yu8BjFsVKImNerRo0cAoLT6MDo6GuHh4XKvM2fOVPkzpNfOyMhQ2JeYmCi79syZM/H6669jyZIlsLS0xNKlS9X+jLFjx6J58+ay96amprLzN23aJNu+efNm5OTkIDIyEl5eXnLXGDVqFNq1a4cdO3YoXP+VV17B/Pnz1Y7nwoULOH/+PLp374733ntPYb+Hh4fc+wYNGigcU69ePQwbNgx37tzBvXv3FPb7+flh4sSJcttmzZoFFxcXbN++HYWFhWrHq4y3t7dCDYpIJMKUKVMAACdOnFDrOtKqeDc3t2rFU5atrS327dsHf39/7N+/H6NGjUKjRo1Qt25dBAUFYe/evRpfu6LfV+k9KGteIMaDen2TGsXKVQuXFR0drVBFa2lpKesxq43PSEpKQkREBADAzMwMbm5uGD16NObMmYOWLVuq/RndunVT2Pbaa6/BysoKV65ckW2Lj4+XfVVWbZqfn4+MjAxkZGTINQW0bt260qrusqTt8X369FHr+OTkZERGRiI6OhppaWkoKCiQ2//w4UOFnsZdu3ZVaCawsrLCq6++iqNHj+L27dto0aKF2jGXV1hYiG+//RY7duzAzZs3kZubK/ezfPjwoVrXefr0KUxMTFCnTh2NY1Hm1VdfxfXr1xEXF4eYmBhcvHgRZ86cwaFDh3Do0CG888472LJlS5WbUir6fZVWuStL4sR4UKImNcrNzQ03b95EWloa/Pz85PYtXrwYixcvBsDbsqWdj6rqn3/+AcDbq8vr27cvjh49qtF1y3J1dVW5PS0tTfZeOk72u+++q/B6eXl5com6qqXBrKwsAED9+vUrPTYxMREdOnRATk4OevTogaCgINjZ2UEsFiM2NhanTp1SSNyA6nuWxpqdnV2lmMt76623cPDgQTRp0gQjRoyQjVXOysrCypUrlcakjJWVFUpKSlBUVAQzMzO5fWIxr0SUSCQqz5fukx5blkgkQpcuXWTD+Bhj2L9/P8aNG4etW7di2LBhGDJkiFpxSlX0+/ry5UsAgLW1dZWuSQwLJWpSo7p06YJTp04hJiYGPXv21MlnxMbGAuA9x3XlyZMnKrfb29vL3tvZ2QHgPZGrUtqsaqnMwcEBAOT+SFDl66+/xrNnzxAVFYV33nlHbt/kyZNx6tQppeepumdpR7Ky911VFy5cwMGDB9G3b1/8+uuvclXg8fHxWLlypdrXkia8zMxMhT94pDEqm0xHSlp6Ved+RCIRBg8ejBkzZmDRokWIjo6uUqKWSCQ4ffo0AOW/r9I/9JQlcWI8qI2a1Kjx48dDLBZjzZo1OqnOS09Px48//ggAVeopXFW///67wrY///wTL1++RJs2bWTbpLNixcXF6SwWAOjQoQMA4NixY5Uem5SUBAAYNGiQ3HaJRIKzZ8+qPO/s2bMK1bQvX77ExYsXYWVlhSZNmlT4udLkW1JSojKmAQMGKLRTK3vWFZE2Ydy5c0dh3//bubuQpv4wDuBfac2ZLdaiBEmKLtoqpje5NMkVLnsZQQSVBVGa9EYUSJDUxZSIkHIU0UVgFs1uoosQFwTB0m6a6SpymF2ESpFtFSU4vDCeLvyfQ3Mnc/UnT+v7gV3oztl+28Rn5/e82Gw2GI1GPHnyBGNjY5rnK59Vfn7+lJ8zOzs7pTUq/H4/BgYG4HA4EmoeFEq1dyppGUo/DNT0R9lsNtTU1CAajWLTpk3qP+iJlK3cVPT09KC8vBzRaBT79u3DypUrf3e5P+T3+xGJRNSfx8bGcOrUKQDjX0YUlZWVMJvNOH36dMLxing8ruaxf0dhYSGcTic6Ojo0p3F9f6Wt5J4nFuo1NDRotggp+vr60NzcnPC78+fPIxaLYdeuXT/NqSv5Vq3CqB+tKRKJ4Ny5c5M+7kQulwsANPvoTSYTduzYgVgspqZZvvfixQs0NTXBbDYnXBl3dnbi5s2bmn3y0WgUTU1NADDleoqvX7+iubkZhw8fxowZM+Dz+TR3UUKhEAwGAyfm/eO49U1/nDLA4tKlS7DZbHC5XMjPz1dHiD579gxdXV2YM2eO5lXNhw8fUFdXB2A8QH78+BHd3d3qwJLq6uqf5oR/l9vtRlFRESoqKmC1WnHv3j309PRgw4YN6ohSAGpF9Pbt21FQUICNGzfCbrdjdHQUAwMDaG9vx+rVq/+XvHlLSwvWrl2LAwcOwO/3o7i4GKOjo4hEInj69Km63Xvo0CFcv34d27Ztw86dOzFv3jw8fvwY4XAYHo8HgUBA8/HLy8tx5MgRBAIB2O12hMNh3L9/H3l5eVOqmC8uLkZWVhYuXryI4eFhdTu3trYWTqcTTqcTt2/fxrt371BUVITBwUG0trbC4/Hgzp07U34fysrKYDab8eDBA9TU1CTd39jYiFAohPr6erS1tcHlcsFkMuHVq1dobW2FiODWrVtqOgEYL2Tbu3cvjh49itLSUtjtdhgMBvT396OtrQ0jIyPweDxJo0CB8Wp1JcDH43G8efMGHR0dePv2LaxWK/x+P9xud9J5yojb9evX//IVO6WJaWwNo39cV1eXVFdXy9KlSyU7O1tmzpwpOTk54na7xefzSSwWSzoH//XzKrfMzExZsGCBlJSUyIkTJ+T58+eazzVZH3UqlD7qYDAoV69eleXLl0tmZqYsXLhQamtrJR6Pa5738uVL2b9/vyxatEiMRqPMnTtXHA6HHDt2TDo7O5PWOVmf72SGhobk+PHjsmTJEjEajWK1WmXVqlXi8/kSjgsGg1JSUiJms1ksFots3rxZuru7E17f98cCEK/XK+3t7bJmzRqZNWuWWCwWqaiokMHBwaR1aPVRi4gEAgEpLCyUrKws9TNURKNRqaqqktzcXDGZTOJwOOTKlSvy+vXrlN+TgwcPisFgkPfv32ve//nzZ/F6vVJQUKD+7eXl5cnu3bslHA4nHT88PCwtLS2yZ88eWbFihVgsFjEYDDJ//nwpKyuTa9euJfWAK33Uyi0jI0Nmz54tixcvli1btsjly5fl06dPP3wNN27cEABy9+7dKb9uSk8ZIpP0BhBRgrq6OtTX1yMYDKY0MvJv9vDhQ6xbtw5er1fdydC73t5eOBwOnD17FidPnpzu5fyS0tJSDA0Nobe3N+UJfZRemKMmorSzbNkyVFVVobGxESMjI9O9nJQFg0E8evQIDQ0NDNLEHDURpaczZ84gNzcX/f39mhXVevblyxdcuHAh5Z5sSk8M1ESUlnJycv6arfqJtm7dOt1LIB1hjpqIiEjHmKMmIiLSMQZqIiIiHWOgJiIi0jEGaiIiIh1joCYiItIxBmoiIiIdY6AmIiLSMQZqIiIiHWOgJiIi0rFvlI5NdnOKRVUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3))\n",
    "plt.xlabel(\"GDP per capita (USD)\")\n",
    "plt.axis([0, 60000, 0, 10])\n",
    "X=np.linspace(0, 60000, 1000)\n",
    "plt.plot(X, t0 + t1*X, \"b\")\n",
    "plt.text(5000, 3.1, r\"$\\theta_0 = 4.85$\", fontsize=14, color=\"b\")\n",
    "plt.text(5000, 2.2, r\"$\\theta_1 = 4.91 \\times 10^{-5}$\", fontsize=14, color=\"b\")\n",
    "save_fig('best_fit_model_plot')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22587.49\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5.962447443188149"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyprus_gdp_per_capita = gdp_per_capita.loc[\"Cyprus\"][\"GDP per capita\"]\n",
    "print(cyprus_gdp_per_capita)\n",
    "cyprus_predicted_life_satisfaction = lin1.predict([[cyprus_gdp_per_capita]])[0][0]\n",
    "cyprus_predicted_life_satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure cyprus_prediction_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3), s=1)\n",
    "plt.xlabel(\"GDP per capita (USD)\")\n",
    "X=np.linspace(0, 60000, 1000)\n",
    "plt.plot(X, t0 + t1*X, \"b\")\n",
    "plt.axis([0, 60000, 0, 10])\n",
    "plt.text(5000, 7.5, r\"$\\theta_0 = 4.85$\", fontsize=14, color=\"b\")\n",
    "plt.text(5000, 6.6, r\"$\\theta_1 = 4.91 \\times 10^{-5}$\", fontsize=14, color=\"b\")\n",
    "plt.plot([cyprus_gdp_per_capita, cyprus_gdp_per_capita], [0, cyprus_predicted_life_satisfaction], \"r--\")\n",
    "plt.text(25000, 5.0, r\"Prediction = 5.96\", fontsize=14, color=\"b\")\n",
    "plt.plot(cyprus_gdp_per_capita, cyprus_predicted_life_satisfaction, \"ro\")\n",
    "save_fig('cyprus_prediction_plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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</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>Portugal</th>\n",
       "      <td>19121.592</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>20732.482</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>25864.721</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          GDP per capita  Life satisfaction\n",
       "Country                                    \n",
       "Portugal       19121.592                5.1\n",
       "Slovenia       20732.482                5.7\n",
       "Spain          25864.721                6.5"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data[7:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.766666666666667"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(5.1+5.7+6.5)/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "backup = oecd_bli, gdp_per_capita\n",
    "\n",
    "def prepare_country_stats(oecd_bli, gdp_per_capita):\n",
    "    oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
    "    oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
    "    gdp_per_capita.rename(columns={\"2015\": \"GDP per capita\"}, inplace=True)\n",
    "    gdp_per_capita.set_index(\"Country\", inplace=True)\n",
    "    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=\"GDP per capita\", inplace=True)\n",
    "    remove_indices = [0, 1, 6, 8, 33, 34, 35]\n",
    "    keep_indices = list(set(range(36)) - set(remove_indices))\n",
    "    return full_country_stats[[\"GDP per capita\", 'Life satisfaction']].iloc[keep_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.96242338]]\n"
     ]
    }
   ],
   "source": [
    "# Code example\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn.linear_model\n",
    "\n",
    "# Load the data\n",
    "oecd_bli = pd.read_csv(datapath + \"oecd_bli_2015.csv\", thousands=',')\n",
    "gdp_per_capita = pd.read_csv(datapath + \"gdp_per_capita.csv\",thousands=',',delimiter='\\t',\n",
    "                             encoding='latin1', na_values=\"n/a\")\n",
    "\n",
    "# Prepare the data\n",
    "country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)\n",
    "X = np.c_[country_stats[\"GDP per capita\"]]\n",
    "y = np.c_[country_stats[\"Life satisfaction\"]]\n",
    "\n",
    "# Visualize the data\n",
    "country_stats.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction')\n",
    "plt.show()\n",
    "\n",
    "# Select a linear model\n",
    "model = sklearn.linear_model.LinearRegression()\n",
    "\n",
    "# Train the model\n",
    "model.fit(X, y)\n",
    "\n",
    "# Make a prediction for Cyprus\n",
    "X_new = [[22587]]  # Cyprus' GDP per capita\n",
    "print(model.predict(X_new)) # outputs [[ 5.96242338]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "oecd_bli, gdp_per_capita = backup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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</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>Brazil</th>\n",
       "      <td>8669.998</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>9009.280</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>13340.905</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>17256.918</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>74822.106</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>80675.308</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>101994.093</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                GDP per capita  Life satisfaction\n",
       "Country                                          \n",
       "Brazil                8669.998                7.0\n",
       "Mexico                9009.280                6.7\n",
       "Chile                13340.905                6.7\n",
       "Czech Republic       17256.918                6.5\n",
       "Norway               74822.106                7.4\n",
       "Switzerland          80675.308                7.5\n",
       "Luxembourg          101994.093                6.9"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "position_text2 = {\n",
    "    \"Brazil\": (1000, 9.0),\n",
    "    \"Mexico\": (11000, 9.0),\n",
    "    \"Chile\": (25000, 9.0),\n",
    "    \"Czech Republic\": (35000, 9.0),\n",
    "    \"Norway\": (60000, 3),\n",
    "    \"Switzerland\": (72000, 3.0),\n",
    "    \"Luxembourg\": (90000, 3.0),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure representative_training_data_scatterplot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(8,3))\n",
    "plt.axis([0, 110000, 0, 10])\n",
    "\n",
    "for country, pos_text in position_text2.items():\n",
    "    pos_data_x, pos_data_y = missing_data.loc[country]\n",
    "    plt.annotate(country, xy=(pos_data_x, pos_data_y), xytext=pos_text,\n",
    "            arrowprops=dict(facecolor='black', width=0.5, shrink=0.1, headwidth=5))\n",
    "    plt.plot(pos_data_x, pos_data_y, \"rs\")\n",
    "\n",
    "X=np.linspace(0, 110000, 1000)\n",
    "plt.plot(X, t0 + t1*X, \"b:\")\n",
    "\n",
    "lin_reg_full = linear_model.LinearRegression()\n",
    "Xfull = np.c_[full_country_stats[\"GDP per capita\"]]\n",
    "yfull = np.c_[full_country_stats[\"Life satisfaction\"]]\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, 110000, 1000)\n",
    "plt.plot(X, t0full + t1full * X, \"k\")\n",
    "plt.xlabel(\"GDP per capita (USD)\")\n",
    "\n",
    "save_fig('representative_training_data_scatterplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure overfitting_model_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_country_stats.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(8,3))\n",
    "plt.axis([0, 110000, 0, 10])\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn import pipeline\n",
    "\n",
    "poly = preprocessing.PolynomialFeatures(degree=30, include_bias=False)\n",
    "scaler = preprocessing.StandardScaler()\n",
    "lin_reg2 = linear_model.LinearRegression()\n",
    "\n",
    "pipeline_reg = pipeline.Pipeline([('poly', poly), ('scal', scaler), ('lin', lin_reg2)])\n",
    "pipeline_reg.fit(Xfull, yfull)\n",
    "curve = pipeline_reg.predict(X[:, np.newaxis])\n",
    "plt.plot(X, curve)\n",
    "plt.xlabel(\"GDP per capita (USD)\")\n",
    "save_fig('overfitting_model_plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "New Zealand    7.3\n",
       "Sweden         7.2\n",
       "Norway         7.4\n",
       "Switzerland    7.5\n",
       "Name: Life satisfaction, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_country_stats.loc[[c for c in full_country_stats.index if \"W\" in c.upper()]][\"Life satisfaction\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Subject Descriptor</th>\n",
       "      <th>Units</th>\n",
       "      <th>Scale</th>\n",
       "      <th>Country/Series-specific Notes</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Estimates Start After</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>6040.957</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kuwait</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>29363.027</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malawi</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>354.275</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>37044.891</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>74822.106</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Subject Descriptor         Units  \\\n",
       "Country                                                                        \n",
       "Botswana     Gross domestic product per capita, current prices  U.S. dollars   \n",
       "Kuwait       Gross domestic product per capita, current prices  U.S. dollars   \n",
       "Malawi       Gross domestic product per capita, current prices  U.S. dollars   \n",
       "New Zealand  Gross domestic product per capita, current prices  U.S. dollars   \n",
       "Norway       Gross domestic product per capita, current prices  U.S. dollars   \n",
       "\n",
       "             Scale                      Country/Series-specific Notes  \\\n",
       "Country                                                                 \n",
       "Botswana     Units  See notes for:  Gross domestic product, curren...   \n",
       "Kuwait       Units  See notes for:  Gross domestic product, curren...   \n",
       "Malawi       Units  See notes for:  Gross domestic product, curren...   \n",
       "New Zealand  Units  See notes for:  Gross domestic product, curren...   \n",
       "Norway       Units  See notes for:  Gross domestic product, curren...   \n",
       "\n",
       "             GDP per capita  Estimates Start After  \n",
       "Country                                             \n",
       "Botswana           6040.957                 2008.0  \n",
       "Kuwait            29363.027                 2014.0  \n",
       "Malawi              354.275                 2011.0  \n",
       "New Zealand       37044.891                 2015.0  \n",
       "Norway            74822.106                 2015.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_per_capita.loc[[c for c in gdp_per_capita.index if \"W\" in c.upper()]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure ridge_model_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3))\n",
    "\n",
    "plt.xlabel(\"GDP per capita\")\n",
    "plt.ylabel('Life satisfaction')\n",
    "\n",
    "plt.plot(list(sample_data[\"GDP per capita\"]), list(sample_data[\"Life satisfaction\"]), \"bo\")\n",
    "plt.plot(list(missing_data[\"GDP per capita\"]), list(missing_data[\"Life satisfaction\"]), \"rs\")\n",
    "\n",
    "X = np.linspace(0, 110000, 1000)\n",
    "plt.plot(X, t0full + t1full * X, \"r--\", label=\"Linear model on all data\")\n",
    "plt.plot(X, t0 + t1*X, \"b:\", label=\"Linear model on partial data\")\n",
    "\n",
    "ridge = linear_model.Ridge(alpha=10**9.5)\n",
    "Xsample = np.c_[sample_data[\"GDP per capita\"]]\n",
    "ysample = np.c_[sample_data[\"Life satisfaction\"]]\n",
    "ridge.fit(Xsample, ysample)\n",
    "t0ridge, t1ridge = ridge.intercept_[0], ridge.coef_[0][0]\n",
    "plt.plot(X, t0ridge + t1ridge * X, \"b\", label=\"Regularized linear model on partial data\")\n",
    "\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.axis([0, 110000, 0, 10])\n",
    "plt.xlabel(\"GDP per capita (USD)\")\n",
    "save_fig('ridge_model_plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
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   "interpreter": {
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  },
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