01_the_machine_learning_landscape.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 1 – The Machine Learning landscape**\n",
    "\n",
    "_This is the code used to generate some of the figures in chapter 1._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code example 1-1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scikit-Learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\""
   ]
  },
  {
   "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": 3,
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "datapath = os.path.join(\"datasets\", \"lifesat\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.96242338]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/miniconda3/envs/tf2/lib/python3.6/site-packages/sklearn/linear_model/base.py:485: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
      "  linalg.lstsq(X, y)\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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Note: you can ignore the rest of this notebook, it just generates many of the figures in chapter 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 7,
   "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": 8,
   "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": 9,
   "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": 9,
     "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": 10,
   "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": 10,
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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": 11,
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GDP per capita       55805.204\n",
       "Life satisfaction        7.200\n",
       "Name: United States, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_country_stats[[\"GDP per capita\", 'Life satisfaction']].loc[\"United States\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "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 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data.to_csv(os.path.join(\"datasets\", \"lifesat\", \"lifesat.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data.loc[list(position_text.keys())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.853052800266436, 4.911544589158484e-05)"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure best_fit_model_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22587.49\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5.96244744318815"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "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 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data[7:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.766666666666667"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(5.1+5.7+6.5)/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "oecd_bli, gdp_per_capita = backup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "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 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/miniconda3/envs/tf2/lib/python3.6/site-packages/numpy/lib/nanfunctions.py:1508: RuntimeWarning: overflow encountered in multiply\n",
      "  sqr = np.multiply(arr, arr, out=arr)\n",
      "/Users/ageron/miniconda3/envs/tf2/lib/python3.6/site-packages/numpy/core/fromnumeric.py:86: RuntimeWarning: overflow encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure overfitting_model_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_country_stats.plot(kind='scatter', 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=60, 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": 32,
   "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": 32,
     "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": 33,
   "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": 33,
     "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": 34,
   "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 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "backup = oecd_bli, gdp_per_capita\n",
    "\n",
    "def prepare_country_stats(oecd_bli, gdp_per_capita):\n",
    "    return sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace this linear model:\n",
    "import sklearn.linear_model\n",
    "model = sklearn.linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with this k-neighbors regression model:\n",
    "import sklearn.neighbors\n",
    "model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.76666667]]\n"
     ]
    }
   ],
   "source": [
    "X = np.c_[country_stats[\"GDP per capita\"]]\n",
    "y = np.c_[country_stats[\"Life satisfaction\"]]\n",
    "\n",
    "# Train the model\n",
    "model.fit(X, y)\n",
    "\n",
    "# Make a prediction for Cyprus\n",
    "X_new = np.array([[22587.0]])  # Cyprus' GDP per capita\n",
    "print(model.predict(X_new)) # outputs [[ 5.76666667]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": true
  },
  "toc_position": {
   "height": "616px",
   "left": "0px",
   "right": "20px",
   "top": "106px",
   "width": "213px"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}