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": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import numpy.random as rnd\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "rnd.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"fundamentals\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and prepare Life satisfaction data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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</td>\n",
       "      <td>2.1</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>76</td>\n",
       "      <td>14.02</td>\n",
       "      <td>72</td>\n",
       "      <td>0.8</td>\n",
       "      <td>31588</td>\n",
       "      <td>47657</td>\n",
       "      <td>...</td>\n",
       "      <td>1.08</td>\n",
       "      <td>50449</td>\n",
       "      <td>92</td>\n",
       "      <td>2.3</td>\n",
       "      <td>85</td>\n",
       "      <td>512</td>\n",
       "      <td>14.41</td>\n",
       "      <td>93</td>\n",
       "      <td>91</td>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>27</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83</td>\n",
       "      <td>7.61</td>\n",
       "      <td>72</td>\n",
       "      <td>0.4</td>\n",
       "      <td>31173</td>\n",
       "      <td>49887</td>\n",
       "      <td>...</td>\n",
       "      <td>1.19</td>\n",
       "      <td>45199</td>\n",
       "      <td>89</td>\n",
       "      <td>1.6</td>\n",
       "      <td>69</td>\n",
       "      <td>500</td>\n",
       "      <td>14.46</td>\n",
       "      <td>75</td>\n",
       "      <td>94</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           2.1                         10.5   \n",
       "Austria               27           3.4                          7.1   \n",
       "\n",
       "Indicator  Dwellings without basic facilities  Educational attainment  \\\n",
       "Country                                                                 \n",
       "Australia                                 1.1                      76   \n",
       "Austria                                   1.0                      83   \n",
       "\n",
       "Indicator  Employees working very long hours  Employment rate  Homicide rate  \\\n",
       "Country                                                                        \n",
       "Australia                              14.02               72            0.8   \n",
       "Austria                                 7.61               72            0.4   \n",
       "\n",
       "Indicator  Household net adjusted disposable income  \\\n",
       "Country                                               \n",
       "Australia                                     31588   \n",
       "Austria                                       31173   \n",
       "\n",
       "Indicator  Household net financial wealth         ...          \\\n",
       "Country                                           ...           \n",
       "Australia                           47657         ...           \n",
       "Austria                             49887         ...           \n",
       "\n",
       "Indicator  Long-term unemployment rate  Personal earnings  \\\n",
       "Country                                                     \n",
       "Australia                         1.08              50449   \n",
       "Austria                           1.19              45199   \n",
       "\n",
       "Indicator  Quality of support network  Rooms per person  Self-reported health  \\\n",
       "Country                                                                         \n",
       "Australia                          92               2.3                    85   \n",
       "Austria                            89               1.6                    69   \n",
       "\n",
       "Indicator  Student skills  Time devoted to leisure and personal care  \\\n",
       "Country                                                                \n",
       "Australia             512                                      14.41   \n",
       "Austria               500                                      14.46   \n",
       "\n",
       "Indicator  Voter turnout  Water quality  Years in education  \n",
       "Country                                                      \n",
       "Australia             93             91                19.4  \n",
       "Austria               75             94                17.0  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Download CSV from http://stats.oecd.org/index.aspx?DataSetCode=BLI\n",
    "datapath = \"datasets/lifesat/\"\n",
    "\n",
    "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": 3,
   "metadata": {
    "collapsed": false
   },
   "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": 3,
     "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": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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</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</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  \n",
       "Albania            3995.383                   2010  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download data from http://goo.gl/j1MSKe (=> imf.org)\n",
    "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": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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</td>\n",
       "      <td>7.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>45</td>\n",
       "      <td>10.41</td>\n",
       "      <td>67</td>\n",
       "      <td>25.5</td>\n",
       "      <td>11664</td>\n",
       "      <td>6844</td>\n",
       "      <td>...</td>\n",
       "      <td>14.97</td>\n",
       "      <td>79</td>\n",
       "      <td>72</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>30</td>\n",
       "      <td>12.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>37</td>\n",
       "      <td>28.83</td>\n",
       "      <td>61</td>\n",
       "      <td>23.4</td>\n",
       "      <td>13085</td>\n",
       "      <td>9056</td>\n",
       "      <td>...</td>\n",
       "      <td>13.89</td>\n",
       "      <td>63</td>\n",
       "      <td>67</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>15</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>94</td>\n",
       "      <td>0.16</td>\n",
       "      <td>69</td>\n",
       "      <td>12.8</td>\n",
       "      <td>19292</td>\n",
       "      <td>3412</td>\n",
       "      <td>...</td>\n",
       "      <td>14.97</td>\n",
       "      <td>65</td>\n",
       "      <td>56</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>12.7</td>\n",
       "      <td>34</td>\n",
       "      <td>40.86</td>\n",
       "      <td>50</td>\n",
       "      <td>1.2</td>\n",
       "      <td>14095</td>\n",
       "      <td>3251</td>\n",
       "      <td>...</td>\n",
       "      <td>13.42</td>\n",
       "      <td>88</td>\n",
       "      <td>62</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>15</td>\n",
       "      <td>3.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>82</td>\n",
       "      <td>3.19</td>\n",
       "      <td>58</td>\n",
       "      <td>1.3</td>\n",
       "      <td>15442</td>\n",
       "      <td>13277</td>\n",
       "      <td>...</td>\n",
       "      <td>15.04</td>\n",
       "      <td>62</td>\n",
       "      <td>77</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>33</td>\n",
       "      <td>1.4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>90</td>\n",
       "      <td>7.41</td>\n",
       "      <td>60</td>\n",
       "      <td>0.9</td>\n",
       "      <td>17852</td>\n",
       "      <td>10919</td>\n",
       "      <td>...</td>\n",
       "      <td>14.20</td>\n",
       "      <td>55</td>\n",
       "      <td>79</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>46</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>57</td>\n",
       "      <td>15.42</td>\n",
       "      <td>62</td>\n",
       "      <td>4.4</td>\n",
       "      <td>14533</td>\n",
       "      <td>17733</td>\n",
       "      <td>...</td>\n",
       "      <td>14.41</td>\n",
       "      <td>49</td>\n",
       "      <td>73</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovak Republic</th>\n",
       "      <td>13</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>92</td>\n",
       "      <td>7.02</td>\n",
       "      <td>60</td>\n",
       "      <td>1.2</td>\n",
       "      <td>17503</td>\n",
       "      <td>8663</td>\n",
       "      <td>...</td>\n",
       "      <td>14.99</td>\n",
       "      <td>59</td>\n",
       "      <td>81</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>16</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>92</td>\n",
       "      <td>6.98</td>\n",
       "      <td>68</td>\n",
       "      <td>0.8</td>\n",
       "      <td>18404</td>\n",
       "      <td>17299</td>\n",
       "      <td>...</td>\n",
       "      <td>14.98</td>\n",
       "      <td>59</td>\n",
       "      <td>85</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>90</td>\n",
       "      <td>3.30</td>\n",
       "      <td>68</td>\n",
       "      <td>4.8</td>\n",
       "      <td>15167</td>\n",
       "      <td>7680</td>\n",
       "      <td>...</td>\n",
       "      <td>14.90</td>\n",
       "      <td>64</td>\n",
       "      <td>79</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>27</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>68</td>\n",
       "      <td>6.16</td>\n",
       "      <td>49</td>\n",
       "      <td>1.6</td>\n",
       "      <td>18575</td>\n",
       "      <td>14579</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>64</td>\n",
       "      <td>69</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>18</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>38</td>\n",
       "      <td>9.62</td>\n",
       "      <td>61</td>\n",
       "      <td>1.1</td>\n",
       "      <td>20086</td>\n",
       "      <td>31245</td>\n",
       "      <td>...</td>\n",
       "      <td>14.95</td>\n",
       "      <td>58</td>\n",
       "      <td>86</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>26</td>\n",
       "      <td>3.9</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>85</td>\n",
       "      <td>5.63</td>\n",
       "      <td>63</td>\n",
       "      <td>0.4</td>\n",
       "      <td>19326</td>\n",
       "      <td>18465</td>\n",
       "      <td>...</td>\n",
       "      <td>14.62</td>\n",
       "      <td>52</td>\n",
       "      <td>88</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>24</td>\n",
       "      <td>4.2</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>55</td>\n",
       "      <td>5.89</td>\n",
       "      <td>56</td>\n",
       "      <td>0.6</td>\n",
       "      <td>22477</td>\n",
       "      <td>24774</td>\n",
       "      <td>...</td>\n",
       "      <td>16.06</td>\n",
       "      <td>69</td>\n",
       "      <td>71</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea</th>\n",
       "      <td>30</td>\n",
       "      <td>2.1</td>\n",
       "      <td>10.4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>82</td>\n",
       "      <td>18.72</td>\n",
       "      <td>64</td>\n",
       "      <td>1.1</td>\n",
       "      <td>19510</td>\n",
       "      <td>29091</td>\n",
       "      <td>...</td>\n",
       "      <td>14.63</td>\n",
       "      <td>76</td>\n",
       "      <td>78</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>21</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>57</td>\n",
       "      <td>3.66</td>\n",
       "      <td>56</td>\n",
       "      <td>0.7</td>\n",
       "      <td>25166</td>\n",
       "      <td>54987</td>\n",
       "      <td>...</td>\n",
       "      <td>14.98</td>\n",
       "      <td>75</td>\n",
       "      <td>71</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>24</td>\n",
       "      <td>1.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>94</td>\n",
       "      <td>22.26</td>\n",
       "      <td>72</td>\n",
       "      <td>0.3</td>\n",
       "      <td>26111</td>\n",
       "      <td>86764</td>\n",
       "      <td>...</td>\n",
       "      <td>14.93</td>\n",
       "      <td>53</td>\n",
       "      <td>85</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>21</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.7</td>\n",
       "      <td>85</td>\n",
       "      <td>16.03</td>\n",
       "      <td>67</td>\n",
       "      <td>2.3</td>\n",
       "      <td>22104</td>\n",
       "      <td>52933</td>\n",
       "      <td>...</td>\n",
       "      <td>14.48</td>\n",
       "      <td>68</td>\n",
       "      <td>68</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>11</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>74</td>\n",
       "      <td>13.87</td>\n",
       "      <td>73</td>\n",
       "      <td>1.2</td>\n",
       "      <td>23815</td>\n",
       "      <td>28290</td>\n",
       "      <td>...</td>\n",
       "      <td>14.87</td>\n",
       "      <td>77</td>\n",
       "      <td>89</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>12</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>73</td>\n",
       "      <td>8.15</td>\n",
       "      <td>64</td>\n",
       "      <td>0.6</td>\n",
       "      <td>28799</td>\n",
       "      <td>48741</td>\n",
       "      <td>...</td>\n",
       "      <td>15.33</td>\n",
       "      <td>80</td>\n",
       "      <td>82</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>21</td>\n",
       "      <td>6.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>72</td>\n",
       "      <td>4.57</td>\n",
       "      <td>62</td>\n",
       "      <td>1.1</td>\n",
       "      <td>28307</td>\n",
       "      <td>83876</td>\n",
       "      <td>...</td>\n",
       "      <td>15.71</td>\n",
       "      <td>89</td>\n",
       "      <td>87</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>16</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>86</td>\n",
       "      <td>5.25</td>\n",
       "      <td>73</td>\n",
       "      <td>0.5</td>\n",
       "      <td>31252</td>\n",
       "      <td>50394</td>\n",
       "      <td>...</td>\n",
       "      <td>15.31</td>\n",
       "      <td>72</td>\n",
       "      <td>95</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>15</td>\n",
       "      <td>2.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>85</td>\n",
       "      <td>3.58</td>\n",
       "      <td>69</td>\n",
       "      <td>1.4</td>\n",
       "      <td>27927</td>\n",
       "      <td>18761</td>\n",
       "      <td>...</td>\n",
       "      <td>14.89</td>\n",
       "      <td>69</td>\n",
       "      <td>94</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>15</td>\n",
       "      <td>1.3</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>89</td>\n",
       "      <td>3.94</td>\n",
       "      <td>72</td>\n",
       "      <td>1.5</td>\n",
       "      <td>29365</td>\n",
       "      <td>67913</td>\n",
       "      <td>...</td>\n",
       "      <td>14.25</td>\n",
       "      <td>61</td>\n",
       "      <td>91</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>30</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73</td>\n",
       "      <td>0.45</td>\n",
       "      <td>74</td>\n",
       "      <td>0.9</td>\n",
       "      <td>27888</td>\n",
       "      <td>77961</td>\n",
       "      <td>...</td>\n",
       "      <td>15.44</td>\n",
       "      <td>75</td>\n",
       "      <td>92</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>27</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83</td>\n",
       "      <td>7.61</td>\n",
       "      <td>72</td>\n",
       "      <td>0.4</td>\n",
       "      <td>31173</td>\n",
       "      <td>49887</td>\n",
       "      <td>...</td>\n",
       "      <td>14.46</td>\n",
       "      <td>75</td>\n",
       "      <td>94</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>13</td>\n",
       "      <td>1.9</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>78</td>\n",
       "      <td>12.70</td>\n",
       "      <td>71</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27029</td>\n",
       "      <td>60778</td>\n",
       "      <td>...</td>\n",
       "      <td>14.83</td>\n",
       "      <td>66</td>\n",
       "      <td>88</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>10</td>\n",
       "      <td>5.1</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88</td>\n",
       "      <td>1.13</td>\n",
       "      <td>74</td>\n",
       "      <td>0.7</td>\n",
       "      <td>29185</td>\n",
       "      <td>60328</td>\n",
       "      <td>...</td>\n",
       "      <td>15.11</td>\n",
       "      <td>86</td>\n",
       "      <td>95</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>18</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>71</td>\n",
       "      <td>12.25</td>\n",
       "      <td>82</td>\n",
       "      <td>0.3</td>\n",
       "      <td>23965</td>\n",
       "      <td>43045</td>\n",
       "      <td>...</td>\n",
       "      <td>14.61</td>\n",
       "      <td>81</td>\n",
       "      <td>97</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>13</td>\n",
       "      <td>2.1</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>76</td>\n",
       "      <td>14.02</td>\n",
       "      <td>72</td>\n",
       "      <td>0.8</td>\n",
       "      <td>31588</td>\n",
       "      <td>47657</td>\n",
       "      <td>...</td>\n",
       "      <td>14.41</td>\n",
       "      <td>93</td>\n",
       "      <td>91</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>13</td>\n",
       "      <td>2.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>75</td>\n",
       "      <td>4.20</td>\n",
       "      <td>60</td>\n",
       "      <td>0.8</td>\n",
       "      <td>23917</td>\n",
       "      <td>31580</td>\n",
       "      <td>...</td>\n",
       "      <td>15.19</td>\n",
       "      <td>70</td>\n",
       "      <td>80</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>15</td>\n",
       "      <td>3.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>78</td>\n",
       "      <td>2.03</td>\n",
       "      <td>73</td>\n",
       "      <td>0.3</td>\n",
       "      <td>26491</td>\n",
       "      <td>44488</td>\n",
       "      <td>...</td>\n",
       "      <td>16.06</td>\n",
       "      <td>88</td>\n",
       "      <td>94</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>18</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>89</td>\n",
       "      <td>11.30</td>\n",
       "      <td>67</td>\n",
       "      <td>5.2</td>\n",
       "      <td>41355</td>\n",
       "      <td>145769</td>\n",
       "      <td>...</td>\n",
       "      <td>14.27</td>\n",
       "      <td>68</td>\n",
       "      <td>85</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>16</td>\n",
       "      <td>3.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>82</td>\n",
       "      <td>2.82</td>\n",
       "      <td>75</td>\n",
       "      <td>0.6</td>\n",
       "      <td>33492</td>\n",
       "      <td>8797</td>\n",
       "      <td>...</td>\n",
       "      <td>15.56</td>\n",
       "      <td>78</td>\n",
       "      <td>94</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>20</td>\n",
       "      <td>4.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86</td>\n",
       "      <td>6.72</td>\n",
       "      <td>80</td>\n",
       "      <td>0.5</td>\n",
       "      <td>33491</td>\n",
       "      <td>108823</td>\n",
       "      <td>...</td>\n",
       "      <td>14.98</td>\n",
       "      <td>49</td>\n",
       "      <td>96</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>12</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>78</td>\n",
       "      <td>3.47</td>\n",
       "      <td>66</td>\n",
       "      <td>0.4</td>\n",
       "      <td>38951</td>\n",
       "      <td>61765</td>\n",
       "      <td>...</td>\n",
       "      <td>15.12</td>\n",
       "      <td>91</td>\n",
       "      <td>86</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</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Indicator        Air pollution  Assault rate  Consultation on rule-making  \\\n",
       "Country                                                                     \n",
       "Brazil                      18           7.9                          4.0   \n",
       "Mexico                      30          12.8                          9.0   \n",
       "Russia                      15           3.8                          2.5   \n",
       "Turkey                      35           5.0                          5.5   \n",
       "Hungary                     15           3.6                          7.9   \n",
       "Poland                      33           1.4                         10.8   \n",
       "Chile                       46           6.9                          2.0   \n",
       "Slovak Republic             13           3.0                          6.6   \n",
       "Czech Republic              16           2.8                          6.8   \n",
       "Estonia                      9           5.5                          3.3   \n",
       "Greece                      27           3.7                          6.5   \n",
       "Portugal                    18           5.7                          6.5   \n",
       "Slovenia                    26           3.9                         10.3   \n",
       "Spain                       24           4.2                          7.3   \n",
       "Korea                       30           2.1                         10.4   \n",
       "Italy                       21           4.7                          5.0   \n",
       "Japan                       24           1.4                          7.3   \n",
       "Israel                      21           6.4                          2.5   \n",
       "New Zealand                 11           2.2                         10.3   \n",
       "France                      12           5.0                          3.5   \n",
       "Belgium                     21           6.6                          4.5   \n",
       "Germany                     16           3.6                          4.5   \n",
       "Finland                     15           2.4                          9.0   \n",
       "Canada                      15           1.3                         10.5   \n",
       "Netherlands                 30           4.9                          6.1   \n",
       "Austria                     27           3.4                          7.1   \n",
       "United Kingdom              13           1.9                         11.5   \n",
       "Sweden                      10           5.1                         10.9   \n",
       "Iceland                     18           2.7                          5.1   \n",
       "Australia                   13           2.1                         10.5   \n",
       "Ireland                     13           2.6                          9.0   \n",
       "Denmark                     15           3.9                          7.0   \n",
       "United States               18           1.5                          8.3   \n",
       "Norway                      16           3.3                          8.1   \n",
       "Switzerland                 20           4.2                          8.4   \n",
       "Luxembourg                  12           4.3                          6.0   \n",
       "\n",
       "Indicator        Dwellings without basic facilities  Educational attainment  \\\n",
       "Country                                                                       \n",
       "Brazil                                          6.7                      45   \n",
       "Mexico                                          4.2                      37   \n",
       "Russia                                         15.1                      94   \n",
       "Turkey                                         12.7                      34   \n",
       "Hungary                                         4.8                      82   \n",
       "Poland                                          3.2                      90   \n",
       "Chile                                           9.4                      57   \n",
       "Slovak Republic                                 0.6                      92   \n",
       "Czech Republic                                  0.9                      92   \n",
       "Estonia                                         8.1                      90   \n",
       "Greece                                          0.7                      68   \n",
       "Portugal                                        0.9                      38   \n",
       "Slovenia                                        0.5                      85   \n",
       "Spain                                           0.1                      55   \n",
       "Korea                                           4.2                      82   \n",
       "Italy                                           1.1                      57   \n",
       "Japan                                           6.4                      94   \n",
       "Israel                                          3.7                      85   \n",
       "New Zealand                                     0.2                      74   \n",
       "France                                          0.5                      73   \n",
       "Belgium                                         2.0                      72   \n",
       "Germany                                         0.1                      86   \n",
       "Finland                                         0.6                      85   \n",
       "Canada                                          0.2                      89   \n",
       "Netherlands                                     0.0                      73   \n",
       "Austria                                         1.0                      83   \n",
       "United Kingdom                                  0.2                      78   \n",
       "Sweden                                          0.0                      88   \n",
       "Iceland                                         0.4                      71   \n",
       "Australia                                       1.1                      76   \n",
       "Ireland                                         0.2                      75   \n",
       "Denmark                                         0.9                      78   \n",
       "United States                                   0.1                      89   \n",
       "Norway                                          0.3                      82   \n",
       "Switzerland                                     0.0                      86   \n",
       "Luxembourg                                      0.1                      78   \n",
       "\n",
       "Indicator        Employees working very long hours  Employment rate  \\\n",
       "Country                                                               \n",
       "Brazil                                       10.41               67   \n",
       "Mexico                                       28.83               61   \n",
       "Russia                                        0.16               69   \n",
       "Turkey                                       40.86               50   \n",
       "Hungary                                       3.19               58   \n",
       "Poland                                        7.41               60   \n",
       "Chile                                        15.42               62   \n",
       "Slovak Republic                               7.02               60   \n",
       "Czech Republic                                6.98               68   \n",
       "Estonia                                       3.30               68   \n",
       "Greece                                        6.16               49   \n",
       "Portugal                                      9.62               61   \n",
       "Slovenia                                      5.63               63   \n",
       "Spain                                         5.89               56   \n",
       "Korea                                        18.72               64   \n",
       "Italy                                         3.66               56   \n",
       "Japan                                        22.26               72   \n",
       "Israel                                       16.03               67   \n",
       "New Zealand                                  13.87               73   \n",
       "France                                        8.15               64   \n",
       "Belgium                                       4.57               62   \n",
       "Germany                                       5.25               73   \n",
       "Finland                                       3.58               69   \n",
       "Canada                                        3.94               72   \n",
       "Netherlands                                   0.45               74   \n",
       "Austria                                       7.61               72   \n",
       "United Kingdom                               12.70               71   \n",
       "Sweden                                        1.13               74   \n",
       "Iceland                                      12.25               82   \n",
       "Australia                                    14.02               72   \n",
       "Ireland                                       4.20               60   \n",
       "Denmark                                       2.03               73   \n",
       "United States                                11.30               67   \n",
       "Norway                                        2.82               75   \n",
       "Switzerland                                   6.72               80   \n",
       "Luxembourg                                    3.47               66   \n",
       "\n",
       "Indicator        Homicide rate  Household net adjusted disposable income  \\\n",
       "Country                                                                    \n",
       "Brazil                    25.5                                     11664   \n",
       "Mexico                    23.4                                     13085   \n",
       "Russia                    12.8                                     19292   \n",
       "Turkey                     1.2                                     14095   \n",
       "Hungary                    1.3                                     15442   \n",
       "Poland                     0.9                                     17852   \n",
       "Chile                      4.4                                     14533   \n",
       "Slovak Republic            1.2                                     17503   \n",
       "Czech Republic             0.8                                     18404   \n",
       "Estonia                    4.8                                     15167   \n",
       "Greece                     1.6                                     18575   \n",
       "Portugal                   1.1                                     20086   \n",
       "Slovenia                   0.4                                     19326   \n",
       "Spain                      0.6                                     22477   \n",
       "Korea                      1.1                                     19510   \n",
       "Italy                      0.7                                     25166   \n",
       "Japan                      0.3                                     26111   \n",
       "Israel                     2.3                                     22104   \n",
       "New Zealand                1.2                                     23815   \n",
       "France                     0.6                                     28799   \n",
       "Belgium                    1.1                                     28307   \n",
       "Germany                    0.5                                     31252   \n",
       "Finland                    1.4                                     27927   \n",
       "Canada                     1.5                                     29365   \n",
       "Netherlands                0.9                                     27888   \n",
       "Austria                    0.4                                     31173   \n",
       "United Kingdom             0.3                                     27029   \n",
       "Sweden                     0.7                                     29185   \n",
       "Iceland                    0.3                                     23965   \n",
       "Australia                  0.8                                     31588   \n",
       "Ireland                    0.8                                     23917   \n",
       "Denmark                    0.3                                     26491   \n",
       "United States              5.2                                     41355   \n",
       "Norway                     0.6                                     33492   \n",
       "Switzerland                0.5                                     33491   \n",
       "Luxembourg                 0.4                                     38951   \n",
       "\n",
       "Indicator        Household net financial wealth          ...            \\\n",
       "Country                                                  ...             \n",
       "Brazil                                     6844          ...             \n",
       "Mexico                                     9056          ...             \n",
       "Russia                                     3412          ...             \n",
       "Turkey                                     3251          ...             \n",
       "Hungary                                   13277          ...             \n",
       "Poland                                    10919          ...             \n",
       "Chile                                     17733          ...             \n",
       "Slovak Republic                            8663          ...             \n",
       "Czech Republic                            17299          ...             \n",
       "Estonia                                    7680          ...             \n",
       "Greece                                    14579          ...             \n",
       "Portugal                                  31245          ...             \n",
       "Slovenia                                  18465          ...             \n",
       "Spain                                     24774          ...             \n",
       "Korea                                     29091          ...             \n",
       "Italy                                     54987          ...             \n",
       "Japan                                     86764          ...             \n",
       "Israel                                    52933          ...             \n",
       "New Zealand                               28290          ...             \n",
       "France                                    48741          ...             \n",
       "Belgium                                   83876          ...             \n",
       "Germany                                   50394          ...             \n",
       "Finland                                   18761          ...             \n",
       "Canada                                    67913          ...             \n",
       "Netherlands                               77961          ...             \n",
       "Austria                                   49887          ...             \n",
       "United Kingdom                            60778          ...             \n",
       "Sweden                                    60328          ...             \n",
       "Iceland                                   43045          ...             \n",
       "Australia                                 47657          ...             \n",
       "Ireland                                   31580          ...             \n",
       "Denmark                                   44488          ...             \n",
       "United States                            145769          ...             \n",
       "Norway                                     8797          ...             \n",
       "Switzerland                              108823          ...             \n",
       "Luxembourg                                61765          ...             \n",
       "\n",
       "Indicator        Time devoted to leisure and personal care  Voter turnout  \\\n",
       "Country                                                                     \n",
       "Brazil                                               14.97             79   \n",
       "Mexico                                               13.89             63   \n",
       "Russia                                               14.97             65   \n",
       "Turkey                                               13.42             88   \n",
       "Hungary                                              15.04             62   \n",
       "Poland                                               14.20             55   \n",
       "Chile                                                14.41             49   \n",
       "Slovak Republic                                      14.99             59   \n",
       "Czech Republic                                       14.98             59   \n",
       "Estonia                                              14.90             64   \n",
       "Greece                                               14.91             64   \n",
       "Portugal                                             14.95             58   \n",
       "Slovenia                                             14.62             52   \n",
       "Spain                                                16.06             69   \n",
       "Korea                                                14.63             76   \n",
       "Italy                                                14.98             75   \n",
       "Japan                                                14.93             53   \n",
       "Israel                                               14.48             68   \n",
       "New Zealand                                          14.87             77   \n",
       "France                                               15.33             80   \n",
       "Belgium                                              15.71             89   \n",
       "Germany                                              15.31             72   \n",
       "Finland                                              14.89             69   \n",
       "Canada                                               14.25             61   \n",
       "Netherlands                                          15.44             75   \n",
       "Austria                                              14.46             75   \n",
       "United Kingdom                                       14.83             66   \n",
       "Sweden                                               15.11             86   \n",
       "Iceland                                              14.61             81   \n",
       "Australia                                            14.41             93   \n",
       "Ireland                                              15.19             70   \n",
       "Denmark                                              16.06             88   \n",
       "United States                                        14.27             68   \n",
       "Norway                                               15.56             78   \n",
       "Switzerland                                          14.98             49   \n",
       "Luxembourg                                           15.12             91   \n",
       "\n",
       "Indicator        Water quality  Years in education  \\\n",
       "Country                                              \n",
       "Brazil                      72                16.3   \n",
       "Mexico                      67                14.4   \n",
       "Russia                      56                16.0   \n",
       "Turkey                      62                16.4   \n",
       "Hungary                     77                17.6   \n",
       "Poland                      79                18.4   \n",
       "Chile                       73                16.5   \n",
       "Slovak Republic             81                16.3   \n",
       "Czech Republic              85                18.1   \n",
       "Estonia                     79                17.5   \n",
       "Greece                      69                18.6   \n",
       "Portugal                    86                17.6   \n",
       "Slovenia                    88                18.4   \n",
       "Spain                       71                17.6   \n",
       "Korea                       78                17.5   \n",
       "Italy                       71                16.8   \n",
       "Japan                       85                16.3   \n",
       "Israel                      68                15.8   \n",
       "New Zealand                 89                18.1   \n",
       "France                      82                16.4   \n",
       "Belgium                     87                18.9   \n",
       "Germany                     95                18.2   \n",
       "Finland                     94                19.7   \n",
       "Canada                      91                17.2   \n",
       "Netherlands                 92                18.7   \n",
       "Austria                     94                17.0   \n",
       "United Kingdom              88                16.4   \n",
       "Sweden                      95                19.3   \n",
       "Iceland                     97                19.8   \n",
       "Australia                   91                19.4   \n",
       "Ireland                     80                17.6   \n",
       "Denmark                     94                19.4   \n",
       "United States               85                17.2   \n",
       "Norway                      94                17.9   \n",
       "Switzerland                 96                17.3   \n",
       "Luxembourg                  86                15.1   \n",
       "\n",
       "Indicator                                       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",
       "Indicator               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",
       "Indicator                            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",
       "Indicator        GDP per capita  Estimates Start After  \n",
       "Country                                                 \n",
       "Brazil                 8669.998                   2014  \n",
       "Mexico                 9009.280                   2015  \n",
       "Russia                 9054.914                   2015  \n",
       "Turkey                 9437.372                   2013  \n",
       "Hungary               12239.894                   2015  \n",
       "Poland                12495.334                   2014  \n",
       "Chile                 13340.905                   2014  \n",
       "Slovak Republic       15991.736                   2015  \n",
       "Czech Republic        17256.918                   2015  \n",
       "Estonia               17288.083                   2014  \n",
       "Greece                18064.288                   2014  \n",
       "Portugal              19121.592                   2014  \n",
       "Slovenia              20732.482                   2015  \n",
       "Spain                 25864.721                   2014  \n",
       "Korea                 27195.197                   2014  \n",
       "Italy                 29866.581                   2015  \n",
       "Japan                 32485.545                   2015  \n",
       "Israel                35343.336                   2015  \n",
       "New Zealand           37044.891                   2015  \n",
       "France                37675.006                   2015  \n",
       "Belgium               40106.632                   2014  \n",
       "Germany               40996.511                   2014  \n",
       "Finland               41973.988                   2014  \n",
       "Canada                43331.961                   2015  \n",
       "Netherlands           43603.115                   2014  \n",
       "Austria               43724.031                   2015  \n",
       "United Kingdom        43770.688                   2015  \n",
       "Sweden                49866.266                   2014  \n",
       "Iceland               50854.583                   2014  \n",
       "Australia             50961.865                   2014  \n",
       "Ireland               51350.744                   2014  \n",
       "Denmark               52114.165                   2015  \n",
       "United States         55805.204                   2015  \n",
       "Norway                74822.106                   2015  \n",
       "Switzerland           80675.308                   2015  \n",
       "Luxembourg           101994.093                   2014  \n",
       "\n",
       "[36 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Indicator\n",
       "GDP per capita       55805.204\n",
       "Life satisfaction        7.200\n",
       "Name: United States, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_country_stats[[\"GDP per capita\", 'Life satisfaction']].loc[\"United States\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "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": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure money_happy_scatterplot\n"
     ]
    },
    {
     "data": {
      "image/png": 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16kWHDh2YMWNGUj+ziCVmhBOEn//850ByPpwL+YP79+8fsJL4ZPfu3SxcuJAR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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109816780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3))\n",
    "plt.axis([0, 60000, 0, 10])\n",
    "position_text = {\n",
    "    \"Hungary\": (5000, 1),\n",
    "    \"Korea\": (18000, 1.7),\n",
    "    \"France\": (29000, 2.4),\n",
    "    \"Australia\": (40000, 3.0),\n",
    "    \"United States\": (52000, 3.8),\n",
    "}\n",
    "for country, pos_text in position_text.items():\n",
    "    pos_data_x, pos_data_y = sample_data.loc[country]\n",
    "    country = \"U.S.\" if country == \"United States\" else country\n",
    "    plt.annotate(country, xy=(pos_data_x, pos_data_y), xytext=pos_text,\n",
    "            arrowprops=dict(facecolor='black', width=0.5, shrink=0.1, headwidth=5))\n",
    "    plt.plot(pos_data_x, pos_data_y, \"ro\")\n",
    "save_fig('money_happy_scatterplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Indicator</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>Australia</th>\n",
       "      <td>50961.865</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>12239.894</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>55805.204</td>\n",
       "      <td>7.2</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Indicator      GDP per capita  Life satisfaction\n",
       "Country                                         \n",
       "Australia           50961.865                7.3\n",
       "Hungary             12239.894                4.9\n",
       "United States       55805.204                7.2\n",
       "Korea               27195.197                5.8\n",
       "France              37675.006                6.5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data.loc[list(position_text.keys())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure tweaking_model_params_plot.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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hIQwcaGZrdH12Ve8ir3decsMRIpKnqjsDr1u7SeA8o/PEKiF8MA0NsGFD5DBC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\nP73yw5ww5AHPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1096b09b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3))\n",
    "plt.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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.8530528002664362, 4.9115445891584838e-05)"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure best_fit_model_plot.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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KCgsLKSwsTGcIxphGpqioiKKiosDPG8lyhNcvuStwuqruTrDdyhHGmIyK1HjC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\njDEhsiRsjDEh+n+qTWZpN5NjigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ed3af60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3))\n",
    "plt.axis([0, 60000, 0, 10])\n",
    "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": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22587.49\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5.9624474431881502"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure cyprus_prediction_plot.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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puiOOBJ4GPgP2Bj4GOgL1gHdUdWDaBBI5ERgEfA10UtVTEpzj7oh8wHNHOHlM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fa123c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3), s=1)\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": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Indicator</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": [
       "Indicator  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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data[7:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.766666666666667"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(5.1+5.7+6.5)/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Jl84OANZHxH0Vx24nZdyiO5gUKwAR8RRwLxtj3+Q8m7brIOC3EfHkGOerX/u3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\nGjMzy9X/B5W3kGbLMTpsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f99b0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.96242338]]\n"
     ]
    }
   ],
   "source": [
    "backup = oecd_bli, gdp_per_capita\n",
    "\n",
    "def prepare_country_stats(oecd_bli, gdp_per_capita):\n",
    "    return sample_data\n",
    "\n",
    "# Code example\n",
    "########################################################################\n",
    "import sklearn\n",
    "import numpy as np\n",
    "import pandas as pd\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",
    "lin_reg_model = sklearn.linear_model.LinearRegression()\n",
    "\n",
    "# Train the model\n",
    "lin_reg_model.fit(X, y)\n",
    "\n",
    "# Make a prediction for Cyprus\n",
    "X_new = [[22587]]  # Cyprus' GDP per capita\n",
    "print(lin_reg_model.predict(X_new)) # outputs [[ 5.96242338]]\n",
    "########################################################################\n",
    "\n",
    "oecd_bli, gdp_per_capita = backup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Indicator</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": [
       "Indicator       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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "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": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure representative_training_data_scatterplot.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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DCEFCQkKBPSwajSbP5Z969erh4+OTY17WYJE8reTnyTkFVEGtQHwKtcpxbn8l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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f1c33c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(8,3))\n",
    "plt.axis([0, 110000, 0, 10])\n",
    "\n",
    "for country, pos_text in position_text2.items():\n",
    "    pos_data_x, pos_data_y = missing_data.loc[country]\n",
    "    plt.annotate(country, xy=(pos_data_x, pos_data_y), xytext=pos_text,\n",
    "            arrowprops=dict(facecolor='black', width=0.5, shrink=0.1, headwidth=5))\n",
    "    plt.plot(pos_data_x, pos_data_y, \"rs\")\n",
    "\n",
    "X=np.linspace(0, 110000, 1000)\n",
    "plt.plot(X, t0 + t1*X, \"b:\")\n",
    "\n",
    "lin_reg_full = linear_model.LinearRegression()\n",
    "Xfull = np.c_[full_country_stats[\"GDP per capita\"]]\n",
    "yfull = np.c_[full_country_stats[\"Life satisfaction\"]]\n",
    "lin_reg_full.fit(Xfull, yfull)\n",
    "\n",
    "t0full, t1full = lin_reg_full.intercept_[0], lin_reg_full.coef_[0][0]\n",
    "X = np.linspace(0, 110000, 1000)\n",
    "plt.plot(X, t0full + t1full * X, \"k\")\n",
    "\n",
    "save_fig('representative_training_data_scatterplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure overfitting_model_plot.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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TTw6Tzz34IHzrWy2fX14emnGGDdtwf309VFfDokWwfHmoj/Jy2Ggj2GYbGDAA5sx5g1Gj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1096cfd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_country_stats.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(8,3))\n",
    "plt.axis([0, 110000, 0, 10])\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn import pipeline\n",
    "\n",
    "poly = preprocessing.PolynomialFeatures(degree=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",
    "save_fig('overfitting_model_plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "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": 22,
     "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": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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</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</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</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</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</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  \n",
       "Kuwait            29363.027                   2014  \n",
       "Malawi              354.275                   2011  \n",
       "New Zealand       37044.891                   2015  \n",
       "Norway            74822.106                   2015  "
      ]
     },
     "execution_count": 23,
     "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": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure ridge_model_plot.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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9zgRCgAPAt8Cyc46w2KmiP18UkaMAlmX9H2YIu9Pv8zExMfbX/fv3p3///l4M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\nkxwzMzOrS05yzMzMrC79H1AGdJ9WtqyqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1096cf2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3))\n",
    "\n",
    "plt.xlabel(\"GDP per capita\")\n",
    "plt.ylabel('Life satisfaction')\n",
    "\n",
    "plt.plot(list(sample_data[\"GDP per capita\"]), list(sample_data[\"Life satisfaction\"]), \"bo\")\n",
    "plt.plot(list(missing_data[\"GDP per capita\"]), list(missing_data[\"Life satisfaction\"]), \"rs\")\n",
    "\n",
    "X = np.linspace(0, 110000, 1000)\n",
    "plt.plot(X, t0full + t1full * X, \"r--\", label=\"Linear model on all data\")\n",
    "plt.plot(X, t0 + t1*X, \"b:\", label=\"Linear model on partial data\")\n",
    "\n",
    "ridge = linear_model.Ridge(alpha=10**9.5)\n",
    "Xsample = np.c_[sample_data[\"GDP per capita\"]]\n",
    "ysample = np.c_[sample_data[\"Life satisfaction\"]]\n",
    "ridge.fit(Xsample, ysample)\n",
    "t0ridge, t1ridge = ridge.intercept_[0], ridge.coef_[0][0]\n",
    "plt.plot(X, t0ridge + t1ridge * X, \"b\", label=\"Regularized linear model on partial data\")\n",
    "\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.axis([0, 110000, 0, 10])\n",
    "save_fig('ridge_model_plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ef65f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.96242338]]\n"
     ]
    }
   ],
   "source": [
    "backup = oecd_bli, gdp_per_capita\n",
    "\n",
    "def prepare_country_stats(oecd_bli, gdp_per_capita):\n",
    "    return sample_data\n",
    "\n",
    "# Code example\n",
    "########################################################################\n",
    "from sklearn import neighbors\n",
    "import numpy as np\n",
    "import pandas as pd\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 k-neighboors regression model\n",
    "k_neigh_reg_model = neighbors.KNeighborsRegressor(n_neighbors=3)\n",
    "\n",
    "# Train the model\n",
    "k_neigh_reg_model.fit(X, y)\n",
    "\n",
    "# Make a prediction for Cyprus\n",
    "X_new = [[22587]]  # Cyprus' GDP per capita\n",
    "print(lin_reg_model.predict(X_new)) # outputs [[ 5.96242338]]\n",
    "########################################################################\n",
    "\n",
    "oecd_bli, gdp_per_capita = backup"
   ]
  }
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