Happiness_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " alcohol-drug-use-disorders-share-total-disease.csv\r\n",
      " datasets_894_813759_2019.csv\r\n",
      " share-with-alcohol-or-drug-use-disorders.csv\r\n",
      "'SYB62_154_201906_Health Personnel.csv'\r\n",
      "'SYB62_230_201904_GDP and GDP Per Capita.csv'\r\n",
      "'SYB62_246_201907_Population Growth, Fertility and Mortality Indicators.csv'\r\n",
      " SYB62_309_201906_Education.csv\r\n",
      "'SYB62_315_201906_Water and Sanitation Services.csv'\r\n",
      "'SYB62_328_201904_Intentional Homicides and Other Crimes.csv'\r\n",
      "'SYB62_329_201904_Labour Force and Unemployment.csv'\r\n"
     ]
    }
   ],
   "source": [
    "!ls Happiness/raw_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>152</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>3.334</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>153</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>3.231</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.499</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>154</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3.203</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>155</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>3.083</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>156</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>2.853</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Overall rank         Country or region  Score  GDP per capita  \\\n",
       "0               1                   Finland  7.769           1.340   \n",
       "1               2                   Denmark  7.600           1.383   \n",
       "2               3                    Norway  7.554           1.488   \n",
       "3               4                   Iceland  7.494           1.380   \n",
       "4               5               Netherlands  7.488           1.396   \n",
       "..            ...                       ...    ...             ...   \n",
       "151           152                    Rwanda  3.334           0.359   \n",
       "152           153                  Tanzania  3.231           0.476   \n",
       "153           154               Afghanistan  3.203           0.350   \n",
       "154           155  Central African Republic  3.083           0.026   \n",
       "155           156               South Sudan  2.853           0.306   \n",
       "\n",
       "     Social support  Healthy life expectancy  Freedom to make life choices  \\\n",
       "0             1.587                    0.986                         0.596   \n",
       "1             1.573                    0.996                         0.592   \n",
       "2             1.582                    1.028                         0.603   \n",
       "3             1.624                    1.026                         0.591   \n",
       "4             1.522                    0.999                         0.557   \n",
       "..              ...                      ...                           ...   \n",
       "151           0.711                    0.614                         0.555   \n",
       "152           0.885                    0.499                         0.417   \n",
       "153           0.517                    0.361                         0.000   \n",
       "154           0.000                    0.105                         0.225   \n",
       "155           0.575                    0.295                         0.010   \n",
       "\n",
       "     Generosity  Perceptions of corruption  \n",
       "0         0.153                      0.393  \n",
       "1         0.252                      0.410  \n",
       "2         0.271                      0.341  \n",
       "3         0.354                      0.118  \n",
       "4         0.322                      0.298  \n",
       "..          ...                        ...  \n",
       "151       0.217                      0.411  \n",
       "152       0.276                      0.147  \n",
       "153       0.158                      0.025  \n",
       "154       0.235                      0.035  \n",
       "155       0.202                      0.091  \n",
       "\n",
       "[156 rows x 9 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_survey = pd.read_csv('Happiness/raw_data/datasets_894_813759_2019.csv')\n",
    "happiness_survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39.0\n"
     ]
    }
   ],
   "source": [
    "quarter = happiness_survey['Overall rank'].count()/4\n",
    "print (quarter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Quartile rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>152</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>3.334</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.411</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>153</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>3.231</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.499</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>154</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3.203</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.025</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>155</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>3.083</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.035</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>156</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>2.853</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.091</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Overall rank         Country or region  Score  GDP per capita  \\\n",
       "0               1                   Finland  7.769           1.340   \n",
       "1               2                   Denmark  7.600           1.383   \n",
       "2               3                    Norway  7.554           1.488   \n",
       "3               4                   Iceland  7.494           1.380   \n",
       "4               5               Netherlands  7.488           1.396   \n",
       "..            ...                       ...    ...             ...   \n",
       "151           152                    Rwanda  3.334           0.359   \n",
       "152           153                  Tanzania  3.231           0.476   \n",
       "153           154               Afghanistan  3.203           0.350   \n",
       "154           155  Central African Republic  3.083           0.026   \n",
       "155           156               South Sudan  2.853           0.306   \n",
       "\n",
       "     Social support  Healthy life expectancy  Freedom to make life choices  \\\n",
       "0             1.587                    0.986                         0.596   \n",
       "1             1.573                    0.996                         0.592   \n",
       "2             1.582                    1.028                         0.603   \n",
       "3             1.624                    1.026                         0.591   \n",
       "4             1.522                    0.999                         0.557   \n",
       "..              ...                      ...                           ...   \n",
       "151           0.711                    0.614                         0.555   \n",
       "152           0.885                    0.499                         0.417   \n",
       "153           0.517                    0.361                         0.000   \n",
       "154           0.000                    0.105                         0.225   \n",
       "155           0.575                    0.295                         0.010   \n",
       "\n",
       "     Generosity  Perceptions of corruption  Quartile rank  \n",
       "0         0.153                      0.393              0  \n",
       "1         0.252                      0.410              0  \n",
       "2         0.271                      0.341              0  \n",
       "3         0.354                      0.118              0  \n",
       "4         0.322                      0.298              0  \n",
       "..          ...                        ...            ...  \n",
       "151       0.217                      0.411              3  \n",
       "152       0.276                      0.147              3  \n",
       "153       0.158                      0.025              3  \n",
       "154       0.235                      0.035              3  \n",
       "155       0.202                      0.091              3  \n",
       "\n",
       "[156 rows x 10 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_survey['Quartile rank'] = happiness_survey['Overall rank']/(quarter + .001)\n",
    "happiness_survey['Quartile rank'] = happiness_survey['Quartile rank'].astype(int)\n",
    "happiness_survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# line_number = 0\n",
    "# for score in range(happiness_survey['Overall rank'].count()):\n",
    "#     if happiness_survey['Quartile rank'][score] == line_number:\n",
    "#         lower = happiness_survey['Score'][score]\n",
    "#     else:\n",
    "#         line_number += 1\n",
    "#         if line_number == 1:\n",
    "#             score_line_1 = (lower + happiness_survey['Score'][score])/2\n",
    "#         elif line_number == 2:\n",
    "#             score_line_2 = (lower + happiness_survey['Score'][score])/2\n",
    "#         else:\n",
    "#             score_line_3 = (lower + happiness_survey['Score'][score])/2\n",
    "# print(score_line_1, score_line_2, score_line_3)\n",
    "# score_min = happiness_survey['Score'].min()\n",
    "# score_max = happiness_survey['Score'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter (happiness_survey['GDP per capita'],happiness_survey['Score'],c=happiness_survey['Quartile rank'])\n",
    "plt.title ('Happiness Report Component')\n",
    "plt.xlabel('GDP per capita')\n",
    "plt.ylabel('Overall Score')\n",
    "plt.plot(np.unique(happiness_survey['GDP per capita']), np.poly1d(np.polyfit(happiness_survey['GDP per capita'], happiness_survey['Score'], 1))(np.unique(happiness_survey['GDP per capita'])),color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter (happiness_survey['Social support'],happiness_survey['Score'],c=happiness_survey['Quartile rank'])\n",
    "plt.title ('Happiness Report Component')\n",
    "plt.xlabel('Social support')\n",
    "plt.ylabel('Overall Score')\n",
    "plt.plot(np.unique(happiness_survey['Social support']), np.poly1d(np.polyfit(happiness_survey['Social support'], happiness_survey['Score'], 1))(np.unique(happiness_survey['Social support'])),color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter (happiness_survey['Healthy life expectancy'],happiness_survey['Score'],c=happiness_survey['Quartile rank'])\n",
    "plt.title ('Happiness Report Component')\n",
    "plt.xlabel('Healthy life expectancy')\n",
    "plt.ylabel('Overall Score')\n",
    "plt.plot(np.unique(happiness_survey['Healthy life expectancy']), np.poly1d(np.polyfit(happiness_survey['Healthy life expectancy'], happiness_survey['Score'], 1))(np.unique(happiness_survey['Healthy life expectancy'])),color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter (happiness_survey['Freedom to make life choices'],happiness_survey['Score'],c=happiness_survey['Quartile rank'])\n",
    "plt.title ('Happiness Report Component')\n",
    "plt.xlabel('Freedom to make life choices')\n",
    "plt.ylabel('Overall Score')\n",
    "plt.plot(np.unique(happiness_survey['Freedom to make life choices']), np.poly1d(np.polyfit(happiness_survey['Freedom to make life choices'], happiness_survey['Score'], 1))(np.unique(happiness_survey['Freedom to make life choices'])),color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter (happiness_survey['Generosity'],happiness_survey['Score'],c=happiness_survey['Quartile rank'])\n",
    "plt.title ('Happiness Report Component')\n",
    "plt.xlabel('Generosity')\n",
    "plt.ylabel('Overall Score')\n",
    "plt.plot(np.unique(happiness_survey['Generosity']), np.poly1d(np.polyfit(happiness_survey['Generosity'], happiness_survey['Score'], 1))(np.unique(happiness_survey['Generosity'])),color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter (happiness_survey['Perceptions of corruption'],happiness_survey['Score'],c=happiness_survey['Quartile rank'])\n",
    "plt.title ('Happiness Report Component')\n",
    "plt.xlabel('Perceptions of corruption')\n",
    "plt.ylabel('Overall Score')\n",
    "plt.plot(np.unique(happiness_survey['Perceptions of corruption']), np.poly1d(np.polyfit(happiness_survey['Perceptions of corruption'], happiness_survey['Score'], 1))(np.unique(happiness_survey['Perceptions of corruption'])),color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Enrollment in primary, secondary and tertiary education levels  Series                                              \n",
       "Afghanistan                                                     Gross enrollment ratio - Primary (female)               2017\n",
       "                                                                Gross enrollment ratio - Primary (male)                 2017\n",
       "                                                                Gross enrollment ratio - Secondary (female)             2017\n",
       "                                                                Gross enrollment ratio - Secondary (male)               2017\n",
       "                                                                Gross enrollment ratio - Tertiary (female)              2014\n",
       "                                                                                                                        ... \n",
       "Zimbabwe                                                        Gross enrollment ratio - Tertiary (female)              2015\n",
       "                                                                Gross enrollment ratio - Tertiary (male)                2015\n",
       "                                                                Students enrolled in primary education (thousands)      2013\n",
       "                                                                Students enrolled in secondary education (thousands)    2013\n",
       "                                                                Students enrolled in tertiary education (thousands)     2015\n",
       "Name: Year, Length: 1867, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education_filter = education.groupby(['Enrollment in primary, secondary and tertiary education levels','Series'])['Year'].max()\n",
    "education_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region/Country/Area</th>\n",
       "      <th>Enrollment in primary, secondary and tertiary education levels</th>\n",
       "      <th>Year</th>\n",
       "      <th>Series</th>\n",
       "      <th>Value</th>\n",
       "      <th>Footnotes</th>\n",
       "      <th>Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2017</td>\n",
       "      <td>Students enrolled in primary education (thousa...</td>\n",
       "      <td>746284.9227</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>United Nations Educational, Scientific and Cul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2017</td>\n",
       "      <td>Gross enrollment ratio - Primary (male)</td>\n",
       "      <td>103.7417</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>United Nations Educational, Scientific and Cul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2017</td>\n",
       "      <td>Gross enrollment ratio - Primary (female)</td>\n",
       "      <td>104.4958</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>United Nations Educational, Scientific and Cul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2017</td>\n",
       "      <td>Students enrolled in secondary education (thou...</td>\n",
       "      <td>590725.2431</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>United Nations Educational, Scientific and Cul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2017</td>\n",
       "      <td>Gross enrollment ratio - Secondary (male)</td>\n",
       "      <td>76.9813</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>United Nations Educational, Scientific and Cul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>716</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2013</td>\n",
       "      <td>Gross enrollment ratio - Secondary (male)</td>\n",
       "      <td>47.5785</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Nations Educational, Scientific and Cul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>716</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2013</td>\n",
       "      <td>Gross enrollment ratio - Secondary (female)</td>\n",
       "      <td>46.6504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Nations Educational, Scientific and Cul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1864</th>\n",
       "      <td>716</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2015</td>\n",
       "      <td>Students enrolled in tertiary education (thous...</td>\n",
       "      <td>135.5750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Nations Educational, Scientific and Cul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>716</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2015</td>\n",
       "      <td>Gross enrollment ratio - Tertiary (male)</td>\n",
       "      <td>8.9410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Nations Educational, Scientific and Cul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>716</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2015</td>\n",
       "      <td>Gross enrollment ratio - Tertiary (female)</td>\n",
       "      <td>8.0387</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Nations Educational, Scientific and Cul...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1867 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Region/Country/Area  \\\n",
       "0                       1   \n",
       "1                       1   \n",
       "2                       1   \n",
       "3                       1   \n",
       "4                       1   \n",
       "...                   ...   \n",
       "1862                  716   \n",
       "1863                  716   \n",
       "1864                  716   \n",
       "1865                  716   \n",
       "1866                  716   \n",
       "\n",
       "     Enrollment in primary, secondary and tertiary education levels  Year  \\\n",
       "0                         Total, all countries or areas              2017   \n",
       "1                         Total, all countries or areas              2017   \n",
       "2                         Total, all countries or areas              2017   \n",
       "3                         Total, all countries or areas              2017   \n",
       "4                         Total, all countries or areas              2017   \n",
       "...                                                 ...               ...   \n",
       "1862                                           Zimbabwe              2013   \n",
       "1863                                           Zimbabwe              2013   \n",
       "1864                                           Zimbabwe              2015   \n",
       "1865                                           Zimbabwe              2015   \n",
       "1866                                           Zimbabwe              2015   \n",
       "\n",
       "                                                 Series        Value  \\\n",
       "0     Students enrolled in primary education (thousa...  746284.9227   \n",
       "1               Gross enrollment ratio - Primary (male)     103.7417   \n",
       "2             Gross enrollment ratio - Primary (female)     104.4958   \n",
       "3     Students enrolled in secondary education (thou...  590725.2431   \n",
       "4             Gross enrollment ratio - Secondary (male)      76.9813   \n",
       "...                                                 ...          ...   \n",
       "1862          Gross enrollment ratio - Secondary (male)      47.5785   \n",
       "1863        Gross enrollment ratio - Secondary (female)      46.6504   \n",
       "1864  Students enrolled in tertiary education (thous...     135.5750   \n",
       "1865           Gross enrollment ratio - Tertiary (male)       8.9410   \n",
       "1866         Gross enrollment ratio - Tertiary (female)       8.0387   \n",
       "\n",
       "      Footnotes                                             Source  \n",
       "0     Estimate.  United Nations Educational, Scientific and Cul...  \n",
       "1     Estimate.  United Nations Educational, Scientific and Cul...  \n",
       "2     Estimate.  United Nations Educational, Scientific and Cul...  \n",
       "3     Estimate.  United Nations Educational, Scientific and Cul...  \n",
       "4     Estimate.  United Nations Educational, Scientific and Cul...  \n",
       "...         ...                                                ...  \n",
       "1862        NaN  United Nations Educational, Scientific and Cul...  \n",
       "1863        NaN  United Nations Educational, Scientific and Cul...  \n",
       "1864        NaN  United Nations Educational, Scientific and Cul...  \n",
       "1865        NaN  United Nations Educational, Scientific and Cul...  \n",
       "1866        NaN  United Nations Educational, Scientific and Cul...  \n",
       "\n",
       "[1867 rows x 7 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education_latest = pd.merge(left=education, right=education_filter, left_on=['Enrollment in primary, secondary and tertiary education levels','Series','Year'], right_on = ['Enrollment in primary, secondary and tertiary education levels','Series','Year'])\n",
    "education_latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Enrollment in primary, secondary and tertiary education levels</th>\n",
       "      <th>Series</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>Students enrolled in primary education (thousa...</td>\n",
       "      <td>746284.9227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>Gross enrollment ratio - Primary (male)</td>\n",
       "      <td>103.7417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>Gross enrollment ratio - Primary (female)</td>\n",
       "      <td>104.4958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>Students enrolled in secondary education (thou...</td>\n",
       "      <td>590725.2431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>Gross enrollment ratio - Secondary (male)</td>\n",
       "      <td>76.9813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Gross enrollment ratio - Secondary (male)</td>\n",
       "      <td>47.5785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Gross enrollment ratio - Secondary (female)</td>\n",
       "      <td>46.6504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1864</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Students enrolled in tertiary education (thous...</td>\n",
       "      <td>135.5750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Gross enrollment ratio - Tertiary (male)</td>\n",
       "      <td>8.9410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Gross enrollment ratio - Tertiary (female)</td>\n",
       "      <td>8.0387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1867 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Enrollment in primary, secondary and tertiary education levels  \\\n",
       "0                         Total, all countries or areas               \n",
       "1                         Total, all countries or areas               \n",
       "2                         Total, all countries or areas               \n",
       "3                         Total, all countries or areas               \n",
       "4                         Total, all countries or areas               \n",
       "...                                                 ...               \n",
       "1862                                           Zimbabwe               \n",
       "1863                                           Zimbabwe               \n",
       "1864                                           Zimbabwe               \n",
       "1865                                           Zimbabwe               \n",
       "1866                                           Zimbabwe               \n",
       "\n",
       "                                                 Series        Value  \n",
       "0     Students enrolled in primary education (thousa...  746284.9227  \n",
       "1               Gross enrollment ratio - Primary (male)     103.7417  \n",
       "2             Gross enrollment ratio - Primary (female)     104.4958  \n",
       "3     Students enrolled in secondary education (thou...  590725.2431  \n",
       "4             Gross enrollment ratio - Secondary (male)      76.9813  \n",
       "...                                                 ...          ...  \n",
       "1862          Gross enrollment ratio - Secondary (male)      47.5785  \n",
       "1863        Gross enrollment ratio - Secondary (female)      46.6504  \n",
       "1864  Students enrolled in tertiary education (thous...     135.5750  \n",
       "1865           Gross enrollment ratio - Tertiary (male)       8.9410  \n",
       "1866         Gross enrollment ratio - Tertiary (female)       8.0387  \n",
       "\n",
       "[1867 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education_latest.drop(['Region/Country/Area','Year',\"Footnotes\",'Source'], axis = 1, inplace = True)\n",
    "education_latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Series</th>\n",
       "      <th>Gross enrollment ratio - Primary (female)</th>\n",
       "      <th>Gross enrollment ratio - Primary (male)</th>\n",
       "      <th>Gross enrollment ratio - Secondary (female)</th>\n",
       "      <th>Gross enrollment ratio - Secondary (male)</th>\n",
       "      <th>Gross enrollment ratio - Tertiary (female)</th>\n",
       "      <th>Gross enrollment ratio - Tertiary (male)</th>\n",
       "      <th>Students enrolled in primary education (thousands)</th>\n",
       "      <th>Students enrolled in secondary education (thousands)</th>\n",
       "      <th>Students enrolled in tertiary education (thousands)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enrollment in primary, secondary and tertiary education levels</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>Afghanistan</th>\n",
       "      <td>84.1501</td>\n",
       "      <td>122.7469</td>\n",
       "      <td>39.6317</td>\n",
       "      <td>69.1179</td>\n",
       "      <td>3.5854</td>\n",
       "      <td>14.5808</td>\n",
       "      <td>6358.7780</td>\n",
       "      <td>2907.8008</td>\n",
       "      <td>370.6100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>108.6033</td>\n",
       "      <td>111.5243</td>\n",
       "      <td>93.0932</td>\n",
       "      <td>98.9879</td>\n",
       "      <td>70.8026</td>\n",
       "      <td>44.4004</td>\n",
       "      <td>174.8360</td>\n",
       "      <td>280.3780</td>\n",
       "      <td>139.6070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>108.7837</td>\n",
       "      <td>114.4966</td>\n",
       "      <td>101.5495</td>\n",
       "      <td>97.8325</td>\n",
       "      <td>57.3124</td>\n",
       "      <td>38.4862</td>\n",
       "      <td>4283.2070</td>\n",
       "      <td>4572.5130</td>\n",
       "      <td>1545.5230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.2630</td>\n",
       "      <td>4.4920</td>\n",
       "      <td>0.5900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viet Nam</th>\n",
       "      <td>108.1904</td>\n",
       "      <td>107.6687</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.3372</td>\n",
       "      <td>25.3230</td>\n",
       "      <td>7801.5600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2307.3610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Asia</th>\n",
       "      <td>96.2134</td>\n",
       "      <td>102.8252</td>\n",
       "      <td>78.4619</td>\n",
       "      <td>85.6535</td>\n",
       "      <td>52.8801</td>\n",
       "      <td>54.7008</td>\n",
       "      <td>27087.0396</td>\n",
       "      <td>25859.3914</td>\n",
       "      <td>11881.3598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>86.0532</td>\n",
       "      <td>98.5701</td>\n",
       "      <td>42.8198</td>\n",
       "      <td>58.8822</td>\n",
       "      <td>6.0707</td>\n",
       "      <td>13.7438</td>\n",
       "      <td>3900.1340</td>\n",
       "      <td>1915.8710</td>\n",
       "      <td>267.4980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>100.8896</td>\n",
       "      <td>98.6845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.4681</td>\n",
       "      <td>4.5603</td>\n",
       "      <td>3284.8410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.6800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>97.9307</td>\n",
       "      <td>99.4543</td>\n",
       "      <td>46.6504</td>\n",
       "      <td>47.5785</td>\n",
       "      <td>8.0387</td>\n",
       "      <td>8.9410</td>\n",
       "      <td>2663.1870</td>\n",
       "      <td>957.4610</td>\n",
       "      <td>135.5750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>224 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Series                                              Gross enrollment ratio - Primary (female)  \\\n",
       "Enrollment in primary, secondary and tertiary e...                                              \n",
       "Afghanistan                                                                           84.1501   \n",
       "Albania                                                                              108.6033   \n",
       "Algeria                                                                              108.7837   \n",
       "American Samoa                                                                            NaN   \n",
       "Andorra                                                                                   NaN   \n",
       "...                                                                                       ...   \n",
       "Viet Nam                                                                             108.1904   \n",
       "Western Asia                                                                          96.2134   \n",
       "Yemen                                                                                 86.0532   \n",
       "Zambia                                                                               100.8896   \n",
       "Zimbabwe                                                                              97.9307   \n",
       "\n",
       "Series                                              Gross enrollment ratio - Primary (male)  \\\n",
       "Enrollment in primary, secondary and tertiary e...                                            \n",
       "Afghanistan                                                                        122.7469   \n",
       "Albania                                                                            111.5243   \n",
       "Algeria                                                                            114.4966   \n",
       "American Samoa                                                                          NaN   \n",
       "Andorra                                                                                 NaN   \n",
       "...                                                                                     ...   \n",
       "Viet Nam                                                                           107.6687   \n",
       "Western Asia                                                                       102.8252   \n",
       "Yemen                                                                               98.5701   \n",
       "Zambia                                                                              98.6845   \n",
       "Zimbabwe                                                                            99.4543   \n",
       "\n",
       "Series                                              Gross enrollment ratio - Secondary (female)  \\\n",
       "Enrollment in primary, secondary and tertiary e...                                                \n",
       "Afghanistan                                                                             39.6317   \n",
       "Albania                                                                                 93.0932   \n",
       "Algeria                                                                                101.5495   \n",
       "American Samoa                                                                              NaN   \n",
       "Andorra                                                                                     NaN   \n",
       "...                                                                                         ...   \n",
       "Viet Nam                                                                                    NaN   \n",
       "Western Asia                                                                            78.4619   \n",
       "Yemen                                                                                   42.8198   \n",
       "Zambia                                                                                      NaN   \n",
       "Zimbabwe                                                                                46.6504   \n",
       "\n",
       "Series                                              Gross enrollment ratio - Secondary (male)  \\\n",
       "Enrollment in primary, secondary and tertiary e...                                              \n",
       "Afghanistan                                                                           69.1179   \n",
       "Albania                                                                               98.9879   \n",
       "Algeria                                                                               97.8325   \n",
       "American Samoa                                                                            NaN   \n",
       "Andorra                                                                                   NaN   \n",
       "...                                                                                       ...   \n",
       "Viet Nam                                                                                  NaN   \n",
       "Western Asia                                                                          85.6535   \n",
       "Yemen                                                                                 58.8822   \n",
       "Zambia                                                                                    NaN   \n",
       "Zimbabwe                                                                              47.5785   \n",
       "\n",
       "Series                                              Gross enrollment ratio - Tertiary (female)  \\\n",
       "Enrollment in primary, secondary and tertiary e...                                               \n",
       "Afghanistan                                                                             3.5854   \n",
       "Albania                                                                                70.8026   \n",
       "Algeria                                                                                57.3124   \n",
       "American Samoa                                                                             NaN   \n",
       "Andorra                                                                                    NaN   \n",
       "...                                                                                        ...   \n",
       "Viet Nam                                                                               31.3372   \n",
       "Western Asia                                                                           52.8801   \n",
       "Yemen                                                                                   6.0707   \n",
       "Zambia                                                                                  3.4681   \n",
       "Zimbabwe                                                                                8.0387   \n",
       "\n",
       "Series                                              Gross enrollment ratio - Tertiary (male)  \\\n",
       "Enrollment in primary, secondary and tertiary e...                                             \n",
       "Afghanistan                                                                          14.5808   \n",
       "Albania                                                                              44.4004   \n",
       "Algeria                                                                              38.4862   \n",
       "American Samoa                                                                           NaN   \n",
       "Andorra                                                                                  NaN   \n",
       "...                                                                                      ...   \n",
       "Viet Nam                                                                             25.3230   \n",
       "Western Asia                                                                         54.7008   \n",
       "Yemen                                                                                13.7438   \n",
       "Zambia                                                                                4.5603   \n",
       "Zimbabwe                                                                              8.9410   \n",
       "\n",
       "Series                                              Students enrolled in primary education (thousands)  \\\n",
       "Enrollment in primary, secondary and tertiary e...                                                       \n",
       "Afghanistan                                                                                 6358.7780    \n",
       "Albania                                                                                      174.8360    \n",
       "Algeria                                                                                     4283.2070    \n",
       "American Samoa                                                                                    NaN    \n",
       "Andorra                                                                                        4.2630    \n",
       "...                                                                                               ...    \n",
       "Viet Nam                                                                                    7801.5600    \n",
       "Western Asia                                                                               27087.0396    \n",
       "Yemen                                                                                       3900.1340    \n",
       "Zambia                                                                                      3284.8410    \n",
       "Zimbabwe                                                                                    2663.1870    \n",
       "\n",
       "Series                                              Students enrolled in secondary education (thousands)  \\\n",
       "Enrollment in primary, secondary and tertiary e...                                                         \n",
       "Afghanistan                                                                                 2907.8008      \n",
       "Albania                                                                                      280.3780      \n",
       "Algeria                                                                                     4572.5130      \n",
       "American Samoa                                                                                    NaN      \n",
       "Andorra                                                                                        4.4920      \n",
       "...                                                                                               ...      \n",
       "Viet Nam                                                                                          NaN      \n",
       "Western Asia                                                                               25859.3914      \n",
       "Yemen                                                                                       1915.8710      \n",
       "Zambia                                                                                            NaN      \n",
       "Zimbabwe                                                                                     957.4610      \n",
       "\n",
       "Series                                              Students enrolled in tertiary education (thousands)  \n",
       "Enrollment in primary, secondary and tertiary e...                                                       \n",
       "Afghanistan                                                                                  370.6100    \n",
       "Albania                                                                                      139.6070    \n",
       "Algeria                                                                                     1545.5230    \n",
       "American Samoa                                                                                 1.6070    \n",
       "Andorra                                                                                        0.5900    \n",
       "...                                                                                               ...    \n",
       "Viet Nam                                                                                    2307.3610    \n",
       "Western Asia                                                                               11881.3598    \n",
       "Yemen                                                                                        267.4980    \n",
       "Zambia                                                                                        56.6800    \n",
       "Zimbabwe                                                                                     135.5750    \n",
       "\n",
       "[224 rows x 9 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education_latest = pd.pivot_table(education_latest, index='Enrollment in primary, secondary and tertiary education levels', columns='Series', values='Value')\n",
    "education_latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(25,25))\n",
    "# plt.scatter (education_latest['Series'], \n",
    "#              education_latest['Enrollment in primary, secondary and tertiary education levels'])\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Quartile rank</th>\n",
       "      <th>Gross enrollment ratio - Primary (female)</th>\n",
       "      <th>Gross enrollment ratio - Primary (male)</th>\n",
       "      <th>Gross enrollment ratio - Secondary (female)</th>\n",
       "      <th>Gross enrollment ratio - Secondary (male)</th>\n",
       "      <th>Gross enrollment ratio - Tertiary (female)</th>\n",
       "      <th>Gross enrollment ratio - Tertiary (male)</th>\n",
       "      <th>Students enrolled in primary education (thousands)</th>\n",
       "      <th>Students enrolled in secondary education (thousands)</th>\n",
       "      <th>Students enrolled in tertiary education (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0</td>\n",
       "      <td>100.1924</td>\n",
       "      <td>100.6418</td>\n",
       "      <td>159.7470</td>\n",
       "      <td>144.9512</td>\n",
       "      <td>94.5181</td>\n",
       "      <td>79.7513</td>\n",
       "      <td>359.635</td>\n",
       "      <td>539.8160</td>\n",
       "      <td>297.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0</td>\n",
       "      <td>101.1915</td>\n",
       "      <td>102.3880</td>\n",
       "      <td>131.0499</td>\n",
       "      <td>127.2971</td>\n",
       "      <td>93.9538</td>\n",
       "      <td>68.8473</td>\n",
       "      <td>471.787</td>\n",
       "      <td>540.6220</td>\n",
       "      <td>314.822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0</td>\n",
       "      <td>100.1830</td>\n",
       "      <td>100.2746</td>\n",
       "      <td>114.1830</td>\n",
       "      <td>119.3684</td>\n",
       "      <td>98.6869</td>\n",
       "      <td>66.2914</td>\n",
       "      <td>444.638</td>\n",
       "      <td>447.5250</td>\n",
       "      <td>284.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0</td>\n",
       "      <td>99.4831</td>\n",
       "      <td>99.5827</td>\n",
       "      <td>117.7392</td>\n",
       "      <td>117.5228</td>\n",
       "      <td>96.7979</td>\n",
       "      <td>51.7824</td>\n",
       "      <td>31.194</td>\n",
       "      <td>35.6510</td>\n",
       "      <td>18.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0</td>\n",
       "      <td>103.2904</td>\n",
       "      <td>103.2833</td>\n",
       "      <td>133.8120</td>\n",
       "      <td>131.6286</td>\n",
       "      <td>85.3463</td>\n",
       "      <td>75.5978</td>\n",
       "      <td>1192.268</td>\n",
       "      <td>1611.0160</td>\n",
       "      <td>836.946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>150</td>\n",
       "      <td>Malawi</td>\n",
       "      <td>3.410</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.089</td>\n",
       "      <td>3</td>\n",
       "      <td>142.3735</td>\n",
       "      <td>137.5486</td>\n",
       "      <td>37.0018</td>\n",
       "      <td>39.2834</td>\n",
       "      <td>0.6044</td>\n",
       "      <td>0.9470</td>\n",
       "      <td>4433.977</td>\n",
       "      <td>998.9400</td>\n",
       "      <td>12.203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>151</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>3.380</td>\n",
       "      <td>0.287</td>\n",
       "      <td>1.163</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.077</td>\n",
       "      <td>3</td>\n",
       "      <td>86.0532</td>\n",
       "      <td>98.5701</td>\n",
       "      <td>42.8198</td>\n",
       "      <td>58.8822</td>\n",
       "      <td>6.0707</td>\n",
       "      <td>13.7438</td>\n",
       "      <td>3900.134</td>\n",
       "      <td>1915.8710</td>\n",
       "      <td>267.498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>152</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>3.334</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.411</td>\n",
       "      <td>3</td>\n",
       "      <td>132.7576</td>\n",
       "      <td>134.0964</td>\n",
       "      <td>40.2957</td>\n",
       "      <td>35.9440</td>\n",
       "      <td>7.1783</td>\n",
       "      <td>8.1138</td>\n",
       "      <td>2540.374</td>\n",
       "      <td>610.9720</td>\n",
       "      <td>80.773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>154</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3.203</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.025</td>\n",
       "      <td>3</td>\n",
       "      <td>84.1501</td>\n",
       "      <td>122.7469</td>\n",
       "      <td>39.6317</td>\n",
       "      <td>69.1179</td>\n",
       "      <td>3.5854</td>\n",
       "      <td>14.5808</td>\n",
       "      <td>6358.778</td>\n",
       "      <td>2907.8008</td>\n",
       "      <td>370.610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>155</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>3.083</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.035</td>\n",
       "      <td>3</td>\n",
       "      <td>91.5495</td>\n",
       "      <td>119.9783</td>\n",
       "      <td>13.9720</td>\n",
       "      <td>21.1773</td>\n",
       "      <td>1.5647</td>\n",
       "      <td>4.3319</td>\n",
       "      <td>813.767</td>\n",
       "      <td>137.8440</td>\n",
       "      <td>12.522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Overall rank         Country or region  Score  GDP per capita  \\\n",
       "0               1                   Finland  7.769           1.340   \n",
       "1               2                   Denmark  7.600           1.383   \n",
       "2               3                    Norway  7.554           1.488   \n",
       "3               4                   Iceland  7.494           1.380   \n",
       "4               5               Netherlands  7.488           1.396   \n",
       "..            ...                       ...    ...             ...   \n",
       "127           150                    Malawi  3.410           0.191   \n",
       "128           151                     Yemen  3.380           0.287   \n",
       "129           152                    Rwanda  3.334           0.359   \n",
       "130           154               Afghanistan  3.203           0.350   \n",
       "131           155  Central African Republic  3.083           0.026   \n",
       "\n",
       "     Social support  Healthy life expectancy  Freedom to make life choices  \\\n",
       "0             1.587                    0.986                         0.596   \n",
       "1             1.573                    0.996                         0.592   \n",
       "2             1.582                    1.028                         0.603   \n",
       "3             1.624                    1.026                         0.591   \n",
       "4             1.522                    0.999                         0.557   \n",
       "..              ...                      ...                           ...   \n",
       "127           0.560                    0.495                         0.443   \n",
       "128           1.163                    0.463                         0.143   \n",
       "129           0.711                    0.614                         0.555   \n",
       "130           0.517                    0.361                         0.000   \n",
       "131           0.000                    0.105                         0.225   \n",
       "\n",
       "     Generosity  Perceptions of corruption  Quartile rank  \\\n",
       "0         0.153                      0.393              0   \n",
       "1         0.252                      0.410              0   \n",
       "2         0.271                      0.341              0   \n",
       "3         0.354                      0.118              0   \n",
       "4         0.322                      0.298              0   \n",
       "..          ...                        ...            ...   \n",
       "127       0.218                      0.089              3   \n",
       "128       0.108                      0.077              3   \n",
       "129       0.217                      0.411              3   \n",
       "130       0.158                      0.025              3   \n",
       "131       0.235                      0.035              3   \n",
       "\n",
       "     Gross enrollment ratio - Primary (female)  \\\n",
       "0                                     100.1924   \n",
       "1                                     101.1915   \n",
       "2                                     100.1830   \n",
       "3                                      99.4831   \n",
       "4                                     103.2904   \n",
       "..                                         ...   \n",
       "127                                   142.3735   \n",
       "128                                    86.0532   \n",
       "129                                   132.7576   \n",
       "130                                    84.1501   \n",
       "131                                    91.5495   \n",
       "\n",
       "     Gross enrollment ratio - Primary (male)  \\\n",
       "0                                   100.6418   \n",
       "1                                   102.3880   \n",
       "2                                   100.2746   \n",
       "3                                    99.5827   \n",
       "4                                   103.2833   \n",
       "..                                       ...   \n",
       "127                                 137.5486   \n",
       "128                                  98.5701   \n",
       "129                                 134.0964   \n",
       "130                                 122.7469   \n",
       "131                                 119.9783   \n",
       "\n",
       "     Gross enrollment ratio - Secondary (female)  \\\n",
       "0                                       159.7470   \n",
       "1                                       131.0499   \n",
       "2                                       114.1830   \n",
       "3                                       117.7392   \n",
       "4                                       133.8120   \n",
       "..                                           ...   \n",
       "127                                      37.0018   \n",
       "128                                      42.8198   \n",
       "129                                      40.2957   \n",
       "130                                      39.6317   \n",
       "131                                      13.9720   \n",
       "\n",
       "     Gross enrollment ratio - Secondary (male)  \\\n",
       "0                                     144.9512   \n",
       "1                                     127.2971   \n",
       "2                                     119.3684   \n",
       "3                                     117.5228   \n",
       "4                                     131.6286   \n",
       "..                                         ...   \n",
       "127                                    39.2834   \n",
       "128                                    58.8822   \n",
       "129                                    35.9440   \n",
       "130                                    69.1179   \n",
       "131                                    21.1773   \n",
       "\n",
       "     Gross enrollment ratio - Tertiary (female)  \\\n",
       "0                                       94.5181   \n",
       "1                                       93.9538   \n",
       "2                                       98.6869   \n",
       "3                                       96.7979   \n",
       "4                                       85.3463   \n",
       "..                                          ...   \n",
       "127                                      0.6044   \n",
       "128                                      6.0707   \n",
       "129                                      7.1783   \n",
       "130                                      3.5854   \n",
       "131                                      1.5647   \n",
       "\n",
       "     Gross enrollment ratio - Tertiary (male)  \\\n",
       "0                                     79.7513   \n",
       "1                                     68.8473   \n",
       "2                                     66.2914   \n",
       "3                                     51.7824   \n",
       "4                                     75.5978   \n",
       "..                                        ...   \n",
       "127                                    0.9470   \n",
       "128                                   13.7438   \n",
       "129                                    8.1138   \n",
       "130                                   14.5808   \n",
       "131                                    4.3319   \n",
       "\n",
       "     Students enrolled in primary education (thousands)  \\\n",
       "0                                              359.635    \n",
       "1                                              471.787    \n",
       "2                                              444.638    \n",
       "3                                               31.194    \n",
       "4                                             1192.268    \n",
       "..                                                 ...    \n",
       "127                                           4433.977    \n",
       "128                                           3900.134    \n",
       "129                                           2540.374    \n",
       "130                                           6358.778    \n",
       "131                                            813.767    \n",
       "\n",
       "     Students enrolled in secondary education (thousands)  \\\n",
       "0                                             539.8160      \n",
       "1                                             540.6220      \n",
       "2                                             447.5250      \n",
       "3                                              35.6510      \n",
       "4                                            1611.0160      \n",
       "..                                                 ...      \n",
       "127                                           998.9400      \n",
       "128                                          1915.8710      \n",
       "129                                           610.9720      \n",
       "130                                          2907.8008      \n",
       "131                                           137.8440      \n",
       "\n",
       "     Students enrolled in tertiary education (thousands)  \n",
       "0                                              297.163    \n",
       "1                                              314.822    \n",
       "2                                              284.042    \n",
       "3                                               18.590    \n",
       "4                                              836.946    \n",
       "..                                                 ...    \n",
       "127                                             12.203    \n",
       "128                                            267.498    \n",
       "129                                             80.773    \n",
       "130                                            370.610    \n",
       "131                                             12.522    \n",
       "\n",
       "[125 rows x 19 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_education = pd.merge(left=happiness_survey, right=education_latest, left_on='Country or region', right_on = 'Enrollment in primary, secondary and tertiary education levels')\n",
    "H_education.dropna(inplace=True)\n",
    "H_education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Quartile rank</th>\n",
       "      <th>Gross enrollment ratio - Primary (female)</th>\n",
       "      <th>Gross enrollment ratio - Primary (male)</th>\n",
       "      <th>Gross enrollment ratio - Secondary (female)</th>\n",
       "      <th>Gross enrollment ratio - Secondary (male)</th>\n",
       "      <th>Gross enrollment ratio - Tertiary (female)</th>\n",
       "      <th>Gross enrollment ratio - Tertiary (male)</th>\n",
       "      <th>Students enrolled in primary education (thousands)</th>\n",
       "      <th>Students enrolled in secondary education (thousands)</th>\n",
       "      <th>Students enrolled in tertiary education (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Overall rank</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.989604</td>\n",
       "      <td>-0.813219</td>\n",
       "      <td>-0.766167</td>\n",
       "      <td>-0.811112</td>\n",
       "      <td>-0.549904</td>\n",
       "      <td>-0.100993</td>\n",
       "      <td>-0.430409</td>\n",
       "      <td>0.968713</td>\n",
       "      <td>0.045390</td>\n",
       "      <td>0.216071</td>\n",
       "      <td>-0.754106</td>\n",
       "      <td>-0.752386</td>\n",
       "      <td>-0.691909</td>\n",
       "      <td>-0.614994</td>\n",
       "      <td>0.166870</td>\n",
       "      <td>0.109533</td>\n",
       "      <td>0.053039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <td>-0.989604</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.807538</td>\n",
       "      <td>0.771068</td>\n",
       "      <td>0.806345</td>\n",
       "      <td>0.560488</td>\n",
       "      <td>0.146829</td>\n",
       "      <td>0.469994</td>\n",
       "      <td>-0.939531</td>\n",
       "      <td>-0.047855</td>\n",
       "      <td>-0.233365</td>\n",
       "      <td>0.760295</td>\n",
       "      <td>0.757439</td>\n",
       "      <td>0.688651</td>\n",
       "      <td>0.616372</td>\n",
       "      <td>-0.152750</td>\n",
       "      <td>-0.098929</td>\n",
       "      <td>-0.043509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP per capita</th>\n",
       "      <td>-0.813219</td>\n",
       "      <td>0.807538</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.775401</td>\n",
       "      <td>0.857967</td>\n",
       "      <td>0.405732</td>\n",
       "      <td>-0.044069</td>\n",
       "      <td>0.350911</td>\n",
       "      <td>-0.776218</td>\n",
       "      <td>-0.051175</td>\n",
       "      <td>-0.240962</td>\n",
       "      <td>0.841581</td>\n",
       "      <td>0.835329</td>\n",
       "      <td>0.794144</td>\n",
       "      <td>0.710267</td>\n",
       "      <td>-0.077380</td>\n",
       "      <td>-0.012373</td>\n",
       "      <td>0.045804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social support</th>\n",
       "      <td>-0.766167</td>\n",
       "      <td>0.771068</td>\n",
       "      <td>0.775401</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.736881</td>\n",
       "      <td>0.438130</td>\n",
       "      <td>0.047131</td>\n",
       "      <td>0.261373</td>\n",
       "      <td>-0.723190</td>\n",
       "      <td>-0.115529</td>\n",
       "      <td>-0.373953</td>\n",
       "      <td>0.736366</td>\n",
       "      <td>0.695704</td>\n",
       "      <td>0.665066</td>\n",
       "      <td>0.575044</td>\n",
       "      <td>-0.183512</td>\n",
       "      <td>-0.127392</td>\n",
       "      <td>-0.065192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <td>-0.811112</td>\n",
       "      <td>0.806345</td>\n",
       "      <td>0.857967</td>\n",
       "      <td>0.736881</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.428509</td>\n",
       "      <td>-0.010142</td>\n",
       "      <td>0.363790</td>\n",
       "      <td>-0.780397</td>\n",
       "      <td>0.075665</td>\n",
       "      <td>-0.181220</td>\n",
       "      <td>0.824919</td>\n",
       "      <td>0.814609</td>\n",
       "      <td>0.801281</td>\n",
       "      <td>0.737639</td>\n",
       "      <td>-0.095192</td>\n",
       "      <td>-0.023130</td>\n",
       "      <td>0.051181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <td>-0.549904</td>\n",
       "      <td>0.560488</td>\n",
       "      <td>0.405732</td>\n",
       "      <td>0.438130</td>\n",
       "      <td>0.428509</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.387456</td>\n",
       "      <td>0.478678</td>\n",
       "      <td>-0.519569</td>\n",
       "      <td>0.182531</td>\n",
       "      <td>-0.014872</td>\n",
       "      <td>0.420591</td>\n",
       "      <td>0.363659</td>\n",
       "      <td>0.221021</td>\n",
       "      <td>0.117171</td>\n",
       "      <td>0.085222</td>\n",
       "      <td>0.097367</td>\n",
       "      <td>0.104941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>-0.100993</td>\n",
       "      <td>0.146829</td>\n",
       "      <td>-0.044069</td>\n",
       "      <td>0.047131</td>\n",
       "      <td>-0.010142</td>\n",
       "      <td>0.387456</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.343494</td>\n",
       "      <td>-0.057336</td>\n",
       "      <td>0.042738</td>\n",
       "      <td>0.020848</td>\n",
       "      <td>-0.014796</td>\n",
       "      <td>-0.021723</td>\n",
       "      <td>-0.135167</td>\n",
       "      <td>-0.168725</td>\n",
       "      <td>-0.010272</td>\n",
       "      <td>-0.021265</td>\n",
       "      <td>-0.080281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <td>-0.430409</td>\n",
       "      <td>0.469994</td>\n",
       "      <td>0.350911</td>\n",
       "      <td>0.261373</td>\n",
       "      <td>0.363790</td>\n",
       "      <td>0.478678</td>\n",
       "      <td>0.343494</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.394990</td>\n",
       "      <td>0.156701</td>\n",
       "      <td>0.072120</td>\n",
       "      <td>0.373563</td>\n",
       "      <td>0.350994</td>\n",
       "      <td>0.257959</td>\n",
       "      <td>0.254273</td>\n",
       "      <td>-0.069007</td>\n",
       "      <td>-0.048516</td>\n",
       "      <td>-0.037942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quartile rank</th>\n",
       "      <td>0.968713</td>\n",
       "      <td>-0.939531</td>\n",
       "      <td>-0.776218</td>\n",
       "      <td>-0.723190</td>\n",
       "      <td>-0.780397</td>\n",
       "      <td>-0.519569</td>\n",
       "      <td>-0.057336</td>\n",
       "      <td>-0.394990</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.043045</td>\n",
       "      <td>0.191683</td>\n",
       "      <td>-0.705549</td>\n",
       "      <td>-0.698717</td>\n",
       "      <td>-0.651073</td>\n",
       "      <td>-0.565736</td>\n",
       "      <td>0.168057</td>\n",
       "      <td>0.110762</td>\n",
       "      <td>0.059149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross enrollment ratio - Primary (female)</th>\n",
       "      <td>0.045390</td>\n",
       "      <td>-0.047855</td>\n",
       "      <td>-0.051175</td>\n",
       "      <td>-0.115529</td>\n",
       "      <td>0.075665</td>\n",
       "      <td>0.182531</td>\n",
       "      <td>0.042738</td>\n",
       "      <td>0.156701</td>\n",
       "      <td>0.043045</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.828502</td>\n",
       "      <td>0.121109</td>\n",
       "      <td>0.064002</td>\n",
       "      <td>-0.016266</td>\n",
       "      <td>-0.042688</td>\n",
       "      <td>0.131198</td>\n",
       "      <td>0.151412</td>\n",
       "      <td>0.116786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross enrollment ratio - Primary (male)</th>\n",
       "      <td>0.216071</td>\n",
       "      <td>-0.233365</td>\n",
       "      <td>-0.240962</td>\n",
       "      <td>-0.373953</td>\n",
       "      <td>-0.181220</td>\n",
       "      <td>-0.014872</td>\n",
       "      <td>0.020848</td>\n",
       "      <td>0.072120</td>\n",
       "      <td>0.191683</td>\n",
       "      <td>0.828502</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.115763</td>\n",
       "      <td>-0.117504</td>\n",
       "      <td>-0.187237</td>\n",
       "      <td>-0.188947</td>\n",
       "      <td>0.020231</td>\n",
       "      <td>0.015398</td>\n",
       "      <td>0.000491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross enrollment ratio - Secondary (female)</th>\n",
       "      <td>-0.754106</td>\n",
       "      <td>0.760295</td>\n",
       "      <td>0.841581</td>\n",
       "      <td>0.736366</td>\n",
       "      <td>0.824919</td>\n",
       "      <td>0.420591</td>\n",
       "      <td>-0.014796</td>\n",
       "      <td>0.373563</td>\n",
       "      <td>-0.705549</td>\n",
       "      <td>0.121109</td>\n",
       "      <td>-0.115763</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967558</td>\n",
       "      <td>0.813671</td>\n",
       "      <td>0.741108</td>\n",
       "      <td>-0.071030</td>\n",
       "      <td>0.008584</td>\n",
       "      <td>0.066851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross enrollment ratio - Secondary (male)</th>\n",
       "      <td>-0.752386</td>\n",
       "      <td>0.757439</td>\n",
       "      <td>0.835329</td>\n",
       "      <td>0.695704</td>\n",
       "      <td>0.814609</td>\n",
       "      <td>0.363659</td>\n",
       "      <td>-0.021723</td>\n",
       "      <td>0.350994</td>\n",
       "      <td>-0.698717</td>\n",
       "      <td>0.064002</td>\n",
       "      <td>-0.117504</td>\n",
       "      <td>0.967558</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.838116</td>\n",
       "      <td>0.797710</td>\n",
       "      <td>-0.076890</td>\n",
       "      <td>0.001367</td>\n",
       "      <td>0.063731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross enrollment ratio - Tertiary (female)</th>\n",
       "      <td>-0.691909</td>\n",
       "      <td>0.688651</td>\n",
       "      <td>0.794144</td>\n",
       "      <td>0.665066</td>\n",
       "      <td>0.801281</td>\n",
       "      <td>0.221021</td>\n",
       "      <td>-0.135167</td>\n",
       "      <td>0.257959</td>\n",
       "      <td>-0.651073</td>\n",
       "      <td>-0.016266</td>\n",
       "      <td>-0.187237</td>\n",
       "      <td>0.813671</td>\n",
       "      <td>0.838116</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.943689</td>\n",
       "      <td>-0.105576</td>\n",
       "      <td>-0.038020</td>\n",
       "      <td>0.046167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross enrollment ratio - Tertiary (male)</th>\n",
       "      <td>-0.614994</td>\n",
       "      <td>0.616372</td>\n",
       "      <td>0.710267</td>\n",
       "      <td>0.575044</td>\n",
       "      <td>0.737639</td>\n",
       "      <td>0.117171</td>\n",
       "      <td>-0.168725</td>\n",
       "      <td>0.254273</td>\n",
       "      <td>-0.565736</td>\n",
       "      <td>-0.042688</td>\n",
       "      <td>-0.188947</td>\n",
       "      <td>0.741108</td>\n",
       "      <td>0.797710</td>\n",
       "      <td>0.943689</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.075497</td>\n",
       "      <td>-0.007696</td>\n",
       "      <td>0.076873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Students enrolled in primary education (thousands)</th>\n",
       "      <td>0.166870</td>\n",
       "      <td>-0.152750</td>\n",
       "      <td>-0.077380</td>\n",
       "      <td>-0.183512</td>\n",
       "      <td>-0.095192</td>\n",
       "      <td>0.085222</td>\n",
       "      <td>-0.010272</td>\n",
       "      <td>-0.069007</td>\n",
       "      <td>0.168057</td>\n",
       "      <td>0.131198</td>\n",
       "      <td>0.020231</td>\n",
       "      <td>-0.071030</td>\n",
       "      <td>-0.076890</td>\n",
       "      <td>-0.105576</td>\n",
       "      <td>-0.075497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.986868</td>\n",
       "      <td>0.921907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Students enrolled in secondary education (thousands)</th>\n",
       "      <td>0.109533</td>\n",
       "      <td>-0.098929</td>\n",
       "      <td>-0.012373</td>\n",
       "      <td>-0.127392</td>\n",
       "      <td>-0.023130</td>\n",
       "      <td>0.097367</td>\n",
       "      <td>-0.021265</td>\n",
       "      <td>-0.048516</td>\n",
       "      <td>0.110762</td>\n",
       "      <td>0.151412</td>\n",
       "      <td>0.015398</td>\n",
       "      <td>0.008584</td>\n",
       "      <td>0.001367</td>\n",
       "      <td>-0.038020</td>\n",
       "      <td>-0.007696</td>\n",
       "      <td>0.986868</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Students enrolled in tertiary education (thousands)</th>\n",
       "      <td>0.053039</td>\n",
       "      <td>-0.043509</td>\n",
       "      <td>0.045804</td>\n",
       "      <td>-0.065192</td>\n",
       "      <td>0.051181</td>\n",
       "      <td>0.104941</td>\n",
       "      <td>-0.080281</td>\n",
       "      <td>-0.037942</td>\n",
       "      <td>0.059149</td>\n",
       "      <td>0.116786</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>0.066851</td>\n",
       "      <td>0.063731</td>\n",
       "      <td>0.046167</td>\n",
       "      <td>0.076873</td>\n",
       "      <td>0.921907</td>\n",
       "      <td>0.930507</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Overall rank     Score  \\\n",
       "Overall rank                                            1.000000 -0.989604   \n",
       "Score                                                  -0.989604  1.000000   \n",
       "GDP per capita                                         -0.813219  0.807538   \n",
       "Social support                                         -0.766167  0.771068   \n",
       "Healthy life expectancy                                -0.811112  0.806345   \n",
       "Freedom to make life choices                           -0.549904  0.560488   \n",
       "Generosity                                             -0.100993  0.146829   \n",
       "Perceptions of corruption                              -0.430409  0.469994   \n",
       "Quartile rank                                           0.968713 -0.939531   \n",
       "Gross enrollment ratio - Primary (female)               0.045390 -0.047855   \n",
       "Gross enrollment ratio - Primary (male)                 0.216071 -0.233365   \n",
       "Gross enrollment ratio - Secondary (female)            -0.754106  0.760295   \n",
       "Gross enrollment ratio - Secondary (male)              -0.752386  0.757439   \n",
       "Gross enrollment ratio - Tertiary (female)             -0.691909  0.688651   \n",
       "Gross enrollment ratio - Tertiary (male)               -0.614994  0.616372   \n",
       "Students enrolled in primary education (thousands)      0.166870 -0.152750   \n",
       "Students enrolled in secondary education (thous...      0.109533 -0.098929   \n",
       "Students enrolled in tertiary education (thousa...      0.053039 -0.043509   \n",
       "\n",
       "                                                    GDP per capita  \\\n",
       "Overall rank                                             -0.813219   \n",
       "Score                                                     0.807538   \n",
       "GDP per capita                                            1.000000   \n",
       "Social support                                            0.775401   \n",
       "Healthy life expectancy                                   0.857967   \n",
       "Freedom to make life choices                              0.405732   \n",
       "Generosity                                               -0.044069   \n",
       "Perceptions of corruption                                 0.350911   \n",
       "Quartile rank                                            -0.776218   \n",
       "Gross enrollment ratio - Primary (female)                -0.051175   \n",
       "Gross enrollment ratio - Primary (male)                  -0.240962   \n",
       "Gross enrollment ratio - Secondary (female)               0.841581   \n",
       "Gross enrollment ratio - Secondary (male)                 0.835329   \n",
       "Gross enrollment ratio - Tertiary (female)                0.794144   \n",
       "Gross enrollment ratio - Tertiary (male)                  0.710267   \n",
       "Students enrolled in primary education (thousands)       -0.077380   \n",
       "Students enrolled in secondary education (thous...       -0.012373   \n",
       "Students enrolled in tertiary education (thousa...        0.045804   \n",
       "\n",
       "                                                    Social support  \\\n",
       "Overall rank                                             -0.766167   \n",
       "Score                                                     0.771068   \n",
       "GDP per capita                                            0.775401   \n",
       "Social support                                            1.000000   \n",
       "Healthy life expectancy                                   0.736881   \n",
       "Freedom to make life choices                              0.438130   \n",
       "Generosity                                                0.047131   \n",
       "Perceptions of corruption                                 0.261373   \n",
       "Quartile rank                                            -0.723190   \n",
       "Gross enrollment ratio - Primary (female)                -0.115529   \n",
       "Gross enrollment ratio - Primary (male)                  -0.373953   \n",
       "Gross enrollment ratio - Secondary (female)               0.736366   \n",
       "Gross enrollment ratio - Secondary (male)                 0.695704   \n",
       "Gross enrollment ratio - Tertiary (female)                0.665066   \n",
       "Gross enrollment ratio - Tertiary (male)                  0.575044   \n",
       "Students enrolled in primary education (thousands)       -0.183512   \n",
       "Students enrolled in secondary education (thous...       -0.127392   \n",
       "Students enrolled in tertiary education (thousa...       -0.065192   \n",
       "\n",
       "                                                    Healthy life expectancy  \\\n",
       "Overall rank                                                      -0.811112   \n",
       "Score                                                              0.806345   \n",
       "GDP per capita                                                     0.857967   \n",
       "Social support                                                     0.736881   \n",
       "Healthy life expectancy                                            1.000000   \n",
       "Freedom to make life choices                                       0.428509   \n",
       "Generosity                                                        -0.010142   \n",
       "Perceptions of corruption                                          0.363790   \n",
       "Quartile rank                                                     -0.780397   \n",
       "Gross enrollment ratio - Primary (female)                          0.075665   \n",
       "Gross enrollment ratio - Primary (male)                           -0.181220   \n",
       "Gross enrollment ratio - Secondary (female)                        0.824919   \n",
       "Gross enrollment ratio - Secondary (male)                          0.814609   \n",
       "Gross enrollment ratio - Tertiary (female)                         0.801281   \n",
       "Gross enrollment ratio - Tertiary (male)                           0.737639   \n",
       "Students enrolled in primary education (thousands)                -0.095192   \n",
       "Students enrolled in secondary education (thous...                -0.023130   \n",
       "Students enrolled in tertiary education (thousa...                 0.051181   \n",
       "\n",
       "                                                    Freedom to make life choices  \\\n",
       "Overall rank                                                           -0.549904   \n",
       "Score                                                                   0.560488   \n",
       "GDP per capita                                                          0.405732   \n",
       "Social support                                                          0.438130   \n",
       "Healthy life expectancy                                                 0.428509   \n",
       "Freedom to make life choices                                            1.000000   \n",
       "Generosity                                                              0.387456   \n",
       "Perceptions of corruption                                               0.478678   \n",
       "Quartile rank                                                          -0.519569   \n",
       "Gross enrollment ratio - Primary (female)                               0.182531   \n",
       "Gross enrollment ratio - Primary (male)                                -0.014872   \n",
       "Gross enrollment ratio - Secondary (female)                             0.420591   \n",
       "Gross enrollment ratio - Secondary (male)                               0.363659   \n",
       "Gross enrollment ratio - Tertiary (female)                              0.221021   \n",
       "Gross enrollment ratio - Tertiary (male)                                0.117171   \n",
       "Students enrolled in primary education (thousands)                      0.085222   \n",
       "Students enrolled in secondary education (thous...                      0.097367   \n",
       "Students enrolled in tertiary education (thousa...                      0.104941   \n",
       "\n",
       "                                                    Generosity  \\\n",
       "Overall rank                                         -0.100993   \n",
       "Score                                                 0.146829   \n",
       "GDP per capita                                       -0.044069   \n",
       "Social support                                        0.047131   \n",
       "Healthy life expectancy                              -0.010142   \n",
       "Freedom to make life choices                          0.387456   \n",
       "Generosity                                            1.000000   \n",
       "Perceptions of corruption                             0.343494   \n",
       "Quartile rank                                        -0.057336   \n",
       "Gross enrollment ratio - Primary (female)             0.042738   \n",
       "Gross enrollment ratio - Primary (male)               0.020848   \n",
       "Gross enrollment ratio - Secondary (female)          -0.014796   \n",
       "Gross enrollment ratio - Secondary (male)            -0.021723   \n",
       "Gross enrollment ratio - Tertiary (female)           -0.135167   \n",
       "Gross enrollment ratio - Tertiary (male)             -0.168725   \n",
       "Students enrolled in primary education (thousands)   -0.010272   \n",
       "Students enrolled in secondary education (thous...   -0.021265   \n",
       "Students enrolled in tertiary education (thousa...   -0.080281   \n",
       "\n",
       "                                                    Perceptions of corruption  \\\n",
       "Overall rank                                                        -0.430409   \n",
       "Score                                                                0.469994   \n",
       "GDP per capita                                                       0.350911   \n",
       "Social support                                                       0.261373   \n",
       "Healthy life expectancy                                              0.363790   \n",
       "Freedom to make life choices                                         0.478678   \n",
       "Generosity                                                           0.343494   \n",
       "Perceptions of corruption                                            1.000000   \n",
       "Quartile rank                                                       -0.394990   \n",
       "Gross enrollment ratio - Primary (female)                            0.156701   \n",
       "Gross enrollment ratio - Primary (male)                              0.072120   \n",
       "Gross enrollment ratio - Secondary (female)                          0.373563   \n",
       "Gross enrollment ratio - Secondary (male)                            0.350994   \n",
       "Gross enrollment ratio - Tertiary (female)                           0.257959   \n",
       "Gross enrollment ratio - Tertiary (male)                             0.254273   \n",
       "Students enrolled in primary education (thousands)                  -0.069007   \n",
       "Students enrolled in secondary education (thous...                  -0.048516   \n",
       "Students enrolled in tertiary education (thousa...                  -0.037942   \n",
       "\n",
       "                                                    Quartile rank  \\\n",
       "Overall rank                                             0.968713   \n",
       "Score                                                   -0.939531   \n",
       "GDP per capita                                          -0.776218   \n",
       "Social support                                          -0.723190   \n",
       "Healthy life expectancy                                 -0.780397   \n",
       "Freedom to make life choices                            -0.519569   \n",
       "Generosity                                              -0.057336   \n",
       "Perceptions of corruption                               -0.394990   \n",
       "Quartile rank                                            1.000000   \n",
       "Gross enrollment ratio - Primary (female)                0.043045   \n",
       "Gross enrollment ratio - Primary (male)                  0.191683   \n",
       "Gross enrollment ratio - Secondary (female)             -0.705549   \n",
       "Gross enrollment ratio - Secondary (male)               -0.698717   \n",
       "Gross enrollment ratio - Tertiary (female)              -0.651073   \n",
       "Gross enrollment ratio - Tertiary (male)                -0.565736   \n",
       "Students enrolled in primary education (thousands)       0.168057   \n",
       "Students enrolled in secondary education (thous...       0.110762   \n",
       "Students enrolled in tertiary education (thousa...       0.059149   \n",
       "\n",
       "                                                    Gross enrollment ratio - Primary (female)  \\\n",
       "Overall rank                                                                         0.045390   \n",
       "Score                                                                               -0.047855   \n",
       "GDP per capita                                                                      -0.051175   \n",
       "Social support                                                                      -0.115529   \n",
       "Healthy life expectancy                                                              0.075665   \n",
       "Freedom to make life choices                                                         0.182531   \n",
       "Generosity                                                                           0.042738   \n",
       "Perceptions of corruption                                                            0.156701   \n",
       "Quartile rank                                                                        0.043045   \n",
       "Gross enrollment ratio - Primary (female)                                            1.000000   \n",
       "Gross enrollment ratio - Primary (male)                                              0.828502   \n",
       "Gross enrollment ratio - Secondary (female)                                          0.121109   \n",
       "Gross enrollment ratio - Secondary (male)                                            0.064002   \n",
       "Gross enrollment ratio - Tertiary (female)                                          -0.016266   \n",
       "Gross enrollment ratio - Tertiary (male)                                            -0.042688   \n",
       "Students enrolled in primary education (thousands)                                   0.131198   \n",
       "Students enrolled in secondary education (thous...                                   0.151412   \n",
       "Students enrolled in tertiary education (thousa...                                   0.116786   \n",
       "\n",
       "                                                    Gross enrollment ratio - Primary (male)  \\\n",
       "Overall rank                                                                       0.216071   \n",
       "Score                                                                             -0.233365   \n",
       "GDP per capita                                                                    -0.240962   \n",
       "Social support                                                                    -0.373953   \n",
       "Healthy life expectancy                                                           -0.181220   \n",
       "Freedom to make life choices                                                      -0.014872   \n",
       "Generosity                                                                         0.020848   \n",
       "Perceptions of corruption                                                          0.072120   \n",
       "Quartile rank                                                                      0.191683   \n",
       "Gross enrollment ratio - Primary (female)                                          0.828502   \n",
       "Gross enrollment ratio - Primary (male)                                            1.000000   \n",
       "Gross enrollment ratio - Secondary (female)                                       -0.115763   \n",
       "Gross enrollment ratio - Secondary (male)                                         -0.117504   \n",
       "Gross enrollment ratio - Tertiary (female)                                        -0.187237   \n",
       "Gross enrollment ratio - Tertiary (male)                                          -0.188947   \n",
       "Students enrolled in primary education (thousands)                                 0.020231   \n",
       "Students enrolled in secondary education (thous...                                 0.015398   \n",
       "Students enrolled in tertiary education (thousa...                                 0.000491   \n",
       "\n",
       "                                                    Gross enrollment ratio - Secondary (female)  \\\n",
       "Overall rank                                                                          -0.754106   \n",
       "Score                                                                                  0.760295   \n",
       "GDP per capita                                                                         0.841581   \n",
       "Social support                                                                         0.736366   \n",
       "Healthy life expectancy                                                                0.824919   \n",
       "Freedom to make life choices                                                           0.420591   \n",
       "Generosity                                                                            -0.014796   \n",
       "Perceptions of corruption                                                              0.373563   \n",
       "Quartile rank                                                                         -0.705549   \n",
       "Gross enrollment ratio - Primary (female)                                              0.121109   \n",
       "Gross enrollment ratio - Primary (male)                                               -0.115763   \n",
       "Gross enrollment ratio - Secondary (female)                                            1.000000   \n",
       "Gross enrollment ratio - Secondary (male)                                              0.967558   \n",
       "Gross enrollment ratio - Tertiary (female)                                             0.813671   \n",
       "Gross enrollment ratio - Tertiary (male)                                               0.741108   \n",
       "Students enrolled in primary education (thousands)                                    -0.071030   \n",
       "Students enrolled in secondary education (thous...                                     0.008584   \n",
       "Students enrolled in tertiary education (thousa...                                     0.066851   \n",
       "\n",
       "                                                    Gross enrollment ratio - Secondary (male)  \\\n",
       "Overall rank                                                                        -0.752386   \n",
       "Score                                                                                0.757439   \n",
       "GDP per capita                                                                       0.835329   \n",
       "Social support                                                                       0.695704   \n",
       "Healthy life expectancy                                                              0.814609   \n",
       "Freedom to make life choices                                                         0.363659   \n",
       "Generosity                                                                          -0.021723   \n",
       "Perceptions of corruption                                                            0.350994   \n",
       "Quartile rank                                                                       -0.698717   \n",
       "Gross enrollment ratio - Primary (female)                                            0.064002   \n",
       "Gross enrollment ratio - Primary (male)                                             -0.117504   \n",
       "Gross enrollment ratio - Secondary (female)                                          0.967558   \n",
       "Gross enrollment ratio - Secondary (male)                                            1.000000   \n",
       "Gross enrollment ratio - Tertiary (female)                                           0.838116   \n",
       "Gross enrollment ratio - Tertiary (male)                                             0.797710   \n",
       "Students enrolled in primary education (thousands)                                  -0.076890   \n",
       "Students enrolled in secondary education (thous...                                   0.001367   \n",
       "Students enrolled in tertiary education (thousa...                                   0.063731   \n",
       "\n",
       "                                                    Gross enrollment ratio - Tertiary (female)  \\\n",
       "Overall rank                                                                         -0.691909   \n",
       "Score                                                                                 0.688651   \n",
       "GDP per capita                                                                        0.794144   \n",
       "Social support                                                                        0.665066   \n",
       "Healthy life expectancy                                                               0.801281   \n",
       "Freedom to make life choices                                                          0.221021   \n",
       "Generosity                                                                           -0.135167   \n",
       "Perceptions of corruption                                                             0.257959   \n",
       "Quartile rank                                                                        -0.651073   \n",
       "Gross enrollment ratio - Primary (female)                                            -0.016266   \n",
       "Gross enrollment ratio - Primary (male)                                              -0.187237   \n",
       "Gross enrollment ratio - Secondary (female)                                           0.813671   \n",
       "Gross enrollment ratio - Secondary (male)                                             0.838116   \n",
       "Gross enrollment ratio - Tertiary (female)                                            1.000000   \n",
       "Gross enrollment ratio - Tertiary (male)                                              0.943689   \n",
       "Students enrolled in primary education (thousands)                                   -0.105576   \n",
       "Students enrolled in secondary education (thous...                                   -0.038020   \n",
       "Students enrolled in tertiary education (thousa...                                    0.046167   \n",
       "\n",
       "                                                    Gross enrollment ratio - Tertiary (male)  \\\n",
       "Overall rank                                                                       -0.614994   \n",
       "Score                                                                               0.616372   \n",
       "GDP per capita                                                                      0.710267   \n",
       "Social support                                                                      0.575044   \n",
       "Healthy life expectancy                                                             0.737639   \n",
       "Freedom to make life choices                                                        0.117171   \n",
       "Generosity                                                                         -0.168725   \n",
       "Perceptions of corruption                                                           0.254273   \n",
       "Quartile rank                                                                      -0.565736   \n",
       "Gross enrollment ratio - Primary (female)                                          -0.042688   \n",
       "Gross enrollment ratio - Primary (male)                                            -0.188947   \n",
       "Gross enrollment ratio - Secondary (female)                                         0.741108   \n",
       "Gross enrollment ratio - Secondary (male)                                           0.797710   \n",
       "Gross enrollment ratio - Tertiary (female)                                          0.943689   \n",
       "Gross enrollment ratio - Tertiary (male)                                            1.000000   \n",
       "Students enrolled in primary education (thousands)                                 -0.075497   \n",
       "Students enrolled in secondary education (thous...                                 -0.007696   \n",
       "Students enrolled in tertiary education (thousa...                                  0.076873   \n",
       "\n",
       "                                                    Students enrolled in primary education (thousands)  \\\n",
       "Overall rank                                                                                 0.166870    \n",
       "Score                                                                                       -0.152750    \n",
       "GDP per capita                                                                              -0.077380    \n",
       "Social support                                                                              -0.183512    \n",
       "Healthy life expectancy                                                                     -0.095192    \n",
       "Freedom to make life choices                                                                 0.085222    \n",
       "Generosity                                                                                  -0.010272    \n",
       "Perceptions of corruption                                                                   -0.069007    \n",
       "Quartile rank                                                                                0.168057    \n",
       "Gross enrollment ratio - Primary (female)                                                    0.131198    \n",
       "Gross enrollment ratio - Primary (male)                                                      0.020231    \n",
       "Gross enrollment ratio - Secondary (female)                                                 -0.071030    \n",
       "Gross enrollment ratio - Secondary (male)                                                   -0.076890    \n",
       "Gross enrollment ratio - Tertiary (female)                                                  -0.105576    \n",
       "Gross enrollment ratio - Tertiary (male)                                                    -0.075497    \n",
       "Students enrolled in primary education (thousands)                                           1.000000    \n",
       "Students enrolled in secondary education (thous...                                           0.986868    \n",
       "Students enrolled in tertiary education (thousa...                                           0.921907    \n",
       "\n",
       "                                                    Students enrolled in secondary education (thousands)  \\\n",
       "Overall rank                                                                                 0.109533      \n",
       "Score                                                                                       -0.098929      \n",
       "GDP per capita                                                                              -0.012373      \n",
       "Social support                                                                              -0.127392      \n",
       "Healthy life expectancy                                                                     -0.023130      \n",
       "Freedom to make life choices                                                                 0.097367      \n",
       "Generosity                                                                                  -0.021265      \n",
       "Perceptions of corruption                                                                   -0.048516      \n",
       "Quartile rank                                                                                0.110762      \n",
       "Gross enrollment ratio - Primary (female)                                                    0.151412      \n",
       "Gross enrollment ratio - Primary (male)                                                      0.015398      \n",
       "Gross enrollment ratio - Secondary (female)                                                  0.008584      \n",
       "Gross enrollment ratio - Secondary (male)                                                    0.001367      \n",
       "Gross enrollment ratio - Tertiary (female)                                                  -0.038020      \n",
       "Gross enrollment ratio - Tertiary (male)                                                    -0.007696      \n",
       "Students enrolled in primary education (thousands)                                           0.986868      \n",
       "Students enrolled in secondary education (thous...                                           1.000000      \n",
       "Students enrolled in tertiary education (thousa...                                           0.930507      \n",
       "\n",
       "                                                    Students enrolled in tertiary education (thousands)  \n",
       "Overall rank                                                                                 0.053039    \n",
       "Score                                                                                       -0.043509    \n",
       "GDP per capita                                                                               0.045804    \n",
       "Social support                                                                              -0.065192    \n",
       "Healthy life expectancy                                                                      0.051181    \n",
       "Freedom to make life choices                                                                 0.104941    \n",
       "Generosity                                                                                  -0.080281    \n",
       "Perceptions of corruption                                                                   -0.037942    \n",
       "Quartile rank                                                                                0.059149    \n",
       "Gross enrollment ratio - Primary (female)                                                    0.116786    \n",
       "Gross enrollment ratio - Primary (male)                                                      0.000491    \n",
       "Gross enrollment ratio - Secondary (female)                                                  0.066851    \n",
       "Gross enrollment ratio - Secondary (male)                                                    0.063731    \n",
       "Gross enrollment ratio - Tertiary (female)                                                   0.046167    \n",
       "Gross enrollment ratio - Tertiary (male)                                                     0.076873    \n",
       "Students enrolled in primary education (thousands)                                           0.921907    \n",
       "Students enrolled in secondary education (thous...                                           0.930507    \n",
       "Students enrolled in tertiary education (thousa...                                           1.000000    "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_education.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter (H_education['Students enrolled in secondary education (thousands)'] / H_education['Students enrolled in primary education (thousands)'],H_education['Score'],c=H_education['Quartile rank'])\n",
    "plt.title ('Happiness Report Comparison')\n",
    "plt.xlabel('Ratio: secondary/primary education')\n",
    "plt.ylabel('Happiness Score')\n",
    "plt.plot(np.unique(H_education['Students enrolled in secondary education (thousands)'] / H_education['Students enrolled in primary education (thousands)']), np.poly1d(np.polyfit(H_education['Students enrolled in secondary education (thousands)'] / H_education['Students enrolled in primary education (thousands)'], H_education['Score'], 1))(np.unique(H_education['Students enrolled in secondary education (thousands)'] / H_education['Students enrolled in primary education (thousands)'])),color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 125 entries, 0 to 131\n",
      "Data columns (total 19 columns):\n",
      " #   Column                                                Non-Null Count  Dtype  \n",
      "---  ------                                                --------------  -----  \n",
      " 0   Overall rank                                          125 non-null    int64  \n",
      " 1   Country or region                                     125 non-null    object \n",
      " 2   Score                                                 125 non-null    float64\n",
      " 3   GDP per capita                                        125 non-null    float64\n",
      " 4   Social support                                        125 non-null    float64\n",
      " 5   Healthy life expectancy                               125 non-null    float64\n",
      " 6   Freedom to make life choices                          125 non-null    float64\n",
      " 7   Generosity                                            125 non-null    float64\n",
      " 8   Perceptions of corruption                             125 non-null    float64\n",
      " 9   Quartile rank                                         125 non-null    int64  \n",
      " 10  Gross enrollment ratio - Primary (female)             125 non-null    float64\n",
      " 11  Gross enrollment ratio - Primary (male)               125 non-null    float64\n",
      " 12  Gross enrollment ratio - Secondary (female)           125 non-null    float64\n",
      " 13  Gross enrollment ratio - Secondary (male)             125 non-null    float64\n",
      " 14  Gross enrollment ratio - Tertiary (female)            125 non-null    float64\n",
      " 15  Gross enrollment ratio - Tertiary (male)              125 non-null    float64\n",
      " 16  Students enrolled in primary education (thousands)    125 non-null    float64\n",
      " 17  Students enrolled in secondary education (thousands)  125 non-null    float64\n",
      " 18  Students enrolled in tertiary education (thousands)   125 non-null    float64\n",
      "dtypes: float64(16), int64(2), object(1)\n",
      "memory usage: 19.5+ KB\n"
     ]
    }
   ],
   "source": [
    "H_education.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter (H_education['Students enrolled in tertiary education (thousands)'] / H_education['Students enrolled in primary education (thousands)'],H_education['Score'],c=H_education['Quartile rank'])\n",
    "plt.title ('Happiness Report Comparison')\n",
    "plt.xlabel('Ratio: tertiary (Graduate)/primary education enrollment')\n",
    "plt.ylabel('Happiness Score')\n",
    "plt.plot(np.unique(H_education['Students enrolled in tertiary education (thousands)'] / H_education['Students enrolled in primary education (thousands)']), np.poly1d(np.polyfit(H_education['Students enrolled in tertiary education (thousands)'] / H_education['Students enrolled in primary education (thousands)'], H_education['Score'], 1))(np.unique(H_education['Students enrolled in tertiary education (thousands)'] / H_education['Students enrolled in primary education (thousands)'])),color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "H_education.to_csv('Happiness/H_education.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region/Country/Area</th>\n",
       "      <th>Water supply and sanitation services</th>\n",
       "      <th>Year</th>\n",
       "      <th>Series</th>\n",
       "      <th>Value</th>\n",
       "      <th>Footnotes</th>\n",
       "      <th>Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2005</td>\n",
       "      <td>Safely managed drinking water sources, urban (...</td>\n",
       "      <td>86.1804</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2005</td>\n",
       "      <td>Safely managed drinking water sources, rural (...</td>\n",
       "      <td>43.0323</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2005</td>\n",
       "      <td>Safely managed drinking water sources, total (...</td>\n",
       "      <td>64.2425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2005</td>\n",
       "      <td>Safely managed sanitation facilities, urban (P...</td>\n",
       "      <td>37.0176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2005</td>\n",
       "      <td>Safely managed sanitation facilities, rural (P...</td>\n",
       "      <td>26.0720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1517</th>\n",
       "      <td>894</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2010</td>\n",
       "      <td>Safely managed drinking water sources, urban (...</td>\n",
       "      <td>47.0679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1518</th>\n",
       "      <td>894</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2017</td>\n",
       "      <td>Safely managed drinking water sources, urban (...</td>\n",
       "      <td>46.2452</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1519</th>\n",
       "      <td>716</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2005</td>\n",
       "      <td>Safely managed sanitation facilities, urban (P...</td>\n",
       "      <td>22.3062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1520</th>\n",
       "      <td>716</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2010</td>\n",
       "      <td>Safely managed sanitation facilities, urban (P...</td>\n",
       "      <td>19.7990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1521</th>\n",
       "      <td>716</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2017</td>\n",
       "      <td>Safely managed sanitation facilities, urban (P...</td>\n",
       "      <td>16.2922</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1522 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Region/Country/Area Water supply and sanitation services  Year  \\\n",
       "0                       1        Total, all countries or areas  2005   \n",
       "1                       1        Total, all countries or areas  2005   \n",
       "2                       1        Total, all countries or areas  2005   \n",
       "3                       1        Total, all countries or areas  2005   \n",
       "4                       1        Total, all countries or areas  2005   \n",
       "...                   ...                                  ...   ...   \n",
       "1517                  894                               Zambia  2010   \n",
       "1518                  894                               Zambia  2017   \n",
       "1519                  716                             Zimbabwe  2005   \n",
       "1520                  716                             Zimbabwe  2010   \n",
       "1521                  716                             Zimbabwe  2017   \n",
       "\n",
       "                                                 Series    Value Footnotes  \\\n",
       "0     Safely managed drinking water sources, urban (...  86.1804       NaN   \n",
       "1     Safely managed drinking water sources, rural (...  43.0323       NaN   \n",
       "2     Safely managed drinking water sources, total (...  64.2425       NaN   \n",
       "3     Safely managed sanitation facilities, urban (P...  37.0176       NaN   \n",
       "4     Safely managed sanitation facilities, rural (P...  26.0720       NaN   \n",
       "...                                                 ...      ...       ...   \n",
       "1517  Safely managed drinking water sources, urban (...  47.0679       NaN   \n",
       "1518  Safely managed drinking water sources, urban (...  46.2452       NaN   \n",
       "1519  Safely managed sanitation facilities, urban (P...  22.3062       NaN   \n",
       "1520  Safely managed sanitation facilities, urban (P...  19.7990       NaN   \n",
       "1521  Safely managed sanitation facilities, urban (P...  16.2922       NaN   \n",
       "\n",
       "                                                 Source  \n",
       "0     World Health Organization (WHO) and United Nat...  \n",
       "1     World Health Organization (WHO) and United Nat...  \n",
       "2     World Health Organization (WHO) and United Nat...  \n",
       "3     World Health Organization (WHO) and United Nat...  \n",
       "4     World Health Organization (WHO) and United Nat...  \n",
       "...                                                 ...  \n",
       "1517  World Health Organization (WHO) and United Nat...  \n",
       "1518  World Health Organization (WHO) and United Nat...  \n",
       "1519  World Health Organization (WHO) and United Nat...  \n",
       "1520  World Health Organization (WHO) and United Nat...  \n",
       "1521  World Health Organization (WHO) and United Nat...  \n",
       "\n",
       "[1522 rows x 7 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanitation = pd.read_csv('Happiness/SYB62_315_201906_Water and Sanitation Services.csv')\n",
    "sanitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Water supply and sanitation services  Series                                                                             \n",
       "Albania                               Safely managed drinking water sources, total (Proportion of population with access)    2017\n",
       "                                      Safely managed sanitation facilities, rural (Proportion of population with access)     2017\n",
       "                                      Safely managed sanitation facilities, total (Proportion of population with access)     2017\n",
       "                                      Safely managed sanitation facilities, urban (Proportion of population with access)     2017\n",
       "Algeria                               Safely managed sanitation facilities, rural (Proportion of population with access)     2017\n",
       "                                                                                                                             ... \n",
       "Western Europe                        Safely managed sanitation facilities, total (Proportion of population with access)     2017\n",
       "                                      Safely managed sanitation facilities, urban (Proportion of population with access)     2017\n",
       "Yemen                                 Safely managed sanitation facilities, urban (Proportion of population with access)     2017\n",
       "Zambia                                Safely managed drinking water sources, urban (Proportion of population with access)    2017\n",
       "Zimbabwe                              Safely managed sanitation facilities, urban (Proportion of population with access)     2017\n",
       "Name: Year, Length: 512, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanitation_filter = sanitation.groupby(['Water supply and sanitation services','Series'])['Year'].max()\n",
    "sanitation_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region/Country/Area</th>\n",
       "      <th>Water supply and sanitation services</th>\n",
       "      <th>Year</th>\n",
       "      <th>Series</th>\n",
       "      <th>Value</th>\n",
       "      <th>Footnotes</th>\n",
       "      <th>Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2017</td>\n",
       "      <td>Safely managed drinking water sources, urban (...</td>\n",
       "      <td>85.1475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2017</td>\n",
       "      <td>Safely managed drinking water sources, rural (...</td>\n",
       "      <td>53.0372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2017</td>\n",
       "      <td>Safely managed drinking water sources, total (...</td>\n",
       "      <td>70.6449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2017</td>\n",
       "      <td>Safely managed sanitation facilities, urban (P...</td>\n",
       "      <td>47.0236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2017</td>\n",
       "      <td>Safely managed sanitation facilities, rural (P...</td>\n",
       "      <td>42.5072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>548</td>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>2017</td>\n",
       "      <td>Safely managed drinking water sources, total (...</td>\n",
       "      <td>44.1303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>862</td>\n",
       "      <td>Venezuela (Boliv. Rep. of)</td>\n",
       "      <td>2017</td>\n",
       "      <td>Safely managed sanitation facilities, total (P...</td>\n",
       "      <td>23.7586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>887</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>2017</td>\n",
       "      <td>Safely managed sanitation facilities, urban (P...</td>\n",
       "      <td>67.0084</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>894</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2017</td>\n",
       "      <td>Safely managed drinking water sources, urban (...</td>\n",
       "      <td>46.2452</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>716</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2017</td>\n",
       "      <td>Safely managed sanitation facilities, urban (P...</td>\n",
       "      <td>16.2922</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Health Organization (WHO) and United Nat...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>512 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Region/Country/Area Water supply and sanitation services  Year  \\\n",
       "0                      1        Total, all countries or areas  2017   \n",
       "1                      1        Total, all countries or areas  2017   \n",
       "2                      1        Total, all countries or areas  2017   \n",
       "3                      1        Total, all countries or areas  2017   \n",
       "4                      1        Total, all countries or areas  2017   \n",
       "..                   ...                                  ...   ...   \n",
       "507                  548                              Vanuatu  2017   \n",
       "508                  862           Venezuela (Boliv. Rep. of)  2017   \n",
       "509                  887                                Yemen  2017   \n",
       "510                  894                               Zambia  2017   \n",
       "511                  716                             Zimbabwe  2017   \n",
       "\n",
       "                                                Series    Value Footnotes  \\\n",
       "0    Safely managed drinking water sources, urban (...  85.1475       NaN   \n",
       "1    Safely managed drinking water sources, rural (...  53.0372       NaN   \n",
       "2    Safely managed drinking water sources, total (...  70.6449       NaN   \n",
       "3    Safely managed sanitation facilities, urban (P...  47.0236       NaN   \n",
       "4    Safely managed sanitation facilities, rural (P...  42.5072       NaN   \n",
       "..                                                 ...      ...       ...   \n",
       "507  Safely managed drinking water sources, total (...  44.1303       NaN   \n",
       "508  Safely managed sanitation facilities, total (P...  23.7586       NaN   \n",
       "509  Safely managed sanitation facilities, urban (P...  67.0084       NaN   \n",
       "510  Safely managed drinking water sources, urban (...  46.2452       NaN   \n",
       "511  Safely managed sanitation facilities, urban (P...  16.2922       NaN   \n",
       "\n",
       "                                                Source  \n",
       "0    World Health Organization (WHO) and United Nat...  \n",
       "1    World Health Organization (WHO) and United Nat...  \n",
       "2    World Health Organization (WHO) and United Nat...  \n",
       "3    World Health Organization (WHO) and United Nat...  \n",
       "4    World Health Organization (WHO) and United Nat...  \n",
       "..                                                 ...  \n",
       "507  World Health Organization (WHO) and United Nat...  \n",
       "508  World Health Organization (WHO) and United Nat...  \n",
       "509  World Health Organization (WHO) and United Nat...  \n",
       "510  World Health Organization (WHO) and United Nat...  \n",
       "511  World Health Organization (WHO) and United Nat...  \n",
       "\n",
       "[512 rows x 7 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanitation_latest = pd.merge(left=sanitation, right=sanitation_filter, left_on=['Water supply and sanitation services','Series','Year'], right_on = ['Water supply and sanitation services','Series','Year'])\n",
    "sanitation_latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Water supply and sanitation services</th>\n",
       "      <th>Series</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>Safely managed drinking water sources, urban (...</td>\n",
       "      <td>85.1475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>Safely managed drinking water sources, rural (...</td>\n",
       "      <td>53.0372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>Safely managed drinking water sources, total (...</td>\n",
       "      <td>70.6449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>Safely managed sanitation facilities, urban (P...</td>\n",
       "      <td>47.0236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>Safely managed sanitation facilities, rural (P...</td>\n",
       "      <td>42.5072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>507</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>Safely managed drinking water sources, total (...</td>\n",
       "      <td>44.1303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>Venezuela (Boliv. Rep. of)</td>\n",
       "      <td>Safely managed sanitation facilities, total (P...</td>\n",
       "      <td>23.7586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>Safely managed sanitation facilities, urban (P...</td>\n",
       "      <td>67.0084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Safely managed drinking water sources, urban (...</td>\n",
       "      <td>46.2452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Safely managed sanitation facilities, urban (P...</td>\n",
       "      <td>16.2922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>512 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Water supply and sanitation services  \\\n",
       "0          Total, all countries or areas   \n",
       "1          Total, all countries or areas   \n",
       "2          Total, all countries or areas   \n",
       "3          Total, all countries or areas   \n",
       "4          Total, all countries or areas   \n",
       "..                                   ...   \n",
       "507                              Vanuatu   \n",
       "508           Venezuela (Boliv. Rep. of)   \n",
       "509                                Yemen   \n",
       "510                               Zambia   \n",
       "511                             Zimbabwe   \n",
       "\n",
       "                                                Series    Value  \n",
       "0    Safely managed drinking water sources, urban (...  85.1475  \n",
       "1    Safely managed drinking water sources, rural (...  53.0372  \n",
       "2    Safely managed drinking water sources, total (...  70.6449  \n",
       "3    Safely managed sanitation facilities, urban (P...  47.0236  \n",
       "4    Safely managed sanitation facilities, rural (P...  42.5072  \n",
       "..                                                 ...      ...  \n",
       "507  Safely managed drinking water sources, total (...  44.1303  \n",
       "508  Safely managed sanitation facilities, total (P...  23.7586  \n",
       "509  Safely managed sanitation facilities, urban (P...  67.0084  \n",
       "510  Safely managed drinking water sources, urban (...  46.2452  \n",
       "511  Safely managed sanitation facilities, urban (P...  16.2922  \n",
       "\n",
       "[512 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanitation_latest.drop(['Region/Country/Area','Year',\"Footnotes\",'Source'], axis = 1, inplace = True)\n",
    "sanitation_latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Series</th>\n",
       "      <th>Safely managed drinking water sources, total</th>\n",
       "      <th>Safely managed sanitation facilities, total (Proportion of population with access)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Water supply and sanitation services</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>70.0232</td>\n",
       "      <td>39.8555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>NaN</td>\n",
       "      <td>17.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>12.5751</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>90.6400</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>86.4764</td>\n",
       "      <td>48.2232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Asia</th>\n",
       "      <td>75.7550</td>\n",
       "      <td>45.9448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Europe</th>\n",
       "      <td>99.0025</td>\n",
       "      <td>96.9255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>176 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Series                                Safely managed drinking water sources, total  \\\n",
       "Water supply and sanitation services                                                 \n",
       "Albania                                                                    70.0232   \n",
       "Algeria                                                                        NaN   \n",
       "American Samoa                                                             12.5751   \n",
       "Andorra                                                                    90.6400   \n",
       "Armenia                                                                    86.4764   \n",
       "...                                                                            ...   \n",
       "Western Asia                                                               75.7550   \n",
       "Western Europe                                                             99.0025   \n",
       "Yemen                                                                          NaN   \n",
       "Zambia                                                                         NaN   \n",
       "Zimbabwe                                                                       NaN   \n",
       "\n",
       "Series                                Safely managed sanitation facilities, total (Proportion of population with access)  \n",
       "Water supply and sanitation services                                                                                      \n",
       "Albania                                                                         39.8555                                   \n",
       "Algeria                                                                         17.6875                                   \n",
       "American Samoa                                                                      NaN                                   \n",
       "Andorra                                                                        100.0000                                   \n",
       "Armenia                                                                         48.2232                                   \n",
       "...                                                                                 ...                                   \n",
       "Western Asia                                                                    45.9448                                   \n",
       "Western Europe                                                                  96.9255                                   \n",
       "Yemen                                                                               NaN                                   \n",
       "Zambia                                                                              NaN                                   \n",
       "Zimbabwe                                                                            NaN                                   \n",
       "\n",
       "[176 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanitation_latest = pd.pivot_table(sanitation_latest, index='Water supply and sanitation services', columns='Series', values='Value')\n",
    "sanitation_latest.drop(['Safely managed drinking water sources, rural (Proportion of population with access)',\n",
    "                        'Safely managed drinking water sources, urban (Proportion of population with access)',\n",
    "                        'Safely managed sanitation facilities, rural (Proportion of population with access)',\n",
    "                        'Safely managed sanitation facilities, urban (Proportion of population with access)'], axis = 1, inplace = True)\n",
    "sanitation_latest.rename(columns = {'Safely managed drinking water sources, total (Proportion of population with access)':'Safely managed drinking water sources, total'}, inplace = True)\n",
    "sanitation_latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Quartile rank</th>\n",
       "      <th>Safely managed drinking water sources, total</th>\n",
       "      <th>Safely managed sanitation facilities, total (Proportion of population with access)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0</td>\n",
       "      <td>99.6331</td>\n",
       "      <td>99.2058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0</td>\n",
       "      <td>96.7347</td>\n",
       "      <td>94.8404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0</td>\n",
       "      <td>98.3430</td>\n",
       "      <td>76.3175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>81.7581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0</td>\n",
       "      <td>99.9510</td>\n",
       "      <td>97.4733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>119</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>4.519</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.164</td>\n",
       "      <td>3</td>\n",
       "      <td>79.9947</td>\n",
       "      <td>27.1651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>124</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>4.461</td>\n",
       "      <td>0.921</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.055</td>\n",
       "      <td>3</td>\n",
       "      <td>92.6575</td>\n",
       "      <td>78.1183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>126</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>4.437</td>\n",
       "      <td>1.043</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.089</td>\n",
       "      <td>3</td>\n",
       "      <td>58.8333</td>\n",
       "      <td>41.0678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>129</td>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>4.374</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.242</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.045</td>\n",
       "      <td>3</td>\n",
       "      <td>9.9191</td>\n",
       "      <td>13.3495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>133</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>4.332</td>\n",
       "      <td>0.820</td>\n",
       "      <td>1.390</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.010</td>\n",
       "      <td>3</td>\n",
       "      <td>92.0069</td>\n",
       "      <td>68.4599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Overall rank Country or region  Score  GDP per capita  Social support  \\\n",
       "0              1           Finland  7.769           1.340           1.587   \n",
       "1              2           Denmark  7.600           1.383           1.573   \n",
       "2              3            Norway  7.554           1.488           1.582   \n",
       "3              4           Iceland  7.494           1.380           1.624   \n",
       "4              5       Netherlands  7.488           1.396           1.522   \n",
       "..           ...               ...    ...             ...             ...   \n",
       "86           119           Georgia  4.519           0.886           0.666   \n",
       "90           124           Tunisia  4.461           0.921           1.000   \n",
       "92           126              Iraq  4.437           1.043           0.980   \n",
       "94           129      Sierra Leone  4.374           0.268           0.841   \n",
       "96           133           Ukraine  4.332           0.820           1.390   \n",
       "\n",
       "    Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
       "0                     0.986                         0.596       0.153   \n",
       "1                     0.996                         0.592       0.252   \n",
       "2                     1.028                         0.603       0.271   \n",
       "3                     1.026                         0.591       0.354   \n",
       "4                     0.999                         0.557       0.322   \n",
       "..                      ...                           ...         ...   \n",
       "86                    0.752                         0.346       0.043   \n",
       "90                    0.815                         0.167       0.059   \n",
       "92                    0.574                         0.241       0.148   \n",
       "94                    0.242                         0.309       0.252   \n",
       "96                    0.739                         0.178       0.187   \n",
       "\n",
       "    Perceptions of corruption  Quartile rank  \\\n",
       "0                       0.393              0   \n",
       "1                       0.410              0   \n",
       "2                       0.341              0   \n",
       "3                       0.118              0   \n",
       "4                       0.298              0   \n",
       "..                        ...            ...   \n",
       "86                      0.164              3   \n",
       "90                      0.055              3   \n",
       "92                      0.089              3   \n",
       "94                      0.045              3   \n",
       "96                      0.010              3   \n",
       "\n",
       "    Safely managed drinking water sources, total  \\\n",
       "0                                        99.6331   \n",
       "1                                        96.7347   \n",
       "2                                        98.3430   \n",
       "3                                       100.0000   \n",
       "4                                        99.9510   \n",
       "..                                           ...   \n",
       "86                                       79.9947   \n",
       "90                                       92.6575   \n",
       "92                                       58.8333   \n",
       "94                                        9.9191   \n",
       "96                                       92.0069   \n",
       "\n",
       "    Safely managed sanitation facilities, total (Proportion of population with access)  \n",
       "0                                             99.2058                                   \n",
       "1                                             94.8404                                   \n",
       "2                                             76.3175                                   \n",
       "3                                             81.7581                                   \n",
       "4                                             97.4733                                   \n",
       "..                                                ...                                   \n",
       "86                                            27.1651                                   \n",
       "90                                            78.1183                                   \n",
       "92                                            41.0678                                   \n",
       "94                                            13.3495                                   \n",
       "96                                            68.4599                                   \n",
       "\n",
       "[61 rows x 12 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_sanitation = pd.merge(left=happiness_survey, right=sanitation_latest, left_on='Country or region', right_on = 'Water supply and sanitation services')\n",
    "H_sanitation.dropna(inplace=True)\n",
    "H_sanitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 176 entries, Albania to Zimbabwe\n",
      "Data columns (total 2 columns):\n",
      " #   Column                                                                              Non-Null Count  Dtype  \n",
      "---  ------                                                                              --------------  -----  \n",
      " 0   Safely managed drinking water sources, total                                        136 non-null    float64\n",
      " 1   Safely managed sanitation facilities, total (Proportion of population with access)  112 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 4.1+ KB\n"
     ]
    }
   ],
   "source": [
    "sanitation_latest.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 61 entries, 0 to 96\n",
      "Data columns (total 12 columns):\n",
      " #   Column                                                                              Non-Null Count  Dtype  \n",
      "---  ------                                                                              --------------  -----  \n",
      " 0   Overall rank                                                                        61 non-null     int64  \n",
      " 1   Country or region                                                                   61 non-null     object \n",
      " 2   Score                                                                               61 non-null     float64\n",
      " 3   GDP per capita                                                                      61 non-null     float64\n",
      " 4   Social support                                                                      61 non-null     float64\n",
      " 5   Healthy life expectancy                                                             61 non-null     float64\n",
      " 6   Freedom to make life choices                                                        61 non-null     float64\n",
      " 7   Generosity                                                                          61 non-null     float64\n",
      " 8   Perceptions of corruption                                                           61 non-null     float64\n",
      " 9   Quartile rank                                                                       61 non-null     int64  \n",
      " 10  Safely managed drinking water sources, total                                        61 non-null     float64\n",
      " 11  Safely managed sanitation facilities, total (Proportion of population with access)  61 non-null     float64\n",
      "dtypes: float64(9), int64(2), object(1)\n",
      "memory usage: 6.2+ KB\n"
     ]
    }
   ],
   "source": [
    "H_sanitation.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(H_sanitation['Safely managed drinking water sources, total'],H_sanitation['Score'],c=H_sanitation['Quartile rank'])\n",
    "plt.title ('Happiness Report Comparison')\n",
    "plt.xlabel('Safe drinking water (% of population)')\n",
    "plt.ylabel('Happiness Score')\n",
    "plt.plot(np.unique(H_sanitation['Safely managed drinking water sources, total']), np.poly1d(np.polyfit(H_sanitation['Safely managed drinking water sources, total'], H_sanitation['Score'], 1))(np.unique(H_sanitation['Safely managed drinking water sources, total'])),color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Prevalence - Alcohol and substance use disorders: Both (age-standardized percent) (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1990</td>\n",
       "      <td>1.530549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1991</td>\n",
       "      <td>1.530105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1992</td>\n",
       "      <td>1.529283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1993</td>\n",
       "      <td>1.530944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1994</td>\n",
       "      <td>1.532076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6151</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.300518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6152</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2013</td>\n",
       "      <td>2.306298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6153</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2014</td>\n",
       "      <td>2.311257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6154</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2015</td>\n",
       "      <td>2.315047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6155</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2016</td>\n",
       "      <td>2.320384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6156 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Entity Code  Year  \\\n",
       "0     Afghanistan  AFG  1990   \n",
       "1     Afghanistan  AFG  1991   \n",
       "2     Afghanistan  AFG  1992   \n",
       "3     Afghanistan  AFG  1993   \n",
       "4     Afghanistan  AFG  1994   \n",
       "...           ...  ...   ...   \n",
       "6151     Zimbabwe  ZWE  2012   \n",
       "6152     Zimbabwe  ZWE  2013   \n",
       "6153     Zimbabwe  ZWE  2014   \n",
       "6154     Zimbabwe  ZWE  2015   \n",
       "6155     Zimbabwe  ZWE  2016   \n",
       "\n",
       "      Prevalence - Alcohol and substance use disorders: Both (age-standardized percent) (%)  \n",
       "0                                              1.530549                                      \n",
       "1                                              1.530105                                      \n",
       "2                                              1.529283                                      \n",
       "3                                              1.530944                                      \n",
       "4                                              1.532076                                      \n",
       "...                                                 ...                                      \n",
       "6151                                           2.300518                                      \n",
       "6152                                           2.306298                                      \n",
       "6153                                           2.311257                                      \n",
       "6154                                           2.315047                                      \n",
       "6155                                           2.320384                                      \n",
       "\n",
       "[6156 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alcohol_drug = pd.read_csv('Happiness/share-with-alcohol-or-drug-use-disorders.csv')\n",
    "alcohol_drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entity\n",
       "Afghanistan                   2016\n",
       "Albania                       2016\n",
       "Algeria                       2016\n",
       "American Samoa                2016\n",
       "Andean Latin America          2016\n",
       "                              ... \n",
       "Western Sub-Saharan Africa    2016\n",
       "World                         2016\n",
       "Yemen                         2016\n",
       "Zambia                        2016\n",
       "Zimbabwe                      2016\n",
       "Name: Year, Length: 228, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alcohol_drug_filter = alcohol_drug.groupby(['Entity'])['Year'].max()\n",
    "alcohol_drug_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Prevalence - Alcohol and substance use disorders: Both (age-standardized percent) (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.809625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2016</td>\n",
       "      <td>2.325538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZA</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.485915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>ASM</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.622432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andean Latin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>2.154291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>Western Sub-Saharan Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.634571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>World</td>\n",
       "      <td>OWID_WRL</td>\n",
       "      <td>2016</td>\n",
       "      <td>2.223560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>YEM</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.404977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>2016</td>\n",
       "      <td>2.143914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2016</td>\n",
       "      <td>2.320384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>228 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Entity      Code  Year  \\\n",
       "0                   Afghanistan       AFG  2016   \n",
       "1                       Albania       ALB  2016   \n",
       "2                       Algeria       DZA  2016   \n",
       "3                American Samoa       ASM  2016   \n",
       "4          Andean Latin America       NaN  2016   \n",
       "..                          ...       ...   ...   \n",
       "223  Western Sub-Saharan Africa       NaN  2016   \n",
       "224                       World  OWID_WRL  2016   \n",
       "225                       Yemen       YEM  2016   \n",
       "226                      Zambia       ZMB  2016   \n",
       "227                    Zimbabwe       ZWE  2016   \n",
       "\n",
       "     Prevalence - Alcohol and substance use disorders: Both (age-standardized percent) (%)  \n",
       "0                                             1.809625                                      \n",
       "1                                             2.325538                                      \n",
       "2                                             1.485915                                      \n",
       "3                                             1.622432                                      \n",
       "4                                             2.154291                                      \n",
       "..                                                 ...                                      \n",
       "223                                           1.634571                                      \n",
       "224                                           2.223560                                      \n",
       "225                                           1.404977                                      \n",
       "226                                           2.143914                                      \n",
       "227                                           2.320384                                      \n",
       "\n",
       "[228 rows x 4 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alcohol_drug_latest = pd.merge(left=alcohol_drug, right=alcohol_drug_filter, left_on=['Entity','Year'], right_on = ['Entity','Year'])\n",
    "alcohol_drug_latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "      <th>Prevalence percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.809625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2.325538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1.485915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>1.622432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andean Latin America</td>\n",
       "      <td>2.154291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>Western Sub-Saharan Africa</td>\n",
       "      <td>1.634571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>World</td>\n",
       "      <td>2.223560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>1.404977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2.143914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2.320384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>228 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Entity  Prevalence percentage\n",
       "0                   Afghanistan               1.809625\n",
       "1                       Albania               2.325538\n",
       "2                       Algeria               1.485915\n",
       "3                American Samoa               1.622432\n",
       "4          Andean Latin America               2.154291\n",
       "..                          ...                    ...\n",
       "223  Western Sub-Saharan Africa               1.634571\n",
       "224                       World               2.223560\n",
       "225                       Yemen               1.404977\n",
       "226                      Zambia               2.143914\n",
       "227                    Zimbabwe               2.320384\n",
       "\n",
       "[228 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alcohol_drug_latest.drop(['Code','Year'], axis = 1, inplace = True)\n",
    "alcohol_drug_latest.rename(columns = {'Prevalence - Alcohol and substance use disorders: Both (age-standardized percent) (%)':'Prevalence percentage'}, inplace = True)\n",
    "alcohol_drug_latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Quartile rank</th>\n",
       "      <th>Entity</th>\n",
       "      <th>Prevalence percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>4.024233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>2.827104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0</td>\n",
       "      <td>Norway</td>\n",
       "      <td>2.817296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>2.139336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1.869502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>152</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>3.334</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.411</td>\n",
       "      <td>3</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>2.785813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>153</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>3.231</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.499</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.147</td>\n",
       "      <td>3</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>2.017210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>154</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3.203</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.025</td>\n",
       "      <td>3</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.809625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>155</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>3.083</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.035</td>\n",
       "      <td>3</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>1.889598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>156</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>2.853</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.091</td>\n",
       "      <td>3</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>1.917005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Overall rank         Country or region  Score  GDP per capita  \\\n",
       "0               1                   Finland  7.769           1.340   \n",
       "1               2                   Denmark  7.600           1.383   \n",
       "2               3                    Norway  7.554           1.488   \n",
       "3               4                   Iceland  7.494           1.380   \n",
       "4               5               Netherlands  7.488           1.396   \n",
       "..            ...                       ...    ...             ...   \n",
       "142           152                    Rwanda  3.334           0.359   \n",
       "143           153                  Tanzania  3.231           0.476   \n",
       "144           154               Afghanistan  3.203           0.350   \n",
       "145           155  Central African Republic  3.083           0.026   \n",
       "146           156               South Sudan  2.853           0.306   \n",
       "\n",
       "     Social support  Healthy life expectancy  Freedom to make life choices  \\\n",
       "0             1.587                    0.986                         0.596   \n",
       "1             1.573                    0.996                         0.592   \n",
       "2             1.582                    1.028                         0.603   \n",
       "3             1.624                    1.026                         0.591   \n",
       "4             1.522                    0.999                         0.557   \n",
       "..              ...                      ...                           ...   \n",
       "142           0.711                    0.614                         0.555   \n",
       "143           0.885                    0.499                         0.417   \n",
       "144           0.517                    0.361                         0.000   \n",
       "145           0.000                    0.105                         0.225   \n",
       "146           0.575                    0.295                         0.010   \n",
       "\n",
       "     Generosity  Perceptions of corruption  Quartile rank  \\\n",
       "0         0.153                      0.393              0   \n",
       "1         0.252                      0.410              0   \n",
       "2         0.271                      0.341              0   \n",
       "3         0.354                      0.118              0   \n",
       "4         0.322                      0.298              0   \n",
       "..          ...                        ...            ...   \n",
       "142       0.217                      0.411              3   \n",
       "143       0.276                      0.147              3   \n",
       "144       0.158                      0.025              3   \n",
       "145       0.235                      0.035              3   \n",
       "146       0.202                      0.091              3   \n",
       "\n",
       "                       Entity  Prevalence percentage  \n",
       "0                     Finland               4.024233  \n",
       "1                     Denmark               2.827104  \n",
       "2                      Norway               2.817296  \n",
       "3                     Iceland               2.139336  \n",
       "4                 Netherlands               1.869502  \n",
       "..                        ...                    ...  \n",
       "142                    Rwanda               2.785813  \n",
       "143                  Tanzania               2.017210  \n",
       "144               Afghanistan               1.809625  \n",
       "145  Central African Republic               1.889598  \n",
       "146               South Sudan               1.917005  \n",
       "\n",
       "[147 rows x 12 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_alcohol_drug = pd.merge(left=happiness_survey, right=alcohol_drug_latest, left_on='Country or region', right_on = 'Entity')\n",
    "H_alcohol_drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(H_alcohol_drug['Prevalence percentage'],H_alcohol_drug['Score'],c=H_alcohol_drug['Quartile rank'])\n",
    "plt.title ('Happiness Report Comparison')\n",
    "plt.xlabel('Alcohol and drug prevalence (in %)')\n",
    "plt.ylabel('Happiness Score')\n",
    "plt.plot(np.unique(H_alcohol_drug['Prevalence percentage']), np.poly1d(np.polyfit(H_alcohol_drug['Prevalence percentage'], H_alcohol_drug['Score'], 1))(np.unique(H_alcohol_drug['Prevalence percentage'])),color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region/Country/Area</th>\n",
       "      <th>Labor force participation and unemployment</th>\n",
       "      <th>Year</th>\n",
       "      <th>Series</th>\n",
       "      <th>Value</th>\n",
       "      <th>Footnotes</th>\n",
       "      <th>Source</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2005</td>\n",
       "      <td>Labor force participation - Total</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2005</td>\n",
       "      <td>Unemployment rate - Total</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2005</td>\n",
       "      <td>Labor force participation - Male</td>\n",
       "      <td>77.3</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2005</td>\n",
       "      <td>Unemployment rate - Male</td>\n",
       "      <td>5.5</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2005</td>\n",
       "      <td>Labor force participation - Female</td>\n",
       "      <td>50.3</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5523</th>\n",
       "      <td>97</td>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Unemployment rate - Total</td>\n",
       "      <td>6.7</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5524</th>\n",
       "      <td>97</td>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Labor force participation - Male</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5525</th>\n",
       "      <td>97</td>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Unemployment rate - Male</td>\n",
       "      <td>6.5</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5526</th>\n",
       "      <td>97</td>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Labor force participation - Female</td>\n",
       "      <td>51.2</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5527</th>\n",
       "      <td>97</td>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Unemployment rate - Female</td>\n",
       "      <td>6.9</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5528 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Region/Country/Area Labor force participation and unemployment  Year  \\\n",
       "0                       1              Total, all countries or areas  2005   \n",
       "1                       1              Total, all countries or areas  2005   \n",
       "2                       1              Total, all countries or areas  2005   \n",
       "3                       1              Total, all countries or areas  2005   \n",
       "4                       1              Total, all countries or areas  2005   \n",
       "...                   ...                                        ...   ...   \n",
       "5523                   97                        European Union (EU)  2019   \n",
       "5524                   97                        European Union (EU)  2019   \n",
       "5525                   97                        European Union (EU)  2019   \n",
       "5526                   97                        European Union (EU)  2019   \n",
       "5527                   97                        European Union (EU)  2019   \n",
       "\n",
       "                                  Series  Value  Footnotes  \\\n",
       "0      Labor force participation - Total   63.7  Estimate.   \n",
       "1              Unemployment rate - Total    5.9  Estimate.   \n",
       "2       Labor force participation - Male   77.3  Estimate.   \n",
       "3               Unemployment rate - Male    5.5  Estimate.   \n",
       "4     Labor force participation - Female   50.3  Estimate.   \n",
       "...                                  ...    ...        ...   \n",
       "5523           Unemployment rate - Total    6.7  Estimate.   \n",
       "5524    Labor force participation - Male   63.7  Estimate.   \n",
       "5525            Unemployment rate - Male    6.5  Estimate.   \n",
       "5526  Labor force participation - Female   51.2  Estimate.   \n",
       "5527          Unemployment rate - Female    6.9  Estimate.   \n",
       "\n",
       "                                                 Source Unnamed: 7 Unnamed: 8  \\\n",
       "0     International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "1     International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "2     International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "3     International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "4     International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "...                                                 ...        ...        ...   \n",
       "5523  International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "5524  International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "5525  International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "5526  International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "5527  International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "\n",
       "     Unnamed: 9  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  \n",
       "3           NaN  \n",
       "4           NaN  \n",
       "...         ...  \n",
       "5523        NaN  \n",
       "5524        NaN  \n",
       "5525        NaN  \n",
       "5526        NaN  \n",
       "5527        NaN  \n",
       "\n",
       "[5528 rows x 10 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labor = pd.read_csv('Happiness/SYB62_329_201904_Labour Force and Unemployment.csv')\n",
    "labor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Labor force participation and unemployment  Series                            \n",
       "Afghanistan                                 Labor force participation - Female    2019\n",
       "                                            Labor force participation - Male      2019\n",
       "                                            Labor force participation - Total     2019\n",
       "                                            Unemployment rate - Female            2019\n",
       "                                            Unemployment rate - Male              2019\n",
       "                                                                                  ... \n",
       "Zimbabwe                                    Labor force participation - Male      2019\n",
       "                                            Labor force participation - Total     2019\n",
       "                                            Unemployment rate - Female            2019\n",
       "                                            Unemployment rate - Male              2019\n",
       "                                            Unemployment rate - Total             2019\n",
       "Name: Year, Length: 1479, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labor_filter = labor.groupby(['Labor force participation and unemployment','Series'])['Year'].max()\n",
    "labor_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region/Country/Area</th>\n",
       "      <th>Labor force participation and unemployment</th>\n",
       "      <th>Year</th>\n",
       "      <th>Series</th>\n",
       "      <th>Value</th>\n",
       "      <th>Footnotes</th>\n",
       "      <th>Source</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2019</td>\n",
       "      <td>Labor force participation - Total</td>\n",
       "      <td>61.2</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2019</td>\n",
       "      <td>Unemployment rate - Total</td>\n",
       "      <td>5.4</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2019</td>\n",
       "      <td>Labor force participation - Male</td>\n",
       "      <td>74.7</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2019</td>\n",
       "      <td>Unemployment rate - Male</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>2019</td>\n",
       "      <td>Labor force participation - Female</td>\n",
       "      <td>47.7</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1474</th>\n",
       "      <td>97</td>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Unemployment rate - Total</td>\n",
       "      <td>6.7</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1475</th>\n",
       "      <td>97</td>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Labor force participation - Male</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1476</th>\n",
       "      <td>97</td>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Unemployment rate - Male</td>\n",
       "      <td>6.5</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1477</th>\n",
       "      <td>97</td>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Labor force participation - Female</td>\n",
       "      <td>51.2</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478</th>\n",
       "      <td>97</td>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>2019</td>\n",
       "      <td>Unemployment rate - Female</td>\n",
       "      <td>6.9</td>\n",
       "      <td>Estimate.</td>\n",
       "      <td>International Labor Organization (ILO), Geneva...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1479 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Region/Country/Area Labor force participation and unemployment  Year  \\\n",
       "0                       1              Total, all countries or areas  2019   \n",
       "1                       1              Total, all countries or areas  2019   \n",
       "2                       1              Total, all countries or areas  2019   \n",
       "3                       1              Total, all countries or areas  2019   \n",
       "4                       1              Total, all countries or areas  2019   \n",
       "...                   ...                                        ...   ...   \n",
       "1474                   97                        European Union (EU)  2019   \n",
       "1475                   97                        European Union (EU)  2019   \n",
       "1476                   97                        European Union (EU)  2019   \n",
       "1477                   97                        European Union (EU)  2019   \n",
       "1478                   97                        European Union (EU)  2019   \n",
       "\n",
       "                                  Series  Value  Footnotes  \\\n",
       "0      Labor force participation - Total   61.2  Estimate.   \n",
       "1              Unemployment rate - Total    5.4  Estimate.   \n",
       "2       Labor force participation - Male   74.7  Estimate.   \n",
       "3               Unemployment rate - Male    5.0  Estimate.   \n",
       "4     Labor force participation - Female   47.7  Estimate.   \n",
       "...                                  ...    ...        ...   \n",
       "1474           Unemployment rate - Total    6.7  Estimate.   \n",
       "1475    Labor force participation - Male   63.7  Estimate.   \n",
       "1476            Unemployment rate - Male    6.5  Estimate.   \n",
       "1477  Labor force participation - Female   51.2  Estimate.   \n",
       "1478          Unemployment rate - Female    6.9  Estimate.   \n",
       "\n",
       "                                                 Source Unnamed: 7 Unnamed: 8  \\\n",
       "0     International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "1     International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "2     International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "3     International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "4     International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "...                                                 ...        ...        ...   \n",
       "1474  International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "1475  International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "1476  International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "1477  International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "1478  International Labor Organization (ILO), Geneva...        NaN        NaN   \n",
       "\n",
       "     Unnamed: 9  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  \n",
       "3           NaN  \n",
       "4           NaN  \n",
       "...         ...  \n",
       "1474        NaN  \n",
       "1475        NaN  \n",
       "1476        NaN  \n",
       "1477        NaN  \n",
       "1478        NaN  \n",
       "\n",
       "[1479 rows x 10 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labor_latest = pd.merge(left=labor, right=labor_filter, left_on=['Labor force participation and unemployment','Series','Year'], right_on = ['Labor force participation and unemployment','Series','Year'])\n",
    "labor_latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Labor force participation and unemployment</th>\n",
       "      <th>Series</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>Labor force participation - Total</td>\n",
       "      <td>61.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>Unemployment rate - Total</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>Labor force participation - Male</td>\n",
       "      <td>74.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>Unemployment rate - Male</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Total, all countries or areas</td>\n",
       "      <td>Labor force participation - Female</td>\n",
       "      <td>47.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1474</th>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>Unemployment rate - Total</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1475</th>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>Labor force participation - Male</td>\n",
       "      <td>63.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1476</th>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>Unemployment rate - Male</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1477</th>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>Labor force participation - Female</td>\n",
       "      <td>51.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478</th>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>Unemployment rate - Female</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1479 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Labor force participation and unemployment  \\\n",
       "0                 Total, all countries or areas   \n",
       "1                 Total, all countries or areas   \n",
       "2                 Total, all countries or areas   \n",
       "3                 Total, all countries or areas   \n",
       "4                 Total, all countries or areas   \n",
       "...                                         ...   \n",
       "1474                        European Union (EU)   \n",
       "1475                        European Union (EU)   \n",
       "1476                        European Union (EU)   \n",
       "1477                        European Union (EU)   \n",
       "1478                        European Union (EU)   \n",
       "\n",
       "                                  Series  Value  \n",
       "0      Labor force participation - Total   61.2  \n",
       "1              Unemployment rate - Total    5.4  \n",
       "2       Labor force participation - Male   74.7  \n",
       "3               Unemployment rate - Male    5.0  \n",
       "4     Labor force participation - Female   47.7  \n",
       "...                                  ...    ...  \n",
       "1474           Unemployment rate - Total    6.7  \n",
       "1475    Labor force participation - Male   63.7  \n",
       "1476            Unemployment rate - Male    6.5  \n",
       "1477  Labor force participation - Female   51.2  \n",
       "1478          Unemployment rate - Female    6.9  \n",
       "\n",
       "[1479 rows x 3 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labor_latest.drop(['Region/Country/Area','Year','Footnotes','Source','Unnamed: 7','Unnamed: 8','Unnamed: 9'], axis = 1, inplace = True)\n",
    "# labor_latest.rename(columns = {'Prevalence - Alcohol and substance use disorders: Both (age-standardized percent) (%)':'Prevalence percentage'}, inplace = True)\n",
    "labor_latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Series</th>\n",
       "      <th>Labor force participation - Female</th>\n",
       "      <th>Labor force participation - Male</th>\n",
       "      <th>Labor force participation - Total</th>\n",
       "      <th>Unemployment rate - Female</th>\n",
       "      <th>Unemployment rate - Male</th>\n",
       "      <th>Unemployment rate - Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labor force participation and unemployment</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>48.8</td>\n",
       "      <td>82.1</td>\n",
       "      <td>66.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>54.5</td>\n",
       "      <td>72.6</td>\n",
       "      <td>63.4</td>\n",
       "      <td>9.1</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>47.1</td>\n",
       "      <td>64.8</td>\n",
       "      <td>55.9</td>\n",
       "      <td>14.9</td>\n",
       "      <td>15.2</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>14.9</td>\n",
       "      <td>67.2</td>\n",
       "      <td>41.2</td>\n",
       "      <td>17.4</td>\n",
       "      <td>8.2</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>41.2</td>\n",
       "      <td>58.8</td>\n",
       "      <td>52.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Europe</th>\n",
       "      <td>53.7</td>\n",
       "      <td>64.1</td>\n",
       "      <td>58.8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Sahara</th>\n",
       "      <td>27.4</td>\n",
       "      <td>79.7</td>\n",
       "      <td>55.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>5.9</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>5.8</td>\n",
       "      <td>70.9</td>\n",
       "      <td>38.4</td>\n",
       "      <td>31.6</td>\n",
       "      <td>12.6</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>70.8</td>\n",
       "      <td>79.7</td>\n",
       "      <td>75.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>78.7</td>\n",
       "      <td>89.0</td>\n",
       "      <td>83.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Series                                      Labor force participation - Female  \\\n",
       "Labor force participation and unemployment                                       \n",
       "Afghanistan                                                               48.8   \n",
       "Africa                                                                    54.5   \n",
       "Albania                                                                   47.1   \n",
       "Algeria                                                                   14.9   \n",
       "American Samoa                                                            41.2   \n",
       "...                                                                        ...   \n",
       "Western Europe                                                            53.7   \n",
       "Western Sahara                                                            27.4   \n",
       "Yemen                                                                      5.8   \n",
       "Zambia                                                                    70.8   \n",
       "Zimbabwe                                                                  78.7   \n",
       "\n",
       "Series                                      Labor force participation - Male  \\\n",
       "Labor force participation and unemployment                                     \n",
       "Afghanistan                                                             82.1   \n",
       "Africa                                                                  72.6   \n",
       "Albania                                                                 64.8   \n",
       "Algeria                                                                 67.2   \n",
       "American Samoa                                                          58.8   \n",
       "...                                                                      ...   \n",
       "Western Europe                                                          64.1   \n",
       "Western Sahara                                                          79.7   \n",
       "Yemen                                                                   70.9   \n",
       "Zambia                                                                  79.7   \n",
       "Zimbabwe                                                                89.0   \n",
       "\n",
       "Series                                      Labor force participation - Total  \\\n",
       "Labor force participation and unemployment                                      \n",
       "Afghanistan                                                              66.0   \n",
       "Africa                                                                   63.4   \n",
       "Albania                                                                  55.9   \n",
       "Algeria                                                                  41.2   \n",
       "American Samoa                                                           52.8   \n",
       "...                                                                       ...   \n",
       "Western Europe                                                           58.8   \n",
       "Western Sahara                                                           55.1   \n",
       "Yemen                                                                    38.4   \n",
       "Zambia                                                                   75.2   \n",
       "Zimbabwe                                                                 83.7   \n",
       "\n",
       "Series                                      Unemployment rate - Female  \\\n",
       "Labor force participation and unemployment                               \n",
       "Afghanistan                                                       12.2   \n",
       "Africa                                                             9.1   \n",
       "Albania                                                           14.9   \n",
       "Algeria                                                           17.4   \n",
       "American Samoa                                                     6.0   \n",
       "...                                                                ...   \n",
       "Western Europe                                                     5.3   \n",
       "Western Sahara                                                    11.1   \n",
       "Yemen                                                             31.6   \n",
       "Zambia                                                             8.3   \n",
       "Zimbabwe                                                           5.6   \n",
       "\n",
       "Series                                      Unemployment rate - Male  \\\n",
       "Labor force participation and unemployment                             \n",
       "Afghanistan                                                      8.1   \n",
       "Africa                                                           6.8   \n",
       "Albania                                                         15.2   \n",
       "Algeria                                                          8.2   \n",
       "American Samoa                                                   4.9   \n",
       "...                                                              ...   \n",
       "Western Europe                                                   5.6   \n",
       "Western Sahara                                                   5.9   \n",
       "Yemen                                                           12.6   \n",
       "Zambia                                                           7.4   \n",
       "Zimbabwe                                                         4.3   \n",
       "\n",
       "Series                                      Unemployment rate - Total  \n",
       "Labor force participation and unemployment                             \n",
       "Afghanistan                                                       8.8  \n",
       "Africa                                                            7.8  \n",
       "Albania                                                          15.1  \n",
       "Algeria                                                           9.9  \n",
       "American Samoa                                                    9.2  \n",
       "...                                                               ...  \n",
       "Western Europe                                                    5.4  \n",
       "Western Sahara                                                    7.2  \n",
       "Yemen                                                            14.0  \n",
       "Zambia                                                            7.8  \n",
       "Zimbabwe                                                          5.0  \n",
       "\n",
       "[250 rows x 6 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labor_latest = pd.pivot_table(labor_latest, index='Labor force participation and unemployment', columns='Series', values='Value')\n",
    "labor_latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Quartile rank</th>\n",
       "      <th>Labor force participation - Female</th>\n",
       "      <th>Labor force participation - Male</th>\n",
       "      <th>Labor force participation - Total</th>\n",
       "      <th>Unemployment rate - Female</th>\n",
       "      <th>Unemployment rate - Male</th>\n",
       "      <th>Unemployment rate - Total</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0</td>\n",
       "      <td>54.9</td>\n",
       "      <td>62.0</td>\n",
       "      <td>58.3</td>\n",
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       "      <td>8.3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>65.8</td>\n",
       "      <td>61.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0</td>\n",
       "      <td>60.2</td>\n",
       "      <td>66.5</td>\n",
       "      <td>63.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0</td>\n",
       "      <td>71.8</td>\n",
       "      <td>80.2</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0</td>\n",
       "      <td>57.9</td>\n",
       "      <td>68.6</td>\n",
       "      <td>63.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.4</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>151</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>3.380</td>\n",
       "      <td>0.287</td>\n",
       "      <td>1.163</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.077</td>\n",
       "      <td>3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>70.9</td>\n",
       "      <td>38.4</td>\n",
       "      <td>31.6</td>\n",
       "      <td>12.6</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>152</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>3.334</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.411</td>\n",
       "      <td>3</td>\n",
       "      <td>84.2</td>\n",
       "      <td>83.5</td>\n",
       "      <td>83.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>154</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3.203</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.025</td>\n",
       "      <td>3</td>\n",
       "      <td>48.8</td>\n",
       "      <td>82.1</td>\n",
       "      <td>66.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>155</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>3.083</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.035</td>\n",
       "      <td>3</td>\n",
       "      <td>64.7</td>\n",
       "      <td>79.8</td>\n",
       "      <td>72.1</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>156</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>2.853</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.091</td>\n",
       "      <td>3</td>\n",
       "      <td>71.9</td>\n",
       "      <td>74.3</td>\n",
       "      <td>73.1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Overall rank         Country or region  Score  GDP per capita  \\\n",
       "0               1                   Finland  7.769           1.340   \n",
       "1               2                   Denmark  7.600           1.383   \n",
       "2               3                    Norway  7.554           1.488   \n",
       "3               4                   Iceland  7.494           1.380   \n",
       "4               5               Netherlands  7.488           1.396   \n",
       "..            ...                       ...    ...             ...   \n",
       "130           151                     Yemen  3.380           0.287   \n",
       "131           152                    Rwanda  3.334           0.359   \n",
       "132           154               Afghanistan  3.203           0.350   \n",
       "133           155  Central African Republic  3.083           0.026   \n",
       "134           156               South Sudan  2.853           0.306   \n",
       "\n",
       "     Social support  Healthy life expectancy  Freedom to make life choices  \\\n",
       "0             1.587                    0.986                         0.596   \n",
       "1             1.573                    0.996                         0.592   \n",
       "2             1.582                    1.028                         0.603   \n",
       "3             1.624                    1.026                         0.591   \n",
       "4             1.522                    0.999                         0.557   \n",
       "..              ...                      ...                           ...   \n",
       "130           1.163                    0.463                         0.143   \n",
       "131           0.711                    0.614                         0.555   \n",
       "132           0.517                    0.361                         0.000   \n",
       "133           0.000                    0.105                         0.225   \n",
       "134           0.575                    0.295                         0.010   \n",
       "\n",
       "     Generosity  Perceptions of corruption  Quartile rank  \\\n",
       "0         0.153                      0.393              0   \n",
       "1         0.252                      0.410              0   \n",
       "2         0.271                      0.341              0   \n",
       "3         0.354                      0.118              0   \n",
       "4         0.322                      0.298              0   \n",
       "..          ...                        ...            ...   \n",
       "130       0.108                      0.077              3   \n",
       "131       0.217                      0.411              3   \n",
       "132       0.158                      0.025              3   \n",
       "133       0.235                      0.035              3   \n",
       "134       0.202                      0.091              3   \n",
       "\n",
       "     Labor force participation - Female  Labor force participation - Male  \\\n",
       "0                                  54.9                              62.0   \n",
       "1                                  58.0                              65.8   \n",
       "2                                  60.2                              66.5   \n",
       "3                                  71.8                              80.2   \n",
       "4                                  57.9                              68.6   \n",
       "..                                  ...                               ...   \n",
       "130                                 5.8                              70.9   \n",
       "131                                84.2                              83.5   \n",
       "132                                48.8                              82.1   \n",
       "133                                64.7                              79.8   \n",
       "134                                71.9                              74.3   \n",
       "\n",
       "     Labor force participation - Total  Unemployment rate - Female  \\\n",
       "0                                 58.3                         7.9   \n",
       "1                                 61.9                         5.5   \n",
       "2                                 63.4                         3.4   \n",
       "3                                 76.0                         2.9   \n",
       "4                                 63.2                         3.7   \n",
       "..                                 ...                         ...   \n",
       "130                               38.4                        31.6   \n",
       "131                               83.8                         1.5   \n",
       "132                               66.0                        12.2   \n",
       "133                               72.1                         6.3   \n",
       "134                               73.1                        13.0   \n",
       "\n",
       "     Unemployment rate - Male  Unemployment rate - Total  \n",
       "0                         8.3                        8.1  \n",
       "1                         5.2                        5.3  \n",
       "2                         4.2                        3.9  \n",
       "3                         3.0                        2.9  \n",
       "4                         3.2                        3.4  \n",
       "..                        ...                        ...  \n",
       "130                      12.6                       14.0  \n",
       "131                       1.3                        1.4  \n",
       "132                       8.1                        8.8  \n",
       "133                       5.0                        5.6  \n",
       "134                      10.1                       11.5  \n",
       "\n",
       "[135 rows x 16 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_labor = pd.merge(left=happiness_survey, right=labor_latest, left_on='Country or region', right_on = 'Labor force participation and unemployment')\n",
    "H_labor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(H_labor['Labor force participation - Total'],H_labor['Score'],c=H_labor['Quartile rank'])\n",
    "plt.title ('Happiness Report Comparison')\n",
    "plt.xlabel('% of labor force employed')\n",
    "plt.ylabel('Happiness Score')\n",
    "plt.plot(np.unique(H_labor['Labor force participation - Total']), np.poly1d(np.polyfit(H_labor['Labor force participation - Total'], H_labor['Score'], 1))(np.unique(H_labor['Labor force participation - Total'])),color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Indicator Name</th>\n",
       "      <th>Indicator Code</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>Fertility rate, total (births per woman)</td>\n",
       "      <td>SP.DYN.TFRT.IN</td>\n",
       "      <td>4.820</td>\n",
       "      <td>4.655</td>\n",
       "      <td>4.471</td>\n",
       "      <td>4.271</td>\n",
       "      <td>4.059</td>\n",
       "      <td>3.842</td>\n",
       "      <td>...</td>\n",
       "      <td>1.768</td>\n",
       "      <td>1.779</td>\n",
       "      <td>1.795</td>\n",
       "      <td>1.813</td>\n",
       "      <td>1.834</td>\n",
       "      <td>1.854</td>\n",
       "      <td>1.872</td>\n",
       "      <td>1.886</td>\n",
       "      <td>1.896</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Fertility rate, total (births per woman)</td>\n",
       "      <td>SP.DYN.TFRT.IN</td>\n",
       "      <td>7.450</td>\n",
       "      <td>7.450</td>\n",
       "      <td>7.450</td>\n",
       "      <td>7.450</td>\n",
       "      <td>7.450</td>\n",
       "      <td>7.450</td>\n",
       "      <td>...</td>\n",
       "      <td>5.977</td>\n",
       "      <td>5.770</td>\n",
       "      <td>5.562</td>\n",
       "      <td>5.359</td>\n",
       "      <td>5.163</td>\n",
       "      <td>4.976</td>\n",
       "      <td>4.800</td>\n",
       "      <td>4.633</td>\n",
       "      <td>4.473</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Fertility rate, total (births per woman)</td>\n",
       "      <td>SP.DYN.TFRT.IN</td>\n",
       "      <td>6.708</td>\n",
       "      <td>6.790</td>\n",
       "      <td>6.872</td>\n",
       "      <td>6.954</td>\n",
       "      <td>7.036</td>\n",
       "      <td>7.116</td>\n",
       "      <td>...</td>\n",
       "      <td>6.194</td>\n",
       "      <td>6.120</td>\n",
       "      <td>6.039</td>\n",
       "      <td>5.953</td>\n",
       "      <td>5.864</td>\n",
       "      <td>5.774</td>\n",
       "      <td>5.686</td>\n",
       "      <td>5.600</td>\n",
       "      <td>5.519</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Fertility rate, total (births per woman)</td>\n",
       "      <td>SP.DYN.TFRT.IN</td>\n",
       "      <td>6.489</td>\n",
       "      <td>6.401</td>\n",
       "      <td>6.282</td>\n",
       "      <td>6.133</td>\n",
       "      <td>5.960</td>\n",
       "      <td>5.773</td>\n",
       "      <td>...</td>\n",
       "      <td>1.660</td>\n",
       "      <td>1.673</td>\n",
       "      <td>1.684</td>\n",
       "      <td>1.690</td>\n",
       "      <td>1.688</td>\n",
       "      <td>1.677</td>\n",
       "      <td>1.659</td>\n",
       "      <td>1.638</td>\n",
       "      <td>1.617</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>AND</td>\n",
       "      <td>Fertility rate, total (births per woman)</td>\n",
       "      <td>SP.DYN.TFRT.IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.270</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>Kosovo</td>\n",
       "      <td>XKX</td>\n",
       "      <td>Fertility rate, total (births per woman)</td>\n",
       "      <td>SP.DYN.TFRT.IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.290</td>\n",
       "      <td>2.240</td>\n",
       "      <td>2.190</td>\n",
       "      <td>2.160</td>\n",
       "      <td>2.130</td>\n",
       "      <td>2.090</td>\n",
       "      <td>2.060</td>\n",
       "      <td>2.020</td>\n",
       "      <td>2.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "      <td>Fertility rate, total (births per woman)</td>\n",
       "      <td>SP.DYN.TFRT.IN</td>\n",
       "      <td>7.938</td>\n",
       "      <td>7.962</td>\n",
       "      <td>7.991</td>\n",
       "      <td>8.026</td>\n",
       "      <td>8.067</td>\n",
       "      <td>8.114</td>\n",
       "      <td>...</td>\n",
       "      <td>4.674</td>\n",
       "      <td>4.554</td>\n",
       "      <td>4.439</td>\n",
       "      <td>4.326</td>\n",
       "      <td>4.214</td>\n",
       "      <td>4.103</td>\n",
       "      <td>3.994</td>\n",
       "      <td>3.890</td>\n",
       "      <td>3.792</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>Fertility rate, total (births per woman)</td>\n",
       "      <td>SP.DYN.TFRT.IN</td>\n",
       "      <td>6.041</td>\n",
       "      <td>6.028</td>\n",
       "      <td>6.010</td>\n",
       "      <td>5.986</td>\n",
       "      <td>5.956</td>\n",
       "      <td>5.920</td>\n",
       "      <td>...</td>\n",
       "      <td>2.598</td>\n",
       "      <td>2.582</td>\n",
       "      <td>2.561</td>\n",
       "      <td>2.537</td>\n",
       "      <td>2.511</td>\n",
       "      <td>2.484</td>\n",
       "      <td>2.457</td>\n",
       "      <td>2.430</td>\n",
       "      <td>2.405</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>Fertility rate, total (births per woman)</td>\n",
       "      <td>SP.DYN.TFRT.IN</td>\n",
       "      <td>7.115</td>\n",
       "      <td>7.169</td>\n",
       "      <td>7.214</td>\n",
       "      <td>7.249</td>\n",
       "      <td>7.274</td>\n",
       "      <td>7.291</td>\n",
       "      <td>...</td>\n",
       "      <td>5.415</td>\n",
       "      <td>5.328</td>\n",
       "      <td>5.233</td>\n",
       "      <td>5.132</td>\n",
       "      <td>5.026</td>\n",
       "      <td>4.918</td>\n",
       "      <td>4.814</td>\n",
       "      <td>4.718</td>\n",
       "      <td>4.633</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Fertility rate, total (births per woman)</td>\n",
       "      <td>SP.DYN.TFRT.IN</td>\n",
       "      <td>7.158</td>\n",
       "      <td>7.215</td>\n",
       "      <td>7.267</td>\n",
       "      <td>7.311</td>\n",
       "      <td>7.347</td>\n",
       "      <td>7.373</td>\n",
       "      <td>...</td>\n",
       "      <td>4.034</td>\n",
       "      <td>4.059</td>\n",
       "      <td>4.058</td>\n",
       "      <td>4.030</td>\n",
       "      <td>3.974</td>\n",
       "      <td>3.896</td>\n",
       "      <td>3.804</td>\n",
       "      <td>3.707</td>\n",
       "      <td>3.615</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country Name Country Code                            Indicator Name  \\\n",
       "0           Aruba          ABW  Fertility rate, total (births per woman)   \n",
       "1     Afghanistan          AFG  Fertility rate, total (births per woman)   \n",
       "2          Angola          AGO  Fertility rate, total (births per woman)   \n",
       "3         Albania          ALB  Fertility rate, total (births per woman)   \n",
       "4         Andorra          AND  Fertility rate, total (births per woman)   \n",
       "..            ...          ...                                       ...   \n",
       "259        Kosovo          XKX  Fertility rate, total (births per woman)   \n",
       "260   Yemen, Rep.          YEM  Fertility rate, total (births per woman)   \n",
       "261  South Africa          ZAF  Fertility rate, total (births per woman)   \n",
       "262        Zambia          ZMB  Fertility rate, total (births per woman)   \n",
       "263      Zimbabwe          ZWE  Fertility rate, total (births per woman)   \n",
       "\n",
       "     Indicator Code   1960   1961   1962   1963   1964   1965  ...   2010  \\\n",
       "0    SP.DYN.TFRT.IN  4.820  4.655  4.471  4.271  4.059  3.842  ...  1.768   \n",
       "1    SP.DYN.TFRT.IN  7.450  7.450  7.450  7.450  7.450  7.450  ...  5.977   \n",
       "2    SP.DYN.TFRT.IN  6.708  6.790  6.872  6.954  7.036  7.116  ...  6.194   \n",
       "3    SP.DYN.TFRT.IN  6.489  6.401  6.282  6.133  5.960  5.773  ...  1.660   \n",
       "4    SP.DYN.TFRT.IN    NaN    NaN    NaN    NaN    NaN    NaN  ...  1.270   \n",
       "..              ...    ...    ...    ...    ...    ...    ...  ...    ...   \n",
       "259  SP.DYN.TFRT.IN    NaN    NaN    NaN    NaN    NaN    NaN  ...  2.290   \n",
       "260  SP.DYN.TFRT.IN  7.938  7.962  7.991  8.026  8.067  8.114  ...  4.674   \n",
       "261  SP.DYN.TFRT.IN  6.041  6.028  6.010  5.986  5.956  5.920  ...  2.598   \n",
       "262  SP.DYN.TFRT.IN  7.115  7.169  7.214  7.249  7.274  7.291  ...  5.415   \n",
       "263  SP.DYN.TFRT.IN  7.158  7.215  7.267  7.311  7.347  7.373  ...  4.034   \n",
       "\n",
       "      2011   2012   2013   2014   2015   2016   2017   2018  2019  \n",
       "0    1.779  1.795  1.813  1.834  1.854  1.872  1.886  1.896   NaN  \n",
       "1    5.770  5.562  5.359  5.163  4.976  4.800  4.633  4.473   NaN  \n",
       "2    6.120  6.039  5.953  5.864  5.774  5.686  5.600  5.519   NaN  \n",
       "3    1.673  1.684  1.690  1.688  1.677  1.659  1.638  1.617   NaN  \n",
       "4      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   NaN  \n",
       "..     ...    ...    ...    ...    ...    ...    ...    ...   ...  \n",
       "259  2.240  2.190  2.160  2.130  2.090  2.060  2.020  2.000   NaN  \n",
       "260  4.554  4.439  4.326  4.214  4.103  3.994  3.890  3.792   NaN  \n",
       "261  2.582  2.561  2.537  2.511  2.484  2.457  2.430  2.405   NaN  \n",
       "262  5.328  5.233  5.132  5.026  4.918  4.814  4.718  4.633   NaN  \n",
       "263  4.059  4.058  4.030  3.974  3.896  3.804  3.707  3.615   NaN  \n",
       "\n",
       "[264 rows x 64 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fertility= pd.read_csv('Happiness/fertility_by_country.csv')\n",
    "fertility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lee/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:4133: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
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    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Fertility rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>1.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>4.473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>5.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1.617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>Kosovo</td>\n",
       "      <td>2.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>3.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>2.405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>4.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>3.615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country Name  Fertility rate\n",
       "0           Aruba           1.896\n",
       "1     Afghanistan           4.473\n",
       "2          Angola           5.519\n",
       "3         Albania           1.617\n",
       "4         Andorra             NaN\n",
       "..            ...             ...\n",
       "259        Kosovo           2.000\n",
       "260   Yemen, Rep.           3.792\n",
       "261  South Africa           2.405\n",
       "262        Zambia           4.633\n",
       "263      Zimbabwe           3.615\n",
       "\n",
       "[264 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fertility_latest = fertility[['Country Name','2018']]\n",
    "fertility_latest.rename(columns =  {'2018': 'Fertility rate'}, inplace = True)\n",
    "fertility_latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Quartile rank</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Fertility rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>1.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>1.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0</td>\n",
       "      <td>Norway</td>\n",
       "      <td>1.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>1.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>152</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>3.334</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.411</td>\n",
       "      <td>3</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>4.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>153</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>3.231</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.499</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.147</td>\n",
       "      <td>3</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>4.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>154</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3.203</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.025</td>\n",
       "      <td>3</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>4.473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>155</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>3.083</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.035</td>\n",
       "      <td>3</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>4.721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>156</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>2.853</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.091</td>\n",
       "      <td>3</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>4.696</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Overall rank         Country or region  Score  GDP per capita  \\\n",
       "0               1                   Finland  7.769           1.340   \n",
       "1               2                   Denmark  7.600           1.383   \n",
       "2               3                    Norway  7.554           1.488   \n",
       "3               4                   Iceland  7.494           1.380   \n",
       "4               5               Netherlands  7.488           1.396   \n",
       "..            ...                       ...    ...             ...   \n",
       "131           152                    Rwanda  3.334           0.359   \n",
       "132           153                  Tanzania  3.231           0.476   \n",
       "133           154               Afghanistan  3.203           0.350   \n",
       "134           155  Central African Republic  3.083           0.026   \n",
       "135           156               South Sudan  2.853           0.306   \n",
       "\n",
       "     Social support  Healthy life expectancy  Freedom to make life choices  \\\n",
       "0             1.587                    0.986                         0.596   \n",
       "1             1.573                    0.996                         0.592   \n",
       "2             1.582                    1.028                         0.603   \n",
       "3             1.624                    1.026                         0.591   \n",
       "4             1.522                    0.999                         0.557   \n",
       "..              ...                      ...                           ...   \n",
       "131           0.711                    0.614                         0.555   \n",
       "132           0.885                    0.499                         0.417   \n",
       "133           0.517                    0.361                         0.000   \n",
       "134           0.000                    0.105                         0.225   \n",
       "135           0.575                    0.295                         0.010   \n",
       "\n",
       "     Generosity  Perceptions of corruption  Quartile rank  \\\n",
       "0         0.153                      0.393              0   \n",
       "1         0.252                      0.410              0   \n",
       "2         0.271                      0.341              0   \n",
       "3         0.354                      0.118              0   \n",
       "4         0.322                      0.298              0   \n",
       "..          ...                        ...            ...   \n",
       "131       0.217                      0.411              3   \n",
       "132       0.276                      0.147              3   \n",
       "133       0.158                      0.025              3   \n",
       "134       0.235                      0.035              3   \n",
       "135       0.202                      0.091              3   \n",
       "\n",
       "                 Country Name  Fertility rate  \n",
       "0                     Finland           1.410  \n",
       "1                     Denmark           1.730  \n",
       "2                      Norway           1.560  \n",
       "3                     Iceland           1.710  \n",
       "4                 Netherlands           1.590  \n",
       "..                        ...             ...  \n",
       "131                    Rwanda           4.044  \n",
       "132                  Tanzania           4.892  \n",
       "133               Afghanistan           4.473  \n",
       "134  Central African Republic           4.721  \n",
       "135               South Sudan           4.696  \n",
       "\n",
       "[136 rows x 12 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_fertility = pd.merge(left=happiness_survey, right=fertility_latest, left_on='Country or region', right_on = 'Country Name')\n",
    "H_fertility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Overall rank</th>\n",
       "      <th>Country or region</th>\n",
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       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
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       "      <td>0.592</td>\n",
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       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0</td>\n",
       "      <td>Norway</td>\n",
       "      <td>1.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>1.710</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1.590</td>\n",
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       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>152</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>3.334</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.411</td>\n",
       "      <td>3</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>4.044</td>\n",
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       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>153</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>3.231</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.499</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.147</td>\n",
       "      <td>3</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>4.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>154</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3.203</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.025</td>\n",
       "      <td>3</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>4.473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>155</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>3.083</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.105</td>\n",
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       "      <td>0.235</td>\n",
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       "      <td>Central African Republic</td>\n",
       "      <td>4.721</td>\n",
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       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>156</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>2.853</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.091</td>\n",
       "      <td>3</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>4.696</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "     Overall rank         Country or region  Score  GDP per capita  \\\n",
       "0               1                   Finland  7.769           1.340   \n",
       "1               2                   Denmark  7.600           1.383   \n",
       "2               3                    Norway  7.554           1.488   \n",
       "3               4                   Iceland  7.494           1.380   \n",
       "4               5               Netherlands  7.488           1.396   \n",
       "..            ...                       ...    ...             ...   \n",
       "131           152                    Rwanda  3.334           0.359   \n",
       "132           153                  Tanzania  3.231           0.476   \n",
       "133           154               Afghanistan  3.203           0.350   \n",
       "134           155  Central African Republic  3.083           0.026   \n",
       "135           156               South Sudan  2.853           0.306   \n",
       "\n",
       "     Social support  Healthy life expectancy  Freedom to make life choices  \\\n",
       "0             1.587                    0.986                         0.596   \n",
       "1             1.573                    0.996                         0.592   \n",
       "2             1.582                    1.028                         0.603   \n",
       "3             1.624                    1.026                         0.591   \n",
       "4             1.522                    0.999                         0.557   \n",
       "..              ...                      ...                           ...   \n",
       "131           0.711                    0.614                         0.555   \n",
       "132           0.885                    0.499                         0.417   \n",
       "133           0.517                    0.361                         0.000   \n",
       "134           0.000                    0.105                         0.225   \n",
       "135           0.575                    0.295                         0.010   \n",
       "\n",
       "     Generosity  Perceptions of corruption  Quartile rank  \\\n",
       "0         0.153                      0.393              0   \n",
       "1         0.252                      0.410              0   \n",
       "2         0.271                      0.341              0   \n",
       "3         0.354                      0.118              0   \n",
       "4         0.322                      0.298              0   \n",
       "..          ...                        ...            ...   \n",
       "131       0.217                      0.411              3   \n",
       "132       0.276                      0.147              3   \n",
       "133       0.158                      0.025              3   \n",
       "134       0.235                      0.035              3   \n",
       "135       0.202                      0.091              3   \n",
       "\n",
       "                 Country Name  Fertility rate  \n",
       "0                     Finland           1.410  \n",
       "1                     Denmark           1.730  \n",
       "2                      Norway           1.560  \n",
       "3                     Iceland           1.710  \n",
       "4                 Netherlands           1.590  \n",
       "..                        ...             ...  \n",
       "131                    Rwanda           4.044  \n",
       "132                  Tanzania           4.892  \n",
       "133               Afghanistan           4.473  \n",
       "134  Central African Republic           4.721  \n",
       "135               South Sudan           4.696  \n",
       "\n",
       "[136 rows x 12 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_fertility = pd.merge(left=happiness_survey, right=fertility_latest, left_on='Country or region', right_on = 'Country Name')\n",
    "H_fertility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(H_fertility['Fertility rate'],H_fertility['Score'],c=H_fertility['Quartile rank'])\n",
    "plt.title ('Happiness Report Comparison')\n",
    "plt.xlabel('Fertility rate')\n",
    "plt.ylabel('Happiness Score')\n",
    "plt.plot(np.unique(H_fertility['Fertility rate']), np.poly1d(np.polyfit(H_fertility['Fertility rate'], H_fertility['Score'], 1))(np.unique(H_fertility['Fertility rate'])),color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>21</td>\n",
       "      <td>312900</td>\n",
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       "      <td>35-54 years</td>\n",
       "      <td>16</td>\n",
       "      <td>308000</td>\n",
       "      <td>5.19</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2156624900</td>\n",
       "      <td>796</td>\n",
       "      <td>Silent</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>female</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>14</td>\n",
       "      <td>289700</td>\n",
       "      <td>4.83</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2156624900</td>\n",
       "      <td>796</td>\n",
       "      <td>Generation X</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>1</td>\n",
       "      <td>21800</td>\n",
       "      <td>4.59</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2156624900</td>\n",
       "      <td>796</td>\n",
       "      <td>G.I. Generation</td>\n",
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       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
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       "      <td>9</td>\n",
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       "      <td>2156624900</td>\n",
       "      <td>796</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>27815</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>female</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>107</td>\n",
       "      <td>3620833</td>\n",
       "      <td>2.96</td>\n",
       "      <td>Uzbekistan2014</td>\n",
       "      <td>0.675</td>\n",
       "      <td>63067077179</td>\n",
       "      <td>2309</td>\n",
       "      <td>Generation X</td>\n",
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       "    <tr>\n",
       "      <th>27816</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>female</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>9</td>\n",
       "      <td>348465</td>\n",
       "      <td>2.58</td>\n",
       "      <td>Uzbekistan2014</td>\n",
       "      <td>0.675</td>\n",
       "      <td>63067077179</td>\n",
       "      <td>2309</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27817</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>male</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>60</td>\n",
       "      <td>2762158</td>\n",
       "      <td>2.17</td>\n",
       "      <td>Uzbekistan2014</td>\n",
       "      <td>0.675</td>\n",
       "      <td>63067077179</td>\n",
       "      <td>2309</td>\n",
       "      <td>Generation Z</td>\n",
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       "    <tr>\n",
       "      <th>27818</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>female</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>44</td>\n",
       "      <td>2631600</td>\n",
       "      <td>1.67</td>\n",
       "      <td>Uzbekistan2014</td>\n",
       "      <td>0.675</td>\n",
       "      <td>63067077179</td>\n",
       "      <td>2309</td>\n",
       "      <td>Generation Z</td>\n",
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       "    <tr>\n",
       "      <th>27819</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>female</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>21</td>\n",
       "      <td>1438935</td>\n",
       "      <td>1.46</td>\n",
       "      <td>Uzbekistan2014</td>\n",
       "      <td>0.675</td>\n",
       "      <td>63067077179</td>\n",
       "      <td>2309</td>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27820 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year     sex          age  suicides_no  population  \\\n",
       "0         Albania  1987    male  15-24 years           21      312900   \n",
       "1         Albania  1987    male  35-54 years           16      308000   \n",
       "2         Albania  1987  female  15-24 years           14      289700   \n",
       "3         Albania  1987    male    75+ years            1       21800   \n",
       "4         Albania  1987    male  25-34 years            9      274300   \n",
       "...           ...   ...     ...          ...          ...         ...   \n",
       "27815  Uzbekistan  2014  female  35-54 years          107     3620833   \n",
       "27816  Uzbekistan  2014  female    75+ years            9      348465   \n",
       "27817  Uzbekistan  2014    male   5-14 years           60     2762158   \n",
       "27818  Uzbekistan  2014  female   5-14 years           44     2631600   \n",
       "27819  Uzbekistan  2014  female  55-74 years           21     1438935   \n",
       "\n",
       "       suicides/100k pop    country-year  HDI for year   gdp_for_year ($)   \\\n",
       "0                   6.71     Albania1987           NaN          2156624900   \n",
       "1                   5.19     Albania1987           NaN          2156624900   \n",
       "2                   4.83     Albania1987           NaN          2156624900   \n",
       "3                   4.59     Albania1987           NaN          2156624900   \n",
       "4                   3.28     Albania1987           NaN          2156624900   \n",
       "...                  ...             ...           ...                 ...   \n",
       "27815               2.96  Uzbekistan2014         0.675         63067077179   \n",
       "27816               2.58  Uzbekistan2014         0.675         63067077179   \n",
       "27817               2.17  Uzbekistan2014         0.675         63067077179   \n",
       "27818               1.67  Uzbekistan2014         0.675         63067077179   \n",
       "27819               1.46  Uzbekistan2014         0.675         63067077179   \n",
       "\n",
       "       gdp_per_capita ($)       generation  \n",
       "0                     796     Generation X  \n",
       "1                     796           Silent  \n",
       "2                     796     Generation X  \n",
       "3                     796  G.I. Generation  \n",
       "4                     796          Boomers  \n",
       "...                   ...              ...  \n",
       "27815                2309     Generation X  \n",
       "27816                2309           Silent  \n",
       "27817                2309     Generation Z  \n",
       "27818                2309     Generation Z  \n",
       "27819                2309          Boomers  \n",
       "\n",
       "[27820 rows x 12 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide = pd.read_csv('Happiness/suicide_by_country.csv')\n",
    "suicide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Albania                 2010\n",
       "Antigua and Barbuda     2015\n",
       "Argentina               2015\n",
       "Armenia                 2016\n",
       "Aruba                   2011\n",
       "                        ... \n",
       "United Arab Emirates    2010\n",
       "United Kingdom          2015\n",
       "United States           2015\n",
       "Uruguay                 2015\n",
       "Uzbekistan              2014\n",
       "Name: year, Length: 101, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_filter = suicide.groupby(['country'])['year'].max()\n",
    "suicide_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2010</td>\n",
       "      <td>male</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>20</td>\n",
       "      <td>241852</td>\n",
       "      <td>8.27</td>\n",
       "      <td>Albania2010</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2010</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>20</td>\n",
       "      <td>371611</td>\n",
       "      <td>5.38</td>\n",
       "      <td>Albania2010</td>\n",
       "      <td>0.722</td>\n",
       "      <td>11926953259</td>\n",
       "      <td>4359</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2010</td>\n",
       "      <td>male</td>\n",
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       "      <td>9</td>\n",
       "      <td>179720</td>\n",
       "      <td>5.01</td>\n",
       "      <td>Albania2010</td>\n",
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       "      <td>11926953259</td>\n",
       "      <td>4359</td>\n",
       "      <td>Generation X</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
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       "      <td>75+ years</td>\n",
       "      <td>2</td>\n",
       "      <td>50767</td>\n",
       "      <td>3.94</td>\n",
       "      <td>Albania2010</td>\n",
       "      <td>0.722</td>\n",
       "      <td>11926953259</td>\n",
       "      <td>4359</td>\n",
       "      <td>Silent</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
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       "      <td>15-24 years</td>\n",
       "      <td>10</td>\n",
       "      <td>279508</td>\n",
       "      <td>3.58</td>\n",
       "      <td>Albania2010</td>\n",
       "      <td>0.722</td>\n",
       "      <td>11926953259</td>\n",
       "      <td>4359</td>\n",
       "      <td>Millenials</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>1175</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>female</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>107</td>\n",
       "      <td>3620833</td>\n",
       "      <td>2.96</td>\n",
       "      <td>Uzbekistan2014</td>\n",
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       "      <td>2309</td>\n",
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       "    <tr>\n",
       "      <th>1176</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>female</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>9</td>\n",
       "      <td>348465</td>\n",
       "      <td>2.58</td>\n",
       "      <td>Uzbekistan2014</td>\n",
       "      <td>0.675</td>\n",
       "      <td>63067077179</td>\n",
       "      <td>2309</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1177</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>male</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>60</td>\n",
       "      <td>2762158</td>\n",
       "      <td>2.17</td>\n",
       "      <td>Uzbekistan2014</td>\n",
       "      <td>0.675</td>\n",
       "      <td>63067077179</td>\n",
       "      <td>2309</td>\n",
       "      <td>Generation Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>female</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>44</td>\n",
       "      <td>2631600</td>\n",
       "      <td>1.67</td>\n",
       "      <td>Uzbekistan2014</td>\n",
       "      <td>0.675</td>\n",
       "      <td>63067077179</td>\n",
       "      <td>2309</td>\n",
       "      <td>Generation Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>female</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>21</td>\n",
       "      <td>1438935</td>\n",
       "      <td>1.46</td>\n",
       "      <td>Uzbekistan2014</td>\n",
       "      <td>0.675</td>\n",
       "      <td>63067077179</td>\n",
       "      <td>2309</td>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1180 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  year     sex          age  suicides_no  population  \\\n",
       "0        Albania  2010    male  55-74 years           20      241852   \n",
       "1        Albania  2010    male  35-54 years           20      371611   \n",
       "2        Albania  2010    male  25-34 years            9      179720   \n",
       "3        Albania  2010    male    75+ years            2       50767   \n",
       "4        Albania  2010    male  15-24 years           10      279508   \n",
       "...          ...   ...     ...          ...          ...         ...   \n",
       "1175  Uzbekistan  2014  female  35-54 years          107     3620833   \n",
       "1176  Uzbekistan  2014  female    75+ years            9      348465   \n",
       "1177  Uzbekistan  2014    male   5-14 years           60     2762158   \n",
       "1178  Uzbekistan  2014  female   5-14 years           44     2631600   \n",
       "1179  Uzbekistan  2014  female  55-74 years           21     1438935   \n",
       "\n",
       "      suicides/100k pop    country-year  HDI for year   gdp_for_year ($)   \\\n",
       "0                  8.27     Albania2010         0.722         11926953259   \n",
       "1                  5.38     Albania2010         0.722         11926953259   \n",
       "2                  5.01     Albania2010         0.722         11926953259   \n",
       "3                  3.94     Albania2010         0.722         11926953259   \n",
       "4                  3.58     Albania2010         0.722         11926953259   \n",
       "...                 ...             ...           ...                 ...   \n",
       "1175               2.96  Uzbekistan2014         0.675         63067077179   \n",
       "1176               2.58  Uzbekistan2014         0.675         63067077179   \n",
       "1177               2.17  Uzbekistan2014         0.675         63067077179   \n",
       "1178               1.67  Uzbekistan2014         0.675         63067077179   \n",
       "1179               1.46  Uzbekistan2014         0.675         63067077179   \n",
       "\n",
       "      gdp_per_capita ($)    generation  \n",
       "0                   4359        Silent  \n",
       "1                   4359  Generation X  \n",
       "2                   4359  Generation X  \n",
       "3                   4359        Silent  \n",
       "4                   4359    Millenials  \n",
       "...                  ...           ...  \n",
       "1175                2309  Generation X  \n",
       "1176                2309        Silent  \n",
       "1177                2309  Generation Z  \n",
       "1178                2309  Generation Z  \n",
       "1179                2309       Boomers  \n",
       "\n",
       "[1180 rows x 12 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_latest = pd.merge(left=suicide, right=suicide_filter, left_on=['country','year'], right_on = ['country','year'])\n",
    "suicide_latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicides/100k pop</th>\n",
       "      <th>gdp_for_year ($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>20</td>\n",
       "      <td>241852</td>\n",
       "      <td>8.27</td>\n",
       "      <td>11926953259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>20</td>\n",
       "      <td>371611</td>\n",
       "      <td>5.38</td>\n",
       "      <td>11926953259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>9</td>\n",
       "      <td>179720</td>\n",
       "      <td>5.01</td>\n",
       "      <td>11926953259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2</td>\n",
       "      <td>50767</td>\n",
       "      <td>3.94</td>\n",
       "      <td>11926953259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>10</td>\n",
       "      <td>279508</td>\n",
       "      <td>3.58</td>\n",
       "      <td>11926953259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1175</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>107</td>\n",
       "      <td>3620833</td>\n",
       "      <td>2.96</td>\n",
       "      <td>63067077179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1176</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>9</td>\n",
       "      <td>348465</td>\n",
       "      <td>2.58</td>\n",
       "      <td>63067077179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1177</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>60</td>\n",
       "      <td>2762158</td>\n",
       "      <td>2.17</td>\n",
       "      <td>63067077179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>44</td>\n",
       "      <td>2631600</td>\n",
       "      <td>1.67</td>\n",
       "      <td>63067077179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>21</td>\n",
       "      <td>1438935</td>\n",
       "      <td>1.46</td>\n",
       "      <td>63067077179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1180 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  suicides_no  population  suicides/100k pop  \\\n",
       "0        Albania           20      241852               8.27   \n",
       "1        Albania           20      371611               5.38   \n",
       "2        Albania            9      179720               5.01   \n",
       "3        Albania            2       50767               3.94   \n",
       "4        Albania           10      279508               3.58   \n",
       "...          ...          ...         ...                ...   \n",
       "1175  Uzbekistan          107     3620833               2.96   \n",
       "1176  Uzbekistan            9      348465               2.58   \n",
       "1177  Uzbekistan           60     2762158               2.17   \n",
       "1178  Uzbekistan           44     2631600               1.67   \n",
       "1179  Uzbekistan           21     1438935               1.46   \n",
       "\n",
       "       gdp_for_year ($)   \n",
       "0            11926953259  \n",
       "1            11926953259  \n",
       "2            11926953259  \n",
       "3            11926953259  \n",
       "4            11926953259  \n",
       "...                  ...  \n",
       "1175         63067077179  \n",
       "1176         63067077179  \n",
       "1177         63067077179  \n",
       "1178         63067077179  \n",
       "1179         63067077179  \n",
       "\n",
       "[1180 rows x 5 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_latest.drop(['sex','age','year','country-year','HDI for year','gdp_per_capita ($)','generation'], axis = 1, inplace = True)\n",
    "# labor_latest.rename(columns = {'Prevalence - Alcohol and substance use disorders: Both (age-standardized percent) (%)':'Prevalence percentage'}, inplace = True)\n",
    "suicide_latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicides/100k pop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>96</td>\n",
       "      <td>2736025</td>\n",
       "      <td>3.508740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>1</td>\n",
       "      <td>91889</td>\n",
       "      <td>1.088270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>3073</td>\n",
       "      <td>39699624</td>\n",
       "      <td>7.740627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>67</td>\n",
       "      <td>2400033</td>\n",
       "      <td>2.791628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>9</td>\n",
       "      <td>95488</td>\n",
       "      <td>9.425268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>111</td>\n",
       "      <td>7839736</td>\n",
       "      <td>1.415864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>4910</td>\n",
       "      <td>61082942</td>\n",
       "      <td>8.038251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>44189</td>\n",
       "      <td>300078511</td>\n",
       "      <td>14.725813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>630</td>\n",
       "      <td>3190795</td>\n",
       "      <td>19.744296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>2095</td>\n",
       "      <td>27313507</td>\n",
       "      <td>7.670198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      suicides_no  population  suicides/100k pop\n",
       "country                                                         \n",
       "Albania                        96     2736025           3.508740\n",
       "Antigua and Barbuda             1       91889           1.088270\n",
       "Argentina                    3073    39699624           7.740627\n",
       "Armenia                        67     2400033           2.791628\n",
       "Aruba                           9       95488           9.425268\n",
       "...                           ...         ...                ...\n",
       "United Arab Emirates          111     7839736           1.415864\n",
       "United Kingdom               4910    61082942           8.038251\n",
       "United States               44189   300078511          14.725813\n",
       "Uruguay                       630     3190795          19.744296\n",
       "Uzbekistan                   2095    27313507           7.670198\n",
       "\n",
       "[101 rows x 3 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_summary = suicide_latest.groupby('country')[['suicides_no','population']].sum()\n",
    "suicide_summary['suicides/100k pop'] = 100000 * suicide_summary['suicides_no']/suicide_summary['population']\n",
    "suicide_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Quartile rank</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicides/100k pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0</td>\n",
       "      <td>731</td>\n",
       "      <td>5181797</td>\n",
       "      <td>14.107075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0</td>\n",
       "      <td>564</td>\n",
       "      <td>5383060</td>\n",
       "      <td>10.477312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0</td>\n",
       "      <td>590</td>\n",
       "      <td>4882909</td>\n",
       "      <td>12.082961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>268773</td>\n",
       "      <td>14.882447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0</td>\n",
       "      <td>1886</td>\n",
       "      <td>14239554</td>\n",
       "      <td>13.244797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>107</td>\n",
       "      <td>Albania</td>\n",
       "      <td>4.719</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.027</td>\n",
       "      <td>2</td>\n",
       "      <td>96</td>\n",
       "      <td>2736025</td>\n",
       "      <td>3.508740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>116</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>4.559</td>\n",
       "      <td>0.850</td>\n",
       "      <td>1.055</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.064</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>2400033</td>\n",
       "      <td>2.791628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>119</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>4.519</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.164</td>\n",
       "      <td>3</td>\n",
       "      <td>192</td>\n",
       "      <td>3458400</td>\n",
       "      <td>5.551700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>130</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>4.366</td>\n",
       "      <td>0.949</td>\n",
       "      <td>1.265</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.047</td>\n",
       "      <td>3</td>\n",
       "      <td>4015</td>\n",
       "      <td>17891558</td>\n",
       "      <td>22.440751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>133</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>4.332</td>\n",
       "      <td>0.820</td>\n",
       "      <td>1.390</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.010</td>\n",
       "      <td>3</td>\n",
       "      <td>7574</td>\n",
       "      <td>40345446</td>\n",
       "      <td>18.772875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Overall rank Country or region  Score  GDP per capita  Social support  \\\n",
       "0              1           Finland  7.769           1.340           1.587   \n",
       "1              2           Denmark  7.600           1.383           1.573   \n",
       "2              3            Norway  7.554           1.488           1.582   \n",
       "3              4           Iceland  7.494           1.380           1.624   \n",
       "4              5       Netherlands  7.488           1.396           1.522   \n",
       "..           ...               ...    ...             ...             ...   \n",
       "71           107           Albania  4.719           0.947           0.848   \n",
       "72           116           Armenia  4.559           0.850           1.055   \n",
       "73           119           Georgia  4.519           0.886           0.666   \n",
       "74           130         Sri Lanka  4.366           0.949           1.265   \n",
       "75           133           Ukraine  4.332           0.820           1.390   \n",
       "\n",
       "    Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
       "0                     0.986                         0.596       0.153   \n",
       "1                     0.996                         0.592       0.252   \n",
       "2                     1.028                         0.603       0.271   \n",
       "3                     1.026                         0.591       0.354   \n",
       "4                     0.999                         0.557       0.322   \n",
       "..                      ...                           ...         ...   \n",
       "71                    0.874                         0.383       0.178   \n",
       "72                    0.815                         0.283       0.095   \n",
       "73                    0.752                         0.346       0.043   \n",
       "74                    0.831                         0.470       0.244   \n",
       "75                    0.739                         0.178       0.187   \n",
       "\n",
       "    Perceptions of corruption  Quartile rank  suicides_no  population  \\\n",
       "0                       0.393              0          731     5181797   \n",
       "1                       0.410              0          564     5383060   \n",
       "2                       0.341              0          590     4882909   \n",
       "3                       0.118              0           40      268773   \n",
       "4                       0.298              0         1886    14239554   \n",
       "..                        ...            ...          ...         ...   \n",
       "71                      0.027              2           96     2736025   \n",
       "72                      0.064              2           67     2400033   \n",
       "73                      0.164              3          192     3458400   \n",
       "74                      0.047              3         4015    17891558   \n",
       "75                      0.010              3         7574    40345446   \n",
       "\n",
       "    suicides/100k pop  \n",
       "0           14.107075  \n",
       "1           10.477312  \n",
       "2           12.082961  \n",
       "3           14.882447  \n",
       "4           13.244797  \n",
       "..                ...  \n",
       "71           3.508740  \n",
       "72           2.791628  \n",
       "73           5.551700  \n",
       "74          22.440751  \n",
       "75          18.772875  \n",
       "\n",
       "[76 rows x 13 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_suicide = pd.merge(left=happiness_survey, right=suicide_summary, left_on='Country or region', right_on = 'country')\n",
    "H_suicide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(H_suicide['suicides/100k pop'],H_suicide['Score'],c=H_suicide['Quartile rank'])\n",
    "plt.title ('Happiness Report Comparison')\n",
    "plt.xlabel('Suicide rate per 100,000')\n",
    "plt.ylabel('Happiness Score')\n",
    "plt.plot(np.unique(H_suicide['suicides/100k pop']), np.poly1d(np.polyfit(H_suicide['suicides/100k pop'], H_suicide['Score'], 1))(np.unique(H_suicide['suicides/100k pop'])),color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}