World_happiness_prep.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fifteen = pd.read_csv('2015.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.03880</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>5</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.03553</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0  Switzerland  Western Europe               1            7.587   \n",
       "1      Iceland  Western Europe               2            7.561   \n",
       "2      Denmark  Western Europe               3            7.527   \n",
       "3       Norway  Western Europe               4            7.522   \n",
       "4       Canada   North America               5            7.427   \n",
       "\n",
       "   Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0         0.03411                   1.39651  1.34951   \n",
       "1         0.04884                   1.30232  1.40223   \n",
       "2         0.03328                   1.32548  1.36058   \n",
       "3         0.03880                   1.45900  1.33095   \n",
       "4         0.03553                   1.32629  1.32261   \n",
       "\n",
       "   Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                   0.94143  0.66557                        0.41978   \n",
       "1                   0.94784  0.62877                        0.14145   \n",
       "2                   0.87464  0.64938                        0.48357   \n",
       "3                   0.88521  0.66973                        0.36503   \n",
       "4                   0.90563  0.63297                        0.32957   \n",
       "\n",
       "   Generosity  Dystopia Residual  \n",
       "0     0.29678            2.51738  \n",
       "1     0.43630            2.70201  \n",
       "2     0.34139            2.49204  \n",
       "3     0.34699            2.46531  \n",
       "4     0.45811            2.45176  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifteen.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 158 entries, 0 to 157\n",
      "Data columns (total 12 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        158 non-null    object \n",
      " 1   Region                         158 non-null    object \n",
      " 2   Happiness Rank                 158 non-null    int64  \n",
      " 3   Happiness Score                158 non-null    float64\n",
      " 4   Standard Error                 158 non-null    float64\n",
      " 5   Economy (GDP per Capita)       158 non-null    float64\n",
      " 6   Family                         158 non-null    float64\n",
      " 7   Health (Life Expectancy)       158 non-null    float64\n",
      " 8   Freedom                        158 non-null    float64\n",
      " 9   Trust (Government Corruption)  158 non-null    float64\n",
      " 10  Generosity                     158 non-null    float64\n",
      " 11  Dystopia Residual              158 non-null    float64\n",
      "dtypes: float64(9), int64(1), object(2)\n",
      "memory usage: 14.9+ KB\n"
     ]
    }
   ],
   "source": [
    "fifteen.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Region', 'Happiness Rank', 'Happiness Score',\n",
       "       'Economy (GDP per Capita)', 'Family', 'Health (Life Expectancy)',\n",
       "       'Freedom', 'Trust (Government Corruption)', 'Generosity',\n",
       "       'Dystopia Residual'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fix2016.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "fifteen.drop(columns=['Standard Error'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 158 entries, 0 to 157\n",
      "Data columns (total 11 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        158 non-null    object \n",
      " 1   Region                         158 non-null    object \n",
      " 2   Happiness Rank                 158 non-null    int64  \n",
      " 3   Happiness Score                158 non-null    float64\n",
      " 4   Economy (GDP per Capita)       158 non-null    float64\n",
      " 5   Family                         158 non-null    float64\n",
      " 6   Health (Life Expectancy)       158 non-null    float64\n",
      " 7   Freedom                        158 non-null    float64\n",
      " 8   Trust (Government Corruption)  158 non-null    float64\n",
      " 9   Generosity                     158 non-null    float64\n",
      " 10  Dystopia Residual              158 non-null    float64\n",
      "dtypes: float64(8), int64(1), object(2)\n",
      "memory usage: 13.7+ KB\n"
     ]
    }
   ],
   "source": [
    "fifteen.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "158"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifteen['Country'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "fix2015 =  fifteen.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "fix2015['year'] = 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.522</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>5</td>\n",
       "      <td>7.427</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0  Switzerland  Western Europe               1            7.587   \n",
       "1      Iceland  Western Europe               2            7.561   \n",
       "2      Denmark  Western Europe               3            7.527   \n",
       "3       Norway  Western Europe               4            7.522   \n",
       "4       Canada   North America               5            7.427   \n",
       "\n",
       "   Economy (GDP per Capita)   Family  Health (Life Expectancy)  Freedom  \\\n",
       "0                   1.39651  1.34951                   0.94143  0.66557   \n",
       "1                   1.30232  1.40223                   0.94784  0.62877   \n",
       "2                   1.32548  1.36058                   0.87464  0.64938   \n",
       "3                   1.45900  1.33095                   0.88521  0.66973   \n",
       "4                   1.32629  1.32261                   0.90563  0.63297   \n",
       "\n",
       "   Trust (Government Corruption)  Generosity  Dystopia Residual  year  \n",
       "0                        0.41978     0.29678            2.51738  2015  \n",
       "1                        0.14145     0.43630            2.70201  2015  \n",
       "2                        0.48357     0.34139            2.49204  2015  \n",
       "3                        0.36503     0.34699            2.46531  2015  \n",
       "4                        0.32957     0.45811            2.45176  2015  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fix2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 158 entries, 0 to 157\n",
      "Data columns (total 12 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        158 non-null    object \n",
      " 1   Region                         158 non-null    object \n",
      " 2   Happiness Rank                 158 non-null    int64  \n",
      " 3   Happiness Score                158 non-null    float64\n",
      " 4   Economy (GDP per Capita)       158 non-null    float64\n",
      " 5   Family                         158 non-null    float64\n",
      " 6   Health (Life Expectancy)       158 non-null    float64\n",
      " 7   Freedom                        158 non-null    float64\n",
      " 8   Trust (Government Corruption)  158 non-null    float64\n",
      " 9   Generosity                     158 non-null    float64\n",
      " 10  Dystopia Residual              158 non-null    float64\n",
      " 11  year                           158 non-null    int64  \n",
      "dtypes: float64(8), int64(2), object(2)\n",
      "memory usage: 14.9+ KB\n"
     ]
    }
   ],
   "source": [
    "fix2015.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "fix2015.rename(columns={'year': 'Year'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "======================================================================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sixteen = pd.read_csv('2016.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Lower Confidence Interval</th>\n",
       "      <th>Upper Confidence Interval</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.526</td>\n",
       "      <td>7.460</td>\n",
       "      <td>7.592</td>\n",
       "      <td>1.44178</td>\n",
       "      <td>1.16374</td>\n",
       "      <td>0.79504</td>\n",
       "      <td>0.57941</td>\n",
       "      <td>0.44453</td>\n",
       "      <td>0.36171</td>\n",
       "      <td>2.73939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.509</td>\n",
       "      <td>7.428</td>\n",
       "      <td>7.590</td>\n",
       "      <td>1.52733</td>\n",
       "      <td>1.14524</td>\n",
       "      <td>0.86303</td>\n",
       "      <td>0.58557</td>\n",
       "      <td>0.41203</td>\n",
       "      <td>0.28083</td>\n",
       "      <td>2.69463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.501</td>\n",
       "      <td>7.333</td>\n",
       "      <td>7.669</td>\n",
       "      <td>1.42666</td>\n",
       "      <td>1.18326</td>\n",
       "      <td>0.86733</td>\n",
       "      <td>0.56624</td>\n",
       "      <td>0.14975</td>\n",
       "      <td>0.47678</td>\n",
       "      <td>2.83137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.498</td>\n",
       "      <td>7.421</td>\n",
       "      <td>7.575</td>\n",
       "      <td>1.57744</td>\n",
       "      <td>1.12690</td>\n",
       "      <td>0.79579</td>\n",
       "      <td>0.59609</td>\n",
       "      <td>0.35776</td>\n",
       "      <td>0.37895</td>\n",
       "      <td>2.66465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>5</td>\n",
       "      <td>7.413</td>\n",
       "      <td>7.351</td>\n",
       "      <td>7.475</td>\n",
       "      <td>1.40598</td>\n",
       "      <td>1.13464</td>\n",
       "      <td>0.81091</td>\n",
       "      <td>0.57104</td>\n",
       "      <td>0.41004</td>\n",
       "      <td>0.25492</td>\n",
       "      <td>2.82596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0      Denmark  Western Europe               1            7.526   \n",
       "1  Switzerland  Western Europe               2            7.509   \n",
       "2      Iceland  Western Europe               3            7.501   \n",
       "3       Norway  Western Europe               4            7.498   \n",
       "4      Finland  Western Europe               5            7.413   \n",
       "\n",
       "   Lower Confidence Interval  Upper Confidence Interval  \\\n",
       "0                      7.460                      7.592   \n",
       "1                      7.428                      7.590   \n",
       "2                      7.333                      7.669   \n",
       "3                      7.421                      7.575   \n",
       "4                      7.351                      7.475   \n",
       "\n",
       "   Economy (GDP per Capita)   Family  Health (Life Expectancy)  Freedom  \\\n",
       "0                   1.44178  1.16374                   0.79504  0.57941   \n",
       "1                   1.52733  1.14524                   0.86303  0.58557   \n",
       "2                   1.42666  1.18326                   0.86733  0.56624   \n",
       "3                   1.57744  1.12690                   0.79579  0.59609   \n",
       "4                   1.40598  1.13464                   0.81091  0.57104   \n",
       "\n",
       "   Trust (Government Corruption)  Generosity  Dystopia Residual  \n",
       "0                        0.44453     0.36171            2.73939  \n",
       "1                        0.41203     0.28083            2.69463  \n",
       "2                        0.14975     0.47678            2.83137  \n",
       "3                        0.35776     0.37895            2.66465  \n",
       "4                        0.41004     0.25492            2.82596  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sixteen.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 157 entries, 0 to 156\n",
      "Data columns (total 13 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        157 non-null    object \n",
      " 1   Region                         157 non-null    object \n",
      " 2   Happiness Rank                 157 non-null    int64  \n",
      " 3   Happiness Score                157 non-null    float64\n",
      " 4   Lower Confidence Interval      157 non-null    float64\n",
      " 5   Upper Confidence Interval      157 non-null    float64\n",
      " 6   Economy (GDP per Capita)       157 non-null    float64\n",
      " 7   Family                         157 non-null    float64\n",
      " 8   Health (Life Expectancy)       157 non-null    float64\n",
      " 9   Freedom                        157 non-null    float64\n",
      " 10  Trust (Government Corruption)  157 non-null    float64\n",
      " 11  Generosity                     157 non-null    float64\n",
      " 12  Dystopia Residual              157 non-null    float64\n",
      "dtypes: float64(10), int64(1), object(2)\n",
      "memory usage: 16.1+ KB\n"
     ]
    }
   ],
   "source": [
    "sixteen.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Region', 'Happiness Rank', 'Happiness Score',\n",
       "       'Standard Error', 'Economy (GDP per Capita)', 'Family',\n",
       "       'Health (Life Expectancy)', 'Freedom', 'Trust (Government Corruption)',\n",
       "       'Generosity', 'Dystopia Residual'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifteen.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fix2016 = sixteen[['Country', 'Region', 'Happiness Rank', 'Happiness Score', 'Economy (GDP per Capita)', 'Family', 'Health (Life Expectancy)', 'Freedom', 'Trust (Government Corruption)', 'Generosity', 'Dystopia Residual']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 157 entries, 0 to 156\n",
      "Data columns (total 11 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        157 non-null    object \n",
      " 1   Region                         157 non-null    object \n",
      " 2   Happiness Rank                 157 non-null    int64  \n",
      " 3   Happiness Score                157 non-null    float64\n",
      " 4   Economy (GDP per Capita)       157 non-null    float64\n",
      " 5   Family                         157 non-null    float64\n",
      " 6   Health (Life Expectancy)       157 non-null    float64\n",
      " 7   Freedom                        157 non-null    float64\n",
      " 8   Trust (Government Corruption)  157 non-null    float64\n",
      " 9   Generosity                     157 non-null    float64\n",
      " 10  Dystopia Residual              157 non-null    float64\n",
      "dtypes: float64(8), int64(1), object(2)\n",
      "memory usage: 13.6+ KB\n"
     ]
    }
   ],
   "source": [
    "fix2016.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 158 entries, 0 to 157\n",
      "Data columns (total 11 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        158 non-null    object \n",
      " 1   Region                         158 non-null    object \n",
      " 2   Happiness Rank                 158 non-null    int64  \n",
      " 3   Happiness Score                158 non-null    float64\n",
      " 4   Economy (GDP per Capita)       158 non-null    float64\n",
      " 5   Family                         158 non-null    float64\n",
      " 6   Health (Life Expectancy)       158 non-null    float64\n",
      " 7   Freedom                        158 non-null    float64\n",
      " 8   Trust (Government Corruption)  158 non-null    float64\n",
      " 9   Generosity                     158 non-null    float64\n",
      " 10  Dystopia Residual              158 non-null    float64\n",
      "dtypes: float64(8), int64(1), object(2)\n",
      "memory usage: 13.7+ KB\n"
     ]
    }
   ],
   "source": [
    "fix2015.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-33-5376083263fb>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  fix2016['Year'] = 2016\n"
     ]
    }
   ],
   "source": [
    "fix2016['Year'] = 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.526</td>\n",
       "      <td>1.44178</td>\n",
       "      <td>1.16374</td>\n",
       "      <td>0.79504</td>\n",
       "      <td>0.57941</td>\n",
       "      <td>0.44453</td>\n",
       "      <td>0.36171</td>\n",
       "      <td>2.73939</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.509</td>\n",
       "      <td>1.52733</td>\n",
       "      <td>1.14524</td>\n",
       "      <td>0.86303</td>\n",
       "      <td>0.58557</td>\n",
       "      <td>0.41203</td>\n",
       "      <td>0.28083</td>\n",
       "      <td>2.69463</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.501</td>\n",
       "      <td>1.42666</td>\n",
       "      <td>1.18326</td>\n",
       "      <td>0.86733</td>\n",
       "      <td>0.56624</td>\n",
       "      <td>0.14975</td>\n",
       "      <td>0.47678</td>\n",
       "      <td>2.83137</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.498</td>\n",
       "      <td>1.57744</td>\n",
       "      <td>1.12690</td>\n",
       "      <td>0.79579</td>\n",
       "      <td>0.59609</td>\n",
       "      <td>0.35776</td>\n",
       "      <td>0.37895</td>\n",
       "      <td>2.66465</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>5</td>\n",
       "      <td>7.413</td>\n",
       "      <td>1.40598</td>\n",
       "      <td>1.13464</td>\n",
       "      <td>0.81091</td>\n",
       "      <td>0.57104</td>\n",
       "      <td>0.41004</td>\n",
       "      <td>0.25492</td>\n",
       "      <td>2.82596</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0      Denmark  Western Europe               1            7.526   \n",
       "1  Switzerland  Western Europe               2            7.509   \n",
       "2      Iceland  Western Europe               3            7.501   \n",
       "3       Norway  Western Europe               4            7.498   \n",
       "4      Finland  Western Europe               5            7.413   \n",
       "\n",
       "   Economy (GDP per Capita)   Family  Health (Life Expectancy)  Freedom  \\\n",
       "0                   1.44178  1.16374                   0.79504  0.57941   \n",
       "1                   1.52733  1.14524                   0.86303  0.58557   \n",
       "2                   1.42666  1.18326                   0.86733  0.56624   \n",
       "3                   1.57744  1.12690                   0.79579  0.59609   \n",
       "4                   1.40598  1.13464                   0.81091  0.57104   \n",
       "\n",
       "   Trust (Government Corruption)  Generosity  Dystopia Residual  Year  \n",
       "0                        0.44453     0.36171            2.73939  2016  \n",
       "1                        0.41203     0.28083            2.69463  2016  \n",
       "2                        0.14975     0.47678            2.83137  2016  \n",
       "3                        0.35776     0.37895            2.66465  2016  \n",
       "4                        0.41004     0.25492            2.82596  2016  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fix2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2016], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fix2016['Year'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "============================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "fix_1516 = pd.concat([fix2015, fix2016])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                          0\n",
       "Region                           0\n",
       "Happiness Rank                   0\n",
       "Happiness Score                  0\n",
       "Economy (GDP per Capita)         0\n",
       "Family                           0\n",
       "Health (Life Expectancy)         0\n",
       "Freedom                          0\n",
       "Trust (Government Corruption)    0\n",
       "Generosity                       0\n",
       "Dystopia Residual                0\n",
       "Year                             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fix_1516.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1516 = fix_1516.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "================================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "seventeen = pd.read_csv('2017.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Country</th>\n",
       "      <th>Happiness.Rank</th>\n",
       "      <th>Happiness.Score</th>\n",
       "      <th>Whisker.high</th>\n",
       "      <th>Whisker.low</th>\n",
       "      <th>Economy..GDP.per.Capita.</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health..Life.Expectancy.</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust..Government.Corruption.</th>\n",
       "      <th>Dystopia.Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Norway</td>\n",
       "      <td>1</td>\n",
       "      <td>7.537</td>\n",
       "      <td>7.594445</td>\n",
       "      <td>7.479556</td>\n",
       "      <td>1.616463</td>\n",
       "      <td>1.533524</td>\n",
       "      <td>0.796667</td>\n",
       "      <td>0.635423</td>\n",
       "      <td>0.362012</td>\n",
       "      <td>0.315964</td>\n",
       "      <td>2.277027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>2</td>\n",
       "      <td>7.522</td>\n",
       "      <td>7.581728</td>\n",
       "      <td>7.462272</td>\n",
       "      <td>1.482383</td>\n",
       "      <td>1.551122</td>\n",
       "      <td>0.792566</td>\n",
       "      <td>0.626007</td>\n",
       "      <td>0.355280</td>\n",
       "      <td>0.400770</td>\n",
       "      <td>2.313707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>3</td>\n",
       "      <td>7.504</td>\n",
       "      <td>7.622030</td>\n",
       "      <td>7.385970</td>\n",
       "      <td>1.480633</td>\n",
       "      <td>1.610574</td>\n",
       "      <td>0.833552</td>\n",
       "      <td>0.627163</td>\n",
       "      <td>0.475540</td>\n",
       "      <td>0.153527</td>\n",
       "      <td>2.322715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>4</td>\n",
       "      <td>7.494</td>\n",
       "      <td>7.561772</td>\n",
       "      <td>7.426227</td>\n",
       "      <td>1.564980</td>\n",
       "      <td>1.516912</td>\n",
       "      <td>0.858131</td>\n",
       "      <td>0.620071</td>\n",
       "      <td>0.290549</td>\n",
       "      <td>0.367007</td>\n",
       "      <td>2.276716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finland</td>\n",
       "      <td>5</td>\n",
       "      <td>7.469</td>\n",
       "      <td>7.527542</td>\n",
       "      <td>7.410458</td>\n",
       "      <td>1.443572</td>\n",
       "      <td>1.540247</td>\n",
       "      <td>0.809158</td>\n",
       "      <td>0.617951</td>\n",
       "      <td>0.245483</td>\n",
       "      <td>0.382612</td>\n",
       "      <td>2.430182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Happiness.Rank  Happiness.Score  Whisker.high  Whisker.low  \\\n",
       "0       Norway               1            7.537      7.594445     7.479556   \n",
       "1      Denmark               2            7.522      7.581728     7.462272   \n",
       "2      Iceland               3            7.504      7.622030     7.385970   \n",
       "3  Switzerland               4            7.494      7.561772     7.426227   \n",
       "4      Finland               5            7.469      7.527542     7.410458   \n",
       "\n",
       "   Economy..GDP.per.Capita.    Family  Health..Life.Expectancy.   Freedom  \\\n",
       "0                  1.616463  1.533524                  0.796667  0.635423   \n",
       "1                  1.482383  1.551122                  0.792566  0.626007   \n",
       "2                  1.480633  1.610574                  0.833552  0.627163   \n",
       "3                  1.564980  1.516912                  0.858131  0.620071   \n",
       "4                  1.443572  1.540247                  0.809158  0.617951   \n",
       "\n",
       "   Generosity  Trust..Government.Corruption.  Dystopia.Residual  \n",
       "0    0.362012                       0.315964           2.277027  \n",
       "1    0.355280                       0.400770           2.313707  \n",
       "2    0.475540                       0.153527           2.322715  \n",
       "3    0.290549                       0.367007           2.276716  \n",
       "4    0.245483                       0.382612           2.430182  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seventeen.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 155 entries, 0 to 154\n",
      "Data columns (total 12 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        155 non-null    object \n",
      " 1   Happiness.Rank                 155 non-null    int64  \n",
      " 2   Happiness.Score                155 non-null    float64\n",
      " 3   Whisker.high                   155 non-null    float64\n",
      " 4   Whisker.low                    155 non-null    float64\n",
      " 5   Economy..GDP.per.Capita.       155 non-null    float64\n",
      " 6   Family                         155 non-null    float64\n",
      " 7   Health..Life.Expectancy.       155 non-null    float64\n",
      " 8   Freedom                        155 non-null    float64\n",
      " 9   Generosity                     155 non-null    float64\n",
      " 10  Trust..Government.Corruption.  155 non-null    float64\n",
      " 11  Dystopia.Residual              155 non-null    float64\n",
      "dtypes: float64(10), int64(1), object(1)\n",
      "memory usage: 14.7+ KB\n"
     ]
    }
   ],
   "source": [
    "seventeen.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Happiness.Rank', 'Happiness.Score', 'Whisker.high',\n",
       "       'Whisker.low', 'Economy..GDP.per.Capita.', 'Family',\n",
       "       'Health..Life.Expectancy.', 'Freedom', 'Generosity',\n",
       "       'Trust..Government.Corruption.', 'Dystopia.Residual'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seventeen.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Region', 'Happiness Rank', 'Happiness Score',\n",
       "       'Economy (GDP per Capita)', 'Family', 'Health (Life Expectancy)',\n",
       "       'Freedom', 'Trust (Government Corruption)', 'Generosity',\n",
       "       'Dystopia Residual', 'Year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fix2015.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "seventeen.rename(columns={'Happiness.Rank': 'Happiness Rank', 'Happiness.Score': 'Happiness Score', 'Economy..GDP.per.Capita.': 'Economy (GDP per Capita)', 'Health..Life.Expectancy.': 'Health (Life Expectancy)', 'Trust..Government.Corruption.': 'Trust (Government Corruption)', 'Dystopia.Residual': 'Dystopia Residual'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 155 entries, 0 to 154\n",
      "Data columns (total 12 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        155 non-null    object \n",
      " 1   Happiness Rank                 155 non-null    int64  \n",
      " 2   Happiness Score                155 non-null    float64\n",
      " 3   Whisker.high                   155 non-null    float64\n",
      " 4   Whisker.low                    155 non-null    float64\n",
      " 5   Economy (GDP per Capita)       155 non-null    float64\n",
      " 6   Family                         155 non-null    float64\n",
      " 7   Health (Life Expectancy)       155 non-null    float64\n",
      " 8   Freedom                        155 non-null    float64\n",
      " 9   Generosity                     155 non-null    float64\n",
      " 10  Trust (Government Corruption)  155 non-null    float64\n",
      " 11  Dystopia Residual              155 non-null    float64\n",
      "dtypes: float64(10), int64(1), object(1)\n",
      "memory usage: 15.7+ KB\n"
     ]
    }
   ],
   "source": [
    "seventeen.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "fix2017 = seventeen.drop(columns=['Whisker.high', 'Whisker.low'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 155 entries, 0 to 154\n",
      "Data columns (total 10 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        155 non-null    object \n",
      " 1   Happiness Rank                 155 non-null    int64  \n",
      " 2   Happiness Score                155 non-null    float64\n",
      " 3   Economy (GDP per Capita)       155 non-null    float64\n",
      " 4   Family                         155 non-null    float64\n",
      " 5   Health (Life Expectancy)       155 non-null    float64\n",
      " 6   Freedom                        155 non-null    float64\n",
      " 7   Generosity                     155 non-null    float64\n",
      " 8   Trust (Government Corruption)  155 non-null    float64\n",
      " 9   Dystopia Residual              155 non-null    float64\n",
      "dtypes: float64(8), int64(1), object(1)\n",
      "memory usage: 13.3+ KB\n"
     ]
    }
   ],
   "source": [
    "fix2017.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = fix2015[['Country', 'Region']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region\n",
       "0  Switzerland  Western Europe\n",
       "1      Iceland  Western Europe\n",
       "2      Denmark  Western Europe\n",
       "3       Norway  Western Europe\n",
       "4       Canada   North America"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "158"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg['Country'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2017 = pd.merge(reg, fix2017, on='Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Norway</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>7</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0  Switzerland  Western Europe               4            7.494   \n",
       "1      Iceland  Western Europe               3            7.504   \n",
       "2      Denmark  Western Europe               2            7.522   \n",
       "3       Norway  Western Europe               1            7.537   \n",
       "4       Canada   North America               7            7.316   \n",
       "\n",
       "   Economy (GDP per Capita)    Family  Health (Life Expectancy)   Freedom  \\\n",
       "0                  1.564980  1.516912                  0.858131  0.620071   \n",
       "1                  1.480633  1.610574                  0.833552  0.627163   \n",
       "2                  1.482383  1.551122                  0.792566  0.626007   \n",
       "3                  1.616463  1.533524                  0.796667  0.635423   \n",
       "4                  1.479204  1.481349                  0.834558  0.611101   \n",
       "\n",
       "   Generosity  Trust (Government Corruption)  Dystopia Residual  \n",
       "0    0.290549                       0.367007           2.276716  \n",
       "1    0.475540                       0.153527           2.322715  \n",
       "2    0.355280                       0.400770           2.313707  \n",
       "3    0.362012                       0.315964           2.277027  \n",
       "4    0.435540                       0.287372           2.187264  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2017['Year'] = 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Switzerland</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.522</td>\n",
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       "      <td>2017</td>\n",
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       "    <tr>\n",
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       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.537</td>\n",
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       "      <th>4</th>\n",
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       "      <td>North America</td>\n",
       "      <td>7</td>\n",
       "      <td>7.316</td>\n",
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       "      <td>0.611101</td>\n",
       "      <td>0.435540</td>\n",
       "      <td>0.287372</td>\n",
       "      <td>2.187264</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0  Switzerland  Western Europe               4            7.494   \n",
       "1      Iceland  Western Europe               3            7.504   \n",
       "2      Denmark  Western Europe               2            7.522   \n",
       "3       Norway  Western Europe               1            7.537   \n",
       "4       Canada   North America               7            7.316   \n",
       "\n",
       "   Economy (GDP per Capita)    Family  Health (Life Expectancy)   Freedom  \\\n",
       "0                  1.564980  1.516912                  0.858131  0.620071   \n",
       "1                  1.480633  1.610574                  0.833552  0.627163   \n",
       "2                  1.482383  1.551122                  0.792566  0.626007   \n",
       "3                  1.616463  1.533524                  0.796667  0.635423   \n",
       "4                  1.479204  1.481349                  0.834558  0.611101   \n",
       "\n",
       "   Generosity  Trust (Government Corruption)  Dystopia Residual  Year  \n",
       "0    0.290549                       0.367007           2.276716  2017  \n",
       "1    0.475540                       0.153527           2.322715  2017  \n",
       "2    0.355280                       0.400770           2.313707  2017  \n",
       "3    0.362012                       0.315964           2.277027  2017  \n",
       "4    0.435540                       0.287372           2.187264  2017  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 149 entries, 0 to 148\n",
      "Data columns (total 12 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        149 non-null    object \n",
      " 1   Region                         149 non-null    object \n",
      " 2   Happiness Rank                 149 non-null    int64  \n",
      " 3   Happiness Score                149 non-null    float64\n",
      " 4   Economy (GDP per Capita)       149 non-null    float64\n",
      " 5   Family                         149 non-null    float64\n",
      " 6   Health (Life Expectancy)       149 non-null    float64\n",
      " 7   Freedom                        149 non-null    float64\n",
      " 8   Generosity                     149 non-null    float64\n",
      " 9   Trust (Government Corruption)  149 non-null    float64\n",
      " 10  Dystopia Residual              149 non-null    float64\n",
      " 11  Year                           149 non-null    int64  \n",
      "dtypes: float64(8), int64(2), object(2)\n",
      "memory usage: 15.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df2017.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 315 entries, 0 to 156\n",
      "Data columns (total 12 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        315 non-null    object \n",
      " 1   Region                         315 non-null    object \n",
      " 2   Happiness Rank                 315 non-null    int64  \n",
      " 3   Happiness Score                315 non-null    float64\n",
      " 4   Economy (GDP per Capita)       315 non-null    float64\n",
      " 5   Family                         315 non-null    float64\n",
      " 6   Health (Life Expectancy)       315 non-null    float64\n",
      " 7   Freedom                        315 non-null    float64\n",
      " 8   Trust (Government Corruption)  315 non-null    float64\n",
      " 9   Generosity                     315 non-null    float64\n",
      " 10  Dystopia Residual              315 non-null    float64\n",
      " 11  Year                           315 non-null    int64  \n",
      "dtypes: float64(8), int64(2), object(2)\n",
      "memory usage: 32.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df1516.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1516 = df1516[['Country', 'Region', 'Happiness Rank', 'Happiness Score', 'Economy (GDP per Capita)', 'Family', 'Health (Life Expectancy)', 'Freedom', 'Generosity', 'Trust (Government Corruption)', 'Dystopia Residual', 'Year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = pd.concat([df1516, df2017])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "====================================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "eighteen = pd.read_csv('2018.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.632</td>\n",
       "      <td>1.305</td>\n",
       "      <td>1.592</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.594</td>\n",
       "      <td>1.456</td>\n",
       "      <td>1.582</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.555</td>\n",
       "      <td>1.351</td>\n",
       "      <td>1.590</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.495</td>\n",
       "      <td>1.343</td>\n",
       "      <td>1.644</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>7.487</td>\n",
       "      <td>1.420</td>\n",
       "      <td>1.549</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Overall rank Country or region  Score  GDP per capita  Social support  \\\n",
       "0             1           Finland  7.632           1.305           1.592   \n",
       "1             2            Norway  7.594           1.456           1.582   \n",
       "2             3           Denmark  7.555           1.351           1.590   \n",
       "3             4           Iceland  7.495           1.343           1.644   \n",
       "4             5       Switzerland  7.487           1.420           1.549   \n",
       "\n",
       "   Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
       "0                    0.874                         0.681       0.202   \n",
       "1                    0.861                         0.686       0.286   \n",
       "2                    0.868                         0.683       0.284   \n",
       "3                    0.914                         0.677       0.353   \n",
       "4                    0.927                         0.660       0.256   \n",
       "\n",
       "   Perceptions of corruption  \n",
       "0                      0.393  \n",
       "1                      0.340  \n",
       "2                      0.408  \n",
       "3                      0.138  \n",
       "4                      0.357  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eighteen.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 156 entries, 0 to 155\n",
      "Data columns (total 9 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Overall rank                  156 non-null    int64  \n",
      " 1   Country or region             156 non-null    object \n",
      " 2   Score                         156 non-null    float64\n",
      " 3   GDP per capita                156 non-null    float64\n",
      " 4   Social support                156 non-null    float64\n",
      " 5   Healthy life expectancy       156 non-null    float64\n",
      " 6   Freedom to make life choices  156 non-null    float64\n",
      " 7   Generosity                    156 non-null    float64\n",
      " 8   Perceptions of corruption     155 non-null    float64\n",
      "dtypes: float64(7), int64(1), object(1)\n",
      "memory usage: 11.1+ KB\n"
     ]
    }
   ],
   "source": [
    "eighteen.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 464 entries, 0 to 148\n",
      "Data columns (total 12 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        464 non-null    object \n",
      " 1   Region                         464 non-null    object \n",
      " 2   Happiness Rank                 464 non-null    int64  \n",
      " 3   Happiness Score                464 non-null    float64\n",
      " 4   Economy (GDP per Capita)       464 non-null    float64\n",
      " 5   Family                         464 non-null    float64\n",
      " 6   Health (Life Expectancy)       464 non-null    float64\n",
      " 7   Freedom                        464 non-null    float64\n",
      " 8   Generosity                     464 non-null    float64\n",
      " 9   Trust (Government Corruption)  464 non-null    float64\n",
      " 10  Dystopia Residual              464 non-null    float64\n",
      " 11  Year                           464 non-null    int64  \n",
      "dtypes: float64(8), int64(2), object(2)\n",
      "memory usage: 47.1+ KB\n"
     ]
    }
   ],
   "source": [
    "temp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp.rename(columns={'Family': 'Social support'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "============================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "eight = eighteen[['Country or region', 'Overall rank', 'Score', 'GDP per capita', 'Healthy life expectancy', 'Freedom to make life choices', 'Generosity', 'Perceptions of corruption', 'Social support']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Overall rank</th>\n",
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       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Social support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>1</td>\n",
       "      <td>7.632</td>\n",
       "      <td>1.305</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.393</td>\n",
       "      <td>1.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Norway</td>\n",
       "      <td>2</td>\n",
       "      <td>7.594</td>\n",
       "      <td>1.456</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.340</td>\n",
       "      <td>1.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>3</td>\n",
       "      <td>7.555</td>\n",
       "      <td>1.351</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.408</td>\n",
       "      <td>1.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>4</td>\n",
       "      <td>7.495</td>\n",
       "      <td>1.343</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.138</td>\n",
       "      <td>1.644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>5</td>\n",
       "      <td>7.487</td>\n",
       "      <td>1.420</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.357</td>\n",
       "      <td>1.549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country or region  Overall rank  Score  GDP per capita  \\\n",
       "0           Finland             1  7.632           1.305   \n",
       "1            Norway             2  7.594           1.456   \n",
       "2           Denmark             3  7.555           1.351   \n",
       "3           Iceland             4  7.495           1.343   \n",
       "4       Switzerland             5  7.487           1.420   \n",
       "\n",
       "   Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
       "0                    0.874                         0.681       0.202   \n",
       "1                    0.861                         0.686       0.286   \n",
       "2                    0.868                         0.683       0.284   \n",
       "3                    0.914                         0.677       0.353   \n",
       "4                    0.927                         0.660       0.256   \n",
       "\n",
       "   Perceptions of corruption  Social support  \n",
       "0                      0.393           1.592  \n",
       "1                      0.340           1.582  \n",
       "2                      0.408           1.590  \n",
       "3                      0.138           1.644  \n",
       "4                      0.357           1.549  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eight.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "eight = eight.rename(columns={'Country or region': 'Country', 'Overall rank': 'Happiness Rank', 'Score': 'Happiness Score', 'GDP per capita': 'Economy (GDP per Capita)', 'Healthy life expectancy': 'Health (Life Expectancy)', 'Freedom to make life choices': 'Freedom', 'Perceptions of corruption': 'Trust (Government Corruption)'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Social support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>1</td>\n",
       "      <td>7.632</td>\n",
       "      <td>1.305</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.393</td>\n",
       "      <td>1.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Norway</td>\n",
       "      <td>2</td>\n",
       "      <td>7.594</td>\n",
       "      <td>1.456</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.340</td>\n",
       "      <td>1.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>3</td>\n",
       "      <td>7.555</td>\n",
       "      <td>1.351</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.408</td>\n",
       "      <td>1.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>4</td>\n",
       "      <td>7.495</td>\n",
       "      <td>1.343</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.138</td>\n",
       "      <td>1.644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>5</td>\n",
       "      <td>7.487</td>\n",
       "      <td>1.420</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.357</td>\n",
       "      <td>1.549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Happiness Rank  Happiness Score  Economy (GDP per Capita)  \\\n",
       "0      Finland               1            7.632                     1.305   \n",
       "1       Norway               2            7.594                     1.456   \n",
       "2      Denmark               3            7.555                     1.351   \n",
       "3      Iceland               4            7.495                     1.343   \n",
       "4  Switzerland               5            7.487                     1.420   \n",
       "\n",
       "   Health (Life Expectancy)  Freedom  Generosity  \\\n",
       "0                     0.874    0.681       0.202   \n",
       "1                     0.861    0.686       0.286   \n",
       "2                     0.868    0.683       0.284   \n",
       "3                     0.914    0.677       0.353   \n",
       "4                     0.927    0.660       0.256   \n",
       "\n",
       "   Trust (Government Corruption)  Social support  \n",
       "0                          0.393           1.592  \n",
       "1                          0.340           1.582  \n",
       "2                          0.408           1.590  \n",
       "3                          0.138           1.644  \n",
       "4                          0.357           1.549  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eight.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "eight['Year'] = 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "df18 = pd.merge(reg, eight, on ='Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>5</td>\n",
       "      <td>7.487</td>\n",
       "      <td>1.420</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.357</td>\n",
       "      <td>1.549</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.495</td>\n",
       "      <td>1.343</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.138</td>\n",
       "      <td>1.644</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.555</td>\n",
       "      <td>1.351</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.408</td>\n",
       "      <td>1.590</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.594</td>\n",
       "      <td>1.456</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.340</td>\n",
       "      <td>1.582</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>7</td>\n",
       "      <td>7.328</td>\n",
       "      <td>1.330</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.291</td>\n",
       "      <td>1.532</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0  Switzerland  Western Europe               5            7.487   \n",
       "1      Iceland  Western Europe               4            7.495   \n",
       "2      Denmark  Western Europe               3            7.555   \n",
       "3       Norway  Western Europe               2            7.594   \n",
       "4       Canada   North America               7            7.328   \n",
       "\n",
       "   Economy (GDP per Capita)  Health (Life Expectancy)  Freedom  Generosity  \\\n",
       "0                     1.420                     0.927    0.660       0.256   \n",
       "1                     1.343                     0.914    0.677       0.353   \n",
       "2                     1.351                     0.868    0.683       0.284   \n",
       "3                     1.456                     0.861    0.686       0.286   \n",
       "4                     1.330                     0.896    0.653       0.321   \n",
       "\n",
       "   Trust (Government Corruption)  Social support  Year  \n",
       "0                          0.357           1.549  2018  \n",
       "1                          0.138           1.644  2018  \n",
       "2                          0.408           1.590  2018  \n",
       "3                          0.340           1.582  2018  \n",
       "4                          0.291           1.532  2018  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df18.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 150 entries, 0 to 149\n",
      "Data columns (total 11 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        150 non-null    object \n",
      " 1   Region                         150 non-null    object \n",
      " 2   Happiness Rank                 150 non-null    int64  \n",
      " 3   Happiness Score                150 non-null    float64\n",
      " 4   Economy (GDP per Capita)       150 non-null    float64\n",
      " 5   Health (Life Expectancy)       150 non-null    float64\n",
      " 6   Freedom                        150 non-null    float64\n",
      " 7   Generosity                     150 non-null    float64\n",
      " 8   Trust (Government Corruption)  149 non-null    float64\n",
      " 9   Social support                 150 non-null    float64\n",
      " 10  Year                           150 non-null    int64  \n",
      "dtypes: float64(7), int64(2), object(2)\n",
      "memory usage: 14.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df18.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                          0\n",
       "Region                           0\n",
       "Happiness Rank                   0\n",
       "Happiness Score                  0\n",
       "Economy (GDP per Capita)         0\n",
       "Health (Life Expectancy)         0\n",
       "Freedom                          0\n",
       "Generosity                       0\n",
       "Trust (Government Corruption)    1\n",
       "Social support                   0\n",
       "Year                             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df18.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "======================================================================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = pd.concat([temp, df18])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 614 entries, 0 to 149\n",
      "Data columns (total 12 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        614 non-null    object \n",
      " 1   Region                         614 non-null    object \n",
      " 2   Happiness Rank                 614 non-null    int64  \n",
      " 3   Happiness Score                614 non-null    float64\n",
      " 4   Economy (GDP per Capita)       614 non-null    float64\n",
      " 5   Social support                 614 non-null    float64\n",
      " 6   Health (Life Expectancy)       614 non-null    float64\n",
      " 7   Freedom                        614 non-null    float64\n",
      " 8   Generosity                     614 non-null    float64\n",
      " 9   Trust (Government Corruption)  613 non-null    float64\n",
      " 10  Dystopia Residual              464 non-null    float64\n",
      " 11  Year                           614 non-null    int64  \n",
      "dtypes: float64(8), int64(2), object(2)\n",
      "memory usage: 62.4+ KB\n"
     ]
    }
   ],
   "source": [
    "temp.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "==================================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "nineteen = pd.read_csv('2019.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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",
       "    </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",
       "  </tbody>\n",
       "</table>\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",
       "   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",
       "   Perceptions of corruption  \n",
       "0                      0.393  \n",
       "1                      0.410  \n",
       "2                      0.341  \n",
       "3                      0.118  \n",
       "4                      0.298  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nineteen.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 156 entries, 0 to 155\n",
      "Data columns (total 9 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Overall rank                  156 non-null    int64  \n",
      " 1   Country or region             156 non-null    object \n",
      " 2   Score                         156 non-null    float64\n",
      " 3   GDP per capita                156 non-null    float64\n",
      " 4   Social support                156 non-null    float64\n",
      " 5   Healthy life expectancy       156 non-null    float64\n",
      " 6   Freedom to make life choices  156 non-null    float64\n",
      " 7   Generosity                    156 non-null    float64\n",
      " 8   Perceptions of corruption     156 non-null    float64\n",
      "dtypes: float64(7), int64(1), object(1)\n",
      "memory usage: 11.1+ KB\n"
     ]
    }
   ],
   "source": [
    "nineteen.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 614 entries, 0 to 149\n",
      "Data columns (total 12 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        614 non-null    object \n",
      " 1   Region                         614 non-null    object \n",
      " 2   Happiness Rank                 614 non-null    int64  \n",
      " 3   Happiness Score                614 non-null    float64\n",
      " 4   Economy (GDP per Capita)       614 non-null    float64\n",
      " 5   Social support                 614 non-null    float64\n",
      " 6   Health (Life Expectancy)       614 non-null    float64\n",
      " 7   Freedom                        614 non-null    float64\n",
      " 8   Generosity                     614 non-null    float64\n",
      " 9   Trust (Government Corruption)  613 non-null    float64\n",
      " 10  Dystopia Residual              464 non-null    float64\n",
      " 11  Year                           614 non-null    int64  \n",
      "dtypes: float64(8), int64(2), object(2)\n",
      "memory usage: 62.4+ KB\n"
     ]
    }
   ],
   "source": [
    "temp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "nine = nineteen[['Country or region', 'Overall rank', 'Score', 'GDP per capita', 'Social support', 'Healthy life expectancy', 'Freedom to make life choices', 'Generosity', 'Perceptions of corruption']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "nine.rename(columns={'Country or region': 'Country', 'Overall rank': 'Happiness Rank', 'Score': 'Happiness Score', 'GDP per capita': 'Economy (GDP per Capita)', 'Healthy life expectancy': 'Health (Life Expectancy)', 'Freedom to make life choices': 'Freedom', 'Perceptions of corruption': 'Trust (Government Corruption)'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>1</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>Denmark</td>\n",
       "      <td>2</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>Norway</td>\n",
       "      <td>3</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>Iceland</td>\n",
       "      <td>4</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>Netherlands</td>\n",
       "      <td>5</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Happiness Rank  Happiness Score  Economy (GDP per Capita)  \\\n",
       "0      Finland               1            7.769                     1.340   \n",
       "1      Denmark               2            7.600                     1.383   \n",
       "2       Norway               3            7.554                     1.488   \n",
       "3      Iceland               4            7.494                     1.380   \n",
       "4  Netherlands               5            7.488                     1.396   \n",
       "\n",
       "   Social support  Health (Life Expectancy)  Freedom  Generosity  \\\n",
       "0           1.587                     0.986    0.596       0.153   \n",
       "1           1.573                     0.996    0.592       0.252   \n",
       "2           1.582                     1.028    0.603       0.271   \n",
       "3           1.624                     1.026    0.591       0.354   \n",
       "4           1.522                     0.999    0.557       0.322   \n",
       "\n",
       "   Trust (Government Corruption)  \n",
       "0                          0.393  \n",
       "1                          0.410  \n",
       "2                          0.341  \n",
       "3                          0.118  \n",
       "4                          0.298  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "nine['Year'] = 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>1</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>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>2</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>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norway</td>\n",
       "      <td>3</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>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>4</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>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>5</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>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Happiness Rank  Happiness Score  Economy (GDP per Capita)  \\\n",
       "0      Finland               1            7.769                     1.340   \n",
       "1      Denmark               2            7.600                     1.383   \n",
       "2       Norway               3            7.554                     1.488   \n",
       "3      Iceland               4            7.494                     1.380   \n",
       "4  Netherlands               5            7.488                     1.396   \n",
       "\n",
       "   Social support  Health (Life Expectancy)  Freedom  Generosity  \\\n",
       "0           1.587                     0.986    0.596       0.153   \n",
       "1           1.573                     0.996    0.592       0.252   \n",
       "2           1.582                     1.028    0.603       0.271   \n",
       "3           1.624                     1.026    0.591       0.354   \n",
       "4           1.522                     0.999    0.557       0.322   \n",
       "\n",
       "   Trust (Government Corruption)  Year  \n",
       "0                          0.393  2019  \n",
       "1                          0.410  2019  \n",
       "2                          0.341  2019  \n",
       "3                          0.118  2019  \n",
       "4                          0.298  2019  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2019 = pd.merge(reg, nine, on='Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 149 entries, 0 to 148\n",
      "Data columns (total 11 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        149 non-null    object \n",
      " 1   Region                         149 non-null    object \n",
      " 2   Happiness Rank                 149 non-null    int64  \n",
      " 3   Happiness Score                149 non-null    float64\n",
      " 4   Economy (GDP per Capita)       149 non-null    float64\n",
      " 5   Social support                 149 non-null    float64\n",
      " 6   Health (Life Expectancy)       149 non-null    float64\n",
      " 7   Freedom                        149 non-null    float64\n",
      " 8   Generosity                     149 non-null    float64\n",
      " 9   Trust (Government Corruption)  149 non-null    float64\n",
      " 10  Year                           149 non-null    int64  \n",
      "dtypes: float64(7), int64(2), object(2)\n",
      "memory usage: 14.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df2019.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "======================================================================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([temp, df2019])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_csv('happiness_data_2015_to_2019.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "========================================================================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "158"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifteen['Country'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "157"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sixteen['Country'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "country15 = fifteen[['Country', 'Region']]\n",
    "country16 = sixteen[['Country', 'Region']]\n",
    "country17 = df2017[['Country', 'Region']]\n",
    "country18 = df18[['Country', 'Region']]\n",
    "country19 = df2019[['Country', 'Region']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "country15 = fifteen[['Country']]\n",
    "country16 = sixteen[['Country']]\n",
    "country17 = df2017[['Country']]\n",
    "country18 = df18[['Country']]\n",
    "country19 = df2019[['Country']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "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>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Rwanda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Benin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Syria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Burundi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Togo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country\n",
       "0    Switzerland\n",
       "1        Iceland\n",
       "2        Denmark\n",
       "3         Norway\n",
       "4         Canada\n",
       "..           ...\n",
       "146       Rwanda\n",
       "147        Benin\n",
       "148        Syria\n",
       "149      Burundi\n",
       "150         Togo\n",
       "\n",
       "[151 rows x 1 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(country15, country16, on='Country') #['Country'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "158"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifteen['Country'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "157"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sixteen['Country'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Switzerland', 'Iceland', 'Denmark', 'Norway', 'Canada', 'Finland',\n",
       "       'Netherlands', 'Sweden', 'New Zealand', 'Australia', 'Israel',\n",
       "       'Costa Rica', 'Austria', 'Mexico', 'United States', 'Brazil',\n",
       "       'Luxembourg', 'Ireland', 'Belgium', 'United Arab Emirates',\n",
       "       'United Kingdom', 'Oman', 'Venezuela', 'Singapore', 'Panama',\n",
       "       'Germany', 'Chile', 'Qatar', 'France', 'Argentina',\n",
       "       'Czech Republic', 'Uruguay', 'Colombia', 'Thailand',\n",
       "       'Saudi Arabia', 'Spain', 'Malta', 'Taiwan', 'Kuwait', 'Suriname',\n",
       "       'Trinidad and Tobago', 'El Salvador', 'Guatemala', 'Uzbekistan',\n",
       "       'Slovakia', 'Japan', 'South Korea', 'Ecuador', 'Bahrain', 'Italy',\n",
       "       'Bolivia', 'Moldova', 'Paraguay', 'Kazakhstan', 'Slovenia',\n",
       "       'Lithuania', 'Nicaragua', 'Peru', 'Belarus', 'Poland', 'Malaysia',\n",
       "       'Croatia', 'Libya', 'Russia', 'Jamaica', 'North Cyprus', 'Cyprus',\n",
       "       'Algeria', 'Kosovo', 'Turkmenistan', 'Mauritius', 'Hong Kong',\n",
       "       'Estonia', 'Indonesia', 'Vietnam', 'Turkey', 'Kyrgyzstan',\n",
       "       'Nigeria', 'Bhutan', 'Azerbaijan', 'Pakistan', 'Jordan',\n",
       "       'Montenegro', 'China', 'Zambia', 'Romania', 'Serbia', 'Portugal',\n",
       "       'Latvia', 'Philippines', 'Somaliland region', 'Morocco',\n",
       "       'Macedonia', 'Mozambique', 'Albania', 'Bosnia and Herzegovina',\n",
       "       'Lesotho', 'Dominican Republic', 'Laos', 'Mongolia', 'Swaziland',\n",
       "       'Greece', 'Lebanon', 'Hungary', 'Honduras', 'Tajikistan',\n",
       "       'Tunisia', 'Palestinian Territories', 'Bangladesh', 'Iran',\n",
       "       'Ukraine', 'Iraq', 'South Africa', 'Ghana', 'Zimbabwe', 'Liberia',\n",
       "       'India', 'Sudan', 'Haiti', 'Congo (Kinshasa)', 'Nepal', 'Ethiopia',\n",
       "       'Sierra Leone', 'Mauritania', 'Kenya', 'Djibouti', 'Armenia',\n",
       "       'Botswana', 'Myanmar', 'Georgia', 'Malawi', 'Sri Lanka',\n",
       "       'Cameroon', 'Bulgaria', 'Egypt', 'Yemen', 'Angola', 'Mali',\n",
       "       'Congo (Brazzaville)', 'Comoros', 'Uganda', 'Senegal', 'Gabon',\n",
       "       'Niger', 'Cambodia', 'Tanzania', 'Madagascar',\n",
       "       'Central African Republic', 'Chad', 'Guinea', 'Ivory Coast',\n",
       "       'Burkina Faso', 'Afghanistan', 'Rwanda', 'Benin', 'Syria',\n",
       "       'Burundi', 'Togo'], dtype=object)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifteen['Country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# country15 = fifteen[['Country']]\n",
    "# country16 = sixteen[['Country']]\n",
    "# country17 = df2017[['Country']]\n",
    "# country18 = df18[['Country']]\n",
    "# country19 = df2019[['Country']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "fifteen['Country'].to_excel('Country List2015.xlsx', sheet_name='2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "sixteen['Country'].to_excel('Country List2016.xlsx', sheet_name='2016')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2017['Country'].to_excel('Country List2017.xlsx', sheet_name='2017')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "df18['Country'].to_excel('Country List2018.xlsx', sheet_name='2018')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2019['Country'].to_excel('Country List2019.xlsx', sheet_name='2019')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Happiness Rank\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Happiness Score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GDP per Capita\n"
     ]
    },
    {
     "data": {
      "image/png": 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du6pUsz7Hjx/Ha6+9hv79+8PDwwP+/v5ISEjAwoUL9a5brLy+SgX3MW7cuIE1a9Zg6tSpsLGxgYmJCVxdXeHq6orOnTtj3LhxuHTpkvZ8KKn4/l5JD/ex+IBvZmaGJUuWaAd3AGjZsiWio6MRFRWlHfgsLS3Rp08f9OnTBy1atMBnn30GoOrjUt4YF/d9/vz5OHjwIPz8/ODt7Y2goCCEhISUWb/k+D3u/i7ZbklnzpxBXFwcjIyM4OTkpE0vLCzE8ePHkZiYiLZt25ZZr1h5x4mK2i+pqv2oa+r1TfniJ0BRURGeeeYZ3L59G1euXNHm5+Xl4ezZs2jfvj2AB6/C58yZg5dffhkvvfQS5s6dq70yL1byvfRXr17FnTt34OjoWKbtdu3alXr1BgAnT56EmZkZ2rZtW+rNAuWpSr369O7dG08//TTef//9cs9Cqvp+93v37mHnzp3429/+pveJXhUlx/Du3bu4evUqHB0dYWdnBzMzM9y8eRN2dnba3+7du8t900FFdDodXF1dsW7dujIHydu3b2PDhg3aeGzatAkjR47E0qVLMWrUKHh4eCAxMbHMwaOimh/28H794osv0KtXL4SFhSEwMBDdu3fH9evXAah5R9jDGjRogK+//hp79uwpM69Ro0YwMjLSztrMzMxKPb/Lu6F78eLFUnX+8ssvaN26NRo2bIiWLVuW2k8WFhbIysrChg0bEBMTU2Zb1tbWWtvVHZesrCxs3boVISEhmD17Nnx9ffH0008jKSmp0vWrur+ravv27bCwsEBERAR27Nih/X388ccQkcd+V1vbtm1hampa6nlXVFSEQYMGYe/evcr7UVvqzRnKvXv3kJaWBuDBwMfHxyM8PByOjo7Q6XQAAHd3d/zf//0f5s2bp51uZ2RkYMSIEQCApUuXAgBmzpwJEcGhQ4ewdOlSbTrw4MNLb731FszMzBASEgIXF5dSl8GKTZ48GRMmTICjoyMGDhyICxcu4MMPP8SLL74Ia2tr7cBz6dIl6HQ67TJNse7du+utVx9TU1OEhYVh/PjxGD16NMaPHw97e3tkZWXhu+++wyeffILWrVujVatW2jr5+fnaOObn5+Pq1av44IMPkJ+fjxkzZlSpXX1CQ0PRvHlz2NraYuXKlWjatCl8fHzQsGFDvPrqq1i5ciWeeuopuLi44PDhw1i1ahWWLFnySG0sXrwYr7zyCsaOHYvJkyejdevWiI+PR2hoKCwsLBAUFATgwVt0T548iYsXL8LCwgJ79uzBvn37ylwqq6jmhz311FMAgAsXLqBx48Zo1qwZjhw5glOnTqF58+aIiorCp59+CuDBC4RHOUusimbNmmHcuHFYuXIlsrKy4O3tDQsLC8TGxiIsLAx+fn7485//DODBmVxERAQ6deqEwsJCvPPOO2XOhpKSkrBw4UIEBgbi3Llz+Oyzz7QPcZbH09MTXbp0wcSJEzF9+nR07doVOTk5+PXXX7FmzRpt3eqOS4MGDWBpaYnIyEg4ODggKysLa9aswY0bN5CXl1fp+FRlf1dFXl4e9u3bhyFDhpS6NAU8eFHTuXNnbN++HdOnT3/kbVtaWmLUqFF4//330bRpU9jZ2WHjxo24e/cuunXrhh07dijrR22qN4Gybt06rFu3DsCD6/nNmjWDl5cXpk6dqh28P/roI7zzzjuYOHEiCgsL4e7uji+++AJt2rTB4cOHsXXrVqxfv147KCxcuBBTpkzBwIEDtc8F+Pr6YsaMGcjKyoKnpyfmzZtX7ml5nz598O6772LNmjX44IMPYGtri8DAQEycOBHAg7evBgQE4L333kN0dHSZz0UYGRlVWm9VOTg4YNeuXdi4cSNCQ0Nx/fp1mJqaQqfTYebMmXjhhRfQsGFDbfkTJ06gd+/eAB5camnZsiV69+6N999/X9n12ZdeegkhISFITU1F165d8emnn2rjO2PGDJiZmWH58uW4desW2rRpg5CQEAwfPvyR2nBwcEBERARWr16NOXPmID09Hba2tnj22WcxefJk7Z7Mm2++ifnz52PkyJFo2LAhXF1dERISggULFuC3336rUs0ltW/fHoMGDUJQUBBefvllTJs2DampqRg3bhxMTExgb2+PZcuWISgoCGfPntXuOagUFBQEOzs7REREYOPGjcjNzUXbtm3h5+dX6pPvixYtwqJFi/Diiy/C1tYW06dPL/N25IEDByI3Nxf+/v5o3Lgxpk2bVum3MhgbG2Pt2rVYv349Nm7ciLfffhtGRkZwcHDA0qVL4e3tDQDVHhczMzOEhYXh3XffxZAhQ9CsWTP07dsXY8eOrfQtvlXd31Vx6NAh3LlzR3ur+MNeffVV/P3vf0dkZGSpe4BVNWvWLJiYmGDu3LnIzs6Gi4sL/vWvf6FFixZ6+1H8oqGuMZK6fg5VS6KjoxEYGIgjR47gT3/6k6HLqZeSk5PRv39/bN68uUYOpDWhPtZMVFfV63soRERUdzBQiIhICV7yIiIiJXiGQkRESjBQiIhICQYKEREpofdzKLdvZ6OoqHq3WZo3t0J6epb+BZ8A7OuT64/UX/b1yVXd/hobG6Fp06fKnac3UIqKpNqBUrydPwr29cn1R+ov+/rkqqn+8pIXEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIl9P4EMNW+Q4cOIDU1xdBlAACysx/89vRTT1mVmWduboq8vILaLslgzM1N0aRJC3h5eRu6FKI6iYFSB6WmpuDGzRRYNmlu6FJwL+tBoBSYNig7Mze/lqsxrHs3U/5QAUr0qBgodZRlk+Zw9hxi6DIQc3gPANSJWgyteCyIqHy8h0JEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlLCtKYbiIk5A2trC7Rtq6vppoiI6rWYmDMAAGdnVwNX8nhqPFDOnj0Fc3NTBgoRkR5nz54CUH8DhZe8iIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRUD2RlZeKrrzYhOzur3Mf61in+d2ZmZo3VyEAhIqoHfvrpKJKTk3Ds2A/lPta3TvG/Dx48WGM1MlCIiOq4rKxMnDt3GoAgJuY0UlNvlnpc3llKyXXOnTuFc+dOARCcPHmy0rOa6jCtka2WcO9eNm7dysZXX22q6abqBHNzU+TlFVRrG6mpN2HcwEJRRaRKfs49pN7N+UM8l1U8j+uLutTX1NSbsLKyKjP9p5+OAhAAgIhg797tpR4fO/YDBg70qXCdwsJCAEYAgKKionKXV4FnKEREddyFC2f/EwoPwiE9/VapxxcunK10nQek0uVVqPEzFEvLp9CkSWMMHz6qppuqE2xsrJGWVr2bXl99tQl3c/MVVUSqmFlYonHjxhg5MtDQpdQ4Fc/j+qIu9bWis19HRxecO3cKhYWFMDExQZMmTXHnzm3tsaOjS6XrPGAEQCpcXgWeoRAR1XE9evRG8SUrIyMjPPecX6nHPXv2qXQdExMTmJg8ONwbGxuXu7wKDBQiojrOysoaHTp0BGAEZ+eOsLVtWerxU0+Vve9Scp0OHdzQoYMbACN06tSp3OVVqPFLXkREVH09evRGenqadnbx8GN964gI0tPTMGDAAOTk1EyNDBQionrAysq61P27hx9XZZ2RIwNhbW2NnJyauWfES15ERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESpjWdAMuLm6wtrao6WaIiOo9Fxc3Q5dQLTUeKM7OrrCxsUZaWmZNN0VEVK85O7sauoRq4SUvIiJSgoFCRERKMFCIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSgoFCRERKMFCIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSgoFCRERKMFCIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSgoFCRERKMFCIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSgoFCRERKMFCIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSwtTQBVD57t1JR8zhPYYuA/fupANAnajF0O7dSUfjln8ydBlEdRYDpQ6yta07By1TKysAwFMNzMrMMzc3RV5eQW2XZDA2bVqjSZMWhi6DqM5ioNRBXl7ehi6hSmxsrJGWlmnoMmrNH62/RI+K91CIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSgoFCRERKMFCIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSgoFCRERKMFCIiEgJBgoRESmh9yeAjY2NlDSkajv1Afv65Poj9Zd9fXJVp7+VrWskIvLYWyYiIvoPXvIiIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIllAbK7t27MXjwYHh7e2Pz5s1l5l+4cAHDhw/HoEGDMG/ePBQUFKhsvlbp6+vBgwcxbNgwDB06FFOmTMHdu3cNUKUa+vpaLCoqCl5eXrVYWc3Q198rV64gICAAQ4cOxbhx457ofRsTEwN/f38MHToUEydOREZGhgGqVCcrKwtDhgxBcnJymXlP0vEJqLyvNXZ8EkVSUlLE09NTbt++LdnZ2fL8889LXFxcqWWee+45+fXXX0VEZM6cObJ582ZVzdcqfX3NzMyUXr16SUpKioiIhIWFyeLFiw1VbrVUZb+KiKSlpcnf/vY38fT0NECV6ujrb1FRkXh7e8uRI0dERGTFihWyfPlyQ5VbLVXZty+//LJERUWJiMg777wjoaGhhihViVOnTsmQIUPE2dlZkpKSysx/Uo5PIpX3tSaPT8rOUI4dO4bu3bujSZMmsLS0xKBBg/Dtt99q869fv46cnBy4ubkBAIYPH15qfn2ir6/5+flYuHAhWrZsCQCwt7fHjRs3DFVutejra7H58+fj73//uwEqVEtff2NiYmBpaYm+ffsCACZNmoTRo0cbqtxqqcq+LSoqQnZ2NgDg/v37sLCwMESpSkRERGDhwoWwtbUtM+9JOj4Blfe1Jo9PygIlNTUVNjY22mNbW1vcvHmzwvk2Njal5tcn+vratGlTDBw4EACQk5ODtWvXYsCAAbVepwr6+goAmzZtgpOTEzp27Fjb5Smnr7+JiYlo0aIF5s6dCz8/PyxcuBCWlpaGKLXaqrJvg4ODMX/+fPTu3RvHjh3DyJEja7tMZZYsWYLOnTuXO+9JOj4BlejWEFsAAAffSURBVPe1Jo9PygKlqKgIRkb//VpjESn1WN/8+qSqfcnMzMSECRPg4OAAPz+/2ixRGX19jY2NxYEDBzBlyhRDlKecvv4WFBTg3//+N15++WVs374dbdq0wbJlywxRarXp62tOTg7mzZuHjRs34ujRoxg1ahRmz55tiFJr3JN0fKqqmjg+KQuUP/3pT0hLS9Mep6WllTrdenj+rVu3yj0dqw/09RV48Ipn1KhRsLe3x5IlS2q7RGX09fXbb79FWloa/P39MWHCBK3f9ZW+/trY2MDOzg4uLi4AgCFDhuDMmTO1XqcK+voaGxuLBg0awNXVFQAwYsQI/Pvf/671OmvDk3R8qoqaOj4pC5SePXvip59+wu+//4779+/jwIED2nVmAGjVqhUaNGiAkydPAgB27txZan59oq+vhYWFmDRpEnx8fDBv3rx6/UpHX1+nTZuG/fv3Y+fOnVi7di1sbW3xxRdfGLDi6tHXX3d3d/z++++4ePEiAODQoUNwdnY2VLnVoq+vdnZ2SElJwZUrVwAAkZGRWpA+aZ6k45M+NXp8UnJr/z927dolzz33nHh7e8vatWtFRGT8+PFy5swZERG5cOGC+Pv7y6BBg2TmzJmSm5ursvlaVVlfDxw4IPb29jJ06FDtb+7cuQau+PHp26/FkpKS6v27vET09/fUqVPi7+8vgwcPlrFjx8qtW7cMWW616OtrVFSUPP/88zJkyBB55ZVXJDEx0ZDlKuHp6am98+lJPT4VK6+vNXl84i82EhGREvykPBERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESpoYugAgAvv76a2zZsgXZ2dnIy8tDmzZtMGPGDO3rXAICAnD9+nVYW1sDePCJ9X79+mHKlCmwsrJCcnIyBg4cCJ1Op21TRBAYGIgXXnihVvqQkZGBDz/8ENHR0TA2NoaRkRFGjx6NF1988bG3+cEHH8DOzg6+vr746KOP4ODgUG+/xof+AJS8+ZioGlauXCkjR46U5ORkbdqxY8ekW7ducv36dRERGTNmjHzzzTfa/Ly8PFmwYIFMnDhRRB58BsbNza3UdlNSUqRz585y4cKFGu9DTk6OPP/887J27VrJz88XEZHk5GQZMGCAREREKGnj4TEgqmt4hkIGdevWLXz66af47rvvSn3VRY8ePRAcHIz79++Xu56ZmRnmzJmDXr16IT4+Hg0aNCizTMuWLWFnZ4eEhAQ4ODho06Ojo/Hee+/hz3/+M65cuQILCwssW7YM7dq1Q15eHt577z38/PPPKCwshJOTE+bPnw8rKyt4eXnB1dUVly5dwsyZM7Uv2AOAffv2wdLSEq+99po2rVWrVggLC0N+fj4A4PDhw1izZg3y8vLw+++/w9fXFzNmzKi0nuDgYDzzzDOwsLDAuXPnsHz5cpiYmKB9+/YICQlBdnY20tLS4ODggLCwsHLHgai28B4KGdSpU6fQrl27cr83ydfXF+3atatwXQsLC/zlL39BbGxsufN//fVXJCYmlvstyOfOnUNAQAB2796N4cOHY9asWQCAtWvXwsTEBNu2bcOuXbtga2uL9957T1vvmWeewTfffFMqTIq35+HhUaYdZ2dnuLm5QUSwYcMGLFu2DNu2bcOWLVuwdu1a/P7775XWU2z06NHo0KED/vGPf2DgwIGIiIiAr68vIiIicODAASQnJyMqKqrCsSKqDTxDIYOSh76oISsrS/t9kXv37sHHxwczZ86scH0jIyM0bNgQwINvxx02bBiAB99X1LRpU6xYsQL/8z//U2Y9BwcH7eu9/f39ERISgtu3byMqKgqZmZk4duwYgAe/HdG8eXNtvYq+EtzIyKhMXx6ev3r1akRFRWHPnj2Ij4+HiGhnYBXVU5FZs2bhxx9/xLp165CQkIDU1FTcu3evwuWJagMDhQzK1dUVV69exe3bt9G0aVNYWVlh586dAIDw8PBKD6r3799HfHw82rdvD+DBGUvxuvqYmJiUO62oqAhz585Fv379AADZ2dnIzc3Vlqnot0/c3NzK/QndyMhInDhxAq+//jr8/PwwYMAAdO7cGf7+/jh48KAWQhXVU5GZM2eisLAQPj4+ePbZZ3Hjxo1KA42oNvCSFxlUy5YtERgYiOnTp+O3337Tpl+/fh2//PILjI3Lf4rm5ORg6dKl6Nu3L1q3bv3I7V68eFH7xuAtW7bA3d0djRo1Qu/evbF582bk5eWhqKgIb775JkJDQ/Vuz9vbG1lZWVi3bh0KCwsBAElJSdq9kGvXriErKwszZsyAl5cXoqOjtTYqq6ckExMT7XfOjx49iqlTp2Lw4MEAgNOnT2vtEhkKz1DI4IKCgrBr1y688cYbuH//PjIzM9G4cWMMHjy41M/rLl++HP/85z9hbGyMgoIC9OzZE/PmzXusNlu0aIGwsDBcv34dzZo1w/LlywEAU6ZMwbvvvgs/Pz8UFhbC0dERwcHBerdnbm6OTz75BCtWrMDzzz8PExMTmJiYYPLkyRg+fDiKiorw7LPPwsfHB+bm5tDpdGjfvj2uXbsGc3PzCuspycvLC6GhocjPz0dQUBCmTp0KS0tLWFlZoUuXLkhMTHyssSBShd82TH840dHRWLx4Mfbs2WPoUgDUvXqIHhcveRERkRI8QyEiIiV4hkJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiU+H8J72UJsn6n9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Social support\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Healthy life expectancy\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Freedom to make life choices\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generosity\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perceptions of corruption\n"
     ]
    },
    {
     "data": {
      "image/png": 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rq91u2rQpgCf7tmvXLixfvlybFxgYiHfffRfAk2PXrFkz5Obm4ty5c/j++++xbt06jBgxAnv37kWtWrUQEBCAsLAwxMTEIDExERcuXAAAo1Nm+fc7r568T5S2atUKsbGxWLZsGS5duoTLly8jNja2wPNJ/sdq/fr10aNHD3z99de4ePEirly5gpiYmALbzf9YAZ70vKh+b9++HZUrV9YeZzVr1oSLiwu++eYbBAUFoXLlykV017i2p8XGxqJ79+5G0/KfHjZnP4pTYoFTo0YNNGjQQLv95ptvIicnBxMnTkRcXBwqVqxY6HoGg8HoyTcxMRFZWVnIysrCuXPn0KZNG+OCy5UrsH6ZMgUvRVWqVKnAtLymPD1GYcyt1xyvv/46XnvttSLnly9fHj169MCIESMKzMt/QS9/TaZqKF++PIYPH17oE3jdunWRmpqqLZdfUf18WnH9yT9m/utUefLCdNCgQfD29sYPP/yAiIgIzJs3D+vXr8emTZsKrFcSx6O45fLGl6cu1j89fmH3q8KmPS3vOlFx9TxL7/Pqzj/m08c2/3LmKO7YPW3BggVwd3dH69atATx50n7jjTfQrVs3eHh44OjRo0UGztO9eboHedssX748/P394eXlpc2rUaMGrl+/juXLl2PUqFGoXbs2ypYtCwcHBzg4OKBFixYYNmwYLl68iLp166Jfv36oW7cuPDw84O7ujjp16qB3797Fbj9/DTt27MDUqVPRvXt3tGjRAgMGDMDhw4fxzTffGC2f/xjFxcUhICAAjo6OcHFxgbe3N3JycrSgzGNuv7Ozs7Fz5048fPgQzZs316YbDAaICMLDw/H2228XWK+w2p5W3GPE3P0oTql+aCCvWQaDAW+++Sbu3LljdFE7Ozsb0dHRaNiwIYAnF+smT56Mv//97+jbty+mTJlS4GLh2bNntf9fvnwZd+/ehZ2dXYFt29ra4uTJk0bTTp48ifLly6N+/fpGH2YojDn1lpSGDRsiISEBDRo00P7dvn0bCxYswP379wtdJ+8VYP5+ZGZmwsXFBSdOnEDDhg2RmJhoNGZsbCw++eQTo3Hyr3/v3j1cvnxZ62dxPWrYsCGio6ONXoHFx8fj3r17Rq9Oi3Lnzh3MmjULOTk56NOnDz755BN89dVXOH/+vPaqM7+SOB62trY4f/689sEJ4Mmnm3r16oU333wTAHDq1CmjdU6dOvW7j3f58uUL3G/zLirnV9x92VTvf/75Z6Npefd1c3pfGn788Ud8+eWXBaZXqVIF5cqVQ61atQAU7I3BYEBSUpLROnfv3jX6MNCpU6dgZWWFhg0bombNmkb36Zo1a6JixYrYsmULvvvuuwLbf+WVV2BlZYVatWph3759uH//PkJDQxEYGAhPT0/cuXMHgPlBvGbNGvj7+2Pu3LkICAhAs2bNcPXq1WLX37ZtG+rVq4dVq1Zh2LBhcHV1xc2bN3/XdvM7cOAA7t69i3nz5iEsLMzon42NjdGHB34vW1tbo/slAAQFBWH27Nklsh8lFjgPHjxAWloa0tLScPPmTURERGDp0qWws7ODTqdDmzZt4OzsjAkTJuDkyZOIjY3F5MmTkZ6ejn79+gEA5s6dCwAYP348JkyYgJycHG1anpCQEJw6dQrR0dEICgqCvb290amJPO+99x527dqFlStXIjExEbt27cLnn3+OPn36oHr16qhatSoA4OLFi9qdLj9z6i0pI0aMwJkzZzBv3jwkJCTgp59+wqRJk5CRkVHkRxbfeOMNdOzYESEhIThx4gQSEhIwefJkVK9eHQ4ODnjvvfcQHh6OFStWIDExEQcPHsSMGTNQqVIlo1dSS5YswdGjRxEbG4ugoCBYW1trryCrVq2KmzdvIikpqcCr0AEDBiAjIwOTJ09GXFwcTpw4gQkTJqBRo0ZwcXExuc81atTA4cOHMWPGDFy4cAFXrlzBtm3b8MorrxQ4pQGUzPHo378/fv31V8yaNQsJCQk4duwYli5dig4dOqB+/frw8fHBRx99hKNHjyIhIQHz5s3DuXPnMGjQILPGz+Pk5ITz588jPDwcSUlJWLZsWaGnKYu7L1etWhXJycm4du1agfVGjBiB6OhoLFiwAJcuXcKRI0cQEhKCDh06WCxwxo0bh8OHD2PChAk4deoUkpOTERkZiTFjxqB27dro2rUrgCe9OXLkCI4cOYLExESEhIQgPT3daCwrKyuMGzcO0dHRiIqKwsyZM+Hr61vkR3ttbGwwbNgwfPzxx1i6dKl2umffvn2YPHkyevbsib/85S+wtrZGZmYm9uzZg2vXruHAgQP48MMPAcDsP061sbHByZMnceHCBSQmJmLZsmXYuXNnsevb2Njg2rVrOHbsGK5du4YdO3ZoL/x+zx/F5tm+fTsaNGiAnj17QqfTaf8aNWoEf39/nD59utAXbeYYPnw4vv32W2zYsAFXr17F5s2bER4eDk9PzxLZjxI7pbZy5UqsXLkSwJOP/NrY2MDT0xOjRo3SXq0tW7YM8+bNQ2BgIHJzc+Hs7Iz169fjb3/7G3744Qds3boVq1at0sLgww8/xMiRI/HWW29pH6vs0aMHxo4di8zMTHh4eGDq1KmFvgV2dXXFggULsHz5cnz22WeoU6cOBg0ahMDAQABAtWrVMHDgQCxevBhRUVH44osvjNa3srIqtt6SpNfrtTrXr1+P6tWrw8PDA0FBQcWuN3/+fMybNw8jR45Ebm4uWrZsiVWrVqFChQpwc3PDwoULsWLFCnz++eewsbFBjx49MG7cOKMx+vbti5kzZyI1NRWtWrXC119/rfW6V69e2L9/P7y9vREaGmq03quvvor//Oc/WLRoEXr37o3KlSvD09MTEydOLPRUztPKlCmD5cuXY/78+Rg4cCCys7Nhb2+P1atXF/o3ISVxPOrWrYuVK1di8eLF6NGjB2xsbPD222/j/fffBwDMmjULixYtwsSJE/HgwQPY2dlh9erVRtfWzNG9e3fExMQgJCQEOTk58PLywuDBg3HmzBmj5Yq7L/fv3x8TJkyAt7d3gY8z63Q6/Pvf/8ann36KtWvXombNmvDx8cHYsWN/V50lyc3NDWvXrsXKlSsxatQoZGRkwMbGBh07dsScOXO0045Dhw7F1atXMXr0aFSoUAFvv/02fHx8jMaqXbs23nrrLQwfPlzr35QpU4rd/rhx49CgQQNs3rwZX331FR49eoT69eujZ8+e2jUILy8vREdHY/bs2Xjw4AHq16+PkSNHYsWKFYiOjoabm5vJ/Zw+fTqmTZsGf39/VK5cGQ4ODpg5cyZmzJiBlJSUQtcZNGgQEhISMG7cOOTm5sLW1hYhISGYPHkyoqOjf9eLhLy/vQkKCir0XXD//v2xatUqbN68GTNmzDB73DydOnXCjBkzsHr1asydOxf169fHwoUL0bZtWzRv3vyZ98NK/sh7OguIiorCoEGDcOjQoWKvh5B5kpOT0bFjR4SGhmoXnEkN3pfpz4pf3klEREowcIiISIkX5pQaERG92PgOh4iIlGDgEBGREgwcIiJS4pn/DufWreK/NvzPolatauxFPuzHb9gLY+zHb162XpQpYwVr66pFzn/mwDEY+JmDPOyFMfbjN+yFMfbjN3+mXvCUGhERKcHAISIiJRg4RESkBAOHiIiUYOAQEZESDBwiIlKCgUNEREowcIiISAkGDhERKcHAISIiJRg4RESkBAOHiIiUYOAQEZESDBwiIlKCgUNEREowcIiISAkGDhERKcHAISIiJZ75J6afV99/vxepqTeeaYz795/81njVqtVMLluhQjlkZ+eYNW6dOq/B07PzM9VGRPSieWkDJzX1Bq7fvIEqNWv94TEeZD4JnJxyFU0v/OixeWPevfWH6yEiepG9tIEDAFVq1kITj25/eP1zP3wHAM80RlFjEhH92fAaDhERKcHAISIiJRg4RESkBAOHiIiUYOAQEZESDBwiIlKCgUNEREowcIiISAkGDhERKcHAISIiJRg4RESkBAOHiIiUYOAQEZESDBwiIlKCgUNEREowcIiISAkGDhERKcHAISIiJRg4RESkBAOHiIiUYOAQEZESDBwiIlKCgUNEREowcIiISAkGDhERKcHAISIiJRg4RESkBAOHiIiUYOAQEZESDBwiIlKCgUNEREowcIiISAkGDhERKcHAISIiJRg4RESkBAOHiIiUYOAQEZESDBwiIlKCgUNEREowcIiISAmLBs65c2dw7twZS5ZAJYzHlIiKYtHAiY7+BdHRv1iyBCphPKZEVBSeUiMiIiUYOEREpAQDh4iIlGDgEBGREgwcIiJSgoFDRERKMHCIiEgJBg4RESnBwCEiIiUYOEREpAQDh4iIlGDgEBGREgwcIiJSgoFDRERKMHCIiEgJBg4RESnBwCEiIiUYOEREpAQDh4iIlGDgEBGREgwcIiJSgoFDRERKMHCIiEgJBg4RESnBwCEiIiUYOEREpAQDh4iIlGDgEBGREgwcIiJSgoFDRERKMHCIiEgJBg4RESnBwCEiIiUYOEREpAQDh4iIlGDgEBGREgwcIiJSgoFDRERKMHCIiEgJBg6RmTIzM7Bx4xrcv5/5XI9ZEkpzXzMyMpRt05xxn5djYMk6VG2bgUNkpsjIo0hOTkJExJHnesySUJr7un//fmXbNGfc5+UYWLIOVdtm4BCZITMzA2fPngYgOHfudIm8EiyNMUtCae/ryZMnC4xZWr0wNe7zcgwsWYfKbZcrtZHN8ODBfWRmZmLjxjUlPnZq6k2UqVipxMd9Vo+zHiD1Xlap7PPzIDX1JqpVq2bpMkpcZORRAAIAEBFERBzBW295PXdjloTS3leDwVBgzNLqhalxn5djYMk6VG6b73CIzBATE43c3FwAQG5uLmJiop/LMUuCJfa1tHphqe3+XpasQ+W2LfoOp0qVqqhSpSr8/QeV+NgbN67BvUePS3zcZ1W+UhXUqFGjVPb5efCyvnOzs7PH2bO/IDc3F2XLloWdnf1zOWZJsMS+llYvLLXdkq7zZdk23+EQmcHFpT0AKwCAlZUV2rZ1fS7HLAmlva9lypQpMGZp9cLUuM/LMbBkHSq3zcAhMkO1atXRtKkjACs0aeKIqlWf/TpVaYxZEkp7X5s3b15gzNLqhalxn5djYMk6VG7boqfUiF4kLi7tcetWWom+AiyNMUtCae5rp06dkJWlZpvmjPu8HANL1qFq2wwcIjNVq1a9xK+9lcaYJaE097V69erIyir4x5+l1QtT4z4vx8CSdajaNk+pERGREgwcIiJSgoFDRERKMHCIiEgJBg4RESnBwCEiIiUYOEREpAQDh4iIlGDgEBGREgwcIiJSgoFDRERKMHCIiEgJBg4RESnBwCEiIiUYOEREpAQDh4iIlGDgEBGREgwcIiJSgoFDRERKMHCIiEgJBg4RESnBwCEiIiUYOEREpAQDh4iIlGDgEBGREgwcIiJSgoFDRERKMHCIiEgJBg4RESnBwCEiIiUYOEREpAQDh4iIlGDgEBGREgwcIiJSgoFDRERKMHCIiEgJBg4RESlRzpIbt7d3suTmqRTwmBJRUSwaOE2aOFhy81QKeEyJqCg8pUZEREowcIiISAkGDhERKcHAISIiJRg4RESkBAOHiIiUYOAQEZESDBwiIlKCgUNEREowcIiISAkGDhERKcHAISIiJRg4RESkBAOHiIiUYOAQEZESDBwiIlKCgUNEREowcIiISAkGDhERKcHAISIiJRg4RESkBAOHiIiUYOAQEZESDBwiIlKCgUNEREowcIiISAkGDhERKcHAISIiJRg4RESkBAOHiIiUYOAQEZESDBwiIlKCgUNEREowcIiISAkGDhERKcHAISIiJRg4RESkBAOHiIiUYOAQEZESDBwiIlKCgUNEREqUs3QBpenB3Vs498N3z7Q+gGcao7Axa9R9rcTGIyJ6Uby0gVOnzrM/qZerVg0AULVieZPLVqhQDtnZOSaXq1H3tRKpjYjoRfPSBo6nZ2el26tduzrS0jKUbpOI6EXCazhERKQEA4eIiJRg4BARkRIMHCIiUoKBQ0RESjBwiIhICQYOEREpwcAhIiIlGDhERKQEA4eIiJRg4BARkRIMHCIiUoKBQ0RESjBwiIhICRGEnGQAAAsySURBVAYOEREpwcAhIiIlGDhERKQEA4eIiJR45p+YLlPGqiTqeCmwF8bYj9+wF8bYj9+8TL0wtS9WIiKKaiEioj8xnlIjIiIlGDhERKQEA4eIiJRg4BARkRIMHCIiUoKBQ0RESjBwiIhICQYOEREpwcAhIiIlzAqcb7/9Ft7e3ujcuTNCQ0MLzI+JiUGvXr3QpUsXTJ06FTk5OSVe6PPCVC/yBAUFYdu2bQorswxT/di/fz/8/PzQvXt3jBw5Evfu3bNAlWqY6sW+ffvg6+sLHx8fBAcHIzs72wJVqmHu4+TgwYPw9PRUWJllmOrHsmXL4OHhAT8/P/j5+RXbsxeamHDjxg3x8PCQO3fuyP3798XX11fi4uKMlvHx8ZGff/5ZREQmT54soaGhpoZ9IZnTixs3bkhgYKA4ODjI1q1bLVSpGqb6kZGRIe3atZMbN26IiMinn34qs2bNslS5pcpUL+7fvy/t27eXtLQ0EREZO3asbNy40VLllipzHiciImlpadK1a1fx8PCwQJXqmNOPwMBAOXXqlIUqVMfkO5yIiAi0adMGNWvWRJUqVdClSxfs3r1bm3/t2jVkZWXByckJANCrVy+j+S8TU70AnryS6dixI7y8vCxUpTqm+vH48WN8+OGHqFu3LgBAr9fj+vXrliq3VJnqRZUqVfD999/j1VdfxcOHD3Hr1i288sorFqy49JjzOAGAadOm4f3337dAhWqZ04+zZ89i+fLl8PX1xcyZM/Ho0SMLVVu6TAZOamoqateurd2uU6cObt68WeT82rVrG81/mZjqBQAMHz4cffr0UV2aRZjqh7W1Nd566y0AQFZWFlasWIFOnTopr1MFc+4b5cuXx6FDh+Du7o47d+6gffv2qstUwpxerFmzBo0bN4ajo6Pq8pQz1Y/79+/Dzs4OEydOxPbt25Geno5//vOflii11JkMHIPBACur375yWkSMbpua/zL5M+2rOcztR0ZGBt555x00atQIPXv2VFmiMub2okOHDoiKioKHhwc++ugjhRWqY6oXsbGx2Lt3L0aOHGmJ8pQz1Y+qVati5cqVsLW1Rbly5TB06FAcOnTIEqWWOpOB89prryEtLU27nZaWhjp16hQ5/9dffzWa/zIx1Ys/G3P6kZqaioCAAOj1esyZM0d1icqY6sXdu3dx9OhR7bavry8uXryotEZVTPVi9+7dSEtLQ+/evfHOO+9o95GXlal+pKSkYMuWLdptEUG5cs/8U2XPJZOB07ZtW0RGRuL27dt4+PAh9u7dCzc3N23+X//6V1SsWBEnT54EAOzYscNo/svEVC/+bEz1Izc3F++++y68vLwwderUl/rdoKleiAgmTpyIlJQUAE+edJs1a2apckuVqV6MHj0ae/bswY4dO7BixQrUqVMH69evt2DFpctUPypVqoRFixYhKSkJIoLQ0FDtVPRLx5xPFnzzzTfi4+MjnTt3lhUrVoiIyPDhw+XMmTMiIhITEyO9e/eWLl26yPjx4+XRo0cl/eGG54apXuSZNGnSS/8pNZHi+7F3717R6/XSvXt37d+UKVMsXHHpMXXf2Ldvn3Tr1k18fX1l3Lhxkp6ebslyS5W5j5OkpKSX/lNqIqb7sXv3bm1+cHDwS/scyl/8JCIiJfhNA0REpAQDh4iIlGDgEBGREgwcIiJSgoFDRERKMHDoD0tOToadnZ32Dbd53wqd/4/YLGXo0KG4ffs2AGDEiBGIj4+3WC3btm2Du7s7hg0bZrEaTHme+kUvr5fzz1lJmUqVKmHHjh3a7Zs3b6Jbt25o2rQpGjVqZLG6jh07pv1/5cqVFqsDAMLCwjBu3Dj4+flZtI7iPE/9opcXA4dKVN26ddGgQQMkJiaiUaNG+O9//4sNGzbAYDCgZs2amD59OmxtbREcHIy7d+8iKSkJ7u7uGDlyJGbPno1Tp06hbNmy6NSpE8aNG4fHjx9j8eLFOH78OHJzc9G4cWNMmzYN1apVg6enJ3x8fHDs2DFkZGTgH//4BwICAjB58mQAwODBg7FixQr0798fn332Gezt7bFp0yasXbsWZcqUwauvvorp06fjjTfeQHBwMKpVq4aLFy/ixo0b0Ov1WLBgAapWrYrPP/8c+/btQ/ny5WFtbY158+YV+AqfjIwMhISE4MKFC7CysoKrqyvGjx+PhQsXIjo6GsnJybhz5w6GDBlitN6WLVvw5ZdfokyZMrC2tsaCBQtQr169YuvM37dbt24VuP3mm29q76aCg4O126r6RVQkS//lKb24kpKSxMnJyWjaqVOnpGXLlpKSkiJRUVESEBAgDx48EBGRI0eOSNeuXUXkyTcxDB48WFtv7ty5Mm7cOMnJyZFHjx5J//795ccff5SlS5fK/PnzxWAwiIjIxx9/LB9++KGIiHh4eMj06dPFYDDI9evXpXXr1nLhwgUREdHpdHLr1i1tuTNnzkhERIR06tRJm75161bx8vISg8EgkyZNkn79+smjR48kOztbevToIVu2bJGUlBRp1qyZ9pffq1evln379hXoRVBQkMyaNUsMBoM8evRIhg4dKsuXLxcRkQEDBsiuXbsKrBMTEyOtW7eWlJQUERH58ssvZfr06SbrzN+3wm6vWrWq0Nsq+kVUHL7DoWeSlZWlnSrKzc2FtbU1Fi1ahHr16mHt2rW4cuUK/P39teXT09Nx9+5dAEDz5s216REREZg8eTLKli2LsmXLYt26dQCARYsWISMjAxEREQCe/MZOrVq1tPUCAgJgZWWF1157Da6urjh27Bj0en2htR45cgTe3t6wsbEB8OS3m+bMmYPk5GQAgKurKypUqAAA0Ol0uHfvHurWrat9y7Wbmxvc3Nzg4uJSYOzDhw9jw4YNsLKyQoUKFeDv74+vv/4a77zzTpG9i4yMRPv27VGvXj0A0N79LFy4sNg68/etsNvFKe1+ERWHgUPP5OlrOPkZDAb4+flh4sSJ2u3U1FTUqFEDwJMfJctTrlw5oy/3vH79OipVqgSDwYApU6agQ4cOAJ78dkj+H6fK/626BoMBZcoU/TkYg8FQYJqIaD+JXqlSJW26lZUVRARlypTBunXrEB0djcjISMydOxeurq4ICgoqMPbTP9th6qfWy5Yta7ROVlYWrl27ZrLO/H17+nZe3XkeP35stGxp94uoOPyUGpWa9u3bIzw8HKmpqQCADRs2YPDgwYUu6+Ligu3bt8NgMCA7OxujR4/G8ePH0b59e4SGhiI7OxsGgwHTp0/HkiVLtPXCwsIAPPmK92PHjmnfwlu2bNkCT/iurq7YuXOn9mmsrVu3ombNmmjQoEGR+3DhwgV069YNtra2CAwMxJAhQxAdHV3ovq5btw4iguzsbGzevBlt27Yttj+tW7dGZGSk1p+NGzdi0aJFf6jOPNbW1jh79iyAJx/g+Omnn4zml3a/iIrDdzhUatq3b48RI0Zg6NChsLKyQrVq1bBs2bJCf6bg/fffx5w5c+Dn54fc3Fx4e3ujc+fOcHNzw4IFC9CzZ0/k5ubCzs4OwcHB2nrJycno1asXsrKyMG3aNPzv//4vAKBr164YOHAgli5dqi3brl07DBkyBIMHD4bBYICNjQ2WL19e7Kv8Ro0awcvLC71790aVKlVQqVIlTJs2rcBy06ZNw+zZs+Hr64vHjx/D1dUV7777brH90ev1mDhxIoYPHw7gya/lzp07F3Xr1v3ddeYZOHAgJkyYgC5duuD1119HmzZtjOaXdr+IisNvi6YXlqenp/ZpKjKN/SJL40sVIiJSgu9wiIhICb7DISIiJRg4RESkBAOHiIiUYOAQEZESDBwiIlKCgUNEREr8P5M0z1Op5XeCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in y.columns:    \n",
    "    plt.figure(figsize=(7, 4))\n",
    "    print(i)\n",
    "    sns.boxplot(x=[i], data=saharan , palette='Paired')\n",
    "#     plt.legend()\n",
    "    plt.title(f\"Boxplot of {i} in Sub-Saharan Africa\", fontsize=15)\n",
    "    plt.xlabel(f\"{i}\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = west_europe.drop(columns=['Country', 'Region', 'Year'])\n",
    "y = saharan.drop(columns=['Country', 'Region', 'Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Happiness Rank\n"
     ]
    },
    {
     "data": {
      "image/png": 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ggFOnThXaaUta/4SEBJP5//rrL5O/7kurUaNGsLa2xvnz59GoUSPtZ+3atdoN3vnz5+PPP/+Er68vJk+ejC1btsBoNGLbtm2Ij4/H9OnT0bhxY7zxxhv45ptvMGzYsDu6+WlhYYGpU6fC2toao0eP1sZiyZIl6NGjB6ZNm4aePXvCzc0Np0+fvuN3Nbm7u8PNzQ2hoaGYM2eOdlPfHBo0aIAnn3wS33//PWrWrAk7OzvtudatW+Pw4cPYt28fWrdurU1v3Lgxjh8/bjL+Fy9eRFhYGDIzMxEdHY2IiAjo9XqEhoZixYoV6NGjhzbGt27jJbV3v1lbWxfav0t6DRo3boz4+HiTs6Njx47hypUreO6557RpBfcDg8GAhIQENGvWDNWqVUPdunWRmppqMg4xMTH4+uuv7+oM6kHycFdfQFZWFtLT05Geno7z588jJiYG8+bNg729PXQ6HVq1agVXV1eMGDECe/fuRWJiIsaMGYOrV6/itddeAwBMmzYNAPD+++9jxIgRMBgM2rR8kydPxp9//on4+HiMGjUKer2+yL808j+3sHDhQiQnJ2Pjxo2YO3cuXn31VVSrVg1Vq1YFABw9ehSXLl0qtHxp6lWpVq1aiIqKwvHjx5GUlIQpU6Zg3759yMnJuaP1X7NmDVauXImTJ09i9uzZiI+P1z4fdCcqV66M119/HZ988gk2bNiAlJQULFmyBBEREdoliDNnzmDy5MmIjY3FmTNn8Ouvv+LatWtwdnZGtWrVsGzZMnz66ac4ffo0Dh8+jG3btmmfMSmt2rVrY9SoUdi3bx+WL1+ujdXevXtx5MgRJCcnY/78+diwYUOhsSqt/v37o1GjRhg/frxZ32rbqlUrREZGwsPDw+Rg7+bmhhs3biA2NtYkUAYMGIADBw5g+vTpOH78OP744w988MEHuHbtmvY25oiICCxZsgQpKSnYt28fYmNj4ezsDADaNh4fH4/MzMwS21Mt/3hw609+iLi4uODQoUNYv349UlJSMH/+fO2dcMXp3bs3rl27hjFjxiApKQlxcXEYMWIEmjZtCg8PD22+xYsXY+PGjTh+/DgmTpyIK1euaPvt4MGD8e2332LlypU4ffo01q5dixkzZphlDO63h/v8qoCFCxdi4cKFAG5es6xVqxZ8fHwQGhqq7Tzz58/H9OnTMXDgQOTl5cHV1RXLly9Hw4YNsW3bNvz8889YtGiRtiNMmjQJQ4YMga+vL6pUqQLg5qeI33vvPWRkZMDb2xvjxo0r8q8KLy8vhIWF4csvv8ScOXNQp04dhISEaB8Ys7GxQZ8+fRAeHo7Y2FhERESYLG9hYXHbelULCwvDlClT8Morr+Dxxx9HixYtMHz4cHzxxRe4fv26Nl9J6//KK69g7dq1mDp1Kpo0aYKFCxfe1RkKALz33nuwtrbGzJkz8c8//6Bhw4aYMmUKgoKCAADjx49HWFgYhg8fjsuXL6NRo0aYPn26FnARERGYO3culixZAmtra3h5eWHMmDF3XEfXrl2xdu1afPrpp3jhhRcwYcIEjB8/Hj169EDlypXh5OSEKVOmYOLEiYUuEZaGlZUVPvroI/To0QMrVqxAcHDwHbdRGh4eHli1ahXatGljMr1ixYpo3rw54uPjTV4rOzs7bftdvnw5qlWrBm9vb+3twC1atMC0adOwaNEihIeHw8bGBi+++KL2fMuWLdGiRQsEBwdj+PDh6Nev323bUykvL6/Qeubr1asXJk6ciJdffhmHDx/G5MmTYTAY4O/vj759++LAgQPFtlu7dm0sXrwYs2bNQteuXVG5cmX4+Phg5MiRJpe8unfvji+++AInT56Ek5MTlixZot1jCw4ORk5ODr7++mt89NFHqFu3LoYMGYK33npL7SCUAQsx559Ej5DY2FiEhIRgx44dqFevXlmXc9+VZv19fHzQrVs3DBky5D5XR/TgsLOzw8yZMxEYGFjWpdx3j8wlLyIiKlsMFCIiUoKXvIiISAmeoRARkRIMFCIiUoKBQkRESpT4OZRLlzJhNN7ZbZYnnrDBhQsZJc9YDnFsisZxKRrHpXgcm6KZe1wqVLBAzZpVi3yuxEAxGuWOAyV/OSoax6ZoHJeicVyKx7EpWlmNCy95ERGREgwUIiJSgoFCRERKMFCIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSgoFCRERKMFCIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSosR/Afyg+O23LUhLO1fWZRSSmXnzfzdXrWpTqvkrVrRCTo7BnCWVqTp16sHHx6+syyCiMvDQBEpa2jmcPX8OVWo8UdalmMjKuBkoBqtKpVsgO9eM1ZStrMsXyroEIipDD02gAECVGk/AwbtTWZdhImHbOgB44OoqC/ljQUTlE++hEBGREgwUIiJSgoFCRERKMFCIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSgoFCRERKMFCIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSgoFCRERKMFCIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSgoFCRERKMFCIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSgoFCRERKMFCIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlDB7oCQkHEBCwgFzd0NEjzAeRx4OVubuID7+LwCAg4OTubsiokcUjyMPB17yIiIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChE9UjIyrmHFiiXIzMxQ1kbBxyraN1ed5u6vJAwUInqk7N4dhdTUFMTE7FLWRsHHKto3V53m7q8kDBQiemRkZFzDwYP7AQgSEvbf1V/jt7aRlnZOe3zw4F84ePCve2rfXHXerg0V/ZWGlVlaLSArKxMZGRlYsWLJPbWTlnYeFSo9pqgqMofcG1lIu3Ljrl7rihWtkJNjMENVDzeOy01paedhY2NT4ny7d0cBEACAiCAmZhd8ff3vqK9b21i/frX2OC8vD4DFPbVvrjpv14aK/kqDZyhE9Mg4fDj+/w/6Nw/+hw/H33MbFy78oz2+6d9wuZv2zVXn7dpQ0V9pmP0MpUqVqqhSpSp69Ai5p3ZWrFiCK9m5iqoic7B+rAqqV69+V6+1rW01pKdfM0NVDzeOy02lPeu1t9fj4MG/kJeXB0tLS9jb6++4r1vbqFGjJi5fvlQgVCwAyF23b646b9eGiv5Kg2coRPTI8PBog/xLUhYWFmjd2uue2wgI6KI9trS0hKVlhXtq31x13q4NFf2VBgOFiB4ZNjbV4OjoDMACDg7OqFq15PsuJbVRp0497bGjowscHV3uqX1z1Xm7NlT0Vxpmv+RFRHQ/eXi0wYUL6ff0V/itbRR8LCL33L656jR3fyVhoBDRI8XGpto937O9tY1bH99r+0W1ae42VPRXEl7yIiIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlLCytwd6PUu5u6CiB5xPI48HMweKA4OTubugogecTyOPBx4yYuIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJSwKusC7kTW5QtI2LaurMswkXX5AgA8cHWVhazLF1C9br2yLoOIyshDEyh16jyYByorGxsAQNVK1qWav2JFK+TkGMxZUpmpXrfeA/s6EZH5PTSB4uPjV9YlKGFrWw3p6dfKugwiIuV4D4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIl/gvgChUs7qrhu12uPODYFI3jUjSOS/E4NkUz57jcrm0LERGz9UxEROUGL3kREZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESygNl7dq16NixI/z8/LBs2TLVzT9U5s+fj4CAAAQEBGDmzJkAgJiYGHTu3Bl+fn6YPXt2GVdYtsLCwjB69GgAHJd8v/32G4KCguDv74+pU6cC4NgAwJo1a7R9KSwsDED5HpeMjAx06tQJqampAIofi8OHDyMoKAgdOnTAuHHjYDAYzFuYKHTu3Dnx9vaWS5cuSWZmpnTu3FmSkpJUdvHQiI6Oltdee02ys7MlJydHQkJCZO3atdKuXTs5ffq05ObmSv/+/WX79u1lXWqZiImJkZYtW8oHH3wg169f57iIyOnTp6VNmzZy9uxZycnJkeDgYNm+fXu5H5usrCxxd3eXCxcuSG5urnTr1k22bt1absflr7/+kk6dOomDg4OkpKTcdv8JCAiQffv2iYjImDFjZNmyZWatTekZSkxMDFq1aoUaNWqgSpUq6NChAzZt2qSyi4eGra0tRo8ejYoVK8La2hrPPfcckpOT0ahRIzRs2BBWVlbo3LlzuRyfy5cvY/bs2Rg0aBAA4MCBAxwXAP/973/RsWNH1KtXD9bW1pg9ezYqV65c7scmLy8PRqMR169fh8FggMFggI2NTbkdlx9//BGTJk1CnTp1ABS//5w5cwY3btyAi4sLACAoKMjsY1Titw3fibS0NNja2mqP69SpgwMHDqjs4qHRpEkT7ffk5GRs3LgRvXv3LjQ+58+fL4vyytTEiRMxbNgwnD17FkDR2015HJdTp07B2toagwYNwtmzZ9G+fXs0adKk3I+NjY0Nhg4dCn9/f1SuXBnu7u7lepv5+OOPTR4XNxa3Tre1tTX7GCk9QzEajbCw+PerjUXE5HF5lJSUhP79+2PUqFFo2LBhuR+fn376CfXr14eHh4c2jdvNTXl5edi9ezemTZuGlStX4sCBA0hJSSn3Y3PkyBH8/PPP2LZtG3bt2oUKFSogOTm53I9LvuL2n7LYr5SeodSrVw9xcXHa4/T0dO20rDzau3cv3n33XYwdOxYBAQH4448/kJ6erj1fHsdnw4YNSE9PR2BgIK5cuYKsrCycOXMGlpaW2jzlcVwAoHbt2vDw8ECtWrUAAC+++CI2bdpU7scmKioKHh4eeOKJJwDcvHTz9ddfl/txyVevXr0ijyu3Tv/nn3/MPkZKz1Bat26N3bt34+LFi7h+/Tq2bNmCtm3bquzioXH27FmEhoYiPDwcAQEBAABnZ2ecPHkSp06dQl5eHtatW1fuxuebb77BunXrsGbNGrz77rvw8fHBokWLyv24AIC3tzeioqJw9epV5OXlYdeuXXjppZfK/dg0bdoUMTExyMrKgojgt99+475UQHFj0aBBAwxZHYIAAAWsSURBVFSqVAl79+4FcPOdcuYeI6VnKHXr1sWwYcMQEhKC3NxcdOvWDU5OTiq7eGh8/fXXyM7OxowZM7RpPXr0wIwZM/DOO+8gOzsb7dq1w0svvVSGVT4YKlWqxHHBzQPDm2++iZ49eyI3Nxeenp4IDg7Gs88+W67Hpk2bNjh06BCCgoJgbW0NvV6Pd955B56enuV6XPLdbv8JDw/H+PHjkZGRAQcHB4SEhJi1Fv7HRiIiUoKflCciIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSgoFCZcrOzg4XL140mRYZGYmBAweapb9x48YhJibGLG2XJDU1Ffb29ggMDNR+fH190adPH6SkpNx1u+YcL6I7ofRzKEQPulu/B+l+e+yxx7BmzRrtsYhg6tSpmD17Nj799NMyrIzo3jFQ6IF28uRJTJkyBZmZmUhPT0fTpk3x2WefoVKlSmjWrBkGDBiAXbt2ISsrC++//z78/PwQGRmJTZs2wWg04u+//0bdunUxY8YM1K1bF3369EGvXr3g6OiI119/He3atcP+/ftx9epVjBw5Er6+vgCAzz//HFu2bIHRaESDBg0wadIk1K1bF1u2bMHnn38OCwsLWFpaYtSoUXB3dy92ekmys7ORlpaG2rVrl7i+er0eb731FqKjo5GWlqZ9CLKgTZs2ITw8HF999RWeffZZ9S8I0W0wUKjM9e3bFxUq/Hv19cqVK7CzswNw86u6u3TpgsDAQOTm5iIoKAjbt29Hhw4dkJeXh8qVKyMyMhJHjhxB79690bx5cwDAnj17EBkZiWeeeQbh4eH4+OOPMXfuXJN+U1JS0KZNG0yYMAGbN2/GtGnT4Ovri9WrVyMxMRE//fQTrKyssHLlSowfPx4LFy7EzJkzER4eDhcXF0RFRSE2Nhbu7u7FTr/VjRs3EBgYCKPRiAsXLqB69erw8/PDW2+9VeL65uTkoGbNmlixYgUOHjyI4OBgdO3aVWt73bp1+OKLL/D999+jfv36yl8nopIwUKjMfffdd9oXIgI37wls3rwZADBy5EhER0dj4cKFSE5ORlpaGrKysrR5e/fuDeDm9z3pdDrs2bMHAODp6YlnnnkGANC9e3cEBgYW6tfa2hrt2rUDADRr1gyXL18GAGzbtg3x8fHawTr/f3EAQEBAAN5++220a9cOnp6eGDBgwG2n36rgJa9du3Zh5MiR8Pb2RtWqVUu1vi+88AIAwMHBATk5Odpz8fHx2LVrF8aOHcswoTLDQKEH2vvvv4+8vDz4+/ujffv2OHv2LAp+W1DBb5w1Go3a4+KmF2Rtba2dGRX8Wm+j0WhyOSknJwdXrlwBAAwbNgxdu3ZFdHQ0IiMjsXjxYqxatarY6bfj5eWFfv36YejQoVi/fj1sbGxKXN9KlSqZ1Jv/XLVq1fDJJ5/gvffeQ/v27fHkk0+WNLREyvFdXvRAi4qKQmhoKDp27AgA2L9/P/Ly8rTnV69eDQBISEjAyZMntctMv//+u/bPhFasWAFvb+9S99mmTRusWrUKGRkZAIA5c+Zg1KhRMBgM8PHxwfXr1xEcHIxJkybh6NGjyMnJKXZ6Sfr374+qVatql+NKWt/iPP300/Dw8ECfPn3wwQcfwGg0lnp9iVThGQo90IYNG4bQ0FBUqVIFNjY2cHd3x+nTp7Xn//zzT/z4448wGo2YPXs2qlevDuDmN1+PHDkS6enpaNy4MaZMmVLqPl999VWcP38e3bt3h4WFBerXr48ZM2bAysoKY8eOxYgRI2BlZQULCwtMmzYNFStWLHZ6SaytrTFhwgS8+eab6NatW4nrW5JBgwbht99+w6JFi7T7MkT3C79tmB5adnZ22L17t8n9F+DfezBffvllGVVGVD7xkhcRESnBMxQiIlKCZyhERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhIif8DOADmA2N8QnEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Happiness Score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GDP per Capita\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Social support\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Healthy life expectancy\n"
     ]
    },
    {
     "data": {
      "image/png": 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Y8InX/ygGBgbw9PTE6NGjS8wzNjYudXkACA4OLvE4Fh/UgKK37l69ehUvvfQSjh49qrPDAaWPFT09PW39c+bM0XmC8uD9nvYxnj17Ng4cOIB+/frB2dkZkyZNKvF2U319/RLrLa5/We0ZGBjAzc0Ne/fuRc+ePXHs2DFMmTLlifv3YN0ebrs0TZs2xRtvvIEzZ84gPDwcHh4e2ryuXbvi22+/xdmzZ6Gvrw9ra2utj61atcIXX3xRYn316tUDAEyfPh0+Pj44cuQIwsLCEBAQgN27d2PdunWlbnNZ6wNK39efdnuB0vfTspT2ZO7BfftJjyOPGqvA0+8/FeG5uChf/AAWFhaidevWSE9PR3x8vDY/NzcXUVFR2lsEs7KyEBAQgKFDh2LQoEGYOXMmsrKydNZ57tw57f/Lly/j9u3bMDMzK9F2y5YtcerUKZ1pp06dgoGBAZo1a1biwPqwJ+mvSq1atUJCQgKaN2+u/cXGxmLFihUAii7+vf7661i/fj1GjhyJbt26aRcnS9tRytq+0hgZGaFbt27Yt28fDh48WOIVXnm1atUKcXFxOtuWlpaGwMBA3L17t8TyrVu3BgDcunVL5z4bN27EwYMHteXmzp2Lxo0bIzAwEF9++aXO2ACA6Oho7f/8/HxER0fD3Nwc9erVQ+PGjXHlyhWd9YeHh+Prr7+Gvr4+WrZsicuXL+v07/fff0ePHj2QnZ2tU9+srCzs2LEDCxcuxPTp0+Hp6YkWLVogOTn5id+51LJlSwC64zsrKwtdunTRPsfk5eWF06dPY/v27WjZsqXSzxWVpnPnzoiIiEBMTAzs7e216R06dEBhYSF27tyJDh06aAfv1q1b48qVK3j11Ve1mjZs2BCLFy9GbGwskpKSMG/ePBgbG8PHxwdffvklAgMDceTIEdy6davEmC1rfZXNwMAABQUF2hstAGhvanmUJz2OPDhWgaI3LxQ/vk+7/1SESg+Ue/fuITU1FampqUhJSUF4eDiCgoJgZmYGExMT2NrawsbGBh9++CFOnTqF2NhYBAQE4M6dOxg8eDCAog9IAcDkyZPx4YcfIj8/X5tWbMGCBfjjjz8QFRWFadOmwdLSstRnmePGjUNoaCi++uorJCQkIDQ0FCtXrsTAgQNRr1497fTIX3/9hfT09BL3f5L+qjRu3Dj8+OOPWLduHRISEnD48GHMnTsXtWvXRs2aNWFkZISrV6/i6NGjuHr1Kn744QctbB48DVSsePuioqKeatB5eXlh586dyMnJUfb5oeJTI4sXL0ZcXBxOnDiB6dOnIzMzs9RnWM2bN0fv3r0xZ84cHDlyBElJSVixYgWCg4O1A29ISAgOHTqEjz/+GO7u7ujWrRumT5+uU4v//d//RWhoKOLi4jB37lxkZGRoj924ceOwceNGbN26FUlJSdizZw+WLFmi9cfd3R116tRBQEAAYmNjcfr0aSxevBidOnXCyy+/jDp16iAlJQXJycmoVasWDA0NcfDgQSQlJeH8+fOYMmUKrl+/XupjU5oWLVrg7bffxoIFC3Dy5EnExcUhICAA9erV006DmZubo3Xr1li7di28vLye6TF5Era2tti9ezeMjY21ugNFrwjat2+Pn3/+WefzJ+7u7mjQoAEmTpyIqKgoxMbGYsqUKTh79ixat26NBg0aIDQ0FPPnz0dcXBzi4uIQGhqKZs2aoUGDBiX2ybLWp1pGRoZ2DHvwr/j4YG1tDT09PaxcuRJXrlzBTz/9VOKdgaXV8EmOI8eOHcOaNWtw+fJlbNq0CaGhoRgxYgSAp99/KkKlB8pXX30Fe3t72Nvbw9HREdOmTYOdnR3Wrl0LPT096Onp4YsvvkCLFi0wZswYDB48GOnp6fj222/RtGlTHDp0CDt27MCCBQtQp04d1K1bF/PmzcOOHTtw6NAhrR1PT09MnDgRw4cPR7NmzbBu3bpST3l169YNgYGBCAkJgZubG5YtW4Zhw4Zh1qxZAIC6devCz88Py5cvx+zZs0vcv6z+qubg4IClS5diz549cHNzw9y5c+Hp6amdNhk2bBh69uyJSZMmwcPDA1u2bMGCBQtgaGiIqKioEuvr3LkzOnXqhKFDh2Lbtm1P3I8ePXqgdu3acHNzK/W0QXmYmppi7dq1+OOPP7THr2PHjqWeyii2aNEidO/eHTNnzoSbmxt+++03BAUFoUuXLrhx4wY+/vhjjBgxQnsWN3/+fFy/fl0LWaDoGsyaNWvQr18/JCUlYdOmTdq1s6FDh2Ly5Mn4+uuv0bt3b3z22WcYP348JkyYAKDoc1Vff/01srKyMHDgQPj7+6Nz587adT0vLy8UFBSgd+/eiImJwWeffYbo6Gi4ublh/PjxqF+/PkaMGFHiVdPjLFmyBJaWlhg/fjwGDRqEvLw8rF+/Xudx8PT0RH5+vpJrW2WxtbXFvXv3Sv3Qop2dHe7du4cuXbpo02rXro0NGzagdu3aGD58OIYOHYr8/Hx88803aNiwIerVq4evvvoKycnJGDRoEAYMGICcnBxtH354nyxrfaqNHz9eO4Y9+Ff8rQFNmzbF/Pnz8csvv6BXr17Ytm2b9vb8R3nS44izszP+/PNP9O3bF9999x2WLVsGJycnAOXbf1TTkyd9rf2CiIiIwLBhw3DkyBE0adKkqrtTbaWnp6Nbt27YunVrqRcoXxSmpqZYunQp+vbtW9VdUSowMBCXL1/GmjVrqrorpIifnx+aNWuGjz/+uKq78kiVflGeXmzp6ek4ceIEQkJC0KZNmxc6TKqj4tNgwcHBlfrMlAhgoNBTysvLw6xZs9CoUSPt0+30/Dh48CC+++47+Pn5KfmaE6KnUe1OeRERUdV4Lt42TERELz4GChERKcFAISIiJcq8KJ+efheFhZVzmaVhw7q4dSur7AWrOdahCOvAGhRjHYo8D3XQ19dDgwZ1Sp1XZqAUFkqlBUpxe8Q6FGMdWINirEOR57kOPOVFRERKMFCIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSgoFCRERKMFCIiEgJBgoRESnBQCEiIiUYKEREpAQDhYiIlGCgEBGREgwUIiJSgoFCRERKMFCIiEiJMn8CmHT9+us+3Lx5o0LbqFmzBnJz8yu0DVXu3i36fes6deoqX/eLVIfyatSoCZycnKu6G0RKMFCe0s2bN3A95QYMX21YcY3k5FXcuhW7l1UUKPk1aqlf+QtUh/K4d/tWVXeBSCkGSjkYvtoQFo5uVd2N50L0ob0AwHqUQ3HtiKoLXkMhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpUeGBEh39J6Kj/6zoZoiI6DEq41hco0LXDiAq6gwAwMLCqqKbIiKiR6iMYzFPeRERkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUqJGRTdw795dZGVlITh4U5nL1qxZA7m5+RXdpWdy82YK9GvVrupuUDWQd/8ebmbcf+y+8SLsE5WBdSjyLHW4eTMFdevWVdwjXXyFQkRESlT4KxRDwzowNKyDIUOGlbmssXE9pKZmVnSXnklw8CZk5ORVdTeoGjCobYj69es/dt94EfaJysA6FHmWOjzJWaJnxVcoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkRI2KbsDS0rqimyAiojJUxrG4wgPFwsKqopsgIqIyVMaxmKe8iIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRIMFCIiUoKBQkRESjBQiIhIiRpV3YEX0b3btxB9aG9Vd+O5cO/2LQBgPcrh3u1bqN+4SVV3g0gZBspTatSo4g8ANWvWQG5ufoW3o0KNunUBAHVqGShf94tUh/Ko37hJpYwnosrCQHlKTk7OFd6GsXE9pKZmVng7zzvWgejFwmsoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUKPMngPX19SqjH1XW3vOKdSjCOrAGxViHIlVdh8e1ryciUol9ISKiaoqnvIiISAkGChERKcFAISIiJRgoRESkBAOFiIiUYKAQEZESDBQiIlKCgUJEREowUIiISIlKC5Q9e/agd+/ecHZ2xpYtW0rMj46ORv/+/eHh4YExY8bgzp07AIBr167Bx8cHrq6uGDduHO7evVtZXVauvDXYtWsX7O3t0bdvX/Tt2xcrVqyo7K4rVVYdjhw5And3d7i7u2PKlCnaY16dxgJQ/jpUt/EAAFlZWXBzc8OVK1dKzIuJiYGXlxdcXFwwa9Ys5OfnA6h+4wEoXx2eq/EgleDGjRvi6Ogo6enpcvfuXXF3d5eLFy/qLDN06FA5fPiwiIgsXrxYPv30UxERee+992Tv3r0iIvLFF1/I0qVLK6PLyj1LDRYuXCh79uyp9D5XhLLqkJGRIba2ttq0devWyUcffSQi1WcsiDxbHarTeBAROXPmjLi5uYmFhYUkJyeXmN+nTx85ffq0iIgEBATIli1bRKR6jQeR8tfheRoPlfIKJTw8HLa2tnj11VdhaGgIFxcX/PzzzzrLFBYWas8wsrOzUbt2beTl5SEyMhIuLi4AAC8vrxL3e1GUtwYAEBUVhV27dsHd3R0ffvghMjIyKr3/qpRVh4SEBPzP//wPWrVqBQBwdHTEgQMHqtVYAMpfB6B6jQcA2LZtG+bNm4dGjRqVmHf16lXcv38f1tbWAP77uFe38QCUrw7A8zUeKiVQbt68CWNjY+12o0aNkJKSorPMjBkzMHv2bNjb2yM8PBxDhgxBeno66tatixo1ir4U2djYuMT9XhTlrQFQtN3jx4/H7t278frrr2PhwoWV2neVyqrDP/7xD9y4cQMXLlwAAISGhuLvv/+uVmMBKH8dgOo1HgDg448/RocOHUqd93Cdih/36jYegPLVofj/52U8VEqgFBYWQk/vv195LCI6t+/fv49Zs2Zh48aNCAsLg7e3N6ZPn15iOQAlbr8oylsDAFi1ahXat28PPT09jBo1Ct0LQecAAAniSURBVL///nul91+VsurwyiuvIDAwEHPmzEH//v3RqFEjGBgYVKuxAJS/DkD1Gg9leVSdqtt4KMvjxsvzNB4qJVCaNGmC1NRU7XZqaqrOy7rY2FjUqlULVlZWAIDBgwfjxIkTMDIyQmZmJgoKCkq934ukvDXIzMzExo0bteVEBC+99FKl9Vu1supQUFCAJk2aYPv27dixYwfMzMzQtGnTajUWgPLXobqNh7I8XKe///4bjRo1qnbjoSyPqsPzNh4qJVDs7Oxw7NgxpKWlITs7G/v27YODg4M2v3nz5rhx4wbi4+MBAAcPHoSlpSUMDAzQoUMH/PTTTwCAkJAQnfu9SMpbA0NDQ6xfvx5nz54FAGzevBk9e/askm1Qoaw66OnpYcSIEUhJSYGIYOPGjejdu3e1GgtA+etQ3cZDWd544w3UqlULp06dAgD88MMPcHBwqHbjoSyPqsNzNx4q6+r/7t27pU+fPuLs7Czr1q0TEZFRo0bJn3/+KSIihw8fFnd3d3Fzc5Phw4dLUlKSiIhcuXJFfH19pVevXjJixAi5fft2ZXVZufLWIDIyUjw9PcXV1VXGjh0rd+7cqbJtUKGsOhw6dEjc3NzE2dlZ5s2bJ7m5uSJSvcaCSPnrUN3GQzFHR0ft3U0P1iEmJkb69+8vLi4uMnnyZMnJyRGR6jceij1tHZ6n8cBfbCQiIiX4SXkiIlKCgUJEREowUIiISAkGChERKcFAISIiJRgoVC6mpqZIS0vTmbZz506MGTOm3OuMiIiAm5sbAODPP//E3LlzS0x/1vV+/vnnCAkJ0frbo0cPjBw5stzrrmyZmZkYNmxYVXeDqFQ1qroDRKW5dOlShXw30wcffKD9HxISgkmTJqFv377K26koGRkZiIqKqupuEJWKgUIVIjc3F8uXL0dkZCQKCgpgbm6O2bNno27dujh06BDWrl2L3NxcpKWlwdPTExMnTtTue/36daxcuRKZmZkICAiAp6cn7t27h0mTJiE+Ph45OTlYtGgRzM3N0b17d2zbtg0tWrQAALz77rvw9fXFO++8U2q/ZsyYgdatWyMlJQVRUVG4cuUK0tPT4e3t/cj+PuzLL7/Evn37UFhYiDfeeAPz5s1D/fr10b9/f3h7e8PHxwfbt2/Hpk2bsG3bNrz33nswNzfHqVOnkJ6ejr59++Lf//43AOCPP/7A8uXLkZ2dDX19fUyYMAGOjo4AgLVr12LXrl2oUaMGmjdvjiVLliAgIAD3799H3759sXPnTuzatQtbt25FXl4eMjIyMHr0aHh7e2Pnzp3Yv38/9PX1kZiYiNq1ayMwMBAtW7ZEamoq5s2bh/j4eOjr62PIkCF455134ObmhiNHjqBevXoQEbi6uuLzzz/HW2+9pXp4UHVVZR+ppBeaiYmJuLm5iYeHh/bXvXt3ee+990REJCgoSJYsWSKFhYUiIvLJJ5/IvHnzpLCwUHx9feXy5csiUvS7IGZmZnLr1i05fvy49OnTR0REduzYoa3r+PHjYmZmJmfOnBERkQ0bNsiwYcNERGTRokUSGBgoIiKJiYnSvXt3yc/P1+nrg+udPn26rF+/XkREfH19JTQ09LH9fdiuXbtk4sSJkpeXJyIiwcHBMmrUKBERuXDhgnTq1EkOHz4sdnZ2EhcXp7UzevRoyc3NlYyMDHFxcZFff/1Vbt++Lc7Oztqnom/cuCEODg5y9epVOXDggDg7O2uf/v7Pf/4jq1evluTkZLG2thYRkaysLBk0aJCkpaWJiMjp06e1eTt27JD27dvL9evXRaToNzOmTZsmIiL+/v5aze7cuSN9+vSRhIQEGTdunGzevFlERMLDw2XQoEGPHwRED+ErFCq3b775BkZGRtrtnTt34pdffgEAHD58GJmZmQgPDwcA5OXloWHDhtDT08OaNWtw+PBh7N27F3FxcRARZGdnP7atpk2bom3btgCAt956Czt27AAAeHt7w9fXF5MmTcLWrVsxYMCAcn053qP6+7BDhw4hKioK/fv3B1D0LbDFfTc1NcWECRMwZswYLFmyBP/85z+1+w0ePBgGBgYwMDCAq6srwsLCoK+vj9TUVPj7+2vL6enp4a+//sKxY8fg6uqK+vXrAwACAgIAQOeX/OrUqYM1a9bgyJEjSEhIwIULF3Dv3j1tvoWFBZo0aQIAMDc3x/79+wEU/RbL1KlTAQD16tXD3r17AQA+Pj5YtmwZfHx8sHXrVgwdOvSp60j/tzFQqEIUFhZi5syZ6N69OwDg7t27yMnJwb1799CvXz+888476NChA/r3748DBw5AyvgGoOKvbgegfX05ALRo0QKmpqY4ePAg9u7di23btintb2nLjRo1Ct7e3gCKTu09+INGFy9exGuvvYazZ8/C09NTm178ux1A0TfC6uvro6CgAC1btsT27du1eSkpKTAyMsLx48d1vq78zp072k9CF7tx4wYGDx6MQYMGoX379nB1dcWhQ4e0+cU/0Abo1qxGjRo6605OTkaDBg1gZ2eH7OxsHDt2DCdPnkRgYOATVo+oCN/lRRXC3t4eW7ZsQW5uLgoLCzFnzhx8+umnSExMRFZWFiZOnAgnJydERERoyzzopZde0n4zuyze3t5YunQprKys0LhxY6X9LW2577//HllZWQCK3jU2bdo0AMC+ffsQERGB3bt34+jRo9ovLALA7t27UVhYiIyMDISGhsLJyQnW1tZITExEZGQkgKLfDHdxcUFKSgrs7Oywf/9+rZ2goCBs3LgRNWrUQEFBAUQE586dg5GREcaPHw97e3stTIq/0v1RunTpor3Cy8zMxPDhw5GQkAA9PT14e3tj1qxZcHNzQ61atcpVS/q/i69QqEKMHz8egYGB6NevHwoKCmBmZoYZM2bA0NAQPXr0QK9evVCzZk2YmJigVatWSExMRM2aNbX7W1tbY9WqVZgwYQL8/Pwe25ajoyNmz56t/cKlyv4+bODAgUhJScGgQYOgp6eH119/HUuWLMH169cxb948rFmzBkZGRliyZAn8/f3Rpk0bAEU/oDZgwADcvXsX3t7e6NKlCwBg5cqVWLp0KXJyciAiWLp0Kd588028+eabuHTpknbaqVWrVvjoo4/w8ssvw8rKCn369MGGDRvQuHFjuLq6Qk9PD506dYKRkRESExMfu61z587F/Pnz4e7uDhHBmDFjtH7269cPgYGBGDx4cLlrSf938duG6YV3+vRpzJ49G3v37n0uf7XPz88PPj4+cHV1requlOnHH3/Erl27sH79+qruCr2A+AqFXmjTp0/HiRMnsGLFiucyTF4kfn5+SEtLw+rVq6u6K/SC4isUIiJSghfliYhICQYKEREpwUAhIiIlGChERKQEA4WIiJRgoBARkRL/D1XE+4fxcq4hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Freedom to make life choices\n"
     ]
    },
    {
     "data": {
      "image/png": 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NGxqckFq3bo3i4mJMnToVCQkJt5wEvV4PS8v/7SI5ORn5+fnIz89HbGwsOnfubFiMpWWF25dvqkz5F1/LlJ1kbh7DGHPrNUfTpk0N5saY8vVaWVnBw8MD77zzToXt7OzstAdOeHh4hT6tra0BwOC1sPLjlrldf+W3M9arsfk2R2Xn1Ng6Y/0BQEZGBoYMGYJmzZrB29sb3bp1Q9OmTTFw4ECD7R599FEsWrQIr732Gr744gvtLa+m5r4qyu6bkJCQCsfDrebUnGPN2G3l/784bWzOy9bdPHb5+90YU3NjYWGBFStW4M0330RERAQiIyMxYcIEDB482OgvjMaUzZGxeo3p3Lkz1q9fj7i4OFy6dEk7X1hYWMDNzQ2HDx9GnTp14O7ubtDHmDFjEBAQUGG8Zs2awdraGt9//z2OHDmCyMhIRERE4IsvvsDixYuN3sbUeBkZGQCM3xfG5vx2/VpaWpo8lxhT9lp0mTp16hjMtbnHyc31lp1/K3NsG2xjcotbKCtSr9ejdevWuHLlCpKSkrT1hYWFiImJ0d5CnJubi5kzZ+Lll1/G4MGDMWvWLOTm5hqMeeLECe3/Z8+eRXZ2NhwcHCrsu2XLlhX+PuPIkSOwsrIy+M34VsypV5VWrVohMTER//jHP9CiRQu0aNECderUwQcffIC0tDS0bt0aQOkLnWXrW7RogfXr12P37t0ASn+TO3r0qMG45eeuVatWSEpKMngWcvnyZZw9exYtW7astl7Kz3NNzOnPP/+MvLw87Q0LPj4+uHLlCgDDB2+HDh3Qvn17TJgwAStXrsS5c+cAmJ77qmjRogWsrKyQnp5ucL9t3779lm8cadmyJc6ePWvwQv/+/fvRrVs37UXZ22nVqhWOHTtmsKzscXHz/dygQQM0adKkwt9bBAYG4tNPPzU5NzExMVi4cCFatWqF0aNH4/PPP8fkyZOxdetWs+anMlxdXaHX6/Htt9+ibdu2Bs/M3d3dcerUKRw7dswgZFq1aoXk5GSD+yA+Ph7Lly8HAPz444/YuHEjOnTogMmTJ2Pbtm3w8PDA999/D6DiLzimxqtpVlZWBudNvV5v8IYeY8w9TsqfQ65evYqzZ8/CwcGhUsd2eWaFzPXr15GZmYnMzEykp6cjKioKISEhcHBwgE6nQ+fOneHq6oq3334bR44cQXx8PGbOnImcnBwMGTIEAPDBBx8AAKZMmYK3334bxcXF2rIy77//Po4ePYqYmBhMmzYN7dq1M7iEVmb8+PH46aefsG7dOiQnJ+Onn37CqlWrMGjQIDRo0AD16tUDAJw+fVo7CZVnTr2qDB8+HDk5OZgxYwZOnz6NmJgYTJkyBcnJyXjqqafQokUL9O7dG7Nnz0ZERARSUlKwfPlyhIeHawfE6NGj8dNPP+GLL77A2bNnERoaahC6ffv2hZ2dHaZMmYKTJ08iNjYWU6ZMwUMPPYQ+ffpUWy/16tXDmTNnkJWVVSNz2qhRI+Tm5mLnzp24cOECdu/ejTlz5gCAdkmtvFGjRqFFixZ45513ICIm574qHnzwQbz66qv48MMP8eOPPyI1NRUbNmzA6tWrjV5KAYCAgADUq1cPM2fORHx8PI4dO4aFCxeiY8eO2uXL2xk7dixiYmKwePFiJCUlYf/+/Xj//ffRtWtXo79MjBkzBuvXr8cPP/yAlJQUhIaG4o8//kDXrl1Nzk2DBg0QFhaGjz76CCkpKTh16hT27t1r8i3+VWFtbQ1XV1ds2bLFIEgAwMPDAwkJCUhKSjK4IjJ+/Hj88MMPWLt2LZKTk7Fv3z68++67sLGxgbW1NQoLC7F48WJ8//33uHDhAqKjo3Hy5Ek4OzsDKD2mr169iqSkJBQUFJgcrzqVlJRo59mb/5X90uHi4oL9+/dj//79SE5Oxvvvv2/y3ZHmHicfffQRIiMjER8fj2nTpqFRo0bw8/Or1LFdnlnXhtatW4d169YBKH0vt52dHXx8fDBhwgQt+T/++GMsXLgQQUFBKCkpgaurK7766is88cQT2Lt3LzZv3oxPP/1UC4A5c+bgjTfeQM+ePbVr8f3798ekSZOQm5sLb29vBAcHG3065unpicWLF2PNmjVYuXIlmjZtihEjRiAoKAgAUL9+fQQGBmLZsmU4ePAgVq9ebXB7CwuL29arUpMmTfD5559j2bJlGDx4MGxsbNCpUyesXLlSO2jnz5+PDz/8ELNmzcK1a9fQsmVLhISEaH9s1qNHDyxcuBChoaFYtmwZ3N3dMXjwYCQmJgIofXr82WefYdGiRXjllVdQt25duLm5ISwsDA899FC19TJ27FiEhoZqb+VVPad+fn6IiYnB/Pnzcf36dTz55JN44403sHbtWsTExBj8oSRQeilg3rx5GDp0KMLDw/Hyyy+bnPuqmDRpEqysrLBkyRJcunQJTzzxBObOnWvwB4Xl2dra4rPPPsPChQsxaNAg1KtXD7169cLUqVPN2p9Op8Mnn3yCFStW4Msvv8TDDz+MPn36YNKkSUa3HzFiBPLz87F06VJcvnwZrVu3xieffKI9e77d3Dz11FNYvXo1Vq1ahQ0bNsDKygqenp6YOXNm5SbLTG5uboiOjkaXLl0Mljdv3hyPPfYYbGxsDC51enl5YcmSJVi7di1WrVoFOzs79O/fH5MnTwZQeo7JyspCSEgI0tLS0LhxYwwYMADjxo0DAPTq1QubNm1C37598eGHH6JXr163Ha86XbhwoUKfZaZNm4bRo0dj1KhRSElJwVtvvQVra2u89NJLJn9xNPc4GTx4MObOnYuMjAx07NgRX3zxhXZuvtNjuzwLud1Fwhpy8OBBjBgxAhEREWjevHltl0NE9Ldx/vx5dO/eHWFhYdobAqoTPyCTiIiUYcgQEZEyd8XlMiIiuj/xmQwRESnDkCEiImUYMkREpIzZn6Fy5Uoe9Pq7++Wbxo3rIysr1/SG96j7vT/g/u/xfu8PuP97vN/7A8zrsU4dCzRqVM/kWGaHjF4vd33IALgnaqyK+70/4P7v8X7vD7j/e7zf+wOqr0deLiMiImUYMkREpAxDhoiIlGHIEBGRMgwZIiJShiFDRETKMGSIiEgZhgwRESnDkCEiImUYMkREpAxDhoiIlGHIEBGRMgwZIiJShiFDRETKMGSIiEgZhgwRESnDkCEiImUYMkREpIzZX79MRPevPXt2ISPjYrWMZW1ticLC4moZqyry8kq/o75evfrVOq45/TVt2hw+Pr7Vut97FUOGiJCRcRFp6Rdh+3Djqg9WUFT1MarB9dzSkCm2fKB6BzbR3/XsrOrd3z2OIUNEAADbhxvD0du/tsuoNrF7/wsANd5T2X6pFF+TISIiZRgyRESkDEOGiIiUYcgQEZEyDBkiIlKGIUNERMowZIiISBmGDBERKcOQISIiZRgyRESkDEOGiIiUYcgQEZEyDBkiIlKGIUNERMowZIiISBmGDBERKcOQISIiZRgyRESkDEOGiIiUYcgQEZEyDBkiIlKGIUNERMowZIiISBmGDBERKcOQISIiZRgyRESkDEOGiIiUYcgQEZEyDBkiIlKGIUNERMowZIiISBmGDBERKcOQISIiZRgyRESkDEOGiIiUYcgQEZEyDBkiIlKGIUNERMowZIiISBmGDBERKcOQIbPExv6J2Ng/a7sMIqqimn4sW9bYnuieFhNzHADg6OhUy5UQUVXU9GOZz2SIiEgZhgwRESnDkCEiImUYMkREpAxDhoiIlGHIEBGRMgwZIiJShiFDRETKMGSIiEgZhgwRESnDkCEiImUYMkREpAxDhoiIlGHIEBGRMgwZIiJShiFDRETKMGSIiEgZhgwRESnDkCEiImUYMkREpAxDhoiIlGHIEBGRMgwZIiJShiFDRETKMGSIiEgZhgwRESnDkCEiImUYMkREpAxDhoiIlGHIEBGRMgwZIiJShiFDRETKMGSIiEgZhgwRESnDkCEiImUYMkREpAxDhoiIlGHIEBGRMgwZIiJSpsZCJjf3GsLDNyAvL/e2y2633Jwxq7vGmqa6hruhRyL6+6ixkImOjsT586mIitp/22W3W27OmNVdY01TXcPd0CMR/X3USMjk5l7DiRN/ABDExv6BvLxco8tuta25Y1Z3jTVNdQ13Q49E9PdiWRM7iY6OBCAAABEp91u04bKePf2Mbtuzp59ZYz711OBqrdHYflVSXUNVxr9+PQ+5ubkID99QbfUYY21ticLCYqX7qE13a38ZGemo84BNbZdxXyjKv46Mq/nKHyuVlZGRjvr169fY/mrkmcypUzEoKSkBAJSUlODUqRijy261rbljVneNNU11DXdDj0T091Ijz2QcHNrhxInjKCkpQd26deHg0A4AjC671bbmjlndNdYk1TVUZXxb23qwta2HoUNHVGtNN2vSpAEyM68p3Udtulv7Cw/fgKsFRbVdxn3BysYWDRs2VP5YqayafoZVI89k3Ny6ALAAAFhYWMDd3dPosltta+6Y1V1jTVNdw93QIxH9vdRIyNSv3wBt2zoDsICjozPq1atvdNmttjV3zOqusaapruFu6JGI/l5q5HIZUPpbdFZWpsFvz8aW3W65OWNWd401TXUNd0OPRPT3UWMhU79+gwrXKI0tu93yym5XlRprmuoa7oYeiejvgx8rQ0REyjBkiIhIGYYMEREpw5AhIiJlGDJERKQMQ4aIiJRhyBARkTIMGSIiUoYhQ0REyjBkiIhIGYYMEREpw5AhIiJlGDJERKQMQ4aIiJRhyBARkTIMGSIiUoYhQ0REyjBkiIhIGYYMEREpw5AhIiJlGDJERKQMQ4aIiJRhyBARkTIMGSIiUoYhQ0REyjBkiIhIGYYMEREpw5AhIiJlGDJERKQMQ4aIiJRhyBARkTIMGSIiUoYhQ0REyjBkiIhIGYYMEREpw5AhIiJlGDJERKSMZW0XQPeGdu1carsEIqoGNf1YZsiQWRwdnWq7BCKqBjX9WOblMiIiUoYhQ0REyjBkiIhIGYYMEREpw5AhIiJlGDJERKQMQ4aIiJRhyBARkTIMGSIiUoYhQ0REyjBkiIhIGYYMEREpw5AhIiJlGDJERKQMQ4aIiJRhyBARkTIMGSIiUoYhQ0REyjBkiIhIGYYMEREpw5AhIiJlGDJERKQMQ4aIiJRhyBARkTIMGSIiUoYhQ0REyjBkiIhIGYYMEREpw5AhIiJlGDJERKQMQ4aIiJRhyBARkTIMGSIiUoYhQ0REyjBkiIhIGYYMEREpw5AhIiJlGDJERKQMQ4aIiJRhyBARkTIMGSIiUsaytgsgorvD9ewsxO79b22XUW2uZ2cBQI33dD07Cw2bNa/Rfd7NGDJEhKZNq++kaG1ticLC4mobr7Is69cHANR7wKpaxzXVX8Nmzat1Pu91DBkigo+Pb7WN1aRJA2RmXqu28e4293t/1Y2vyRARkTIMGSIiUoYhQ0REyjBkiIhIGYYMEREpw5AhIiJlGDJERKQMQ4aIiJRhyBARkTIMGSIiUoYhQ0REyjBkiIhIGYYMEREpw5AhIiJlGDJERKQMQ4aIiJRhyBARkTIMGSIiUsbsr1+uU8dCZR3V5l6ps7Lu9/6A+7/H+70/4P7v8X7vDzDdo7lzYCEiUh0FERER3YyXy4iISBmGDBERKcOQISIiZRgyRESkDEOGiIiUYcgQEZEyDBkiIlKGIUNERMowZIiISJl7LmS2b9+O3r17w9fXF2FhYbfcbtq0adiyZUsNVlZ9TPX4yy+/oF+/fujbty/eeOMNXL16tRaqrBpTPf78888ICAhAnz59MGPGDBQWFtZClZVn7nG6b98++Pj41GBl1cdUjx9//DG8vb3Rr18/9OvX77bzcDcy1V9SUhICAwPRt29fjB49+r57HJ46dUq77/r16wdPT0/4+/vf+U7kHnLx4kXx9vaWK1euSF5engQEBEhCQkKFbYKCgsTJyUk2b95cS5VWnqker127Jh4eHnLx4kUREVmxYoXMmzevtsqtFFM95uXlSZcuXSQzM1NERCZNmiTh4eG1Ve4dM+c4FRHJzMyUF154Qby9vWuhyqoxp8egoCA5evRoLVVYNab60+v14uvrKxERESIisnTpUlmyZEltlVsp5h6nIiLXr1+XPn36yKFDh+54P/fUM2NFa6AAAAzkSURBVJmoqCh07twZDz/8MGxtbdGrVy/s2LHDYJvt27eje/fu8PPzq6Uqq8ZUj0VFRZgzZw6aNWsGALC3t0daWlptlVsppnq0tbXFnj178Mgjj+DGjRvIysrCQw89VIsV3xlzjlMAeOedd/Dmm2/WQoVVZ06PJ06cwJo1axAQEIC5c+eioKCglqq9c6b6i42Nha2tLby8vAAA48aNwyuvvFJb5VaKuccpAKxZswYdOnTA888/f8f7uadCJiMjA02aNNF+btq0KdLT0w22GTNmDAYNGlTTpVUbUz02atQIPXv2BADk5+dj7dq16NGjR43XWRXm3I9WVlaIiIhAt27dcOXKFXTp0qWmy6w0c/rbsGED2rRpA2dn55our1qY6jEvLw8ODg6YOnUqtm7dipycHISGhtZGqZViqr+UlBQ88sgjmDVrFl588UXMmTMHtra2tVFqpZlznALAtWvX8M0331T6F6J7KmT0ej0sLP738dIiYvDz/cDcHq9du4bXX38dzz77LF588cWaLLHKzO2xa9euOHjwILy9vfHee+/VYIVVY6q/+Ph47Nq1C2+88UZtlFctTPVYr149rFu3Di1btoSlpSVGjRqFiIiI2ii1Ukz1V1xcjN9//x0vv/wytm7diieeeAKLFi2qjVIrzdzH4ffff48ePXqgcePGldrPPRUyzZs3R2ZmpvZzZmYmmjZtWosVVT9zeszIyMCwYcNgb2+PBQsW1HSJVWaqx+zsbERGRmo/BwQE4PTp0zVaY1WY6m/Hjh3IzMzEwIED8frrr2v3573EVI9//fUXNm3apP0sIrC0NPvrq2qdqf6aNGmCFi1aoF27dgAAf39//PnnnzVeZ1WYez795Zdf0Lt370rv554KGXd3d0RHR+Py5cu4ceMGdu3apV0TvV+Y6rGkpATjxo2Dn58fgoOD78lncqZ6FBFMnToVf/31F4DSk3L79u1rq9w7Zqq/t956Czt37sR3332HtWvXomnTpvjqq69qseI7Z6pHGxsbLF26FKmpqRARhIWFaZd57wWm+nN1dcXly5cRFxcHANizZw8cHR1rq9xKMed8KiKIjY2Fq6tr5XdUqbcl1KLvv/9e+vTpI76+vrJ27VoRERkzZoz8+eefBttNnz79nnx3mcjte9y1a5fY29tL3759tX+zZs2q5YrvnKn78eeffxZ/f38JCAiQyZMnS05OTm2We8fMPU5TU1PvyXeXiZjucceOHdr6GTNmSEFBQW2We8dM9Xf8+HEZOHCg9O7dW0aNGiWXLl2qzXIrxVSPly5dEnd39yrtg9+MSUREytxTl8uIiOjewpAhIiJlGDJERKQMQ4aIiJRhyBARkTIMmb8xe3t7BAQEGHzSanBwsLL9zZ07FyEhIUrG/vbbb2vlU359fHwQExNT6dsHBgZix44dSE9Px9ChQwEAubm5GDp0KPr06YNdu3ZVuUZ7e3tcvnz5jm4THByMqKioKu+b6N75E1xS4osvvoCdnV1tl1FlR44cQevWrWu7jEpr1qwZwsPDAZR+xHpWVhZ+/vnnWqvnXvwkCbo7MWTIqLZt26J79+6Ii4vDsmXLYGtriwULFiA7OxslJSUIDAzESy+9BKD0r53//e9/o6ioCDY2Npg+fTpcXV2Rm5uL4OBgxMXFoWnTpqhbty6ee+45AEBCQgLmzp2L7OxsWFhYYNSoUejfvz8OHjyIjz76CI8++ijOnj2LBx98EK+//jq+/PJLnD17Fr6+vpg1a5ZBrT///DP27NmDAwcOwMbGBoMHD8aiRYsQHR2NunXrwsnJCTNnzkT9+vUNbhcSEoKUlBSkp6cjMzMTjo6O6NSpE7Zt24bz589j6tSp8Pf3x6VLl/Duu+8iKysLmZmZeOyxx7BixQqDz3LKy8vD66+/DhcXF0ydOhXp6emYO3cu0tLSUFRUhD59+mDcuHG3nO/z588jICAAmzdvxqxZs5Ceno5+/frh66+/xsmTJ7Fs2TLcuHEDderUwZtvvglvb+8KY/zxxx+YP38+bty4ASsrK0ybNg1ubm5ar3/88Qeys7MxevRo7RODV69ejR9++AF169bF008/jdmzZ6NJkyYIDAzEK6+8ghdeeAF79+7FihUroNfrYWtri/fffx/PPvssjh49arSuzMxMTJ8+HVeuXAFQ+hl0kyZNutNDkO4XVfyDUbqH6XQ68ff3N/j0gLK/WtbpdLJ161YRESkqKpLevXvLiRMnREQkJydH/Pz85NixY3L27Fnx9/eXy5cvi4hIfHy8eHh4SF5enixYsECmTZsmer1esrKyxMvLS1atWiVFRUXSvXt32blzp4iUfq+Fp6enHD16VH777TdxcHCQ2NhYEREZPXq0DBkyRAoKCiQrK0scHR2179Ipb/r06fLpp5+KiMjKlSvlzTfflMLCQikpKZEZM2bI7NmzK9xm1apV4u3tLTk5OXLjxg3p0KGDLFy4UERKP3HA19dXRETWr18va9asEZHS7xEZM2aMfPbZZyIi4u3tLVFRUTJkyBBtGxGRwMBA2b17t4iI5OfnS2BgoPzwww8Vahg+fLj89NNPkpqaKi4uLiIi8ttvv0mfPn1ERCQ7O1t8fX0lNTVVmysvLy+5cOGCwTiFhYXi4eEhe/fuFRGRmJgY8ff3l5KSEtHpdFq9sbGx0rZtWyksLJRNmzbJkCFDJC8vT5uPUaNGGdSVmZkpzz33nHZ/7Ny5U0aPHn3buj7++GNtvvPy8mTSpEn33Cc2UPXhM5m/udtdLiv77ojk5GSkpKQYPIPIz8/HyZMnISLIyMjAq6++qq2zsLBASkoKoqOjMWvWLFhYWMDOzk777Krk5GQUFBTA19cXQOmlIl9fX+zfvx+dOnXC448/jjZt2gAAnnzySTRo0ADW1taws7NDvXr1cPXqVe37dIz59ddfMXnyZFhZWQEofd1jwoQJRrd1d3dHgwYNAJR+1Lmnp6e23+zsbADAyJEjcfjwYXz++edITk5GQkKCwUf0T506FZaWlhgxYgQA4Pr16zh06BCuXr2KlStXasvi4uLu+IMGjx8/jszMTIP6LSwscPr0afzjH//QlsXHx6NOnTro1q0bgNJnotu3b9fWl32joYODAwoLC5Gbm4tff/0VAwYM0D6ifsSIEfjkk08MvoX06NGjaN26tXZ/+Pr6wtfXFxEREbesy9PTE6+//jrS0tLg7u6Of/3rX9oc098PQ4ZuqezkU1JSggYNGuC7777T1l26dAkNGjTAN998Azc3N6xYsUJbl5aWpn2aq5T71KK6detq4938wZ4iguLiYgCAtbW1wbo7/fTemz/CXK/Xo6ioyOi25uxr6dKl+PPPPzFw4EB06tQJxcXFBn2NHz8eBw8exNKlSzF79mzo9XqICMLDw/Hggw8CAC5fvowHHnjgjvoASueqZcuW+Pbbb7Vl6enpFX4xqFu3boU5jY+PxzPPPGPQV9k2ImJ0nsrug1uNKyI4ffr0beuysrLC7t27ER0djd9++w2DBg3CunXr0LZt2zvun+59fHcZmfT000/DxsZGC5m0tDT4+/vjxIkTcHNzw4EDB5CYmAgAiIiIQN++fZGfnw9PT09s2rQJer0eV69exe7duwEAzzzzDCwtLbV3TqWnp2Pnzp1wd3evdI1169bVTpCenp7YuHEjioqKoNfrERYWBg8Pj0qPHRkZiZEjR6J///5o3LgxoqKiUFJSoq13cnLCe++9hx07diAyMhL169eHi4sLPv/8cwBATk4OXn75Za3/O+Hi4oJz587h0KFDAErfFNCrV68KXy71zDPPwMLCAgcOHABQ+s2NI0eOhF6vv+XYnp6e2Lx5M65fvw4A+PLLL9GhQweD4HV2dkZiYiISEhIAALt378bUqVNvW9eyZcsQGhqKHj16IDg4GK1atdJuT38/fCZDJllbWyM0NBQLFizAp59+iuLiYkycOFF7EX/u3LmYMmWK9p0h//73v1GvXj3885//xJw5c+Dn5wc7OzvodDoApd96GRoaivnz5yMkJAQlJSWYMGECOnfujIMHD1aqRi8vL+1Lo8aPH4/Fixejf//+KC4uhpOTE2bPnl3p/idMmIAlS5Zg5cqVsLKyQvv27ZGSkmKwjZ2dHebMmYNZs2Zh+/btWLZsGebNm4eAgAAUFhbC398fffv2veN929nZYdWqVViyZAkKCgogIliyZAkef/xxg+2sra0REhKCDz74AEuWLIGVlRVCQkIqPFMr76WXXkJaWhoGDRoEvV6PFi1aYNmyZQbbPPLII1i2bBmmT5+OkpIS1K9fH8uXL79tXSNHjsSMGTPg7+8Pa2tr2Nvbo0+fPnfcO90f+CnMRESkDC+XERGRMgwZIiJShiFDRETKMGSIiEgZhgwRESnDkCEiImUYMkREpAxDhoiIlPl/8xCeWQETEuAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generosity\n"
     ]
    },
    {
     "data": {
      "image/png": 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CcHAwrl69ioEDB+Lnn39GXFwcLl++jBUrVmDIkCFo3LgxGjVqhGrVqmHw4MFYunQpfvzxR8TFxWHbtm1YvXq1xiGNkrRr1w62traYMGECzp49i1u3bmHmzJmIjIyESqUqdr1Ro0Zh27Zt+P7773Hnzh3s2rUL27dvx+DBg2FgYIB79+5h7ty5iIqKwr179xAREYH09HTY2NgU2lbBuMbExCAzMxPAs2ev7du3xzfffAN3d/cin7XrkoODA44fP460tDS0bt1aY56joyMOHDig8UpH27iZmZnh+PHjmDVrFq5cuYI7d+4gNDQUNWvWVA6tVq9eHdevX0dKSkqZr4eyysjIUO57z/+lpKQAAFQqFUxMTLB27Vr8+eefOH78uHIIsThNmzZF9+7dMWfOHJw4cQI3btzAggULEBsbCx8fH2W527dvY/78+bh58ybCw8Oxbds2DBs2DAAwaNAgpKWlwd/fH3/88QdiYmIwadIk3L59G3/72990Pg6vGx46K8KGDRuwYcMGAEClSpVQp04duLq6YsyYMcqzvVWrVmHBggUYMWIE8vPzYWdnhx07dqBJkyaIjIzEnj17sHHjRuXBavbs2Rg9ejQ6deqkHHLo1asXJkyYgIyMDLi4uGDGjBlFHjpr3749Fi5ciHXr1mH58uWoV68efHx8lE9Lm5qawtvbG0uWLEFUVBRWr16tsb6BgUGJ9ZZWwfmJrVu3YtmyZbh37x6MjIygUqkwadIk9O3bF9WqVQMATJgwAcbGxli0aBGSk5PRpEkTBAYGwtPTs1T7MjAwwOrVq7Fw4UKMHj0aOTk5sLCwwMaNG0t8FfbJJ58gOzsb69atw9y5c5V3ABacc5s5cyYWLlyIyZMn49GjR2jWrBkWLFhQZMAXvJ23f//+mDx5MoYMGQLg2fV25MiRUr/b7GUUBKC9vb3GoSrg2Xma3bt3o127dsq00ozbunXr8NVXX8Hb2xs5OTmwsrLCpk2bUKNGDQDA8OHDsWbNGpw6dQphYWFluh7KKjAwEIGBgYWmm5iY4Pz58zA1NcXixYuxZMkSuLm5oWXLlpg2bZpyjqo4QUFBWLx4Mfz8/JCVlQULCwts2rRJ49yKvb09srKy4OnpiTp16mDixIkYNGgQAMDc3BxbtmzBkiVL0K9fP1StWhVt27bF8uXLpT/ZeB0YCG0HTUnnoqKi4OPjg2PHjqFBgwb6Lof+ou3bt+O7777Dvn379F0K6Yi/vz/i4+Nf6iuHqHh8RUNUShcvXsSNGzewdu1ajU/lE1HJeI6GqJSio6Mxa9YsODk5KR/eJSLteOiMiIik4isaIiKSikFDRERSMWiIiEgqre86S03NhFr9cqdx6tY1RUrKy309eEXEvt8cb2LPAPt+09Sta4rU1EzUrl39L62nNWjUavHSQVOwnTcR+35zvIk9A+z7TVOWvnnojIiIpGLQEBGRVAwaIiKSikFDRERSMWiIiEgqBg0REUnFoCEiIqkYNEREJBWDhoiIpGLQEBGRVAwaIiKSikFDRERSMWiIiEgqBg0REUnFoCEiIqkYNEREJBWDhoiIpGLQEBGRVFp/ypnoRUeOHERiYnyx8ytXNkJOTl45VlR2mZnPfve9enXTl9qOvnuuV68BXF07623/RCVh0NBflpgYjwcJ8TCpVbfoBZ7mlm9BLyEr41nQ5BlVebkN6bHnrEcpets3UWkwaKhMTGrVRSuXHvou46XFRv4bACp0LwU9EL2qeI6GiIikYtAQEZFUDBoiIpKKQUNERFIxaIiISCoGDRERScWgISIiqRg0REQkFYOGiIikYtAQEZFUDBoiIpKKQUNERFIxaIiISCoGDRERScWgISIiqRg0REQkFYOGiIikYtAQEZFUDBoiIpKKQUNERFIxaIiISCoGDRERScWgISIiqRg0REQkFYOGiIikYtAQEZFUDBoiIpKKQUNERFIxaIiISCoGDRERScWgISIiqRg0REQkFYOGiIikYtAQEZFUDBoiIpKKQUNERFIxaIiISCoGDRERScWgISIiqRg0REQklZHsHcTG/o4aNaqiaVOV7F0REVExYmN/BwC0amVd7vuWHjQxMb+hcmUjBg0RkR7FxPwGQD9Bw0NnREQkFYOGiIikYtAQEZFUDBoiIpKKQUNERFIxaIiISCoGDRERScWgISIiqRg0REQkFYOGiIikYtAQEZFUDBoiIpKKQUNERFIxaIiISCoGDRERScWgISIiqRg0REQkFYOGiIikYtAQEZFUDBoiIpKKQUNERFIxaIiISCoGDRERScWgISIiqRg0REQkFYOGiIikYtAQEZFUDBoiIpKKQUNERFIxaIiISCoGDRERScWgISIiqRg0REQkFYOGiIikYtAQEZFUDBoiIpKKQUNERFIxaIiISCoGDRERScWgISIiqRg0REQkFYOGiIikMpK9g6ysTCQnZ2Lnzm2yd/XKqVzZCDk5efouQ+cSExNgWKWqvsug/8rNzkLi42y93Mde19u4NhWx78TEBJiamupl33xFQ0REUkl/RWNiUh21apnB03OA7F29cszNayApKV3fZejczp3b8Phprr7LoP8yrmoCMzMzfPKJT7nv+3W9jWtTEfvW51ElvqIhIiKpGDRERCQVg4aIiKRi0BARkVQMGiIikopBQ0REUjFoiIhIKgYNERFJxaAhIiKpGDRERCQVg4aIiKRi0BARkVQMGiIikopBQ0REUjFoiIhIKgYNERFJxaAhIiKpGDRERCQVg4aIiKRi0BARkVQMGiIikopBQ0REUjFoiIhIKgYNERFJxaAhIiKpGDRERCQVg4aIiKRi0BARkVQMGiIikopBQ0REUjFoiIhIKgYNERFJxaAhIiKpGDRERCQVg4aIiKRi0BARkVQMGiIikopBQ0REUjFoiIhIKgYNERFJxaAhIiKpjGTvwMrKFjVqVJW9GyIiKoGVla3e9i09aFq1soa5eQ0kJaXL3hURERWjVStrve2bh86IiEgqBg0REUnFoCEiIqkYNEREJBWDhoiIpGLQEBGRVAwaIiKSikFDRERSMWiIiEgqBg0REUnFoCEiIqkYNEREJBWDhoiIpGLQEBGRVAwaIiKSikFDRERSMWiIiEgqBg0REUnFoCEiIqkYNEREJBWDhoiIpGLQEBGRVAwaIiKSikFDRERSMWiIiEgqBg0REUnFoCEiIqkYNEREJBWDhoiIpGLQEBGRVAwaIiKSikFDRERSMWiIiEgqBg0REUnFoCEiIqkYNEREJBWDhoiIpGLQEBGRVAwaIiKSikFDRERSMWiIiEgqI30XQBVT1qMUxEb+W99lvLSsRykAUKF7yXqUArP6DfRdBlGxGDT0l9WrV/KDWuXKRsjJySunal6OkakpAKB6FeOX2o4+ezar30DrdUKkTwwa+stcXTuXON/cvAaSktLLqZpXw5vYM1Fp8RwNERFJxaAhIiKpGDRERCQVg4aIiKRi0BARkVQMGiIikopBQ0REUjFoiIhIKgYNERFJxaAhIiKpGDRERCQVg4aIiKRi0BARkVQMGiIikopBQ0REUjFoiIhIKgYNERFJxaAhIiKptP6Us6GhgU52pKvtVDTs+83xJvYMsO83TVn6NhBCCAm1EBERAeChMyIikoxBQ0REUjFoiIhIKgYNERFJxaAhIiKpGDRERCQVg4aIiKRi0BARkVQMGiIikkqnQbNv3z64ubmhc+fOCAkJKTT/8uXL8PT0RJcuXTBjxgzk5eXpcvd6o63vAlOnTkVoaGg5ViaPtp5//vln9OzZEx4eHhg9ejQeP36shyp1T1vfhw4dgru7O7p37w5/f3/k5OTooUrdK+1t/OjRo3B1dS3HyuTS1veqVavg4uKCnj17omfPniWOTUWhreebN2/C29sbHh4eGDZsWOnu20JH4uPjhYuLi0hNTRWZmZnC3d1dXLt2TWOZ7t27i/PnzwshhAgICBAhISG62r3elKbv+Ph4MWLECGFtbS327Nmjp0p1R1vP6enpwsnJScTHxwshhAgODhZBQUH6KldntPWdmZkpnJ2dRVJSkhBCiAkTJoidO3fqq1ydKc1tXAghkpKSRNeuXYWLi4seqtS90vQ9YsQIER0dracKdU9bz2q1WnTu3FkcO3ZMCCHE4sWLxaJFi7RuV2evaE6dOgUHBwfUqlULJiYm6NKlCw4cOKDMv3fvHrKzs2FrawsA8PT01JhfUWnrG3j2DOGjjz5Ct27d9FSlbmnrOTc3F7Nnz0b9+vUBAC1atMCDBw/0Va7OaOvbxMQER44cwVtvvYUnT54gJSUFNWvW1GPFulGa2zgAzJw5E2PHjtVDhXKUpu+LFy9i3bp1cHd3R2BgIJ4+faqnanVDW8+xsbEwMTHBhx9+CAAYOXIkBg4cqHW7OguaxMREmJubK5fr1auHhISEYuebm5trzK+otPUNAL6+vvDy8irv0qTR1nPt2rXRqVMnAEB2djbWr1+Pjz/+uNzr1LXSXNfGxsY4duwYOnbsiNTUVDg7O5d3mTpXmr63bdsGS0tL2NjYlHd50mjrOzMzExYWFvDz88PevXuRlpaGNWvW6KNUndHW859//om33noL06dPR+/evTF79myYmJho3a7OgkatVsPA4H9fHy2E0LisbX5F9br2VZLS9pyeno7PPvsMLVu2RO/evcuzRClK23eHDh0QFRUFFxcXzJkzpxwrlENb31evXsXBgwcxevRofZQnjba+q1evjg0bNuCdd96BkZERhg4dimPHjumjVJ3R1nNeXh7+85//oH///ti7dy+aNGmCr776Sut2dRY0DRo0QFJSknI5KSkJ9erVK3Z+cnKyxvyKSlvfr6PS9JyYmIgBAwagRYsW+PLLL8u7RCm09f3o0SOcOHFCuezu7o4//vijXGuUQVvfBw4cQFJSEvr06YPPPvtMue4rOm19379/H7t371YuCyFgZKT1J75eadp6Njc3R7NmzWBlZQUA6NGjB37//Xet29VZ0Dg6OuLXX3/Fw4cP8eTJExw8eFA5jgcAjRo1QpUqVXDu3DkAQHh4uMb8ikpb368jbT3n5+dj5MiR6NatG2bMmPHavMLT1rcQAn5+frh//z6AZw/A9vb2+ipXZ7T1/fnnn+Onn35CeHg41q9fj3r16mHHjh16rFg3tPVdtWpVLF68GHFxcRBCICQkRDlkXFFp69nOzg4PHz7ElStXAABHjhxBq1attG9Yl+9YiIiIEN27dxedO3cW69evF0II4evrK37//XchhBCXL18Wffr0EV26dBGTJk0ST58+1eXu9UZb3wWmTZv2WrzrTIiSez548KBo0aKF8PDwUP6mT5+u54p1Q9t1fejQIdGjRw/h7u4uJk6cKNLS0vRZrs6U9jYeFxf32rzrTAjtfR84cECZ7+/v/1o8pmnr+bfffhN9+vQRbm5uYujQoSI5OVnrNvkLm0REJBW/GYCIiKRi0BARkVQMGiIikopBQ0REUjFoiIhIKgYNVWi7d++Gl5cX3Nzc8PHHH2PIkCG4cOGCvssqUkJCAj755BMAQFxcHMaNG6fniojKR8X+GCu90ZYtW4YzZ84gODgYjRo1AgD8+uuvGDFiBEJDQ9GwYUM9V6ipfv362LlzJ4Bnnyq/deuWnisiKh/8HA1VSMnJyfjoo49w6NChQl9/ExYWBisrK5iamiIwMBAPHjxAbm4uunfvjpEjR+Lu3bsYPHgwOnTogAsXLiAtLQ1+fn7Kp7q/+eYbHDx4EGq1Go0aNVK+idrb2xtmZma4efMm+vfvj06dOmHOnDm4d+8ehBDo1asXfH19kZeXh6CgIERHR8PY2BiNGzfGggULkJqaCnd3d5w9exZdu3ZFQkICWrdujQ8++ADXr1/H0qVLAQBnz57FvHnzEBYWVu7jSiSFrE+XEsl06NAh0bt37xKX8fb2FocPHxZCCJGdnS28vb3FDz/8IOLi4oRKpRJHjhwRQjz7dHfHjh2FEELs3btXTJgwQeTm5gohhNi5c6fw9fUVQggxaNAgERAQoGx/4MCBYvPmzUIIIdLS0oS7u7v497//Lc6cOSO6du0q1Gq1EEKIRYsWiXPnzom4uDhha2srhBDi9OnTonv37kIIIZKTk4W9vb1ITU0VQgjh5+cnvqHcaBAAAALFSURBVP3225cfJKJXBA+dUYUkXnghnpGRofwuRlZWFlxcXHDmzBk8fvwYy5cvV6ZfuXIF1tbWMDY2RocOHQAAlpaWePToEQAgMjISMTEx6NOnD4Bn32b75MkTZT8ffPCBsq3o6Ghs3rwZAFCjRg14enri+PHjmDFjBipVqgQvLy84OzujS5cusLa2xt27d4vspW7duujYsSPCw8PRq1cvnDhxArNnz9bVUBHpHYOGKiRra2vcunULqampqF27NkxNTREeHg4AWLlyJe7fvw8hBHbu3Ilq1aoBAB4+fIgqVaogNTUVxsbGMDR89l6YF3/OwtfXV/n24ZycHI2fqi347Q21Wl0o7NRqNfLy8lCzZk2Eh4cjOjoap0+fxoQJEzBs2DAl2IoycOBAzJkzB0ZGRujcuTOqV6+ug1EiejXwXWdUIdWvXx8+Pj4YP3688m3JwLNfco2Ojkb16tVha2uLLVu2AADS0tLQv39/HD58uMTtOjs7Y/fu3cjIyAAALF++HFOnTi20nKmpKWxsbJTfVE9PT0dYWBgcHR0RGRmJwYMHw87ODuPGjUOvXr1w8eJFjfUrVaqE3Nxc5bK9vT0MDQ2xadMm5Z1pRK8LvqKhCmvixImIiIjA5MmT8eTJE6Snp8PMzAxubm4YOHAgUlJSEBQUBHd3d+Tk5KBHjx7w8PAo9hAWAHh5eSEhIQH9+vWDgYEB/u///q/YH3ZasmQJAgMDERoaipycHLi7u8PT0xNqtRrHjx9Hjx49YGJiAjMzMwQFBWms++6776JKlSro27cvvv/+exgYGMDT0xM//vgjWrZsqdNxItI3vuuM6BWQl5eHsWPHwsPDA25ubvouh0ineOiMSM+uX7+Odu3aoXbt2ujatau+yyHSOb6iISIiqfiKhoiIpGLQEBGRVAwaIiKSikFDRERSMWiIiEgqBg0REUn1/7ByyYuLUPNCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perceptions of corruption\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in x.columns:    \n",
    "    plt.figure(figsize=(7, 4))\n",
    "    print(i)\n",
    "    sns.boxplot(x=[i], data=west_europe, palette='Paired')\n",
    "#     plt.legend()\n",
    "    plt.title(f\"Boxplot of {i} in Western Europe\", fontsize=15)\n",
    "    plt.xlabel(f\"{i}\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness 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>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>123</td>\n",
       "      <td>4.507</td>\n",
       "      <td>0.33024</td>\n",
       "      <td>0.95571</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.40840</td>\n",
       "      <td>0.21488</td>\n",
       "      <td>0.08786</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>124</td>\n",
       "      <td>4.436</td>\n",
       "      <td>0.45407</td>\n",
       "      <td>0.86908</td>\n",
       "      <td>0.35874</td>\n",
       "      <td>0.24232</td>\n",
       "      <td>0.21900</td>\n",
       "      <td>0.17461</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>125</td>\n",
       "      <td>4.419</td>\n",
       "      <td>0.36471</td>\n",
       "      <td>0.99876</td>\n",
       "      <td>0.41435</td>\n",
       "      <td>0.42215</td>\n",
       "      <td>0.37542</td>\n",
       "      <td>0.05839</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>126</td>\n",
       "      <td>4.369</td>\n",
       "      <td>0.44025</td>\n",
       "      <td>0.59207</td>\n",
       "      <td>0.36291</td>\n",
       "      <td>0.46074</td>\n",
       "      <td>0.18093</td>\n",
       "      <td>0.28105</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>Central and Eastern Europe</td>\n",
       "      <td>127</td>\n",
       "      <td>4.350</td>\n",
       "      <td>0.76821</td>\n",
       "      <td>0.77711</td>\n",
       "      <td>0.72990</td>\n",
       "      <td>0.19847</td>\n",
       "      <td>0.07855</td>\n",
       "      <td>0.03900</td>\n",
       "      <td>2015</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>154</td>\n",
       "      <td>3.203</td>\n",
       "      <td>0.35000</td>\n",
       "      <td>0.51700</td>\n",
       "      <td>0.36100</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.15800</td>\n",
       "      <td>0.02500</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>152</td>\n",
       "      <td>3.334</td>\n",
       "      <td>0.35900</td>\n",
       "      <td>0.71100</td>\n",
       "      <td>0.61400</td>\n",
       "      <td>0.55500</td>\n",
       "      <td>0.21700</td>\n",
       "      <td>0.41100</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Syria</td>\n",
       "      <td>Middle East and Northern Africa</td>\n",
       "      <td>149</td>\n",
       "      <td>3.462</td>\n",
       "      <td>0.61900</td>\n",
       "      <td>0.37800</td>\n",
       "      <td>0.44000</td>\n",
       "      <td>0.01300</td>\n",
       "      <td>0.33100</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>145</td>\n",
       "      <td>3.775</td>\n",
       "      <td>0.04600</td>\n",
       "      <td>0.44700</td>\n",
       "      <td>0.38000</td>\n",
       "      <td>0.22000</td>\n",
       "      <td>0.17600</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Togo</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>139</td>\n",
       "      <td>4.085</td>\n",
       "      <td>0.27500</td>\n",
       "      <td>0.57200</td>\n",
       "      <td>0.41000</td>\n",
       "      <td>0.29300</td>\n",
       "      <td>0.17700</td>\n",
       "      <td>0.08500</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>191 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country                           Region  Happiness Rank  \\\n",
       "122  Sierra Leone               Sub-Saharan Africa             123   \n",
       "123    Mauritania               Sub-Saharan Africa             124   \n",
       "124         Kenya               Sub-Saharan Africa             125   \n",
       "125      Djibouti               Sub-Saharan Africa             126   \n",
       "126       Armenia       Central and Eastern Europe             127   \n",
       "..            ...                              ...             ...   \n",
       "143   Afghanistan                    Southern Asia             154   \n",
       "144        Rwanda               Sub-Saharan Africa             152   \n",
       "146         Syria  Middle East and Northern Africa             149   \n",
       "147       Burundi               Sub-Saharan Africa             145   \n",
       "148          Togo               Sub-Saharan Africa             139   \n",
       "\n",
       "     Happiness Score  GDP per Capita  Social support  Healthy life expectancy  \\\n",
       "122            4.507         0.33024         0.95571                  0.00000   \n",
       "123            4.436         0.45407         0.86908                  0.35874   \n",
       "124            4.419         0.36471         0.99876                  0.41435   \n",
       "125            4.369         0.44025         0.59207                  0.36291   \n",
       "126            4.350         0.76821         0.77711                  0.72990   \n",
       "..               ...             ...             ...                      ...   \n",
       "143            3.203         0.35000         0.51700                  0.36100   \n",
       "144            3.334         0.35900         0.71100                  0.61400   \n",
       "146            3.462         0.61900         0.37800                  0.44000   \n",
       "147            3.775         0.04600         0.44700                  0.38000   \n",
       "148            4.085         0.27500         0.57200                  0.41000   \n",
       "\n",
       "     Freedom to make life choices  Generosity  Perceptions of corruption  Year  \n",
       "122                       0.40840     0.21488                    0.08786  2015  \n",
       "123                       0.24232     0.21900                    0.17461  2015  \n",
       "124                       0.42215     0.37542                    0.05839  2015  \n",
       "125                       0.46074     0.18093                    0.28105  2015  \n",
       "126                       0.19847     0.07855                    0.03900  2015  \n",
       "..                            ...         ...                        ...   ...  \n",
       "143                       0.00000     0.15800                    0.02500  2019  \n",
       "144                       0.55500     0.21700                    0.41100  2019  \n",
       "146                       0.01300     0.33100                    0.14100  2019  \n",
       "147                       0.22000     0.17600                    0.18000  2019  \n",
       "148                       0.29300     0.17700                    0.08500  2019  \n",
       "\n",
       "[191 rows x 11 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low = df[df['Happiness Score'] <= 4.5085]\n",
    "low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yYsQIEhISaN++PS+//PJNe507dy47duzgm2++4ccff+Ty5cuW51q3bo3BYODJJ58kPT39pj3fSLVq1Rg1apRlqe2auLg4unfvjr29Pa6urgQGBhIbG4uPjw+1a9emfPkb//L+5S9/wd3dHYCaNWuSlpZGbGws8fHxBAUFAZCTk0O1atV47rnn8PT0pHr16pb9mzVrhpubGwA1atQgIyOj0PwxMTFUrlyZJ554ArPZjJ2dHTt27KBjx45FamnUqBEODoV/DQ8ePEiHDh0s9X/xxRcANGzYkN27d/Ppp5/yyy+/kJ2dXewxexCcO5dZ6HHlyuWLbLM16tE22HqPtt4f2EaPdnYGPD3din2+VAPStWW277//nv379zNmzJgbBiSDwVDozNK1a22st1v/Q2oymXBycip0LdPZs2ct//iXxIoVK9i6dSs9e/akVatW/PLLL5jNZp577jk2b95MTEwMW7ZsYf369SxfvrzYefr27YuXlxdeXl60bNmSsWPHWp5zdna29HOrnovTq1cvtm7dymeffVao/+uZzWaMRiMALi4uxc51/XG8VofRaGTAgAG88sorAFy6dAl7e3vS0tKKzHWtnxv1AbBu3TqSk5Px8fEBri5jhoWF3TAg3ahOBweHQscqJSUFV1dX3n33XSpUqECHDh3o3LkzmzZtKrZHERGRO1HqH/P39/dn/vz5NG7cuEjAuaZly5aWkBMfH89vv/1m2R4VFYXJZOLMmTP897//LbRf+fLlefzxxy377t27l379+t1WfXv37qVXr1507dqV3Nxcjh49islkYs6cOWzcuJHu3bszefJkjhw5AlwNMtdCyDXp6ekkJiYycuRI2rVrx7Zt24qMKWnPNzN9+nTL2RQAb29vNmzYgNFo5MqVK0RFRd3wAnJ7e3sKCgpuOre3tzeRkZFcvnyZgoKCYpcqb+X8+fPs27ePTZs2sX37drZv386GDRvYv38/p0+fLtEcf/nLX9i5c6elljFjxnD48GH27t3Lm2++iZ+fH7t27QK45XEWERH5I0r1DBJAhw4dePfdd2/6CaU333yT8ePH06VLF+rUqWNZburbty/Hjx/H39+fxx57jCeffLLIvnPnzmXKlCksW7YMR0dHQkNDC519uMb6GiS4ev3SgAEDmDJlCkuXLsXNzY1mzZqRlJRESEgIY8aMISIiAnt7e2bPng1A27Ztee+995g9ezbPPvsscPUTez169KBLly44ODjg7e1NTk7OTZeAiuv5ZqpVq8bo0aOZNGkScPWsUmJiIt26dSM/P5/AwEA6duxIXFxcof2aNm3KkiVLmDdvHnXq1Lnh3D4+Phw9epSePXtiNBpp27Yt3bt358yZM7es63qRkZG0b9+eqlWrWrbVqFEDHx8f1qxZQ9u2bW85x1NPPcXLL79M7969MZlMdOzYkVatWjFixAj69u2Ls7MzDRo04LHHHiMpKYlatWrdVo0iIiK3YjDf6KppERv0xleTOJd1sazLuCu+HvKRrkGyUerx/mfr/YFt9Hira5D0TdoiIiIiVhSQRERERKwoIImIiIhYUUASERERsaKAJCIiImJFAUlERETEigKSiIiIiBUFJBERERErCkgiIiIiVkr9ViMi94qP+04r6xLumpy8vLIuQUTEpikgyQPjwoUsTCbdWUdERG5NS2wiIiIiVhSQRERERKwoIImIiIhYUUASERERsaKAJCIiImJFn2KTB4anp1tZl3DbcvPzuJSeW9ZliIg8cBSQ5IExM/p90rLTyrqM2zInOBRQQBIR+bNpiU1ERETEigKSiIiIiBUFJBERERErCkgiIiIiVhSQRERERKwoIImIiIhYUUASERERsaKAJCIiImJFAUlERETEigLSfeibb74hKCiIrl27EhgYyLJly/7wXDt27GD58uUALF68mMWLF9+tMotVUFBAmzZtmDZt2i3H/vTTT7z77rulXpOIiMj1dKuR+0xKSgqzZ88mIiICDw8PLl++TEhICLVr18bX1/e25zt8+HApVHlzO3fupEmTJkRHRzN27FhcXV2LHdukSROaNGnyJ1YnIiKigHTfSUtLIz8/n5ycHADKlSvHrFmzcHZ2BuDQoUN88MEH5Obm4uHhwfvvv0+tWrUICQlh+PDheHl5kZSURP/+/Vm6dClhYWEAVKtWDYD4+Hh69+5NSkoKQUFBjBgxAqPRyJw5czhw4ABGo5GgoCAGDhxIXFwcc+fOxWQyUa9ePapXr05KSgqnTp3izJkzvPTSS7z++utFeoiIiKBjx46YzWY2b95Mjx49AIiKimLZsmXY29tTvXp15s6dy6FDh1iyZAkrVqzgwIEDhIaGkpOTw6VLl5gwYQJ+fn5/xmEXEZEHjALSfaZBgwb4+vri5+dHw4YN8fLyIjAwkFq1apGXl8fo0aP58MMPadq0KdHR0YwePZp169bdcK4nnniC3r17AxAcHMzixYu5cOECYWFhZGVl4ePjwyuvvEJUVBQA69evJy8vj0GDBtG4cWMAEhMT2bFjB+XLl2fx4sUcO3aMVatWkZmZiZ+fH/369aNChQqW17x48SL79u1jxowZ2Nvbs3LlSktA+vDDDwkPD8fT05PZs2dz4sSJQvWuXLmS6dOnU7duXWJjY5kxY8YDEZAqVy5fKmPvV+rRNth6j7beH9h+jwpI96GpU6fyxhtvsGfPHvbs2UPPnj2ZN28ejz/+OBUqVKBp06YA+Pv7M3nyZDIzM0s8d9u2bXFycqJixYp4eHiQkZFBbGwsCQkJ7N+/H4Ds7GyOHTvGE088Qe3atSlf/v//knh5eeHk5ISnpyfu7u5kZmYWCkgbN27E29ubhx9+GF9fXyZNmsSRI0do1KgRHTp0oE+fPvj5+fHCCy/QsGFD4uLiLPvOnTuXHTt28M033/Djjz9y+fLlOz2U94Vz50r2/lWuXL7EY+9X6tE22HqPtt4f2EaPdnYGPD3din2+xAHpzJkzZGRkYDabLdueeuqpO6tObltMTAzZ2dl07tyZ4OBggoODCQ8PZ+3atYwePbrIeLPZjNFotPwMVy+SLo6Dw///ShgMBsv+48aNo1OnTsDVs0DlypXj0KFDuLi4FNr/2lLf9ftfLyIigtTUVHx8fACws7MjLCyM999/n4kTJ3L06FF27tzJuHHjGD58OI888ohl3759++Ll5YWXlxctW7Zk7NixJTpmIiIit6tEAWnhwoX885//xNPT07LNYDCwbdu2UitMbszFxYVp06bRtGlTqlevjtlsJiEhgYYNG1KnTh3S09OJj4+nadOmbNmyhWrVquHu7o6Hhwe//vor3t7efPfdd5b57O3tyc3Nvelrent7Ex4eTocOHcjLy6Nv375MnTr1tms/fPgwZ8+eZdeuXZZgFRcXx9ChQ3nrrbcICgpixYoV/O1vfyM/P5+EhARLQEpPTycxMZGvvvoKJycn5s2bZwl+IiIid1uJAlJkZCT//ve/qVq1amnXI7fg7e3N8OHDGTp0KPn5+cDVZbFhw4bh5OREaGgo06ZN48qVKzz88MOEhoYCMHjwYMaPH8+6desKfdqtefPmvP3221SqVKnY1+zduzenTp2ie/fuFBQUEBQUhJeXV6Hlr5KIiIggKCio0FknLy8vateuTVRUFG+++Savvvoqzs7OeHp6MmvWLH799VcA3N3d6dGjB126dMHBwQFvb29ycnLIzs7moYceuq06REREbsVgtl4DuYF+/fqxatWqP6MekVIzM/p90rLTyrqM2zInOFTXIF1HPdoGW+/R1vsD2+jxrlyD1LJlS+bMmYOvr2+h//vXNUgiIiJii0oUkCIiIoCr3+B8ja5BEhEREVtVooC0ffv20q5DRERE5J5RooCUnZ3NnDlz2LVrFwUFBbRu3Zp3330XN7fi1+5ERERE7lclulntzJkzycvL46OPPuLjjz/GYDCU6EajIiIiIvejEp1B+vHHH9m4caPl8fTp0+nSpUupFSUiIiJSlkp0BsloNGIymSyPTSYT9vb2pVaUiIiISFkq8cf8//73v9OnTx8AVq9ejZeXV6kWJiIiIlJWShSQxo8fz8cff8yCBQswGo20bduWN954o7RrExERESkTJfombREpG7n5eVxKv/m98q6xhW+2vRX1aBtsvUdb7w9so8c7+ibtPn36sHr1apo1a4bBYLBsN5vNGAwG/vvf/969SkVK2YULWZhM+v8BERG5tZsGpIULFwKwadOmP6UYERERkXvBTQNSlSpVADh48GCR51xdXcnJyaFu3bqlU5mIiIhIGSnRRdqRkZEcOnQIb29v7O3tiY2NpUaNGly6dIm//e1v9OrVq7TrFBEREfnTlCggGQwG1q5dazlbdPr0aaZPn87KlSvp27evApKIiIjYlBJ9UeS5c+cKLaXVqFGDlJQU3Nzc9IWRIiIiYnNKdAbp4YcfZs2aNfTo0QOz2cy6detwd3fn5MmThb5hW+RedrOPc5a1vPw8Mkr4cX4RESl9JQpIM2bM4K233mLq1KkYDAaeffZZZs2aRVRUFK+//npp1yhyV3y5dzqZOWllXcYNDfedDyggiYjcK0oUkGrWrElYWBiXLl3C3t6ecuXKATB06NBSLU5ERESkLJT4GqQhQ4bw0ksvkZOTw6BBg0hNTS3t2kRERETKRIkC0tSpU/Hz88PZ2ZkKFSrQoEEDJk6cWNq1iYiIiJSJEgWkM2fO0LNnT+zs7HB0dGTcuHEkJyeXdm0iIiIiZaJEAclgMBT6tFpWVpY+vSYiIiI2q0QXaXfq1ImxY8eSmZlJWFgYX3/9Nf7+/qVdm4iIiEiZKFFAGjp0KBs2bMBkMrFv3z569epFz549S7s2ERERkTJRooAE8Ne//pW//vWvlsd79+6ldevWpVKUiIiISFm66TVIhw8fpnfv3gwdOpSLFy8C8PvvvzN8+HB9QaSIiIjYrJueQZo6dSr+/v78/vvvfPLJJ3h5eTF+/HiefvppIiMj/6waycrKYv78+Rw8eBB7e3sqVKjA+PHjeeqpp/7QfBMmTGD48OE89thjJd6nfv36HDt27A+9XnHGjx9PixYtCAoKKvJaDRo0KLTt+eefZ9SoUbc1/+nTp/nkk0+YMWPGHdd6vaSkJF588cVC9+cD6NmzJ/369burryUiIlIWbhqQMjMzefXVVzEajbzwwgtER0czdepUunTp8mfVh8lk4rXXXsPLy4sNGzbg4ODA/v37ee2119i8eTMeHh63PWdcXBzDhg0rhWrvnrsRQH///XdOnz59F6opqkqVKn9qSBYREfkz3TQgubq6AmBvb09ubi5Lly6lUaNGf0ph18TFxZGcnMybb76Jnd3VFUFvb29mzpxp+aqBpUuXEh0djdFopE2bNowbN44zZ84wfPhw6tWrR0JCAp6enixcuJDw8HBSU1MZMmQIq1atIjg4mKZNm5KQkMBXX33Fv/71L2JjY8nIyKBKlSqEhoZSqVKlG9aWkpLCO++8Q2ZmJqmpqXTv3p2RI0cSERHB7t27ycjI4PTp07Ru3ZopU6ZgNpuZNWsWMTExVKlSBaPRSIsWLW7reERHR7N8+XJycnLIy8tjxowZPPvssyxfvpz169djZ2dH06ZNef/995k+fTpJSUlMnTqV9957r9jjNHjwYDw8PHBxcSEwMPCGtd+O68+2RUREcODAAWbNmoWPj0+hYx0TE8Py5csxGAw89dRTTJo0iXLlytGyZUs6duzIDz/8QLly5Zg3bx7Vq1cnPj6emTNnkpOTg4eHB1OnTqVGjRq3VZuIiEhJ3PQaJLPZbPnZw8PjTw9HAEeOHKFBgwaWcHRN+/bt8fT0ZNeuXRw+fJi1a9eyYcMGUlJS2LhxIwBHjx7llVdeYdOmTVSoUIGoqCiGDBlClSpVWLp0qeXsU7t27di6dStZWVmcOHGCsLAwtm7dyqOPPmqZ60Y2bdpEQEAA4eHhREVF8eWXX1qu1frhhx9YtGgRGzduZMeOHRw7doytW7dy5MgRNm3axMKFC/ntt9+Knbtbt26F/uzevRuTyURYWBj/+Mc/2LhxI4MHD2bp0qUYjUY+/fRT1q1bR0REBPn5+aSkpDBx4kQaN27Me++9d9PjdPLkSebOncvy5cuLrd1aampqkaP2+cIAAB8lSURBVBpLsgR57VifP3+ef/zjH6xYsYKoqChcXV1ZsmQJABcvXqRZs2ZERUXRpUsXpk+fTl5eHhMnTmT+/PmsX7+eV155hUmTJt3y9URERP6Im55BMplMZGRkWILS9T8DuLu7l251gJ2dHc7OzsU+HxsbS3x8vOU6npycHKpVq8Zzzz2Hp6enJdTVq1ePjIyMG87x9NNPA1CrVi3efvttvv76a06ePMmhQ4eoWbNmsa89aNAg9u/fz+eff87x48fJz8/nypUrADRr1gw3NzcAatSoQUZGBgcOHKBTp044OjpSsWJF2rVrV+zcxS1fffTRR2zfvp2TJ09y4MAB7OzssLe3p1mzZvTo0QNfX19eeeUVqlatSmJiYomPU/Xq1S1jb1S7tT+6xHbtWB88eJAOHTpYQmqvXr2YMGECAM7OzpZPTHbv3p0FCxaQmJjI6dOnC304ICsr67Zf/15WuXL5e2KOe516tA223qOt9we23+NNA9Ivv/yCt7e3JRR5eXlZnjMYDCQkJJRudUDjxo356quvMJvNGAwGy/YFCxbQqlUrjEYjAwYM4JVXXgHg0qVL2Nvbk5aWVihYGQyGQuHuetfGHT58mDFjxjBw4EBeeOEF7Ozsit0HYNasWZw+fZqAgAD8/PzYt2+fZfyNXtu6BgeHEn/LAgCXL1+mR48edO3alebNm1O/fn1WrVoFwMcff8yhQ4fYtWsXgwcPZt68eYX2vdlxcnFxueHxuL7223Wt34KCghvObf1N7Gaz2TLWzs7O8l6bTCbs7e0xmUxUr17dEsqMRiPnz5+/7bruZefOZd7R/pUrl7/jOe516tE22HqPtt4f2EaPdnYGPD3din/+ZjsfPXqUhIQEjh49WuTPnxGOAP7yl7/g6enJkiVLMBqNAOzevZuIiAieeOIJvL29iYyM5PLlyxQUFDBs2DC2bt160znt7e0tc13v4MGDtGjRgj59+vD4448TExNzw3HX7N27l0GDBuHv78/JkydJSUm56S1YWrZsSXR0NHl5eWRkZLB79+4SHoWrEhMTMRgMDB06FC8vL7799luMRiMXL16kc+fOPPnkk4wcOZLWrVtz7Ngx7O3tLaHjjxynP8rDw4Pjx49jNpvZvn37Dce0aNGC7du3k56eDkB4eLglgF+5csWyX0REBO3ataNOnTpkZGTwn//8B4B169YxduzYUqlfRETk9k5hlAGDwcDHH3/MzJkzCQgIwMHBAQ8PD5YuXUqlSpXw8fHh6NGj9OzZE6PRSNu2benevTtnzpwpds7nn3+eIUOGsGzZskLbO3fuzPDhwwkMDASunr1KSkoqdp6//e1vvPXWW7i4uPDII4/ccryfnx8//fQTAQEBVKpUqcjH5K/XrVu3Qo9r1apFaGgoDRs2xN/fH4PBQJs2bfj++++pWLEivXr1okePHri6ulK7dm2Cg4PJzc0lMzOTcePGMXfu3Ns+Tjdz7Rqk6zVv3pyJEycyZswYhg4dSqVKlXjuuedIS0srsn+DBg3429/+RkhICPn5+Tz11FNMnTrV8vw333xDaGgoVapUYfbs2Tg5ObFw4UI++OADcnNzcXNzY/bs2X+odhERkVsxmP/I+olIKSqN75wC+HLvdDJzioa1e8Fw3/laYisB9WgbbL1HW+8PbKPHO1piExEREXkQKSDJPac0zh6JiIjcDgUkERERESsKSCIiIiJWFJBERERErCggiYiIiFhRQBIRERGxooAkIiIiYkUBSURERMTKPX+rEZG7ZUDriWVdQrHy8vPKugQREbmOApI8MC5cyMJk0p11RETk1rTEJiIiImJFAUlERETEigKSiIiIiBUFJBERERErCkgiIiIiVvQpNnlgeHq6lcq8+QV5pKfllsrcIiJSNhSQ5IHx7U8zuZKXdtfn7frcHEABSUTElmiJTURERMSKApKIiIiIFQUkERERESsKSCIiIiJWFJBERERErCggiYiIiFhRQBIRERGxooAkIiIiYkUBSURERMSKAtKf4JtvviEoKIiuXbsSGBjIsmXL/vBcO3bsYPny5QAsXryYxYsX360yiwgPD2fTpk2lMvcvv/xC/fr12bp16y3Hrl69mtWrV5dKHSIiIjeiW42UspSUFGbPnk1ERAQeHh5cvnyZkJAQateuja+v723Pd/jw4VKo8sb++9//0qJFi1KZe926dbz44ousWbOGF1544aZj+/TpUyo1iIiIFEcBqZSlpaWRn59PTk4OAOXKlWPWrFk4OzsDcOjQIT744ANyc3Px8PDg/fffp1atWoSEhDB8+HC8vLxISkqif//+LF26lLCwMACqVasGQHx8PL179yYlJYWgoCBGjBiB0Whkzpw5HDhwAKPRSFBQEAMHDqSgoIApU6Zw/Phxzp8/T/369VmwYAEFBQWMHj2a8+fPAzBs2DBcXV3Zvn07+/fvp3LlyjRs2JDJkydz9uxZDAYDY8aMoVWrVixevJhDhw6RnJzMyy+/THR0NE2aNOH777/n4sWLTJw4kfbt2xc6Jvn5+URFRbFq1Sp69+7Nb7/9Rs2aNQGYPXs2e/fuxc7ODj8/P4YPH245SzZixAhWrlxJZGQkV65cwdHRkfnz51OnTp3SfyNFROSBooBUyho0aICvry9+fn40bNgQLy8vAgMDqVWrFnl5eYwePZoPP/yQpk2bEh0dzejRo1m3bt0N53riiSfo3bs3AMHBwSxevJgLFy4QFhZGVlYWPj4+vPLKK0RFRQGwfv168vLyGDRoEI0bN8ZsNuPo6MiaNWswmUwMGDCAnTt3kp2dzWOPPcbSpUtJSEhg48aNvP322/j4+NCiRQvatm3LqFGjCA4OxtfXl9TUVPr27cuGDRsAyMvLY8uWLQBER0eTn5/PmjVr2L59OwsXLiwSkHbu3Em1atWoXbs2fn5+rFmzhnHjxnHmzBl27drF5s2buXLlChMmTCA39/9vApuVlcV3333HihUrcHFxYeHChaxatYpJkybd9fdNREQebApIf4KpU6fyxhtvsGfPHvbs2UPPnj2ZN28ejz/+OBUqVKBp06YA+Pv7M3nyZDIzM0s8d9u2bXFycqJixYp4eHiQkZFBbGwsCQkJ7N+/H4Ds7GyOHTtGv379cHd3Z9WqVZw4cYLExESys7Np1qwZCxYsICUlheeff55hw4YVeZ19+/Zx4sQJFi1aBEBBQQGnT58GsNR/fU0A9erVIz09vchc69atIyAgAIDOnTszduxYRo4cSdWqVXF2dqZ379506NCBsWPHWs60Abi5uTF//nw2b95MYmIiu3fvpmHDhiU+VqWpcuXyZV0CcO/UUZrUo22w9R5tvT+w/R4VkEpZTEwM2dnZdO7cmeDgYIKDgwkPD2ft2rWMHj26yHiz2YzRaLT8DFfDSHEcHP7/LTQYDJb9x40bR6dOnQC4ePEi5cqVY9u2bSxatIj+/fsTFBREWloaZrOZxx9/nOjoaHbv3s2OHTv45z//aTkjdI3JZOLLL7/E3d0dgNTUVDw9Pfnuu+9wcXEpNPZaqDEYDEXqvXDhArt37+bnn3/mX//6F2azmUuXLvHtt9/SpUsXvv76aw4cOMCuXbvo3bs3K1assOybnJxMSEgIL7/8Mu3ataNSpUokJCQUf/D/ROfOlTzUlpbKlcvfE3WUJvVoG2y9R1vvD2yjRzs7A56ebsU//yfW8kBycXFh/vz5JCUlAVdDT0JCAg0bNqROnTqkp6cTHx8PwJYtW6hWrRru7u54eHjw66+/AvDdd99Z5rO3t79pYALw9vYmPDyc/Px8Ll++TN++fTl06BCxsbH4+/sTHBxMhQoViIuLw2g0snLlShYvXoy/vz/vvfceFy9eJCsrC3t7e0tY8/b25quvvgLg119/JTAwkCtXrtz28YiMjMTb25tdu3axfft2duzYwdChQwkLC+PIkSO8/PLLNG/enLfffpu6dety8uRJy74//fQTtWrVYuDAgTRp0oTvvvvOUp+IiMjdpDNIpczb25vhw4czdOhQ8vPzgatLUMOGDcPJyYnQ0FCmTZvGlStXePjhhwkNDQVg8ODBjB8/nnXr1hX6tNu18FCpUqViX7N3796cOnWK7t27U1BQQFBQEF5eXri7uzN27Fg2b96Mo6Mjzz77LElJSbz22muMHj2awMBA7O3tGTduHBUqVKBVq1YsWLCA8uXLM3HiRCZPnkxgYCAAc+bMwc2t+ORdnPXr1zNq1KhC2/r168eyZctwdnbmmWeeISAgAFdXV5599lnatWvHzz//DEDr1q1ZvXo1nTt3xmw207x5c44fP37bNYiIiNyKwXxtHUfExn3700yu5KXd9Xm7PjfnnjjVbAunvG9FPdoGW+/R1vsD2+hRS2wiIiIit0kBSURERMSKApKIiIiIFQUkERERESsKSCIiIiJWFJBERERErCggiYiIiFhRQBIRERGxooAkIiIiYkW3GpEHRscmE0pl3vyCvFKZV0REyo4CkjwwLlzIwmTSnXVEROTWtMQmIiIiYkUBSURERMSKApKIiIiIFQUkERERESsKSCIiIiJWFJBERERErOhj/vLA8PR0K/HYgoI80tJyS7EaERG5lykgyQPjyKnZ5BWklWjsM3VnAQpIIiIPKi2xiYiIiFhRQBIRERGxooAkIiIiYkUBSURERMSKApKIiIiIFQUkERERESsKSCIiIiJWFJBERERErCggiYiIiFi5ZwJSXFwcISEht7XP9eO7det2W/sWFBTQpk0bpk2bdlv73Y7Vq1ezevXqUpu/JBYvXszixYuLbDebzSxfvpxu3brRrVs3unfvzubNm29r7pSUFF577TUAxo8fT0RERJExERERjB8//o8VLyIiUkbu61uNHDhwwPJzZGTkbe27c+dOmjRpQnR0NGPHjsXV1fVul0efPn3u+px3S2hoKEeOHGHlypWUL1+es2fP8vLLL+Ph4UGrVq1KNEfVqlX57LPPSrlSERGRP989H5AKCgqYMmUKx48f5/z589SvX58FCxYwb948AF566SW+/vpr6tevz7Fjx1i8eDEpKSmcOnWKM2fO8NJLL/H6668XmTciIoKOHTtiNpvZvHkzPXr0AK6eCXF1deXIkSNcunSJ0aNHExkZydGjR/Hz82P8+PEYjUbmzJnDgQMHMBqNBAUFMXDgQOLi4pg7dy4mk4l69epRvXp1AEaMGEFUVBSffPIJBoOBJk2aMG3aNC5evMg777xDZmYmqampdO/enZEjRxaqMysri3feeYeUlBRSU1Np2bIlH3zwAQcOHODTTz/FxcWF//3vf9SvX5958+bh5OTEsmXLCA8Px8PDgwoVKtC0adNCc16+fJkvv/ySjRs3Ur58eQAeeeQRFixYYAmKK1euJDIykitXruDo6Mj8+fOpU6cOPj4+NG3alISEBObOncvf//53tm/fDkBMTAwrV64kPz+f119/nc6dOwNw6tQp+vXrR0ZGBs8//zxjxozBYDCwYcMGvvzyS0wmE0899RTvvfcezs7ON33trl27smfPHq5cucLs2bNp3Ljx3fpVExERsbjnA9IPP/yAo6Mja9aswWQyMWDAAHbu3MnEiRNZsWIFX3/9dZF9jh07xqpVq8jMzMTPz49+/fpRoUIFy/MXL15k3759zJgxA3t7e1auXGkJSACpqamsWbOG9evXM2HCBLZu3YqzszPt2rVj2LBhbNq0CYD169eTl5fHoEGDLP9QJyYmsmPHDsqXL29Z2kpJSWHmzJlERETwyCOPMG7cOHbu3MmpU6cICAige/fuZGZm0r59e0JCQqhYsaKllpiYGBo2bMiiRYvIy8ujS5cu/Pzzz5ZjEx0dTZUqVejZsyd79uyhcuXKrFu3jvXr12MwGOjVq1eRgHTixAkcHByoVatWoe3XxmVlZfHdd9+xYsUKXFxcWLhwIatWrWLSpEkAtGvXjg8//JCkpKRC+1+5coXw8HAuXLhAcHAwzZs3ByApKYnIyEjc3NwYMGAA27Zto1atWoSHhxMWFoazszPz58/n888/p3///jd9bXd3d9auXcuKFSv49NNPb7h8eLdUrly+1OYuLfdjzbdLPdoGW+/R1vsD2+/xng9IzZs3x93dnVWrVnHixAkSExPJzs6+6T5eXl44OTnh6emJu7s7mZmZhQLSxo0b8fb25uGHH8bX15dJkyZx5MgRGjVqBFwNAADVqlWjXr16eHp6Alf/cc7IyCA2NpaEhAT2798PQHZ2NseOHeOJJ56gdu3alrMy1/zwww88++yzPPLIIwDMnTvX8tz+/fv5/PPPOX78OPn5+Vy5cqXQvgEBAcTHx/PFF19w4sQJ0tPTLf3Xq1fPMmfdunXJyMjg5MmTtG/fnnLlygHw4osvYjKZCs1pZ2eHk5NTscfPzc2N+fPns3nzZhITE9m9ezcNGza0PP/000/fcL/u3bvj4OBA1apVeeaZZ/jxxx8B8PHxsYQ+f39/Dhw4wNmzZzl16hQ9e/YEID8/n0aNGt3ytdu2bWvp/d///nexPdwN585llur8d1vlyuXvu5pvl3q0Dbbeo633B7bRo52dAU9Pt2Kfv+cD0rZt21i0aBH9+/cnKCiItLQ0zGbzTfdxdna2/GwwGIqMj4iIIDU1FR8fH+BqYAgLC+P9998HwNHR0TLWwaHoITIajYwbN45OnToBV89IlStXjkOHDuHi4lJkvIODAwaDwfL44sWLACxdupTTp08TEBCAn58f+/btK1LrihUr2Lp1Kz179qRVq1b88ssvljE36tO6XwcHB/Ly8grNWbduXXJycvj999+pVq2aZfvmzZs5f/48nTp1IiQkhJdffpl27dpRqVIlEhISbnh8r2dvb2/52WQyWY7j9cfQZDLh4OCA0WjE39+fiRMnAleX/YxGI8nJySV67euPp4iIyN12z3yKrTixsbH4+/sTHBxMhQoViIuLw2g0Alf/QS4oKLit+Q4fPszZs2eJiYlh+/btbN++nU8//ZSoqCiysrJKNIe3tzfh4eHk5+dz+fJl+vbty6FDh4od36RJEw4dOsS5c+cAmDFjBtu2bWPv3r0MGjQIf39/Tp48SUpKSpGzPXv37qVXr1507dqV3Nxcjh49WmTM9Vq2bMmOHTvIzMwkNzeXb7/9tsgYFxcX+vXrx5QpUyw9JyUlsWDBAurWrctPP/1ErVq1GDhwIE2aNOG7776zHPOb2bx5M2azmTNnznD48GGaNGkCXL0g/tKlS+Tm5rJlyxZatWqFl5cX3377LRcuXMBsNjNlyhS+/PLLP/zaIiIid9M9dQbpP//5D82aNbM8DgwMpF+/fowdO5bNmzfj6OjIs88+a7n2xdfXl27dut3w4+XFiYiIICgoqNCZHi8vL2rXrk1UVFSJ5ujduzenTp2ie/fuFBQUEBQUhJeXF3FxcTccX7VqVd59910GDRqEyWTimWeeISgoCFdXV9566y1cXFx45JFHaNy4MUlJSdSsWdOy74ABA5gyZQpLly7Fzc2NZs2aFRlzvYYNGzJgwAB69OhBhQoVCp0hut6oUaP46KOP6NmzJw4ODtjb2zNmzBjatGnD5cuXWb16NZ07d8ZsNtO8eXOOHz9+y+Py0EMPERQUREFBAe+//75lWa1OnToMGTKES5cuERAQQJs2bQAYPnw4AwYMwGQy0bBhQ4YMGUJBQcEfem0REZG7yWC+1XqViI04cmo2eQVpJRr7TN1Z9936ui1cE3Ar6tE22HqPtt4f2EaPt7oG6Z5fYhMRERH5sykgiYiIiFhRQBIRERGxooAkIiIiYkUBSURERMSKApKIiIiIFQUkERERESsKSCIiIiJWFJBERERErNxTtxoRKU2Nar1d4rEFBXm3HiQiIjZLAUkeGBcuZGEy6c46IiJyawpI8sCwszOUdQmlTj3aBvV4/7P1/uD+7/FW9etmtSIiIiJWdJG2iIiIiBUFJBERERErCkgiIiIiVhSQRERERKwoIImIiIhYUUASERERsaKAJCIiImJFAUlERETEigKSiIiIiBUFJLF5UVFRdO7cmU6dOrFq1aqyLueuWLJkCV26dKFLly7MmTMHgH379hEYGEinTp0IDQ0t4wrvntmzZzN+/HjA9nrcvn07QUFB+Pv7M336dMD2eoyMjLT8rs6ePRuwjR6zsrIICAggKSkJKL6nhIQEgoKCeOGFF3j33XcpKCgoq5Jvm3WPa9asISAggMDAQCZMmEBe3tWbet/PPd6UWcSGnT171tyhQwdzWlqa+fLly+bAwEDz8ePHy7qsO7J3715zr169zLm5uea8vDxz//79zVFRUeb27dubf/vtN3N+fr751VdfNcfExJR1qXds3759Zi8vL/Pbb79tvnLlik31+Ntvv5nbtGljTk5ONufl5Zn79OljjomJsakes7Ozzc2bNzdfuHDBnJ+fb+7Ro4d527Zt932Phw4dMgcEBJifeuop8+nTp2/6u9mlSxfzDz/8YDabzeYJEyaYV61aVZall5h1jydOnDB37NjRnJmZaTaZTOa33nrLvHz5crPZfP/2eCs6gyQ2bd++fXh7e+Pu7s5DDz3ECy+8wDfffFPWZd2RypUrM378eJycnHB0dKRu3bokJiZSq1YtatSogYODA4GBgfd9n+np6YSGhjJ06FAA4uPjbarHb7/9ls6dO/PII4/g6OhIaGgorq6uNtWj0WjEZDJx5coVCgoKKCgowM3N7b7vMTw8nPfee48qVaoAxf9unjlzhpycHJ555hkAgoKC7pterXt0cnLivffew83NDYPBwJNPPsnvv/9+X/d4Kw5lXYBIaUpNTaVy5cqWx1WqVCE+Pr4MK7pz9erVs/ycmJhIdHQ0L7/8cpE+U1JSyqK8u2by5MmMGjWK5ORk4Mbv5f3c46lTp3B0dGTo0KEkJyfz/PPPU69ePZvq0c3NjZEjR+Lv74+rqyvNmze3iffxgw8+KPS4uJ6st1euXPm+6dW6x8cee4zHHnsMgIsXL7Jq1Spmzpx5X/d4KzqDJDbNZDJhMBgsj81mc6HH97Pjx4/z6quv8tZbb1GjRg2b6vPrr7/m0UcfpWXLlpZttvZeGo1GYmNjmTFjBmvWrCE+Pp7Tp0/bVI9Hjx5l3bp17Nixg927d2NnZ0diYqJN9QjF/27a2u8sQEpKCgMGDCA4OBgvLy+b7PEanUESm/bII4/wn//8x/L43LlzllPG97Pvv/+eN998k3feeYcuXbpw4MABzp07Z3n+fu9zy5YtnDt3jm7dupGRkUF2djZnzpzB3t7eMuZ+77FSpUq0bNmSihUrAuDn58c333xjUz3u2bOHli1b4unpCVxdfvn8889tqke4+t+ZG/39s95+/vz5+7rX//3vfwwePJiQkBBeffVVoGjv93uP19MZJLFprVq1IjY2losXL3LlyhX+/e9/065du7Iu644kJyczbNgw5s2bR5cuXQB4+umnOXnyJKdOncJoNLJp06b7us/ly5ezadMmIiMjefPNN/Hx8WHZsmU21WOHDh3Ys2cPly5dwmg0snv3bl588UWb6rFBgwbs27eP7OxszGYz27dvt7nfVSj+799jjz2Gs7Mz33//PXD1E333a69ZWVkMGjSIkSNHWsIRYFM9WtMZJLFpVatWZdSoUfTv35/8/Hx69OhB06ZNy7qsO/L555+Tm5vLrFmzLNt69+7NrFmzGDFiBLm5ubRv354XX3yxDKu8+5ydnW2qx6effprBgwfTt29f8vPzad26NX369KFOnTo202ObNm04cuQIQUFBODo60qRJE0aMGEHr1q1tpke4+e/mvHnzmDhxIllZWTz11FP079+/jKv9Y9auXcv58+dZvnw5y5cvB8DHx4eRI0faTI/WDGaz2VzWRYiIiIjcS7TEJiIiImJFAUlERETEigKSiIiIiBUFJBERERErCkgiIiIiVhSQRETkpl599VUuXrxY1mWI/KkUkERE5Kb27t1b1iWI/OkUkERE7mNr166lS5cuBAYG0r9/f5KTk1mzZg0BAQF07dqVV199lZMnTwIwfvx4Pv/8c8u+1z/28fFh8eLF9O3blw4dOvDhhx8CMGHCBAAGDBhguXGwyINA36QtInKfOnr0KPPmzWP9+vU8+uijfPHFFwwcOBCTycSaNWuoWLEiERERDBs2jM2bN99yvuzsbL766itSUlLo2LEjwcHBzJw5k4iICL788kvLfeNEHgQ6gyQicp+KjY2lTZs2PProowAMHDgQX19fOnfubAkzQUFBpKSkkJSUdMv5fH19gau36PH09CQjI6P0ihe5xykgiYjcp+zt7TEYDJbHOTk5nD59usg4s9lMQUEBBoOB6+8ulZ+fX2ics7Oz5WfrsSIPGgUkEZH7lJeXF7GxsaSmpgLwf+3bPQ5EUBSG4W/YABKdRMcW7IBN3GgsgZWo7ECj1dBo1TZhD2Kmm2RupZGY5H02cHK6N/en73sty6JxHL+/zoZhkOd5iuNYvu9r2zZJ0r7vWtf10hzXdXUcxz1LAA/FGyQA+FNpmqppGlVVJUkKw1DTNGmeZ5VlqfM8FQSBuq6T4zgyxqiua+V5riiKlGXZpTlFUcgYo7ZtlSTJnSsBj/F6c4YKAADwgys2AAAAC4EEAABgIZAAAAAsBBIAAICFQAIAALAQSAAAABYCCQAAwEIgAQAAWD7Q2lUSz4ydrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 4))\n",
    "sns.countplot(y='Region', data=low, palette='summer', order=low['Region'].value_counts().index)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Countries in Sub-Saharan Africa is most often ranked last Happiness Score. Let's compare it with Western Europe, so we could get a clearer information between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness 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>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.522</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>6</td>\n",
       "      <td>7.406</td>\n",
       "      <td>1.29025</td>\n",
       "      <td>1.31826</td>\n",
       "      <td>0.88911</td>\n",
       "      <td>0.64169</td>\n",
       "      <td>0.23351</td>\n",
       "      <td>0.41372</td>\n",
       "      <td>2015</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>115</td>\n",
       "      <td>4.587</td>\n",
       "      <td>0.33100</td>\n",
       "      <td>1.05600</td>\n",
       "      <td>0.38000</td>\n",
       "      <td>0.25500</td>\n",
       "      <td>0.17700</td>\n",
       "      <td>0.11300</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>152</td>\n",
       "      <td>3.334</td>\n",
       "      <td>0.35900</td>\n",
       "      <td>0.71100</td>\n",
       "      <td>0.61400</td>\n",
       "      <td>0.55500</td>\n",
       "      <td>0.21700</td>\n",
       "      <td>0.41100</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Benin</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>102</td>\n",
       "      <td>4.883</td>\n",
       "      <td>0.39300</td>\n",
       "      <td>0.43700</td>\n",
       "      <td>0.39700</td>\n",
       "      <td>0.34900</td>\n",
       "      <td>0.17500</td>\n",
       "      <td>0.08200</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>145</td>\n",
       "      <td>3.775</td>\n",
       "      <td>0.04600</td>\n",
       "      <td>0.44700</td>\n",
       "      <td>0.38000</td>\n",
       "      <td>0.22000</td>\n",
       "      <td>0.17600</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Togo</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>139</td>\n",
       "      <td>4.085</td>\n",
       "      <td>0.27500</td>\n",
       "      <td>0.57200</td>\n",
       "      <td>0.41000</td>\n",
       "      <td>0.29300</td>\n",
       "      <td>0.17700</td>\n",
       "      <td>0.08500</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country              Region  Happiness Rank  Happiness Score  \\\n",
       "0     Switzerland      Western Europe               1            7.587   \n",
       "1         Iceland      Western Europe               2            7.561   \n",
       "2         Denmark      Western Europe               3            7.527   \n",
       "3          Norway      Western Europe               4            7.522   \n",
       "5         Finland      Western Europe               6            7.406   \n",
       "..            ...                 ...             ...              ...   \n",
       "142  Burkina Faso  Sub-Saharan Africa             115            4.587   \n",
       "144        Rwanda  Sub-Saharan Africa             152            3.334   \n",
       "145         Benin  Sub-Saharan Africa             102            4.883   \n",
       "147       Burundi  Sub-Saharan Africa             145            3.775   \n",
       "148          Togo  Sub-Saharan Africa             139            4.085   \n",
       "\n",
       "     GDP per Capita  Social support  Healthy life expectancy  \\\n",
       "0           1.39651         1.34951                  0.94143   \n",
       "1           1.30232         1.40223                  0.94784   \n",
       "2           1.32548         1.36058                  0.87464   \n",
       "3           1.45900         1.33095                  0.88521   \n",
       "5           1.29025         1.31826                  0.88911   \n",
       "..              ...             ...                      ...   \n",
       "142         0.33100         1.05600                  0.38000   \n",
       "144         0.35900         0.71100                  0.61400   \n",
       "145         0.39300         0.43700                  0.39700   \n",
       "147         0.04600         0.44700                  0.38000   \n",
       "148         0.27500         0.57200                  0.41000   \n",
       "\n",
       "     Freedom to make life choices  Generosity  Perceptions of corruption  Year  \n",
       "0                         0.66557     0.29678                    0.41978  2015  \n",
       "1                         0.62877     0.43630                    0.14145  2015  \n",
       "2                         0.64938     0.34139                    0.48357  2015  \n",
       "3                         0.66973     0.34699                    0.36503  2015  \n",
       "5                         0.64169     0.23351                    0.41372  2015  \n",
       "..                            ...         ...                        ...   ...  \n",
       "142                       0.25500     0.17700                    0.11300  2019  \n",
       "144                       0.55500     0.21700                    0.41100  2019  \n",
       "145                       0.34900     0.17500                    0.08200  2019  \n",
       "147                       0.22000     0.17600                    0.18000  2019  \n",
       "148                       0.29300     0.17700                    0.08500  2019  \n",
       "\n",
       "[289 rows x 11 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sahara_west = df[(df['Region'] == 'Western Europe') | (df['Region'] == 'Sub-Saharan Africa')]\n",
    "sahara_west"
   ]
  }
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