happiness_index.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2a940ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c8149e37",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./data/world-happiness-report-2021.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dfefe876",
   "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 name</th>\n",
       "      <th>Regional indicator</th>\n",
       "      <th>Ladder score</th>\n",
       "      <th>Standard error of ladder score</th>\n",
       "      <th>upperwhisker</th>\n",
       "      <th>lowerwhisker</th>\n",
       "      <th>Logged 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>Ladder score in Dystopia</th>\n",
       "      <th>Explained by: Log GDP per capita</th>\n",
       "      <th>Explained by: Social support</th>\n",
       "      <th>Explained by: Healthy life expectancy</th>\n",
       "      <th>Explained by: Freedom to make life choices</th>\n",
       "      <th>Explained by: Generosity</th>\n",
       "      <th>Explained by: Perceptions of corruption</th>\n",
       "      <th>Dystopia + residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.842</td>\n",
       "      <td>0.032</td>\n",
       "      <td>7.904</td>\n",
       "      <td>7.780</td>\n",
       "      <td>10.775</td>\n",
       "      <td>0.954</td>\n",
       "      <td>72.000</td>\n",
       "      <td>0.949</td>\n",
       "      <td>-0.098</td>\n",
       "      <td>0.186</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.446</td>\n",
       "      <td>1.106</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.481</td>\n",
       "      <td>3.253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.620</td>\n",
       "      <td>0.035</td>\n",
       "      <td>7.687</td>\n",
       "      <td>7.552</td>\n",
       "      <td>10.933</td>\n",
       "      <td>0.954</td>\n",
       "      <td>72.700</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.179</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.502</td>\n",
       "      <td>1.108</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.485</td>\n",
       "      <td>2.868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.571</td>\n",
       "      <td>0.036</td>\n",
       "      <td>7.643</td>\n",
       "      <td>7.500</td>\n",
       "      <td>11.117</td>\n",
       "      <td>0.942</td>\n",
       "      <td>74.400</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.292</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.566</td>\n",
       "      <td>1.079</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.413</td>\n",
       "      <td>2.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.554</td>\n",
       "      <td>0.059</td>\n",
       "      <td>7.670</td>\n",
       "      <td>7.438</td>\n",
       "      <td>10.878</td>\n",
       "      <td>0.983</td>\n",
       "      <td>73.000</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.673</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.482</td>\n",
       "      <td>1.172</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.170</td>\n",
       "      <td>2.967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.464</td>\n",
       "      <td>0.027</td>\n",
       "      <td>7.518</td>\n",
       "      <td>7.410</td>\n",
       "      <td>10.932</td>\n",
       "      <td>0.942</td>\n",
       "      <td>72.400</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.338</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.501</td>\n",
       "      <td>1.079</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.384</td>\n",
       "      <td>2.798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.512</td>\n",
       "      <td>0.120</td>\n",
       "      <td>3.748</td>\n",
       "      <td>3.276</td>\n",
       "      <td>7.926</td>\n",
       "      <td>0.787</td>\n",
       "      <td>48.700</td>\n",
       "      <td>0.715</td>\n",
       "      <td>-0.131</td>\n",
       "      <td>0.915</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.405</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.467</td>\n",
       "      <td>0.074</td>\n",
       "      <td>3.611</td>\n",
       "      <td>3.322</td>\n",
       "      <td>9.782</td>\n",
       "      <td>0.784</td>\n",
       "      <td>59.269</td>\n",
       "      <td>0.824</td>\n",
       "      <td>-0.246</td>\n",
       "      <td>0.801</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.415</td>\n",
       "      <td>0.068</td>\n",
       "      <td>3.548</td>\n",
       "      <td>3.282</td>\n",
       "      <td>7.676</td>\n",
       "      <td>0.552</td>\n",
       "      <td>61.400</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.167</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.493</td>\n",
       "      <td>1.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.145</td>\n",
       "      <td>0.058</td>\n",
       "      <td>3.259</td>\n",
       "      <td>3.030</td>\n",
       "      <td>7.943</td>\n",
       "      <td>0.750</td>\n",
       "      <td>56.201</td>\n",
       "      <td>0.677</td>\n",
       "      <td>-0.047</td>\n",
       "      <td>0.821</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.649</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.075</td>\n",
       "      <td>1.205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>2.523</td>\n",
       "      <td>0.038</td>\n",
       "      <td>2.596</td>\n",
       "      <td>2.449</td>\n",
       "      <td>7.695</td>\n",
       "      <td>0.463</td>\n",
       "      <td>52.493</td>\n",
       "      <td>0.382</td>\n",
       "      <td>-0.102</td>\n",
       "      <td>0.924</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1.895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country name  Regional indicator  Ladder score  \\\n",
       "0        Finland      Western Europe         7.842   \n",
       "1        Denmark      Western Europe         7.620   \n",
       "2    Switzerland      Western Europe         7.571   \n",
       "3        Iceland      Western Europe         7.554   \n",
       "4    Netherlands      Western Europe         7.464   \n",
       "..           ...                 ...           ...   \n",
       "144      Lesotho  Sub-Saharan Africa         3.512   \n",
       "145     Botswana  Sub-Saharan Africa         3.467   \n",
       "146       Rwanda  Sub-Saharan Africa         3.415   \n",
       "147     Zimbabwe  Sub-Saharan Africa         3.145   \n",
       "148  Afghanistan          South Asia         2.523   \n",
       "\n",
       "     Standard error of ladder score  upperwhisker  lowerwhisker  \\\n",
       "0                             0.032         7.904         7.780   \n",
       "1                             0.035         7.687         7.552   \n",
       "2                             0.036         7.643         7.500   \n",
       "3                             0.059         7.670         7.438   \n",
       "4                             0.027         7.518         7.410   \n",
       "..                              ...           ...           ...   \n",
       "144                           0.120         3.748         3.276   \n",
       "145                           0.074         3.611         3.322   \n",
       "146                           0.068         3.548         3.282   \n",
       "147                           0.058         3.259         3.030   \n",
       "148                           0.038         2.596         2.449   \n",
       "\n",
       "     Logged GDP per capita  Social support  Healthy life expectancy  \\\n",
       "0                   10.775           0.954                   72.000   \n",
       "1                   10.933           0.954                   72.700   \n",
       "2                   11.117           0.942                   74.400   \n",
       "3                   10.878           0.983                   73.000   \n",
       "4                   10.932           0.942                   72.400   \n",
       "..                     ...             ...                      ...   \n",
       "144                  7.926           0.787                   48.700   \n",
       "145                  9.782           0.784                   59.269   \n",
       "146                  7.676           0.552                   61.400   \n",
       "147                  7.943           0.750                   56.201   \n",
       "148                  7.695           0.463                   52.493   \n",
       "\n",
       "     Freedom to make life choices  Generosity  Perceptions of corruption  \\\n",
       "0                           0.949      -0.098                      0.186   \n",
       "1                           0.946       0.030                      0.179   \n",
       "2                           0.919       0.025                      0.292   \n",
       "3                           0.955       0.160                      0.673   \n",
       "4                           0.913       0.175                      0.338   \n",
       "..                            ...         ...                        ...   \n",
       "144                         0.715      -0.131                      0.915   \n",
       "145                         0.824      -0.246                      0.801   \n",
       "146                         0.897       0.061                      0.167   \n",
       "147                         0.677      -0.047                      0.821   \n",
       "148                         0.382      -0.102                      0.924   \n",
       "\n",
       "     Ladder score in Dystopia  Explained by: Log GDP per capita  \\\n",
       "0                        2.43                             1.446   \n",
       "1                        2.43                             1.502   \n",
       "2                        2.43                             1.566   \n",
       "3                        2.43                             1.482   \n",
       "4                        2.43                             1.501   \n",
       "..                        ...                               ...   \n",
       "144                      2.43                             0.451   \n",
       "145                      2.43                             1.099   \n",
       "146                      2.43                             0.364   \n",
       "147                      2.43                             0.457   \n",
       "148                      2.43                             0.370   \n",
       "\n",
       "     Explained by: Social support  Explained by: Healthy life expectancy  \\\n",
       "0                           1.106                                  0.741   \n",
       "1                           1.108                                  0.763   \n",
       "2                           1.079                                  0.816   \n",
       "3                           1.172                                  0.772   \n",
       "4                           1.079                                  0.753   \n",
       "..                            ...                                    ...   \n",
       "144                         0.731                                  0.007   \n",
       "145                         0.724                                  0.340   \n",
       "146                         0.202                                  0.407   \n",
       "147                         0.649                                  0.243   \n",
       "148                         0.000                                  0.126   \n",
       "\n",
       "     Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
       "0                                         0.691                     0.124   \n",
       "1                                         0.686                     0.208   \n",
       "2                                         0.653                     0.204   \n",
       "3                                         0.698                     0.293   \n",
       "4                                         0.647                     0.302   \n",
       "..                                          ...                       ...   \n",
       "144                                       0.405                     0.103   \n",
       "145                                       0.539                     0.027   \n",
       "146                                       0.627                     0.227   \n",
       "147                                       0.359                     0.157   \n",
       "148                                       0.000                     0.122   \n",
       "\n",
       "     Explained by: Perceptions of corruption  Dystopia + residual  \n",
       "0                                      0.481                3.253  \n",
       "1                                      0.485                2.868  \n",
       "2                                      0.413                2.839  \n",
       "3                                      0.170                2.967  \n",
       "4                                      0.384                2.798  \n",
       "..                                       ...                  ...  \n",
       "144                                    0.015                1.800  \n",
       "145                                    0.088                0.648  \n",
       "146                                    0.493                1.095  \n",
       "147                                    0.075                1.205  \n",
       "148                                    0.010                1.895  \n",
       "\n",
       "[149 rows x 20 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cec6fb3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ls = df.iloc[:,[0, 2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "109b5ded",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 149 artists>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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5GCS2LyExHhCfpyGxfx4levWJz7fGtTyOBLsGNSz5KbLSX0HCDNDbzAp3enFPGXNOkiTpIFKclxHu3uTud6CypNfQuMcmZFEPRtZrHRLiS5GluzMq0ZqORLNHHK4G+DaaVHUIaiH6OooZb4XEFdTTe1C8fwdZw4YmWq2JxPtfKM4NakTyH5TVPQ2VcwH8omVCWMack/YireckeS8pzssAMxtuZhvExxko+epmlE09GDX8mICSs1ZBNc+PIpE8BjUB2a/ikDvFcTYE/g58BtUoDzKzmSj+3Ae506tQrPtJyuEYNyFhnoemUxUu+B+ipieDge1QVzFQzDtJOowU6CRZkE4XZzNrMrNxZvZYzD3utYhtjzSzc9pwjmFm9qmKzyPN7KzF2O+eJT1XK8c4CTUqGWdmxVzkDyAX9ZeQC3ouihnPAv7n7s+6+3dQ05JaJNBPIevYgGFImKfEvgehxK+3Yv3v4v3ZlPXV/eNc05BF/k58fitem+OvOo75NGWjk55L+z0kyfuR86OTpKTTxRn1mR7h7puirOFjO+Acw1ASFgDuPsbdv77wzd/dbselPbG7/8Tdh7t7byS+s4E/AE+4+xxkPT8Sm08FNjCzwWZWjQSyGXUYuwWJOnGc21Dy2JXIEl8HCfCryFU9AIl6QxzjRcrRkqsBI4ga5orLvQmJ+zjUGGVeLP9oK/eVMeckSZIOoiuIcyWjgfXN7KNmdr+ZPWxmt5jZqi03DAH7p5k9GH87xfIPhiU+LvbvC5wO7BLLjjOzUWZ2XWx/qpn9yczuMLMXzOzrFeeYGa99zOxWM3vIzMab2f6xfJiZPWlmfzCzx83sJjOrf597NFSzfKmZzUbJYA8DH0Hu5BrgRpQhvSfKoj4LieX26DebDnwY2Bf4PhLbV2LdBqj2+U3kFm+M5R9DLvPeyDrugx4M7kPiXYUGZBgqozqPcubzRa18/xlzTjqEtKCTpAvVOZtZDbAPEqa7gO3d3c3saDTq8Nstdvkt8Gt3v8vMhgL/RZbl8cBX3P1uM+uDXLTfBY4vGoS0aLMJsBHKgu4LPG1m57p7Y8X6ucCB7j7dzAYB95nZtbFuA+Awd/+Cmf0Ddf/66yJutRn4PerqdXKc+/hYPh0J9Azgl2gCVR1wEhLT/8Yx6mKb0UhgvwBcg7wDPWPdkDhe0flrDHoA8PjcB02/Wo3StT4QZXAPQtb5s2hK1cWLuJ8k6RBaCnS2+0xWJLqCONeb2bh4PxrV/Q5HM42HoAzlF1vZbw9g47JnBv3CSr4bTXm6FLgqmoC83zVc7+7zgHlmNhmNXHylYr0BPzWzXZGIrhHbALzo7sX1j0Uu9PdjLySwf0A9tg0J5L9j3UTUJGQQarF5K7B2bAOyhvshy3p3FDc+Frgc9e2eh8R2Cko0+2Ac9yn0MDEw7mNA/M1D3/NsVCN9D7LK6+NcM1vegLtfgOq2GTlypLdcnyRJkrSdruDWLmLOI9z9a+7egBKZznH3zVA3rdYSkqqAHSr2XcPdZ7j76ahvdD2ycDdajGuYV/G+ifc+tByOLNqt3X0Eit0W1/R++7aGAd8Avu3uG8UxHFm/q6BJUYfHtpvF+Z5GU65AQlqURc1A2d2PoNIpkPVbhVzYY5AQv40aktRRJny9EusKEb8ZudO3pKyD7omGeyx4A+nWTpYxhbs7Xd7JikBXEOfWWAklNkEpOC25Cfhq8cHMRsTreu4+3t1/joRpIyRgfZfyeia7e6OZ7Yas2KXhv6jt5u9bLJ+NBHNdFGu+B7n4z0YJXx+P7QYgwZ6MksBOR53DQII6C91v/1hWi0S2yFDvhyzzNdFDwbw4/vaUMefdKec777OU95sk7UoKdLK801XF+VTgiuhJPWUh23wdGGlmj5rZE5RZ3t+MsqxHUFet/6CSoPlm9oiZHdeG67k0zjUGWbRPteEYwLttPS9EseKHzOwxJJ5zY5MX414MJWTtEuefApxG6dq+DcWNays+GxLUzZEV/wYS4IY47q2xbQ2qY36IcvJU0epzG+R1eAllgzeiBLIkSZJkGWHuGS7sbIqscHfvY2bfBU5ELukfufupZtaEhlZ8FCWJDUJZ2t9G8eDVUaz+8/H5RdTS8z4k2ieiGukZKOY8A1nvBwIXuPuXzWxSHHd1ZJHPRqJch+ZVD1jY9Y8cOdLHjBnTpntPCyhpK5kglnR3zGysu49sbV1XtZxXZP5O6dLfOF6bkdV7FOXYx1OQCBdTp3ZGv6ehpicgsS06iw1A5VHNyO29DxLfw83sUJTd/RpyaRuKazciIS86i71LxpyTziYf7JLlmRTnrkEv4G0zewW4F8WK10cZ6I8gN/R9qN64SOIyFE8/J97PQe7p3qjdp8f7TWL7MSgJbTQS3LdRDLoP8Iu4jrNR+VUzqr1+HYl/jo1MuiQp0MnySlcopVrhcfd3H5LMbBhwnbu/iBqNYGYNqEPYhqiMCxSj7o0s5mZUrzwdWcNzkHC/VXGaHVHi1yBkCc9GmfANwGMoNj0h9jPkRm+OfWe0cs1ZSpUkSdJBpOXcvdgRZWzPQaVTk1GJ13OoqclUVM88AGWYF6I5G2VfFxY38Vok3H0YaHL3K2N/Qy7zCfH+zpYXkm7tJEmSjiMt567Hu1ZoDMo4s2L5Gkg8Qa7mnkhYn0WZ3E1ISHsAV1EOsqiK/Xsid/UGKHHs58DP4rg1ZrYd5YjJYSjmDBL2JOmStObazmSxpLuTlvMSYmZuZn+p+FxjZm8WvbrbganI8gW5oQ9C5VInIWv4WygZ7AkkwtsRs5zj71kk0ncjcXXkur4YWcG3xPYg0V0FWeKgeumV4/07KAMcZKEnSZIky4gU5yVnFrBpxYCLPSmzq5cad58JvGZmu6PM7L+gMY/boLrlf7j7mcCmFbvdA6yFxHciUN1i6lYNSjoraqnfQu7w42K/IuFrPfQgUAzCOBgJ93v+neRUqiRJko4jxblt/AcNkQA4DLisWGFmK5vZNdEc5T4z29zMVjOzSWY2z8zmmlmjmZ0QU61Gx7Srh8ysGFF5NvBPlPD1f6gpyG5IfI8ws4eAochlvQdwAHJHDyDmNoclXzQPKUqk6oAvx/KihvlF1LITZDX/Is5Tj6zuemShL0DGnJOuTGZxJ92dFOclIJqB1COL8g8x3Wpz4P6KzU4DHnb3zVHzj0tQT+wfunsdyox+DXUtmwzs6e5boUzr38UxXorX2ajV5nzUmews5Ja+E1m7Dajk6k1UMlVMrnLUWawWCW1fZH03UrYxnUoZkx5fcf3FvTQg93cTqoFOkm5F9uFOujMpzkvGHDSoYzgSxPOAG1psszNyRePut6GOW83ufp6ZrYLGWn4H9cy+AXjVzOYiS3h4lFJdgtznxdCJicBOSOx3R61Lh8b6jeIco4AXkLt6PmVW9jw0P3oV1Da0GSWEVcU9NKAks+bY72ZKy3m1OEZDyy8i3dpJkiQdR4pz23kYDai4DBiBYsIgYTvVzI6MzwNRJvRdqMvXS8DzSDQHAs+guO9ayH28ZrwvfpuPxnaXIcGsQpnWbyExHY1KqX4c+49C1vCdwLnIdX0QmkJ1JBLhU1ByWRVlm84qNIjjDmRhPxnbVMf9LUC6tZPuQlrQSXckxXnJqEfzp59C7S9/6+7jW2xzJ2oWQri9i2zpe5FVOhklaP0Fie7asbyIAxc8iyz1aci1/Hgsnx7nmI0EfiXUEWwgytxeDz0g7EJZdvUz9CBBnHs3ZJkPRjHqaZSW84nIHb4xEvYmVF+dJEmSLCNSnJeMwq29EbA3sK+ZWYttTkUu5B+h0qS3kUV7PBLLkcDtyDp+GDUBKbKo5yBXsgPTY4LVFCTA+yJruTewK2Us+mmUNDYbWc+vI6EdjeZaA7yMRLZXrBsc1/RInG8NFHfuh4T5b2gkZzFnOotGk25PWs9JdyLFeQkJwcTd70UZ0oNR3+qxsXwamgT1fXffHrmga4Bj3H3T2PZ6JIovReLY95C1u1PFeYqBFY5c4W8jK3eyu/dDQt2fcqRkbxTLHoIs8P2RqINKsK5AAu7oAaEaWfTPx7IPoIcG0MPCBkioGyuOU/k9ZMw5SZKkg0hxbiNmthESuKlIPDc2szozW4n3dtT6LLCnmT2PYtOroclRh1bMiG5k4fRFYluF5lJvgNzh01GW9tmoJOoR1FHMUD/sKyjLoe5ElnM15Yzmt5F1THzuhyzrz6EHjwaUdPaeJiQZc06SJOk4UpyXjHozG2dm44DLgSPcvcndJwL/QOVRlyJ3dSVvuPsn3X094EHgm+5+HBLVZ4FjUK3xRMKVbGYvmNlYZJmvhMS3CdUy3xvb3YqsYoB73H272B5370XZJ/tu5G4nzl/EyTdCFnOREDYBifmrcS1Tkev8PdnaSdIdSdd20l3I3tpLgLtXL2LdCcAJlcvM7A7gi+4+pWLxNcgyHePuP6hYPizi16cCr7v7unGMDyCxvxyNfHTK320Q8H3gz8AnzGxfJKQNZnY/KrcizufIrf3L+GtEsebXkMXcgzIpbTWUYFa0/yxqoyvvN6dSJUmSdBBpOXcslwGHtlh2KBUdxVrwISSglWyN4sJPI+F0FAt+y913A1aNZc+jrOtVUZz7AuTObkaZ2ZfGfmeihLVaYLy7r4mysuvifEXm9kvIWn+OBUdPAunWTrovaT0n3YEU547lSmA/M6uDd2c1rw70MrN7o2XnFWbWJyZQ3YCs4v7h1p6GxLQ/8DVKcb4BWM3MLkcdyaqQKF+DhLVfnKeoZe6BWnzWoDh1TRyrh5k9Gp+LmPebsc+GqPvZQr0FSZIkSceQ4tyBuPtU4AE01hFkNd+KJkztEW07xyDBbEbW7HooA7sHEuXByM28Kfq9+gMHxvFWQaVTRUewR1E51tvAN5GL+ymUhf1ArDsDxbcbgKmRLd6ILOb7kKv7HcpEsTXb59tIkiRJFpcU546n0rV9KHIVbwzcHYllR6BErznIpd0Tdfl6FFmxryDxnRfbFHXVr4RbuxZZ0xPi+H2QO7uwkJ9AFvVmsfx3KBbdCPQ1s8+iB4EeKLu7FxLvYlJVTRxzAbKUKkmSpONIce54rgF2N7OtkDg+DNzs7iPib2NKK/Ud5EY+ELmUB6FJUbVI1KtRPBlgLTObiCzdamRxF3OgG5CIz0clWL9Cgmwodv1GvB+AYtMO3Aj8Bv2b2Ijy38YcZK0vQMack+5Mxp2Trk6Kcxsxs5PM7PEYDTnOzLZrbbuYz3wHGmBxGXId72Rm68dx9qaM6zYiS3U4EsS5SBzHoaYgf4rPDUhQQb/ho/F+WLz2R8L8CBLh02P7uUjEn4jruBu5ueegJiZjkcVcjTqMzUIPBjsv4deTJF2eFOikK5OlVG3AzHYA9gO2cvd5ZjaIVmYeV3AZcBVwqLu/GUMxLotEsVVRprSjJK7pyMI+DZVJ1aGxkPWoJKpICmtGsegmZGU7SgCbi1zjw5BlXNle9OeoFeeOSHSnI1GuQS1BV0MDLzaN9VcAn0L13bXu/m6jlCylSpIk6TjScm4bQ4Ap7j4PwN2nuPskADPb3cweNrPxZvYnM6tz96tRaVJR7zwdWaUfo7SAQZ3FbgMeAk6mTPS6EDUG+QlwDnJ/gyztm1CzkL9SZlwXseliLvRa8XocKs2aipLGihKp15DIXxD7zEcCPRz9G3mhUpjjPtOtnXR70npOuiopzm3jJhTzfcbMms3sWTN7xMweRkMjDnH3zZBF+qWFHcTdJ6CZ0I1ooMZolBQ2Gc1mvhZZzB9AiVojkSVbixLA6pCQzqbMCP8xspxB1rwhQQY1E5mHHgzq0KQqQ3HrAcgC3xq1/hwAbPl+95AkSZK0PynObSDiyFujtpvzkej9GjgfqHP3Z2LTPyN38eIcszIj+hrUKWw/JJ7vaBPfH81ofgC5o6egLOzzKftmn1IckjLL+mco67toYHICst4duAe5rye7e3/0oFC07ZwR13bL4txDknRHct5z0hXJmHMbcfcm4A4zawC+ikqi7kSihpn1QW0y1zGz8chSrYpGJFeiBiCPIyv3B7HPNsjVvQOymhvieOcBfczsd8CeaOhFUUI1C/hyHL/okd2Msrq/BvwLZXwPQhb5qsD28dlR/LkJmG1mW8TtDYzjDC7up5X7z5hzkiRJB5GWcxsws+ExGQrkdj4HZTSfDDRGJvZclFx1KrAbStYq3MtDgUnuvgmyTreN5RehoRYnIcGcR0WHLnf/Chpm0YTadRqymAciF3YzEvRG1CWs4OTiELHNMSgu3YwsaGL7H6LGJtPRQA6AGjN7T2/tjDknyxtpQSddiRTnttEH+LOZPRGf70GlTh9GgnoFqmfeEjgauAWJ7DloetU7lPXKNwM7hnU9CDUeWQnFf9dAv9HfUCY1lNndfZA4P4cSt5orjtlIOXmKOO4zKI5chRLBnkBC3is+T0cW+TyU8NaXMmFsThu+oyRJkqSNpDgvAjMbWIyINLPXzezV6Or1R2BUNBCZ4+4HRcb2vciS3hvFm9dA5VYjUEb07sAngVfdfVSc5g3ktt4VCeNRSNDHIkFdHTUg2dzMHkDx6EtieRNK6voVSgpz5IaehlzSZ1R8noZc4SBxHxSvU5FA90CtOsfF+6GU5WHrt/xuskNYkiRJx5HivAjcfWrRyQsJ6K8rOnu9Z8axmW2ELOS3kOU5390bzWw3ZJUu6lxvIRf3V5BFWx3LX0Nu7puQsNcCH0TiXfx+OyCBnRWvTyJLuwFZ1ushN/Vsys5ghh4GzgQ+E/uBrORG9NBQlH79qJV7Tbd2slySru2kK5AJYUuImV0MXOfuV8ai3mFN90GCOAZ15joc+I6ZjUHx4XnIVb0hMNTM/o1qicehKVUPx/FOQhbwE8AaZjYWCfJUlND1IEroWgUJbC2KU+9H6freK9YNj1dHAlwf6+cAxyJ3+68o49szUKzc0ICOnWL74W3+wpIkSZIlJi3npWdWWNZHI1E7Ktzdb6Fa5cORIG/v7tcia3QyqlfeFs1wvtrdt0RtPu8BLkdZ2deiNpv1KJlsdZR41o8Ff7u9kbB6XEMVEtk3UAOTsXHeJhSXfhz4H/By/E1CWeNnoy5iNcAnUHx7PovufpYkyx1pPSedTYpz+/KAu79Y8XkwKmX6tLuPq1h+u7vPcPc3UXLYv2N5NYpLHwOsA2yFrNeiJeftSISPYMFs7HGUNc0nxDENxaz7oWSvk2JZD2R9j0M9uPuhBDRDHcTOQclln4htHMW1FyBjzkmSJB3HcivOZuZmdkbF5+PN7NQlPMYoM9uxYtEhKGmqKtYXYlcwqzg3iuW+A0wEdjGzN83suthuXsU+zRWf70dtOJ8EZrr7ZmGVfwQldBlq4zkXuakLNkZNSQyJswF/AbZBYyR3Qxb0fBSHbkKTp/pQ9up2ZDkXLTvPBLZD8edJrXw3GXNOkiTpIJZbcUaCd1AMpVhizKwGzVXescWqNynrlfenzICuZBYSuUbgANQMZNYSnP6jqF767RD6K5B7+15UnvVT4GDKXtp/QsLrKEO7N8rqPgPFrp9BrvSaWL4nEuUnUNy5Hgn6kShprGgJOjnu7+IluPYkWS5I13bSmSzP4jwfdbA6ruUKM1vbzG6NcY+3mtnQWH6xmZ1pZrejuO+xwHGR8DU0dp8JfNnM5gKfZeGiewfQ191noZGO1SiGC7CKmd0TSWCroWxqkEAehEqn+qCM77koZr0LsoAd+AUS1Nmx38dRfLsKlUe9A/wdubKHx7JVkZV+cMV1DEZu9IfjuIejBw7inIW/euOWN5du7SRJko5jeRZngN8Bh5vZSi2WnwNc4u6bA5cCZ1Ws2xDYw90PZsHyqc+hRKr+KEa7FbB50RPb3e9w9/0qjnM+8ICZ9USDKz4DvOHuF6PksV0jCWw/5IomtqujbLf5JmpsUgd8AZVFvYas+b9Q/n69Uex4KvB5JOxF968GZP0+G9s7csU7cm+fTlkzPTG+mzfi/VRkbd/Q8otNt3aSJEnHsVyLs7tPRw07vt5i1Q6o6xZI5HauWHdF9M1eGNe4e7O7P0FpgbZ27kfRTOXDUGb0KcBu0VXsGuA/ZvYYGpixSey2C/CMu89G7vDrkUDWoVj0tijmvS6ynIu2mnNQadV8JOqNKEO7KbZrRKJbRTmpCvSQ0QNlltcgQd6C0l2+cmw7ZBHfR5Ist6RrO+kslmtxDn6DrMnei9imcnDD+8WGK5O5bKFbiWdRPHgvlPl8e5RZzQQed/dNUXy5GPE4GRhsZs+g0Y9PIKu3MY7zOnJzbw38X+xzPeqJPRFY2d17IaHdOu7F0UNCsf07KNlrHhLuKpS9PR/NfT47tl+TsjwrS6mSFZYU6KQzWO7F2d2noX7Wn69YfA/KYgbFWe9ayO4zKK3TttAHZTr3QjXLBU3Ag2Z2K0ryWt3M9gf+gwS9F4px/wolgtWhyVNDkPA+ipLCHDUy+QbRVczMTo9teiErem6cs2hA0gt5CgpX+peAfdEDwH+Rl6E5rruZhTyAZMw5SZKk41juxTk4AwlVwdeBo8zsURQL/sZC9vs3cGD01t5lSU4YIyO3QhZvIfAbRnLZJigD+kMorjyDMgFtT9STezqle7nInm5CwrsuZT1yE3oI2AAlnX0bCepdSNR7IdE9lzLOfA1qyVmFSrRGI/GeSCnIQyk7yL3n30nGnJMkSTqO5bZ9Z5GoFe/foOwdjbtPQMLYcp8jW3x+BrXcLBi9sHO0XG5mnwZuROI7F5ju7hsCRILaaJQAtiESUUfZ0UXN8aHIkp6ERPdvyHquBr6HRB8U965FYj4TJYbVogeDR1AM+Sb0cFKI+y3Ivd0Luc7XiXPORZ3EVkFJYb3RA8Krrd1nkqwoFK7tCad/pJOvJFlRWFEs587gMFTO9Diyeg+DdxuXXAo8ENvdGa/zkbBa/P2CMt7bhNzy1ciy3YTS3bw/Sv6qQ27vevRA8DsUd65CJVUbACfGscajmHIDsA+yxB0loVUht/l8ZPHX0kpv7XRrJ0mSdBwpzh2AmQ1ElvmFaLzkWsDnQphPRclYE5Egjo3X2lh2NppmtXlsNxe53Q+Ow09DSWAFhyDruxGJcjVKDDsRCew89Dvvg0T9BWQxv4wS0d4CJiCreyaKea9N2VzFKa30yntMt3aywpHJYcmyYrl1a3cyH0d11F8EMLPVUbb2VCSId6Da4VNQtrYhi/YDyMXcWHGsgZStQKuocM8jMf1cLH8ZlY2dHOf8J/p930aCuwOy3ufF+dZGwr86inE3xfItkTW/AbLSa1iy7mZJslzTUqDT1Z10BGk5tyNmNjPeHgZcXSx390moNOoRVC61OvAHoCnKqeaj32IOcjV7WNm1yNJdh1LEbwN+H4c+Nfabh9zU30YiW4Xqpxvjb+s47txYPxbFqOtQPPomFIf+C/B9VG89n/Lhbdel/nKSJEmSxSYt5w7A3Ue1suwsFuxEVinmDUhEt0UiuSqKJc9EceTxKEbdjKzcIvu7cF3XAL8ERgJ7IBG/ibLhyNTYt2gmsh6y3lcFBqCkt9dR7PlwymEY8+IYWyGrvPJ+LkDtURk5cmRlnXiSJEmylKTl3AGY2eVmtm/F54vN7GAz62lmF5nZ+OirXR2bFF26ipKvWlS/3BMliQ1G1nM1Sib7XOz3Duo+VoOajIyknOl8ZWwzELgVdfuqRg8C85Al/g5yh9chN/cQZL3PQT2/6+JYw1q5x4w5J0mSdBDdSpzNbDUz+7uZPW9mT5jZDWa2YSddy5ZmdmG8P9LM3gTqzexxlAD2qVjXAw2XuAH4CoC7b4Zc329G7+2a2GcYEunRqKf2vSjrumizORl4x91vjsvoB/wTGBOf51DGjg9GAzSqgU+jDOzpse5CFEfeCyWbORLqldEQjF5ImInrPWCpv7AkWU4Z9t3rM1EsaXe6jThHDPZq4A53Xy/aYJ7IIvpbdzAnoszqgsuBOe6+CbJG9zGzOpQlfae7zwF+juK6uPtTwEto5nIjigf3Re7ndZD7ug/K+j4L1TY/GIMzIOqS3f08lDDmSHRB7vBZKLY9C9Usj4t11cA3KV3j/4nXvujBYJO4lhdjeXMxtauSLKVKkiTpOLqNOKNxiY0hRgC4+zh3H23il2b2WLiMDwEws1FmdoeZXWlmT5nZpSHymNm+sewuMzvLzK6L5Sub2TUxTvI+M9u85YWYWV80keqRVtbVoFrj8cDeKDGrd4yhrAI2qhgXuQVqNnI3Euq3UXx4NWQBr4/GNe6HZjpvEee4JtavambHoPrkBmSZv4aah1yBks4moKYiIGu4qKcGMHffIo71BHJ390fTuL4a29xP2eq08j7TrZ0kFaT1nLQn3UmcN0VZxq1xEDACidcewC/NrEh+2hJZihujhKedwpV8PrCPu+9MObcY4DTg4agzPpEWiVDBSOCxFssOQaL8KnIPnwEcRdnBaw9kIW+PMqIbkLX6JSTGt8dx10Fu7jWR5TwD2A5Zw/XRcnQjyhrmX8T55iD39wPIWv4CEtUN0e+8LXJpL1C3bGbHxfoNgONRrfQ3gcJ1vgHK3k6SJEmWEd1JnBfFzsBl7t4UrTr/h9zFAA+4+yvu3oxcu8OQuL3g7oXr9rIWxypcz7cBA1uZBz0EiVgll1MmUo0HNkMlSJOAf8QYykaU5HUtEktHIt0jjrk/EuxHkNBPQY1JLgWeB86Oh4YJsU9VbL8Hcl1PRW70B+LYfZCoOyrJeh09KByAxL0JuIhyLOV5yMp+K67/MpTNPYokSd6XtJ6T9qI7ifPjqF63NRY1urFyxGPRW3pR27e2rmWp0BzKMY/lRu593N3RwIyd3X0gcllXNvGYjbqG3RLHmQs8h+Lpv0HC+CoS51pkuW4ff3eZ2SgUj24AnqQc6zg3llXF+tmxrrinYjTllDh2DfpummPf+aiM6inkXh8DfDKOPbWVe82Yc5IkSQfRncT5NqDOzL5QLDCzbczsg6ij1SFmVm1mg5HF+sBCjgMSoHXNbFh8PqRi3Z1IpAghnOLu01vs/ySKBy+MnZGl2xorIYGch8R3Vlzv51C8dyIqp3oVZU07Sux6HfhO7F+I7kmU/bh7IOu5DlnvxcCNyymzsXshN/u3kJDXIff+kyhR7GyUfDYs9nk7lr/d8iYy5pwkrZPZ20l70G3EOSzSA4E9o5TqcdQhaxKyOh9F7uDbgBPc/fVFHGsOmvB0o5ndhUTtnVh9KjAyYrunA0e0sv9TwEqRGFZwSIyWfBTFuX+0kNOfh1psfhiJ86pIWEcht/cm8fdWbN8zrm3d2OZGFJvuARxNaTk7av/psex2ZEH/NI4zAbnHp6OGJecjwd8VlVM1I+H/VhxjAvpujYqyqiRJFo8U6GRpMGneIjYwm1k5GtHMjgRGuvtXF77XUl6U2cXAde5+pZmtjJponOXuF7XjOfq4+8zI3r4OJZNNRYL5W3c/fyH7/Rb1zj4DjYG8sMX6UcC/UIy3Pu7j+Fg3M8ZJjkLJV3uhpLNDUReuosPXD5DFWoxvfALNYD4ZWbw9UULav2N/kJV8IXromI1EeDCyegchsV274phvo5j0G8iKH4aGahTZ3sMoJ2Q97+4L9RSMHDnSx4wZs7DViyT/B5Ysz2Tf7WRRmNlYdx/Z2rou3b4zErH+C1zQnsIcfMHMjkAW6DBUGvVc1CYPW8j1VCHrfSISx4XVWI929/3MrB542Myudve7i4ccd78DuMPMGmL7y1E2+T9RjfQrKGnsKFQ+NsLM3gJmuPsAM5uMxji+gdzi9ciVPRXFsYvRkjMoBfp+JM5TKWua56IOYj0p23u+GvtciR4UBsR5Wn4XxwDHAAwd+p4y6CRJkmQpWCq3tpmtbWa3Rk3wrUWzimhXeVbU875gZh+P5VVm9nsze9zMrosOXx9fyOH7oAYZf3P3c2P/EVF7/KiZXW1mA2L5HWb2czN7wMyeMbNdYnkvM/tHbH+5md1vZiMB3P3X7j4CxYdnIlHC3ee5+9MLuabdkCifCxzs7n9Z1PcT7vNxyL28KK5C1vEAJNLDkPu6F9DL1IO7L9DfzOYgS7gHckOPRdbtDUhgidf5SKhB8efC/b0eEuN1kDhPim0MZbg/EO/3p0yEW/19rj9JklYo4s/pIUqWlMUR5/qIpY4zs3HADyvWnYNGI26O4pmVgx2GIOHbD8VuQfXIw1CZztFojOHCOBO4y91/XbHsEuD/4nzjkfu3oMbdt0U1usXyLwNvxfY/opVsb3efhqzUl8zsMjM7PCzk1jgMlRddDexnZrUL2Q6AeHjYACWZtUYRD/4FcjUX4YMeyG3ejDLM90JiO9fd6ylnQU9AsWXQd1lkolfF8YqktL5IYKvRg8j2yOJupMxmL+LW0+Kcr6LGKM20kpmeJEmSdByLI85z3H1E8YdmEBfsAPwt3v8FiXHBNe7e7O5PULp/dwauiOWvUwpLa9wG7G9mq8C7Lu7+7v6/WP9nFhxleFW8jqV0S+8M/B3A3R9DSWPvwd2PRv2vH0Cx4D9VrjczN7NfA/ui2O8xKKa710KufZdIDHsdxZxfj25lO1Yc82JkJYMeYI6Neyis3Q8iq3tunLMaZavfgOLiTShOPgO5wueiWPO1KIHrdNSYBZQFviUS2ivQQ1Rv5Oa+MfariuOBBH61uL4qWnm4yFKqJEmSjqO9s7Urs8sq64utxevi8HfkPr6hRVb0wijOV9QyL9H53H18WOl7oqERLY/9KeRGHo+SsIYiS7o1Roe1vhnwJTPbGmVa77iQ7auR6/kfaAAGlN9lPXI9X4nEdRvklaiJv62Rm7sn+j0L6/njyDJuRnHmA2P9UUi05yCRP5SyjvkI5PYGJbS9EO9XbnnBWUqVJEnScSytON9D2Xf5cOCu99n+LuDgiD2vyvt0nnL336As5kloQtNgMzs73M6fQZ3A3u98nwQws42RWL6Lmf3QzPaL7OmCEcidW2zzMSR2rwM3ufsw5I7+DbCXmQ2vjLsTfazDMj4WlUFdE++Pi/BA0Q5zbTTqsQq4D7ntx8e6O+NaDPg8ekAosqeLcqmBSJinIRGuQROtQCK7Uhy7J2U4ogqJ8sQ41qooWQz0XV8R26xPKdSL83CUJMkiyPhzsiQsrTh/HTgqXLifQfWyi+KfKBP5MVRnez9lffHCmI2sxvHI/ftZVO4zggXj363xeyTojyIRfLTyfO5+ChL4E8zs6YipnwYcCe8K80hkga4FbFXRyrMRZTXfE9d2DYpH/5WydeiGcZ1VyBPwBHCKuxcC+jJwQnwvc9BDwSZEKAHFjN9Bon0U8grMRN9fLYpRb045q/loFK829D0/gYS8mjLkcE1cd5GBPd/dPxHbvUkp7vORm38G5Wzod0m3dpIkScfxvqVUlTXO8fli4OJ4PwGNNGy5z5GtHcPdm83s+KgvHoj+5z9+MfY/qnhvZlsCDyI3bZ2ZXYQsyD+Y2bfc/XYzO9U0uakaWcu/Qq7gQ4G/mtk+7j6top56XzObgAT0o8BFZvYJd7826qx7RH3yGciC748Etxg2sR5KuBoU53wwLncP5AKfgtzMF7r7tRW3dg0S2TeQOO6DBBUz+xFlm84Ni+8c+BlKFrsSeSueRYK8NeoyNhCJ+CjUgnM6ssK3RtnfWwLHufuuZlYIN3ENq8a+05ClPgI9AGzb4ifKUqokSZIOpDM6hF0XFupo4EeL6uTVGu7+AmU28ldi2WYo/vtn08Qp0BSrY1CN729RPPUAZOl+diGHn+LuW6FY9/GtrF8XJUqdH3/NSDyvcfetUR9tQ4ILSszaPZathMQaM2tGDwp/RB3DRsQ2ZyEXdB0S+2fj+AAfQUL6OZTM9SEkvNsgwa2K137x/sa41pWQUBdjI5uB78U1ADSZxlkOQg9rT6MHhdXiPpwoM0uSpH1I13byfixzcXb3UZH5vXFY4W2hSPSqnCD1FHILbxjrbnf318I9/Cqwnbv/B1nqwxZy3NYyvivZGU1x+jxlwtYDwA/Ddf5xJH69Y10z8J9IDrsXxXRBLuM6Ss/BTsiq/ShlYtveSHjXRA1DpiI3dzFJa/W4r7fR79gAPBzXNR8J+bz4fFFs1+TuG6LGLo3I8u+Bst4bYr+vxPW/hNzuzoJTu5IkaQdSoJNF0W16axeY2brI9TqZxZ9G1VzxuUicWtQ+TYvY5jfIyiw4D7mDV0aJa09TuoohHh5Qq82eIeIW17FWbD+Pcj5zT2SB/x49JExH1u+GqGb6gDheUaP8GooL/wQlcRU+5g8gwZ+KvAYDgaejCcumsc0T8epxz03oAaQGiXYDSoQ738w2r/wSMuacJEnScXQrcTZNnDoPOCcGYVROkNoQCdPCunstDRfE662oM1gvysEWc9DIx3rga0hA3xM3RyI6B4lfYd3+HZVXNSFr9jdIEGciAS3c1YWVOwzFm0ECOhglhD2FYutFVnVR29Ts7oVyVqGHgjPQw0UT+q4akQv8lVhmca0DKbPD6919gRrxLKVKkqWnMoM7Lemkku4gzkWHssfRDOSbUEY1yLqsNrPxqD/1ke4+byHHaQ++AewW5xtLGdudhJqAfJbSGgWJ3eHxfhPU4Ws6EtZalNTWG1nAG6OmJrXxujNyhfdEVmw9ejg4quL4a8Q1OPCx2KYpjtkMYGZbINEuLO1dYp/mOFctaniyfpzrTiTONbFsCGWLzyRJOpAU6KTgfadSJQvHWkzsimWjgFvcvSYyva9HsebH0cPDozHwwt29zszGoOQr0MPGV5D1vAawEeVDQD9UVlaN4tXPx/pGFIeuQnOZNweeQRZ5DeoU9n+ojnot5CLvjcICRWvO+1FSWj+UODaasqNYDXCZuxcPGe8hp1IlSfuRk6xWHGwRU6m6g+XcZWkpzLHsDmRFgwRuFWBjd9+qwjXcgEQV1G6zHsWNp6Gkrjtimx1RnfOPkHU8JNY3xz73xjGaUcOVHZFwr4N+2+mojMyQ+BZWcBUqsxqMxHktyjj6dshFPg8JczOtDL5It3aSdAzp4k4gxbnDiC5gfwA+4u7Pm9loMxtRsUl9JFn1R7HfjZDl/GHkCq9HSV5DkEsc5NJ+ENUkD6HseFaPaqT7Iuv3bSSsfVHDknlItGtRolgVqs3ugcR5g4rr+kVs+xfKrO9+S/dtJEmSJEtCinPHUAf8CzggSrwALiQ6jxElVWFJT0cDROaj2uWJKKb+FrJseyARngucjNqRFlnqD6PksedQ2VMRRx4e699C1vVMyrafc1A2+Npx7sMoE8luQG5xULlYMRf6/UZeJknSzqT1vGLTbuJsZk2RuPWImT1UOYGpnY5/rJktrHlIu2NmR5rZOYuzPOZJV8YNGlGzk89XLLuCcszk51iw1WkD+i2KdqavouzpR1CCV+HGXhmNelwltpuFpnOtiUS1GbnHC/Hug9zVv0Sx6yYkzq/EuseB7yN3eg/UJGVm/FXHOd5EceoFyFKqJEmSjqM9LeditOQWwPdQm8l2w93Pc/dL2vOYHUgzsnC3MbMTAdx9NnAzsH+s+1uLfQxleg9HJVWPI9GsBh5ClvirSFSb0CzngiZkORf12TPRA8IEFH/+LmUDlDORdf02anIyJpZNQIlgvZHlPxm5vP9AORijvNiMOSdJh5PW84pLR7m1+yGXKiZ+aWaPmdl4Mzsklg8xszvD2n6smNRkZjPN7Cdhgd8X06uIftnHx/svmNmDsc0/zaxXywsws23N7B4zezheh8fyI83sKjO70cyeNbNfVOxzlJk9Y2b/Q127lhgz2wtZuXchgfuMmX0+encDXIpixrdFG9NjkdU6D3ULew01D9ma0j29N2rfOTD+apGFDLAbaiu6CnKJv1WxzQZIrPshl3YzSi67mXKG85HASUjEb0DWclUcowfwrbZ8D0mStA8p0Csm7SnORT3yUyi+WjTpOAhlCm+B3Ka/NLMhaD7yf6O95hbAuNi+N3BfWOB3Al9o5VxXufs2sc2TLOg+LngK2NXdtwROQWMeC0YAh6BY7iFmtlZc02lIlPdEdccL45C419fMbA6qHb40hPlk5EXYCsV7r49l9ajk6UXgtPAyjEDu5mYknjuh7O15wAAUF25A1vKv3b03alYCaq+5C+oc1hjHXx8JcVHrPR3FrRuQ9f3TOM930TCOosHJM8jqXile5yGRfwNZz0U70ndJt3aSJEnH0RFu7Y1QxvElZmaomcZl7t7k7m+gEY3boKzjo8zsVGAzdy8swQbguni/sB7Xm0b283iU2bxJK9usBFxhZo8Bv26xza3u/o67z0Wu5LVRCdEd7v6muzegpiYL43LgS8gV3B+5g7+M6pU3Bp4Lq/gIYIC7r4Nivbej7/wmADPbCZUpNVHWL38ViXXxB0rYus7M+qHkrOY4TxVyfxfi/g8kyPMpW3JORAK8Bpou5cgq7odqpZ9EMeuZ6EGoHlnVRbewouxqAdKtnSTLjiyvWvHoELe2u9+LEpEGs5D+1+5+Jxq48Crwl4pkr0YvO6MsrMf1xcBXYxrVaci6a8mP0PCLTdFAicptKruIVZ5jSTqyDEFTrIpjvYOszZuRRb4hcjd/yMz6IzF8ADg9RmfuHdt+C7mge1B29uqDLOdaJLY1wNXITf58XOeLsXxd9Ds+gsT9lThOMQlrCHowGIfc48VAjNWRiG8Y2xXdxR5HiWdGaTEXU7aSJOlEUqBXHDpEnM1sIyQCU5Fr+hAzq47e2LsCD5jZ2sBkd/8DGp241UIP+F76Aq9F5vPCOletRDnq8MjFOOb9wCgzGxjH/cT7bH8TsJaZPYNiu1uhLlw7UTb9WBkJ6MXIGm0Ero3jnw487+63x/LvoOlPE1Hs9z4Utz4YCXZR7lQI7LFITPug9pufjnVFMxGP49bFcfaM/eYjkS8eSjzO2YT6lv8n9p8A/DXe93if7yJJkiRpRzoi5jwOuX2PcPcmZPE9iiy724ATYobzKGCcmT2MBOi3S3Cu7yMxvRnFllvjF8DPzOxuFpwS1Sru/hpwKooT34JitIvafiZK2joGieBPUdLWD5E4X4kE9iHK2uFalBn9DIqzb2BmbyJh3Bb4OhLhotf1lmhQRSG0s+K1GO9YvP4PfcezUBZ4Y1zHdCS8I1ACWSMS+nORG3vV+DwEifhHkIt+dlzPsXHd67Zy/xlzTpIk6SCyt3Y7YWYfRzHm7wNj3b3azGqQcPZCQy5eBD7k7reb2W2oK9hVKC7/iLsfYWZ3ICH9M3AO8AIS8l6xXTNKCpuMYuWOxjoWGeu9kUX8E/Qw8BmUyd2EhPwwZKHXI4GeGOeoi+1uQ1b866g8awSaAvbtFvd7DHowYejQoVu/9NJLbfre0k2XJG0je3B3f7K3dgdgZsPNrLLt5QiUQf0UUGVmT6BEqpUpZzpDGfsuJkKBLNrPmNkM5PbfE1mztbF/DyTCt8WxLmTBRLkqlJh2ZRyrBsWs58XnRpQx3geVcvWt2G8jFJPfGyWGzUfCvCpy1fdAXo4kSZJkGZHi3Hb6AH82syfM7FGUPX1qZIDPRcJYjdzkv6vY7wYzm43EfEAsm4hc4dOQ+382cHzFPhbbbFmx/axYPglZ2s2obA0ksEcBx8W19AJOQL/3S8jqvglZxq/F60zUDeyPcW/NlMM5ikz6d0m3dpIkScfRWiZ0shi4+1g0Bao1mqK++l1UVQYoOetWJIY7x7Lp8bo+iqNfgNp7zkAW8hvAUGTJrorKuOqQlT6MBRO2nkVu8Cno930IWcfHovj/dGBTVDr2MrK4i/adG6MSt6nIDf7v2O89D3Et3NoL+RqSJOkoipBQureXT9JyXnYUMeHPA4fG+0HAAciVDbKEN0C1zoVLG1Qf/RpwEbJq145tz0Ix5rdj+/7I7W2oVGo1VHNuaE50cfxiZOXmsX9vZG0bEuXB6GHgC+gh4O12uP8kSTqAzNtYPklx7gAq5zybmZvZGcjqnQ98DInhbch6HgLsHpu/guLBRcY4aOTjxkhoQeI5Po51cCx7Frmh30ZZ2m+gbPaikYojF7vHvlfE56L/9vzYx5C13gPVbM9DbvH+bf82kiRJkiUlxbnjmYdiwZ9FIggS3MOQW7ooN3NkKfdECVjrUg6wuC7WOxLQtZCQPxvHW42yo1ddbLctsBf6jR3NcZ5T8Xk0ilsPQw8Ig2J9XVznYBQTr0YjKRcgY85JkiQdR4pzxzMfNfPYDSVinYhi1Ycgi/S56LE9H1mtrwNPx77T0ECKjyNX9CvuXo1KofpT/n5XIAv41VhWDMG4EQl+NbKAG1GTlKJVqKMJYk3IYv8Fco87ZdzZKGPi75LtO5MkSTqOTAhbNkxB4rg5iuP2QWI9lHLyVA1qsTkAuZ63R3Fjp2zH2dvMvoBi1qsga7gZJZh9DbmjDYlvXzSeEmS918c5mpBrvVcc+yexfBZKQuuFsrb7UTZvaa09apIkXYRMDlv+SMt52XAgmjb19fjch7LtZpWZ/ReVT92AYr0fBdZDgz9GI8GchlzP30aW7FTgbiTGO6He3m9SDsu4BVnqjpqjfA+Jd3NcSxWyxneIZT1RfXUzGoxR1EeDOp0tQLq1kyRJOo4U52WAu49Clu3nkRV8CPAHNJyjgbLlpwN/B/6FuolthSzkN4HfxzY/QNbvAFTHbCimvTJqy3lKnHYvFNO2WHYycln3ie2LiVbbIKu5GmV/V6PEsrWQ5U5sswDp1k6SJOk4UpyXEe4+DY10/DLQ7O7nxapa1KSkFon1uWiYx1DUYOQtJNrrI6H9I5okVYVafM6j7CJ2IuXc6pcpS7TeRm71JmRhT4hjboGs+cJt/TtkOQ9GVnkxO/qxdvgKkiTpYLKsavkhxXnZcgayeIeaWdHSsxEYicSyFiV1TY/Pj6Ka5oEoXl1s04gyqz+HMrqfQcI7B4mvo/nNRWeykUjEq1Asehiy2ItpV2/F+7uRuDcjsR8S+xeu8iRJujgp0MsHKc7tiJk1FZO54u+77t7HzO4ws5Hu/gZqy9mIunQ9ipKxnkGiOxMNzvg3ikH3RoI+FcWJAZ6kjB1Xx77NKCY9j3IM5BmUWdY9kLg3xd+mKFt7OnJzPx3LR6K4d1F6NSSudWzLe82Yc5IkSceR4ty+zHH3ERV/p7eyzeNIVP+LBljMRS0970VdwECZ1Q1oJOX3UYnVOOTW7otE+A4krhfGPgNRB7EeKIb8LVQv3RDri/rmh5Cge5zHY5vnUUz8r5StQxvinCe1vImMOSdJ1yWt5+5PivOy57Z4Ncpypu2QYA6M5QOBlVC2dT+Uub0dC85e3he5yHsjsa8G9kOi2hclc21CmcH9CWSdb0tZIlWD4tGboLaeO8e5V41j1KB/Izu171eQJElHkwLdvUlxbl/qW7i1D2m5gWuA9lzUXKQWWa+rIzf2DFQqtRaaTlUkcDnK9r6BcsykoznMk5CrG5SVvRVqxTmdcnIVaErVO8D1lJnhc1G8eSrqNvYAEvsfxD5FO8/zW7mPdGsnSZJ0ECnO7UtLt/blC9nO3f2TwGlI/M5FpU893H1T4AlkMTsS7aLeuI7STf1tlGU9FcWHAX6G6qIHxv4bxnIDPoRKtCbFsioUax6IBLtPnO91yvGVTchN/umWN5Bu7SRJko4jxblz+RMS0b2QCK5hZv2RG/lqd98MiW1h/U5BrnBDWdVroGYju6A49CrAmqh1Z1O8NlP23F4F9fieF8cbiUq2Donr2IbSIge5v2uBtc1svfa++SRJOpZ0bXdfUpzbCTObWfF+XzN71swWOujYzPqg2uXZ0Vv7LNRf+yvIav2Umd0NjEAiWY/abr6E6pQLAW2Ov1rkpjZU41zUPxf9uhtR3XNhOc9Cnb/+h9zqNehBYFX0oNC34nKPcvfnl+wbSZKkK5AC3T1JcW5f6s3sOTRlqhE1HHkPMVLSgBOAXmY2DnXt+hWyhk9AgrwZEs656LeqQvHoRpTI1YBEeQ4S6ydRXHoTZCnXo0Svq+OvF0ouuyfe74hqnufGtlujaVdzUIeyYvBF0Sms8h4y5pwkSdJBpDi3L6PQd7qFu2/s7t+NQRW9gT+a2T/NrFdsO5pysMVw1F5z2zjGqSgT+8vuvgplhvcOyCIehkqx3orldZRi+yQqr5oRx24CPowGbRRUoeSvwt09C1ntZ8T7dYB/xrIq5DZfgIw5J0n3Ydh3r08LupuR4tx+1KGEqwPc/amK5Ve5+zbuvgUSzs8DFEljsU0x07kGubFHoYYkB8b6u+J1D+RyrgL+j/L3exWVXtWjdpzHoQSvWiTA61C6qZuQVT4yzlEb+9aiwRo3xHUUgzIMNS1JkqSbkwLdfUhxbj8akbv4m2ZW2Yt6UzN7ycxeQz2zNylWmNkG6Dc4DrXprAceRA1JBlRuG3wCCfx05IqGstXn6/H5AeBKJKqz4vM04KbYrtndd0EzoPvEeWbFvoOQtQyy0j9M1Fa3TAhLt3aSJEnHkeLcfjQDn0TDJAZVLL8YWaNnoNKpngBm1hsNwmgC3nL3d1B98lpxjOeQRQtlE5AtUAx5DnKJ1wEvu/v6wDGxzcmovGoSEv0NUGLYvkiwa8zMkHu8CbjZ3Qt17YtqoUHlXb9Ece13WiaEpVs7SZKk40hxbkfcfTYaRtHfzD4fi/si93Ef4GzgY5EANgO4Blmze5jZaDTHuTdK7qoDBpvZbFRq1YRc3gCvu3sDck+vb2brxnkLVgZWQxngg+McRQzaUEvQ1WP/nczs4djPkOscYCPK6Vd1S/fNJEnSVUjXdvcgxbn9KUYynmxm+yMhPBr4DLKir0XJVoZiyjOAT6Fe2EVW9kRg3Xj/DKpvrkZW7FeBDc3sE7HsZjTUYl0Ad/8fspJnoXnODajb2HeQe3s+sDfq1T0PtQj1uPabKGuqn4+/old3kiRJsoyo6ewLWF6I8iiQ0M13942KdWa2KjDD3c8ws52Ac4C+7j7TzC4G7nT3B82sCcWcv44mQc1AzUEKN/mTSEyrgPOQu/yDwEeQsE+O7YpRkEWm9yiU4DUAdRxbAzUeMdS0pOi1PTaO1YDc4Q2oN/eVrdzvBcAFACNHjvSW65MkSZK2k5Zz+zMViWAlKwNTzGwI8EfgEHefWbG+pbhVIeG8BNUePwJ8CVnNs2L9SkiYD0ZJYfXAymZ2Yix7BsW390Gi+7PY73Uk3qvHOUahODXIYn8TifWzKO7dE5V9LUDGnJMkSTqOFOd2JkT3NTPbHcDMVkZZz/ehBLD/Q2MbK/mEmX0J1Spvj2LRAEcgYd4e1T5/F9U434r6Ytcggf0Xyha/FzgAdf3aGOgf6zciEtFiWTVyldehWPbGse7flNngm6AYd1/UfztJkuWEjDt3fVKcO4bPopjzOORWPg0laG0T74vpVUXnradR3Bkkli8gN/XvUDtNR1bst2KbzVFm9xuxzWpIqCdQ1kQ/jWLO8+NY98bynqgDWdHY5LeU/w6eRvHolZDl/Xgsf6LlDWYpVZIkSceR4twBuPsT7r5bxXSqS939f+7eMxqPFNOrJrn7ke5+HLKGi+lTI5GI7oss3Fmx/iLkkv4xsmbXQ/XKK8U226E4cxMSWkeiPYCyRKoaifue8fkgNIDDkbW8Fuo8NgTFo0G10guQbu0k6d6k9dy1SXHuejQBDxMNQ1BsuA9qYPIv1Mnr98hVXYXKrLZGLvGewPg4zodR4ldT7NM/ltfH36Xx+REUBy+ytPvGX13Fsq+16x0mSdIlyLaeXZcU567FSfHaHK9VFe9fRY1BamP5fCSe2yMBbkaWbn9k+U5DYm3Iel4Hubeb0SzoavQAcBcq9SrKpY6O481Hgg9lE5QkSZZDCpFOoe46pDh3HXYBjqecDnU7GilZWK/9UElTM+oQdn+8fwMlmL2Cfs/VUKb1qkjIpyCh/QnK1K5C4rxTHO8A4Dok5HWoXroeCXNt7PueOueMOSdJknQcKc5dADPbAXXj+jXluMZ6VAI1F1myU5ALuxGJ72xk+Q5GzU0uj8N9BNUoz0WdyZxy8lRDbLMdEv0PxvmeRTHpocAPYp99UHZ5TVxLy2vOmHOSJEkHkeLcOfQys1eKP9QXezYSYVAjko2RBV2PXNAfQBnXdch9PSiWVwHnoxItkIXtyPKtRxZ0M/BD4Iuxzc6oQ9ggZIEfgNqGjkUdweYja/oTse/K7Xv7SZJ0VdK13TVIcV5GmNlJZva4mT0KPIoahdyIrOGvIQv2GGSpXoPKoIrmJM+hjOuVgJfjrxqJ6Hwk3AcjId0D1UTPQU1FCpf0zmjOM3GsHZEVvhoS6PnuvjtlO8/7UWtQQ+K/AOnWTpIk6ThSnJcB4bbeD9jK3TdHAjrR3Y+OsquZyEr+OeowdgESxLFxiKLU6XR3Xxu4hbL3tiOX9p2x7a9QPXQ1GkP5EhLYbyBXdzH84g4k3P9AsegpZnYD8NM47jbArrFtMRij8p7SrZ0kSdJBpDgvG4YAU9x9HoC7T3H3SWZ2h5l9NOY/zwJORwJai3phF/wYWblNZjYTuar7IqGdjOqWX0RCulasqwVeQ/XOTbHt8bFND/SA0Ii6jm2M4s0boZ7bRSZ3QWZrJ8kKRGZudz4pzh2Ama1qZn8zsxfMbCyKB29qZs+Y2e/N7IMtdumBYsmfc/f1UPy5aLfpqKVmNWUjke2Bq5HQvh3b9EH9s5tRI5KZ7j4UCXU1qn++NtZPie17Ird4YU0/G9sW7vEvx/m+0A5fS5IkSbKYpDi3M2ZmKGZ8p7uv6+5bA59EmdjHoMESl5vZkRW7Fb2rrzCzOUioV0aubkPTqeYhC7oXKpt6ltIiboztPo5afzYCVWb2OnJNNyE3+WNxntGotKo3inFXAbj73sAW8fm/yJKH9w7yyJhzkiRJB5Li3P58CGhw9/OKBe7+krufhYZWDETTpQ4GNkMlS/XIgn0MxYjfQqL7VhyiD0oQm4es2sGoP3YN6gRWJHHdg0S9FtUwG6WLuhg1OR/Fk78NjInl8ymbndwYr88hq3wOcocvQMack2T5J5uTdB4pzu3PJrSYOmVmw81sg4pFI5AIg9zO84F57j4y9h+AfptC9e5Fnb/qkNj+GA2jKJK1psX77ShndB+BpksVseMfIau4CYl+P2AryladVRH7fiaONRw9TIDc6kmSJMkyIsW5gzGz36FRjA8hV/KhKAHr1NjkERT77WVmbyJxXImyM9gUJLSNqIUnKKu7R7zvgxK5ive/Q1awI6u3OM6oeO2BmpT0Q0K+SWxXcBFlclmPuLbtW95XurWTJEk6jhTn9udxZJEC4O5fQTXFU1Fi2D/c/SB3n4Ks3jEoo3oaKpnqg8T19ThEFXCNu28FTELZ3FcDJ8b6F1CdtKNJUx+g/F2bkcjWojizI1f1WOQiJ66tcurUN1HGdzXKIJ8FfN3MNq+8yXRrJ8mKRbq2ly0pzktIZTORmMm8XYtNbgN6mtmXKpYVAyQmACPMrMrM1gK2jeWnILf1AUg06yi7hTmwdzQvKfpdH4cs8PnIyq5ClvW1KOZd7NeAGpE4ZSvPgbHPW7HfX1AjEuIajkPWulNmgfdw90eX4GtKkmQ5JAV62dGp4mxmwyLOWbnsVDN7TwJSi21GmtlZ8X6Ume3YhnNPMLNBC1m3pZm5me3dYnmrzUQqt3F3R2Me9zSzF83sAeDPyGq+G7mMx6OErZ6x7idICHsjcX4RWb1fRFnevVH8eDsknHOBj6JkriGxzXzgO8DnKq5lW+CqOPZHkVg3olGSg2J5FUo2a0ZWNZQtQ4v+3Vcu+ttMkmRFIQV62dBmcTazgWE5jjOz183s1YrPPVpse6yZfXbpL1e4+xh3/3p8HIVcs8W5JpjZeDN7xMxuMrPVWj3IojkMjVI8LI5ZPAwMQeLYYGZVRTOR2OYUM3swHjb6A8e4+zpI4L7j7pcjq3Und98EtdKcgARzFSSac5Bw1yI3OMCmqJnIupTx6U1RzfMpsU8PZFVfA/wPWd1VZjYb+Ep8Z1PiuDWUoyTnxzKQG30getjoF8eoj+3+2PILyphzkiRJx9FmcXb3qe4+wt1HAOcBvy4+u3tDi23Pc/dLlvQc0UHr52b2QDTw2CWWjzKz68xsGHAscFw8FOwS9zQBid6mwNmxz8AQ64fN7HzKRKmW5yzqhY8E9jKznu4+BjgTtbbcAQnixWZ2TcRdHwd6u/s27r5pHPu8sJq3QoMqWvIh1M96g3jfAz0MnIXE+k3UlnNf1C3sFSSWT7j788jC/jlKBhsTx5yK6p+LhLCnkaCbmX2YslxqPiqPmoYs5M3RA0UPZN3/HMWyi2lUh7XyPWXMOUmSpIOoef9NFh8z+wJqtNED1cl+xt1nm9mpKOZ5CfAfd9/azLZAk5GeiX2fR3W/GwI7mdnhqJTnSXff1swuBf5uZs/GNtPcfYKZnYe6Yf0qjrMycJ67X29mnwHODRF/KK4JVJp0DDDazOYDP3b3y83scjQR6kV3f97M5gI/jJ7T34/zXoQs6F1R4tRlKJv6ETP7FHIX9wR2cPc1zOwZ4EIzOwl933VxDcOB1c1sHOWDwt+RqPYATorP/YAz0ISon1ImcvVGVvmryJqfgRqNDEDZ4MT11lEOryialUwCflFxLQPQA0f/+PxVJNrV8bmyDCxJkhWcStf2hNM/0olXsvzS3jHnq8J63ALVxn6+cqW7T0bJUv2AXdB0pl5mtjYw2d1no4lLZ7n7liim2j92n4QabOwNfAnY0MxqeS/1wOkhemdSxnIHoMERWyIhm4/mGe8B/NLMhiAxPBo9BPRANcDrVRz7JeT27Y/E9a/Ap9BDxsbIRbwWcjn/O/bpB/R0921QDHlgxfFuD8/DJbHPoch9bWgghaH48sGowcgmwI5mNgE9BKyGrOHigWBjNACjSOayWN+A5jTfhx4QpgPPx/omJNIDUWZ2TSx/CVniHvewAOnWTpIk6TjaW5w3NbPRZjYeOByJSUvuQYMUdkXtIVdGQj061q8FHBnHWAtNVgKJxNwYHvEOsiBXXch1FJbof+IYs5BAFdOVdo79m939DRSn3Qa1rNwAxXInIqHfm9K92xjXez1K+vpoXMshSOzHIjHtFa+gzOj6eFi4GsWC+yKX84axzT6UDyENSFC/hKzbHnGteyDL/zZ3HxbLHo9rakSehIOAj8T9N6BM69PjGKchz8R8lCQ2Js5zJxLrZmQ9nx/bD0cPNIaGYiz4BadbO0kSMkGso2hvcb4Y+Kq7b4bEoGcr24xGYrw2iqk6Erc7wyV9IHB2HOMZStcrLDgpyZGVNwNZuAVzgCsi9v1Zyi5XM9EDA7GsdyvXtgvwBkqiuhXFWv+JxBz0fR0IfB0JeRVKpOoV1zMKJWXNQ/FukOjNQi7oi4BX3H0GKrmqjRKpDWm9C1efinv+D+ostk/0354d566Jcww0sydQOZbH+hmoNAr0UPRibP8NZPGDapzvQxb0dcidPo8ys7sJWL+Va0uSJAFSoDuC9hbnvsBr4W4+fCHb3Al8GnjW3ZtRLHhPNFHpNuTKLuYYL8wyruTfwIEVCWHTUC3xoyFWx8Z2k4FdzewhJFBzkRU7GFnxDyAxvgo4Cgn1f5E47xHHmIsyl3/p7hsiIV4HWbh3IjE8BLmDx5jZ/bHP7919R3c/GYknSPgGIGGtRl6Gv6EHmqL++PTYDiS2B1B6A3ogq74BPcBMRJ6BIgZdH/sWfbM3Q9Z+M3oAeRqVTq0D7BbfST1yzVfHvlXx/uXF+B2SJFmByR7c7Uu7JoShpKn7UbxyPAtatABEEhdIzECDFvpH3TBmtj+azvQqiuluE9vNRglRuPsdZvZyvH8GZRsT+zcDX4jSoWLZMKDJ3feKz4YE9TZkZZ7g7q8jd3otskivRWL2U5Qgdby7b2pmdyAR/pW7H2lmI9GoxwlmdgRykTcAH3P3jeN+vhYWck3c97HufjHyNGBmPSknTzWjGcs/QjHq8ykfVvZF/bEnUiZ61cc9DEbTr6Yi0Z+ELOX5SHz/iXIAquJ3GURZy1wwGpWlFV3FZlOOs1wAd78AuABg5MiR3nJ9kiRJ0nZMPTOS1ghRvy7Ko4plp1KRHb4Yx1is7c1sprv3MbOZyHL/M4r1Pg6cixK6ihKp/wBbomYi81AseiAS0jdQAtdKqLnI+rHPD4DfxjGuQx3CPgOcA3wMufmfiePWAjejZLjhwEtRs115vcegjHeGDh269UsvvURbyCftJFk+ySzu98fMxsbAo/fQ3pZztyIalPwGWefzUH30N8MaX5z970Cegt1QQtfn3X20mY1CVuUVyFptMrNPox7YT6G68CLJak3khu4dZV3VyGMwDQlrTyTCDSihbB8UD98PiW4TytruH8fZgHJM5KbIUu6BOodZfN6TcpzkSsjq7oMs6jdQPsCHY1tDHoQkSZLF5v0evFO8F80K01vbWrQKDdf2g0CNu6/n7hujYRIt49w9raJVKIr3VlITbTK/iazTSt5CQlwP7OHuo5H1+usorVoNuZd3Q+7nIr48M9ZdCNyCxLQGZYcXGdgzYtsiI/6l2P+HKJ58byybEOvPRfHvRmQ93xyveyLr/GZksfdCDyqTKedMv9sStCBLqZIkSTqOFdly3g2J033FAncfB2BmP0Su3lpkRRax868DuwMzrZzPfFV03zodGG5mt6J4Maju+EPIOv0CSnrbA9g44u6rIst0a/Rb/BTYxt33jdj5q6jsbC6a99w73OTbIlHuizqKXYrizX1Rn+7tgb3Qw0Fx7UcgC3oOEu0rUBb6apQDNT6AOoONRG7uN2L7YS2/vBZu7UV8zUmSJO+lNcs6remSFcZyboVNUdIUsGCrUNQM5GuU7t/1zWx3lJBVjazJVZC792fAv9B3OQ118uqHXNGHUDbw+LGZ/QeJ8A7RfGQSatN5PnJDrwVcY2bfi/Oegxq1DEK10i8gIV8bZWqDrOlRsc185DbfO84zgLLLV08kykVm+FnI+p6LLOSesf9ace7GWN4b+JaZrcgPckmSLAMyB6Uk/4e7IDXRKnRf5KKei8SzBlmVjUjEZiGreHWUbT4RZX8/6O7TImu7qEV+GVm/z6LpUd9B2d+/jHOuEvsbStAaSTnC8adI5Hsj4TQUcz4XPVw0o6EYv0ElXvVInIciEf45ZVeywcBjKMbdhLLKPxz3Mi2u4yTUq/v7wLfd/c9m9kbs34dyalWSJEmHkBa1WJEs55Zp6Y8jca1cflW8jkUDK15BZU0bxKsB/3D3jd19OHITH4QE86+U3b+2oKwx/hwSw6HIvX0YMNLMJiMLuAqVPw1FbuiNKJu3vE3ZLGUOysquQu7ky+Ick5G4Ph338oHYvhn4MnKNN8dxhgDfQsK9PmXXtGFxbX+gbP15TjQ7WRl1H3t7gS8zY85JkiQdxookzkX9b8FtqH63ssXoemb2QRRX7oVizC+huPReKFHqWXh3wMZMNKVqFqprrkGx25lILF+JzO/pqOb4wai//geaFvUScm1/AFnJv0eC+z1ktU9DIgyKWV8b7+fEMU9EDwI/Rg8QRQORSehB4izkGi8ytofHdnOAk5Fg74Lc2446o/2k4vsaix5Q3vPvJNt3JkmSdBwrjDi7+0zUvWz3WDQAieqqMRFrG2SRTkIWZw3qGnYhsKG7P4F6ah8bDUVuRtneh8Z+V5nZ06gl6Z1E+ZGZ7cOCDwWgRKzrUZx4VcrhFrsicT4BCfxclJgGSmB7DoluXRzzDGQVn0E5n3lNZJE3I5f272PdHCTcX0Kiv3N8bkDubot9+8b7tVBDkrVppQlJkiTJsmJFjEWvMOIcfBY4OYZQ3Aac4u4fcff1kNB+092fRW0yX4mkraOJsZbIQp2MhO+bqEXpSBTbrUNTpvZEfcWno4Yee6G48y7uPiGOYyjuPA/Fl69Hgrwhil2/hmK81xPWsrsfi4TUUQezYUiMa5AAVyELeD801KIKZVv/CQl9HXKT16D48rEonn5WrG9GZVfD4hoPjO2akNW9AOnWTpIk6TiyQ1gHEXXU9wEXuvsfYtk2yF0+Cg2fGIxiumOA7ZCAvoY6gBWNUU5GtdJ7IAt5E2TxvkA5zvJ1JKRfA45EAjsAudxPR67rYrpVD/SwsBqKca+ERHpN5MbfMo7xIvIsbA7Md/cFBDo7hCVJsqxZ3hLDskNYJ+DubmYHAr8xs+8iF/UEZHG/hcT1EeRGHoo6fG2HYs89kYW8M6phbkQWcNFN7GEUOy6oiu1PQrHiesoSqt3RQ8LqyBqvQ4lhtUjQj0Fx5irkGZgZ+/ZBol/MfE6SJOlUKh/mlzehbsmK5tZeprj7JHf/ZHQg2yRc6M/G6tHRs/t+ZC3fgVzd9yKBrUFx7YGx/VAUK57t7h8Gzo7lzci9XfyWuyBXeCMwLpZtjYS4ry7L69ADQjNy098W2/0e+FWs648eBkBJYUmSJMkyIsV5MTAzN7MzKj4fH5262srjwFZm1gfVQP8BieEWqPZ4DeT+fhy5veejeHIPlMA2G7mg56DfsLm4NGSNH4qs3W/E8sJD8jYwI0ZZFpnd5yJLGhRn/k5cS0+UrNaMsssXIGPOSZIkHUeK8+IxDzjIzAa10/FuQ+J3NnCju/8AZUxPQfFlB/7m7vug+PM8JJp9UPnTvrHtbUi4H0NJa59D1nExsGJIHGsGclH3imMUzUsa0ANATWy3L7KcH4pzFmMj32p5A1lKlSRJZ7K856tkzHnxmI+mTB2H4ro/QW07MbPBLDhl6pvufreZjUcu5neQkB7n7peY2V/QOMgDUB2xRcvQfigxbCKykEfEoI7VUWLWcUh0H0azmbdGolmDYtMz4zqLYRWvoY5kx8Wx+yKhLSZTrRPvt2RBcQY1Qin+bWQZVZIkXZKWAr08xaHTcl58fgccbmYrtVheOWXqYFQXDXA3cllvgpK/donl26MErQaUUd2A4sH1SCh/iMS1KV6rgRfdfSNkPQ+l7Pm9bmxzP7KKf4PEtAaVZE2Lc96GRlf2QuK/BbKKJ6I483zgSWB/JOi1SKwbWci/kXRrJ0mSdBwpzouJu08HLkGx3kr2QK0uZ6IErn5mtjZlU5FTUab2ZtEAZQ3kVj4WWdRTkEjuiVzWPZAwT0PJYP2AnczMUT11P+C/SJz/F6+jiLGUKAmsGVn2LyPreiRlM5JXkRu8GbgBZZD3Qs1GirahzSg+XSSa3dHy+0i3dpIkXY3lydWd4rxk/AZZoJVUoXriMcDh7r4GckPPRdZyI7Jmq4GLgGvcfTZwPPADd9863l8V2zahuPDmlCVMbwNnxvrqimv4AJpKZcjavRMYgUR1PZTY1ReJfuGmrvzNP4HGW/ZCMemvxfKaWFYXx06SJOkWDPvu9cuFSKc4LwHuPg1lLtcCP4gM7ptQCdRawDFmNiI2b0LtOTdA2dObx+dHIku7J/ArM3sRifYGqCa56AJWg5Ky3kZiPQt4HlnUj8U5qlD8eVZ8/iJlR6//Iat8HvDBits4gXKM5WrAKXHOt1AZF3G8Byk7hyVJknQrurtIpzgvOUUf6yY0kepU5DZeE1mix1K6h+9HWdQbIKGrR9ZoFRLda1EceXSs/2vFeQy185yFsq2/Ep/noXGRxDUUVi7AlZQW8qBYXoWEeiYS/W8id3fR7vM89LAxL/abj0q06lDCWhV6+FiAjDknSZJ0HCnOi4G796l4/wal2/oC4Ah3PwTFeu+OHthHoNacG8XfBcDHkYX6DeR+noLivLsCm8Uxf4wEex6KL4PKod5CbvNpKImssHxvoRTTp5Dbey4S/tnoIeFqJPSFBbwPcnNPQxnfE+P4RSvQ6XGcRpQ4RhxzATLmnCRJd6C7Ws9ZStU2Ckv1q6gpyCwkqB+O91XALHffxswuB1Z399vN7Hdo+MZeSCAfQT20X0YZ3Wcid3IVyspujuPWoKQvRxZtYQVPRNZ4Myq5+g0S47tQqdQcZLVXIUE3FLPuheLRY5CAF+0/56HY8/A4XuFir2+fry1JkmTZ0x1LrtJybgPuXoXaaK6BemEPQ2L6M3fvjcTz9Zh+NRxoMrO+yCo9HdUgX43c0lNR+dVQ5IoegmLGR6LfpwZ1CeuBXNvNqDHJdcgqb6YcknEiKtOqjWO/jMTZ0bjJJjRn+kHKyVdPoxh20Yu7GBn5VmxvcY0tv4N0aydJknQQaTkvPb9BLuVCzECC9iXUeOQgM9sf9bD+FfrOzd1HmNlzqCzqiyihDCSyTWjc4+pI6PsD/45ljrLAn0fNRwy5yWuRmH8ftQDdFIlvY2yzfqwv4tR94v30WEcc+99onvWg+NyEHgIWoMVUqiX+0pIkSTqL7mBJp+W8lEQG96XIjfw1M/s9ij/viJqKgBLHHkMiuxELTnmag+LAxfs9kCgOQL9PTySuuwN/j+1WQeVb1ahuucjsno1i29ugGumBcZw5SLAtzr8/ZQLY6kjYQe7rwhq/CZVZVdOKOCdJkiwvdMW4dIpz+3A6Ssz6N5qNvBkS5EFhHe+J4skNSCSrzazoGIa7vxBvDdUwn4bahILc2c/HcXvHeYrtZ1O217wIxbDXRjHl6cBhSHjrkIU/F7mob0Si/ReUGNY/jjGHssXnbiiTHBTDTpIkWW4pSq+6ilCnOLeRlhnc7t7L3Y+MIRZfQW7sX6LOXE+izOqNkFU9191HI9G7Lw5jqCRrL5Q0VjQEeQW5mR9EFm0tegi4Bbm5m5H4Ho2s3f9DDwGPuPu/4hhNqNVnA3JXfyCOsykS/8YW92TIGn8n9p3Ryv1nzDlJkqSDyJhzO2Bmw4HmilnNI4CXUCz4EuASd3/TzAaixh/NLfZfN5bNRcL4I+Bkyjaav0Cx57koXn2imV2DWoFugJK9JqAe2usjl/VDcXhHDVB2Rg8KN7r7x8ysGVng/wfcg0q/oMzc/iuwEvAxlNXd8p7fjTkDM83s6ff5mgah8rHuTt5H12J5uI/l4R5gOboP+/kyu4+1F7Yixbl96AOcbWb9kdv5OSRcs9BM5Dtju0eRRf2hin37oUYgN6MsbICNUbwYZBW/jHpl/xD12X4ExbinufsrZgZyfQ9r5drmIHc3SHhXMrPd0EPAwxXbFG0610DW8kjk7q6Mj7+Lu1+A6rcXCzMb4+4jF3f7rkreR9diebiP5eEeIO+jvUlxbgfcfSxKAGuNuortjgEws6Yos6pFSWN/Ac5092Yzq0LNSD6K4sKvA5+M7aYDc9x9CzO7Ggk0sc1kyt9zBvBXM1snzr8RclE/gUq2Dkeu7P8Wl8aCceUGd9/YzHqgB4z2mmOdJEmSLAYZc+4E3L3a3Ue4+ybuvoW7/8rdm2Nds7uf6O6bISHezd3fcfc73H2/isP8FsWjcfdhRBcvd78YKNzr/ZAgv4iyrh04zd2PBiYBE919Aur9XXAnMasaZYjXoGSzJEmSZBmRlvNyjLs/YmYPA4+j+PLdi7HbacBlZvYQ6sn9cjtdzmK7wLs4eR9di+XhPpaHe4C8j3bF3L2zryFJkiRJkgrSrZ0kSZIkXYwU5yRJkiTpYqQ4Jx2Kma1lZreb2ZNm9riZfaOzr6ktmFlPM3vAzB6J+zits6+prZhZtZk9bGbXdfa1tBUzm2Bm481snJm9pw6/u2Bm/c3sSjN7Kv4b2aGzr2lJMbPh8TsUf9PN7JudfV1LipkdF/9tP2Zml5lZz069now5Jx2JmQ0Bhrj7QzGZayxwgLs/0cmXtkSYisl7u/tMM6tFpWffcPf73mfXLoeZfQvVsfdrUQHQbTCzCcBId+/WTS/M7M/AaHe/MEoXe7n72518WW3GzIp+/9u5+0udfT2Li5mtgf6b3tjd55jZP4AbovqlU0jLOelQ3P01d38o3s9ArUzX6NyrWnJczIyPtfHX7Z5szWxN4CNoTGnSiZhZP2BX4I8A7t7QnYU52B14vjsJcwU1QL2Z1aD5BJM682JSnJNlhpkNQ81U7u/kS2kT4Q4ehxq+3Ozu3fE+fgOcQIsWst0QB24ys7HRSrY7si4aaHNRhBkuNLPenX1RS8mhwGWdfRFLiru/ikb6voxm3b/j7jd15jWlOCfLBDPrA/wT+Ka7T+/s62kL7t7k7iOANYFtzWzTTr6kJcLM9gMmR0e77s5O7r4VGrf6FTPbtbMvqA3UAFsB57r7lqgb33c795LaTrjlPwZc0dnXsqSY2QA0SncdNEa3t5l9ujOvKcU56XAiRvtP4FJ3v6qzr2dpCdfjHWj8Z3diJ+BjEa/9O/AhM/tr515S23D3SfE6Gbga2LZzr6hNvAK8UuGBuRKJdXdlH+Ahd3+jsy+kDewBvOjub7p7I3AVC2/JvExIcU46lEik+iPwpLuf2dnX01bMbHAMNsHM6tF/zE916kUtIe7+PXdfM9q9Hgrc5u6dah20BTPrHcmFhBt4L+Cxzr2qJcfdXwcmxlQ7ULy2WyVKtuAwuqFLO3gZ2N7MesX/s3ZH+TGdRrbvTDqanYDPAOMjXgtworvf0HmX1CaGAH+ObNQq4B/u3m1Lkbo5qwJXxzS2GuBv7n5j515Sm/kacGm4hF8Ajurk62kTZtYL2BP4YmdfS1tw9/vN7Eo0anc+mtjXqW08s5QqSZIkSboY6dZOkiRJki5GinOSJEmSdDFSnJMkSZKki5HinCRJkiRdjBTnJEmSJOlipDgnSZIkSRcjxTlJkiRJuhj/D8dlEqpKWF+iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(ls.iloc[:,1], ls.iloc[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "832eb307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      7.842\n",
       "1      7.620\n",
       "2      7.571\n",
       "3      7.554\n",
       "4      7.464\n",
       "       ...  \n",
       "144    3.512\n",
       "145    3.467\n",
       "146    3.415\n",
       "147    3.145\n",
       "148    2.523\n",
       "Name: Ladder score, Length: 149, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls.iloc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ac9c0222",
   "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 name</th>\n",
       "      <th>Ladder score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>7.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>7.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>3.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>3.467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>3.415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>3.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2.523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country name  Ladder score\n",
       "0        Finland         7.842\n",
       "1        Denmark         7.620\n",
       "2    Switzerland         7.571\n",
       "3        Iceland         7.554\n",
       "4    Netherlands         7.464\n",
       "..           ...           ...\n",
       "144      Lesotho         3.512\n",
       "145     Botswana         3.467\n",
       "146       Rwanda         3.415\n",
       "147     Zimbabwe         3.145\n",
       "148  Afghanistan         2.523\n",
       "\n",
       "[149 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, [0,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1c4ea31e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'Regional indicator': 'Region'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a6f801f",
   "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 name</th>\n",
       "      <th>Region</th>\n",
       "      <th>Ladder score</th>\n",
       "      <th>Standard error of ladder score</th>\n",
       "      <th>upperwhisker</th>\n",
       "      <th>lowerwhisker</th>\n",
       "      <th>Logged 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>Ladder score in Dystopia</th>\n",
       "      <th>Explained by: Log GDP per capita</th>\n",
       "      <th>Explained by: Social support</th>\n",
       "      <th>Explained by: Healthy life expectancy</th>\n",
       "      <th>Explained by: Freedom to make life choices</th>\n",
       "      <th>Explained by: Generosity</th>\n",
       "      <th>Explained by: Perceptions of corruption</th>\n",
       "      <th>Dystopia + residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.842</td>\n",
       "      <td>0.032</td>\n",
       "      <td>7.904</td>\n",
       "      <td>7.780</td>\n",
       "      <td>10.775</td>\n",
       "      <td>0.954</td>\n",
       "      <td>72.000</td>\n",
       "      <td>0.949</td>\n",
       "      <td>-0.098</td>\n",
       "      <td>0.186</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.446</td>\n",
       "      <td>1.106</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.481</td>\n",
       "      <td>3.253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.620</td>\n",
       "      <td>0.035</td>\n",
       "      <td>7.687</td>\n",
       "      <td>7.552</td>\n",
       "      <td>10.933</td>\n",
       "      <td>0.954</td>\n",
       "      <td>72.700</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.179</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.502</td>\n",
       "      <td>1.108</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.485</td>\n",
       "      <td>2.868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.571</td>\n",
       "      <td>0.036</td>\n",
       "      <td>7.643</td>\n",
       "      <td>7.500</td>\n",
       "      <td>11.117</td>\n",
       "      <td>0.942</td>\n",
       "      <td>74.400</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.292</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.566</td>\n",
       "      <td>1.079</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.413</td>\n",
       "      <td>2.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.554</td>\n",
       "      <td>0.059</td>\n",
       "      <td>7.670</td>\n",
       "      <td>7.438</td>\n",
       "      <td>10.878</td>\n",
       "      <td>0.983</td>\n",
       "      <td>73.000</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.673</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.482</td>\n",
       "      <td>1.172</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.170</td>\n",
       "      <td>2.967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.464</td>\n",
       "      <td>0.027</td>\n",
       "      <td>7.518</td>\n",
       "      <td>7.410</td>\n",
       "      <td>10.932</td>\n",
       "      <td>0.942</td>\n",
       "      <td>72.400</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.338</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.501</td>\n",
       "      <td>1.079</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.384</td>\n",
       "      <td>2.798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.512</td>\n",
       "      <td>0.120</td>\n",
       "      <td>3.748</td>\n",
       "      <td>3.276</td>\n",
       "      <td>7.926</td>\n",
       "      <td>0.787</td>\n",
       "      <td>48.700</td>\n",
       "      <td>0.715</td>\n",
       "      <td>-0.131</td>\n",
       "      <td>0.915</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.405</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.467</td>\n",
       "      <td>0.074</td>\n",
       "      <td>3.611</td>\n",
       "      <td>3.322</td>\n",
       "      <td>9.782</td>\n",
       "      <td>0.784</td>\n",
       "      <td>59.269</td>\n",
       "      <td>0.824</td>\n",
       "      <td>-0.246</td>\n",
       "      <td>0.801</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.415</td>\n",
       "      <td>0.068</td>\n",
       "      <td>3.548</td>\n",
       "      <td>3.282</td>\n",
       "      <td>7.676</td>\n",
       "      <td>0.552</td>\n",
       "      <td>61.400</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.167</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.493</td>\n",
       "      <td>1.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.145</td>\n",
       "      <td>0.058</td>\n",
       "      <td>3.259</td>\n",
       "      <td>3.030</td>\n",
       "      <td>7.943</td>\n",
       "      <td>0.750</td>\n",
       "      <td>56.201</td>\n",
       "      <td>0.677</td>\n",
       "      <td>-0.047</td>\n",
       "      <td>0.821</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.649</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.075</td>\n",
       "      <td>1.205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>2.523</td>\n",
       "      <td>0.038</td>\n",
       "      <td>2.596</td>\n",
       "      <td>2.449</td>\n",
       "      <td>7.695</td>\n",
       "      <td>0.463</td>\n",
       "      <td>52.493</td>\n",
       "      <td>0.382</td>\n",
       "      <td>-0.102</td>\n",
       "      <td>0.924</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1.895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country name              Region  Ladder score  \\\n",
       "0        Finland      Western Europe         7.842   \n",
       "1        Denmark      Western Europe         7.620   \n",
       "2    Switzerland      Western Europe         7.571   \n",
       "3        Iceland      Western Europe         7.554   \n",
       "4    Netherlands      Western Europe         7.464   \n",
       "..           ...                 ...           ...   \n",
       "144      Lesotho  Sub-Saharan Africa         3.512   \n",
       "145     Botswana  Sub-Saharan Africa         3.467   \n",
       "146       Rwanda  Sub-Saharan Africa         3.415   \n",
       "147     Zimbabwe  Sub-Saharan Africa         3.145   \n",
       "148  Afghanistan          South Asia         2.523   \n",
       "\n",
       "     Standard error of ladder score  upperwhisker  lowerwhisker  \\\n",
       "0                             0.032         7.904         7.780   \n",
       "1                             0.035         7.687         7.552   \n",
       "2                             0.036         7.643         7.500   \n",
       "3                             0.059         7.670         7.438   \n",
       "4                             0.027         7.518         7.410   \n",
       "..                              ...           ...           ...   \n",
       "144                           0.120         3.748         3.276   \n",
       "145                           0.074         3.611         3.322   \n",
       "146                           0.068         3.548         3.282   \n",
       "147                           0.058         3.259         3.030   \n",
       "148                           0.038         2.596         2.449   \n",
       "\n",
       "     Logged GDP per capita  Social support  Healthy life expectancy  \\\n",
       "0                   10.775           0.954                   72.000   \n",
       "1                   10.933           0.954                   72.700   \n",
       "2                   11.117           0.942                   74.400   \n",
       "3                   10.878           0.983                   73.000   \n",
       "4                   10.932           0.942                   72.400   \n",
       "..                     ...             ...                      ...   \n",
       "144                  7.926           0.787                   48.700   \n",
       "145                  9.782           0.784                   59.269   \n",
       "146                  7.676           0.552                   61.400   \n",
       "147                  7.943           0.750                   56.201   \n",
       "148                  7.695           0.463                   52.493   \n",
       "\n",
       "     Freedom to make life choices  Generosity  Perceptions of corruption  \\\n",
       "0                           0.949      -0.098                      0.186   \n",
       "1                           0.946       0.030                      0.179   \n",
       "2                           0.919       0.025                      0.292   \n",
       "3                           0.955       0.160                      0.673   \n",
       "4                           0.913       0.175                      0.338   \n",
       "..                            ...         ...                        ...   \n",
       "144                         0.715      -0.131                      0.915   \n",
       "145                         0.824      -0.246                      0.801   \n",
       "146                         0.897       0.061                      0.167   \n",
       "147                         0.677      -0.047                      0.821   \n",
       "148                         0.382      -0.102                      0.924   \n",
       "\n",
       "     Ladder score in Dystopia  Explained by: Log GDP per capita  \\\n",
       "0                        2.43                             1.446   \n",
       "1                        2.43                             1.502   \n",
       "2                        2.43                             1.566   \n",
       "3                        2.43                             1.482   \n",
       "4                        2.43                             1.501   \n",
       "..                        ...                               ...   \n",
       "144                      2.43                             0.451   \n",
       "145                      2.43                             1.099   \n",
       "146                      2.43                             0.364   \n",
       "147                      2.43                             0.457   \n",
       "148                      2.43                             0.370   \n",
       "\n",
       "     Explained by: Social support  Explained by: Healthy life expectancy  \\\n",
       "0                           1.106                                  0.741   \n",
       "1                           1.108                                  0.763   \n",
       "2                           1.079                                  0.816   \n",
       "3                           1.172                                  0.772   \n",
       "4                           1.079                                  0.753   \n",
       "..                            ...                                    ...   \n",
       "144                         0.731                                  0.007   \n",
       "145                         0.724                                  0.340   \n",
       "146                         0.202                                  0.407   \n",
       "147                         0.649                                  0.243   \n",
       "148                         0.000                                  0.126   \n",
       "\n",
       "     Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
       "0                                         0.691                     0.124   \n",
       "1                                         0.686                     0.208   \n",
       "2                                         0.653                     0.204   \n",
       "3                                         0.698                     0.293   \n",
       "4                                         0.647                     0.302   \n",
       "..                                          ...                       ...   \n",
       "144                                       0.405                     0.103   \n",
       "145                                       0.539                     0.027   \n",
       "146                                       0.627                     0.227   \n",
       "147                                       0.359                     0.157   \n",
       "148                                       0.000                     0.122   \n",
       "\n",
       "     Explained by: Perceptions of corruption  Dystopia + residual  \n",
       "0                                      0.481                3.253  \n",
       "1                                      0.485                2.868  \n",
       "2                                      0.413                2.839  \n",
       "3                                      0.170                2.967  \n",
       "4                                      0.384                2.798  \n",
       "..                                       ...                  ...  \n",
       "144                                    0.015                1.800  \n",
       "145                                    0.088                0.648  \n",
       "146                                    0.493                1.095  \n",
       "147                                    0.075                1.205  \n",
       "148                                    0.010                1.895  \n",
       "\n",
       "[149 rows x 20 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "64eb327c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reindex(sorted(df.columns), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fa945e39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country name</th>\n",
       "      <th>Region</th>\n",
       "      <th>Ladder score</th>\n",
       "      <th>Standard error of ladder score</th>\n",
       "      <th>upperwhisker</th>\n",
       "      <th>lowerwhisker</th>\n",
       "      <th>Logged 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>Ladder score in Dystopia</th>\n",
       "      <th>Explained by: Log GDP per capita</th>\n",
       "      <th>Explained by: Social support</th>\n",
       "      <th>Explained by: Healthy life expectancy</th>\n",
       "      <th>Explained by: Freedom to make life choices</th>\n",
       "      <th>Explained by: Generosity</th>\n",
       "      <th>Explained by: Perceptions of corruption</th>\n",
       "      <th>Dystopia + residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.842</td>\n",
       "      <td>0.032</td>\n",
       "      <td>7.904</td>\n",
       "      <td>7.780</td>\n",
       "      <td>10.775</td>\n",
       "      <td>0.954</td>\n",
       "      <td>72.000</td>\n",
       "      <td>0.949</td>\n",
       "      <td>-0.098</td>\n",
       "      <td>0.186</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.446</td>\n",
       "      <td>1.106</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.481</td>\n",
       "      <td>3.253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.620</td>\n",
       "      <td>0.035</td>\n",
       "      <td>7.687</td>\n",
       "      <td>7.552</td>\n",
       "      <td>10.933</td>\n",
       "      <td>0.954</td>\n",
       "      <td>72.700</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.179</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.502</td>\n",
       "      <td>1.108</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.485</td>\n",
       "      <td>2.868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.571</td>\n",
       "      <td>0.036</td>\n",
       "      <td>7.643</td>\n",
       "      <td>7.500</td>\n",
       "      <td>11.117</td>\n",
       "      <td>0.942</td>\n",
       "      <td>74.400</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.292</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.566</td>\n",
       "      <td>1.079</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.413</td>\n",
       "      <td>2.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.554</td>\n",
       "      <td>0.059</td>\n",
       "      <td>7.670</td>\n",
       "      <td>7.438</td>\n",
       "      <td>10.878</td>\n",
       "      <td>0.983</td>\n",
       "      <td>73.000</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.673</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.482</td>\n",
       "      <td>1.172</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.170</td>\n",
       "      <td>2.967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.464</td>\n",
       "      <td>0.027</td>\n",
       "      <td>7.518</td>\n",
       "      <td>7.410</td>\n",
       "      <td>10.932</td>\n",
       "      <td>0.942</td>\n",
       "      <td>72.400</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.338</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.501</td>\n",
       "      <td>1.079</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.384</td>\n",
       "      <td>2.798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.512</td>\n",
       "      <td>0.120</td>\n",
       "      <td>3.748</td>\n",
       "      <td>3.276</td>\n",
       "      <td>7.926</td>\n",
       "      <td>0.787</td>\n",
       "      <td>48.700</td>\n",
       "      <td>0.715</td>\n",
       "      <td>-0.131</td>\n",
       "      <td>0.915</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.405</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.467</td>\n",
       "      <td>0.074</td>\n",
       "      <td>3.611</td>\n",
       "      <td>3.322</td>\n",
       "      <td>9.782</td>\n",
       "      <td>0.784</td>\n",
       "      <td>59.269</td>\n",
       "      <td>0.824</td>\n",
       "      <td>-0.246</td>\n",
       "      <td>0.801</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.415</td>\n",
       "      <td>0.068</td>\n",
       "      <td>3.548</td>\n",
       "      <td>3.282</td>\n",
       "      <td>7.676</td>\n",
       "      <td>0.552</td>\n",
       "      <td>61.400</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.167</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.493</td>\n",
       "      <td>1.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.145</td>\n",
       "      <td>0.058</td>\n",
       "      <td>3.259</td>\n",
       "      <td>3.030</td>\n",
       "      <td>7.943</td>\n",
       "      <td>0.750</td>\n",
       "      <td>56.201</td>\n",
       "      <td>0.677</td>\n",
       "      <td>-0.047</td>\n",
       "      <td>0.821</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.649</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.075</td>\n",
       "      <td>1.205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>2.523</td>\n",
       "      <td>0.038</td>\n",
       "      <td>2.596</td>\n",
       "      <td>2.449</td>\n",
       "      <td>7.695</td>\n",
       "      <td>0.463</td>\n",
       "      <td>52.493</td>\n",
       "      <td>0.382</td>\n",
       "      <td>-0.102</td>\n",
       "      <td>0.924</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1.895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country name              Region  Ladder score  \\\n",
       "0        Finland      Western Europe         7.842   \n",
       "1        Denmark      Western Europe         7.620   \n",
       "2    Switzerland      Western Europe         7.571   \n",
       "3        Iceland      Western Europe         7.554   \n",
       "4    Netherlands      Western Europe         7.464   \n",
       "..           ...                 ...           ...   \n",
       "144      Lesotho  Sub-Saharan Africa         3.512   \n",
       "145     Botswana  Sub-Saharan Africa         3.467   \n",
       "146       Rwanda  Sub-Saharan Africa         3.415   \n",
       "147     Zimbabwe  Sub-Saharan Africa         3.145   \n",
       "148  Afghanistan          South Asia         2.523   \n",
       "\n",
       "     Standard error of ladder score  upperwhisker  lowerwhisker  \\\n",
       "0                             0.032         7.904         7.780   \n",
       "1                             0.035         7.687         7.552   \n",
       "2                             0.036         7.643         7.500   \n",
       "3                             0.059         7.670         7.438   \n",
       "4                             0.027         7.518         7.410   \n",
       "..                              ...           ...           ...   \n",
       "144                           0.120         3.748         3.276   \n",
       "145                           0.074         3.611         3.322   \n",
       "146                           0.068         3.548         3.282   \n",
       "147                           0.058         3.259         3.030   \n",
       "148                           0.038         2.596         2.449   \n",
       "\n",
       "     Logged GDP per capita  Social support  Healthy life expectancy  \\\n",
       "0                   10.775           0.954                   72.000   \n",
       "1                   10.933           0.954                   72.700   \n",
       "2                   11.117           0.942                   74.400   \n",
       "3                   10.878           0.983                   73.000   \n",
       "4                   10.932           0.942                   72.400   \n",
       "..                     ...             ...                      ...   \n",
       "144                  7.926           0.787                   48.700   \n",
       "145                  9.782           0.784                   59.269   \n",
       "146                  7.676           0.552                   61.400   \n",
       "147                  7.943           0.750                   56.201   \n",
       "148                  7.695           0.463                   52.493   \n",
       "\n",
       "     Freedom to make life choices  Generosity  Perceptions of corruption  \\\n",
       "0                           0.949      -0.098                      0.186   \n",
       "1                           0.946       0.030                      0.179   \n",
       "2                           0.919       0.025                      0.292   \n",
       "3                           0.955       0.160                      0.673   \n",
       "4                           0.913       0.175                      0.338   \n",
       "..                            ...         ...                        ...   \n",
       "144                         0.715      -0.131                      0.915   \n",
       "145                         0.824      -0.246                      0.801   \n",
       "146                         0.897       0.061                      0.167   \n",
       "147                         0.677      -0.047                      0.821   \n",
       "148                         0.382      -0.102                      0.924   \n",
       "\n",
       "     Ladder score in Dystopia  Explained by: Log GDP per capita  \\\n",
       "0                        2.43                             1.446   \n",
       "1                        2.43                             1.502   \n",
       "2                        2.43                             1.566   \n",
       "3                        2.43                             1.482   \n",
       "4                        2.43                             1.501   \n",
       "..                        ...                               ...   \n",
       "144                      2.43                             0.451   \n",
       "145                      2.43                             1.099   \n",
       "146                      2.43                             0.364   \n",
       "147                      2.43                             0.457   \n",
       "148                      2.43                             0.370   \n",
       "\n",
       "     Explained by: Social support  Explained by: Healthy life expectancy  \\\n",
       "0                           1.106                                  0.741   \n",
       "1                           1.108                                  0.763   \n",
       "2                           1.079                                  0.816   \n",
       "3                           1.172                                  0.772   \n",
       "4                           1.079                                  0.753   \n",
       "..                            ...                                    ...   \n",
       "144                         0.731                                  0.007   \n",
       "145                         0.724                                  0.340   \n",
       "146                         0.202                                  0.407   \n",
       "147                         0.649                                  0.243   \n",
       "148                         0.000                                  0.126   \n",
       "\n",
       "     Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
       "0                                         0.691                     0.124   \n",
       "1                                         0.686                     0.208   \n",
       "2                                         0.653                     0.204   \n",
       "3                                         0.698                     0.293   \n",
       "4                                         0.647                     0.302   \n",
       "..                                          ...                       ...   \n",
       "144                                       0.405                     0.103   \n",
       "145                                       0.539                     0.027   \n",
       "146                                       0.627                     0.227   \n",
       "147                                       0.359                     0.157   \n",
       "148                                       0.000                     0.122   \n",
       "\n",
       "     Explained by: Perceptions of corruption  Dystopia + residual  \n",
       "0                                      0.481                3.253  \n",
       "1                                      0.485                2.868  \n",
       "2                                      0.413                2.839  \n",
       "3                                      0.170                2.967  \n",
       "4                                      0.384                2.798  \n",
       "..                                       ...                  ...  \n",
       "144                                    0.015                1.800  \n",
       "145                                    0.088                0.648  \n",
       "146                                    0.493                1.095  \n",
       "147                                    0.075                1.205  \n",
       "148                                    0.010                1.895  \n",
       "\n",
       "[149 rows x 20 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cffb20f8",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Country Name'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Country Name'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-1fc01240cdbf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Country Name'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Finnland'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3022\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3023\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3025\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Country Name'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "da6b778f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fedb6419fa0>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ls.iloc[:,1], ls.iloc[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1be3d9da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fedb5618940>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(ls.iloc[:,1], ls.iloc[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "456a0703",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib.pyplot' has no attribute 'cluster'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-795f0800f2a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcluster\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'cluster'"
     ]
    }
   ],
   "source": [
    "plt.cluster(ls.iloc[:,1], ls.iloc[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f9f48ff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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