WHA_Country.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# World Happiness Analysis by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show path to csv files\n",
    "original_data = \"Resources/completData.csv\"\n",
    "\n",
    "# Read csv files through Pandas\n",
    "data = pd.read_csv(original_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank 2015</th>\n",
       "      <th>Happiness Score 2015</th>\n",
       "      <th>Economy (GDP per Capita) 2015</th>\n",
       "      <th>Family 2015</th>\n",
       "      <th>Health (Life Expectancy) 2015</th>\n",
       "      <th>Freedom 2015</th>\n",
       "      <th>Trust (Government Corruption) 2015</th>\n",
       "      <th>Generosity 2015</th>\n",
       "      <th>...</th>\n",
       "      <th>Trust (Government Corruption) 2018</th>\n",
       "      <th>Generosity 2018</th>\n",
       "      <th>Happiness Rank 2019</th>\n",
       "      <th>Happiness Score 2019</th>\n",
       "      <th>Economy (GDP per Capita) 2019</th>\n",
       "      <th>Family 2019</th>\n",
       "      <th>Health (Life Expectancy) 2019</th>\n",
       "      <th>Freedom 2019</th>\n",
       "      <th>Trust (Government Corruption) 2019</th>\n",
       "      <th>Generosity 2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.587</td>\n",
       "      <td>1.397</td>\n",
       "      <td>1.350</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.297</td>\n",
       "      <td>...</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.256</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.480</td>\n",
       "      <td>1.452</td>\n",
       "      <td>1.526</td>\n",
       "      <td>1.052</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.561</td>\n",
       "      <td>1.302</td>\n",
       "      <td>1.402</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.436</td>\n",
       "      <td>...</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.353</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.527</td>\n",
       "      <td>1.325</td>\n",
       "      <td>1.361</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.649</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.341</td>\n",
       "      <td>...</td>\n",
       "      <td>0.408</td>\n",
       "      <td>0.284</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.522</td>\n",
       "      <td>1.459</td>\n",
       "      <td>1.331</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.347</td>\n",
       "      <td>...</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.286</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0.271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.427</td>\n",
       "      <td>1.326</td>\n",
       "      <td>1.323</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.458</td>\n",
       "      <td>...</td>\n",
       "      <td>0.291</td>\n",
       "      <td>0.321</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.278</td>\n",
       "      <td>1.365</td>\n",
       "      <td>1.505</td>\n",
       "      <td>1.039</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank 2015  Happiness Score 2015  \\\n",
       "0  Switzerland  Western Europe                  1.0                 7.587   \n",
       "1      Iceland  Western Europe                  2.0                 7.561   \n",
       "2      Denmark  Western Europe                  3.0                 7.527   \n",
       "3       Norway  Western Europe                  4.0                 7.522   \n",
       "4       Canada   North America                  5.0                 7.427   \n",
       "\n",
       "   Economy (GDP per Capita) 2015  Family 2015  Health (Life Expectancy) 2015  \\\n",
       "0                          1.397        1.350                          0.941   \n",
       "1                          1.302        1.402                          0.948   \n",
       "2                          1.325        1.361                          0.875   \n",
       "3                          1.459        1.331                          0.885   \n",
       "4                          1.326        1.323                          0.906   \n",
       "\n",
       "   Freedom 2015  Trust (Government Corruption) 2015  Generosity 2015  ...  \\\n",
       "0         0.666                               0.420            0.297  ...   \n",
       "1         0.629                               0.141            0.436  ...   \n",
       "2         0.649                               0.484            0.341  ...   \n",
       "3         0.670                               0.365            0.347  ...   \n",
       "4         0.633                               0.330            0.458  ...   \n",
       "\n",
       "   Trust (Government Corruption) 2018  Generosity 2018  Happiness Rank 2019  \\\n",
       "0                               0.357            0.256                  6.0   \n",
       "1                               0.138            0.353                  4.0   \n",
       "2                               0.408            0.284                  2.0   \n",
       "3                               0.340            0.286                  3.0   \n",
       "4                               0.291            0.321                  9.0   \n",
       "\n",
       "   Happiness Score 2019  Economy (GDP per Capita) 2019  Family 2019  \\\n",
       "0                 7.480                          1.452        1.526   \n",
       "1                 7.494                          1.380        1.624   \n",
       "2                 7.600                          1.383        1.573   \n",
       "3                 7.554                          1.488        1.582   \n",
       "4                 7.278                          1.365        1.505   \n",
       "\n",
       "   Health (Life Expectancy) 2019  Freedom 2019  \\\n",
       "0                          1.052         0.572   \n",
       "1                          1.026         0.591   \n",
       "2                          0.996         0.592   \n",
       "3                          1.028         0.603   \n",
       "4                          1.039         0.584   \n",
       "\n",
       "   Trust (Government Corruption) 2019  Generosity 2019  \n",
       "0                               0.343            0.263  \n",
       "1                               0.118            0.354  \n",
       "2                               0.410            0.252  \n",
       "3                               0.341            0.271  \n",
       "4                               0.308            0.285  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Happiness Rank 2015</th>\n",
       "      <th>Happiness Score 2015</th>\n",
       "      <th>Economy (GDP per Capita) 2015</th>\n",
       "      <th>Family 2015</th>\n",
       "      <th>Health (Life Expectancy) 2015</th>\n",
       "      <th>Freedom 2015</th>\n",
       "      <th>Trust (Government Corruption) 2015</th>\n",
       "      <th>Generosity 2015</th>\n",
       "      <th>Happiness Rank 2016</th>\n",
       "      <th>Happiness Score 2016</th>\n",
       "      <th>...</th>\n",
       "      <th>Trust (Government Corruption) 2018</th>\n",
       "      <th>Generosity 2018</th>\n",
       "      <th>Happiness Rank 2019</th>\n",
       "      <th>Happiness Score 2019</th>\n",
       "      <th>Economy (GDP per Capita) 2019</th>\n",
       "      <th>Family 2019</th>\n",
       "      <th>Health (Life Expectancy) 2019</th>\n",
       "      <th>Freedom 2019</th>\n",
       "      <th>Trust (Government Corruption) 2019</th>\n",
       "      <th>Generosity 2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>153.0</td>\n",
       "      <td>3.575</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.365</td>\n",
       "      <td>154.0</td>\n",
       "      <td>3.360</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.191</td>\n",
       "      <td>154.0</td>\n",
       "      <td>3.203</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>95.0</td>\n",
       "      <td>4.959</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.143</td>\n",
       "      <td>109.0</td>\n",
       "      <td>4.655</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.149</td>\n",
       "      <td>107.0</td>\n",
       "      <td>4.719</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>68.0</td>\n",
       "      <td>5.605</td>\n",
       "      <td>0.939</td>\n",
       "      <td>1.078</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.078</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.055</td>\n",
       "      <td>88.0</td>\n",
       "      <td>5.211</td>\n",
       "      <td>1.002</td>\n",
       "      <td>1.160</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>30.0</td>\n",
       "      <td>6.574</td>\n",
       "      <td>1.054</td>\n",
       "      <td>1.248</td>\n",
       "      <td>0.787</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.115</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.650</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.062</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.086</td>\n",
       "      <td>1.092</td>\n",
       "      <td>1.432</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>127.0</td>\n",
       "      <td>4.350</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.079</td>\n",
       "      <td>121.0</td>\n",
       "      <td>4.360</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.077</td>\n",
       "      <td>116.0</td>\n",
       "      <td>4.559</td>\n",
       "      <td>0.850</td>\n",
       "      <td>1.055</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>23.0</td>\n",
       "      <td>6.810</td>\n",
       "      <td>1.044</td>\n",
       "      <td>1.256</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.058</td>\n",
       "      <td>44.0</td>\n",
       "      <td>6.084</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.056</td>\n",
       "      <td>108.0</td>\n",
       "      <td>4.707</td>\n",
       "      <td>0.960</td>\n",
       "      <td>1.427</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>75.0</td>\n",
       "      <td>5.360</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.594</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.169</td>\n",
       "      <td>96.0</td>\n",
       "      <td>5.061</td>\n",
       "      <td>...</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.177</td>\n",
       "      <td>94.0</td>\n",
       "      <td>5.175</td>\n",
       "      <td>0.741</td>\n",
       "      <td>1.346</td>\n",
       "      <td>0.851</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>136.0</td>\n",
       "      <td>4.077</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.401</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.091</td>\n",
       "      <td>147.0</td>\n",
       "      <td>3.724</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.083</td>\n",
       "      <td>151.0</td>\n",
       "      <td>3.380</td>\n",
       "      <td>0.287</td>\n",
       "      <td>1.163</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>85.0</td>\n",
       "      <td>5.129</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.196</td>\n",
       "      <td>106.0</td>\n",
       "      <td>4.795</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.221</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.107</td>\n",
       "      <td>0.578</td>\n",
       "      <td>1.058</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>115.0</td>\n",
       "      <td>4.610</td>\n",
       "      <td>0.271</td>\n",
       "      <td>1.033</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.190</td>\n",
       "      <td>131.0</td>\n",
       "      <td>4.193</td>\n",
       "      <td>...</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.132</td>\n",
       "      <td>146.0</td>\n",
       "      <td>3.663</td>\n",
       "      <td>0.366</td>\n",
       "      <td>1.114</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Happiness Rank 2015  Happiness Score 2015  \\\n",
       "Country                                                  \n",
       "Afghanistan                153.0                 3.575   \n",
       "Albania                     95.0                 4.959   \n",
       "Algeria                     68.0                 5.605   \n",
       "Argentina                   30.0                 6.574   \n",
       "Armenia                    127.0                 4.350   \n",
       "...                          ...                   ...   \n",
       "Venezuela                   23.0                 6.810   \n",
       "Vietnam                     75.0                 5.360   \n",
       "Yemen                      136.0                 4.077   \n",
       "Zambia                      85.0                 5.129   \n",
       "Zimbabwe                   115.0                 4.610   \n",
       "\n",
       "             Economy (GDP per Capita) 2015  Family 2015  \\\n",
       "Country                                                   \n",
       "Afghanistan                          0.320        0.303   \n",
       "Albania                              0.879        0.804   \n",
       "Algeria                              0.939        1.078   \n",
       "Argentina                            1.054        1.248   \n",
       "Armenia                              0.768        0.777   \n",
       "...                                    ...          ...   \n",
       "Venezuela                            1.044        1.256   \n",
       "Vietnam                              0.632        0.912   \n",
       "Yemen                                0.546        0.681   \n",
       "Zambia                               0.470        0.916   \n",
       "Zimbabwe                             0.271        1.033   \n",
       "\n",
       "             Health (Life Expectancy) 2015  Freedom 2015  \\\n",
       "Country                                                    \n",
       "Afghanistan                          0.303         0.234   \n",
       "Albania                              0.813         0.357   \n",
       "Algeria                              0.618         0.286   \n",
       "Argentina                            0.787         0.450   \n",
       "Armenia                              0.730         0.198   \n",
       "...                                    ...           ...   \n",
       "Venezuela                            0.721         0.429   \n",
       "Vietnam                              0.747         0.594   \n",
       "Yemen                                0.401         0.356   \n",
       "Zambia                               0.299         0.488   \n",
       "Zimbabwe                             0.335         0.259   \n",
       "\n",
       "             Trust (Government Corruption) 2015  Generosity 2015  \\\n",
       "Country                                                            \n",
       "Afghanistan                               0.097            0.365   \n",
       "Albania                                   0.064            0.143   \n",
       "Algeria                                   0.174            0.078   \n",
       "Argentina                                 0.085            0.115   \n",
       "Armenia                                   0.039            0.079   \n",
       "...                                         ...              ...   \n",
       "Venezuela                                 0.111            0.058   \n",
       "Vietnam                                   0.104            0.169   \n",
       "Yemen                                     0.079            0.091   \n",
       "Zambia                                    0.125            0.196   \n",
       "Zimbabwe                                  0.081            0.190   \n",
       "\n",
       "             Happiness Rank 2016  Happiness Score 2016  ...  \\\n",
       "Country                                                 ...   \n",
       "Afghanistan                154.0                 3.360  ...   \n",
       "Albania                    109.0                 4.655  ...   \n",
       "Algeria                     38.0                 6.355  ...   \n",
       "Argentina                   26.0                 6.650  ...   \n",
       "Armenia                    121.0                 4.360  ...   \n",
       "...                          ...                   ...  ...   \n",
       "Venezuela                   44.0                 6.084  ...   \n",
       "Vietnam                     96.0                 5.061  ...   \n",
       "Yemen                      147.0                 3.724  ...   \n",
       "Zambia                     106.0                 4.795  ...   \n",
       "Zimbabwe                   131.0                 4.193  ...   \n",
       "\n",
       "             Trust (Government Corruption) 2018  Generosity 2018  \\\n",
       "Country                                                            \n",
       "Afghanistan                               0.036            0.191   \n",
       "Albania                                   0.032            0.149   \n",
       "Algeria                                   0.135            0.055   \n",
       "Argentina                                 0.054            0.062   \n",
       "Armenia                                   0.028            0.077   \n",
       "...                                         ...              ...   \n",
       "Venezuela                                 0.052            0.056   \n",
       "Vietnam                                   0.079            0.177   \n",
       "Yemen                                     0.064            0.083   \n",
       "Zambia                                    0.082            0.221   \n",
       "Zimbabwe                                  0.099            0.132   \n",
       "\n",
       "             Happiness Rank 2019  Happiness Score 2019  \\\n",
       "Country                                                  \n",
       "Afghanistan                154.0                 3.203   \n",
       "Albania                    107.0                 4.719   \n",
       "Algeria                     88.0                 5.211   \n",
       "Argentina                   47.0                 6.086   \n",
       "Armenia                    116.0                 4.559   \n",
       "...                          ...                   ...   \n",
       "Venezuela                  108.0                 4.707   \n",
       "Vietnam                     94.0                 5.175   \n",
       "Yemen                      151.0                 3.380   \n",
       "Zambia                     138.0                 4.107   \n",
       "Zimbabwe                   146.0                 3.663   \n",
       "\n",
       "             Economy (GDP per Capita) 2019  Family 2019  \\\n",
       "Country                                                   \n",
       "Afghanistan                          0.350        0.517   \n",
       "Albania                              0.947        0.848   \n",
       "Algeria                              1.002        1.160   \n",
       "Argentina                            1.092        1.432   \n",
       "Armenia                              0.850        1.055   \n",
       "...                                    ...          ...   \n",
       "Venezuela                            0.960        1.427   \n",
       "Vietnam                              0.741        1.346   \n",
       "Yemen                                0.287        1.163   \n",
       "Zambia                               0.578        1.058   \n",
       "Zimbabwe                             0.366        1.114   \n",
       "\n",
       "             Health (Life Expectancy) 2019  Freedom 2019  \\\n",
       "Country                                                    \n",
       "Afghanistan                          0.361         0.000   \n",
       "Albania                              0.874         0.383   \n",
       "Algeria                              0.785         0.086   \n",
       "Argentina                            0.881         0.471   \n",
       "Armenia                              0.815         0.283   \n",
       "...                                    ...           ...   \n",
       "Venezuela                            0.805         0.154   \n",
       "Vietnam                              0.851         0.543   \n",
       "Yemen                                0.463         0.143   \n",
       "Zambia                               0.426         0.431   \n",
       "Zimbabwe                             0.433         0.361   \n",
       "\n",
       "             Trust (Government Corruption) 2019  Generosity 2019  \n",
       "Country                                                           \n",
       "Afghanistan                               0.025            0.158  \n",
       "Albania                                   0.027            0.178  \n",
       "Algeria                                   0.114            0.073  \n",
       "Argentina                                 0.050            0.066  \n",
       "Armenia                                   0.064            0.095  \n",
       "...                                         ...              ...  \n",
       "Venezuela                                 0.047            0.064  \n",
       "Vietnam                                   0.073            0.147  \n",
       "Yemen                                     0.077            0.108  \n",
       "Zambia                                    0.087            0.247  \n",
       "Zimbabwe                                  0.089            0.151  \n",
       "\n",
       "[140 rows x 40 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group data with Country as index\n",
    "complet_data_Summary = data.groupby(\"Country\")\n",
    "complet_data_Summary.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Country',\n",
       " 'Region',\n",
       " 'Happiness Rank 2015',\n",
       " 'Happiness Score 2015',\n",
       " 'Economy (GDP per Capita) 2015',\n",
       " 'Family 2015',\n",
       " 'Health (Life Expectancy) 2015',\n",
       " 'Freedom 2015',\n",
       " 'Trust (Government Corruption) 2015',\n",
       " 'Generosity 2015',\n",
       " 'Happiness Rank 2016',\n",
       " 'Happiness Score 2016',\n",
       " 'Economy (GDP per Capita) 2016',\n",
       " 'Family 2016',\n",
       " 'Health (Life Expectancy) 2016',\n",
       " 'Freedom 2016',\n",
       " 'Trust (Government Corruption) 2016',\n",
       " 'Generosity 2016',\n",
       " 'Happiness Rank 2017',\n",
       " 'Happiness Score 2017',\n",
       " 'Economy (GDP per Capita) 2017',\n",
       " 'Family 2017',\n",
       " 'Health (Life Expectancy) 2017',\n",
       " 'Freedom 2017',\n",
       " 'Trust (Government Corruption) 2017',\n",
       " 'Generosity 2017',\n",
       " 'Happiness Rank 2018',\n",
       " 'Happiness Score 2018',\n",
       " 'Economy (GDP per Capita) 2018',\n",
       " 'Family 2018',\n",
       " 'Health (Life Expectancy) 2018',\n",
       " 'Freedom 2018',\n",
       " 'Trust (Government Corruption) 2018',\n",
       " 'Generosity 2018',\n",
       " 'Happiness Rank 2019',\n",
       " 'Happiness Score 2019',\n",
       " 'Economy (GDP per Capita) 2019',\n",
       " 'Family 2019',\n",
       " 'Health (Life Expectancy) 2019',\n",
       " 'Freedom 2019',\n",
       " 'Trust (Government Corruption) 2019',\n",
       " 'Generosity 2019']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trust (Government Corruption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Gov Trust Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0.0580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0.0432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0.1462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0.0644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>0.0388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>0.0714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>0.0920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>0.0672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>0.0974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>0.0900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Gov Trust Average\n",
       "Country                       \n",
       "Afghanistan             0.0580\n",
       "Albania                 0.0432\n",
       "Algeria                 0.1462\n",
       "Argentina               0.0644\n",
       "Armenia                 0.0388\n",
       "...                        ...\n",
       "Venezuela               0.0714\n",
       "Vietnam                 0.0920\n",
       "Yemen                   0.0672\n",
       "Zambia                  0.0974\n",
       "Zimbabwe                0.0900\n",
       "\n",
       "[140 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of Trust (Government Corruption) per year\n",
    "Trust_df = data[[\"Country\", \"Trust (Government Corruption) 2015\", \"Trust (Government Corruption) 2016\", \"Trust (Government Corruption) 2017\", \"Trust (Government Corruption) 2018\", \"Trust (Government Corruption) 2019\"]]\n",
    "Trust_df = Trust_df.groupby(\"Country\")\n",
    "Trust_df.mean()\n",
    "\n",
    "#Find the averages of the data\n",
    "GOV_df = Trust_df.mean().reset_index()\n",
    "GOV_df[\"Gov Trust Average\"] = GOV_df.mean(axis=1)\n",
    "GOV_df\n",
    "\n",
    "# Filter the DataFrame down only to those columns to chart\n",
    "GOV_plot = GOV_df[[\"Country\", \"Gov Trust Average\"]]\n",
    "\n",
    "# Set the index to be \"Country\" so they will be used as labels\n",
    "GOV_plot = GOV_plot.set_index(\"Country\")\n",
    "GOV_plot.head(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Gov Trust Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rwanda</th>\n",
       "      <td>0.4734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>0.4672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>0.4296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>0.4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>0.3986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>0.3974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>0.3798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qatar</th>\n",
       "      <td>0.3558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>0.3440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>0.3374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Gov Trust Average\n",
       "Country                       \n",
       "Rwanda                  0.4734\n",
       "Singapore               0.4672\n",
       "Denmark                 0.4296\n",
       "New Zealand             0.4000\n",
       "Finland                 0.3986\n",
       "Sweden                  0.3974\n",
       "Switzerland             0.3798\n",
       "Qatar                   0.3558\n",
       "Norway                  0.3440\n",
       "Luxembourg              0.3374"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List top 10 Countries with the highest average Government Trust Average\n",
    "\n",
    "highest_10_GOV = GOV_plot.nlargest(10, ['Gov Trust Average'])\n",
    "highest_10_GOV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use DataFrame.plot() in order to create a bar chart of the data\n",
    "highest_10_GOV.plot(kind=\"bar\", figsize=(5,3))\n",
    "\n",
    "# Set a title for the chart\n",
    "plt.title(\"Average Government Trust Score Per Country\")\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gov Trust Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>0.0016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>0.0046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>0.0078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moldova</th>\n",
       "      <td>0.0090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>0.0128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>0.0168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <td>0.0176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>0.0184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovakia</th>\n",
       "      <td>0.0230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>0.0282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Gov Trust Average\n",
       "Country                                  \n",
       "Bosnia and Herzegovina             0.0016\n",
       "Romania                            0.0046\n",
       "Bulgaria                           0.0078\n",
       "Moldova                            0.0090\n",
       "Indonesia                          0.0128\n",
       "Portugal                           0.0168\n",
       "Lithuania                          0.0176\n",
       "Ukraine                            0.0184\n",
       "Slovakia                           0.0230\n",
       "Italy                              0.0282"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List top 10 Countries with the lowest average Government Trust Average\n",
    "\n",
    "lowest_10_GOV = GOV_plot.nsmallest(10, ['Gov Trust Average'])\n",
    "lowest_10_GOV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Generocity Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0.2678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0.1678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0.0690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0.0924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>0.0824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Generocity Average\n",
       "Country                        \n",
       "Afghanistan              0.2678\n",
       "Albania                  0.1678\n",
       "Algeria                  0.0690\n",
       "Argentina                0.0924\n",
       "Armenia                  0.0824"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of Generocity per year\n",
    "Generocity_df = data[[\"Country\", \"Generosity 2015\", \"Generosity 2016\", \"Generosity 2017\", \"Generosity 2018\", \"Generosity 2019\"]]\n",
    "Generocity_df = Generocity_df.groupby(\"Country\")\n",
    "Generocity_df.mean()\n",
    "\n",
    "#Find the averages of the data\n",
    "Gen_df = Generocity_df.mean().reset_index()\n",
    "Gen_df[\"Generocity Average\"] = Gen_df.mean(axis=1)\n",
    "Gen_df\n",
    "\n",
    "# Filter the DataFrame down only to those columns to chart\n",
    "Gen_plot = Gen_df[[\"Country\", \"Generocity Average\"]]\n",
    "\n",
    "# Set the index to be \"Country\" so they will be used as labels\n",
    "Gen_plot = Gen_plot.set_index(\"Country\")\n",
    "Gen_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Generocity Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Myanmar</th>\n",
       "      <td>0.7236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>0.5348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>0.4916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malta</th>\n",
       "      <td>0.4812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haiti</th>\n",
       "      <td>0.4484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhutan</th>\n",
       "      <td>0.4346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>0.4328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Syria</th>\n",
       "      <td>0.4314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>0.4248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>0.4192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Generocity Average\n",
       "Country                           \n",
       "Myanmar                     0.7236\n",
       "Indonesia                   0.5348\n",
       "Thailand                    0.4916\n",
       "Malta                       0.4812\n",
       "Haiti                       0.4484\n",
       "Bhutan                      0.4346\n",
       "New Zealand                 0.4328\n",
       "Syria                       0.4314\n",
       "United Kingdom              0.4248\n",
       "Iceland                     0.4192"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List top 10 Countries with the highest average Generocity Average\n",
    "highest_10_generocity = Gen_plot.nlargest(10, ['Generocity Average'])\n",
    "highest_10_generocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use DataFrame.plot() in order to create a bar chart of the data\n",
    "highest_10_generocity.plot(kind=\"bar\", figsize=(5,3))\n",
    "\n",
    "# Set a title for the chart\n",
    "plt.title(\"Average Generocity Score Per Country\")\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Generocity Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <td>0.0250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>0.0414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>0.0456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>0.0490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>0.0496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gabon</th>\n",
       "      <td>0.0498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>0.0504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>0.0516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>0.0572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Generocity Average\n",
       "Country                       \n",
       "Greece                  0.0000\n",
       "Lithuania               0.0250\n",
       "Morocco                 0.0414\n",
       "Russia                  0.0456\n",
       "Azerbaijan              0.0490\n",
       "China                   0.0496\n",
       "Gabon                   0.0498\n",
       "Tunisia                 0.0504\n",
       "Georgia                 0.0516\n",
       "Venezuela               0.0572"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List top 10 Countries with the lowest average Generocity Average\n",
    "lowest_10_generocity = Gen_plot.nsmallest(10, ['Generocity Average'])\n",
    "lowest_10_generocity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Health (Life Expectancy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Health Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0.2546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0.7876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0.6652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0.7608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>0.6980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Health Average\n",
       "Country                    \n",
       "Afghanistan          0.2546\n",
       "Albania              0.7876\n",
       "Algeria              0.6652\n",
       "Argentina            0.7608\n",
       "Armenia              0.6980"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of Health per year\n",
    "Health_df = data[[\"Country\", \"Health (Life Expectancy) 2015\",\"Health (Life Expectancy) 2016\", \"Health (Life Expectancy) 2017\", \"Health (Life Expectancy) 2018\", \"Health (Life Expectancy) 2019\"]]\n",
    "Health_df = Health_df.groupby(\"Country\")\n",
    "Health_df.mean()\n",
    "\n",
    "#Find the averages of the data\n",
    "Heal_df = Health_df.mean().reset_index()\n",
    "Heal_df[\"Health Average\"] = Heal_df.mean(axis=1)\n",
    "Heal_df\n",
    "\n",
    "# Filter the DataFrame down only to those columns to chart\n",
    "Health_plot = Heal_df[[\"Country\", \"Health Average\"]]\n",
    "\n",
    "# Set the index to be \"Country\" so they will be used as labels\n",
    "Health_plot = Health_plot.set_index(\"Country\")\n",
    "Health_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Health Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>1.0140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>0.9790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>0.9502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>0.9484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>0.9286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>0.9282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>0.9178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0.9162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>0.9146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cyprus</th>\n",
       "      <td>0.9138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Health Average\n",
       "Country                    \n",
       "Singapore            1.0140\n",
       "Japan                0.9790\n",
       "Spain                0.9502\n",
       "South Korea          0.9484\n",
       "Italy                0.9286\n",
       "Switzerland          0.9282\n",
       "Iceland              0.9178\n",
       "France               0.9162\n",
       "Australia            0.9146\n",
       "Cyprus               0.9138"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List top 10 Countries with the highest average Health Average\n",
    "highest_10_health = Health_plot.nlargest(10, ['Health Average'])\n",
    "highest_10_health"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use DataFrame.plot() in order to create a bar chart of the data\n",
    "highest_10_health.plot(kind=\"bar\", figsize=(5,3))\n",
    "\n",
    "# Set a title for the chart\n",
    "plt.title(\"Average Life Expectancy Per Country\")\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Health Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sierra Leone</th>\n",
       "      <td>0.0496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chad</th>\n",
       "      <td>0.0948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ivory Coast</th>\n",
       "      <td>0.1116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>0.1122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mali</th>\n",
       "      <td>0.1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cameroon</th>\n",
       "      <td>0.2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Congo (Kinshasa)</th>\n",
       "      <td>0.2076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burundi</th>\n",
       "      <td>0.2116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guinea</th>\n",
       "      <td>0.2416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0.2546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Health Average\n",
       "Country                         \n",
       "Sierra Leone              0.0496\n",
       "Chad                      0.0948\n",
       "Ivory Coast               0.1116\n",
       "Nigeria                   0.1122\n",
       "Mali                      0.1996\n",
       "Cameroon                  0.2014\n",
       "Congo (Kinshasa)          0.2076\n",
       "Burundi                   0.2116\n",
       "Guinea                    0.2416\n",
       "Afghanistan               0.2546"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List top 10 Countries with the lowest average Health Average\n",
    "lowest_10_health = Health_plot.nsmallest(10, ['Health Average'])\n",
    "lowest_10_health"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Family Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0.4098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0.7550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>1.0742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.3308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>0.8908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Family Average\n",
       "Country                    \n",
       "Afghanistan          0.4098\n",
       "Albania              0.7550\n",
       "Algeria              1.0742\n",
       "Argentina            1.3308\n",
       "Armenia              0.8908"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of Family per year\n",
    "Family_df = data[[\"Country\", \"Family 2015\", \"Family 2016\", \"Family 2017\", \"Family 2018\", \"Family 2019\"]]\n",
    "Family_df = Family_df.groupby(\"Country\")\n",
    "Family_df.mean()\n",
    "\n",
    "#Find the averages of the data\n",
    "Fam_df = Family_df.mean().reset_index()\n",
    "Fam_df[\"Family Average\"] = Fam_df.mean(axis=1)\n",
    "Fam_df\n",
    "\n",
    "# Filter the DataFrame down only to those columns to chart\n",
    "Family_plot = Fam_df[[\"Country\", \"Family Average\"]]\n",
    "\n",
    "# Set the index to be \"Country\" so they will be used as labels\n",
    "Family_plot = Family_plot.set_index(\"Country\")\n",
    "Family_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Family Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>1.4928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>1.4478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>1.4450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>1.4398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>1.4344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>1.4340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>1.4312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>1.4174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1.4090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1.3994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Family Average\n",
       "Country                    \n",
       "Iceland              1.4928\n",
       "Denmark              1.4478\n",
       "Ireland              1.4450\n",
       "New Zealand          1.4398\n",
       "Finland              1.4344\n",
       "Uzbekistan           1.4340\n",
       "Norway               1.4312\n",
       "Switzerland          1.4174\n",
       "Australia            1.4090\n",
       "Spain                1.3994"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List top 10 Countries with the highest average Family Average\n",
    "highest_10_Family = Family_plot.nlargest(10, ['Family Average'])\n",
    "highest_10_Family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use DataFrame.plot() in order to create a bar chart of the data\n",
    "highest_10_Family.plot(kind=\"bar\", figsize=(5,3))\n",
    "\n",
    "# Set a title for the chart\n",
    "plt.title(\"Average GDP Per Country\")\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Family Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Togo</th>\n",
       "      <td>0.3236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>0.3404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Syria</th>\n",
       "      <td>0.3560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0.4098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malawi</th>\n",
       "      <td>0.4344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burundi</th>\n",
       "      <td>0.4708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>0.4814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>0.5880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>0.6088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haiti</th>\n",
       "      <td>0.6162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Family Average\n",
       "Country                    \n",
       "Togo                 0.3236\n",
       "Benin                0.3404\n",
       "Syria                0.3560\n",
       "Afghanistan          0.4098\n",
       "Malawi               0.4344\n",
       "Burundi              0.4708\n",
       "Georgia              0.4814\n",
       "India                0.5880\n",
       "Pakistan             0.6088\n",
       "Haiti                0.6162"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List top 10 Countries with the lowest average Family Average\n",
    "lowest_10_Family = Family_plot.nsmallest(10, ['Family Average'])\n",
    "lowest_10_Family "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Freedom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Freedom Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>0.6558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>0.6380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cambodia</th>\n",
       "      <td>0.6378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>0.6258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>0.6216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Freedom Average\n",
       "Country                    \n",
       "Uzbekistan           0.6558\n",
       "Norway               0.6380\n",
       "Cambodia             0.6378\n",
       "Denmark              0.6258\n",
       "Finland              0.6216"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Summary of Freedom score\n",
    "Freedom_df = data[[\"Country\", \"Freedom 2015\", \"Freedom 2016\", \"Freedom 2017\", \"Freedom 2018\", \"Freedom 2019\"]]\n",
    "Freedom_df = Freedom_df.groupby(\"Country\")\n",
    "Freedom_df.mean()\n",
    "\n",
    "#Find the averages of the data\n",
    "Free_df = Freedom_df.mean().reset_index()\n",
    "Free_df[\"Freedom Average\"] = Free_df.mean(axis=1)\n",
    "Free_df\n",
    "\n",
    "#sort data in ascending order based on Average\n",
    "Free_df.sort_values(by=['Freedom Average'], inplace=True, ascending=False)\n",
    "Free_df\n",
    "\n",
    "# Filter the DataFrame down only to those columns to chart\n",
    "Freedom_plot = Free_df[[\"Country\", \"Freedom Average\"]]\n",
    "\n",
    "# Set the index to be \"Country\" so they will be used as labels\n",
    "Freedom_plot = Freedom_plot.set_index(\"Country\")\n",
    "Freedom_plot.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Freedom Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>0.6558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>0.6380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cambodia</th>\n",
       "      <td>0.6378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>0.6258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>0.6216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>0.6208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>0.6180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>0.6176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>0.6176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>0.6110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Freedom Average\n",
       "Country                     \n",
       "Uzbekistan            0.6558\n",
       "Norway                0.6380\n",
       "Cambodia              0.6378\n",
       "Denmark               0.6258\n",
       "Finland               0.6216\n",
       "Switzerland           0.6208\n",
       "Iceland               0.6180\n",
       "New Zealand           0.6176\n",
       "Sweden                0.6176\n",
       "Canada                0.6110"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List top 10 Countries with the highest average Freedom Average\n",
    "highest_10_Freedom = Freedom_plot.nlargest(10, ['Freedom Average'])\n",
    "highest_10_Freedom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use DataFrame.plot() in order to create a bar chart of the data\n",
    "highest_10_Freedom.plot(kind=\"bar\", figsize=(5,3))\n",
    "\n",
    "# Set a title for the chart\n",
    "plt.title(\"Average Freedom Score Per Country\")\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Freedom Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Syria</th>\n",
       "      <td>0.0818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>0.0858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haiti</th>\n",
       "      <td>0.0892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burundi</th>\n",
       "      <td>0.1014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0.1178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mauritania</th>\n",
       "      <td>0.1320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>0.1688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>0.1766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madagascar</th>\n",
       "      <td>0.1774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chad</th>\n",
       "      <td>0.1776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Freedom Average\n",
       "Country                                \n",
       "Syria                            0.0818\n",
       "Greece                           0.0858\n",
       "Haiti                            0.0892\n",
       "Burundi                          0.1014\n",
       "Afghanistan                      0.1178\n",
       "Mauritania                       0.1320\n",
       "Ukraine                          0.1688\n",
       "Bosnia and Herzegovina           0.1766\n",
       "Madagascar                       0.1774\n",
       "Chad                             0.1776"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List top 10 Countries with the lowest average Freedom Average\n",
    "lowest_10_Freedom = Freedom_plot.nsmallest(10, ['Freedom Average'])\n",
    "lowest_10_Freedom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Happiness Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Happiness Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>3.5128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>4.7126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>5.6676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>6.4594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>4.3932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Happiness Average\n",
       "Country                       \n",
       "Afghanistan             3.5128\n",
       "Albania                 4.7126\n",
       "Algeria                 5.6676\n",
       "Argentina               6.4594\n",
       "Armenia                 4.3932"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of Happiness Score per year\n",
    "Happiness_df = data[[\"Country\", \"Happiness Score 2015\", \"Happiness Score 2016\", \"Happiness Score 2017\", \"Happiness Score 2018\", \"Happiness Score 2019\"]]\n",
    "Happiness_df = Happiness_df.groupby(\"Country\")\n",
    "Happiness_df.mean()\n",
    "\n",
    "#Find the averages of the data\n",
    "Happ_df = Happiness_df.mean().reset_index()\n",
    "Happ_df[\"Happiness Average\"] = Happ_df.mean(axis=1)\n",
    "Happ_df\n",
    "\n",
    "# Filter the DataFrame down only to those columns to chart\n",
    "Happiness_plot = Happ_df[[\"Country\", \"Happiness Average\"]]\n",
    "\n",
    "# Set the index to be \"Country\" so they will be used as labels\n",
    "Happiness_plot = Happiness_plot.set_index(\"Country\")\n",
    "Happiness_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Happiness Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>7.5460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>7.5410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>7.5378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>7.5114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>7.5110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>7.4046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>7.3506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>7.3192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>7.3130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>7.2762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Happiness Average\n",
       "Country                       \n",
       "Denmark                 7.5460\n",
       "Norway                  7.5410\n",
       "Finland                 7.5378\n",
       "Switzerland             7.5114\n",
       "Iceland                 7.5110\n",
       "Netherlands             7.4046\n",
       "Canada                  7.3506\n",
       "Sweden                  7.3192\n",
       "New Zealand             7.3130\n",
       "Australia               7.2762"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List top 10 Countries with the highest average Happiness Average\n",
    "highest_10_Happiness = Happiness_plot.nlargest(10, ['Happiness Average'])\n",
    "highest_10_Happiness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use DataFrame.plot() in order to create a bar chart of the data\n",
    "\n",
    "#highest_10_Happiness.plot(kind=\"bar\", figsize=(5,3))\n",
    "highest_10_Happiness.plot(kind=\"bar\", figsize=(7,5))\n",
    "\n",
    "plt.ylim(7.2, 7.6)\n",
    "#plt.yticks(np.arange(7.2, 7.6, step=1))\n",
    "\n",
    "# Set a title for the chart\n",
    "plt.title(\"Average Happiness Score Per Country\")\n",
    "\n",
    "# Save and Show plot\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Images/AverageHappiness_Country.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.5460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Norway</td>\n",
       "      <td>7.5410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Finland</td>\n",
       "      <td>7.5378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>7.5114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.5110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.4046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Canada</td>\n",
       "      <td>7.3506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>7.3192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>7.3130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Australia</td>\n",
       "      <td>7.2762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Happiness Average\n",
       "0      Denmark             7.5460\n",
       "1       Norway             7.5410\n",
       "2      Finland             7.5378\n",
       "3  Switzerland             7.5114\n",
       "4      Iceland             7.5110\n",
       "5  Netherlands             7.4046\n",
       "6       Canada             7.3506\n",
       "7       Sweden             7.3192\n",
       "8  New Zealand             7.3130\n",
       "9    Australia             7.2762"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#highest_10_Happiness.head(10)\n",
    "df=highest_10_Happiness.reset_index()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = df[\"Country\"]\n",
    "values = df[\"Happiness Average\"]\n",
    "\n",
    "# Change the bar colors here\n",
    "plt.bar(labels, values, color=['red', 'red', 'red', 'red', 'red', 'red', 'blue','red','green','green'])\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylim(7.2, 7.6)\n",
    "\n",
    "plt.title(\"Average Happiness Score Per Country\")\n",
    "plt.ylabel('Happiness Score (Avg)')\n",
    "plt.xlabel('Country')\n",
    "\n",
    "# Save and Show plot\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Images/AverageHappiness_Country.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Happiness Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Burundi</th>\n",
       "      <td>3.0790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Syria</th>\n",
       "      <td>3.2922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rwanda</th>\n",
       "      <td>3.4386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tanzania</th>\n",
       "      <td>3.4660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>3.5128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Togo</th>\n",
       "      <td>3.5442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>3.6258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madagascar</th>\n",
       "      <td>3.7454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>3.8300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liberia</th>\n",
       "      <td>3.8392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Happiness Average\n",
       "Country                       \n",
       "Burundi                 3.0790\n",
       "Syria                   3.2922\n",
       "Rwanda                  3.4386\n",
       "Tanzania                3.4660\n",
       "Afghanistan             3.5128\n",
       "Togo                    3.5442\n",
       "Yemen                   3.6258\n",
       "Madagascar              3.7454\n",
       "Botswana                3.8300\n",
       "Liberia                 3.8392"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List top 10 Countries with the lowest average Happiness Average\n",
    "lowest_10_Happiness = Happiness_plot.nsmallest(10, ['Happiness Average'])\n",
    "lowest_10_Happiness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Economy (GDP per Capita)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Economy GDP Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0.3570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0.9386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>1.0130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.1110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>0.8392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Economy GDP Average\n",
       "Country                         \n",
       "Afghanistan               0.3570\n",
       "Albania                   0.9386\n",
       "Algeria                   1.0130\n",
       "Argentina                 1.1110\n",
       "Armenia                   0.8392"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of Happiness per year\n",
    "Economy_df = data[[\"Country\", \"Economy (GDP per Capita) 2015\",\"Economy (GDP per Capita) 2016\", \"Economy (GDP per Capita) 2017\", \"Economy (GDP per Capita) 2018\", \"Economy (GDP per Capita) 2019\" ]]\n",
    "Economy_df = Economy_df.groupby(\"Country\")\n",
    "Economy_df.mean()\n",
    "\n",
    "#Find the averages of the data\n",
    "Econ_df = Economy_df.mean().reset_index()\n",
    "Econ_df[\"Economy GDP Average\"] = Econ_df.mean(axis=1)\n",
    "Econ_df\n",
    "\n",
    "# Filter the DataFrame down only to those columns to chart\n",
    "Economy_plot = Econ_df[[\"Country\", \"Economy GDP Average\"]]\n",
    "\n",
    "# Set the index to be \"Country\" so they will be used as labels\n",
    "Economy_plot = Economy_plot.set_index(\"Country\")\n",
    "Economy_plot.head()\n",
    "Economy_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Economy GDP Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Qatar</th>\n",
       "      <td>1.7436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>1.6378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>1.5922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kuwait</th>\n",
       "      <td>1.5556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>1.5192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>1.4722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>1.4604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>1.4560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>1.4396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>1.4110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Economy GDP Average\n",
       "Country                           \n",
       "Qatar                       1.7436\n",
       "Luxembourg                  1.6378\n",
       "Singapore                   1.5922\n",
       "Kuwait                      1.5556\n",
       "Norway                      1.5192\n",
       "Switzerland                 1.4722\n",
       "Ireland                     1.4604\n",
       "United States               1.4560\n",
       "Saudi Arabia                1.4396\n",
       "Netherlands                 1.4110"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List top 10 Countries with the highest average Economy GDP Averag\n",
    "highest_10_Economy = Economy_plot.nlargest(10, ['Economy GDP Average'])\n",
    "highest_10_Economy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JDB8+vEbyly5dytSpU3n33XeZP38+8+fP57rrrmPcuHFI4tBDD+W8885jhx12oHXr1gAMHjyY0aNHV7RRbDPqs88+o3379pSVlXHrrbeyYkVIkrjnnnsyceJEVq5cyUcffcQTTzwBQJcuXVi0aFGlqfLLL79co/uoDh8ROpknt0aYY//99+eKK65g/PjxnHXWWXz55Ze0bNmSRx55hNNPP51TTz2VHj160LRpU8aMGVNpJFiV9u3b87vf/Y5BgwZhZhx44IEVI7easvPOO7PRRhsxcuTIguc333xzxo8fzwUXXMD7779P27ZtadOmTcU6I4SR12233cby5cvp3r07jz32WMV0fU3cfffd7L333pXuc+jQoZx//vl8/fXXDBs2jL59+1asZQL85S9/4YwzzqBnz54VD5AbbrhhtbZPP/10Dj/8cO666y4GDRpUMVo8/PDDefTRR+nevTvbbbcd/fr1Y+ONN2a99dZjwoQJnH322Xz22WeUl5fzox/9iG7dutXoXorRIHOW9OnTx4rFIyylQbUbb6fPq6++yg47FMzz7kQWLlzIwIEDee211ygry84k7vPPP6dVq1YsWbKEXXbZhWnTprH55pvX+PpC3y1JMwoFgPERoeM0YG655RYuvPBCrrrqqkwpQYAhQ4bw6aef8s033/DLX/6yVkqwtrgidJwGzIgRIxgxYkR9d6NeyK0LloJsPWIcx3EK4CPCBkRW1ySLmao4ztpS270PHxE69UqLFi1YsmRJrb+4jlOMXDzCnE1iTfARoVOvbLXVVixYsACPQekkSS5CdU1xRejUK82aNatxFGHHSQtXhBkmq2uSjlOVNa4RSrpZ0seSCgZVlTRQ0meSZsXXxXnn9pf0uqR5kn6WZMcdx3GSos7pPCNP5VJ6mtmlAJKaANcR8pV0BYZL6lqXzjqO46RB0uk889kFmGdmbwFIugMYCrxS7VXOOotPxZ2GSlLmM7tJmi3pIUk57+ctgffy6iyIZY7jOA2KJDZLZgIdzexzSQcC9wCdgUIWskWNxSSdApwC0KFDhwS65TiOUzPqPCI0s6Vm9nl8/yDQTFIbwgjwO3lVtwIWVtPOjWbWx8z61DQ8kOM4ThLUeUQoaXPgIzMzSbsQlOsS4FOgs6StgfcJeY+Pqas8x6kpvibp1JQk0nkeAZwmqRz4Ejjagr9UuaQzgf8ATYCbzazuoWQdp4FSasXrij45arJrXG08bzMbDYwucu5B4MG165rjOE5pcM8Sx3FqxLo8AvXoM47jZB4fETqO0yAp5QjUR4SO42QeV4SO42QeV4SO42QeV4SO42QeV4SO42QeV4SO42QeV4SO42QeV4SO42QeV4SO42QeV4SO42SeJLLYHStpTnw9LalX3rn5kubG7HYvJNlxx3GcpEgii93bwAAz6wlcBtxY5fygmN2uz9p10XEcJ13qnMXOzJ7OO3yWEJLfcRyn0ZD0GuEPgIfyjg2YLGlGTM7kOI7T4EgsDJekQQRFuGde8R5mtlBSW+BhSa+Z2ZQi13sWO8dx6oVERoSSegI3AUPNbEmu3MwWxr8fA5MISd8L4lnsHMepL+qsCCV1AO4GjjezN/LKN5C0Ye49MBgouPPsOI5TnySRxe5ioDVwvSSA8rhD3A6YFMuaAmPN7N8p3IPjOE6dSCKL3ShgVIHyt4Beq1/hOI7TsHDPEsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMk8SWewk6S+S5sVMdr3zzu0v6fV47mdJdtxxHCcpkshidwDQOb5OAf4KIKkJcF083xUYLqlrXTrrOI6TBmtUhDHHyP+qqTIUuMUCzwKbSGpPCMs/z8zeMrNvgDtiXcdxnAZFEmuEWwLv5R0viGXFyh3HcRoUSShCFSizasoLNyKdIukFSS8sWrQogW45juPUjCQU4QLgO3nHWwELqykviGexcxynvkhCEd4HjIi7x7sCn5nZB8B0oLOkrSWtBxwd6zqO4zQokshi9yBwIDAP+AIYGc+VSzoT+A/QBLjZzF5O4R4cx3HqRBJZ7Aw4o8i5BwmK0nEcp8HiniWO42QeV4SO42QeV4SO42QeV4SO42QeV4SO42QeV4SO42QeV4SO42QeV4SO42QeV4SO42QeV4SO42QeV4SO42QeV4SO42QeV4SO42SeGinCNWWjk/RTSbPi6yVJKyRtGs/NlzQ3nnsh6RtwHMepKzWJR5jLRrcfIer0dEn3mdkruTpmdiVwZax/MHCumeUnfBpkZosT7bnjOE5C1GREWNtsdMOBcUl0znEcpxTURBHWOBudpPUJOZAn5hUbMFnSDEmnrG1HHcdx0mKNU2Nql43uYGBalWnxHma2UFJb4GFJr8VcyZWFBCV5CkCHDh1q0C3HcZxkqMmIsDbZ6I6myrTYzBbGvx8DkwhT7dXwLHaO49QXNVGENcpGJ2ljYABwb17ZBpI2zL0HBgMvJdFxx3GcpKhJ8qaC2egknRrP3xCrHgpMNrPleZe3AyZJyskaa2b/TvIGHMdx6kpN1ggLZqPLU4C54zHAmCplbwG96tRDx3GclHHPEsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMo8rQsdxMk9S6TwHSvosL6XnxTW91nEcp75JJJ1n5CkzG7KW1zqO49QbaaTzTOpax3GckpBkOs/dJM2W9JCkbrW81nEcp95IKp3nTKCjmX0u6UDgHqBzDa8NQjydp+M49UQi6TzNbKmZfR7fPwg0k9SmJtfmteHpPB3HqRcSSecpaXPFVHWSdontLqnJtY7jOPVNUuk8jwBOk1QOfAkcbWYGFLw2pXtxHMdZKxJJ52lmo4HRNb3WcRynIeGeJY7jZB5XhI7jZB5XhI7jZB5XhI7jZB5XhI7jZB5XhI7jZB5XhI7jZB5XhI7jZB5XhI7jZB5XhI7jZB5XhI7jZB5XhI7jZB5XhI7jZJ6kstgdK2lOfD0tqVfeufmS5sbsdi8k2XnHcZwkSCqL3dvAADP7RNIBwI1Av7zzg8xscYL9dhzHSYxEstiZ2dNm9kk8fJYQkt9xHKdRkGQWuxw/AB7KOzZgsqQZMUGT4zhOgyKpLHahojSIoAj3zCvew8wWSmoLPCzpNTObUuBaz2LnOE69kEgWOwBJPYGbgKFmtiRXbmYL49+PgUmEqfZqeBY7x3Hqi6Sy2HUA7gaON7M38so3kLRh7j0wGHgpqc47juMkQVJZ7C4GWgPXx6ye5WbWB2gHTIplTYGxZvbvVO7EcRxnLUkqi90oYFSB694CelUtdxzHaUi4Z4njOJnHFaHjOJnHFaHjOJnHFaHjOJnHFaHjOJnHFaHjOJnHFaHjOJnHFaHjOJnHFaHjOJnHFaHjOJnHFaHjOJnHFaHjOJnHFaHjOJknqSx2kvSXeH6OpN41vdZxHKe+WaMizMtidwDQFRguqWuVagcAnePrFOCvtbjWcRynXkkki108vsUCzwKbSGpfw2sdx3HqlaSy2BWrU9sMeI7jOCUnqSx2xerUJgNeRRY74HNJr9egb1VpAxRMJK/fr0VrDUeWy3N5Li8ZeR0LFdZEEdYki12xOuvV4FogZLEDbqxBf4oi6YWYKyV1SinL5bk8l5euvESy2MXjEXH3eFfgMzP7oIbXOo7j1CtJZbF7EDgQmAd8AYys7tpU7sRxHGctSSqLnQFn1PTaFKnT1LoBy3J5Ls/lpShPQYc5juNkF3excxwn87gidBwn87gidJx1DEllkjaq7340JhrtGqGkMmCOmXUvoczeBYo/A94xs/JS9SMtJG1tZm+vqSxBeUOAB81sZRrtZwlJY4FTgRXADGBj4CozuzIleS2AHwDdgBa5cjM7KQ15UeYewCwzWy7pOKA3cI2ZvVPnthurIgSQdDvwczN7t0TyniV8+HMIXjPd4/vWwKlmNjlheesDPwY6mNnJkjoDXczsgSTl5MmbaWa9q5TNMLOdU5J3G7AbMBH4p5m9mpKcw6o7b2Z3Jyzvfop4UEV5309SXpQ5y8x2lHQssDNwATDDzHomLSvKuwt4DTgGuBQ4FnjVzM5JQ16UOQfoBfQEbgX+ARxmZgPq2naNzGcaMO2BlyU9DyzPFabxRYvMB36Qs4WMkXR+ClwG3A0kqgiBfxKe7rvF4wXAXUCiilDS9oQn+8ZVlMZG5D3tk8bMjotTuOHAPyUZ4Z7HmdmyBEUdHP+2BXYHHovHg4AnCP+7JPlj/HsYsDlwWzweTvgOpUEzSc2AQ4DRZvZt/DzTYlszO1LSUDP7VxyR/idFeQDlZmaShhJGgv+QdEISDTd2RfjrEsvbPt8g3MxekbSTmb0lFXKrrjPbmNkwScOjvC+VjqAuwBBgE1YpDYBlwMkpyKvAzJZKmgi0BH4EHAr8VNJfzOzahGSMBJD0ANA1ej0RIyRdl4SMKvKejO1fZmb9807dL2lK0vIifyMo2dnAFEkdgaUpyQL4Nv79VFJ34EOgU4ryAJZJ+jlwHNA/hvlrlkTDjVoR5r5wJeQNSX8lhBMDGBbLmrPqi5Ek30hqSZxmSdoG+DppIWZ2L3CvpN3M7Jmk2y+GpIOBk4BtCFOdXczs47gk8CqQiCLMo1NOCUY+ArZLWEY+m0n6rpm9BWG9FdgsDUFm9hfgL3lF70galIasyI2S/h/wS4LbbCvg4hTlQfi9HUOYlX0oqQOQyBpoY18j3JXwY9mBEOChCbDczFLZMYtK6XRgT8Ia4VTgeuArYH0z+zxhefsBFxGC2k4G9gBONLMnEpZzvpn9QdK1FFjbMrOzk5SXJ/cW4CYzW22UJGkfM3s0YXmjCcGDxxHu82hCvMyzkpSTJ29/ggfEW7GoE/BDM0t8CimpHfBbYAszOyAu2+xmZv9IWta6SGNXhC8Qvsx3AX2AEUBnM/tFCrKaAP8xs32TbruIvDLgCOBRYFeC4n3WzAqGHqqjrIPN7P5i6y1m9q+kZdYXcQ10r3g4xcwmpSyvObB9PHzNzBIf0Uc5DxHWVy80s16SmgIvmlmPhOUcZ2a3STqv0HkzuypJeVHmMqrffKrzwKdRT40BzGyepCZmtoKw4P50SnJWSPpC0sZm9lkaMqrIWynpTDO7E/i/lGXdH/+WVOGVekQPFTvESW+OVMfOhJFgU6CXJMzslhTktDGzO+MaWi7gyYoU5GwQ/26YQtsFMbMNASRdSliLvJUwMDg2qX40dkX4RQzvNUvSH4APWPWPSoOvgLmSHqbyLnUqU0fgYUk/AcZXkfe/NIRJ2oxgdtGVyrZhe6chDxjN6iP6bVOSlRsN/p6we6z4shSXUm4lrH/OItj3QRjZpKEIl0tqzar15F0JNq6JYmZ/i39LvVEJ8D0z65d3/FdJzwF/qGvDjV0RHk/wjjkTOJcQBLZam7E68n+kPDqrQs44NT+yjwHfTUne7QSlexDBOPcEYFFKsoDSjegjfwAOTstesQB9CLvUpVh/Oo+wabGNpGmETZkj0xIm6bvANYRlGwOeAc7NbQylxIpoJ3lHlDmcVQ+YumFmjfYFnFOTMn/V+POcEf/OySt7MkV5UwhT4lsISupcYHaK8qaV+PO8C2hfIlnNCQObbgRD/2ZA8xTlPUsYiDSNr+OA51K+x07AvYQQ/YuAewiWAHVuu7FvlhTyhHjRzHZKSd7bFN5VTWWEFg1kTwNytmhPAH8zszRMdZD0rJntKuk/BFOMhcAEM9smJXkdCSYs6xGU4MbA9WY2LyV51xAMnO8hzwzJEvYsyZP3OLAj8HwVeWl4lhT6LaxWlqC856zyNLXi+5OGvLRplFPjaGB8DLC1pPzQ/xsCS1IUnZ8joQVh6rFpivL+SniyXx+Pj49lo1KS9xtJGxPc+q4leJacm5IsCOtni8xsKaUxjt+IEEF9cF6Zkd7mySUptVuBpM0JmSFbStqJVQnTNgLWT0Fe7vv+uKSfsWqaOoyUl43iGvbJrNp8ApLxb26UI8I4ktga+B3ws7xTywjTupIFQJA01cz2TKnt2WbWa01lCRL1wG4AABJpSURBVMprYWZfpdF2EXm3ENaYlgBPxddUM/ukVH1o7ESTpxMJD+kX8k4tA8YkPdrNmxUVzFCZ1uwoyn6a8B2ZQd7aoJlNrHPbjVER1heqHH2mjPDlOy1FxTQTONLM3ozH3yVMVdOa7swjTFWfIqzfTbMSmApJ2oJgM/kTgkFwKjMVlThiSinNgyQdnoRCaMgoBpZIo+1GOTXOUQ92aH/Ke19O8O08KiVZEAI6PC7pLcITuCMxMVYamNm20W1pL4Lv8fWSPk3ry6cQSmkvoAdhAXw0QQmnxa2EiCnfIy9iSoryCpkHdU5DkJlNlHQQqyv5S9OQBxB9jKuaWqVhGpTjAUkHWsiDlCiNekRYxLNkWzO7sF47liDRM6ELQRGm5pkQZW1FUEwDCOGO/keYqv4uJXmLgTeBG4DHzWx+GnLy5L1oZjtJmmNmPeNm1H8sJTtJxdy7OXmx7Gkz2z0FWTcQ1gQHATcRRtjPm9kPkpYV5f0KGEhQhA8CBxC+K0ekIS/KXEawE/6a4NufmB1oox4RQmnt0OJGwq9YtYv7JHBpWtPHOJXL+TYb8JSkG1Jcx3uXkIv6t2Z2akoyKjCzNpK6ET7PyxXiLb5uZsenJLLUEVNKafC/e1Tuc8zs15L+RLoeNEcQHpYvmtnI6Ot8U4rysOhhkgaNXRGW2rPkZuAlVk2Hjyf4d6ZlxH0LYdE7F4VlOGF6l5ah7E4EpXtM3BH8L8GOMBXHfYVYhB0IU/5OBPOZNKNVlzpiyvGE5Zp8g//DU5L1Zfz7RVxzXULYUEyLLy24gZbH/+PHpGfoX0H8/3Wm8nS8zqHNGvvUuCvBTKAZwRuiGfDXFO3QVlusTXMBt9S7xrH9VgRluBfBSNbMrFNKsuYQIvhMJQRAWJCGnCwg6ZeEB+Y+hBiLRojs88uU5F0P/IKwNPVj4HNCGP3U1rAljQLOAbYiuC3uCjyTxNJGoxwRxrWdKwlrgvMJawXtgGvjVHknM3sxBdFfStrTzKbGfuzBqidxGrwoaVczezbK6wdMS0tYXHNtDjxNUE79LYF8EEVkNQEmm9lP0mi/iqyCkVJyWMIRUyTNpfpoKWmEz/9DXD+eqBCAtgXBNz5xJAn4nZl9Ctwg6d/ARmY2Jw15eZwD9CVEYRqkEFk9EfvTRqkICbu36wMdLYZ0j8PzPyoETt2fdKYFpwH/imuFImwmJBIqvAj9gBGScjlZOgCv5n5oKfygDjCzVH2Lc1iI5pOKB1ABShYpJTKkxPIg+Pr2BogK8etofpW4qZWZmaR7CJF1SHuTK4+vzOwrSUhqbmavSeqSRMONVREeSIg7WPHUtRDy/TSCGcYBaQg1s1mEUEob5WSmISeP/VNuvyqnqUAmgBRNMGZFz6C7qBxdJ9FF/rh50AQ428yuTrLtIvIqRtHR+L+zmT2iENg30d9cqT1L8nhWUl8zm56ijKoskLQJwUXyYUmfENxA60yjXCOU9IaZFQyxXt25BOS2Juwa53ZxpxJ2jdN060NSWyovDqeStU/Sj/MOWxBGNq+maHD8zwLFlqK8x80szfD1VeWdDJwCbGpm28Rd8RvMbJ8EZeR7lkxnlSJMxbMkT+4rhDQH7xAeYjlTllSy5hWQP4CwufZvM/umzu01UkV4D3B3VePNaKB7pJkNTUnuwwSPi1xWsmOBgZZS1GpJ3ycsA2xB2JXrSFBM3dKQV0B+c+A+M/teKeSljaTLCT+eqvEdZ6YkbxawCyEqy06xbK4lHDU6tltSz5I40l2NNNaUtcq/uSCWQHzOxqoItyTYSH1J8Ds0wiJqS+BQM3s/Jbmr5fjNGc2mJG82sDfwSDQEHgQMN7NT0pBXQP7/IxjlpuINEQ24ryXkYsmNsM9Ja/dYIRpMVSxFg+rnzKxfniF3U2BmkqMmhQRYc3IKSNLFBBOddwif5dtJyaqmDxsQ0ogeY2YHpdB+If/m3LFZAv7NjXKNMCq6fpL2JrgUCXjIEk72U4DHJR0N3BmPjyDdiBvfmtkSSWWSyszscUm/T0tYld3OJoTgnqm5aBFsMMeyyi7yuFi2XxrCSjktjjwp6ReE9bv9CMbx9ycs43KCGQmShhA+w+EEm9AbCO6EiRPtdw8kRIHaH5gY5SWOmW0dd6q/k9qyUGMcEZYarUoeI4LBds7otwz4PAkXnyJyHyE8aX8HtCFMj/sm7aIlaSszW1BlulNOCMBwgMWcJklTD3aZJc30Fn+8owhhv0RIgH6TJfijy7crlXQzwTPn9/E48XiEUaEPJyjYxwnLDNemZWtaRfZqM7LEsBJG7PVXrSPybkAYmTUlmOmcDbROQc7rFIj0Swjw8GaK9/cIYQTTJL6OAx5NUd5DBK+g2fG4KTA3JVllwEsl+I7MIXjIlBGmw33yzr2SgryVBNfSrfPK3kr7PqOc6wgDgcTbbpRT4/pEUk9WDwyZys6cmS3PO0wzw9y5BHOEA83svwAK2dCOIQRgSIuTCBFariaMuJ9mVZ6WNChVpjcsuJ/NltTBUprORf5M8LJYSthIewEgmtJ8UN2Fa8nOBG+SRxSiIt1BeIiVgkHAqZLmk/BOtSvCWhCnHj2Bl1k1PU4twrEK53P9jBCA88eWUKIcM3tQ0tfAQ5IOIUzn+hI8S1ILkhoVROJh66uhJJne8mgPvCzpeSrvUid2z2Z2s0JqhbbA7LxTH5JCyDYLHlsvAhdEz6rhwHoKeZUnmdmNScvMIxX7YPA1wloh6RUz61pCeb8mGIyOJTz9jibk3HidEBB2YMLy9iQYqz4NHGUpRbmJO5vFMDO7LCW5OxNysXQnBM/YjGBuNbvaC9deXsHRtJk9mYa8+kJSGWGD62hL0dc4ytqTYKD+T4XQ/a0sgZ1xV4S1QNI/gD+Z2Sslklc0QY4SDL5QZTOoOSFc1QoSjPdWRd6PCxRvQIge3drMWiUpr4rspqyK7/i6pZQIy0kehRiIfYAuZradQpSdu8xsj7q27VPj2vEv4BlJHxKCQ6ZtTb9S0lHAhHicH/QysSeYpRjnrYi8ikjfkjYkONOPJKw3/anYdXVF0pvAlWZ2Q17ZA2aWqG9wkSUNSOnBkiEOJZgFzQQws4Xx+1NnXBHWjpsJMebmkm7cvBzHEpJoX0/4YT0LHBd9Vs8sgfzUiN4C5xHu8V9A7zTXIyPfAoMUovj80IJr1pZJCynlg6UUXhcNiG/MzCTl1ngTiz3qirB2vGtm9625WjLEzZCDi5yeWqp+JI2kKwnBbG8EepjZ5yUS/YWZDZN0PiHa91EkOLKuJ3KeVSJEJ/okvt+EEHE80ShMkjayEOCkoAJOWfHeKelvwCbRj/sk4O9JNOxrhLVAIRjlJgTvgFIkCE8tj2t9Imkl4fMrp7IiSnXqmHN1i+9zAUw3NbO2acgrJQo5S+6zmNhI0gHAvmZWaD22LnIeMLMhxdzeLMV0nlH+fuQZqJvZw4m064qw5qj00VJSy+OaRSQdbHleMtGT5gRLMdNbqSi1H/y6hivCBkya7mZZRNKjViUEVqGyxki0JXyKEBnJCF46/S3hyEGqnNt7NSylSD5R9mHA7wk2kyLBGYSvEdYCSdsBfwXamVn36GXyfTP7TUoiU8vjmiUUsgGuD7SJEXXyg5duUW8dS5bhhFiZkwiKcEosS5rcrn4LginLbMLn2RN4jhCrMy3+ABxsZonnovYRYS2Q9CQh6frf8taaXjKz7inJSy2Pa5aQdA7wI4LSy49ovBT4u5mNrpeOpYCkVqXYfJJ0B3C5mc2Nx92Bn5jZiSnKnJaEzWDBtl0R1hxJ082sb5VFd5++NhIknWVm1665ZuND0u6EvMKtzKyDpF4EE6HTU5JXsshBcUoMwe99c4L3U6KblT41rh2LJW3DKl/VI0jBsV3S9hYS0xRcj0lzHWZdRNLeZvYY8H7ej6qCtHb9S8zVhNBY9wGY2WxJ/VOU96qkm6i8Jpn4lDWSb0L2BWHXOEcivv6uCGvHGQTbt+0lvQ+8TfgCJM15hFwX+V4W+UP3VCIqr8MMAB6jsE1makEzSo2ZvafKybdSiawTGUnI6nhOPJ5CWD9PnJz/sqQ9zKxSOtsY+KHO+NS4FkhqYSGd4AZAmZktk9TGzBYnLGcXgvH2h/H4BEL49fnAJeuYt4CTAJImAFcRwprtSohd2cfMjq7XjiVIoUCzSQWf9RFh7Zgu6WRblXD9cEL06KSz5t0A7Btl9I8yzgJ2JIxIjyh+qVOM6Gv8LMHMZEqpgmeUiFMJ7phbAguAyYTUAKmQZ1BdiTQMqiXtBuwObCbpvLxTG5FQLERXhLXjGOBmSU8QdiBbk840tUneqG8YcGM0op6okBnNWTu6Av2AvYA/StqeEK360PrtViJ0MbNj8wvitHFakfp1Jd9QuwUh70y1fs91YD1CFO6mQL4f91ISGhT41LiWxMCltxLyxvY3s3kpyHgJ2NFCBOXXgFPMbEruXFrmOus6MQRXX8Ka4Z6EB9kcM/thvXYsAdKcNtaiD1PNLDU7QkkdzewdSRtY5ejtdcZHhLUgxiPchmA8uh1wv6TRZnZdwqLGETKgLSakLH0qyt+WdCMqr+ssJUQOuopgP7iknvtTZ0oxbSwiN1/BlhFGiGlH3dlCIRJ2KyBREyFXhLXjJWCUhWH02wqh3q9KWoiZXS7pUUKo98m2atheRlgrdNaO4YSR4OnAqOjLPcXSTwObJqlPG4uQb9FQTtjIOypFeRDys6RiIuRTYydzxLXBAwjeJm3NrGU9d6nO5KaN9d2PNFGM2F7FoSGRSO0+IqwFpdwpc5JH0kTCzvs8wnLD8cDz9dqpOiLpz2b2I2B0LmBpPpZgoqgCsg8CuhE2S3Ly0ozk8170oDGFBPNnk5ARtyvC2lHKnTInIST1Bd4DriCEeT+OYJfZlrDckUqSqhJxa/z7x1IKjfEP1yek2LyJMA1P+6FSyETojCQa9qlxHUl7p8ypO5JmEoKU/i+uKd3BKrvMHczM7TJriaQ5ZtYz728r4G4zG7zGixsgPiKsBfW0U+bUnXXeLjPaDF4CdCT8rnORitJatvky/v1CIZvcEhJOC5BDJUj/6oqwdlTdKXubMD12GjZNJDU1s3JgH4Ifd4515TfwD+BcqkQzT5EHJG0CXElYbjASyh9SgEI2gxXpX4E6K0KfGtcRST8ysz/Xdz+c4ki6EDgQWExIcNTbzCzaZf4rrRh3pUQFcmCXUHZzoIWZpW7jqlXpX38A3EnIM/5xndt1RVg3JL1rZh3qux9O9USbz5xd5vJYth0hfl+jD2sm6QqCAfXdVI7Vl+i95Tae8gKCjCBsPL1DigFBtHr612sswfSvrgjriKT3zOw79d0PJ9tIerxAsZlZor7w9bHxpMrpX69LIwK3K8I64iNCJ0vkGzBLug5YZGaXxOO0IlSnnv51XVkoTpWYO6TQE0NAo/dKcBovVfyLIXxPFwNTzeztFESWfOPJzMrSaDcfV4Q1wMzcRMZpqBT6bnYCLpR0iZndkbC8dTIgiE+NHWcdJG4uPJJGGK51cePJFaHjrKPkBydwqif1ubfjOKVH0t5AYuYl6zq+Rug4jRhJc1l9I29TQiL7EaXvUePEp8aO04iR1LFKkQFLkg5lv67jitBxnMzja4SO42QeV4SO42QeV4ROvSBpc0l3SHpT0iuSHoy2aEm1PzCGdXecNeKK0Ck5kgRMAp4ws23MrCvwC6BdgmIGEtJcFpLv1hJOJVwROvXBIOBbM7shV2Bms4Cpkq6U9JKkuZKGQcXo7oFcXUmjJZ0Y38+X9GtJM+M120vqRMhvca6kWZL2kjRG0lUxSsuVkv4rabPYRpmkeZLalOoDcBoW/mR06oPuhEjKVTmMEM6pF9AGmC5pSg3aW2xmvSWdDvzEzEbF5EKfm9kfAST9ANiOEEJqhaRPCbHt/gzsC8w2s8V1vjOnUeIjQqchsScwzsxWmNlHwJNA3xpcd3f8O4MQcKAYd5lZLoz9zawyOD4J+Gftu+usK7gidOqDl4GdC5SrSP1yKn9XW1Q5n4vIvILqZzkVRsZm9h7wUXRF6wc8VF2HnXUbV4ROffAY0FzSybmCGAL+E2CYpCZx/a4/IVfuO0BXSc0lbUyIg7cmlrHmDIM3AbcBd+aNFJ0M4orQKTkW3JkOBfaL5jMvE1JRjgXmALMJyvJ8M/swjt7ujOduB16sgZj7gUNzmyVF6twHtMKnxZnHXeyczCKpD3C1mRVTlE5G8F1jJ5NI+hlwGmHn2Mk4PiJ0HCfz+Bqh4ziZxxWh4ziZxxWh4ziZxxWh4ziZxxWh4ziZxxWh4ziZ5/8DSM8yBWxy+EoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use DataFrame.plot() in order to create a bar chart of the data\n",
    "highest_10_Economy.plot(kind=\"bar\", figsize=(5,3))\n",
    "\n",
    "# Set a title for the chart\n",
    "plt.title(\"Average GDP Per Country\")\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Economy GDP Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Burundi</th>\n",
       "      <td>0.0624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Congo (Kinshasa)</th>\n",
       "      <td>0.0624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liberia</th>\n",
       "      <td>0.0892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niger</th>\n",
       "      <td>0.1266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malawi</th>\n",
       "      <td>0.1426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Togo</th>\n",
       "      <td>0.2658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madagascar</th>\n",
       "      <td>0.2660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guinea</th>\n",
       "      <td>0.2734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ethiopia</th>\n",
       "      <td>0.2934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burkina Faso</th>\n",
       "      <td>0.3146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Economy GDP Average\n",
       "Country                              \n",
       "Burundi                        0.0624\n",
       "Congo (Kinshasa)               0.0624\n",
       "Liberia                        0.0892\n",
       "Niger                          0.1266\n",
       "Malawi                         0.1426\n",
       "Togo                           0.2658\n",
       "Madagascar                     0.2660\n",
       "Guinea                         0.2734\n",
       "Ethiopia                       0.2934\n",
       "Burkina Faso                   0.3146"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List top 10 Countries with the lowest average Economy GDP Averag\n",
    "lowest_10_Economy = Economy_plot.nsmallest(10, ['Economy GDP Average'])\n",
    "lowest_10_Economy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge Averages to one dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gov Trust Average</th>\n",
       "      <th>Generocity Average</th>\n",
       "      <th>Health Average</th>\n",
       "      <th>Family Average</th>\n",
       "      <th>Economy GDP Average</th>\n",
       "      <th>Freedom Average</th>\n",
       "      <th>Happiness Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>0.4296</td>\n",
       "      <td>0.3188</td>\n",
       "      <td>0.8654</td>\n",
       "      <td>1.4478</td>\n",
       "      <td>1.3966</td>\n",
       "      <td>0.6258</td>\n",
       "      <td>7.5460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>0.3440</td>\n",
       "      <td>0.3290</td>\n",
       "      <td>0.8734</td>\n",
       "      <td>1.4312</td>\n",
       "      <td>1.5192</td>\n",
       "      <td>0.6380</td>\n",
       "      <td>7.5410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>0.3986</td>\n",
       "      <td>0.2178</td>\n",
       "      <td>0.8738</td>\n",
       "      <td>1.4344</td>\n",
       "      <td>1.3570</td>\n",
       "      <td>0.6216</td>\n",
       "      <td>7.5378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>0.3798</td>\n",
       "      <td>0.2776</td>\n",
       "      <td>0.9282</td>\n",
       "      <td>1.4174</td>\n",
       "      <td>1.4722</td>\n",
       "      <td>0.6208</td>\n",
       "      <td>7.5114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>0.1402</td>\n",
       "      <td>0.4192</td>\n",
       "      <td>0.9178</td>\n",
       "      <td>1.4928</td>\n",
       "      <td>1.3866</td>\n",
       "      <td>0.6180</td>\n",
       "      <td>7.5110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>0.2986</td>\n",
       "      <td>0.4150</td>\n",
       "      <td>0.8786</td>\n",
       "      <td>1.3496</td>\n",
       "      <td>1.4110</td>\n",
       "      <td>0.5896</td>\n",
       "      <td>7.4046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>0.3058</td>\n",
       "      <td>0.3896</td>\n",
       "      <td>0.9008</td>\n",
       "      <td>1.3874</td>\n",
       "      <td>1.3880</td>\n",
       "      <td>0.6110</td>\n",
       "      <td>7.3506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>0.3974</td>\n",
       "      <td>0.3366</td>\n",
       "      <td>0.8990</td>\n",
       "      <td>1.3686</td>\n",
       "      <td>1.4040</td>\n",
       "      <td>0.6176</td>\n",
       "      <td>7.3192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.4328</td>\n",
       "      <td>0.8916</td>\n",
       "      <td>1.4398</td>\n",
       "      <td>1.3176</td>\n",
       "      <td>0.6176</td>\n",
       "      <td>7.3130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>0.3144</td>\n",
       "      <td>0.4162</td>\n",
       "      <td>0.9146</td>\n",
       "      <td>1.4090</td>\n",
       "      <td>1.3948</td>\n",
       "      <td>0.6050</td>\n",
       "      <td>7.2762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Gov Trust Average  Generocity Average  Health Average  \\\n",
       "Country                                                              \n",
       "Denmark                 0.4296              0.3188          0.8654   \n",
       "Norway                  0.3440              0.3290          0.8734   \n",
       "Finland                 0.3986              0.2178          0.8738   \n",
       "Switzerland             0.3798              0.2776          0.9282   \n",
       "Iceland                 0.1402              0.4192          0.9178   \n",
       "Netherlands             0.2986              0.4150          0.8786   \n",
       "Canada                  0.3058              0.3896          0.9008   \n",
       "Sweden                  0.3974              0.3366          0.8990   \n",
       "New Zealand             0.4000              0.4328          0.8916   \n",
       "Australia               0.3144              0.4162          0.9146   \n",
       "\n",
       "             Family Average  Economy GDP Average  Freedom Average  \\\n",
       "Country                                                             \n",
       "Denmark              1.4478               1.3966           0.6258   \n",
       "Norway               1.4312               1.5192           0.6380   \n",
       "Finland              1.4344               1.3570           0.6216   \n",
       "Switzerland          1.4174               1.4722           0.6208   \n",
       "Iceland              1.4928               1.3866           0.6180   \n",
       "Netherlands          1.3496               1.4110           0.5896   \n",
       "Canada               1.3874               1.3880           0.6110   \n",
       "Sweden               1.3686               1.4040           0.6176   \n",
       "New Zealand          1.4398               1.3176           0.6176   \n",
       "Australia            1.4090               1.3948           0.6050   \n",
       "\n",
       "             Happiness Average  \n",
       "Country                         \n",
       "Denmark                 7.5460  \n",
       "Norway                  7.5410  \n",
       "Finland                 7.5378  \n",
       "Switzerland             7.5114  \n",
       "Iceland                 7.5110  \n",
       "Netherlands             7.4046  \n",
       "Canada                  7.3506  \n",
       "Sweden                  7.3192  \n",
       "New Zealand             7.3130  \n",
       "Australia               7.2762  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge all average columns together and order in top 10 Countries with highest Happiness Average\n",
    "# Merging Data on Country\n",
    "Comparison_data = pd.merge(GOV_plot, Gen_plot, on = [\"Country\"], how = \"outer\")\n",
    "Comparison_data = pd.merge(Comparison_data, Health_plot, on = [\"Country\"], how = \"outer\")\n",
    "Comparison_data = pd.merge(Comparison_data, Family_plot, on = [\"Country\"], how = \"outer\")\n",
    "Comparison_data = pd.merge(Comparison_data, Economy_plot, on = [\"Country\"], how = \"outer\")\n",
    "Comparison_data = pd.merge(Comparison_data, Freedom_plot, on = [\"Country\"], how = \"outer\")\n",
    "Comparison_data = pd.merge(Comparison_data, Happiness_plot, on = [\"Country\"], how = \"outer\")\n",
    "\n",
    "top_ten_Comparison_data = Comparison_data.nlargest(10, ['Happiness Average'])\n",
    "top_ten_Comparison_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export to CSV\n",
    "Comparison_data.to_csv(\"Resources/Country_output.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Gov Trust Average</th>\n",
       "      <th>Generocity Average</th>\n",
       "      <th>Health Average</th>\n",
       "      <th>Family Average</th>\n",
       "      <th>Economy GDP Average</th>\n",
       "      <th>Freedom Average</th>\n",
       "      <th>Happiness Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Qatar</th>\n",
       "      <td>0.3558</td>\n",
       "      <td>0.2912</td>\n",
       "      <td>0.7686</td>\n",
       "      <td>1.1698</td>\n",
       "      <td>1.7436</td>\n",
       "      <td>0.6040</td>\n",
       "      <td>6.4218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>0.3374</td>\n",
       "      <td>0.2458</td>\n",
       "      <td>0.9034</td>\n",
       "      <td>1.3434</td>\n",
       "      <td>1.6378</td>\n",
       "      <td>0.5840</td>\n",
       "      <td>6.9360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>0.4672</td>\n",
       "      <td>0.3032</td>\n",
       "      <td>1.0140</td>\n",
       "      <td>1.2312</td>\n",
       "      <td>1.5922</td>\n",
       "      <td>0.5536</td>\n",
       "      <td>6.5428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kuwait</th>\n",
       "      <td>0.1822</td>\n",
       "      <td>0.1718</td>\n",
       "      <td>0.6952</td>\n",
       "      <td>1.1848</td>\n",
       "      <td>1.5556</td>\n",
       "      <td>0.5060</td>\n",
       "      <td>6.1486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>0.3440</td>\n",
       "      <td>0.3290</td>\n",
       "      <td>0.8734</td>\n",
       "      <td>1.4312</td>\n",
       "      <td>1.5192</td>\n",
       "      <td>0.6380</td>\n",
       "      <td>7.5410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>0.3798</td>\n",
       "      <td>0.2776</td>\n",
       "      <td>0.9282</td>\n",
       "      <td>1.4174</td>\n",
       "      <td>1.4722</td>\n",
       "      <td>0.6208</td>\n",
       "      <td>7.5114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>0.2998</td>\n",
       "      <td>0.3884</td>\n",
       "      <td>0.8790</td>\n",
       "      <td>1.4450</td>\n",
       "      <td>1.4604</td>\n",
       "      <td>0.5722</td>\n",
       "      <td>6.9644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>0.1410</td>\n",
       "      <td>0.3552</td>\n",
       "      <td>0.8216</td>\n",
       "      <td>1.3286</td>\n",
       "      <td>1.4560</td>\n",
       "      <td>0.5070</td>\n",
       "      <td>6.9988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>0.2314</td>\n",
       "      <td>0.1236</td>\n",
       "      <td>0.6662</td>\n",
       "      <td>1.1814</td>\n",
       "      <td>1.4396</td>\n",
       "      <td>0.4174</td>\n",
       "      <td>6.3760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>0.2986</td>\n",
       "      <td>0.4150</td>\n",
       "      <td>0.8786</td>\n",
       "      <td>1.3496</td>\n",
       "      <td>1.4110</td>\n",
       "      <td>0.5896</td>\n",
       "      <td>7.4046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Gov Trust Average  Generocity Average  Health Average  \\\n",
       "Country                                                                \n",
       "Qatar                     0.3558              0.2912          0.7686   \n",
       "Luxembourg                0.3374              0.2458          0.9034   \n",
       "Singapore                 0.4672              0.3032          1.0140   \n",
       "Kuwait                    0.1822              0.1718          0.6952   \n",
       "Norway                    0.3440              0.3290          0.8734   \n",
       "Switzerland               0.3798              0.2776          0.9282   \n",
       "Ireland                   0.2998              0.3884          0.8790   \n",
       "United States             0.1410              0.3552          0.8216   \n",
       "Saudi Arabia              0.2314              0.1236          0.6662   \n",
       "Netherlands               0.2986              0.4150          0.8786   \n",
       "\n",
       "               Family Average  Economy GDP Average  Freedom Average  \\\n",
       "Country                                                               \n",
       "Qatar                  1.1698               1.7436           0.6040   \n",
       "Luxembourg             1.3434               1.6378           0.5840   \n",
       "Singapore              1.2312               1.5922           0.5536   \n",
       "Kuwait                 1.1848               1.5556           0.5060   \n",
       "Norway                 1.4312               1.5192           0.6380   \n",
       "Switzerland            1.4174               1.4722           0.6208   \n",
       "Ireland                1.4450               1.4604           0.5722   \n",
       "United States          1.3286               1.4560           0.5070   \n",
       "Saudi Arabia           1.1814               1.4396           0.4174   \n",
       "Netherlands            1.3496               1.4110           0.5896   \n",
       "\n",
       "               Happiness Average  \n",
       "Country                           \n",
       "Qatar                     6.4218  \n",
       "Luxembourg                6.9360  \n",
       "Singapore                 6.5428  \n",
       "Kuwait                    6.1486  \n",
       "Norway                    7.5410  \n",
       "Switzerland               7.5114  \n",
       "Ireland                   6.9644  \n",
       "United States             6.9988  \n",
       "Saudi Arabia              6.3760  \n",
       "Netherlands               7.4046  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort data in ascending order based on Average\n",
    "# It is possible to sort based upon multiple columns\n",
    "Comp_data_df = Comparison_data.sort_values(\n",
    "    [\"Economy GDP Average\", \"Freedom Average\"], ascending=False)\n",
    "Comp_data_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ANOVA\n",
    "\n",
    "* Analysis of variance, or ANOVA, is a statistical method that separates observed variance data into different components to use for additional tests. A one-way ANOVA is used for three or more groups of data, to gain information about the relationship between the dependent and independent variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=394.32714337574123, pvalue=7.526071762437509e-217)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform the ANOVA\n",
    "st.f_oneway(Comp_data_df[\"Gov Trust Average\"], \n",
    "            Comp_data_df[\"Generocity Average\"], \n",
    "            Comp_data_df[\"Health Average\"], \n",
    "            Comp_data_df[\"Family Average\"], \n",
    "            Comp_data_df[\"Economy GDP Average\"], \n",
    "            Comp_data_df[\"Freedom Average\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between both factors is 0.39\n"
     ]
    }
   ],
   "source": [
    "# The next example will compute the Pearson correlation coefficient between \"IEconomic GDP Average\" and \"Freedom Average\"\n",
    "GDP = Comp_data_df.iloc[:,4]\n",
    "Free = Comp_data_df.iloc[:,5]\n",
    "correlation = st.pearsonr(GDP,Free)\n",
    "print(f\"The correlation between both factors is {round(correlation[0],2)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between both factors is 0.39\n"
     ]
    },
    {
     "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": [
    "# Compare the calcualted Pearson's r to the plots\n",
    "plt.scatter(GDP,Free)\n",
    "plt.xlabel('Country GDP Per Capita')\n",
    "plt.ylabel('Average Freedom Score Per Country Per Year (L)')\n",
    "print(f\"The correlation between both factors is {round(correlation[0],2)}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Economy GDP Average  Generocity Average    -0.025530\n",
       "Generocity Average   Economy GDP Average   -0.025530\n",
       "Health Average       Generocity Average     0.032844\n",
       "Generocity Average   Health Average         0.032844\n",
       "                     Family Average         0.069850\n",
       "Family Average       Generocity Average     0.069850\n",
       "Generocity Average   Happiness Average      0.150190\n",
       "Happiness Average    Generocity Average     0.150190\n",
       "Gov Trust Average    Family Average         0.259132\n",
       "Family Average       Gov Trust Average      0.259132\n",
       "Generocity Average   Gov Trust Average      0.307336\n",
       "Gov Trust Average    Generocity Average     0.307336\n",
       "Health Average       Gov Trust Average      0.319317\n",
       "Gov Trust Average    Health Average         0.319317\n",
       "Generocity Average   Freedom Average        0.343940\n",
       "Freedom Average      Generocity Average     0.343940\n",
       "                     Health Average         0.370710\n",
       "Health Average       Freedom Average        0.370710\n",
       "Gov Trust Average    Economy GDP Average    0.375456\n",
       "Economy GDP Average  Gov Trust Average      0.375456\n",
       "Freedom Average      Economy GDP Average    0.390637\n",
       "Economy GDP Average  Freedom Average        0.390637\n",
       "Happiness Average    Gov Trust Average      0.451979\n",
       "Gov Trust Average    Happiness Average      0.451979\n",
       "Family Average       Freedom Average        0.487547\n",
       "Freedom Average      Family Average         0.487547\n",
       "Gov Trust Average    Freedom Average        0.509484\n",
       "Freedom Average      Gov Trust Average      0.509484\n",
       "Happiness Average    Freedom Average        0.583155\n",
       "Freedom Average      Happiness Average      0.583155\n",
       "Family Average       Health Average         0.641513\n",
       "Health Average       Family Average         0.641513\n",
       "Family Average       Economy GDP Average    0.700215\n",
       "Economy GDP Average  Family Average         0.700215\n",
       "Family Average       Happiness Average      0.785501\n",
       "Happiness Average    Family Average         0.785501\n",
       "                     Health Average         0.792512\n",
       "Health Average       Happiness Average      0.792512\n",
       "Happiness Average    Economy GDP Average    0.817523\n",
       "Economy GDP Average  Happiness Average      0.817523\n",
       "                     Health Average         0.847967\n",
       "Health Average       Economy GDP Average    0.847967\n",
       "Gov Trust Average    Gov Trust Average      1.000000\n",
       "Family Average       Family Average         1.000000\n",
       "Economy GDP Average  Economy GDP Average    1.000000\n",
       "Health Average       Health Average         1.000000\n",
       "Generocity Average   Generocity Average     1.000000\n",
       "Freedom Average      Freedom Average        1.000000\n",
       "Happiness Average    Happiness Average      1.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate the correlation matrix and find the strongest positive and negative correlations\n",
    "all_data = Comp_data_df.corr()\n",
    "all_data.unstack().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     Gov Trust Average  Generocity Average  Health Average  \\\n",
      "Gov Trust Average             1.000000            0.307336        0.319317   \n",
      "Generocity Average            0.307336            1.000000        0.032844   \n",
      "Health Average                0.319317            0.032844        1.000000   \n",
      "Family Average                0.259132            0.069850        0.641513   \n",
      "Economy GDP Average           0.375456           -0.025530        0.847967   \n",
      "\n",
      "                     Family Average  Economy GDP Average  Freedom Average  \\\n",
      "Gov Trust Average          0.259132             0.375456         0.509484   \n",
      "Generocity Average         0.069850            -0.025530         0.343940   \n",
      "Health Average             0.641513             0.847967         0.370710   \n",
      "Family Average             1.000000             0.700215         0.487547   \n",
      "Economy GDP Average        0.700215             1.000000         0.390637   \n",
      "\n",
      "                     Happiness Average  \n",
      "Gov Trust Average             0.451979  \n",
      "Generocity Average            0.150190  \n",
      "Health Average                0.792512  \n",
      "Family Average                0.785501  \n",
      "Economy GDP Average           0.817523  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print (all_data.head())\n",
    "sns.heatmap(all_data.corr(),annot=True, cmap='ocean')\n",
    "\n",
    "plt.savefig('Images/Correlation_heatmap.png', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression - Top Ten Countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trust (Government Corruption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = -0.02x + 7.44\n",
      "The r-value is: 0.00031983404351783513\n"
     ]
    },
    {
     "data": {
      "image/png": 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TzMzKNa2JKSI6gP0AJI0GOoF5JbP+NiLeVJyQ5/8GcDjwEHCLpKsi4q5mxWtmZhsbqE7qw4B7I+KBGuc/GLgnIu6LiOeAS4CjmxadmZm9yEAliBOAi7spe6Wk2yT9TNKeedo04MHCPA/laS8i6VRJCyUtXLFiReMiNjMb4ZqeICSNA44CLi8pvhXYOSL2Bb4GzK8sVjJvaW96RFwQEe0R0T5lypRGhGxmZgzMGcSRwK0R8Wh1QUSsjoin8vhPgbGSJpPOGHYszLoDsHwAYjUzs2wgEsSJdNO8JGk7ScrjB+d4VgK3ALtL2iWfgZwAXDUAsZqZWdbUH8pJ2pR0JdJphWmzASLifOA44H2S1gFdwAmRfpixTtLpwAJgNHBhRNzZzFjNzGxj/qGcmdkI1pIfypmZ2dDmBGFmZqWcIMzMrJQThJmZlXKCMDOzUk4QZmZWygnCzMxKOUGYmVkpJwgzMyvlBGFmZqWcIMzMrJQThJmZlXKCMDOzUk4QZmZWygnCzMxKOUGYmVkpJwgzMyvlBGFmZqWcIMzMrFRNCULSzpJen8fbJE1oblhmZtZqvSYISe8FrgC+nSftAMxvZlBmZtZ6tZxBfAB4FbAaICL+AmzTzKDMzKz1akkQz0bEc5UXksYA0byQzMxsMKglQVwv6V+ANkmHA5cD/9vcsMzMrNVqSRBnASuApcBpwE+Bf21mUGZm1npjeiqUNAq4PSL2Ar4zMCGZmdlg0OMZREQ8D9wmaacBisfMzAaJHs8gsu2BOyXdDDxdmRgRR/W0kKQZwKWFSS8BzomI80rmPQj4I3B8RFyRpy0D1gDrgXUR0V5DrGZm1iC1JIjP9GXFEdEB7AcgaTTQCcyrni+XfRFYULKa10bE433ZvpmZ9U+vndQRcT1wNzAhD3/K0+pxGHBvRDxQUvZB4MfAY3Wu08zMmqiWX1K/FbgZeAvwVuAmScfVuZ0TgItL1j0NeDNwfskyAVwraZGkU+vcnpmZ9VMtTUyfBA6KiMcAJE0Bfkm6/UavJI0DjgLOLik+DzgrItZLqi57VUQsl7QN8AtJd0fEDSXrPxU4FWCnndyXbmbWKLX8DmJUJTlkK2tcruJI4NaIeLSkrB24JHdIHwd8U9IsgIhYnv8+Ruq7OLhs5RFxQUS0R0T7lClT6gjLzMx6UssZxM8lLWBDE9HxwM/q2MaJlDQvAUTELpVxSd8Hro6I+ZI2IyWmNXn8DcBn69immZn1U68JIiLmSDoGOAQQcEFEvOhqpDKSNgUOJ/0CuzJtdl5vWb9DxbbAvNzsNAa4KCJ+Xss2zcysMRTR8333JO0CPBwRz+TXbcC2EbGs+eHVp729PRYuXNjqMMzMhgxJi7r7nVktfQmXA88XXq/P08zMbBirJUGMKd7uO4+Pa15IZmY2GNSSIFZIeuG2GpKOBvzrZjOzYa6Wq5hmAz+S9HVSJ/WDwElNjcrMzFqulquY7gVeIWlzUqf2muaHNcDe9ja4uPRK3N6NGQNtbTB+/IuHtrbmlo2q5+coZmb16TZBSPon0rMgKvdP+ihwrKQHgA9HxP0DEeCAePLJvi+7bh2sWZOGkWTcuPIk1uykuMkm8OJf3ZtZE/R0BvEF4BUAkt4EvIP0o7f9SfdOmtn06AbKNdf0fdm1a6GrC555ZuOhq6t8eiPLerlEuameey4N/UmuQ1Ejk189y4wd68RoA66nBBER8bc8fgzw3YhYBCyS9P7mhzZEjB2bhokTWx3JwHn++ZQcGpH46l3m2WdbW/dKTFZu881hwoQ0jB0Ld965cfmWW8I228CUKTB58oZhypQ0FMumTElJ0lqmpwSh3O/wN9Ltur9ZKBvf1KhscBs1asM325Fk/fqUoJqR+Lor6+pK21y7ttW1r81TT6Xh4YfLy594Ig0dHQMbV7NNnAhz58JOO6XEOGZM+VBr2SA5W+wpQZwHLAFWk54BsRBA0v5AN3vfbBgbPRo23TQNI8lzz2048K9Zs+FvZbw4rFkDq1en8ZUr4Ze/bHX0A2P1ajjttN7nq9WoUfUllm22gQVlz1zrn24TRERcmG/Stw1wW6HoEeDdDY/EzAanceNgq63SMFJEpIP+44+nYcWKNDz2WPpbnD5xIuy5J8yalS6iWLfuxcPateXTG1XWpCbuHi9zjYhO0qNCi9N89mBmw5sEW2yRhl13bXU0LeML6c3MrJQThJmZlarlmdS7Stokjx8q6UOSJjU/NDMza6VaziB+DKyXtBvwXWAX4KKmRmVmZi1XS4J4PiLWAW8GzouIjwDbNzcsMzNrtVoSxFpJJwLvAq7O08Y2LyQzMxsMakkQ7wZeCXwhIu7PjyD9YXPDMjOzVqvldt93AR8CkLQlMCEi/r3ZgZmZWWvVchXTdZImStqK9Ivq70n6cvNDMzOzVqqliWmLiFhNuqPr9yLiQOD1zQ3LzMxarZYEMUbS9sBb2dBJbWZmw1wtCeKzwALg3oi4RdJLgL80NywzM2u1WjqpLwcuL7y+Dzi2mUGZmVnr9ZogJL0U+BawbUTsJWkf4KiI+HzTozMboeYv7mTugg6Wr+pi6qQ25sycwaz9p7U6LBthamli+g5wNrAWICJuB05oZlBmI9n8xZ2cfeVSOld1EUDnqi7OvnIp8xd39rqsWSPVkiA2jYibq6ata0YwZgZzF3TQtXb9RtO61q5n7oJh9phOG/RqSRCPS9oVCABJx1HDI0clzZC0pDCslnRGN/MeJGl9Xndl2hGSOiTdI+kTNdbHbMhbvqqrrulmzdJrHwTwAeACYA9JncD9wDt6WygiOoD9ACSNJj2Zbl71fLnsi6QrpYrTvgEcDjwE3CLpqvyrbrNhbeqkNjpLksHUSW0tiMZGsl7PICLivoh4PTAF2CMiDomIZXVu5zDSZbIPlJR9kHRL8ccK0w4G7snbfg64BDi6zm2aDUlzZs6gbezojaa1jR3NnJkzWhSRjVS1XMW0Cemy1umkH80BEBGfrWM7JwAXl6x7Guk24q8DDioUTQMeLLx+CHh5N/GdCpwKsNNOO9URktngVLlayVcxWavV0sT0E+BJYBHwbL0bkDQOOIp0JVS184CzImJ9JfFUFiuZN8rWHxEXkJrAaG9vL53HbKiZtf80JwRruVoSxA4RcUQ/tnEkcGtEPFpS1g5ckpPDZOCNktaRzhh2LMYALO9HDGZmVqdaEsSNkvaOiKV93MaJlDQvAUTELpVxSd8Hro6I+ZLGALvnZ090kpqo3tbH7ZuZWR/UkiAOAU6WdD+piUlARMQ+vS0oaVPSlUinFabNJq3g/O6Wi4h1kk4nXdk0GrgwIu6sIVYzM2uQWhLEkX1deUT8Ddi6alppYoiIk6te/xT4aV+3bWZm/dNtgpA0MT8HYs0AxmNmZoNET2cQFwFvIl29FGx8ZVEAL2liXGZm1mLdJoiIeFP+u0t385iZ2fBVSx8Eko4hdVYH8NuImN/UqMzMrOV6vdWGpG8Cs4GlwB3AbEnfaHZgZmbWWrWcQbwG2CsiKndz/QEpWZiZ2TBWy+2+O4DiTY52BG5vTjhmZjZY1HIGsTXwJ0mVhwYdBPxB0lUAEXFUs4IzM7PWqSVBnNP0KMzMbNDpNUFExPWStiM9oyGAWyLikaZHZmZmLVXLVUzvAW4GjgGOA/4o6ZRmB2ZmZq1VSxPTHGD/iFgJIGlr4EbgwmYGZmZmrVXLVUwPsfH9mNaw8dPezMxsGKrlDKITuEnST0h9EEcDN0v6KEBEfLmJ8ZmZWYvUkiDuzUPFT/LfCY0Px8zMBotarmL6zEAEYmZmg0uvCULSFODjwJ7A+Mr0iHhdE+MyM7MWq6WT+kfA3cAuwGeAZcAtTYzJzMwGgVoSxNYR8V1gbURcHxGnAK9oclxmZtZitXRSr81/H5b0j8ByYIfmhWRmZoNBLQni85K2AD4GfA2YCHykqVGZmVnL1XIV09V59Engtc0Nx8zMBotuE4Skr5F+GFcqIj7UlIjMzGxQ6OkMYmFh/DPAp5oci5mZDSLdJoiI+EFlXNIZxddmZjb81XKZK/TQ1GRmZsNTrQnCzMxGmJ46qdew4cxhU0mrK0VARMTEZgdnZuXmL+5k7oIOlq/qYuqkNubMnMGs/ae1OiwbZnrqg+jX3VolzQAuLUx6CXBORJxXmOdo4HPA88A64IyI+F0uW0Z69sR6YF1EtPcnHrPhYv7iTs6+cilda9cD0Lmqi7OvXArgJGENVcsP5fokIjqA/QAkjSY9V2Je1Wy/Aq6KiJC0D3AZsEeh/LUR8XizYjQbiuYu6HghOVR0rV3P3AUdThDWUE1LEFUOA+6NiAeKEyPiqcLLzXBnuFmvlq/qqmu6WV8NVCf1CcDFZQWS3izpbuAa4JRCUQDXSlok6dTuVizpVEkLJS1csWJFQ4M2G4ymTmqra7pZXzU9QUgaBxwFXF5WHhHzImIPYBapP6LiVRFxAHAk8AFJ/9DN8hdERHtEtE+ZMqXB0ZsNPnNmzqBt7OiNprWNHc2cmTNaFJENVwNxBnEkcGtEPNrTTBFxA7CrpMn59fL89zFS38XBzQ7UbCiYtf80zj1mb6ZNakPAtEltnHvM3u5/sIYbiD6IE+m+eWk3Ut9ESDoAGAeslLQZMCoi1uTxNwCfHYBYzYaEWftPc0KwpmtqgpC0KXA4cFph2myAiDgfOBY4SdJaoAs4PieLbYF5kioxXhQRP29mrGZmtjFFDJ8Lh9rb22PhwoW9z2hmZgBIWtTd78x8qw0zMyvlBGFmZqWcIMzMrJQThJmZlXKCMDOzUk4QZmZWygnCzMxKOUGYmVkpJwgzMyvlBGFmZqWcIMzMrJQThJmZlXKCMDOzUgP1TGozs2Fj/uJO5i7oYPmqLqZOamPOzBnD8vkcThBmZnWYv7iTs69cStfa9QB0ruri7CuXAgy7JOEmJjOzOsxd0PFCcqjoWrueuQs6WhRR8zhBmJnVYfmqrrqmD2VOEGZmdZg6qa2u6UOZE4SZWR3mzJxB29jRG00TqS/iVf/+a+Yv7mxNYE3gTmozszpUOqLnLuigc1UXAiKXDbcOa59BmJnVadb+0/j9J17HtEltLySHiuHUYe0EYWbWR8O9w9oJwsysj4Z7h7UThJlZH5V1WLeNHc2cmTNaFFFjuZPazKyPih3Ww/G2G04QZmb9MGv/acMmIVRzE5OZmZVqWoKQNEPSksKwWtIZVfMcLen2XL5Q0iGFsiMkdUi6R9InmhWnmZmVa1oTU0R0APsBSBoNdALzqmb7FXBVRISkfYDLgD3y/N8ADgceAm6RdFVE3NWseM3MbGMD1cR0GHBvRDxQnBgRT0VE5Xcmm7HhB4kHA/dExH0R8RxwCXD0AMVqZmYMXII4Abi4rEDSmyXdDVwDnJInTwMeLMz2UJ5WtvypuXlq4YoVKxoYspnZyNb0BCFpHHAUcHlZeUTMi4g9gFnA5yqLlc3azfIXRER7RLRPmTKlESGbmRkDcwZxJHBrRDza00wRcQOwq6TJpDOGHQvFOwDLmxeimZlVG4gEcSLdNy/tJkl5/ABgHLASuAXYXdIu+QzkBOCqAYjVzMyypv5QTtKmpCuRTitMmw0QEecDxwInSVoLdAHH507rdZJOBxYAo4ELI+LOZsZqZmYb04aLiIa+9vb2WLhwYavDMDMbMiQtioj2sjL/ktrMzEo5QZiZWSknCDMzK+UEYWZmpZwgzMyslBOEmZmVcoIwM7NSThBmZlbKCcLMzEr5mdRmw9T8xZ3MXdDB8lVdTJ3UxpyZM4bts5OtOZwgzIah+Ys7OfvKpXStXQ9A56ouzr5yKYCThNXMTUxmw9DcBR0vJIeKrrXrmbugo0UR2VDkBGE2DC1f1VXXdLMyThBmw9DUSW11TTcr4wRhNgzNmTmDtrGjN5rWNnY0c2bOaFFENhS5k9psGKp0RPsqJusPJwizYWrW/tOcEKxf3MRkZmalnCDMzKyUE4SZmZVygjAzs1JOEGZmVkoR0eoYGkbSCuCBJm9mMvB4k7cx0IZbnYZbfWD41Wm41QeGbp12jogpZQXDKkEMBEkLI6K91XE00nCr03CrDwy/Og23+sDwrJObmMzMrJQThJmZlXKCqN8FrQ6gCYZbnYZbfWD41Wm41QeGYZ3cB2FmZhYYaIUAAAikSURBVKV8BmFmZqWcIMzMrJQTRIGkIyR1SLpH0idKyveQ9AdJz0o6s6psmaSlkpZIWjhwUXevhvq8XdLtebhR0r61Ltsq/azTUNxHR+e6LJG0UNIhtS7bKv2s05DbR4X5DpK0XtJx9S47aEWEh9QPMxq4F3gJMA64DXhZ1TzbAAcBXwDOrCpbBkxudT3qrM/fA1vm8SOBm2pddqjVaQjvo83Z0Fe4D3D3MNhHpXUaqvuoMN+vgZ8Cxw3mfVTP4DOIDQ4G7omI+yLiOeAS4OjiDBHxWETcAqxtRYB1qqU+N0bEE/nlH4Edal22RfpTp8Golvo8FfloA2wGRK3Ltkh/6jQY1fo+fxD4MfBYH5YdtJwgNpgGPFh4/VCeVqsArpW0SNKpDY2sb+qtzz8DP+vjsgOlP3WCIbqPJL1Z0t3ANcAp9SzbAv2pEwzBfSRpGvBm4Px6lx3s/ES5DVQyrZ5vNq+KiOWStgF+IenuiLihQbH1Rc31kfRa0sG00hbc3/eiWfpTJxii+ygi5gHzJP0D8Dng9bUu2wL9qRMMzX10HnBWRKyXNpp9sO6jmvkMYoOHgB0Lr3cAlte6cEQsz38fA+aRTi9bqab6SNoH+C/g6IhYWc+yLdCfOg3ZfVSRD5S7Sppc77IDqD91Gqr7qB24RNIy4Djgm5Jm1bjs4NbqTpDBMpDOpu4DdmFDh9Ke3cz7aQqd1KR21AmF8RuBIwZ7fYCdgHuAv+/rezGE6jRU99FubOjQPQDoJH0zHcr7qLs6Dcl9VDX/99nQST0o91E9g5uYsohYJ+l0YAHp6oMLI+JOSbNz+fmStgMWAhOB5yWdAbyMdJvfefn0cgxwUUT8vBX1qKilPsA5wNakbzwA6yKivbtlW1KRgv7UCdiWobmPjgVOkrQW6AKOj3T0Gcr7qLROkobqPqpr2YGIu1F8qw0zMyvlPggzMyvlBGFmZqWcIMzMrJQThJmZlXKCMDOzUk4QVjdJW+e7bS6R9IikzsLrcX1c5xmSNu2h/ApJL8njm0v6lqR7JS3Ot2V4b1/rM1hJOlnS1B7Kz5R0t6Q7JN0m6aQBjm+SpPcXXk+VdEU/1vdLSVs2JjprBCcIq1tErIyI/SJiP9L9Z75SeR0Rz0nqy+9rzgBKE4SkPYHREXFfnvRfwBPA7hGxP3AEsFUftlkTSaObte5enAyUJoh8Hf7hwMERsRfwD5Tf2qFUdZ36WMdJwAsJIiKWR8RxPczfm/8prs8GgVb/Us/D0B7Ivyon/YL0y8BvgC/x4l+b3wFMJ/1C9hrSr0rvAI4HPgQ8BywFflOyjX8DTs7ju5J+nTqqm3gEzM3rXkr6ERbApcAbC/N9n/SDrdF5/luA24HTcvmhuS4XAXfl19cBVwB3Az9iw++IluUY/0D6IeUBpB9H3QvMLmxzTmE7n8nTpgN/Ar4D3AlcC7SRbtnwFNABLAHaqur5V2DXbt6Dw4DFuf4XApsU4jwH+B1wQsnr64D2PO9kYFkePxn4CfDzHM+n8vRLSD90W5Lfw+nAHblsPPC9HMNi4LWFdV2Z1/UX4D8KcW9ZWd7D4BhaHoCHoT2wcYK4mvRN/4XphfkqCeJY4DuF6Vvkv8vo5jkAwPXA3nn8KGBeD/EcC/yCdODfNh9ItyfdbfMHeZ5xpLtstgGnAv+ap2+SD/C7kBLC08AuuexQ4EnS/XRGkZLBIYXY35fHv0JKABOAKcBjefobSA+1V17+atK3/unAOmC/PN9lwDvy+AsH7Ko6TgCe6Kb+43PdXppf/zdwRiHOjxfmrX79wvZ4cYJ4mPQL9ba8L9spJIQ83wuvgY8B38vje+T9MD6v6z5gi/z6AWDHwjr+Amzd6s+1hzS4icka6fKIWN/LPEuB10v6oqRXR8STNax3e2BFWYGkT+a+j8pN0A4BLo6I9RHxKCm5HES67ffrJG1CepDQDRHRRTpwnyRpCXAT6SC4e17XzRFxf2FzN0fEQxHxPOlb8/RC2VWF+t0UEWsiYgXwjKRJeTtvIH2bvpV00Kxs5/6IWJLHF1Wtt7TadH9X0Bl5fX/Or39ASkQVl1bNX/26O7+I1LTYRToDOKSX+Q8hNRkREXeTEsFLc9mvIuLJiHiGdHa2c2G5x+imWc0GnhOENdLThfF1bPz5Gg+QD1wHkg6k50o6p4b1dlWWJx1Q9pU0Kq/vC5H6Qibm8tJ2+Hwwug6YSWrWuqQw/wdjQx/KLhFxbUl9AJ4tjK9n49vlV8qer5rv+TyfgHML29ktIr5bw3rL6rIaeLrSaV+lt36I6jp1t8/GV81XnZB6u0dPT3H0VN/xpP1tg4AThDXLMlJbPJIOIDXbkK/K+VtE/BD4z8o8wBpS00mZP5HuAEpE3ENqBvp8pWNV0ng2HJBuAI6XNFrSFNK355tz2SXAu4FXk/oIyH/fJ2lsXtdLJW3Wr5qXWwCcImnzvJ1p+ZkHPenpPTkX+IakiXl9E/MDdu4GpkvaLc/3TtJZVC2WkZI3pD6QosMlbSWpDZgF/L6X+G4A3p5jeynpLrsdPW1c6S592+U4bBDw3VytWX7MhqabW4BKk8fewFxJz5Me3fq+PP0C4GeSHo6I11at6xpSH8Av8+v3kDpF75H0f6RvnGflsnnAK0md4EFqY38kl11LapO/KtIjICFdETUduDUfoFaQDoANFRHXSvo74A/5bqVPAe8gfYPuzveB8yV1Aa/MzTsV3yI92/mWfFfUtcCXIuIZSe8GLs9Xk93Ci5901p3/BC6T9E7S85WLfkdqMtqNdJfVhQCSfi/pDlIT3jcK838zx76UdGZyckQ8W/VAnWoHAn+MiHU1xmtN5ru52qCXv7X+hvS0sd76OKzBJJ1M6rw+vcnb+Sopef+qmdux2rmJyQa9/M35Uwyx5/la3e5wchhcfAZhZmalfAZhZmalnCDMzKyUE4SZmZVygjAzs1JOEGZmVur/AxqO18y4lEjpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression X and Y axis\n",
    "x_values = top_ten_Comparison_data[\"Gov Trust Average\"]\n",
    "y_values = top_ten_Comparison_data[\"Happiness Average\"]\n",
    "\n",
    "# Perform a linear regression on temperature vs. latitude\n",
    "(slope, intercept, rvalue, pvalue, stderr) = st.linregress(x_values, y_values)\n",
    "\n",
    "# Get regression values\n",
    "regress_values = x_values * slope + intercept\n",
    "\n",
    "# Create line equation string\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "print(line_eq)\n",
    "\n",
    "correlation = st.pearsonr(x_values, y_values)\n",
    "\n",
    "# Create Plot\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "\n",
    "# Label plot and annotate the line equation\n",
    "plt.xlabel(\"Trust (Government Corruption)\")\n",
    "plt.ylabel(\"Happiness Score\")\n",
    "plt.title(f'Average Happiness Score by Govenment Trust')\n",
    "#plt.annotate(line_eq,(5,10),fontsize=15,color=\"r\")\n",
    "\n",
    "# Print r value\n",
    "print(f\"The r-value is: {rvalue**2}\")\n",
    "\n",
    "# Save and Show plot\n",
    "plt.savefig(\"Images/Linear_Regression_GOVTrust.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = -0.95x + 7.77\n",
      "The r-value is: 0.3891533029316568\n"
     ]
    },
    {
     "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": [
    "# Linear regression X and Y axis\n",
    "x_values = top_ten_Comparison_data[\"Generocity Average\"]\n",
    "y_values = top_ten_Comparison_data[\"Happiness Average\"]\n",
    "\n",
    "# Perform a linear regression on temperature vs. latitude\n",
    "(slope, intercept, rvalue, pvalue, stderr) = st.linregress(x_values, y_values)\n",
    "\n",
    "# Get regression values\n",
    "regress_values = x_values * slope + intercept\n",
    "\n",
    "# Create line equation string\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "print(line_eq)\n",
    "\n",
    "correlation = st.pearsonr(x_values, y_values)\n",
    "\n",
    "# Create Plot\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "\n",
    "# Label plot and annotate the line equation\n",
    "plt.xlabel(\"Generocity\")\n",
    "plt.ylabel(\"Happiness Score\")\n",
    "plt.title(f'Average Happiness Score by Generocity')\n",
    "#plt.annotate(line_eq,(5,10),fontsize=15,color=\"r\")\n",
    "\n",
    "# Print r value\n",
    "print(f\"The r-value is: {rvalue**2}\")\n",
    "\n",
    "# Save and Show plot\n",
    "plt.savefig(\"Images/Linear_Regression_Generocity.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Health (Life Expectancy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = -1.62x + 8.88\n",
      "The r-value is: 0.10050001600594217\n"
     ]
    },
    {
     "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": [
    "# Linear regression X and Y axis\n",
    "x_values = top_ten_Comparison_data[\"Health Average\"]\n",
    "y_values = top_ten_Comparison_data[\"Happiness Average\"]\n",
    "\n",
    "# Perform a linear regression on temperature vs. latitude\n",
    "(slope, intercept, rvalue, pvalue, stderr) = st.linregress(x_values, y_values)\n",
    "\n",
    "# Get regression values\n",
    "regress_values = x_values * slope + intercept\n",
    "\n",
    "# Create line equation string\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "print(line_eq)\n",
    "\n",
    "correlation = st.pearsonr(x_values, y_values)\n",
    "\n",
    "# Create Plot\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "\n",
    "# Label plot and annotate the line equation\n",
    "plt.xlabel(\"Health (Life Expectancy)\")\n",
    "plt.ylabel(\"Happiness Score\")\n",
    "plt.title(f'Average Happiness Score by Health (Life Expectancy)')\n",
    "#plt.annotate(line_eq,(5,10),fontsize=15,color=\"r\")\n",
    "\n",
    "# Print r value\n",
    "print(f\"The r-value is: {rvalue**2}\")\n",
    "\n",
    "# Save and Show plot\n",
    "plt.savefig(\"Images/Linear_Regression_Health.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 1.36x + 5.5\n",
      "The r-value is: 0.26936631068187095\n"
     ]
    },
    {
     "data": {
      "image/png": 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+uK1syBB49FHYfPPKxVVAOd5/iohe21il1dfXR2NjY6XDMLNa09wMEybAbbe1lW23Hdx7L2ywQeXiKgNJMyKivlBdybqYJI2TNCt3Wyrp1A7r7Cnp1dw6Z+bqDpA0R9JTkk4vVZxm1o+9/HK6QtuQIW3J4YAD4I034PHH+3xy6E7JupgiYg6wI4CkAUATMKXAqvdGxMH5gmz984F9geeBByXdFBGPlypeM+tH5s6F974X3nyzreykk9L0GAMGdP64fqZcg9R7A09HxLNFrr8b8FREPBMRbwPXAtU/s5WZVbfGxjTwvMUWbcnhJz9JA88XXujk0EG5EsRRwDWd1H1I0sOS/irpfVnZaGB+bp3ns7JVSDpRUqOkxkWLFvVexGbWd9xyS0oMu+7aVnbllSkxnHFG5eKqciVPEJIGA4cA1xeofgjYLCI+AJwHNLQ+rMC6BUfTI+KiiKiPiPqRI0f2Rshm1ldccklKDAcd1FZ2++0pMXz+85WLq0aUowVxIPBQRLzUsSIilkbEsmz5FmCQpA1JLYZNc6tuAiwoQ6xmVusi4MwzU2L40pfayh9+ONXts0/lYqsx5TgP4mg66V6S9G7gpYgISbuREtbLwBJga0mbkwa3jwI+U4ZYzaxWrViRzmy+8sq2svXWg1mzYMyYysVVw0qaICQNIR2JdFKu7GSAiLgQOAL4sqQVQDNwVKQTM1ZI+howFRgAXBYRj5UyVjOrUa+/nrqQ7rmnrWzHHeGuu2DEiMrF1Qf4RDkzq00LF8Luu8O8eW1l48fDddfB2mtXLKxaU5ET5czMSuLf/4a11oKNN25LDl//epomo6HByaEXOUGYWW2477408LzNNmmwGeCcc9LyueempGG9ypP1mVl1a2iAQw9tX3bttXDkkZWJpx9xyjWz6nTBBanFkE8Od9+dWgxODmXhFoSZVY8IOP10+N//bV/+6KPwvvcVfoyVjBOEmVXe8uXpzObrrmsre/e7YcYMGDWqcnH1c04QZlY5r72WLuV5331tZbvvnqbeHj68cnEZ4DEIM6uEF15ILYPhw9uSw6c/DW+/ne47OVQFJwgzK58nnkgDz6NGpSQBMHEirFyZupcGDapsfNaOE4SZld6996bE8N73tpWde24alP7f/011VnU8BmFmpXP99anrKG/y5FXPa7Cq5BaEmfW+X/witQryyWH69NRicHKoGW5BmFnvWLkSTjsNfv3r9uVPPgnjxlUmJlsjThBmtmaam2HIkPZlm20G99+fJtSzmuUuJjPrmZdeSt1I+eTw0Y+mcxvmzXNy6APcgjCz1fP446tOezFsGCxeDIMHVyYmKwm3IMysOHfckVoM+eTw4Q+nsYelS50c+iAnCDPr2qWXpsSwzz5tZSedlI5Imj7d5zD0YU4QZlbY6aenL/8TTmgr+8UvUmK48MLKxWVlU9QYhKTNgK0j4m+S6oCBEfFaaUMzs4r45Cfh5pvblzU0pOs9W7/SbYKQ9CXgRGB9YEtgE+BCYO/ShmZmZdPSAptvDvPnty9vbIRddqlMTFZxxbQgvgrsBtwPEBH/lrRRSaMys/JYvBhGjly1/NlnYcyY8sdjVaWYMYi3IuLt1juSBgJRupDMrORmz07jCx2Tw6uvpjEGJwejuARxj6TvAnWS9gWuB/5c2rDMrCRuvDElhh12aF++fHlKDL4Og+UUkyC+AywCZgMnAbcA/1XKoMysl/34xykxTJjQvnzlypQYBvqcWVtVl+8KSWsBj0TE9sDF5QnJzHrNIYfAnzs0+EeMgFdeqUw8VlO6bEFExErgYUnukDSrJcOGpRZDPjmMH59aC04OVqRiupjeAzwm6Q5JN7XeunuQpHGSZuVuSyWd2sm6u0pqkXRErmyepNnZYxuLf0pm/VRESgoSLFvWVv6Tn6S6hobKxWY1qZiOxx/0ZMMRMQfYEUDSAKAJmNJxvazuf4CpBTazV0Qs7sn+zfqNN96AoUNXLb/xxtTFZNZD3bYgIuIe4ElgWHZ7IitbHXsDT0fEswXqvg78CVi4mts069+eeSa1Fjomh3vvTS0GJwdbQ90mCEmfBh4APgV8Grg/3xVUpKOAawpsezRwKOnM7I4CuE3SDEknrub+zPquGTNSYthyy/blTU0pMeyxR2Xisj6nmC6m7wG7RsRCAEkjgb8BNxSzA0mDgUOAMwpU/wr4TkS0aNUZIT8SEQuys7Zvl/RkREwrsP0TSVOBMMYn91hfdu21cPTRq5a/9Zan2raSKGaQeq3W5JB5ucjHtToQeCgiXipQVw9cK2kecARwgaQJABGxIPu7kDR2sVuhjUfERRFRHxH1IwtNGWBW6844I7UYOiaHiHRzcrASKaYFcaukqbR1ER0J/HU19nE0BbqXACJi89ZlSb8Hbo6IBklDSYnptWx5P+CHq7FPs9q3115w993tyzbbLF3O06wMuk0QETFR0mHAHoCAiyJilaORCpE0BNiXdAZ2a9nJ2Xa7mlB+Y2BK1u00ELg6Im4tZp9mNa/QBXg+/3m48sryx2L9miK6nndP0ubACxHxZna/Dtg4IuaVPrzVU19fH42NPmXCalAErFWg5/bXv4ZTTil/PNZvSJoREfWF6ooZS7geWJm735KVmdmaev311GLomBxuuy0lDScHq6BixiAG5qf7joi3syOTzKynnnsujSd09K9/wdZblz8eswKKaUEskvTOGTeSxgM+u9msJ6ZPTy2GjslhyZLUYnBysCpSTAviZOAqSb8hDVLPB75Q0qjM+prLLoMvfnHV8hUrYMCA8sdjVoRijmJ6GvigpHeRBrVfK31YZn3EKafAeeetWt7NwSFm1aDTLiZJn5SUbwefBvw9m811884eZ2bAzjunrqR8cthhh7aT28xqQFdjEGeTriSHpIOBzwHHAzdReO4kM2udbnvmzLayk09OSeHhhysXl1kPdJUgIiLeyJYPAy6NiBkRcQngOS3MWrW0tCWGvIsvTonht7+tTFxma6irMQhl4w5vkKbrviBXt05JozKrBc3NMGTIquXTpsFHP1r+eMx6WVcJ4lfALGAp6RoQjQCSdgJeKENsZtVp0SLYaKNVy+fNK3xug1mN6jRBRMRl2SR9GwH5ztMXgeNKHZhZ1XnySdhuu/ZlgwfD0qWw9tqVicmshLo8US4imiJiZkSszJW9EBHPlT40sypx111pfCGfHHbdFVauTNdicHKwPmp1rutg1r9ccUVKDB//eFvZccelgecHHig866pZH+IEYdbRf/1X+vI/9ti2sv/5n5QYLrusYmGZlVu3Z1JL2hJ4PiLekrQnsANwZUQsKXVwZmV12GEwpcOlTm64AQ4/vDLxmFVYMS2IPwEtkrYCLgU2B64uaVRm5dLSAltumVoM+eRw332pxeDkYP1YMZP1rYyIFZIOBX4VEedJmtnto8yq2VtvwToFTueZOxfGji17OGbVqJgWxHJJRwPHADdnZYNKF5JZCb3yCnzgA6smh1deSS0GJwezdxSTII4DPgScHRFzs4n6/lDasMx62XPPwYgRsP768MgjqWzCBHj77ZQYRoyobHxmVaiY6b4fB04BkLQeMCwiflbqwMx6xcMPw447ti8766x082GqZl3qtgUh6W5JwyWtTzqj+nJJvyh9aGZr4PbbUwLIJ4dLLkmthe9/38nBrAjFdDGtGxFLSTO6Xh4RuwD7lDYssx668sr05b/ffm1lt9ySEkOhK7qZWaeKSRADJb0H+DRtg9Rm1SMCzj47JYZjjmkrb2xMdQceWLnYzGpYMYe5/hCYCkyPiAclbQH8u7RhmRWhpQW+/OV03YVWQ4bAo4/C5r7oodmaKmaQ+nrg+tz9ZwCfPWSV09ycjkC67ba2su22g3vvhQ02qFxcZn1MMVNtbAP8Ftg4IraXtANwSET8uOTRlUHDzCYmTZ3DgiXNjBpRx8T9xzFhp9GVDssKefll+MhHYM6ctrIDDoDJk6GurnJxmfVRxYxBXAycASwHiIhHgKNKGVS5NMxs4ozJs2la0kwATUuaOWPybBpmNlU6NMubOzclgA03bEsOJ50EK1bAX//q5GBWIsUkiCER8UCHshWlCKbcJk2dQ/PylnZlzctbmDR1TiePsLKaMSMNPG+xBbz5Zio7++x0HYYLL4QBAyobn1kfV0yCWJzN6BoAko6giEuOShonaVbutlTSqZ2su6uklmzbrWUHSJoj6SlJpxf5fFbLgiXNq1VuZXLLLSkx1Ne3lV15ZToi6bvf9TkMZmVSzFFMXwUuAraV1ATMBT7X3YMiYg6wI4CkAUATMKXjelnd/5COlMqXnQ/sCzwPPCjppuys7l4zakQdTQWSwagR7rKoiEsvhRNOaF92++2wj0+7MauEblsQEfFMROwDjAS2jYg9ImLeau5nb+DpiHi2QN3XSVOKL8yV7QY8le37beBaYPxq7rNbE/cfR92g9t0UdYMGMHH/cb29K+tMBJx5ZmoV5JPDrFmpzsnBrGKKOYppbdJhrWNJJ80BEBE/XI39HAVcU2Dbo4FDgY8Du+aqRgPzc/efB3bvJL4TgRMBxowZsxoh8c7RSj6KqQJWrEgJ4Yor2srWWy8lhtV8Hc2sNIrpYroReBWYAby1ujuQNBg4hHQkVEe/Ar4TES1q369cqJM5Cm0/Ii4idYFRX19fcJ2uTNhptBNCOb3+Ohx0ENxzT1vZjjvCXXd5RlWzKlNMgtgkIg5Yg30cCDwUES8VqKsHrs2Sw4bAJyStILUYNs3HACxYgxis0hYuhA9+MB2y2mr8eLjuOlh77crFZWadKiZB/EPS+yNidg/3cTQFupcAIuKd+RAk/R64OSIaJA0Ets6uPdFE6qL6TA/3b5X073/DuHFpPKHVKafAL38JaxVzEJ2ZVUoxCWIP4FhJc0ldTAIiInbo7oGShpCORDopV3YyaQMXdva47BKnXyMd2TQAuCwiHisiVqsW990HH/pQ+7JzzoFvfrMy8ZjZaismQfR4KsyIeAPYoENZwcQQEcd2uH8LcEtP920VcuONaZ6kvGuvhSOPrEw8ZtZjnSYIScOz60C8VsZ4rFZdcAF89avty+6+Gz72sYqEY2ZrrqsWxNXAwaSjl4L2RxYFsEUJ47JasHJl4ekuHn0U3ve+8sdjZr2q0wQREQdnfz2xvrX3xhswdOiq5U1NMGpU+eMxs5IoZgwCSYeRBqsDuDciGkoalVWnF14onACefx5G+1wSs76m2+MMJV0AnAzMBh4FTpZ0fqkDsyry0ENpKoyOyWHZsnT4qpODWZ9UTAviY8D2EdE6m+sVpGRhfd2UKXDYYauWt7T4HAazfqCYT/kcID85zqbAI6UJx6rCT36SWgz55LDNNqm1EOHkYNZPFPNJ3wB4QtLdku4GHgdGSrpJ0k0ljc7K68gjU2L43vfayj73uZQU5vgiSmb9TTFdTGeWPAqrrDFjYP789mWTJsG3vlWZeMysKnSbICLiHknvJl2jIYAHI+LFkkdmpdXSAgMLvPw33QSf/GT54zGzqlPMUUwnAA8AhwFHAPdJOr7UgVmJLFuWupE6JoeHH05dSU4OZpYppotpIrBTRLwMIGkD4B/AZaUMzHrZc8/BZputWv7ii7DxxuWPx8yqXjGD1M/Tfj6m12h/tTerZvfdl1oMHZNDc3NqMTg5mFknimlBNAH3S7qRNAYxHnhA0mkAEfGLEsZnPXXNNfCZApfQWLkyJQwzs24U04J4Gmig7ZKfNwIvAMOym1WT//7vlADyyWHnndvOYXByMLMiFXMU0w/KEYitoYMPhr/8pX3ZSSfBhZ1el8nMrEvdJghJI4FvA+8D1mktj4iPlzAuK9b668Mrr7QvO/98+MpXKhOPmfUZxYxBXAVcR7o2xMnAMcCiUgZl3VixAgYNWrX8tttg333LH4+Z9UlFTbUREZcCyyPinog4HvhgieOyQpYsSWMIHZPDE0+k8QUnBzPrRcW0IJZnf1+QdBCwANikdCHZKubPT9NhdLR4MWywwarlZma9oJgE8WNJ6wLfBM4DhgP/WdKoLJk3D7bfHl5/vX35W2/B4MEVCcnM+o9ijmK6OVt8FdirtOEYkC7Qs8su7cv23BPuvNOHqZpZ2XSaICSdR9u5D6uIiFNKElF/NnUqHHBA+3dV99kAAAzDSURBVLLLL4djj61IOGbWv3XVgmjMLf8AOKvEsfRfl18Ox3eY/3DqVNhvv8rEY2ZGFwkiIq5oXZZ0av6+9YII+NGP4KwOefehh2CnnSoTk5lZTjGD1NBFV5OtppYWOPFEuCw3Ge6wYTB7duHZVs3MKqTYBGFr6o034JBD4I472sre/364++50NrSZWZXpapD6NdpaDkMkLW2tAiIihpc6uD5h8WL40Ifgqafayg46CG64AdZZp/PH1YiGmU1MmjqHBUuaGTWijon7j2PCTqMrHZaZ9YKuxiDWaKZWSeNIU3S02gI4MyJ+lVtnPPAjYCWwAjg1Iv6e1c0jXXuiBVgREfVrEk/ZPf00bLcdLF/eVvbVr8K558JaxZzAXv0aZjZxxuTZNC9vAaBpSTNnTJ4N4CRh1geUrIspIuYAOwJIGkC6rsSUDqvdAdwUESFpB+CPwLa5+r0iYnGpYiyJBx6A3XdvX/azn8F3vlOZeEpo0tQ57ySHVs3LW5g0dY4ThFkfUK4xiL2BpyPi2XxhRCzL3R1KLQ+G//nPaYwh7+qr4eijKxNPGSxY0rxa5WZWW8rV13EUcE2hCkmHSnoS+AuQPxkggNskzZB0YmcblnSipEZJjYsWVWCS2d/9Lp3dnE8Od96ZDmPtw8kBYNSIutUqN7PaUvIEIWkwcAhwfaH6iJgSEdsCE0jjEa0+EhE7AwcCX5X0H508/qKIqI+I+pEjR/Zy9J2IgO9+NyWGk09uK589O9Xt1T9mJJm4/zjqBg1oV1Y3aAAT9x9XoYjMrDeVo4vpQOChiHipq5UiYpqkLSVtGBGLI2JBVr5Q0hRgN2BaGeLt3IoVadqLq65qKxs5Mp3ctkn/m+C2dZzBRzGZ9U3lSBBH03n30laksYmQtDMwGHhZ0lBgrYh4LVveD/hhGWItbNky+MQn4N5728rq6+Fvf4N1161YWNVgwk6jnRDM+qiSJghJQ4B9gZNyZScDRMSFwOHAFyQtB5qBI7NksTEwRWnm0oHA1RFxayljLeill2DXXdP1GFodfngafPZ022bWxymidg8c6qi+vj4aGxu7X7E7c+bAttu2LzvtNDjnHE+3bWZ9iqQZnZ1n1jfO2Oot06enBJBPDr/8ZRp4/vnPnRzMrF/xXEwAt94KBx7Yvuz66+GIIyoTj5lZFXCCgPbJYdo0+OhHKxeLmVmVcIIAmDs3HcK61VaVjsTMrGo4QQCMHVvpCMzMqo4Hqc3MrCAnCDMzK8gJwszMCnKCMDOzgpwgzMysICcIMzMryAnCzMwKcoIwM7OCfKJclWuY2eQL8phZRThBVLGGmU2cMXk2zctbAGha0swZk2cDOEmYWcm5i6mKTZo6553k0Kp5eQuTps6pUERm1p84QVSxBUuaV6vczKw3OUFUsVEj6lar3MysNzlBVLGJ+4+jbtCAdmV1gwYwcf9xFYrI+pqGmU185Gd3svnpf+EjP7uThplNlQ7JqogHqatY60C0j2KyUvBBENYdJ4gqN2Gn0f6wWkl0dRCE33MG7mIy67d8EIR1xwnCrJ/yQRDWHScIs37KB0FYdzwGYdZP+SAI644ThFk/5oMgrCvuYjIzs4JKliAkjZM0K3dbKunUDuuMl/RIVt8oaY9c3QGS5kh6StLppYrTzMwKK1kXU0TMAXYEkDQAaAKmdFjtDuCmiAhJOwB/BLbN1j8f2Bd4HnhQ0k0R8Xip4jUzs/bK1cW0N/B0RDybL4yIZRER2d2hQOvybsBTEfFMRLwNXAuML1OsZmZG+RLEUcA1hSokHSrpSeAvwPFZ8Whgfm6157OyQo8/Meuealy0aFEvhmxm1r+VPEFIGgwcAlxfqD4ipkTEtsAE4EetDyu0aiePvygi6iOifuTIkb0RspmZUZ4WxIHAQxHxUlcrRcQ0YEtJG5JaDJvmqjcBFpQuRDMz66gcCeJoOu9e2kqSsuWdgcHAy8CDwNaSNs9aIEcBN5UhVjMzy5T0RDlJQ0hHIp2UKzsZICIuBA4HviBpOdAMHJkNWq+Q9DVgKjAAuCwiHitlrGZm1p7aDiKqffX19dHY2FjpMMzMaoakGRFRX6jOZ1KbmVlBThBmZlaQE4SZmRXkBGFmZgU5QZiZWUFOEGZmVpAThJmZFeQEYWZmBTlBmJlZQb4mtfVLDTObmDR1DguWNDNqRB0T9x/nazObdeAEYf1Ow8wmzpg8m+blLQA0LWnmjMmzAZwkzHLcxWT9zqSpc95JDq2al7cwaeqcCkVkVp2cIKzfWbCkebXKzforJwjrd0aNqFutcrP+ygnC+p2J+4+jbtCAdmV1gwYwcf9xFYrIrDp5kNr6ndaBaB/FZNY1JwjrlybsNNoJwawb7mIyM7OCnCDMzKwgJwgzMyvICcLMzApygjAzs4IUEZWOoddIWgQ8W+k4OtgQWFzpIIpQK3FC7cRaK3FC7cRaK3FC7cS6WUSMLFTRpxJENZLUGBH1lY6jO7USJ9ROrLUSJ9ROrLUSJ9RWrJ1xF5OZmRXkBGFmZgU5QZTeRZUOoEi1EifUTqy1EifUTqy1EifUVqwFeQzCzMwKcgvCzMwKcoIwM7OCnCB6SNJlkhZKerST+vGSHpE0S1KjpD1ydSMk3SDpSUlPSPpQlcb5n5Iek/SopGskrVOqOIuJNbferpJaJB2RKztA0hxJT0k6vRrjlLSppLuy1/wxSd8oZZxrEmuufICkmZJurtY4y/l56oVYy/qZWmMR4VsPbsB/ADsDj3ZS/y7axnh2AJ7M1V0BnJAtDwZGVFucwGhgLlCX3f8jcGwl/6fZOgOAO4FbgCNyZU8DW2T/z4eB91ZhnO8Bds6WhwH/KmWcaxJrru404Grg5mqNs5yfpzV8/cv+mVrTm1sQPRQR04D/66J+WWTvAmAoEACShpPeYJdm670dEUuqLc7MQKBO0kBgCLCgVHFmsXQZa+brwJ+Ahbmy3YCnIuKZiHgbuBYYX5ooex5nRLwQEQ9ly68BT5C+NEpmDf6nSNoEOAi4pDTRtelpnOX+PGX76PH/lDJ/ptaUE0QJSTpU0pPAX4Djs+ItgEXA5VnT/RJJQysWJIXjjIgm4BzgOeAF4NWIuK1yUYKk0cChwIUdqkYD83P3n6fEX7xd6SLO/DpjgZ2A+8sTVadxdBXrr4BvAyvLGlQBXcRZjZ+ngrFW42eqO04QJRQRUyJiW2AC8KOseCCpefrbiNgJeB0oaZ95dwrFKWk90q/wzYFRwFBJn6tclED6wvpORLR0KFeBdSt5/HZncQIg6V2kX5enRsTSska2qoKxSjoYWBgRMyoT1io6+59W3eeJzv+n1fiZ6pIvOVoGETFN0paSNiT9un0+Ilp/Od5A5d/QwCpx7gXMjYhFAJImAx8G/lDBEOuBayVBmgjtE5JWkP6nm+bW24TKNt0LxhkRDZIGkZLDVRExuYIxtursf7o7cIikTwDrAMMl/SEiKvWF1lmc91F9n6fOYh1E9X2muuQEUSKStgKejoiQtDNp8Ozl7P58SeMiYg6wN/B4tcVJagZ/UNIQoDmLs7FScQJExOaty5J+Txo4bcj6c7eWtDnQBBwFfKYyUXYZp0h95U9ExC8qFV9eZ7ECDcAZWfmewLcqmBy6ipNq+jxBl6//7lTZZ6o7ThA9JOkaYE9gQ0nPA2eRfiEQERcChwNfkLSc9GY4MjcY/HXgKkmDgWeA46owzvsl3QA8BKwAZlLiqQOKiLWgiFgh6WvAVNLRI5dFxGPVFifwEeDzwGxJs7Ky70bELVUYa1mtYZxl+zzBGr1Py/6ZWlOeasPMzAryILWZmRXkBGFmZgU5QZiZWUFOEGZmVpAThJmZFeQEYVakbGbOWbnb2DXc3iHKZp6V9H1J3+qNOM16i8+DMCtec0Ts2Fsbi4ibgJt6a3tmvc0tCLMekvQuSXdIekjSbEnjs/Kx2bUJLsnm/b9K0j6Spkv6t6TdsvWOlfSbDtvcUtJDuftbS6qW+ZCsn3GCMCteXa57aQrwJnBoROxMmrvq59l0GgBbAb8mXWNjW9LUH3sA3wK+29kOIuJp4FVJrS2V44Dfl+LJmHXHXUxmxWvXxZRNvPcTSf9BmhJ7NLBxVj03ImZn6z0G3JHNdzUbGNvNfi4BjpN0GnAk6XoXZmXnFoRZz30WGAnskiWOl0gznwK8lVtvZe7+Srr/YfYn4EDgYGBGRLzcaxGbrQYnCLOeW5d0zYTlkvYCNuuNjUbEm6SJB38LXN4b2zTrCScIs567CqiX1EhqTTzZy9sOoKqvOGZ9m2dzNatC2TkR60bEf1c6Fuu/PEhtVmWyI6S2BD5e6Visf3MLwszMCvIYhJmZFeQEYWZmBTlBmJlZQU4QZmZWkBOEmZkV9P8BEdLfnlPO1l0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression X and Y axis\n",
    "x_values = top_ten_Comparison_data[\"Family Average\"]\n",
    "y_values = top_ten_Comparison_data[\"Happiness Average\"]\n",
    "\n",
    "# Perform a linear regression on temperature vs. latitude\n",
    "(slope, intercept, rvalue, pvalue, stderr) = st.linregress(x_values, y_values)\n",
    "\n",
    "# Get regression values\n",
    "regress_values = x_values * slope + intercept\n",
    "\n",
    "# Create line equation string\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "print(line_eq)\n",
    "\n",
    "correlation = st.pearsonr(x_values, y_values)\n",
    "\n",
    "# Create Plot\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "\n",
    "# Label plot and annotate the line equation\n",
    "plt.xlabel(\"Family\")\n",
    "plt.ylabel(\"Happiness Score\")\n",
    "plt.title(f'Average Happiness Score by Family')\n",
    "#plt.annotate(line_eq,(5,10),fontsize=15,color=\"r\")\n",
    "\n",
    "# Print r value\n",
    "print(f\"The r-value is: {rvalue**2}\")\n",
    "\n",
    "# Save and Show plot\n",
    "plt.savefig(\"Images/Linear_Regression_Family.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Freedom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 4.8x + 4.47\n",
      "The r-value is: 0.3192480402098517\n"
     ]
    },
    {
     "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": [
    "# Linear regression X and Y axis\n",
    "x_values = top_ten_Comparison_data[\"Freedom Average\"]\n",
    "y_values = top_ten_Comparison_data[\"Happiness Average\"]\n",
    "\n",
    "# Perform a linear regression on temperature vs. latitude\n",
    "(slope, intercept, rvalue, pvalue, stderr) = st.linregress(x_values, y_values)\n",
    "\n",
    "# Get regression values\n",
    "regress_values = x_values * slope + intercept\n",
    "\n",
    "# Create line equation string\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "print(line_eq)\n",
    "\n",
    "correlation = st.pearsonr(x_values, y_values)\n",
    "\n",
    "# Create Plot\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "\n",
    "# Label plot and annotate the line equation\n",
    "plt.xlabel(\"Freedom\")\n",
    "plt.ylabel(\"Happiness Score\")\n",
    "plt.title(f'Average Happiness Score by Freedom')\n",
    "#plt.annotate(line_eq,(5,10),fontsize=15,color=\"r\")\n",
    "\n",
    "# Print r value\n",
    "print(f\"The r-value is: {rvalue**2}\")\n",
    "\n",
    "# Save and Show plot\n",
    "plt.savefig(\"Images/Linear_Regression_Freedom.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Economy (GDP per Capita)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 0.83x + 6.26\n",
      "The r-value is: 0.18363932424794108\n"
     ]
    },
    {
     "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": [
    "# Linear regression X and Y axis\n",
    "x_values = top_ten_Comparison_data[\"Economy GDP Average\"]\n",
    "y_values = top_ten_Comparison_data[\"Happiness Average\"]\n",
    "\n",
    "# Perform a linear regression on temperature vs. latitude\n",
    "(slope, intercept, rvalue, pvalue, stderr) = st.linregress(x_values, y_values)\n",
    "\n",
    "# Get regression values\n",
    "regress_values = x_values * slope + intercept\n",
    "\n",
    "# Create line equation string\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "print(line_eq)\n",
    "\n",
    "correlation = st.pearsonr(x_values, y_values)\n",
    "\n",
    "# Create Plot\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "\n",
    "# Label plot and annotate the line equation\n",
    "plt.xlabel(\"Economy (GDP per Capita)\")\n",
    "plt.ylabel(\"Happiness Score\")\n",
    "plt.title(f'Average Happiness Score by Economy (GDP per Capita)')\n",
    "#plt.annotate(line_eq,(5,10),fontsize=15,color=\"r\")\n",
    "\n",
    "# Print r value\n",
    "print(f\"The r-value is: {rvalue**2}\")\n",
    "\n",
    "# Save and Show plot\n",
    "plt.savefig(\"Images/Linear_Regression_EconomyGDP.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 2.33x + 3.26\n",
      "The r-value is: 0.6683439315322117\n",
      "The p-value is: 6.992878703184945e-35\n"
     ]
    },
    {
     "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": [
    "# Economy (GDP per Capita) all Countries\n",
    "\n",
    "# Linear regression X and Y axis\n",
    "x_values = Comp_data_df[\"Economy GDP Average\"]\n",
    "y_values = Comp_data_df[\"Happiness Average\"]\n",
    "\n",
    "# Perform a linear regression on temperature vs. latitude\n",
    "(slope, intercept, rvalue, pvalue, stderr) = st.linregress(x_values, y_values)\n",
    "\n",
    "# Get regression values\n",
    "regress_values = x_values * slope + intercept\n",
    "\n",
    "# Create line equation string\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "print(line_eq)\n",
    "\n",
    "correlation = st.pearsonr(x_values, y_values)\n",
    "\n",
    "# Create Plot\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "\n",
    "# Label plot and annotate the line equation\n",
    "plt.xlabel(\"Economy (GDP per Capita)\")\n",
    "plt.ylabel(\"Happiness Score\")\n",
    "plt.title(f'Average Happiness Score by Economy (GDP per Capita)')\n",
    "#plt.annotate(line_eq,(5,10),fontsize=15,color=\"r\")\n",
    "\n",
    "# Print r value\n",
    "print(f\"The r-value is: {rvalue**2}\")\n",
    "print(f\"The p-value is: {pvalue}\")\n",
    "\n",
    "# Save and Show plot\n",
    "plt.savefig(\"Images/Linear_Regression_EconomyGDP_All.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "?st.linregress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Happiness Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia and New Zealand</th>\n",
       "      <td>7.294600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central and Eastern Europe</th>\n",
       "      <td>5.437643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eastern Asia</th>\n",
       "      <td>5.514850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America and Caribbean</th>\n",
       "      <td>6.003100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle East and Northern Africa</th>\n",
       "      <td>5.242200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>7.174700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southeastern Asia</th>\n",
       "      <td>5.389475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southern Asia</th>\n",
       "      <td>4.580657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub-Saharan Africa</th>\n",
       "      <td>4.190935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Europe</th>\n",
       "      <td>6.789330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Happiness Average\n",
       "Region                                            \n",
       "Australia and New Zealand                 7.294600\n",
       "Central and Eastern Europe                5.437643\n",
       "Eastern Asia                              5.514850\n",
       "Latin America and Caribbean               6.003100\n",
       "Middle East and Northern Africa           5.242200\n",
       "North America                             7.174700\n",
       "Southeastern Asia                         5.389475\n",
       "Southern Asia                             4.580657\n",
       "Sub-Saharan Africa                        4.190935\n",
       "Western Europe                            6.789330"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of Happiness per year\n",
    "Happiness_df = data[[\"Country\", \"Region\", \"Happiness Score 2015\", \"Happiness Score 2016\", \"Happiness Score 2017\", \"Happiness Score 2018\", \"Happiness Score 2019\"]]\n",
    "Happiness_df = Happiness_df.groupby(\"Region\")\n",
    "Happiness_df.mean()\n",
    "\n",
    "#Find the averages of the data\n",
    "Happ_df = Happiness_df.mean().reset_index()\n",
    "Happ_df[\"Happiness Average\"] = Happ_df.mean(axis=1)\n",
    "Happ_df\n",
    "\n",
    "# Filter the DataFrame down only to those columns to chart\n",
    "Happiness_plot = Happ_df[[\"Region\", \"Happiness Average\"]]\n",
    "\n",
    "# Set the index to be \"Region\" so they will be used as labels\n",
    "Happiness_plot = Happiness_plot.set_index(\"Region\")\n",
    "Happiness_plot.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia and New Zealand</td>\n",
       "      <td>7.294600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>North America</td>\n",
       "      <td>7.174700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Western Europe</td>\n",
       "      <td>6.789330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Latin America and Caribbean</td>\n",
       "      <td>6.003100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>5.514850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Central and Eastern Europe</td>\n",
       "      <td>5.437643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Southeastern Asia</td>\n",
       "      <td>5.389475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Middle East and Northern Africa</td>\n",
       "      <td>5.242200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>4.580657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>4.190935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Region  Happiness Average\n",
       "0        Australia and New Zealand           7.294600\n",
       "5                    North America           7.174700\n",
       "9                   Western Europe           6.789330\n",
       "3      Latin America and Caribbean           6.003100\n",
       "2                     Eastern Asia           5.514850\n",
       "1       Central and Eastern Europe           5.437643\n",
       "6                Southeastern Asia           5.389475\n",
       "4  Middle East and Northern Africa           5.242200\n",
       "7                    Southern Asia           4.580657\n",
       "8               Sub-Saharan Africa           4.190935"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp=Happiness_plot.reset_index()\n",
    "dp.sort_values(by=['Happiness Average'], inplace=True, ascending=False)\n",
    "dp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = dp[\"Region\"]\n",
    "values = dp[\"Happiness Average\"]\n",
    "\n",
    "# Change the bar colors here\n",
    "plt.bar(labels, values, color=['green', 'blue', 'red', 'magenta', 'purple', 'yellow', 'orange','grey','black','brown'])\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.ylim(4, 7.5)\n",
    "\n",
    "plt.title(\"Average Happiness Score Per Region\")\n",
    "plt.ylabel('Happiness Score (Avg)')\n",
    "plt.xlabel('Region')\n",
    "\n",
    "# Save and Show plot\n",
    "plt.savefig(\"Images/AverageHappiness_Region.png\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geopandas World Map\n",
    "\n",
    "* Geopandas provides a high-level interface to the matplotlib library for making maps. Mapping shapes is as easy as using the plot() method on a GeoSeries or GeoDataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>pop_est</th>\n",
       "      <th>continent</th>\n",
       "      <th>name</th>\n",
       "      <th>iso_a3</th>\n",
       "      <th>gdp_md_est</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>920938</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>FJI</td>\n",
       "      <td>8374.0</td>\n",
       "      <td>MULTIPOLYGON (((180.00000 -16.06713, 180.00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53950935</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>TZA</td>\n",
       "      <td>150600.0</td>\n",
       "      <td>POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>603253</td>\n",
       "      <td>Africa</td>\n",
       "      <td>W. Sahara</td>\n",
       "      <td>ESH</td>\n",
       "      <td>906.5</td>\n",
       "      <td>POLYGON ((-8.66559 27.65643, -8.66512 27.58948...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35623680</td>\n",
       "      <td>North America</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1674000.0</td>\n",
       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -122.9742...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>326625791</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>18560000.0</td>\n",
       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -120.0000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pop_est      continent                      name iso_a3  gdp_md_est  \\\n",
       "0     920938        Oceania                      Fiji    FJI      8374.0   \n",
       "1   53950935         Africa                  Tanzania    TZA    150600.0   \n",
       "2     603253         Africa                 W. Sahara    ESH       906.5   \n",
       "3   35623680  North America                    Canada    CAN   1674000.0   \n",
       "4  326625791  North America  United States of America    USA  18560000.0   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((180.00000 -16.06713, 180.00000...  \n",
       "1  POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...  \n",
       "2  POLYGON ((-8.66559 27.65643, -8.66512 27.58948...  \n",
       "3  MULTIPOLYGON (((-122.84000 49.00000, -122.9742...  \n",
       "4  MULTIPOLYGON (((-122.84000 49.00000, -120.0000...  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View first 5 rows of the World shape file data\n",
    "world.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 177 entries, 0 to 176\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   pop_est     177 non-null    int64   \n",
      " 1   continent   177 non-null    object  \n",
      " 2   name        177 non-null    object  \n",
      " 3   iso_a3      177 non-null    object  \n",
      " 4   gdp_md_est  177 non-null    float64 \n",
      " 5   geometry    177 non-null    geometry\n",
      "dtypes: float64(1), geometry(1), int64(1), object(3)\n",
      "memory usage: 8.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Inspect count of the World shape file data\n",
    "world.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "Int64Index: 128 entries, 0 to 127\n",
      "Data columns (total 13 columns):\n",
      " #   Column               Non-Null Count  Dtype   \n",
      "---  ------               --------------  -----   \n",
      " 0   pop_est              128 non-null    int64   \n",
      " 1   continent            128 non-null    object  \n",
      " 2   name                 128 non-null    object  \n",
      " 3   iso_a3               128 non-null    object  \n",
      " 4   gdp_md_est           128 non-null    float64 \n",
      " 5   geometry             128 non-null    geometry\n",
      " 6   Gov Trust Average    128 non-null    float64 \n",
      " 7   Generocity Average   128 non-null    float64 \n",
      " 8   Health Average       128 non-null    float64 \n",
      " 9   Family Average       128 non-null    float64 \n",
      " 10  Economy GDP Average  128 non-null    float64 \n",
      " 11  Freedom Average      128 non-null    float64 \n",
      " 12  Happiness Average    128 non-null    float64 \n",
      "dtypes: float64(8), geometry(1), int64(1), object(3)\n",
      "memory usage: 14.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Merge data sets\n",
    "\n",
    "# merge both data sets using name/Country as unique identifiers\n",
    "for_plotting = world.merge(Comp_data_df, left_on = 'name', right_on = 'Country')\n",
    "\n",
    "# check the occurrence\n",
    "for_plotting.info() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alvin\\anaconda3\\envs\\PythonData\\lib\\site-packages\\geopandas\\plotting.py:376: UserWarning: The GeoSeries you are attempting to plot is empty. Nothing has been displayed.\n",
      "  UserWarning,\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "cannot convert float NaN to integer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-59-24b9a71543aa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m ax = for_plotting.plot(column='Happiness Average', cmap = 'YlGnBu', figsize=(15,10), k=3, legend = True, \n\u001b[0;32m      4\u001b[0m                                 \u001b[0mlegend_kwds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'label'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m\"Happiness Score Per Country\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'orientation'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m\"horizontal\"\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m                                 missing_kwds={'color': 'lightgrey'})\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'World Happiness Report \\n 2015 - 2019'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfontdict\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'fontsize'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\geopandas\\geodataframe.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    919\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[0mthere\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    920\u001b[0m         \"\"\"\n\u001b[1;32m--> 921\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mplot_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    922\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    923\u001b[0m     \u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_dataframe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\geopandas\\plotting.py\u001b[0m in \u001b[0;36mplot_dataframe\u001b[1;34m(df, column, cmap, color, ax, cax, categorical, legend, scheme, k, vmin, vmax, markersize, figsize, legend_kwds, categories, classification_kwds, missing_kwds, aspect, **style_kwds)\u001b[0m\n\u001b[0;32m    822\u001b[0m             \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_cmap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mlegend_kwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    823\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 824\u001b[1;33m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    825\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    826\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m()\u001b[0m\n\u001b[0;32m    715\u001b[0m     \u001b[0mthe\u001b[0m \u001b[0mcurrent\u001b[0m \u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    716\u001b[0m     \"\"\"\n\u001b[1;32m--> 717\u001b[1;33m     \u001b[0mgcf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    718\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    719\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mdraw_idle\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1945\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1946\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1947\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1948\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1949\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdeprecated\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"3.2\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    391\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[0;32m    392\u001b[0m               else nullcontext()):\n\u001b[1;32m--> 393\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    394\u001b[0m             \u001b[1;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    395\u001b[0m             \u001b[1;31m# don't forget to call the superclass.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1734\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1735\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[1;32m-> 1736\u001b[1;33m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[0;32m   1737\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1738\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'figure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    135\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    136\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 137\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    138\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    139\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2628\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2629\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2630\u001b[1;33m         \u001b[0mmimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2631\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2632\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'axes'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    135\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    136\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 137\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    138\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    139\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1225\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_gid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1227\u001b[1;33m         \u001b[0mticks_to_draw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1228\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[0;32m   1229\u001b[0m                                                                 renderer)\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1101\u001b[0m         \u001b[0mthe\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m  \u001b[0mReturn\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mlist\u001b[0m \u001b[0mof\u001b[0m \u001b[0mticks\u001b[0m \u001b[0mthat\u001b[0m \u001b[0mwill\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mdrawn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1102\u001b[0m         \"\"\"\n\u001b[1;32m-> 1103\u001b[1;33m         \u001b[0mmajor_locs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_majorticklocs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1104\u001b[0m         \u001b[0mmajor_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1105\u001b[0m         \u001b[0mmajor_ticks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_majorticklocs\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1346\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_majorticklocs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1347\u001b[0m         \u001b[1;34m\"\"\"Get the array of major tick locations in data coordinates.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1348\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1349\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1350\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_minorticklocs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   2201\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2202\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2203\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2205\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[1;34m(self, vmin, vmax)\u001b[0m\n\u001b[0;32m   2209\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[0;32m   2210\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[1;32m-> 2211\u001b[1;33m         \u001b[0mlocs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2212\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2213\u001b[0m         \u001b[0mprune\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[1;34m(self, vmin, vmax)\u001b[0m\n\u001b[0;32m   2148\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'auto'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2149\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2150\u001b[1;33m                 nbins = np.clip(self.axis.get_tick_space(),\n\u001b[0m\u001b[0;32m   2151\u001b[0m                                 max(1, self._min_n_ticks - 1), 9)\n\u001b[0;32m   2152\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   2193\u001b[0m         \u001b[0msize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_size\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2194\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2195\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlength\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2196\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2197\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m31\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: cannot convert float NaN to integer"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "cannot convert float NaN to integer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(close, block)\u001b[0m\n\u001b[0;32m     41\u001b[0m             display(\n\u001b[0;32m     42\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 43\u001b[1;33m                 \u001b[0mmetadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     44\u001b[0m             )\n\u001b[0;32m     45\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.py\u001b[0m in \u001b[0;36m_fetch_figure_metadata\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    179\u001b[0m         \u001b[1;31m# the background is transparent\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    180\u001b[0m         ticksLight = _is_light([label.get_color()\n\u001b[1;32m--> 181\u001b[1;33m                                 \u001b[1;32mfor\u001b[0m \u001b[0maxes\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    182\u001b[0m                                 \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    183\u001b[0m                                 for label in axis.get_ticklabels()])\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    181\u001b[0m                                 \u001b[1;32mfor\u001b[0m \u001b[0maxes\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m                                 \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 183\u001b[1;33m                                 for label in axis.get_ticklabels()])\n\u001b[0m\u001b[0;32m    184\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mticksLight\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mticksLight\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mticksLight\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    185\u001b[0m             \u001b[1;31m# there are one or more tick labels, all with the same lightness\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_ticklabels\u001b[1;34m(self, minor, which)\u001b[0m\n\u001b[0;32m   1318\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mminor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1319\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_minorticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1320\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_majorticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1321\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1322\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_majorticklines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_majorticklabels\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1274\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_majorticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1275\u001b[0m         \u001b[1;34m'Return a list of Text instances for the major ticklabels.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1276\u001b[1;33m         \u001b[0mticks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1277\u001b[0m         \u001b[0mlabels1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mticks\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1278\u001b[0m         \u001b[0mlabels2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mticks\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_major_ticks\u001b[1;34m(self, numticks)\u001b[0m\n\u001b[0;32m   1429\u001b[0m         \u001b[1;34m'Get the tick instances; grow as necessary.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1430\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnumticks\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1431\u001b[1;33m             \u001b[0mnumticks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_majorticklocs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1432\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1433\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajorTicks\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mnumticks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_majorticklocs\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1346\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_majorticklocs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1347\u001b[0m         \u001b[1;34m\"\"\"Get the array of major tick locations in data coordinates.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1348\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1349\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1350\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_minorticklocs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   2201\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2202\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2203\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2205\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[1;34m(self, vmin, vmax)\u001b[0m\n\u001b[0;32m   2209\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[0;32m   2210\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[1;32m-> 2211\u001b[1;33m         \u001b[0mlocs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2212\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2213\u001b[0m         \u001b[0mprune\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[1;34m(self, vmin, vmax)\u001b[0m\n\u001b[0;32m   2148\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'auto'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2149\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2150\u001b[1;33m                 nbins = np.clip(self.axis.get_tick_space(),\n\u001b[0m\u001b[0;32m   2151\u001b[0m                                 max(1, self._min_n_ticks - 1), 9)\n\u001b[0;32m   2152\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   2193\u001b[0m         \u001b[0msize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_size\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2194\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2195\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlength\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2196\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2197\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m31\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: cannot convert float NaN to integer"
     ]
    }
   ],
   "source": [
    "#plot merged file.\n",
    "\n",
    "ax = for_plotting.plot(column='Happiness Average', cmap = 'YlGnBu', figsize=(15,10), k=3, legend = True, \n",
    "                                legend_kwds={'label': \"Happiness Score Per Country\",'orientation':\"horizontal\"},\n",
    "                                missing_kwds={'color': 'lightgrey'})\n",
    "                                \n",
    "ax.set_title('World Happiness Report \\n 2015 - 2019', fontdict= {'fontsize':14})\n",
    "\n",
    "plt.savefig(\"Images/WHR_ChoroplethMap.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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