COVID-19 cases-vs-World-Happiness_Data-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COVID-19 vs World-Happiness Data Analysis Notebook\n",
    "------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modules are imported.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\loicb\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "print('Modules are imported.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the COVID-19 Confirmed Cases Dataset from JHU CSSE repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_dataset_csv = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
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       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
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       "      <th>0</th>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Albania</td>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>51424</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.03390</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>89416</td>\n",
       "      <td>90014</td>\n",
       "      <td>90579</td>\n",
       "      <td>91121</td>\n",
       "      <td>91638</td>\n",
       "      <td>92102</td>\n",
       "      <td>92597</td>\n",
       "      <td>93065</td>\n",
       "      <td>93507</td>\n",
       "      <td>93933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7127</td>\n",
       "      <td>7162</td>\n",
       "      <td>7190</td>\n",
       "      <td>7236</td>\n",
       "      <td>7288</td>\n",
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       "      <td>7446</td>\n",
       "      <td>7466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15729</td>\n",
       "      <td>15804</td>\n",
       "      <td>15925</td>\n",
       "      <td>16061</td>\n",
       "      <td>16161</td>\n",
       "      <td>16188</td>\n",
       "      <td>16277</td>\n",
       "      <td>16362</td>\n",
       "      <td>16407</td>\n",
       "      <td>16484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 335 columns</p>\n",
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       "  Province/State Country/Region       Lat       Long  1/22/20  1/23/20  \\\n",
       "0            NaN    Afghanistan  33.93911  67.709953        0        0   \n",
       "1            NaN        Albania  41.15330  20.168300        0        0   \n",
       "2            NaN        Algeria  28.03390   1.659600        0        0   \n",
       "3            NaN        Andorra  42.50630   1.521800        0        0   \n",
       "4            NaN         Angola -11.20270  17.873900        0        0   \n",
       "\n",
       "   1/24/20  1/25/20  1/26/20  1/27/20  ...  12/8/20  12/9/20  12/10/20  \\\n",
       "0        0        0        0        0  ...    47716    47851     48053   \n",
       "1        0        0        0        0  ...    44436    45188     46061   \n",
       "2        0        0        0        0  ...    89416    90014     90579   \n",
       "3        0        0        0        0  ...     7127     7162      7190   \n",
       "4        0        0        0        0  ...    15729    15804     15925   \n",
       "\n",
       "   12/11/20  12/12/20  12/13/20  12/14/20  12/15/20  12/16/20  12/17/20  \n",
       "0     48116     48229     48527     48718     48952     49161     49378  \n",
       "1     46863     47742     48530     49191     50000     50637     51424  \n",
       "2     91121     91638     92102     92597     93065     93507     93933  \n",
       "3      7236      7288      7338      7382      7382      7446      7466  \n",
       "4     16061     16161     16188     16277     16362     16407     16484  \n",
       "\n",
       "[5 rows x 335 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the shape of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(271, 335)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete the useless columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>51424</td>\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>89416</td>\n",
       "      <td>90014</td>\n",
       "      <td>90579</td>\n",
       "      <td>91121</td>\n",
       "      <td>91638</td>\n",
       "      <td>92102</td>\n",
       "      <td>92597</td>\n",
       "      <td>93065</td>\n",
       "      <td>93507</td>\n",
       "      <td>93933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>7162</td>\n",
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       "      <td>7382</td>\n",
       "      <td>7382</td>\n",
       "      <td>7446</td>\n",
       "      <td>7466</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15729</td>\n",
       "      <td>15804</td>\n",
       "      <td>15925</td>\n",
       "      <td>16061</td>\n",
       "      <td>16161</td>\n",
       "      <td>16188</td>\n",
       "      <td>16277</td>\n",
       "      <td>16362</td>\n",
       "      <td>16407</td>\n",
       "      <td>16484</td>\n",
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      ],
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       "  Province/State Country/Region  1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  \\\n",
       "0            NaN    Afghanistan        0        0        0        0        0   \n",
       "1            NaN        Albania        0        0        0        0        0   \n",
       "2            NaN        Algeria        0        0        0        0        0   \n",
       "3            NaN        Andorra        0        0        0        0        0   \n",
       "4            NaN         Angola        0        0        0        0        0   \n",
       "\n",
       "   1/27/20  1/28/20  1/29/20  ...  12/8/20  12/9/20  12/10/20  12/11/20  \\\n",
       "0        0        0        0  ...    47716    47851     48053     48116   \n",
       "1        0        0        0  ...    44436    45188     46061     46863   \n",
       "2        0        0        0  ...    89416    90014     90579     91121   \n",
       "3        0        0        0  ...     7127     7162      7190      7236   \n",
       "4        0        0        0  ...    15729    15804     15925     16061   \n",
       "\n",
       "   12/12/20  12/13/20  12/14/20  12/15/20  12/16/20  12/17/20  \n",
       "0     48229     48527     48718     48952     49161     49378  \n",
       "1     47742     48530     49191     50000     50637     51424  \n",
       "2     91638     92102     92597     93065     93507     93933  \n",
       "3      7288      7338      7382      7382      7446      7466  \n",
       "4     16161     16188     16277     16362     16407     16484  \n",
       "\n",
       "[5 rows x 333 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv.drop([\"Lat\",\"Long\"],axis=1,inplace=True)\n",
    "corona_dataset_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregate rows by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>47716</td>\n",
       "      <td>47851</td>\n",
       "      <td>48053</td>\n",
       "      <td>48116</td>\n",
       "      <td>48229</td>\n",
       "      <td>48527</td>\n",
       "      <td>48718</td>\n",
       "      <td>48952</td>\n",
       "      <td>49161</td>\n",
       "      <td>49378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>44436</td>\n",
       "      <td>45188</td>\n",
       "      <td>46061</td>\n",
       "      <td>46863</td>\n",
       "      <td>47742</td>\n",
       "      <td>48530</td>\n",
       "      <td>49191</td>\n",
       "      <td>50000</td>\n",
       "      <td>50637</td>\n",
       "      <td>51424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>89416</td>\n",
       "      <td>90014</td>\n",
       "      <td>90579</td>\n",
       "      <td>91121</td>\n",
       "      <td>91638</td>\n",
       "      <td>92102</td>\n",
       "      <td>92597</td>\n",
       "      <td>93065</td>\n",
       "      <td>93507</td>\n",
       "      <td>93933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7127</td>\n",
       "      <td>7162</td>\n",
       "      <td>7190</td>\n",
       "      <td>7236</td>\n",
       "      <td>7288</td>\n",
       "      <td>7338</td>\n",
       "      <td>7382</td>\n",
       "      <td>7382</td>\n",
       "      <td>7446</td>\n",
       "      <td>7466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15729</td>\n",
       "      <td>15804</td>\n",
       "      <td>15925</td>\n",
       "      <td>16061</td>\n",
       "      <td>16161</td>\n",
       "      <td>16188</td>\n",
       "      <td>16277</td>\n",
       "      <td>16362</td>\n",
       "      <td>16407</td>\n",
       "      <td>16484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 331 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan           0        0        0        0        0        0        0   \n",
       "Albania               0        0        0        0        0        0        0   \n",
       "Algeria               0        0        0        0        0        0        0   \n",
       "Andorra               0        0        0        0        0        0        0   \n",
       "Angola                0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  12/8/20  12/9/20  12/10/20  \\\n",
       "Country/Region                             ...                               \n",
       "Afghanistan           0        0        0  ...    47716    47851     48053   \n",
       "Albania               0        0        0  ...    44436    45188     46061   \n",
       "Algeria               0        0        0  ...    89416    90014     90579   \n",
       "Andorra               0        0        0  ...     7127     7162      7190   \n",
       "Angola                0        0        0  ...    15729    15804     15925   \n",
       "\n",
       "                12/11/20  12/12/20  12/13/20  12/14/20  12/15/20  12/16/20  \\\n",
       "Country/Region                                                               \n",
       "Afghanistan        48116     48229     48527     48718     48952     49161   \n",
       "Albania            46863     47742     48530     49191     50000     50637   \n",
       "Algeria            91121     91638     92102     92597     93065     93507   \n",
       "Andorra             7236      7288      7338      7382      7382      7446   \n",
       "Angola             16061     16161     16188     16277     16362     16407   \n",
       "\n",
       "                12/17/20  \n",
       "Country/Region            \n",
       "Afghanistan        49378  \n",
       "Albania            51424  \n",
       "Algeria            93933  \n",
       "Andorra             7466  \n",
       "Angola             16484  \n",
       "\n",
       "[5 rows x 331 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated = corona_dataset_csv.groupby(\"Country/Region\").sum()\n",
    "corona_dataset_aggregated.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the shape of the new dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(191, 331)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find maximum infection rate for all countries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = list(corona_dataset_aggregated.index)\n",
    "max_infection_rates = []\n",
    "for c in countries : \n",
    "    max_infection_rates.append(corona_dataset_aggregated.loc[c].diff().max())\n",
    "corona_dataset_aggregated[\"max_infection_rate\"] = max_infection_rates"
   ]
  },
  {
   "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>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>1/30/20</th>\n",
       "      <th>1/31/20</th>\n",
       "      <th>...</th>\n",
       "      <th>12/9/20</th>\n",
       "      <th>12/10/20</th>\n",
       "      <th>12/11/20</th>\n",
       "      <th>12/12/20</th>\n",
       "      <th>12/13/20</th>\n",
       "      <th>12/14/20</th>\n",
       "      <th>12/15/20</th>\n",
       "      <th>12/16/20</th>\n",
       "      <th>12/17/20</th>\n",
       "      <th>max_infection_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>47851</td>\n",
       "      <td>48053</td>\n",
       "      <td>48116</td>\n",
       "      <td>48229</td>\n",
       "      <td>48527</td>\n",
       "      <td>48718</td>\n",
       "      <td>48952</td>\n",
       "      <td>49161</td>\n",
       "      <td>49378</td>\n",
       "      <td>915.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>45188</td>\n",
       "      <td>46061</td>\n",
       "      <td>46863</td>\n",
       "      <td>47742</td>\n",
       "      <td>48530</td>\n",
       "      <td>49191</td>\n",
       "      <td>50000</td>\n",
       "      <td>50637</td>\n",
       "      <td>51424</td>\n",
       "      <td>879.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>90014</td>\n",
       "      <td>90579</td>\n",
       "      <td>91121</td>\n",
       "      <td>91638</td>\n",
       "      <td>92102</td>\n",
       "      <td>92597</td>\n",
       "      <td>93065</td>\n",
       "      <td>93507</td>\n",
       "      <td>93933</td>\n",
       "      <td>1133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7162</td>\n",
       "      <td>7190</td>\n",
       "      <td>7236</td>\n",
       "      <td>7288</td>\n",
       "      <td>7338</td>\n",
       "      <td>7382</td>\n",
       "      <td>7382</td>\n",
       "      <td>7446</td>\n",
       "      <td>7466</td>\n",
       "      <td>299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15804</td>\n",
       "      <td>15925</td>\n",
       "      <td>16061</td>\n",
       "      <td>16161</td>\n",
       "      <td>16188</td>\n",
       "      <td>16277</td>\n",
       "      <td>16362</td>\n",
       "      <td>16407</td>\n",
       "      <td>16484</td>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 332 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan           0        0        0        0        0        0        0   \n",
       "Albania               0        0        0        0        0        0        0   \n",
       "Algeria               0        0        0        0        0        0        0   \n",
       "Andorra               0        0        0        0        0        0        0   \n",
       "Angola                0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  12/9/20  12/10/20  12/11/20  \\\n",
       "Country/Region                             ...                                \n",
       "Afghanistan           0        0        0  ...    47851     48053     48116   \n",
       "Albania               0        0        0  ...    45188     46061     46863   \n",
       "Algeria               0        0        0  ...    90014     90579     91121   \n",
       "Andorra               0        0        0  ...     7162      7190      7236   \n",
       "Angola                0        0        0  ...    15804     15925     16061   \n",
       "\n",
       "                12/12/20  12/13/20  12/14/20  12/15/20  12/16/20  12/17/20  \\\n",
       "Country/Region                                                               \n",
       "Afghanistan        48229     48527     48718     48952     49161     49378   \n",
       "Albania            47742     48530     49191     50000     50637     51424   \n",
       "Algeria            91638     92102     92597     93065     93507     93933   \n",
       "Andorra             7288      7338      7382      7382      7446      7466   \n",
       "Angola             16161     16188     16277     16362     16407     16484   \n",
       "\n",
       "                max_infection_rate  \n",
       "Country/Region                      \n",
       "Afghanistan                  915.0  \n",
       "Albania                      879.0  \n",
       "Algeria                     1133.0  \n",
       "Andorra                      299.0  \n",
       "Angola                       355.0  \n",
       "\n",
       "[5 rows x 332 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop the useless columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_data=pd.DataFrame (corona_dataset_aggregated[\"max_infection_rate\"])"
   ]
  },
  {
   "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>max_infection_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>915.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>879.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>1133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                max_infection_rate\n",
       "Country/Region                    \n",
       "Afghanistan                  915.0\n",
       "Albania                      879.0\n",
       "Algeria                     1133.0\n",
       "Andorra                      299.0\n",
       "Angola                       355.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the World Happiness Report dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv = pd.read_csv (\"data/worldwide_happiness_report.csv\")"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Overall rank Country or region  Score  GDP per capita  Social support  \\\n",
       "0             1           Finland  7.769           1.340           1.587   \n",
       "1             2           Denmark  7.600           1.383           1.573   \n",
       "2             3            Norway  7.554           1.488           1.582   \n",
       "3             4           Iceland  7.494           1.380           1.624   \n",
       "4             5       Netherlands  7.488           1.396           1.522   \n",
       "\n",
       "   Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
       "0                    0.986                         0.596       0.153   \n",
       "1                    0.996                         0.592       0.252   \n",
       "2                    1.028                         0.603       0.271   \n",
       "3                    1.026                         0.591       0.354   \n",
       "4                    0.999                         0.557       0.322   \n",
       "\n",
       "   Perceptions of corruption  \n",
       "0                      0.393  \n",
       "1                      0.410  \n",
       "2                      0.341  \n",
       "3                      0.118  \n",
       "4                      0.298  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop the useless columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "useless_cols= [\"Overall rank\",\"Score\"]"
   ]
  },
  {
   "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>Country or region</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norway</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country or region  GDP per capita  Social support  Healthy life expectancy  \\\n",
       "0           Finland           1.340           1.587                    0.986   \n",
       "1           Denmark           1.383           1.573                    0.996   \n",
       "2            Norway           1.488           1.582                    1.028   \n",
       "3           Iceland           1.380           1.624                    1.026   \n",
       "4       Netherlands           1.396           1.522                    0.999   \n",
       "\n",
       "   Freedom to make life choices  Generosity  Perceptions of corruption  \n",
       "0                         0.596       0.153                      0.393  \n",
       "1                         0.592       0.252                      0.410  \n",
       "2                         0.603       0.271                      0.341  \n",
       "3                         0.591       0.354                      0.118  \n",
       "4                         0.557       0.322                      0.298  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.drop(useless_cols,axis=1, inplace=True)\n",
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change the indices of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv.set_index(\"Country or region\",inplace = True)"
   ]
  },
  {
   "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>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country or region</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>Finland</th>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   GDP per capita  Social support  Healthy life expectancy  \\\n",
       "Country or region                                                            \n",
       "Finland                     1.340           1.587                    0.986   \n",
       "Denmark                     1.383           1.573                    0.996   \n",
       "Norway                      1.488           1.582                    1.028   \n",
       "Iceland                     1.380           1.624                    1.026   \n",
       "Netherlands                 1.396           1.522                    0.999   \n",
       "\n",
       "                   Freedom to make life choices  Generosity  \\\n",
       "Country or region                                             \n",
       "Finland                                   0.596       0.153   \n",
       "Denmark                                   0.592       0.252   \n",
       "Norway                                    0.603       0.271   \n",
       "Iceland                                   0.591       0.354   \n",
       "Netherlands                               0.557       0.322   \n",
       "\n",
       "                   Perceptions of corruption  \n",
       "Country or region                             \n",
       "Finland                                0.393  \n",
       "Denmark                                0.410  \n",
       "Norway                                 0.341  \n",
       "Iceland                                0.118  \n",
       "Netherlands                            0.298  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Join COVID-19 Confirmed Cases Dataset and World Happiness Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### COVID-19 Confirmed Cases Dataset:"
   ]
  },
  {
   "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>max_infection_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>915.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>879.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>1133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                max_infection_rate\n",
       "Country/Region                    \n",
       "Afghanistan                  915.0\n",
       "Albania                      879.0\n",
       "Algeria                     1133.0\n",
       "Andorra                      299.0\n",
       "Angola                       355.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(191, 1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### World happiness report Dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country or region</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>Finland</th>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   GDP per capita  Social support  Healthy life expectancy  \\\n",
       "Country or region                                                            \n",
       "Finland                     1.340           1.587                    0.986   \n",
       "Denmark                     1.383           1.573                    0.996   \n",
       "Norway                      1.488           1.582                    1.028   \n",
       "Iceland                     1.380           1.624                    1.026   \n",
       "Netherlands                 1.396           1.522                    0.999   \n",
       "\n",
       "                   Freedom to make life choices  Generosity  \\\n",
       "Country or region                                             \n",
       "Finland                                   0.596       0.153   \n",
       "Denmark                                   0.592       0.252   \n",
       "Norway                                    0.603       0.271   \n",
       "Iceland                                   0.591       0.354   \n",
       "Netherlands                               0.557       0.322   \n",
       "\n",
       "                   Perceptions of corruption  \n",
       "Country or region                             \n",
       "Finland                                0.393  \n",
       "Denmark                                0.410  \n",
       "Norway                                 0.341  \n",
       "Iceland                                0.118  \n",
       "Netherlands                            0.298  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156, 6)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>max_infection_rate</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>915.0</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>879.0</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>1133.0</td>\n",
       "      <td>1.002</td>\n",
       "      <td>1.160</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>18326.0</td>\n",
       "      <td>1.092</td>\n",
       "      <td>1.432</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>2476.0</td>\n",
       "      <td>0.850</td>\n",
       "      <td>1.055</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             max_infection_rate  GDP per capita  Social support  \\\n",
       "Afghanistan               915.0           0.350           0.517   \n",
       "Albania                   879.0           0.947           0.848   \n",
       "Algeria                  1133.0           1.002           1.160   \n",
       "Argentina               18326.0           1.092           1.432   \n",
       "Armenia                  2476.0           0.850           1.055   \n",
       "\n",
       "             Healthy life expectancy  Freedom to make life choices  \\\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",
       "             Generosity  Perceptions of corruption  \n",
       "Afghanistan       0.158                      0.025  \n",
       "Albania           0.178                      0.027  \n",
       "Algeria           0.073                      0.114  \n",
       "Argentina         0.066                      0.050  \n",
       "Armenia           0.095                      0.064  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = corona_data.join(happiness_report_csv,how=\"inner\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Correlation matrix "
   ]
  },
  {
   "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>max_infection_rate</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max_infection_rate</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.116368</td>\n",
       "      <td>0.083433</td>\n",
       "      <td>0.088402</td>\n",
       "      <td>-0.091153</td>\n",
       "      <td>-0.112274</td>\n",
       "      <td>0.004334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP per capita</th>\n",
       "      <td>0.116368</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.757521</td>\n",
       "      <td>0.859431</td>\n",
       "      <td>0.394799</td>\n",
       "      <td>-0.097441</td>\n",
       "      <td>0.311759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social support</th>\n",
       "      <td>0.083433</td>\n",
       "      <td>0.757521</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.751632</td>\n",
       "      <td>0.456317</td>\n",
       "      <td>-0.060586</td>\n",
       "      <td>0.203333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <td>0.088402</td>\n",
       "      <td>0.859431</td>\n",
       "      <td>0.751632</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.423146</td>\n",
       "      <td>-0.052984</td>\n",
       "      <td>0.311566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <td>-0.091153</td>\n",
       "      <td>0.394799</td>\n",
       "      <td>0.456317</td>\n",
       "      <td>0.423146</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.259240</td>\n",
       "      <td>0.446845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>-0.112274</td>\n",
       "      <td>-0.097441</td>\n",
       "      <td>-0.060586</td>\n",
       "      <td>-0.052984</td>\n",
       "      <td>0.259240</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.326430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <td>0.004334</td>\n",
       "      <td>0.311759</td>\n",
       "      <td>0.203333</td>\n",
       "      <td>0.311566</td>\n",
       "      <td>0.446845</td>\n",
       "      <td>0.326430</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              max_infection_rate  GDP per capita  \\\n",
       "max_infection_rate                      1.000000        0.116368   \n",
       "GDP per capita                          0.116368        1.000000   \n",
       "Social support                          0.083433        0.757521   \n",
       "Healthy life expectancy                 0.088402        0.859431   \n",
       "Freedom to make life choices           -0.091153        0.394799   \n",
       "Generosity                             -0.112274       -0.097441   \n",
       "Perceptions of corruption               0.004334        0.311759   \n",
       "\n",
       "                              Social support  Healthy life expectancy  \\\n",
       "max_infection_rate                  0.083433                 0.088402   \n",
       "GDP per capita                      0.757521                 0.859431   \n",
       "Social support                      1.000000                 0.751632   \n",
       "Healthy life expectancy             0.751632                 1.000000   \n",
       "Freedom to make life choices        0.456317                 0.423146   \n",
       "Generosity                         -0.060586                -0.052984   \n",
       "Perceptions of corruption           0.203333                 0.311566   \n",
       "\n",
       "                              Freedom to make life choices  Generosity  \\\n",
       "max_infection_rate                               -0.091153   -0.112274   \n",
       "GDP per capita                                    0.394799   -0.097441   \n",
       "Social support                                    0.456317   -0.060586   \n",
       "Healthy life expectancy                           0.423146   -0.052984   \n",
       "Freedom to make life choices                      1.000000    0.259240   \n",
       "Generosity                                        0.259240    1.000000   \n",
       "Perceptions of corruption                         0.446845    0.326430   \n",
       "\n",
       "                              Perceptions of corruption  \n",
       "max_infection_rate                             0.004334  \n",
       "GDP per capita                                 0.311759  \n",
       "Social support                                 0.203333  \n",
       "Healthy life expectancy                        0.311566  \n",
       "Freedom to make life choices                   0.446845  \n",
       "Generosity                                     0.326430  \n",
       "Perceptions of corruption                      1.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>max_infection_rate</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>915.0</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>879.0</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>1133.0</td>\n",
       "      <td>1.002</td>\n",
       "      <td>1.160</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>18326.0</td>\n",
       "      <td>1.092</td>\n",
       "      <td>1.432</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>2476.0</td>\n",
       "      <td>0.850</td>\n",
       "      <td>1.055</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             max_infection_rate  GDP per capita  Social support  \\\n",
       "Afghanistan               915.0           0.350           0.517   \n",
       "Albania                   879.0           0.947           0.848   \n",
       "Algeria                  1133.0           1.002           1.160   \n",
       "Argentina               18326.0           1.092           1.432   \n",
       "Armenia                  2476.0           0.850           1.055   \n",
       "\n",
       "             Healthy life expectancy  Freedom to make life choices  \\\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",
       "             Generosity  Perceptions of corruption  \n",
       "Afghanistan       0.158                      0.025  \n",
       "Albania           0.178                      0.027  \n",
       "Algeria           0.073                      0.114  \n",
       "Argentina         0.066                      0.050  \n",
       "Armenia           0.095                      0.064  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting GDP per capita vs maximum infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20b28a8f288>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data [\"GDP per capita\"]\n",
    "y = data [\"max_infection_rate\"]\n",
    "sns.scatterplot (x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": [
    "sns.regplot(x, np.log(y))\n",
    "# Saving the Seaborn Figure:\n",
    "plt.savefig('img/GDPConfirmed.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Social support vs maximum infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20b28e4eb88>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data [\"Social support\"]\n",
    "y = data [\"max_infection_rate\"]\n",
    "sns.scatterplot (x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": [
    "sns.regplot(x, np.log(y))\n",
    "# Saving the Seaborn Figure:\n",
    "plt.savefig('img/SocialConfirmed.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Healthy life expectancy vs maximum infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20b28f020c8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data [\"Healthy life expectancy\"]\n",
    "y = data [\"max_infection_rate\"]\n",
    "sns.scatterplot (x,np.log(y))"
   ]
  },
  {
   "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": [
    "sns.regplot(x, np.log(y))\n",
    "# Saving the Seaborn Figure:\n",
    "plt.savefig('img/LifeConfirmed.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Freedom to make life choices vs maximum infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20b270420c8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data [\"Freedom to make life choices\"]\n",
    "y = data [\"max_infection_rate\"]\n",
    "sns.scatterplot (x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.regplot(x, np.log(y))\n",
    "# Saving the Seaborn Figure:\n",
    "plt.savefig('img/FreedomConfirmed.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Generosity vs maximum infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20b290af208>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x05grgZ7usW/isOmWl/bV7hm+tK8Qy3mE7bVH0XPNc9pISy9LK00a/N39raX/Z8TfnOyp1dt94vnB0cBfvu2CGzdx87lHj/ndsOmWTrOaPcPOCGJjFL32MD3XWkEeyGTaKKoPLFVSSSsFrtYpreo56W15U6u3a1CzB7xveHwPPcxkoJ7uzpqbmFdfUUR1HmF77UEnq9SbrPbcwN7MrW0T9VpHzezuJhJGmJz/6yq/MbMuYGm0zcmeWr3dPUMjseRuZ/V0M3/mNC4+6fDRap/5M6cxq6f5tED5PPr2mzo6VrFnaCTUB0vQnmu9q4xvnnVk5gY8Nc4hWRUk5/8p4H8BPWb2YvlmYAi4Isa2ZUKtPO3Bc6bHkrvt6DAWzullxrQpTVflVKcpymWkz7740pixiitPW8asnnBbFU4W9OpdZdRLa6V5wFMVOpJVQXL+nwc+b2afd/dPtaBNsYtyULFebxdoOHc7UfuanWA10cDuftO6OO3qTbH3YuuNDfR0d2ZuwFMVOpJVYdI+D5jZ/u7+AoCZzQKOcffvNnJgM/s4cCbFCqJfAqe7+0uNPFYYcdSi1wvIjQTMuGvlJ0pT7BsutKQXW6+qZVZPN7N6ujM14KkKHcmqMAu7Pejub6i67RfufkTog5odAPyE4s5gg2b2beBf3f2aer8T1cJuad+YPO72TbT/b3dXZ8PHDns1NTxcYNvuvewbKTCls4N5+02lqyubq4XkuTxV0i+Khd1qvTNDTRKr8bs9ZrYPmA78dxOPFVjac7Rxt2+iNEWjvdiwVyuFgvNo/+7MlXTWo7WOJIvCdLU2mNk/mNmhZvZqM7sU2NjIQd39KeALwG+Bp4EX3P2O6vuZ2Woz22BmG/r7+xs51DhpX48+7vZNVI7Z6PaQ2weGuPTOLVy0YjE3rD6Ki1Ys5tI7t9Qt0dR2hSLJC5P26QUuAt5RuukO4HPuPhD6oGazKS4T8T5gJ/Ad4EZ3/3q934kq7ZP29WeiaN9kaYio0xTPvjDIr/sHWHPTy1VCa1cu4Xf7epm/f8+4+0+Uejpg9vSG2yEi4zWd9ikF+U+a2X7uvrvJ9rwDeNzd+0uNuxk4Gqgb/KOS9lmUzbYvyIdH1GmKEWc08EOxJ7/mpk18++y31Lx/oxUyUX5oKU8veRdmhu/RZrYZ2Fz6/vVm9tUGj/tb4Cgzm25mBhwHPNLgY4WW9lmUzbQvTEql3taTYXmdzWDqXVU2sm1dlDNpo56VW+8Y23a9xG+fH+CpHXt4fqDx51ckDmEGbC8F3gXcCuDuD5nZ2xo5qLvfb2Y3Aj8HhoFfoAljkQg6YNxMeqm61zylqyP0ZjBhr26inEkb96zcWs/tulVLmD9zGgvn9KausyH5FKq2zt2frLqp4RIUd/+su7/W3Q9391PdfW+jjyUv6+nu5GsffhM3rD6Ky09dyhEHzqoZiBsddK3Va9790nDonnzYq5taH2p9+01laHgk9JVL3BVVtZ7bC27cxBPb92hQW1IjTM//STM7GnAz6wbOp4WpGplcoeA8++LeMZu9lHuc1YG40QBYK7CddvUD3PrR5bGOo1SPExxx4CwuPGER77vivgmvXGrl9qOelVt9jEKh9mS56d2dqSkpFgnT8z8HOA84ANgKvKH0vbRAkPx8vR7nftO6xgXiRktK6+5XPDQS+ThK5Tk7znVnvHm0zecfd9i4ZbOrr1zq5fZn90wJfaUyURurj/HcwBDHL5435n4LZvewZ2iE4hCXSPKCLOy21t3XAMe6+wda0CapEjQ/Xy8w11pKupEJXYWCY2bceM5b2D4wxGXrH+MXT+6MZZ5EvXO+9aPLGRwaYaTOIHNlz3qi3H5UFV+1jnH29Rv5xplHsvnpXeM28+nuNPp37VWVkSQuSNrnj8zsM8CnKNbjS4sFHaAMk84IO+haKxivXbmEa+99nI+/c1Hka9lMdM4HzJ5O/669k57rRKmtqMpd6x2jw4yvfuCNzJ7eTcGdkYJz95ZnmdI5l7Ov35jKOSZxUEltegVJ+9wGPAcsMbMXzWxX5f8xt08Inp8PW0IZZtC1VjBec9Mm/ubEw2MJXpOdc5BzjWu2dGU6ykrLUFcfY/rUDjrMeP+V9/H2des57eoHWHbIXL74w181NbM5qvLcVmhFSa00LsiSzhcAF5jZ99z9pBa0SaoE7dHHOYGtXjB291h6crUGeM8/7jBGvBj85vR2T3qucay4WX0FdPzieVz2waWc8/WxvfmRAqO3QfG5OufrG/m79/w+K5ceyKyeKewc3Mdl6x8LPAic9tnp1bTRTbqFmeGrwE8yl7Fhglhci4y1et36ynPu228qF56waNwmM4vmz5jwXOP4MKxcx6gcwL//4Fa+ffZbcPfRYzz9wmDND8tXzerhtKsfGDcWEPTYWQqmaV9EMe8CB38zew+wFphHcScvA9zdZ8bUttSJs+c12QYuSS9J0ep16yvPuVAosPnpXfz9e18/2lsOGvSi/jAsFAp86OhDxq1j1Gkwf9bL6xLV+7B8YvuecdVYN597dKBjZy2YaqObdAtT6nkJcKK77+/uM919Rp4CP8S3GmWQ3GjSS1I0uuJns8ec09vNc7uHuOh7D/O+K+7j4h9s5hPvWjQ6wavV6q1jNFKVxq41JnH5B5fypbseHXO/etVYtaR9RdpqjSzjIa0TZpLXs+6e60ldcfW8Jrqcn9PbnZpqiSTWrd8+MMTZVbnzNTdt4uKTDg8d9KJI2QVdx6jW1VpnB/TvHjuRPUzwztquYWm4YpX6wgT/DWZ2A/BdYPQv2N1vjrxVKRXXZexEHypbnt3FpXduYeXSA5nT283g0DCv2r8nNbtexT0GUu+5OWRub6igF1XKLmw5beWHZaHgTQXvLAZTbXSTXmGC/0xgD3B8xW0O5Cb4x9XzqhdQzIxL79wyLsd8+alL+b1XzEz8Td+K6pN6z830qZ2RrHYadrC0mb+BKIJ3o8FU9fZSLfBmLkmLajOXZsXxJqoXRH9n+hQe3PoCF/9g87jgl4YKj1bshxzVB0yUG8hkLZBmrURUotXwZi5mdqG7X2JmX6bY0x/D3c+PqI2ZEMdlbL0e4faBIeb0docaZ2hlYBoaHqFvv6ljyh7r1a2HbVfl/efPnMrN5x7NvuFCw+cUVcqu3K5CoYCZMTQ8Mvo6pTWQZq1EVFojSNqnPMibfLe7jdX6UCnn+IMGrVb38Hq6O8fV31fWrVcG8JGC87f/spk7Nm8LtMH7ROdRnuUa5gMuipRduV21UnFXnraMw/r2Y8fgvtRdEWStRFRaI7K0j5l92d3/IpIHqyEtaZ9WGx4usGXbrkDrwbQiDVNp266XeM9X7x13vJvPPZq5vVNrrgX0hdu3jC4GV69dE53HnN7uyDahCRucy+26aMXimqm4b555JKdcdX/qUiut/ruQdKmX9omyZGR5hI8lJV1dHfzeK2YGqq+v18MrFAqxrAezb7j2uvX7hgt11wI655hDR7+v1/OcqKfazFyLZudKlNs1q2dKzfZt27U38jkgUVC9vdQSptpHEhJ0nKFWXvv4xfN4bmAolpUkJ8qj1wvgs3qmjLlfVI/bihRGuV07B/fVbF91oE9LamWiKqOsDV5LdNJRLC6RqNXD+8y7F48Gfoi2RzpRj7LebNRy4Jyo59nI47Zilmu5XTdtfJK1K5eMnb176lJu2jh2l9M0zb6tddWjVTfzLcqc/y/c/YhIHqyGvOb8w6ruyQ0Nj9Qscbz7wmNZMKsntjWJag3aXn7qUub2dtPR0RGq2meyx21lbr2y2mfEGV3MbXbPFB7t352pckqNBeRDw6WeFQ8wzd1fqrptrrs/V/r2i022USJQnSKqt+nJY9t2M7B3uOngVC8lFXRCU70g3+zjxmWiFFzWZt+qCijfwqR9fmZmR5W/MbOVwL3l7939mgjblXlp2XSjVgpl7colfOmuR2MfkJxsgLXRtEPSi9zVk9Z21ZO1heIkWmEGfE8Brjaz9cCrgDnAH8bRqKxLOjVRqdxTvmH1UWzdMcjOwX2j5ZbApL28OAcENfkoWVlbKE6iFWYzl1+a2eeA64FdwNvcfWtsLcuwtAW1jg6ju6uTv/rOQ6FmuMb9Iaa0Q7KSTqFJsgKnfczsn4C/BJYApwPfN7Pz4mpYlqUxqDVS691MTX2QtFeUaYe0pNmyJmupKolOmLTPw8CZXiwPeryU//+HeJqVbWncwaiRXl6jH2JBrxiiSjukKc0mkhVa1TMG7RKMGi0FDPN7UYwpqGRRpL4oSj0PAz4PLAamlW9391c32KBZwFXA4RRXCz3D3X/ayGOlTbvkUhvtmYe5YohildQgx9NMVpGxwqR9vgZ8FrgUOJZi3r+Zd88XgdvcfZWZdQPhFlVPuXbYwajRD7FWp70mO167XImJRClMnX+Pu99FMVX0hLv/DQ2WeprZTOBtwD8BuPuQu+9s5LHaVVoGMBsZEGz1QmKTHa+ZgWuRdhWm5/+SmXUAj5rZR4GngHkNHvfVQD/wNTN7PbAR+Ji7D1TeycxWA6sBDjrooAYPlT1Z76m2Ou012fHSWH0lkrQwPf+/pJiaOR9YCnwQOK3B43YBbwT+X2k9oAHgk9V3cvcr3H2Zuy/r6+tr8FDZ0w491VaXEJaP98r9i73/p18YHL1i0kzWfEjL1XJWhOn5O8UJXgcDU0q3XUmx7j+srcBWd7+/9P2N1Aj+eZX3nmqjWz4WCoWay1cf1refZrK2uaxfLSchTPD/BnAB8Eug0MxB3f0ZM3vSzBa5+xbgOGBzM4/ZTuIaMM1CxUvYN3Hl/at32KqcWR1VGioLz2EepW1WfRaECf797n5rhMf+C+AbpUqf/6JYPSTEs+ZKVnpGYd/Elfevt8PW4NAw26HpQJ2V5zCP8n613Igwwf+zZnYVcBewt3yju9/cyIHd/UFg3MQDiWfANCs9o7Bv4sr719th65FndnHxDzaPBmqgod57Vp7DPErjrPq0CzPgezrwBuAE4I9L/1bE0SiJfsA0Kz2jsIOzlfe/bP1j43bYWrtyCZetf2w0UO8cHGp496pmn0MNSMZH+xSHF6bn/3p3//3YWiKxqtczGik4hYKnJm0RNuVVef9fPLmTa+99nG+eeSQAjzyza8zy1eXN5Z954SX+/r2vZ+fgPi5b/1jg3nszvUuljOLVLrPqWynw2j5mdiVwqbsnMjCbpbV90qhQcB555sUxlTBrVy7h2nsf53MnL4klbdHo4Gij1T6V998+MDRuvZ/jF8/j/ONewzlfH/scfOH2LXzllCM4YPbEk8ybCeB5Xn9Ig+TJanptH+CtwIfM7HGKOX8D3N0bKfWUFuvoMOb2dnPRisXM6pkyZlOXz/5x9KmfZgJl2KUxat2/1hXEZ969mFOuun9Mzn7NTZu4+KTDxywFUQ5UU7o66OowBodeDlqN9i6zknaLmq540itM8D8htlZIS3R0dIwphYT4BsWSHhytlQaoF4APmdvLnN7umoFq3aolXHLbFvp37x0NWo20P68Dkkn/HUh9gQd8S+v5jPsXZ+MkWq0cFEtDT7d60LzeYPL0qZ10dFjNQHXBjZs455hDm55l3a4DkpMNYqfh70BqC9Pzl4xr5aBYGnu69QaT5/YWe6D1AtWsnimjXzcatGo997N7pmQ6Fx4kpZPGvwMpUvDPmVYtNZ3GzcEn+/CrF6h2Du4b/bqZoFX53LdDLjxISieNfwdSpJ28JDbDwwW27d7L8EiBrs4O5u03la6uMFNLWitozj+K4JyG6p9mq3Ce2rGH5Wt/NO72e9YcO6ZyStU+yYqi2kcksELBebR/d6Z6ttVXBuVqn6+cckTkQSvpXHgUVx5BUzrtsLFRO0pvN0wyrdFlqScaQGzFDNnKQeJ5M6bxO73xLEud9DLTUSwb3q6D2Hmhnr/EopGe7US9USDzOfJKSefCo7jy0KzabFPwl1g0UuUx0QAi0Fb14kkHzqiqcJTSyS6lfSQWjaQEJuqNJp0jj0OrdzurpJSNqOefM62qvGikZztZb1T14tFJ+spDkqeef46Uc+qNLGfciLA924l6o+qpRq/RKw8tTd0eVOefI2moLZ/MRFcmqhdPXjtMTsubenX+6vnnSBby5hP1RluZI1fvtrYoSihhXIgAAAnISURBVEQlHZTzz5EpXR018+ZTUjzrNgl56d02ciWVhQ6EBKN3fY50dRjrVo3d5nDdqiV0tVFAi0LY3m0WrxIaHf9JenKaREfBP0cGh0a45LYtXLRiMTesPoqLVizmktu2MDiU7V5b1ME3TO+21YPoUWk0faOB9/ahtE+OdHd10r97L2dfv3H0trT12hrZwjHqFE2YCVBZ3ayk0fSNSkTbh3r+OZL2Xlsjveg4BiDDPE9ZzYE3k75JcnKaREc9/xxJe6+tkV50HME3zPOU1c1Kkl5bSJKn4J8zaV6LpZFAHlfwDfo8ZTWIpr0jIPFT8JfUaCSQJx18sxxE09wRkPhphq+kRjnnf+mdW1i59EDm9HYzb8ZUXrV/z4Q7gGnmr0h9qdzJy8w6gQ3AU+6+Ism2SPI6OozD+vbjY+94DWdfvzFw9U5cPVh9qEg7S7ra52PAIwm3QVJkx+C+0cAPyS0fkNX6fZGgEgv+ZrYAeDdwVVJtkGhFMdkqLaWTWsNG2l2SaZ//C1wIzKh3BzNbDawGOOigg1rULGlEVJOt0lI6mZYPIZG4JNLzN7MVwDZ33zjR/dz9Cndf5u7L+vr6WtQ6aURUPeW0TERrxRo2WVwTSNpHUj3/5cCJZvZHwDRgppl93d0/mFB7pElR9ZTjLJ0MM4AbdwlpXlYOlfRKJPi7+6eATwGY2THAJxT4sy3KdE0c1Tthg23c9ftZXRNI2kfS1T7SJtKSrqmnkbRUnGvYaExBkpb4DF93Xw+sT7gZ0qS0z3RNW7BNy8C25Jd6/hKZNK/2mLZNSNJ+pSTtT8s7SC6Eyfm3amavZhBLK6RyeQeRVgmalmplFY4WVpMkKe0juREkLaWZvdHQHIb0U89fpELaBoazSHMYskE9f5EKaRsYziJdPWWDgr9IBVXhNE9XT9mgtI9IhbTPV8gCzWHIBvX8Raqkeb5CFujqKRvU8xeRSOnqKRsU/EUkcprDkH5K+4iI5JCCv4hIDin4i4jkkIK/iEgOKfiLiOSQqn1EYqalmyWNFPxFYqRFziStlPYRiZEWOZO0UvAXiZEWOZO0UvAXiZGWiJa0UvAXiZEWOZO00oCvSIy0yJmklYK/SMy0yJmkkdI+IiI5pOAvIpJDCv4iIjmk4C8ikkMK/iIiOWTunnQbAjGzfuCJiB92LvBcxI+ZVnk517ycJ+hc21XU53qwu/dV35iZ4B8HM9vg7suSbkcr5OVc83KeoHNtV606V6V9RERySMFfRCSH8h78r0i6AS2Ul3PNy3mCzrVdteRcc53zFxHJq7z3/EVEcikXwd/MTjCzLWb2azP7ZI2fTzWzG0o/v9/MFra+lc0LcJ5vM7Ofm9mwma1Koo1RCXCu/9PMNpvZJjO7y8wOTqKdUQhwrueY2S/N7EEz+4mZLU6inVGY7Fwr7rfKzNzMMlsBFOB1/bCZ9Zde1wfN7MxIG+Dubf0P6AQeA14NdAMPAYur7nMucFnp6z8Dbki63TGd50JgCXAdsCrpNsd8rscC00tf/3kWX9MQ5zqz4usTgduSbndc51q63wzgbuA+YFnS7Y7xdf0w8JW42pCHnv+bgV+7+3+5+xDwz8BJVfc5Cbi29PWNwHFmlrUF1yc9T3f/jbtvAgpJNDBCQc71R+6+p/TtfcCCFrcxKkHO9cWKb3uBrA7kBXmvAlwMXAK81MrGRSzoucYmD8H/AODJiu+3lm6reR93HwZeAOa0pHXRCXKe7SLsuX4E+LdYWxSfQOdqZueZ2WMUg+L5LWpb1CY9VzM7AjjQ3X/QyobFIOjf8MpS6vJGMzswygbkIfjX6sFX94yC3Cft2uEcggp8rmb2QWAZsC7WFsUn0Lm6+z+6+6HAGuAzsbcqHhOeq5l1AJcCf9WyFsUnyOv6fWChuy8BfsjL2YlI5CH4bwUqPzEXAP9d7z5m1gXsDzzfktZFJ8h5totA52pm7wA+DZzo7ntb1LaohX1d/xn4k1hbFJ/JznUGcDiw3sx+AxwF3JrRQd9JX1d3317xd3slsDTKBuQh+P8MOMzMDjGzbooDurdW3edW4EOlr1cB/+6lEZcMCXKe7WLScy2lBy6nGPi3JdDGqAQ518Mqvn038GgL2xelCc/V3V9w97nuvtDdF1IcyznR3Tck09ymBHldX1nx7YnAI5G2IOlR7xaNrP8R8CuKo+ufLt32vyn+4QBMA74D/Bp4AHh10m2O6TzfRLHHMQBsB/4j6TbHeK4/BJ4FHiz9uzXpNsd4rl8E/qN0nj8CXpd0m+M616r7riej1T4BX9fPl17Xh0qv62ujPL5m+IqI5FAe0j4iIlJFwV9EJIcU/EVEckjBX0QkhxT8RURySMFf2pqZzTezb5rZf5nZRjP7qZmdnHS7ysxsmZl9qfT1MWZ2dNJtknzoSroBInEpLc73XeBadz+ldNvBFCfMxHXMLi+uDxWIFycolScpHQPsBu6NoWkiY6jnL+3sD4Ehd7+sfIO7P+HuXzazTjNbZ2Y/Ky2cdTaM9r7XlxbS+k8z+0Z5hVczW2pmPy5dQdxenoFZuv//MbMfAx8zs4NLewiU9xI4qHS/95rZw2b2kJndXXG8H5T2kDgH+Hhp7fY/MLPHzWxK6X4zzew35e9FmqWev7Sz1wE/r/OzjwAvuPubzGwqcI+Z3VH62RGl3/1v4B5guZndD3wZOMnd+83sfcDngDNKvzPL3d8OYGbfB65z92vN7AzgSxTX2/lr4F3u/pSZzapsjLv/xswuA3a7+xdKj7Oe4nIN36U4/f8md9/X5HMiAij4S46Y2T8CbwWGgCeAJfbyjmb7A4eVfvaAu28t/c6DFDfB2UlxUbE7SxcCncDTFQ9/Q8XXbwHeU/r6eorLLEPxg+QaM/s2cHOAJl8FXEgx+J8OnBXwVEUmpeAv7ew/gJXlb9z9PDObSzHH/lvgL9z99spfMLNjgMoVQEcovk+M4lpIb6lzrIEJ2uGl459jZkdS7M0/aGZvmKjx7n6PmS00s7cDne7+8ET3FwlDOX9pZ/8OTDOzP6+4bXrp/9uBP6/Iqb/GzHoneKwtQJ+ZvaV0/ylm9ro6972XYpoG4APAT0q/c6i73+/ufw08x9glfQF2UVy2uNJ1wLeAr03QNpHQFPylbXlx1cI/Ad5eGjx9gOKGGGsoplQ2Az83s4cpLv9c90rYi1vtrQLWmtlDFFfQrFeWeT5wupltAk4FPla6fZ0VN1p/mOIetA9V/d73gZPLA76l274BzKb4ASASGa3qKZJipTGJk9z91KTbIu1FOX+RlDKzLwP/g+K67yKRUs9fRCSHlPMXEckhBX8RkRxS8BcRySEFfxGRHFLwFxHJIQV/EZEc+v93nJhsc37VSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data [\"Generosity\"]\n",
    "y = data [\"max_infection_rate\"]\n",
    "sns.scatterplot (x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "sns.regplot(x, np.log(y))\n",
    "# Saving the Seaborn Figure:\n",
    "plt.savefig('img/GenerosityConfirmed.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Perceptions of corruption vs maximum infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20b28f8b588>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data [\"Perceptions of corruption\"]\n",
    "y = data [\"max_infection_rate\"]\n",
    "sns.scatterplot (x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": [
    "sns.regplot(x, np.log(y))\n",
    "# Saving the Seaborn Figure:\n",
    "plt.savefig('img/CorruptionConfirmed.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplots(figsize=(18, 12))\n",
    "sns.heatmap(data.corr(), annot=True, linewidths=.5, cmap=\"jet\")\n",
    "# Saving the Seaborn Figure:\n",
    "plt.savefig('img/heatmapconfirmed.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the COVID-19 Recoveries Cases Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_dataset_csv = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Delete the useless columns\n",
    "corona_dataset_csv.drop([\"Lat\",\"Long\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### Aggregate rows by Country\n",
    "corona_dataset_aggregated = corona_dataset_csv.groupby(\"Country/Region\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Find maximum recovery rate for all countries\n",
    "countries = list(corona_dataset_aggregated.index)\n",
    "max_recovery_rates = []\n",
    "for c in countries : \n",
    "    max_recovery_rates.append(corona_dataset_aggregated.loc[c].diff().max())\n",
    "corona_dataset_aggregated[\"max_recovery_rates\"] = max_recovery_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Drop the useless columns \n",
    "corona_data=pd.DataFrame (corona_dataset_aggregated[\"max_recovery_rates\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the World Happiness Report dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv = pd.read_csv (\"data/worldwide_happiness_report.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "useless_cols= [\"Overall rank\",\"Score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv.drop(useless_cols,axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv.set_index(\"Country or region\",inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = corona_data.join(happiness_report_csv,how=\"inner\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Correlation matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>max_recovery_rates</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max_recovery_rates</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.093277</td>\n",
       "      <td>0.081335</td>\n",
       "      <td>0.065809</td>\n",
       "      <td>-0.090630</td>\n",
       "      <td>-0.144466</td>\n",
       "      <td>-0.027924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP per capita</th>\n",
       "      <td>0.093277</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.757521</td>\n",
       "      <td>0.859431</td>\n",
       "      <td>0.394799</td>\n",
       "      <td>-0.097441</td>\n",
       "      <td>0.311759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social support</th>\n",
       "      <td>0.081335</td>\n",
       "      <td>0.757521</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.751632</td>\n",
       "      <td>0.456317</td>\n",
       "      <td>-0.060586</td>\n",
       "      <td>0.203333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <td>0.065809</td>\n",
       "      <td>0.859431</td>\n",
       "      <td>0.751632</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.423146</td>\n",
       "      <td>-0.052984</td>\n",
       "      <td>0.311566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <td>-0.090630</td>\n",
       "      <td>0.394799</td>\n",
       "      <td>0.456317</td>\n",
       "      <td>0.423146</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.259240</td>\n",
       "      <td>0.446845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>-0.144466</td>\n",
       "      <td>-0.097441</td>\n",
       "      <td>-0.060586</td>\n",
       "      <td>-0.052984</td>\n",
       "      <td>0.259240</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.326430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <td>-0.027924</td>\n",
       "      <td>0.311759</td>\n",
       "      <td>0.203333</td>\n",
       "      <td>0.311566</td>\n",
       "      <td>0.446845</td>\n",
       "      <td>0.326430</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              max_recovery_rates  GDP per capita  \\\n",
       "max_recovery_rates                      1.000000        0.093277   \n",
       "GDP per capita                          0.093277        1.000000   \n",
       "Social support                          0.081335        0.757521   \n",
       "Healthy life expectancy                 0.065809        0.859431   \n",
       "Freedom to make life choices           -0.090630        0.394799   \n",
       "Generosity                             -0.144466       -0.097441   \n",
       "Perceptions of corruption              -0.027924        0.311759   \n",
       "\n",
       "                              Social support  Healthy life expectancy  \\\n",
       "max_recovery_rates                  0.081335                 0.065809   \n",
       "GDP per capita                      0.757521                 0.859431   \n",
       "Social support                      1.000000                 0.751632   \n",
       "Healthy life expectancy             0.751632                 1.000000   \n",
       "Freedom to make life choices        0.456317                 0.423146   \n",
       "Generosity                         -0.060586                -0.052984   \n",
       "Perceptions of corruption           0.203333                 0.311566   \n",
       "\n",
       "                              Freedom to make life choices  Generosity  \\\n",
       "max_recovery_rates                               -0.090630   -0.144466   \n",
       "GDP per capita                                    0.394799   -0.097441   \n",
       "Social support                                    0.456317   -0.060586   \n",
       "Healthy life expectancy                           0.423146   -0.052984   \n",
       "Freedom to make life choices                      1.000000    0.259240   \n",
       "Generosity                                        0.259240    1.000000   \n",
       "Perceptions of corruption                         0.446845    0.326430   \n",
       "\n",
       "                              Perceptions of corruption  \n",
       "max_recovery_rates                            -0.027924  \n",
       "GDP per capita                                 0.311759  \n",
       "Social support                                 0.203333  \n",
       "Healthy life expectancy                        0.311566  \n",
       "Freedom to make life choices                   0.446845  \n",
       "Generosity                                     0.326430  \n",
       "Perceptions of corruption                      1.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplots(figsize=(18, 12))\n",
    "sns.heatmap(data.corr(), annot=True, linewidths=.5, cmap=\"jet\")\n",
    "# Saving the Seaborn Figure:\n",
    "plt.savefig('img/heatmaprecoveries.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the COVID-19 Deaths Cases Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_dataset_csv = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Delete the useless columns\n",
    "corona_dataset_csv.drop([\"Lat\",\"Long\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### Aggregate rows by Country\n",
    "corona_dataset_aggregated = corona_dataset_csv.groupby(\"Country/Region\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Find maximum recovery rate for all countries\n",
    "countries = list(corona_dataset_aggregated.index)\n",
    "max_death_rates = []\n",
    "for c in countries : \n",
    "    max_death_rates.append(corona_dataset_aggregated.loc[c].diff().max())\n",
    "corona_dataset_aggregated[\"max_death_rates\"] = max_death_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Drop the useless columns \n",
    "corona_data=pd.DataFrame (corona_dataset_aggregated[\"max_death_rates\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the World Happiness Report dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv = pd.read_csv (\"data/worldwide_happiness_report.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "useless_cols= [\"Overall rank\",\"Score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv.drop(useless_cols,axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv.set_index(\"Country or region\",inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = corona_data.join(happiness_report_csv,how=\"inner\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Correlation matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>max_death_rates</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max_death_rates</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.154235</td>\n",
       "      <td>0.150124</td>\n",
       "      <td>0.212021</td>\n",
       "      <td>0.118066</td>\n",
       "      <td>-0.188672</td>\n",
       "      <td>-0.085317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP per capita</th>\n",
       "      <td>0.154235</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.757521</td>\n",
       "      <td>0.859431</td>\n",
       "      <td>0.394799</td>\n",
       "      <td>-0.097441</td>\n",
       "      <td>0.311759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social support</th>\n",
       "      <td>0.150124</td>\n",
       "      <td>0.757521</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.751632</td>\n",
       "      <td>0.456317</td>\n",
       "      <td>-0.060586</td>\n",
       "      <td>0.203333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <td>0.212021</td>\n",
       "      <td>0.859431</td>\n",
       "      <td>0.751632</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.423146</td>\n",
       "      <td>-0.052984</td>\n",
       "      <td>0.311566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <td>0.118066</td>\n",
       "      <td>0.394799</td>\n",
       "      <td>0.456317</td>\n",
       "      <td>0.423146</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.259240</td>\n",
       "      <td>0.446845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>-0.188672</td>\n",
       "      <td>-0.097441</td>\n",
       "      <td>-0.060586</td>\n",
       "      <td>-0.052984</td>\n",
       "      <td>0.259240</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.326430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <td>-0.085317</td>\n",
       "      <td>0.311759</td>\n",
       "      <td>0.203333</td>\n",
       "      <td>0.311566</td>\n",
       "      <td>0.446845</td>\n",
       "      <td>0.326430</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              max_death_rates  GDP per capita  Social support  \\\n",
       "max_death_rates                      1.000000        0.154235        0.150124   \n",
       "GDP per capita                       0.154235        1.000000        0.757521   \n",
       "Social support                       0.150124        0.757521        1.000000   \n",
       "Healthy life expectancy              0.212021        0.859431        0.751632   \n",
       "Freedom to make life choices         0.118066        0.394799        0.456317   \n",
       "Generosity                          -0.188672       -0.097441       -0.060586   \n",
       "Perceptions of corruption           -0.085317        0.311759        0.203333   \n",
       "\n",
       "                              Healthy life expectancy  \\\n",
       "max_death_rates                              0.212021   \n",
       "GDP per capita                               0.859431   \n",
       "Social support                               0.751632   \n",
       "Healthy life expectancy                      1.000000   \n",
       "Freedom to make life choices                 0.423146   \n",
       "Generosity                                  -0.052984   \n",
       "Perceptions of corruption                    0.311566   \n",
       "\n",
       "                              Freedom to make life choices  Generosity  \\\n",
       "max_death_rates                                   0.118066   -0.188672   \n",
       "GDP per capita                                    0.394799   -0.097441   \n",
       "Social support                                    0.456317   -0.060586   \n",
       "Healthy life expectancy                           0.423146   -0.052984   \n",
       "Freedom to make life choices                      1.000000    0.259240   \n",
       "Generosity                                        0.259240    1.000000   \n",
       "Perceptions of corruption                         0.446845    0.326430   \n",
       "\n",
       "                              Perceptions of corruption  \n",
       "max_death_rates                               -0.085317  \n",
       "GDP per capita                                 0.311759  \n",
       "Social support                                 0.203333  \n",
       "Healthy life expectancy                        0.311566  \n",
       "Freedom to make life choices                   0.446845  \n",
       "Generosity                                     0.326430  \n",
       "Perceptions of corruption                      1.000000  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplots(figsize=(18, 12))\n",
    "sns.heatmap(data.corr(), annot=True, linewidths=.5, cmap=\"jet\")\n",
    "# Saving the Seaborn Figure:\n",
    "plt.savefig('img/heatmapdeaths.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}