covid19 data analysis notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Welcome to Covid19 Data Analysis Notebook\n",
    "------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's Import the modules "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modules are imported.\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": [
    "## Task 2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.1: importing covid19 dataset\n",
    "importing \"Covid19_Confirmed_dataset.csv\" from \"./Dataset\" folder. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
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       "      <th>...</th>\n",
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       "      <td>1092</td>\n",
       "      <td>1176</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1463</td>\n",
       "      <td>1531</td>\n",
       "      <td>1703</td>\n",
       "      <td>1828</td>\n",
       "      <td>1939</td>\n",
       "      <td>2171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
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       "      <td>Albania</td>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>634</td>\n",
       "      <td>663</td>\n",
       "      <td>678</td>\n",
       "      <td>712</td>\n",
       "      <td>726</td>\n",
       "      <td>736</td>\n",
       "      <td>750</td>\n",
       "      <td>766</td>\n",
       "      <td>773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</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>2811</td>\n",
       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</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",
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       "      <td>717</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>745</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>25</td>\n",
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       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 104 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region      Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
       "0            NaN    Afghanistan  33.0000  65.0000        0        0        0   \n",
       "1            NaN        Albania  41.1533  20.1683        0        0        0   \n",
       "2            NaN        Algeria  28.0339   1.6596        0        0        0   \n",
       "3            NaN        Andorra  42.5063   1.5218        0        0        0   \n",
       "4            NaN         Angola -11.2027  17.8739        0        0        0   \n",
       "\n",
       "   1/25/20  1/26/20  1/27/20  ...  4/21/20  4/22/20  4/23/20  4/24/20  \\\n",
       "0        0        0        0  ...     1092     1176     1279     1351   \n",
       "1        0        0        0  ...      609      634      663      678   \n",
       "2        0        0        0  ...     2811     2910     3007     3127   \n",
       "3        0        0        0  ...      717      723      723      731   \n",
       "4        0        0        0  ...       24       25       25       25   \n",
       "\n",
       "   4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \n",
       "0     1463     1531     1703     1828     1939     2171  \n",
       "1      712      726      736      750      766      773  \n",
       "2     3256     3382     3517     3649     3848     4006  \n",
       "3      738      738      743      743      743      745  \n",
       "4       25       26       27       27       27       27  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv=pd.read_csv(\"Datasets/covid19_Confirmed_dataset.csv\")\n",
    "corona_dataset_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's check the shape of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(266, 104)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.2: Delete the useless columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_dataset_csv.drop([\"Lat\",\"Long\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>4006</td>\n",
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       "      <td>1401</td>\n",
       "      <td>1473</td>\n",
       "      <td>1523</td>\n",
       "      <td>1596</td>\n",
       "      <td>1677</td>\n",
       "      <td>1746</td>\n",
       "      <td>1808</td>\n",
       "      <td>1867</td>\n",
       "      <td>1932</td>\n",
       "      <td>2066</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
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       "      <td>0</td>\n",
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       "      <td>104</td>\n",
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       "      <td>105</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
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       "      <td>106</td>\n",
       "      <td>106</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
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       "      <td>2971</td>\n",
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       "      <td>2994</td>\n",
       "      <td>3002</td>\n",
       "      <td>3004</td>\n",
       "      <td>3016</td>\n",
       "      <td>3016</td>\n",
       "      <td>3025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Province/State       Country/Region  1/22/20  1/23/20  \\\n",
       "0                           NaN          Afghanistan        0        0   \n",
       "1                           NaN              Albania        0        0   \n",
       "2                           NaN              Algeria        0        0   \n",
       "3                           NaN              Andorra        0        0   \n",
       "4                           NaN               Angola        0        0   \n",
       "5                           NaN  Antigua and Barbuda        0        0   \n",
       "6                           NaN            Argentina        0        0   \n",
       "7                           NaN              Armenia        0        0   \n",
       "8  Australian Capital Territory            Australia        0        0   \n",
       "9               New South Wales            Australia        0        0   \n",
       "\n",
       "   1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  1/29/20  ...  4/21/20  \\\n",
       "0        0        0        0        0        0        0  ...     1092   \n",
       "1        0        0        0        0        0        0  ...      609   \n",
       "2        0        0        0        0        0        0  ...     2811   \n",
       "3        0        0        0        0        0        0  ...      717   \n",
       "4        0        0        0        0        0        0  ...       24   \n",
       "5        0        0        0        0        0        0  ...       23   \n",
       "6        0        0        0        0        0        0  ...     3031   \n",
       "7        0        0        0        0        0        0  ...     1401   \n",
       "8        0        0        0        0        0        0  ...      104   \n",
       "9        0        0        3        4        4        4  ...     2969   \n",
       "\n",
       "   4/22/20  4/23/20  4/24/20  4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  \\\n",
       "0     1176     1279     1351     1463     1531     1703     1828     1939   \n",
       "1      634      663      678      712      726      736      750      766   \n",
       "2     2910     3007     3127     3256     3382     3517     3649     3848   \n",
       "3      723      723      731      738      738      743      743      743   \n",
       "4       25       25       25       25       26       27       27       27   \n",
       "5       24       24       24       24       24       24       24       24   \n",
       "6     3144     3435     3607     3780     3892     4003     4127     4285   \n",
       "7     1473     1523     1596     1677     1746     1808     1867     1932   \n",
       "8      104      104      105      106      106      106      106      106   \n",
       "9     2971     2976     2982     2994     3002     3004     3016     3016   \n",
       "\n",
       "   4/30/20  \n",
       "0     2171  \n",
       "1      773  \n",
       "2     4006  \n",
       "3      745  \n",
       "4       27  \n",
       "5       24  \n",
       "6     4428  \n",
       "7     2066  \n",
       "8      106  \n",
       "9     3025  \n",
       "\n",
       "[10 rows x 102 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.3: Aggregating the rows by the country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>4/21/20</th>\n",
       "      <th>4/22/20</th>\n",
       "      <th>4/23/20</th>\n",
       "      <th>4/24/20</th>\n",
       "      <th>4/25/20</th>\n",
       "      <th>4/26/20</th>\n",
       "      <th>4/27/20</th>\n",
       "      <th>4/28/20</th>\n",
       "      <th>4/29/20</th>\n",
       "      <th>4/30/20</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",
       "      <td>Afghanistan</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1092</td>\n",
       "      <td>1176</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1463</td>\n",
       "      <td>1531</td>\n",
       "      <td>1703</td>\n",
       "      <td>1828</td>\n",
       "      <td>1939</td>\n",
       "      <td>2171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>609</td>\n",
       "      <td>634</td>\n",
       "      <td>663</td>\n",
       "      <td>678</td>\n",
       "      <td>712</td>\n",
       "      <td>726</td>\n",
       "      <td>736</td>\n",
       "      <td>750</td>\n",
       "      <td>766</td>\n",
       "      <td>773</td>\n",
       "    </tr>\n",
       "    <tr>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2811</td>\n",
       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>717</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "    <tr>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Antigua and Barbuda</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Argentina</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3031</td>\n",
       "      <td>3144</td>\n",
       "      <td>3435</td>\n",
       "      <td>3607</td>\n",
       "      <td>3780</td>\n",
       "      <td>3892</td>\n",
       "      <td>4003</td>\n",
       "      <td>4127</td>\n",
       "      <td>4285</td>\n",
       "      <td>4428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Armenia</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1401</td>\n",
       "      <td>1473</td>\n",
       "      <td>1523</td>\n",
       "      <td>1596</td>\n",
       "      <td>1677</td>\n",
       "      <td>1746</td>\n",
       "      <td>1808</td>\n",
       "      <td>1867</td>\n",
       "      <td>1932</td>\n",
       "      <td>2066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>6645</td>\n",
       "      <td>6652</td>\n",
       "      <td>6662</td>\n",
       "      <td>6677</td>\n",
       "      <td>6694</td>\n",
       "      <td>6714</td>\n",
       "      <td>6721</td>\n",
       "      <td>6744</td>\n",
       "      <td>6752</td>\n",
       "      <td>6766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Austria</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14873</td>\n",
       "      <td>14925</td>\n",
       "      <td>15002</td>\n",
       "      <td>15071</td>\n",
       "      <td>15148</td>\n",
       "      <td>15225</td>\n",
       "      <td>15274</td>\n",
       "      <td>15357</td>\n",
       "      <td>15402</td>\n",
       "      <td>15452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  \\\n",
       "Country/Region                                                              \n",
       "Afghanistan                0        0        0        0        0        0   \n",
       "Albania                    0        0        0        0        0        0   \n",
       "Algeria                    0        0        0        0        0        0   \n",
       "Andorra                    0        0        0        0        0        0   \n",
       "Angola                     0        0        0        0        0        0   \n",
       "Antigua and Barbuda        0        0        0        0        0        0   \n",
       "Argentina                  0        0        0        0        0        0   \n",
       "Armenia                    0        0        0        0        0        0   \n",
       "Australia                  0        0        0        0        4        5   \n",
       "Austria                    0        0        0        0        0        0   \n",
       "\n",
       "                     1/28/20  1/29/20  1/30/20  1/31/20  ...  4/21/20  \\\n",
       "Country/Region                                           ...            \n",
       "Afghanistan                0        0        0        0  ...     1092   \n",
       "Albania                    0        0        0        0  ...      609   \n",
       "Algeria                    0        0        0        0  ...     2811   \n",
       "Andorra                    0        0        0        0  ...      717   \n",
       "Angola                     0        0        0        0  ...       24   \n",
       "Antigua and Barbuda        0        0        0        0  ...       23   \n",
       "Argentina                  0        0        0        0  ...     3031   \n",
       "Armenia                    0        0        0        0  ...     1401   \n",
       "Australia                  5        6        9        9  ...     6645   \n",
       "Austria                    0        0        0        0  ...    14873   \n",
       "\n",
       "                     4/22/20  4/23/20  4/24/20  4/25/20  4/26/20  4/27/20  \\\n",
       "Country/Region                                                              \n",
       "Afghanistan             1176     1279     1351     1463     1531     1703   \n",
       "Albania                  634      663      678      712      726      736   \n",
       "Algeria                 2910     3007     3127     3256     3382     3517   \n",
       "Andorra                  723      723      731      738      738      743   \n",
       "Angola                    25       25       25       25       26       27   \n",
       "Antigua and Barbuda       24       24       24       24       24       24   \n",
       "Argentina               3144     3435     3607     3780     3892     4003   \n",
       "Armenia                 1473     1523     1596     1677     1746     1808   \n",
       "Australia               6652     6662     6677     6694     6714     6721   \n",
       "Austria                14925    15002    15071    15148    15225    15274   \n",
       "\n",
       "                     4/28/20  4/29/20  4/30/20  \n",
       "Country/Region                                  \n",
       "Afghanistan             1828     1939     2171  \n",
       "Albania                  750      766      773  \n",
       "Algeria                 3649     3848     4006  \n",
       "Andorra                  743      743      745  \n",
       "Angola                    27       27       27  \n",
       "Antigua and Barbuda       24       24       24  \n",
       "Argentina               4127     4285     4428  \n",
       "Armenia                 1867     1932     2066  \n",
       "Australia               6744     6752     6766  \n",
       "Austria                15357    15402    15452  \n",
       "\n",
       "[10 rows x 100 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_agg=corona_dataset_csv.groupby(\"Country/Region\").sum()\n",
    "corona_agg.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 100)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_agg.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.4: Visualizing data related to a country for example China\n",
    "visualization always helps for better understanding of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x245b13e2e48>"
      ]
     },
     "execution_count": 8,
     "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": [
    "corona_agg.loc[\"China\"].plot()\n",
    "corona_agg.loc[\"Italy\"].plot()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task3: Calculating a good measure \n",
    "we need to find a good measure reperestend as a number, describing the spread of the virus in a country. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x245b16a8948>"
      ]
     },
     "execution_count": 9,
     "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": [
    "corona_agg.loc['China'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x245b179b0c8>"
      ]
     },
     "execution_count": 10,
     "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": [
    "corona_agg.loc['China'][:3].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### task 3.1: caculating the first derivative of the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x245b180c888>"
      ]
     },
     "execution_count": 11,
     "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": [
    "corona_agg.loc[\"China\"].diff().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### task 3.2: find maxmimum infection rate for China"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15136.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_agg.loc[\"China\"].diff().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6557.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_agg.loc[\"Italy\"].diff().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9630.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_agg.loc[\"Spain\"].diff().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3.3: find maximum infection rate for all of the countries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries=list(corona_agg.index)\n",
    "max_infection_rates=[]\n",
    "for c in countries:\n",
    "    max_infection_rates.append(corona_agg.loc[c].diff().max())\n",
    "corona_agg[\"max_infection\"]=max_infection_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>4/22/20</th>\n",
       "      <th>4/23/20</th>\n",
       "      <th>4/24/20</th>\n",
       "      <th>4/25/20</th>\n",
       "      <th>4/26/20</th>\n",
       "      <th>4/27/20</th>\n",
       "      <th>4/28/20</th>\n",
       "      <th>4/29/20</th>\n",
       "      <th>4/30/20</th>\n",
       "      <th>max_infection</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",
       "      <td>Afghanistan</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1176</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1463</td>\n",
       "      <td>1531</td>\n",
       "      <td>1703</td>\n",
       "      <td>1828</td>\n",
       "      <td>1939</td>\n",
       "      <td>2171</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>634</td>\n",
       "      <td>663</td>\n",
       "      <td>678</td>\n",
       "      <td>712</td>\n",
       "      <td>726</td>\n",
       "      <td>736</td>\n",
       "      <td>750</td>\n",
       "      <td>766</td>\n",
       "      <td>773</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>745</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 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  ...  4/22/20  4/23/20  4/24/20  \\\n",
       "Country/Region                             ...                              \n",
       "Afghanistan           0        0        0  ...     1176     1279     1351   \n",
       "Albania               0        0        0  ...      634      663      678   \n",
       "Algeria               0        0        0  ...     2910     3007     3127   \n",
       "Andorra               0        0        0  ...      723      723      731   \n",
       "Angola                0        0        0  ...       25       25       25   \n",
       "\n",
       "                4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \\\n",
       "Country/Region                                                         \n",
       "Afghanistan        1463     1531     1703     1828     1939     2171   \n",
       "Albania             712      726      736      750      766      773   \n",
       "Algeria            3256     3382     3517     3649     3848     4006   \n",
       "Andorra             738      738      743      743      743      745   \n",
       "Angola               25       26       27       27       27       27   \n",
       "\n",
       "                max_infection  \n",
       "Country/Region                 \n",
       "Afghanistan             232.0  \n",
       "Albania                  34.0  \n",
       "Algeria                 199.0  \n",
       "Andorra                  43.0  \n",
       "Angola                    5.0  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_agg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3.4: create a new dataframe with only needed column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_data= pd.DataFrame(corona_agg[\"max_infection\"])"
   ]
  },
  {
   "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</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andorra</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Angola</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                max_infection\n",
       "Country/Region               \n",
       "Afghanistan             232.0\n",
       "Albania                  34.0\n",
       "Algeria                 199.0\n",
       "Andorra                  43.0\n",
       "Angola                    5.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task4: \n",
    "- Importing the WorldHappinessReport.csv dataset\n",
    "- selecting needed columns for our analysis \n",
    "- join the datasets \n",
    "- calculate the correlations as the result of our analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.1 : importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv=pd.read_csv(\"Datasets/worldwide_happiness_report.csv\")"
   ]
  },
  {
   "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>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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.2: let's drop the useless columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "useless_cols=[\"Overall rank\",\"Score\",\"Generosity\",\"Perceptions of corruption\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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",
       "    </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  \n",
       "0                         0.596  \n",
       "1                         0.592  \n",
       "2                         0.603  \n",
       "3                         0.591  \n",
       "4                         0.557  "
      ]
     },
     "execution_count": 23,
     "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": [
    "### Task 4.3: changing the indices of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv.set_index(\"Country or region\", inplace=True)\n",
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task4.4: now let's join two dataset we have prepared  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Corona Dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andorra</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Angola</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                max_infection\n",
       "Country/Region               \n",
       "Afghanistan             232.0\n",
       "Albania                  34.0\n",
       "Algeria                 199.0\n",
       "Andorra                  43.0\n",
       "Angola                    5.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### wolrd happiness report Dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country or region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\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",
       "    </tr>\n",
       "    <tr>\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",
       "    </tr>\n",
       "    <tr>\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",
       "    </tr>\n",
       "    <tr>\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",
       "    </tr>\n",
       "    <tr>\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",
       "    </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  \n",
       "Country or region                                \n",
       "Finland                                   0.596  \n",
       "Denmark                                   0.592  \n",
       "Norway                                    0.603  \n",
       "Iceland                                   0.591  \n",
       "Netherlands                               0.557  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>232.0</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>199.0</td>\n",
       "      <td>1.002</td>\n",
       "      <td>1.160</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Argentina</td>\n",
       "      <td>291.0</td>\n",
       "      <td>1.092</td>\n",
       "      <td>1.432</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Armenia</td>\n",
       "      <td>134.0</td>\n",
       "      <td>0.850</td>\n",
       "      <td>1.055</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             max_infection  GDP per capita  Social support  \\\n",
       "Afghanistan          232.0           0.350           0.517   \n",
       "Albania               34.0           0.947           0.848   \n",
       "Algeria              199.0           1.002           1.160   \n",
       "Argentina            291.0           1.092           1.432   \n",
       "Armenia              134.0           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  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=corona_data.join(happiness_report_csv,how=\"inner\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.5: correlation matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>max_infection</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250118</td>\n",
       "      <td>0.191958</td>\n",
       "      <td>0.289263</td>\n",
       "      <td>0.078196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GDP per capita</td>\n",
       "      <td>0.250118</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.759468</td>\n",
       "      <td>0.863062</td>\n",
       "      <td>0.394603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Social support</td>\n",
       "      <td>0.191958</td>\n",
       "      <td>0.759468</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.765286</td>\n",
       "      <td>0.456246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Healthy life expectancy</td>\n",
       "      <td>0.289263</td>\n",
       "      <td>0.863062</td>\n",
       "      <td>0.765286</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.427892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Freedom to make life choices</td>\n",
       "      <td>0.078196</td>\n",
       "      <td>0.394603</td>\n",
       "      <td>0.456246</td>\n",
       "      <td>0.427892</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              max_infection  GDP per capita  Social support  \\\n",
       "max_infection                      1.000000        0.250118        0.191958   \n",
       "GDP per capita                     0.250118        1.000000        0.759468   \n",
       "Social support                     0.191958        0.759468        1.000000   \n",
       "Healthy life expectancy            0.289263        0.863062        0.765286   \n",
       "Freedom to make life choices       0.078196        0.394603        0.456246   \n",
       "\n",
       "                              Healthy life expectancy  \\\n",
       "max_infection                                0.289263   \n",
       "GDP per capita                               0.863062   \n",
       "Social support                               0.765286   \n",
       "Healthy life expectancy                      1.000000   \n",
       "Freedom to make life choices                 0.427892   \n",
       "\n",
       "                              Freedom to make life choices  \n",
       "max_infection                                     0.078196  \n",
       "GDP per capita                                    0.394603  \n",
       "Social support                                    0.456246  \n",
       "Healthy life expectancy                           0.427892  \n",
       "Freedom to make life choices                      1.000000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5: Visualization of the results\n",
    "our Analysis is not finished unless we visualize the results in terms figures and graphs so that everyone can understand what you get out of our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>232.0</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>199.0</td>\n",
       "      <td>1.002</td>\n",
       "      <td>1.160</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Argentina</td>\n",
       "      <td>291.0</td>\n",
       "      <td>1.092</td>\n",
       "      <td>1.432</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Armenia</td>\n",
       "      <td>134.0</td>\n",
       "      <td>0.850</td>\n",
       "      <td>1.055</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             max_infection  GDP per capita  Social support  \\\n",
       "Afghanistan          232.0           0.350           0.517   \n",
       "Albania               34.0           0.947           0.848   \n",
       "Algeria              199.0           1.002           1.160   \n",
       "Argentina            291.0           1.092           1.432   \n",
       "Armenia              134.0           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  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.1: Plotting GDP vs maximum Infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x245b3882f88>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mLjl9flX2uAMQ4g5yKOJvMMpI/w8ol8r+avD+e8Cfxi6RiIW82oWTJO6Eq1YdrvWcll84byl9vdk6cBvJ1azDC6Mck3jeGp33xDnTWHf5aYmYseIMcijqbzByRrOZTXP3PQnJ0xD5FKLRDVnBtdT+AAemTeaS0+czb3Y/UydPYHZ/ayaXdn7UjXwDWXcSrSjO8XwkhjE8chAziy27uNl5i/IM50X+2Gsf1Rx4GeVooWnAcWZ2MrDK3VdHF1MkTR5jzZMsnVAxNQxMm8xH37oglno47YwaG9UEancU2m4btlKrqNEMY8++kWpU1M0XvS72563VmU1eyONvMAxRzEdrgLcCtwO4+8Nm9oZEpBJtk7dY86Sn0pUf4JVnLqwqBDh0YZioxF3wrZ3jZVW1tJ5ynNADZ33uV6PgSkJdnM9b0fNV8vYbDEukldPcfcuYTflWeV1M0lnBUR1oSZdOqPwAZ/RNKuToLAzN2jDJmkVjI6CGhkePgpNYhKjeeYuiEKC4mflRZgpbAhOSB6WuLwEeT0Ys0S5JjrJaGfUnPZWu/ACf37WvkKOzMDRrw2bLZ8bJ2FHwxi07+fIPf8LXVr0Ody/cqD4JijrTiTJTuAj4MHAM5dpFrw7ei5zSaJTVbphcK6P+pOO/Kz/Ak+cezvXnLGk6OitiqGCzNkzThl1vFPyRNy/gqMOmFHJUnxRFnOmEWWTn6mDZzdPc/fdSkEkkSBy2/VY6nzSchj09xhH9k5nR1zvu6KyooYLN2jANG3atz+LIwybHUndI5IumIalm9gjlCqb3u3tmlUwVkhoPcYTJtXqMvCzckpdQwVYYrw2TVnZFVabdThIhqd8FfgH0m9mLBMtw0v5ynCID4jAxtDrqz2LpxnoUNVQQxm/DpG3YafosRHaEKZ19KXCpmX3D3c9OQSaRIHGYGIrqQKsQt5klLzMgaK5425E1ijLNU5uIaIR2NEshdAZxhckV0YFWIc5QwSIsXVmhXVnDBgsUqU3EoUQpnf07wNXAHMqmo1TNR/IpxIdGcfG1QZH8E+3KGtanUKQ26QYSK3MB/CXwdndXbkLByYttP0viaoMi+SfalTWs2bBynsVzZ3DRqScyo28SO4cOUCqVYrsWkRxRlMILUghCjCbpMNA4Z3Vx+ZOaKdPeiRN4y8I5nL9s3qgaVNefu4SB6VO6blZaNKKYjz4DHAV8Hdhf2e7utyYj2mhkPhJJE/fSm+12flGP3Uz+tEJKSyVn646XeO8X75cJKQckaT46DHgJeEvNNgdSUQpCJEmrHWaSkVhRQkDDyB9V1lZnKT09xoQeS9ysJt9YMoRWCu5+QZKCCJEl7cTgx+GfqNfBRfEBbN87zJo7N3PlmQurNvw1d27mz1csGiVbWFnbnVWkYVZTIl0yhClzcZm7/6WZfZbyzGAU7n5JIpKJ3NANI7IsHcaNOrhZ03pDd6ylUukQG/7VKxe17NxtN1FtZt8krj93Catu3NA0wbGV50uJdMkRZqZQcS7LoN+FdMuILM3a92M7wQk91O3gbr94eejM8YPOIetIXH7LJr626nUtydiOkiyVnCcG9/CZf/1PrjxzIbP6e5kzfTJHH953yDPT6vNVpKivohEmo/mbwf8vj7efmX3W3f8gLsHyRjeMluvRLSOytFb5qtcJXn/OEgamTR7VyW3dMcTQ8MHQPgB3r9tJRl1ut0I7SrL2mfneY9uq3633zLT6fBV1AZsiEGmRnSYsj/FYuaKbMzS7ZURW64Rdd/lp3LZ6eSKzoXqd4KqbNnDJ6fNH7Vfp4MJmjsddmrydrO8oz0yrz1dRF7ApAlGijwpLu6P8bhkt1yNPI7KkZ2tpJPU16gTnze6vtnMrHVzcM512oqqiPDOtPl9Fr7+VZzpeKaSxfkAnm5bi7GzaaaesfBtx39tGneDUyRPa6uCS6CRbVZJjn5m3LJzDn/7WQoZHDjK4e/8oudp5vpSZnwyhk9eaHshso7svjuVgdWg1eS3p9QNm9fd2vCM2jo6x3U49i3o6SSiiRsecPzCNHUMHOmZgUXlmSqUSv9g7fEgUUm0bdvKgKg9ETV4L7VMwsyl1ts2uefuZsMdKkzjXD6hnv0x6Qfo8EEdF1HbbKQvfRhL3tp7vYv7ANJ4Y3NPQZ1XEpUMrz0xPT09VIUD9Nixyxd1OJIr56EEz+5C73wdgZiuBTwG/BuDufx+/eO2T9PoBRXTEZjEya7edxruPSV1PUvd2rNljcPf+hj6ros9Ei/j76HaiRB+9F/ismX3azP4B+BDwxnZObmYTzGyjmX2rneOMR9LrByS9IH3cZBVJ1W47NbqPM/smJXY9ad3b8TrOos9EG7XhwZIXYsbTjURZZOcR4M+Bi4DTgIvdfWub5/9DfpUclwhJhxoWLTQuq06m3XZqdB93DB1I7HrGU0RxmnPGUz5FH2nP6u/l+nOXjGrDq1cu4s++/Vj1HhXRPNbJhDYfmdnfAicCiyibjL5pZp9z98+3cmIzOxb4LcqK5o9bOUZYkoxSKFpoXFadTBztVO8+Jnk99WSe2TeJJwb3xGrOGS8CZ/ve4dyEBLdCT48xu793VE2ma+7YzMYtO/nE2w92TcZ8kYjiU3gU+KCXw5V+YmavBf6qjXP/NXAZML3RDmZ2IXAhwHHHHdfGqZKlSKFxWeYdJNFOSV9PFPt/q9c2nsJMM9M6rF8mqg+np6eHT37rsbr3qJtzgPJKFPPRGq+JX3X3Xe7+gVZOamZnAtvcfUOTc65196XuvnRgYKCVU4kxFM3c1Yy0rydp5/NYn1UamdZR/Eyt+KTGu0dFN491IlHMR/MpRxstBKrhqe5+QgvnXQ6cZWZvC451mJnd5O7ntHAsEYGimbuakfb1RJmZxBUVlfRMNMpovZWR/Xj3KE8Z86JMFPPR3wGfANZQdjRfALT0y3P3jwMfBzCzU4GPSiHEQ5iOqEjmrjCkeT2VUe+aOzezcsncagXQmX2TRu1XJFt5GrWKGt2jtMxjIjxRlEKfu99lZubuzwBXmdm/UVYUIgcUqSMqKj09xvyBafzhm35t3CzdrG3lYWcppZJzsOSJ1ypqRKfNXDuBKHkK+8ysB3jCzC42sxXAnHYFcPd73P3Mdo8jsgs37TZ2DB1omqWbpq18bEjnyEgptN1/+95h/uzbj3H1ykWjbP7Xn7uk7mg9CR+OMprzRZSZwh8BU4FLgE9SNiGdl4RQojXktEuHMO0cdUTdqv+h3uzwHz/4G6FnKcMjB/neY9sY3D08Kmx0doPza2Tf+URRCg7cCLwcqBhQv0A5b0HkADnt0iFMO0exlbdj9qs3O9y2e3/owUHlWjZu2cmqGzdUr+W21Y2XR+k0n5QYTRTz0T9QdjavBM4M/t6ehFBJ06kZlEULN837fWgkX5h2jhJK2o7Zr96spZLwVkujwUHRnhmRPFFmCoPufntikqREJztjizS1z/t9aCZfmHYOO6Jux+w3dtayeO4MDu+bxI0fOIWf/uIl/uauJxjcs79hR1+kZ0akQ+j1FMzsdOB3gbuA/ZXt7n5rMqKNptX1FMaSRV3+pMhjHfqwMuX9PqQpX+25Fs+dwUWnnsis/l6OntHHUYdNGfee1iqvgWmTueyMBVx686aqIrv+nCW8bMYUZvRl/2yIbIi6nkKUmcIFwCso+xNKwTYHUlEKcdEpztg8jrSjyJT3+xBFvnaVc23uw/nL5nH5LZtC39OxI/13r71vlBlq1U0buG31cikEEZooPoWTg5IT57v7BcHf+xOTLCGKVuq6EXkMP40iU97vQ1j54ihFXunYrzrrpKpCgPD3tNZMlWdFK4pBFKVwn5ktTEySlOgUx1oeR9pRZMr7fQgrX1zKuafHcPdYFiKqJU+KVhSDKOaj1wPnm9lPKPsUDHB3L1RIaqc41vIYfhpFprzfh7Dyxamc272nKhkh4iCKUjgjMSlSphPirKPGwafhkI7aKeX9PoSRL07l3G6nnndFK4pB6OijrIkr+qiTaNbZl0rOzqFhntu5j1U3Na7Tk6ZMnUbcDv9uaz+RPFGjj6QUCkArHUWls3p+1z6u/MajuQ397ATUkYs8k2RIqoiJqKtctTISrThA/887T25o8+7Wzizu6867GUyIKEgppEzUTr7VEswVB+jOoQN1bd6TJvbkLs8hDfKY3yFEnogSktpVJFWXJ2oIY6vRLRUH6HX3PHVIWeQvnLeUiT2WuzyHNMhjfocQeUIzhTokOZqM2sm3Gt1SG8lyzR2b+eTZJzFvdj9TJ09gdv9knts1lLs8hzTIY36HEHlCM4U6JDmajJpg1GqSV2144ufeu5iTjjmc446YypzpU0atjRtWjvHIe7XTWpTgJcT4SCnUIcnRZNROPkoJ5nrfbbSiVVwZxXGUeUiTvGdSC5E1CkmtQ9IVMluJfhkZKbFtz34OHCwxaUIPc6ZNZuLE9nR6GDma7ZP3aqf16NaoK9GdKCQ1BpIuFxA1hHFkpMSPX9jNRTUJaNeds4RXHDm9LcXQTI4wvpUi2ujzGEIah6KSshNxIKVQh7yVC9i2Z39VIUC5073opg18bdXrOHpGX5Nvt06YcNg81mAqGnEENijUVsSFfAoNGM8enzYHDpbqjsZHDpYafCMewswCZKNvnzgCGxRqK+JCM4UCMGlCT93R+MQJh+r0OE0IYWYBeZtVFZE4THBFNOOJfKKZQgGYM20y152zZNRo/LpzljBn2mi7eNyRQGFnAXmaVRWROMJkFWor4kLRRynSzii+En00crDExDrRR6WS8/yL+3jX9ffGGgkk52XyyKcgkkTRRzml3R/txIk9DZ3KlWPv3T8Suwkhj5E6nUYcJjiZ8URcyHyUEkk6AivH3r53uK4JYVKb+QxJUqRs6CSJwwQnM56Ig/z2Fh1Gko7AyrHrFb/79DsWsWffSC4726JlQwvRDch8lBJJxvNXjr1xy06uuWMzV565kFn9vRzeN4nLbt7E4J793H7xcg6WyJVpodWy4EKI5NBMISWSjOevPfbGLTv55LceY/9Iictu3sTGLTsZmDaZ53buy92IXGGUQuSPTGYKZjYXuAE4CigBa939M1nIkhZJOgJrjz104CBPbdvDNXdsZuOWnQBccvr86hrNkJ8RubKhhcgfWc0URoA/cfdXAq8FPmxmCzOSJTWSdARWjn3sjD6OOnwKg3v2A+VOdt7s/lyOyJUNHS9y2os4yGSm4O7PAc8Fr3eb2ePAMcBjWcjTSdSbkTieyxG5wijjQ3kKIi4y9ymY2fHAYuD+Op9daGbrzWz94OBg2qIVlrEzktn9k3M7IlcYZTyo9pGIi0yjj8xsGnAL8Efu/uLYz919LbAWyhnNScnR6Vm7RR2Rd/p9iRM57UVcZKYUzGwSZYXwD+5+a1ZydMu0u2iZyd1yX+JCTnsRF5mYj8zMgL8FHnf3v8pChgqaducT3ZdoyGkv4iKrmcJy4FzgETN7KNj23939O2kLoml3PtF9iUZRTYQif2QVffTvQC6e1qJNu8fa2Wf2TWLH0IGO6wiKdl/yQNFMhCKfZB59lDVFmnbXqxX04xd2c8Vtm3KVqRwHRbovQnQSWk+B4kS5DO7ez4pr1x0yer7yzIWsunFD9X3WmcpxUZT7IkSe0XoKLVCUaXcjO/uMvkmj3neK3b0o90WITqLrzUdFotGSizuHDox6L7u7EKJVpBQKRD07+3XnLOGWDVuq72V3F0K0g3wKBaNboo+EEPEgn0KHU8/OLru7ECIuZD4SQghRRTMF0fUkEfqqcFpRVKQURFeTROE9FfMTRUbmI9HVJFF4T8X8RJGRUhBdTRKF91TMTxQZKQXR1TRKCGwnATCJYwqRFlIKoqtJovCeivmJIqPkNdH1KPpIdDJKXhMiIkkU3lMxP1FUpBREZiQ1mtYoXYjWkVIQmZBULL9yBIRoDzmaRSYkFcuvHAEh2kNKQWRCUrH8yhEQoj2kFEQmJBXLrxwBIdpDSkFkQlKx/MoREKI9lKcgMkPRR0Ikj/IURGFIKpZfOQJCtI7MR0IIIapIKQghhKgipSCEEKKKlIIQQogqUgpCCCGqZKYUzOwMM9tsZk+a2ceykkMIIcSvyCQk1cwmAJ8H3gxsBR40s9vd/bEs5CkCir3PFo8AWEkAAAmkSURBVLW/6BayylM4BXjS3Z8GMLN/As4GpBTqoMqf2aL2F91EVuajY4AtNe+3BttEHVT5M1vU/qKbyEop1BteHVJvw8wuNLP1ZrZ+cHAwBbHyiSp/ZovaX3QTWSmFrcDcmvfHAs+O3cnd17r7UndfOjAwkJpweUOVP7NF7S+6iayUwoPAfDObZ2a9wHuA2zOSJfeo8me2qP1FN5FZlVQzexvw18AE4Evu/ufj7d/tVVIV/ZItan9RVApTJdXdvwN8J6vzFw1V/swWtb/oFpTRLIQQooqUghBCiCpSCkIIIapIKQghhKgipSCEEKJKZiGpUTGzQeCZNg8zG/hFDOKkRdHkBcmcBkWTFyRzGjSS9+XuHjr7tzBKIQ7MbH2UeN2sKZq8IJnToGjygmROg7jklflICCFEFSkFIYQQVbpNKazNWoCIFE1ekMxpUDR5QTKnQSzydpVPQQghxPh020xBCCHEOEgpCCGEqNIRSsHMzjCzzWb2pJl9rM7nk83sq8Hn95vZ8TWffTzYvtnM3pojmf/YzB4zs01mdpeZvbzms4Nm9lDwl9o6FCFkfp+ZDdbI9sGaz843syeCv/NzIu+aGln/08x21nyWehub2ZfMbJuZPdrgczOzvwmuZ5OZ/XrNZ6m3b0iZfy+QdZOZ/dDMTq757Kdm9kjQxqnVxQ8h86lmtqvm/v+Pms/GfaYykvfSGlkfDZ7dI4LPorexuxf6j/J6DE8BJwC9wMPAwjH7rAauC16/B/hq8HphsP9kYF5wnAk5kfk0YGrw+vcrMgfv9+S0nd8HfK7Od48Ang7+zwxez8xa3jH7/wHldT2ybOM3AL8OPNrg87cB/0J5OdvXAvdn1b4RZF5WkQX4zYrMwfufArNz2M6nAt9q95lKS94x+74d+H47bdwJM4VTgCfd/Wl3Hwb+CTh7zD5nA18OXt8MnG5mFmz/J3ff7+4/AZ4Mjpe5zO5+t7u/FLy9j/KSpVkSpp0b8VbgTnf/pbvvAO4EzkhIzgpR5f1d4CsJyzQu7v4D4Jfj7HI2cIOXuQ+YYWYvI5v2BZrL7O4/DGSCfDzHYdq5Ee38BlomorxtP8edoBSOAbbUvN8abKu7j7uPALuAWSG/mwRRz/sByiPEClPMbL2Z3Wdmv52EgHUIK/PKwFRws5lV1uHOop1DnzMwzc0Dvl+zOYs2bkaja8rqOY7K2OfYge+Z2QYzuzAjmRrxOjN72Mz+xcxeFWzLdTub2VTKg4FbajZHbuPMVl6LkXprIo6Ns220T5jvJkHo85rZOcBS4L/VbD7O3Z81sxOA75vZI+7+VAJyjhKlzraxMn8T+Iq77zeziyjPzt4Y8rtxE+Wc7wFudveDNduyaONm5O05Do2ZnUZZKby+ZvPyoI3nAHea2Y+DUXHW/IhyvaA9Vl42+OvAfPLfzm8H1rl77awicht3wkxhKzC35v2xwLON9jGzicDhlKdjYb6bBKHOa2ZvAq4AznL3/ZXt7v5s8P9p4B5gcZLCBjSV2d2318j5BWBJ2O8mQJRzvocxU+6M2rgZja4pq+c4FGa2CPgicLa7b69sr2njbcBtpGO6bYq7v+jue4LX3wEmmdlsct7OjP8ch2/jpJ0kKThhJlJ2rM3jV86fV43Z58OMdjR/LXj9KkY7mp8mHUdzGJkXU3ZqzR+zfSYwOXg9G3iCdJxdYWR+Wc3rFcB9wesjgJ8Ess8MXh+RtbzBfgsoO+Ms6zYOznc8jR2gv8VoR/MDWbVvBJmPo+yrWzZmez8wveb1D4EzciLzUZXngXIn+rOgzUM9U2nLG3xeGej2t9vGqdyAFBrsbcB/Bp3oFcG2/0V5hA0wBfjn4OF8ADih5rtXBN/bDPxmjmT+V+AF4KHg7/Zg+zLgkeCBfAT4QI5k/hTwH4FsdwOvqPnu+4P2fxK4IA/yBu+vAv5izPcyaWPKo7zngAOUR6UfAC4CLgo+N+DzwfU8AizNsn1DyvxFYEfNc7w+2H5C0L4PB8/MFTmS+eKa5/g+ahRavWcqa3mDfd5HOWim9nsttbHKXAghhKjSCT4FIYQQMSGlIIQQooqUghBCiCpSCkIIIapIKQghhKgipSAKh5kdaWb/aGZPB+n795rZiuCzSoXLjUE1yx+Y2Zk1373KzH5eU1HyrOyuJBpm9h0zmxH8rc5aHtGZSCmIQhEUMvw68AN3P8Hdl1BOSKwttPZv7r7Y3RcAlwCfM7PTaz5f4+6vBt4JfMnMYvsdBOWtE/ldufvb3H0nMINy5V8hYkdKQRSNNwLD7n5dZYO7P+Pun623s7s/RDlh7eI6nz0OjFDOWq4SzCZuNLPvB+sTfKjms0vN7MGg6N//DLYdb2aPm9m1lOvmzB1zvNcEawk8bGYPmNn04Dv/ZmY/Cv6WBfueGsxubrPyehrXVZRMUBt/NvAXwInBbOfTZjbNymtu/CionZ945U7RuXRCQTzRXbyKcscbhR8Bl47daGa/AZSAwTrfWUS5lEQ/sNHMvg2cRLkw2imUs4tvN7M3UC6DsIByJvGoEbyZ9QJfBd7t7g+a2WHAELANeLO77zOz+ZSzVpcGXzuF8lofzwDfBX6Hcsn3Ch8DTgpmO5V6Xivc/cVAadxnZre7MlNFC0gpiEJjZp+nXHlz2N1f02i3Me8/ElSf3U25s67XeX7D3YeAITO7m3JH/XrgLcDGYJ9plJXEz4BnvLzGwVgWAM+5+4NQLrYWyN1P2az1auAg8Gs133nAy4X4MLOvBOe9mcYY8L8DBVWiXM75SOD5cb4jRF2kFETR+A9gZeWNu384GB2Pt9TgYuDxmvdr3P2aJucZqygqJao/5e7X135g5eVd9zY4jtU5FsBHKNe2OpmyGXdfk3OPx+8BA8ASdz9gZj+lXO9LiMjIpyCKxvcpL4Dz+zXbpjbaOSjbfCXlQnJRONvMppjZLMrLMz4I3AG838ymBcc+JqhTPx4/Bo42s9cE35leU779OXcvAedSXuqxwilmNi/wJbwb+Pcxx9wNTK95fziwLVAIpwEvR4gW0UxBFAp392AltDVmdhllf8Be4PKa3f6rmW2krCy2AZe4+10RT/UA8G3KpZ8/6eW69M+a2SuBe8tBUOwBzqFs/mkk77CZvRv4rJn1UfYnvAm4FrjFzN5JuaJs7UzjXsrO5P8C/IByHfzaY243s3VWXsj9X4CrgW9aeWH2hygrIiFaQlVShRiDmV0F7AlhYkri3KcCH3X3M5vtK0QSyHwkhBCiimYKQgghqmimIIQQooqUghBCiCpSCkIIIapIKQghhKgipSCEEKLK/wdN5p7pfuD3GAAAAABJRU5ErkJggg==\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\"]\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x245b396d548>"
      ]
     },
     "execution_count": 46,
     "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": [
    "sns.regplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.2: Plotting Social support vs maximum Infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x245b395dd88>"
      ]
     },
     "execution_count": 47,
     "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\"]\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x245b34be448>"
      ]
     },
     "execution_count": 48,
     "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": [
    "sns.regplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.3: Plotting Healthy life expectancy vs maximum Infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x245b38466c8>"
      ]
     },
     "execution_count": 49,
     "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\"]\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x245b399d208>"
      ]
     },
     "execution_count": 50,
     "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": [
    "sns.regplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.4: Plotting Freedom to make life choices vs maximum Infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x245b2b3ca88>"
      ]
     },
     "execution_count": 51,
     "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\"]\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x245b37cfc48>"
      ]
     },
     "execution_count": 52,
     "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": [
    "sns.regplot(x,np.log(y))"
   ]
  }
 ],
 "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
}