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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       "      <td>151.2093</td>\n",
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       "      <td>3016</td>\n",
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       "                 Province/State       Country/Region      Lat      Long  \\\n",
       "0                           NaN          Afghanistan  33.0000   65.0000   \n",
       "1                           NaN              Albania  41.1533   20.1683   \n",
       "2                           NaN              Algeria  28.0339    1.6596   \n",
       "3                           NaN              Andorra  42.5063    1.5218   \n",
       "4                           NaN               Angola -11.2027   17.8739   \n",
       "5                           NaN  Antigua and Barbuda  17.0608  -61.7964   \n",
       "6                           NaN            Argentina -38.4161  -63.6167   \n",
       "7                           NaN              Armenia  40.0691   45.0382   \n",
       "8  Australian Capital Territory            Australia -35.4735  149.0124   \n",
       "9               New South Wales            Australia -33.8688  151.2093   \n",
       "\n",
       "   1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  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        0        0        3        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 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(10)"
   ]
  },
  {
   "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": [
    "df = corona_dataset_csv.drop(['Lat',\"Long\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <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>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",
       "      <th>5</th>\n",
       "      <td>NaN</td>\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>...</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",
       "      <th>6</th>\n",
       "      <td>NaN</td>\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>...</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",
       "      <th>7</th>\n",
       "      <td>NaN</td>\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>...</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",
       "      <th>8</th>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</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>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>105</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2969</td>\n",
       "      <td>2971</td>\n",
       "      <td>2976</td>\n",
       "      <td>2982</td>\n",
       "      <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": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.3: Aggregating the rows by the country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_dataset_aggregated = corona_dataset_csv.groupby(\"Country/Region\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</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",
       "      <th>Afghanistan</th>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</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",
       "      <th>Albania</th>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</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",
       "      <th>Algeria</th>\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>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",
       "      <th>Andorra</th>\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",
       "      <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",
       "      <th>Angola</th>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</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",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>17.0608</td>\n",
       "      <td>-61.7964</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",
       "      <th>Argentina</th>\n",
       "      <td>-38.4161</td>\n",
       "      <td>-63.6167</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",
       "      <th>Armenia</th>\n",
       "      <td>40.0691</td>\n",
       "      <td>45.0382</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",
       "      <th>Australia</th>\n",
       "      <td>-255.9695</td>\n",
       "      <td>1129.8623</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>...</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",
       "      <th>Austria</th>\n",
       "      <td>47.5162</td>\n",
       "      <td>14.5501</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 × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Lat       Long  1/22/20  1/23/20  1/24/20  1/25/20  \\\n",
       "Country/Region                                                                 \n",
       "Afghanistan           33.0000    65.0000        0        0        0        0   \n",
       "Albania               41.1533    20.1683        0        0        0        0   \n",
       "Algeria               28.0339     1.6596        0        0        0        0   \n",
       "Andorra               42.5063     1.5218        0        0        0        0   \n",
       "Angola               -11.2027    17.8739        0        0        0        0   \n",
       "Antigua and Barbuda   17.0608   -61.7964        0        0        0        0   \n",
       "Argentina            -38.4161   -63.6167        0        0        0        0   \n",
       "Armenia               40.0691    45.0382        0        0        0        0   \n",
       "Australia           -255.9695  1129.8623        0        0        0        0   \n",
       "Austria               47.5162    14.5501        0        0        0        0   \n",
       "\n",
       "                     1/26/20  1/27/20  1/28/20  1/29/20  ...  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                  4        5        5        6  ...     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 102 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 102)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2300fc93308>"
      ]
     },
     "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_dataset_aggregated.loc[\"China\"].plot()\n",
    "corona_dataset_aggregated.loc[\"Italy\"].plot()\n",
    "corona_dataset_aggregated.loc[\"Spain\"].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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230132bd748>"
      ]
     },
     "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_dataset_aggregated.loc['China'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23015f87788>"
      ]
     },
     "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": [
    "corona_dataset_aggregated.loc[\"China\"][3:10].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### task 3.1: caculating the first derivative of the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230111fb7c8>"
      ]
     },
     "execution_count": 12,
     "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_dataset_aggregated.loc[\"China\"].diff().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### task 3.2: find maxmimum infection rate for China"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15136.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"China\"].diff().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6557.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"Italy\"].diff().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1893.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"India\"].diff().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3.3: find maximum infection rate for all of the countries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
    "# print(max_infection_rates)\n",
    "corona_dataset_aggregated['max infection rate'] = max_infection_rates"
   ]
  },
  {
   "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>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",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</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 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>33.0000</td>\n",
       "      <td>65.0000</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>232.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</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>34.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\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>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>199.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\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",
       "      <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>43.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</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>29.0766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Lat     Long  1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan     33.0000  65.0000        0        0        0        0        0   \n",
       "Albania         41.1533  20.1683        0        0        0        0        0   \n",
       "Algeria         28.0339   1.6596        0        0        0        0        0   \n",
       "Andorra         42.5063   1.5218        0        0        0        0        0   \n",
       "Angola         -11.2027  17.8739        0        0        0        0        0   \n",
       "\n",
       "                1/27/20  1/28/20  1/29/20  ...  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 rate  \n",
       "Country/Region                      \n",
       "Afghanistan               232.0000  \n",
       "Albania                    34.0000  \n",
       "Algeria                   199.0000  \n",
       "Andorra                    43.0000  \n",
       "Angola                     29.0766  \n",
       "\n",
       "[5 rows x 103 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3.4: create a new dataframe with only needed column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_data = pd.DataFrame(corona_dataset_aggregated['max infection rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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 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>232.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>43.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>29.0766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                max infection rate\n",
       "Country/Region                    \n",
       "Afghanistan               232.0000\n",
       "Albania                    34.0000\n",
       "Algeria                   199.0000\n",
       "Andorra                    43.0000\n",
       "Angola                     29.0766"
      ]
     },
     "execution_count": 22,
     "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": 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>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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness_report = pd.read_csv(\"Datasets/worldwide_happiness_report.csv\")\n",
    "world_happiness_report.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156, 9)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness_report.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.2: let's drop the useless columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_dropped = ['Overall rank','Score','Generosity','Perceptions of corruption']\n",
    "world_happiness_report.drop(columns_to_dropped,axis=1 , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness_report.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.3: changing the indices of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>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",
       "      <th>Finland</th>\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",
       "      <th>Denmark</th>\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",
       "      <th>Norway</th>\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",
       "      <th>Iceland</th>\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",
       "      <th>Netherlands</th>\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": [
    "world_happiness_report.set_index(['Country or region'],inplace=True)\n",
    "world_happiness_report.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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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 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>232.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>43.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>29.0766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                max infection rate\n",
       "Country/Region                    \n",
       "Afghanistan               232.0000\n",
       "Albania                    34.0000\n",
       "Algeria                   199.0000\n",
       "Andorra                    43.0000\n",
       "Angola                     29.0766"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### wolrd happiness report Dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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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       "    }\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",
       "      <th>Finland</th>\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",
       "      <th>Denmark</th>\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",
       "      <th>Norway</th>\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",
       "      <th>Iceland</th>\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",
       "      <th>Netherlands</th>\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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness_report.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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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       "    }\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>max infection rate</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",
       "    </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>267.0</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>391.0</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>386.0</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>99.0</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>1346.0</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  max infection rate  \n",
       "Country or region                                                    \n",
       "Finland                                   0.596               267.0  \n",
       "Denmark                                   0.592               391.0  \n",
       "Norway                                    0.603               386.0  \n",
       "Iceland                                   0.591                99.0  \n",
       "Netherlands                               0.557              1346.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = world_happiness_report.join(corona_data).copy()\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.5: correlation matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>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>max infection rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GDP per capita</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.754906</td>\n",
       "      <td>0.835462</td>\n",
       "      <td>0.379079</td>\n",
       "      <td>0.251203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social support</th>\n",
       "      <td>0.754906</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.719009</td>\n",
       "      <td>0.447333</td>\n",
       "      <td>0.194233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <td>0.835462</td>\n",
       "      <td>0.719009</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.390395</td>\n",
       "      <td>0.291478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <td>0.379079</td>\n",
       "      <td>0.447333</td>\n",
       "      <td>0.390395</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.080998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max infection rate</th>\n",
       "      <td>0.251203</td>\n",
       "      <td>0.194233</td>\n",
       "      <td>0.291478</td>\n",
       "      <td>0.080998</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              GDP per capita  Social support  \\\n",
       "GDP per capita                      1.000000        0.754906   \n",
       "Social support                      0.754906        1.000000   \n",
       "Healthy life expectancy             0.835462        0.719009   \n",
       "Freedom to make life choices        0.379079        0.447333   \n",
       "max infection rate                  0.251203        0.194233   \n",
       "\n",
       "                              Healthy life expectancy  \\\n",
       "GDP per capita                               0.835462   \n",
       "Social support                               0.719009   \n",
       "Healthy life expectancy                      1.000000   \n",
       "Freedom to make life choices                 0.390395   \n",
       "max infection rate                           0.291478   \n",
       "\n",
       "                              Freedom to make life choices  max infection rate  \n",
       "GDP per capita                                    0.379079            0.251203  \n",
       "Social support                                    0.447333            0.194233  \n",
       "Healthy life expectancy                           0.390395            0.291478  \n",
       "Freedom to make life choices                      1.000000            0.080998  \n",
       "max infection rate                                0.080998            1.000000  "
      ]
     },
     "execution_count": 33,
     "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": 34,
   "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>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>max infection rate</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",
       "    </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>267.0</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>391.0</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>386.0</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>99.0</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>1346.0</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  max infection rate  \n",
       "Country or region                                                    \n",
       "Finland                                   0.596               267.0  \n",
       "Denmark                                   0.592               391.0  \n",
       "Norway                                    0.603               386.0  \n",
       "Iceland                                   0.591                99.0  \n",
       "Netherlands                               0.557              1346.0  "
      ]
     },
     "execution_count": 34,
     "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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230137027c8>"
      ]
     },
     "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['GDP per capita']\n",
    "y = data['max infection rate']\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\chinu\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\numpy\\linalg\\linalg.py:1965: RuntimeWarning: invalid value encountered in greater\n",
      "  large = s > cutoff\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230146cfa08>"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2301370e548>"
      ]
     },
     "execution_count": 37,
     "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": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\chinu\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\numpy\\linalg\\linalg.py:1965: RuntimeWarning: invalid value encountered in greater\n",
      "  large = s > cutoff\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23014656f88>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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22dJ12kki6rJpItV691EIFL6IXAj8fPGh7xbnGbfCTcAdqvpGEYkBw5EOYQwtVim8lFqxgHv2H+eGqy9oKD5SvYLfum6UP/6V9ry/mWJrh4Xs6dW9l2yb7KriLxmeJ47Nk/eUWMTheZMjbTFAYXYE7wXeyaneQr8C7FLVzzV1QZFx4CFgm4asnrAdgTHI1Fr9DtLgknbTjzMDSlSmen7v8aPcet9Bnj2Z5swervrv3X+cm+7aR77gcSKdDx5UmBiJEnFd3nvFeafJ1dYdAfAO4GdUdQFARG4E7gGaMgTANuAI8LfFncYDwHtL5zeM1YZVCp9OL2slqndn1/78OfzMueuCrp4Vef4l5RtxBFfgx8/O8uF/PMHWyRTXXn4ul2ybLK/SO20obr3vIBFHOJHzEATHEXxV5rMe60cj3HrfwZauG6aOQIDKPCiP1obXR4CLgL9U1ZcT1CR86LSLilwrIveLyP1Hjhxp4XKG0VvC5KUPG72qlSjtzg6fTDMWj/DMiUU+/I+PcPuDz3CyKs+/pHx9VY7M5VDAFTg0m+amu/bxd997gpvu2sexhSzjiQjHFrLcdNc+7t1/vO1yP3syTSLqkPd8SpsoEch7Pomow3Mn0y2dP4wh+Fvg+yJyvYhcD+wB/qaFax4CDqnq94u/30ZgGJagqrtUdYeq7tiwYUMLlzOM3rJ5IkW6Kqd8mCqFl6MXM36zBY8//87jCErEdfB8JR5xiTjCrfcdPO34kvI9vpADAUcEEcH3lYgj/MMDh4g4gbtPCL7XOlernDmeJJP3iboOJYe6KkTdIGNp43gSESERdVmTjLJhLM6GsXjo84cJFv+JiOwmSCMV4O2q+mBT/5rgfM+JyEEReaGq/gS4Evhxs+czjH6n2TbEq51O10rkPZ903iNTbOjm+cpTM4uMJyJBrmKRWivqM8eTHFvIkvd8HCdYhpeUbyLqkM57JKJL19LtWJ0vx5sv3sxNd+1jJOZyIp0PAtcKY6koCvzOq87lnPUjTZ+/piEQkXFVPSkik8ATxa/Sc5Oq2sr+593A3xczhvZjs5CNVcwwVwo3QysZVpl8ML1rIVvgv+87PdBbUu6VrroTizkWcz7X/PWeJX7+kvJ1RFBfUUBRJlJxZhZyAOw/skAs4jCRijEaj5RX5+1ERLjsBetJxly+8v0n2T89R85T4lGXczeMteVvqWbWkIj8s6q+vlhIVnlQqaCsa8sZyxoyeoWlfXaXRjOsPD+IL6QrVv2wNNCbiJ4q+HrNi87gjh8fLj9+YjHHsYU8k6koEyOx8nGlLJx79x9n13f388SxBSKusH4kRq7gc3wxz1g8wkK2UI6Yrk1GiUaWz+AJi4gQdYVYxCEecYlHHOIRZ0l2VYPnC5U1VDd9tB8wQ2D0gtWa9tnPxu2aXXtOyyZazBWYGktwy7WXoqpkCz7pnMdi3qvZz+d9X3votJV/Ou+xbiTOmy/eXC4Em8sUSEYdJkfipx33J2+6sPxYZfFY0MzOYSIVZyFX4PhCjmzBJxl1+ejrXhTKCNy7/zi33h+cb9OaFO+4bCtXvugMYm44pR/2MwxrCMK0ob5TVa+s95hhrDZWY9pnv08/q+41pKrEXYcnjy3w7GyabN4PNbP32ZPpIBZQQcl/X1kIds1f76l5XCW1XjMSizASi6Aoc5nCikYg6gar+/sOHOfPdj9OzBXWj8Q5kc7xyTt+QioWCfUZdOIzrJk1JCKJYnxgvYhMiMhk8WsrcFZTVzOMAWI1pn2GaZ/cSzZPpMotHPKeT87zmcsGO4J0Lvzg9lKWTSXL+e/DHtfoayKOw0g8wuRIjI1rEjxv3QibJ1NMjSf4yvefIh5xGIlHm/oMOvEZrrQjuA74XQKl/wCnagdOAn/e9BWNVUk/uxuaZTUOiOn29LMwfxcld89izuNXL9rE//ftn5L38kt8+402cysFekuZPbXOE/a4lV6TLfh4qvzmZeewcU2CeMTFLWYZLffvb/Uz6MRnWHNHoKo3qeo5wPtVdZuqnlP8ulBV/6zpKxqrjmaGiQwCq3FATDdrGlb6u8gWPGYX8zw3m+HJ4uCWE4s5LnreBO+94jzWjcSZyxRYNxJvKvh6ybbJ8nmOzmU5vpAjk/e49b6DSwq+Ko8Lcz1HhF/YvoEPX7WdM9ckSec8Nq1N8Ue//BJe/7KzSMUiS4zAcv/+sXikpc+gE59hmBYTvoisVdUTACIyAVyjqn/R9FWNVcVq9KXD6kz7bLWmoZGdX+Xfha9B8VbBy3PTnfvYsq620mpXM7fSOW66ax+jxSyhUvXvezml7GtdT6SUveNw3/7jfPGeJ3j6RLr87/6H3/rZFa9f675Q1fICo95nsNz73Ym6lDCG4J2qWnYFqeqMiLwTMENgAKt72Ho3B8R0w73WinFrJEh512OHeeDJ4/iqRN1TefaxiMOzs+0vuKpFqU1EKdZTUpzVvXmcotIvKf5S+mbp3/3Hd+xtODhb676YTef5xBteXPczqPl+X31Bwx1a6xHGEDgiIqVOoSLiArGmr2isOlajL73bdDObp1njVm/nly14pHMedz02zaf+9SdAEFgseMr0XAZI4DrS9oKrlaiVPXR4Ls2aZLSs8GOR2t12brxjL9MnM3gaDLvZMBYvB2dXeh9Xui/CfAYrvd+3XHtpW/8uwvQa+hfgH0TkShG5ArgFuKNtEhgDz2r0pXeS3XunuWbXHi678S6u2bWnvBPo52weOD2LSouK8cljCzx1bJGnZ9IcX8jx5XueJOJIsdeNlP7n6EK261O8zhxPki0ELSIirkM0EvQY2rpulHWjccYS0RWNwO690/x0eh5fg1nIBV955kSGgufX3fG2el90M2stjCH4IHAX8NvA7wB3Ar/fdkmMgaUXDcQGlVoBxJ8ePtn3qapnr02ykC1Q8HxyheBrvpjaWdm1s9SsbSQWYWo8XuzgGfTpaaXqNixR12E0EWH9WJx3vepcQIJ+QRK0oCj4hFbGJQNd+i9oPAeHT2br7nhbvS+6GdgP03TOF5EvAncVm8QZxmkM67D1EmH9+7W2+3lPSee9vnOvlXr3LOYK/PLLNnHTXfso+LpiqmVlP59SwVWpWrfdRkBEiEccElGXRNRZkroJ8OoLNhJxnab96QdnFjljLM4zsxnwg9bPqkpBw63sw94X3QoK1yJMZfHVwKcI4gLniMjLgBtU9eq2S2MYA0gj/v1aAcRYxAmdSdJJcoVix868d1oB1yXbJnkv59Wd0/vyzWv4yr1P4flKzBXGEhEirtsWl1ApkycZdUkWlX+9lgytLFJKfv6z1iQ5Op8l5/m4jrBtcqRtC59uBoVrESZY/HHgEmA3gKr+sFhdbBgGjaXP1gognjcVdJHsdqpqtuCRyflkCoHyr57LW0291M579x/njh8fZk0iwkLOI1fwOZEu8NZLzmpqNxD02HdIRFwS0aAJm+O0MherMUqr8qgrnLN+pGygP3TV+W27RjeDwrUIYwgKqjrbbPc7w1jtNJI+u9J2vxvutVzhlNLP5Pwlvv12UErXHBuJM1lsj5/Oezx4cJZfC/F61xHikWC1H4+21nmzHXSjlqQf0q/DGIJHROQtgCsi5wHvAb7XWbEMY3BoJH22m0VqpdYNmbxHJh98r+7V0+6Zuys1e1uOiBMMeYlX+Pj7jU4Z6FJc4MhclumTGQTwgZjrMJaIcM760bZfsxZhDMG7gY8AWeCrBOmk/08nhTKMQaLRoF6nFEvB88uKP1sIfl6pzXxlz/7KmbuVVbeNstzgl+qGbImoSyrmkopFVkzdXM1UxgXWJCJMzweDbiIO5DyfI/M53nJJZ7OrKlmp++jfFX98p6p+RFUvLn79oapmuiSfYfQ9vUqfzRY8TmbyTM9lOHh8kaeOL3L4ZIbZdJ5M3lvRCMDSqtt2zdx988WbKfhBBpSi5WExv/FzW5kaD7pwnrU2ydpUbGiNACyNCyzkvGKKKnh+sCOYGotxz/5WhkA2xko7gleIyPOA3xCRL3Oq+ygALY6qNIxVRaf9+56vQWA375MteKH78q9Eo26cMFyybZL3OS/ga/cf5NnZoC/Pb/3CuUOdWrwclXGBUiaSK4KnyrYNo6hq38QI/oqggngbS9tQQzC60spGDaNDZAveKTdP3ifvtTeoC+HcOGEoZfakohGSMZdtG0b537pYPTxo7N47zcl0nmdn0yQiLq4IGgxEJuYGu6Ru15DUNASq+lngsyLyl6r6212TyDCGjE6s9sPQTC9+OKX4gzx+t+eZPTA48zBKsYFULIgl5Twfv5iyKyJsHI33pEVLmMri3y42mjuj8nhVfaqTghnGaqSUyRN8dW61H4awBWIQtG0oBXmTUXfZXP5mlHE7FHi/j9+spBQbWJMMBtgcnc+SVZ+Y67BlMslCzmNqLNF1QxamsvhdwPXAYYLsJghcQy/tnFiGsTrIFzN5siEzebpNrQIxtxhEThQVf9RdObDbjDJu9jXVhmOQ5mFUxgbGk1HGk1FUldl0njt+7xfadp1yI8MNW18S5vgw6aO/C7xQVY+1JpphrG5yhWDGbq642s8V/LqVuv2CI0Ki1LYhVj+fv1ohzyxkG1bGJQVe8JQDswvloOknv/XYsq+pZTgWcwU2jieWHFurIKvXLqRutGyvfJ9QvxDmNWEMwUFgtjXRDGP1oKoVCv9UJ85u+PWXo5misFKztmTUJRlrzM9frZAPHJ3nuZNZXEdIRBzWj8YZT0brVscenFnEFXhmNoNDkDXj+8q+I/PlMaeVSvvEYo6oK3i+cuBo0XCI4DgSqmFfP7iQKmtOCp7P4bkseS9o571773Rb5KjeIYUhzJH7gd0i8k2CojIAVPVPGhfRMAaLkk8/5/lk86dW/P3i3mmkKCzqOiSLrp5afv4wVCqak+k8xxaCYijPVwqe8kxxAlnElRVXupsnUjx4cAYHKcsiQFSEj37jEabnsni+Eo84FIpFVutSUU6kC4gE7ivfV7I5n5PpPMBpBX2VO4CT6TwjcZc1yWD30A4XUqM7jFJl+Se/9RhPHEsTdRzOXpsg5/ltM0rLtayoRxhD8FTxK4ZNJjNWMdWB3FzBJ+9p3yj95VhuFOPMYpZPfPPHjCUibFqb4h2XbeV/uWAj/77vaFvcIiVFczKd5+DMIr4GCjwYYQiicHguUw561uK6y7fxji/fjyvBa7U4s2BNMsLBmTRRV4g4gevo2EIOV+DIfI5YxMGRU4YjHhXWjcSYGIkvadsBLNkBPDebIZ3ziEdcxhLR8vvVbL5+szuMndunuPnu/Wz1dcmqvV1xjeXcT/UIkzX0f7cklWH0Ib6vZbdO1utt9k4rVBeFLeQKzCzkUWDruhQn0jk+ecdPeHx6ntt+8HRb3CKbJ1IcODrPsYUcpRBI2VSq4qsiKnWrq3dun+IFU6McOLpQbFkdjIF8bjZoXOA6xXEwpSY8aNFgaNlw+ChnjSVYyHnc8XuXLjn/Nbv2LHGRxCNO0L5hLls2BPX88yut+MMEqWu9vpON5irdT2GpaQhE5DOq+rsi8k9UfM4lbB6B0a+Ubr5903PkCj4RB54/Nc6vXbqFS7atK670B0/pL8eZ40mOL2YZiUVwHOHEiTziCHHXwXEcUjGHxVyBz//7ATaMxduSWXPd5du47isPAOBIoJAhUNwR1+HsNQmmxhKhzvvB12wvr6rLbh3fJ+YE5y2FLUSg4EPUgYIfGBtHYN1IjIjrMDWWOO3c1cp2/WicZ2bTZApB+416PaHqrfjrKfOVXt/JoHFlY0PECbUtWOmgUq+hT7csmWF0iTt/fJiP/9Oj5AseJxZy5Xr4/Ufm+KNv7W1oVGK7O3O2k3jUZSTm8q5XncsN33yMnOeTdFwyBQ9HhPWj8fKxyajLQs5jS5tGYe7cPsVYIsJitoAqeKpEXMERyBbdaWGLoZbrxhp1hNlMnmPzeXwUkeAaoKTiERayHqU2RTOLwe7no6970Wnnrla248ko2UIwcW02na/b+bXein805vL4kfnybmb9aHxJXGSl13c6aFxqeSLXPfFwmONXqix+oPj931qSyDA6RME7FcQtuXk+d9fjCHAyU0CcYMas7ysLOY/1sQi33ncwlDLvRGfOMNQyPsFA+yDDZyQWKc8fInQAABm0SURBVI9jrB7FOBKLkIq5jFesVNN5j5FiJWu7VqDnTY2VlexcJs+RuSyZgsdILNJww73qPk2llfS6UZhdzJP1fCKOw9R4gljEYSKlHJkLpoVFHGHDaHzZ6y3XFTYWcfnk//rSUPLVWvHvO3ySqz5zNz+dnkc12BXl8Xn6RJq1qWjZKK20Y+hG0LgRhrf9nzFQlAalH1/I8exsmiePLfDU8UWem80ws5gLVla+Xx6cnvf8JW6FvOc31FCtE50561EyPscWsmXj87nvPM5Pn5tj67oUZ4wnGE9El8zkhUBh3HLtpXz3g1fw2Te/nFjEZTFXQFXL7Qp+87JzyqMwKx9vto3BdZdvK59vNB5h45oEm9am+OybX96W1ewNV1/A1nWjrBuNc8nWddz81legBIp0LBFl24ZRtm8c5/lTo8xna/vCR2Iuh2bS7JueJ+rUj1tUstzw+KPzWeayHgeOLhBxJMhc0sBdVW2U6g2f37l9iomROFvXjXDeGWOMJ2OkYhGirgRunS4SPqxsGF2gMke/Mk8/bI5+qZFa1HUo+FocNh6kTjbSUK0TnTnrcet9B8uuBEeCSV3pvMeX7nmS17zkzFDnWGnwzUvPXtu2gTidGLBTLxVz893h/eqV/vnzpkZJ5z0W843FhZbbUcws5pkciXJ0Phd0DEVwJXCNnbN+hNliGmut11cb336YTgbhWkwkqucPiMh6VT3aObGMYaAT6ZqlRmqjcZeZhTyeBOcaiUVCNVQrUaszZyoW4X1fe6itcYOI45CKu0zPZ5hIRnGcUxv1ZpRCrZbYrbbKXk5R33LtpfVfGPLcH7jtIeYywc7u6FyWD9z2EJ9644VlmRsZANSOthPLGbvZdJ51I3FOpgvlhYZI0Ep6OaM0EnPZf3QBgHPWpfjo61601LhVxDFOpvNB76GCTyrmtq3ALAxhdgT3icg7VXUPgIj8KvDHwAtauXCxkd39wNOq+vpWzmX0P56vp/rudLDZWmUjtYK/QK7gE3OFsydG6irtSv/8SCxSdjmUOnOWfs97fstxg6jrMBqPkIq75XYOz5scKSqFU4YgrB+/060TOl2Ve+Mde5lZzAOKr5Ap+GTnc/zhPz7Mv3/oSqCxXUi7VtrVxvOaXXuYnsuwYSzOMycy+AQLF9eRJUVsn/zWY+w7Mk/Ucdg4HifiOsvuSErG7chcplyYJ8BI3O1qrCCMIXgL8AUR2Q2cBawDrmjDtd8LPAaMt+FcRp+gquS9pe6dXKH9Q9JXolYjtZWoDg5nijdt1BHmMgU2jieJOkLe1yXFW+m8FzoAHXUdUjGXkXiERPT0Xj6Njrws0Y3WCZ1u7Lb/6AKqilexGVTg0InMkpVx2F1Np9IzS59R1BXOXBPn8MksBVW2TY7woavOB4Iitum5TDBnAHh2NstZaxNl33+1/Kmow8GZRVQhEXHYuDbBWCLa1cZ5YQrKHhaR/0SQTjoHXK6qh1q5qIicDbwO+E/A+1o5l9E7qouyBqEStxbLVegCjCdjfP5tFwJwzV/vaThuEHUdRuJBJs9yyr+SZv3u3ei+WbnCLrkwcp7PoZl021wYpeK0cssjDYxBs7UOzRjVelR/Ri/fMrHkMyoVsXm+4oogIvgEWU7nrB9ZsiOpNOARJxhVWdmjsJuxgjAxgr8BziVoO/0C4J9E5M9U9c9buO5ngN8Hxla47rXAtQBbtmxp4VJGO/B8XdJVM9tgUVY/5+RDuOBw2IleiWiQ4pmK12/fXE0zfvxuBBxLK+xSLyGHQHEJlHcfQNPuqXPWpdh7eD74paJaOe5K07UO7Q5mV5671nlKjfQ8X8kXi94ijpDz9LQdSaUBj7kOBS/o1VGqfO7mlLIwrqFHgN/UYJl3QEQuBZpuOCcirwemVfUBEdlZ6zhV3QXsAtixY8fgLTEHmIJ3Klsn2wbXTq9y8hshjJJfaaJXPOoyGo8wEnOJNKj8W6UbrY1LK+zpuUxQoyeAChvXJMqtoxfzftPuqQ9ddT7v/Lv7yZd8QwIRESZHY03/Ozo9R3o5giKzBRyCrhi+Qs5TYq6smDFUqnoWDYryuj2lrO5frKr+qVbs9VV1VlXf0cI1fw64WkSeAG4FrhCRr7RwPqMFKvPzn5vNlPPzD59cmp/fCr3IyW+UN1+8mYIfrNqU4Ht1ltEl2yZ57xXnsW4kzlymwIaxOH/42vN5446z2bQ2yZpktOtGAJbm9DdTI7B77zTX7NrDZTfexTW79pRbQFdSyu1XBV+DnPmzir7sZNTlwLHF8uo2KH5rLB9+5/Yp3v2q5xOPODgOJCMOG8ZiRF23qyMbW6XUytt1HaLOKTdXxHVOq2GorDMYT0Y5a00SxxFcJ2iZ0WhhXiuEcQ2dR5Al9CKg3NBDVZv6dFT1D4A/KJ57J/B+VX1rM+cyGqPSvVOaj9uNwSmdzMlvl8sp7NjGn3/hBq56yZlNuX06RStukDBpm5XXuWjLxLK7D2DJbqr0eyNunfe8+gVtq3Xo1QCauWyBTWsTHJ3PkVMhFXFYPxrD19N3RtVxjIgrXTcAJcK4hv4W+Djwp8CrgLdT7uBi9COVRVl5L0jb7GWjtbC+9UZpt8upVrZRPOoyGoswEu++2ycszbpBSmmbpYZxqkH/nhvv2Bu6bUPeU7atH2m5hUW7lHcvB9CU3HTbNoyWH1vMFZZtitfJOEajhDEESVW9U0REVZ8ErheR7xIYh5ZQ1d3A7lbPM+xkC8V5uH04OAVW9q23wnKZPo2kc67EICj/SppVovuPLuAIp/r7C6houQiqmlrKC6ibpbOSjO1U3r2cYdxotlIv4hjLEcYQZETEAfYVB9k/DfRe8iGkclpWZQuGflL6yxHW7dIo7XY5DZryL9HtFXAt5bXS6raejO1U3r1s29BPq/xGCDu8PgW8B/gEQTHZr3dSKONUZ81Khd9p104nUzybKfKqRztcTrGIw1g82naffzd91K0o0XPWpXj8yAJS0ZfJV3j++sYzdVZa3daTsZ3KuxtZVCvRL6v8RghTUHZf8cd5gviA0Uaqm6yVfu5GELeSQUjxrKYRl1OlkTtrTZK3v3Irv/iSjeX2Du2k2yv0VpToh646n/ff9hDz2UJQBOUIa+PRcpUsNG/UKl93ZC7LxvH4kucrZWyn8u5UMdlqpu4SSER2iMh/FZEfiMiPSl/dEK7E3ufmaqa1DRIFL8gPPrGYY/pkhkMzizxxbJGnZ9Icmcsym86TznUnk6eaQUjxrKY6nXPdSHzZwTMlIzeTzrIuFWM2nePT3/4p9zx+rCNyVa5+m0mlbJR67Y4rqU4VBfj0Gy/k5ZsnWJMICptK/4bde6fLRm16LrPEqNW7F6tfJ8DTJzLlIfPVMraaAltJKdV1aizBbDrfs0ycQSKMa+jvgQ8AD1OcHNptIo50NfLfDnKn+fJ7o+DD0ou2y+1gJZeTI0Iq7vL1B58mGXUYiQer5hHX6WjwsNs+6rAr4Jo7lasvKJ9jTXFkZOm5VNRpyu1U7QrauCbBoZk0h+cyjCUip8nYbt96WPdMr9JM+40whuCIqt7ecUnq0M3IfyP4fuDayVa5dvo9gFtNp1I8l6OTsQjXCZTPSNwNdjciPDOb7qpi7raPOqwSXclPDyz73IFji5w3NbrkPGHeu2pjOJaIsmmt8tzJbM0xkd32rfcyzbTfCGMIPi4inwfuBLKlB1X16x2Tqga9GNhQSWEZhb9ahqB3KsWzmk7EIkrKfzQeIRk73effbcXcCx91GCW60k5FYdnngKbqA5Z7zyOuw0VbJto2w6BVeplm2m+ESZN4O/Ay4DXALxW/ejI/oJuR/1LrhWPz2SWjEUutFxayhVVjBCC8v71V2hWLcEQYTUQ4c02SLZMpNozFlzUC0F7/cxj61Ue9Uiyh1nPb1o809d51+z1vhoMziy1XQ68WwuwILlTVl3Rckjp06g+pMje/XwuyukUnUjyraSUWISKMFPv5p2Juua9LPXqR292PKYT1dirLPffR120HGn/vBiGfvtdppv1EGEOwR0RepKo/7rg0NfB8ZWos0fIfUqnXTil4243cfGMpjcYiRIJdw2giQirq4jjNdTfpR8Xcbeop55Wea+a96/f33NJMTyH1Vr4i8hjBPIIDBDECAVRVX9p58QJ27Nih999/f+jjK8ci5r3iV0G7OiXLWJ7KGEFlLKLSDVXK9hmJRUi2oPwNox6lrKF+3bW0iog8oKo76h0XZkfwmjbI0xGqi7HynnZ9LKLRGLXaTVx67jpS8aCnfynbp1dYSuHw0O+7lm4RprL4yW4IUo8lbp0BH4s47JRiEY5IeYZvIz7/TtJqSqEZEWMQCbMj6Dm5gs+Tx5bvhmi0TjfHSEqF8h/pE+VfSSsphZaXbgwqA9Fi0db8naPksz+2kF2S13/v/uNtu4Yjwmg8wtR4gudNpjhjPMFoPNJ3RgBaSynsdmsJw2gXA7EjMDpHp3r6RxyHZMxdUuE7CLSSUtiu1hLmXjK6zUDsCIzO8ezJNIno0j+DZnsMRRyHNckoZ61NsmVdUORVWh0PCq0UQjXS/K0WzTZ5M4xWMEMw5Jw5niSTX5pl1UiPIdeRYPB2UfmvG42TiLa/tXO3aKUquB3VtOZeMnqBuYaGnGZ6DDVb4TsoNJtS2I5q2uXcSwXP5wdPzXDZjXeZq8joCGYIhpxGxkgmihW+o7FIS0Veq9kH3mpeenWMYi6T5+kTGSKWiWR0EDMExoo9hkqjHNs1x9dSLFemuu3Bc7MZAM4YS5RdRcPaIdPoHGYIjNOIRRxG4xFSsQixSHvDSNb6d2Wq3UsKbFqbYLzCXTSsHTLDsJp3m53EDIEBQNQNlP9IvP3Kv5JuT+8aRCrdS9fs2sP0XGbJ88PaIbMetttsHssaGmJiEYeJVCzoRz+ZYmIk1lEjAO1JsRwmBqGvf79gGVfNY4ZgyIhHXSZHYmyeTHH2RHeUfyWm2BqjX4fc9CM2aKZ5zDU0BCSip3r7tCPg2wqDMLCk37AOmeGwQTPNY4ZglZKMueU5vm6f9fM3xWZ0Ahs00zxmCFYRyXJXz/5T/obRaWy32TxmCAYYkWDKlyl/wwiw3WZzmCEYMEozfEujHE35G4bRKmYIBgBT/oZhdBIzBH2M+fwNw+gGZgj6CPP5G4bRC7puCERkM/BlYCPgA7tU9aZuy9EvmNvHMIxe04sdQQH4v1T1ByIyBjwgIt9W1R/3QJaeYW4fwzD6ha4bAlV9Fni2+POciDwGbAJWvSGIR11GY5G2tXQ2DMNoBz2NEYjIVuDlwPeXee5a4FqAs86uPS2r3ym1dB6JR4ia8jcMow/pmSEQkVHgvwC/q6onq59X1V3ALoCXvOwi7bJ4LdGtls61sJ7shmE0Qk8MgYhECYzA36vq13shQ7uJusVsn7hLPNK74e3Wk90wjEbp+nJVgknnfwM8pqp/0u3rt5Oo67AmGeWstUk2T6aYHIn11AiA9WQ3DKNxerEj+Dng14CHReSHxcc+rKr/rQeyNEzUdUgVM34S0d4q/eWwCWCGYTRKL7KG/h0YqHzJktsnFXP7UvlXYj3ZDcNoFEtjWQYRIRlzWTcSZ/Nkquz26XcjADYBzDCMxrEWE0UcEVIxl1Q8Qirq4gxokZf1ZDcMo1GG2hCUlH/J7RPEsQcf68luGEYjDJ0hcETKfX1Wk/I3DMNolqEwBCXlPxqPkIya8jcMw6hk1RoCU/6GYRjhWFWGwJS/YRhG4wy8IXCdINXTlL9hGEZzDKQhcJ2gdcJI3DXlbxiG0SIDYwhM+RuGYXSGgTAEUddhy2TKlL9hGEYHGIgWE45gRsAwDKNDDIQhMAzDMDqHGQLDMIwhxwyBYRjGkGOGwDAMY8gxQ2AYhjHkmCEwDMMYcgaijsAwjMFk995pbr57PwdnFtlsQ5L6FtsRGIbREXbvneZjtz/K9FyGtcko03MZPnb7o+zeO91r0YwqzBAYhtERbr57P1E3aA0jEnyPusLNd+/vtWhGFWYIDMPoCAdnFklG3SWPJaMuh2YWeySRUQszBIZhdITNEynSeW/JY+m8x9kTqR5JZNTCDIFhGB3husu3kfeUxVwB1eB73lOuu3xbr0UzqjBDYBhGR9i5fYobrr6AqbEEs+k8U2MJbrj6Assa6kMsfdQwjI6xc/uUKf4BwHYEhmEYQ44ZAsMwjCHHDIFhGMaQY4bAMAxjyDFDYBiGMeSIqvZahrqIyBzwk17LUYf1wNFeC1GHQZARBkNOk7E9DIKMMBhyLifj81R1Q70XDkr66E9UdUevhVgJEbnfZGwPgyCnydgeBkFGGAw5W5HRXEOGYRhDjhkCwzCMIWdQDMGuXgsQApOxfQyCnCZjexgEGWEw5GxaxoEIFhuGYRidY1B2BIZhGEaHMENgGIYx5PSVIRCR14jIT0TkcRH50DLPi4h8tvj8j0Tkoj6U8f8oyvYjEfmeiFzYbzJWHHexiHgi8sZuyle8dl0ZRWSniPxQRB4VkX/rtoxFGep93mtE5J9E5KGinG/vsnxfEJFpEXmkxvP9cM/Uk7Hn90xRjhXlrDiul/dNXRmbum9UtS++ABf4H8A2IAY8BLyo6pjXAt8CBLgU+H4fyvhKYKL481X9KGPFcXcB/w14Y7/JCKwFfgxsKf4+1ad/kx8Gbiz+vAE4DsS6KOPlwEXAIzWe7+k9E1LGnt4zYeWs+JvoyX0T8r1s6r7ppx3BJcDjqrpfVXPArcAbqo55A/BlDdgDrBWRM/tJRlX9nqrOFH/dA5zdRflCyVjk3cB/Aaa7KVyRMDK+Bfi6qj4FoKr9KqcCYyIiwCiBISh0S0BVvbt4zVr0+p6pK2Mf3DMlOeq9l9Db+yaMjE3dN/1kCDYBByt+P1R8rNFjOkmj138HwWqsm9SVUUQ2Ab8C/FUX5aokzPv4AmBCRHaLyAMi8n92TbpThJHzz4DzgWeAh4H3qqrfHfFC0et7plF6cc+Eog/umzA0dd/0U4sJWeax6tzWMMd0ktDXF5FXEfxRX9ZRiZa59DKPVcv4GeCDquoFC9muE0bGCPAK4EogCdwjIntU9aedFq6CMHL+IvBD4ArgXODbIvJdVT3ZaeFC0ut7JjQ9vGfC0uv7JgxN3Tf9ZAgOAZsrfj+bYJXV6DGdJNT1ReSlwOeBq1T1WJdkKxFGxh3ArcU/5vXAa0WkoKr/2B0RQ3/WR1V1AVgQkbuBC4FuGoIwcr4d+KQGDtnHReQAsB24tzsi1qXX90woenzPhKXX900YmrtvehGUqRHkiAD7gXM4FZi7oOqY17E08HVvH8q4BXgceGW/vo9Vx3+R7geLw7yP5wN3Fo9NAY8AL+5DOf8SuL748xnA08D6Lsu5ldrBw57eMyFl7Ok9E1bOquO6ft+EfC+bum/6ZkegqgUReRfwLwSR+S+o6qMi8lvF5/+KIFL/WoI/mkWC1Vi/yfgxYB3wF8WVQ0G72LUwpIw9JYyMqvqYiNwB/Ajwgc+r6oppfb2QE/gE8EUReZhA2X5QVbvWrlhEbgF2AutF5BDwcSBaIV9P75mQMvb0nmlAzp5TT8Zm7xtrMWEYhjHk9FPWkGEYhtEDzBAYhmEMOWYIDMMwhhwzBIZhGEOOGQLDMIwhxwyBMZCIyEeK3RV/VOy0+DNNnGOHiHy2zjE7ReSfm5e0s4jIWhH5j72Wwxhs+qaOwDDCIiI/C7weuEhVsyKynqDgqyFU9X7g/nbL1y1ExCXoNvkfgb/osTjGAGM7AmMQOZOgjD4LoKpHVfUZABG5UkQeFJGHi73b48XHLy72un9IRO4VkbHK1b6IXFJ8/sHi9xeuJICIXFA8zw+Lu5LzRGRrZZ94EXm/iFxf/Hm3iHymeO5HROSS4uPXi8jfichdIrJPRN5ZfFxE5FPFYx8WkTcVH98pIt8Rka8SNLn7JHBuUY5PtfVdNoYG2xEYg8i/Ah8TkZ8C/z/wNVX9NxFJEJT+X6mqPxWRLwO/LSJ/AXwNeJOq3ici40C66px7gcuL1cSvBv4I+NUVZPgt4CZV/XsRiRFUHp9RR+4RVX2liFwOfAF4cfHxlxK0fxgBHhSRbwI/C7yMoE/MeuC+Yt8YCNpjv1hVD4jI1uLPL6tzbcOoiRkCY+BQ1XkReQXw88CrgK9JMD3sQeCAnuq0+CXgdwh6rzyrqvcVX38SoKqD5BrgSyJyHkF3zmgdMe4BPiIiZxP0f98XoiPlLcXr3y0i4yKytvj4N1Q1DaRF5DsEiv4y4BZV9YDDEkyauhg4SdAv6EC9ixlGWMw1ZAwkquqp6m5V/TjwLoLVey1NLNRvvfwJ4Duq+mLgl4BEnet/FbiaYGfxLyJyBcFAmsp7qvoc1TLoCo+vZFUWVpLNMBrFDIExcIjIC4sr9xIvA54kcO9sFZHnFx//NeDfio+fJSIXF18/JiLVu+E1BJ1DAd4WQoZtwH5V/SxwO4F75zAwJSLrirGJ11e9rOTnvwyYVdXZ4uNvEJGEiKwjaCh2H3A38CYRcUVkA8GIwuVaW88BY/XkNYyVMNeQMYiMAp8rulYKBJ01r1XVjATD4/9zUdHfB/yVquaKwdbPiUiSYBX/6qpz/r8ErqH3EcykrcebgLeKSB54DrhBVfMicgPwfeAAgQGqZEZEvgeMA79R8fi9wDcJ2jF/QlWfEZH/ShAneIhgh/D7qvqciGyvPKGqHhOR/14MUn9LVT8QQnbDWIJ1HzWMLiAiu4H3F1NWKx+/HphX1U/3Qi7DAHMNGYZhDD22IzAMwxhybEdgGIYx5JghMAzDGHLMEBiGYQw5ZggMwzCGHDMEhmEYQ87/BCpE4iB37XmzAAAAAElFTkSuQmCC\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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2301483ad48>"
      ]
     },
     "execution_count": 39,
     "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": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\chinu\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\numpy\\linalg\\linalg.py:1965: RuntimeWarning: invalid value encountered in greater\n",
      "  large = s > cutoff\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230148bb8c8>"
      ]
     },
     "execution_count": 40,
     "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": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23015f622c8>"
      ]
     },
     "execution_count": 45,
     "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": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\chinu\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\numpy\\linalg\\linalg.py:1965: RuntimeWarning: invalid value encountered in greater\n",
      "  large = s > cutoff\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23015f1fac8>"
      ]
     },
     "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": [
    "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}