Covid19 data analysis notebook - solved.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Welcome to Covid19 Data Analysis Notebook\n",
    "------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's Import the modules "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modules are imported.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "print('Modules are imported.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.1: importing covid19 dataset\n",
    "importing \"Covid19_Confirmed_dataset.csv\" from \"./Dataset\" folder. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Province/State</th>\n",
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       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
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       "      <td>4006</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
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       "      <td>0</td>\n",
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       "      <td>26</td>\n",
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       "      <td>27</td>\n",
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       "    <tr>\n",
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       "      <td>-61.7964</td>\n",
       "      <td>0</td>\n",
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       "      <td>24</td>\n",
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       "      <td>-63.6167</td>\n",
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       "      <th>7</th>\n",
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       "      <td>1523</td>\n",
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       "      <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>-35.4735</td>\n",
       "      <td>149.0124</td>\n",
       "      <td>0</td>\n",
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       "      <td>106</td>\n",
       "      <td>106</td>\n",
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       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-33.8688</td>\n",
       "      <td>151.2093</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>...</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 × 104 columns</p>\n",
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      ],
      "text/plain": [
       "                 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('Covid19_Confirmed_dataset.csv')\n",
    "corona_dataset_csv.head(10)\n",
    "#We will notice data is from 22 January 2020 to 30 April 2020"
   ]
  },
  {
   "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    #Tuple with 266 rows and 104 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Province/State', 'Country/Region', 'Lat', 'Long', '1/22/20', '1/23/20',\n",
       "       '1/24/20', '1/25/20', '1/26/20', '1/27/20',\n",
       "       ...\n",
       "       '4/21/20', '4/22/20', '4/23/20', '4/24/20', '4/25/20', '4/26/20',\n",
       "       '4/27/20', '4/28/20', '4/29/20', '4/30/20'],\n",
       "      dtype='object', length=104)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = corona_dataset_csv.columns\n",
    "columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.2: Delete the useless columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Latitude and Longitude are not important features for us here\n",
    "corona_dataset_csv.drop([\"Lat\",\n",
    "                        \"Long\"],\n",
    "                        axis=1,\n",
    "                        #default value, annotation axis=0 which is equal to rows\n",
    "                        inplace = True   #will change the corona dataset too\n",
    "                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.3: Aggregating the rows by the country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_dataset_aggregated = corona_dataset_csv.groupby(\"Country/Region\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>1/30/20</th>\n",
       "      <th>1/31/20</th>\n",
       "      <th>...</th>\n",
       "      <th>4/21/20</th>\n",
       "      <th>4/22/20</th>\n",
       "      <th>4/23/20</th>\n",
       "      <th>4/24/20</th>\n",
       "      <th>4/25/20</th>\n",
       "      <th>4/26/20</th>\n",
       "      <th>4/27/20</th>\n",
       "      <th>4/28/20</th>\n",
       "      <th>4/29/20</th>\n",
       "      <th>4/30/20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>609</td>\n",
       "      <td>634</td>\n",
       "      <td>663</td>\n",
       "      <td>678</td>\n",
       "      <td>712</td>\n",
       "      <td>726</td>\n",
       "      <td>736</td>\n",
       "      <td>750</td>\n",
       "      <td>766</td>\n",
       "      <td>773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>717</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan           0        0        0        0        0        0        0   \n",
       "Albania               0        0        0        0        0        0        0   \n",
       "Algeria               0        0        0        0        0        0        0   \n",
       "Andorra               0        0        0        0        0        0        0   \n",
       "Angola                0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  4/21/20  4/22/20  4/23/20  \\\n",
       "Country/Region                             ...                              \n",
       "Afghanistan           0        0        0  ...     1092     1176     1279   \n",
       "Albania               0        0        0  ...      609      634      663   \n",
       "Algeria               0        0        0  ...     2811     2910     3007   \n",
       "Andorra               0        0        0  ...      717      723      723   \n",
       "Angola                0        0        0  ...       24       25       25   \n",
       "\n",
       "                4/24/20  4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \n",
       "Country/Region                                                                 \n",
       "Afghanistan        1351     1463     1531     1703     1828     1939     2171  \n",
       "Albania             678      712      726      736      750      766      773  \n",
       "Algeria            3127     3256     3382     3517     3649     3848     4006  \n",
       "Andorra             731      738      738      743      743      743      745  \n",
       "Angola               25       25       26       27       27       27       27  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.head()\n",
    "#After aggregation, the index of the df is the column at which we aggregated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 100)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.shape\n",
    "#we have 187 countries, 100 dates"
   ]
  },
  {
   "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": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1/22/20      548\n",
       "1/23/20      643\n",
       "1/24/20      920\n",
       "1/25/20     1406\n",
       "1/26/20     2075\n",
       "1/27/20     2877\n",
       "1/28/20     5509\n",
       "1/29/20     6087\n",
       "1/30/20     8141\n",
       "1/31/20     9802\n",
       "2/1/20     11891\n",
       "2/2/20     16630\n",
       "2/3/20     19716\n",
       "2/4/20     23707\n",
       "2/5/20     27440\n",
       "2/6/20     30587\n",
       "2/7/20     34110\n",
       "2/8/20     36814\n",
       "2/9/20     39829\n",
       "2/10/20    42354\n",
       "2/11/20    44386\n",
       "2/12/20    44759\n",
       "2/13/20    59895\n",
       "2/14/20    66358\n",
       "2/15/20    68413\n",
       "2/16/20    70513\n",
       "2/17/20    72434\n",
       "2/18/20    74211\n",
       "2/19/20    74619\n",
       "2/20/20    75077\n",
       "           ...  \n",
       "4/1/20     82361\n",
       "4/2/20     82432\n",
       "4/3/20     82511\n",
       "4/4/20     82543\n",
       "4/5/20     82602\n",
       "4/6/20     82665\n",
       "4/7/20     82718\n",
       "4/8/20     82809\n",
       "4/9/20     82883\n",
       "4/10/20    82941\n",
       "4/11/20    83014\n",
       "4/12/20    83134\n",
       "4/13/20    83213\n",
       "4/14/20    83306\n",
       "4/15/20    83356\n",
       "4/16/20    83403\n",
       "4/17/20    83760\n",
       "4/18/20    83787\n",
       "4/19/20    83805\n",
       "4/20/20    83817\n",
       "4/21/20    83853\n",
       "4/22/20    83868\n",
       "4/23/20    83884\n",
       "4/24/20    83899\n",
       "4/25/20    83909\n",
       "4/26/20    83912\n",
       "4/27/20    83918\n",
       "4/28/20    83940\n",
       "4/29/20    83944\n",
       "4/30/20    83956\n",
       "Name: China, Length: 100, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"China\"]\n",
    "#will return pandas series"
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20d4c9fc828>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc['China'].plot()\n",
    "#will plot the values on different date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20d4cd43ba8>"
      ]
     },
     "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['Egypt'].plot()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20d4cd9fa58>"
      ]
     },
     "execution_count": 13,
     "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": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20d4ce2fdd8>"
      ]
     },
     "execution_count": 14,
     "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": [
    "#Spread of the virus in China for the first 3 dates only\n",
    "corona_dataset_aggregated.loc['China'][:3].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the 1st 24 hrs, an increase in case (550 to 650) by 100\n",
    "\n",
    "In the 2nd 24 hrs, an increase (650 to 900) by 250\n",
    "\n",
    "We want to find a measure for new cases, so either say average or maximum number of new cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### task 3.1: caculating the first derivative of the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20d4ce9fda0>"
      ]
     },
     "execution_count": 15,
     "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": [
    "This plot shows us the change in infection rate day by day and what we are looking for is the maximum number."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### task 3.2: find maxmimum infection rate for China"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15136.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"China\"].diff().max()\n",
    "#In only 24 hrs, the difference was 15136"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6557.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"Italy\"].diff().max()\n",
    "#In only 24 hrs, the difference was 6557"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9630.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"Spain\"].diff().max()\n",
    "#In only 24 hrs, the difference was 9630"
   ]
  },
  {
   "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": [
    {
     "data": {
      "text/plain": [
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       " 8.0]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "max_infection_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_dataset_aggregated[\"max_infection_rates\"] = max_infection_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>1/30/20</th>\n",
       "      <th>1/31/20</th>\n",
       "      <th>...</th>\n",
       "      <th>4/22/20</th>\n",
       "      <th>4/23/20</th>\n",
       "      <th>4/24/20</th>\n",
       "      <th>4/25/20</th>\n",
       "      <th>4/26/20</th>\n",
       "      <th>4/27/20</th>\n",
       "      <th>4/28/20</th>\n",
       "      <th>4/29/20</th>\n",
       "      <th>4/30/20</th>\n",
       "      <th>max_infection_rates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1176</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1463</td>\n",
       "      <td>1531</td>\n",
       "      <td>1703</td>\n",
       "      <td>1828</td>\n",
       "      <td>1939</td>\n",
       "      <td>2171</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>634</td>\n",
       "      <td>663</td>\n",
       "      <td>678</td>\n",
       "      <td>712</td>\n",
       "      <td>726</td>\n",
       "      <td>736</td>\n",
       "      <td>750</td>\n",
       "      <td>766</td>\n",
       "      <td>773</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>745</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan           0        0        0        0        0        0        0   \n",
       "Albania               0        0        0        0        0        0        0   \n",
       "Algeria               0        0        0        0        0        0        0   \n",
       "Andorra               0        0        0        0        0        0        0   \n",
       "Angola                0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  4/22/20  4/23/20  4/24/20  \\\n",
       "Country/Region                             ...                              \n",
       "Afghanistan           0        0        0  ...     1176     1279     1351   \n",
       "Albania               0        0        0  ...      634      663      678   \n",
       "Algeria               0        0        0  ...     2910     3007     3127   \n",
       "Andorra               0        0        0  ...      723      723      731   \n",
       "Angola                0        0        0  ...       25       25       25   \n",
       "\n",
       "                4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \\\n",
       "Country/Region                                                         \n",
       "Afghanistan        1463     1531     1703     1828     1939     2171   \n",
       "Albania             712      726      736      750      766      773   \n",
       "Algeria            3256     3382     3517     3649     3848     4006   \n",
       "Andorra             738      738      743      743      743      745   \n",
       "Angola               25       26       27       27       27       27   \n",
       "\n",
       "                max_infection_rates  \n",
       "Country/Region                       \n",
       "Afghanistan                   232.0  \n",
       "Albania                        34.0  \n",
       "Algeria                       199.0  \n",
       "Andorra                        43.0  \n",
       "Angola                          5.0  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_data = pd.DataFrame(corona_dataset_aggregated[\"max_infection_rates\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rates</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>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                max_infection_rates\n",
       "Country/Region                     \n",
       "Afghanistan                   232.0\n",
       "Albania                        34.0\n",
       "Algeria                       199.0\n",
       "Andorra                        43.0\n",
       "Angola                          5.0"
      ]
     },
     "execution_count": 23,
     "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": [],
   "source": [
    "happiness_report_csv = pd.read_csv(\"worldwide_happiness_report.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.2: let's drop the useless columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "useless_cols = [\"Overall rank\", \"Score\", \"Generosity\", \"Perceptions of corruption\"]"
   ]
  },
  {
   "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",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country or region</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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": [
    "happiness_report_csv.drop(useless_cols, axis=1, inplace=True)\n",
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.3: changing the indices of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv.set_index(\"Country or region\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country or region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task4.4: now let's join two dataset we have prepared  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Corona Dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        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_rates</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>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                max_infection_rates\n",
       "Country/Region                     \n",
       "Afghanistan                   232.0\n",
       "Albania                        34.0\n",
       "Algeria                       199.0\n",
       "Andorra                        43.0\n",
       "Angola                          5.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.shape    #Tuple with 187 countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### wolrd happiness report Dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "        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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156, 4)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.shape    #156 countries, less than corona data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rates</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>232.0</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0</td>\n",
       "      <td>1.002</td>\n",
       "      <td>1.160</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>291.0</td>\n",
       "      <td>1.092</td>\n",
       "      <td>1.432</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>134.0</td>\n",
       "      <td>0.850</td>\n",
       "      <td>1.055</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             max_infection_rates  GDP per capita  Social support  \\\n",
       "Afghanistan                232.0           0.350           0.517   \n",
       "Albania                     34.0           0.947           0.848   \n",
       "Algeria                    199.0           1.002           1.160   \n",
       "Argentina                  291.0           1.092           1.432   \n",
       "Armenia                    134.0           0.850           1.055   \n",
       "\n",
       "             Healthy life expectancy  Freedom to make life choices  \n",
       "Afghanistan                    0.361                         0.000  \n",
       "Albania                        0.874                         0.383  \n",
       "Algeria                        0.785                         0.086  \n",
       "Argentina                      0.881                         0.471  \n",
       "Armenia                        0.815                         0.283  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inner join\n",
    "data = corona_data.join(happiness_report_csv,\n",
    "                how = \"inner\"    #method/type of join\n",
    "                )\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.5: correlation matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rates</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max_infection_rates</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250118</td>\n",
       "      <td>0.191958</td>\n",
       "      <td>0.289263</td>\n",
       "      <td>0.078196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP per capita</th>\n",
       "      <td>0.250118</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.759468</td>\n",
       "      <td>0.863062</td>\n",
       "      <td>0.394603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social support</th>\n",
       "      <td>0.191958</td>\n",
       "      <td>0.759468</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.765286</td>\n",
       "      <td>0.456246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <td>0.289263</td>\n",
       "      <td>0.863062</td>\n",
       "      <td>0.765286</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.427892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <td>0.078196</td>\n",
       "      <td>0.394603</td>\n",
       "      <td>0.456246</td>\n",
       "      <td>0.427892</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              max_infection_rates  GDP per capita  \\\n",
       "max_infection_rates                      1.000000        0.250118   \n",
       "GDP per capita                           0.250118        1.000000   \n",
       "Social support                           0.191958        0.759468   \n",
       "Healthy life expectancy                  0.289263        0.863062   \n",
       "Freedom to make life choices             0.078196        0.394603   \n",
       "\n",
       "                              Social support  Healthy life expectancy  \\\n",
       "max_infection_rates                 0.191958                 0.289263   \n",
       "GDP per capita                      0.759468                 0.863062   \n",
       "Social support                      1.000000                 0.765286   \n",
       "Healthy life expectancy             0.765286                 1.000000   \n",
       "Freedom to make life choices        0.456246                 0.427892   \n",
       "\n",
       "                              Freedom to make life choices  \n",
       "max_infection_rates                               0.078196  \n",
       "GDP per capita                                    0.394603  \n",
       "Social support                                    0.456246  \n",
       "Healthy life expectancy                           0.427892  \n",
       "Freedom to make life choices                      1.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is +ve correlation between max_infection-rate and all other features"
   ]
  },
  {
   "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rates</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>232.0</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0</td>\n",
       "      <td>1.002</td>\n",
       "      <td>1.160</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>291.0</td>\n",
       "      <td>1.092</td>\n",
       "      <td>1.432</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>134.0</td>\n",
       "      <td>0.850</td>\n",
       "      <td>1.055</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             max_infection_rates  GDP per capita  Social support  \\\n",
       "Afghanistan                232.0           0.350           0.517   \n",
       "Albania                     34.0           0.947           0.848   \n",
       "Algeria                    199.0           1.002           1.160   \n",
       "Argentina                  291.0           1.092           1.432   \n",
       "Armenia                    134.0           0.850           1.055   \n",
       "\n",
       "             Healthy life expectancy  Freedom to make life choices  \n",
       "Afghanistan                    0.361                         0.000  \n",
       "Albania                        0.874                         0.383  \n",
       "Algeria                        0.785                         0.086  \n",
       "Argentina                      0.881                         0.471  \n",
       "Armenia                        0.815                         0.283  "
      ]
     },
     "execution_count": 36,
     "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20d4cf22828>"
      ]
     },
     "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[\"GDP per capita\"]\n",
    "y = data[\"max_infection_rates\"]\n",
    "sns.scatterplot(x,y)\n",
    "\n",
    "#We can see the values need different scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20d4cf8fd30>"
      ]
     },
     "execution_count": 38,
     "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": [
    "#Will apply log scaling to y\n",
    "x = data[\"GDP per capita\"]\n",
    "y = data[\"max_infection_rates\"]\n",
    "sns.scatterplot(x,np.log(y))\n",
    "\n",
    "#Now we can see +ve correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20d4cffcdd8>"
      ]
     },
     "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": [
    "#RegPlot\n",
    "x = data[\"GDP per capita\"]\n",
    "y = data[\"max_infection_rates\"]\n",
    "\n",
    "sns.regplot(x,np.log(y))\n",
    "\n",
    "#Line fitted, +ve slope seen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.2: Plotting Social support vs maximum Infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20d4d0689b0>"
      ]
     },
     "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": [
    "x = data[\"Social support\"]\n",
    "y = data[\"max_infection_rates\"]\n",
    "\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20d4d0c70b8>"
      ]
     },
     "execution_count": 41,
     "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_rates\"]\n",
    "\n",
    "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20d4d1397b8>"
      ]
     },
     "execution_count": 42,
     "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_rates\"]\n",
    "\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20d4d1829e8>"
      ]
     },
     "execution_count": 43,
     "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_rates\"]\n",
    "\n",
    "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20d4d1e0c88>"
      ]
     },
     "execution_count": 44,
     "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_rates\"]\n",
    "\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20d4d246940>"
      ]
     },
     "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_rates\"]\n",
    "\n",
    "sns.regplot(x,np.log(y))"
   ]
  }
 ],
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