covid19 data analysis notebook(2).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": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</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",
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       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>33.0000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1092</td>\n",
       "      <td>1176</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1463</td>\n",
       "      <td>1531</td>\n",
       "      <td>1703</td>\n",
       "      <td>1828</td>\n",
       "      <td>1939</td>\n",
       "      <td>2171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\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",
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       "      <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>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2811</td>\n",
       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <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 × 104 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region      Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
       "0            NaN    Afghanistan  33.0000  65.0000        0        0        0   \n",
       "1            NaN        Albania  41.1533  20.1683        0        0        0   \n",
       "2            NaN        Algeria  28.0339   1.6596        0        0        0   \n",
       "3            NaN        Andorra  42.5063   1.5218        0        0        0   \n",
       "4            NaN         Angola -11.2027  17.8739        0        0        0   \n",
       "\n",
       "   1/25/20  1/26/20  1/27/20  ...  4/21/20  4/22/20  4/23/20  4/24/20  \\\n",
       "0        0        0        0  ...     1092     1176     1279     1351   \n",
       "1        0        0        0  ...      609      634      663      678   \n",
       "2        0        0        0  ...     2811     2910     3007     3127   \n",
       "3        0        0        0  ...      717      723      723      731   \n",
       "4        0        0        0  ...       24       25       25       25   \n",
       "\n",
       "   4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \n",
       "0     1463     1531     1703     1828     1939     2171  \n",
       "1      712      726      736      750      766      773  \n",
       "2     3256     3382     3517     3649     3848     4006  \n",
       "3      738      738      743      743      743      745  \n",
       "4       25       26       27       27       27       27  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv = pd.read_csv(\"Datasets/covid19_Confirmed_dataset.csv\")\n",
    "corona_dataset_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's check the shape of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(266, 104)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.2: Delete the useless columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_dataset_csv.drop([\"Lat\",\"Long\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3256</td>\n",
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       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>25</td>\n",
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       "      <td>27</td>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1401</td>\n",
       "      <td>1473</td>\n",
       "      <td>1523</td>\n",
       "      <td>1596</td>\n",
       "      <td>1677</td>\n",
       "      <td>1746</td>\n",
       "      <td>1808</td>\n",
       "      <td>1867</td>\n",
       "      <td>1932</td>\n",
       "      <td>2066</td>\n",
       "    </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",
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       "      <td>0</td>\n",
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       "      <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",
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       "    <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",
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       "      <td>4</td>\n",
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       "      <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": [
    "corona_dataset_csv.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.3: Aggregating the rows by the country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "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",
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       "\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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 100)"
      ]
     },
     "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.axes._subplots.AxesSubplot at 0xe06e130>"
      ]
     },
     "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()"
   ]
  },
  {
   "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 0xe0f7490>"
      ]
     },
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe12cbf8>"
      ]
     },
     "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\"][:3].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 0x1701448>"
      ]
     },
     "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": [
       "9630.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"Spain\"].diff().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3.3: find maximum infection rate for all of the countries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = list(corona_dataset_aggregated.index)\n",
    "max_infection_rates = []\n",
    "for c in countries:\n",
    "    max_infection_rates.append(corona_dataset_aggregated.loc[c].diff().max())\n",
    "corona_dataset_aggregated[\"max_infection_rate\"] = max_infection_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>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_rate  \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": 19,
     "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_data = pd.DataFrame(corona_dataset_aggregated[\"max_infection_rate\"])"
   ]
  },
  {
   "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>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>232.0</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_rate\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": 21,
     "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(\"Datasets/worldwide_happiness_report.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 26,
     "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": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "useless_cols = [\"Overall rank\",\"Score\",\"Generosity\",\"Perceptions of corruption\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country or region</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.3: changing the indices of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.set_index(\"Country or region\",inplace=True)\n",
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task4.4: now let's join two dataset we have prepared  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Corona Dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 1)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.head()\n",
    "corona_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### wolrd happiness report Dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156, 4)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()\n",
    "happiness_report_csv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rate</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </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_rate  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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = corona_data.join(happiness_report_csv,how=\"inner\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.5: correlation matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rate</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max_infection_rate</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_rate  GDP per capita  \\\n",
       "max_infection_rate                      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_rate                  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_rate                                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": 50,
     "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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rate</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </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_rate  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": 51,
     "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": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x54b8b80>"
      ]
     },
     "execution_count": 58,
     "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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x54e7538>"
      ]
     },
     "execution_count": 59,
     "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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x5523f28>"
      ]
     },
     "execution_count": 60,
     "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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x556b538>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.3: Plotting Healthy life expectancy vs maximum Infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eb86d0>"
      ]
     },
     "execution_count": 62,
     "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": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x54e70d0>"
      ]
     },
     "execution_count": 63,
     "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": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe1ac928>"
      ]
     },
     "execution_count": 70,
     "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": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe204250>"
      ]
     },
     "execution_count": 77,
     "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}