covid19 data analysis notebook (1).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 folder. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
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       "      <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",
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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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       "      <th>4</th>\n",
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       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
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       "      <td>26</td>\n",
       "      <td>27</td>\n",
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       "      <td>27</td>\n",
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       "      <td>17.0608</td>\n",
       "      <td>-61.7964</td>\n",
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       "      <td>24</td>\n",
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       "      <th>6</th>\n",
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       "      <th>7</th>\n",
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       "      <td>45.0382</td>\n",
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       "      <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>-35.4735</td>\n",
       "      <td>149.0124</td>\n",
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       "      <td>104</td>\n",
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       "      <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>-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",
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       "      <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",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-12.4634</td>\n",
       "      <td>130.8456</td>\n",
       "      <td>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>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-28.0167</td>\n",
       "      <td>153.4000</td>\n",
       "      <td>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>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>1026</td>\n",
       "      <td>1026</td>\n",
       "      <td>1026</td>\n",
       "      <td>1030</td>\n",
       "      <td>1033</td>\n",
       "      <td>1034</td>\n",
       "      <td>1033</td>\n",
       "      <td>1033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-34.9285</td>\n",
       "      <td>138.6007</td>\n",
       "      <td>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>437</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-41.4545</td>\n",
       "      <td>145.9707</td>\n",
       "      <td>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>201</td>\n",
       "      <td>205</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>212</td>\n",
       "      <td>214</td>\n",
       "      <td>218</td>\n",
       "      <td>219</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-37.8136</td>\n",
       "      <td>144.9631</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1336</td>\n",
       "      <td>1336</td>\n",
       "      <td>1337</td>\n",
       "      <td>1343</td>\n",
       "      <td>1346</td>\n",
       "      <td>1349</td>\n",
       "      <td>1349</td>\n",
       "      <td>1354</td>\n",
       "      <td>1361</td>\n",
       "      <td>1364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-31.9505</td>\n",
       "      <td>115.8605</td>\n",
       "      <td>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>546</td>\n",
       "      <td>546</td>\n",
       "      <td>546</td>\n",
       "      <td>548</td>\n",
       "      <td>549</td>\n",
       "      <td>549</td>\n",
       "      <td>549</td>\n",
       "      <td>550</td>\n",
       "      <td>551</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Austria</td>\n",
       "      <td>47.5162</td>\n",
       "      <td>14.5501</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14873</td>\n",
       "      <td>14925</td>\n",
       "      <td>15002</td>\n",
       "      <td>15071</td>\n",
       "      <td>15148</td>\n",
       "      <td>15225</td>\n",
       "      <td>15274</td>\n",
       "      <td>15357</td>\n",
       "      <td>15402</td>\n",
       "      <td>15452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>40.1431</td>\n",
       "      <td>47.5769</td>\n",
       "      <td>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>1480</td>\n",
       "      <td>1518</td>\n",
       "      <td>1548</td>\n",
       "      <td>1592</td>\n",
       "      <td>1617</td>\n",
       "      <td>1645</td>\n",
       "      <td>1678</td>\n",
       "      <td>1717</td>\n",
       "      <td>1766</td>\n",
       "      <td>1804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahamas</td>\n",
       "      <td>25.0343</td>\n",
       "      <td>-77.3963</td>\n",
       "      <td>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>65</td>\n",
       "      <td>65</td>\n",
       "      <td>72</td>\n",
       "      <td>73</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>26.0275</td>\n",
       "      <td>50.5500</td>\n",
       "      <td>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>1973</td>\n",
       "      <td>2027</td>\n",
       "      <td>2217</td>\n",
       "      <td>2518</td>\n",
       "      <td>2588</td>\n",
       "      <td>2647</td>\n",
       "      <td>2723</td>\n",
       "      <td>2811</td>\n",
       "      <td>2921</td>\n",
       "      <td>3040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 104 columns</p>\n",
       "</div>"
      ],
      "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",
       "10            Northern Territory            Australia -12.4634  130.8456   \n",
       "11                    Queensland            Australia -28.0167  153.4000   \n",
       "12               South Australia            Australia -34.9285  138.6007   \n",
       "13                      Tasmania            Australia -41.4545  145.9707   \n",
       "14                      Victoria            Australia -37.8136  144.9631   \n",
       "15             Western Australia            Australia -31.9505  115.8605   \n",
       "16                           NaN              Austria  47.5162   14.5501   \n",
       "17                           NaN           Azerbaijan  40.1431   47.5769   \n",
       "18                           NaN              Bahamas  25.0343  -77.3963   \n",
       "19                           NaN              Bahrain  26.0275   50.5500   \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",
       "10        0        0        0        0        0        0  ...       28   \n",
       "11        0        0        0        0        0        0  ...     1024   \n",
       "12        0        0        0        0        0        0  ...      437   \n",
       "13        0        0        0        0        0        0  ...      201   \n",
       "14        0        0        0        0        1        1  ...     1336   \n",
       "15        0        0        0        0        0        0  ...      546   \n",
       "16        0        0        0        0        0        0  ...    14873   \n",
       "17        0        0        0        0        0        0  ...     1480   \n",
       "18        0        0        0        0        0        0  ...       65   \n",
       "19        0        0        0        0        0        0  ...     1973   \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",
       "10       28       28       28       28       28       28       28       28   \n",
       "11     1024     1026     1026     1026     1030     1033     1034     1033   \n",
       "12      438      438      438      438      438      438      438      438   \n",
       "13      205      207      207      207      212      214      218      219   \n",
       "14     1336     1337     1343     1346     1349     1349     1354     1361   \n",
       "15      546      546      548      549      549      549      550      551   \n",
       "16    14925    15002    15071    15148    15225    15274    15357    15402   \n",
       "17     1518     1548     1592     1617     1645     1678     1717     1766   \n",
       "18       65       72       73       78       80       80       80       80   \n",
       "19     2027     2217     2518     2588     2647     2723     2811     2921   \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",
       "10       28  \n",
       "11     1033  \n",
       "12      438  \n",
       "13      221  \n",
       "14     1364  \n",
       "15      551  \n",
       "16    15452  \n",
       "17     1804  \n",
       "18       81  \n",
       "19     3040  \n",
       "\n",
       "[20 rows x 104 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor_data_csv=pd.read_csv(\"covid19_Confirmed_dataset.csv\")\n",
    "cor_data_csv.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's check the shape of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 266 entries, 0 to 265\n",
      "Columns: 104 entries, Province/State to 4/30/20\n",
      "dtypes: float64(2), int64(100), object(2)\n",
      "memory usage: 216.2+ KB\n"
     ]
    }
   ],
   "source": [
    "cor_data_csv.shape\n",
    "cor_data_csv.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.2: Delete the useless columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=cor_data_csv.drop([\"Lat\",\"Long\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>...</th>\n",
       "      <th>4/21/20</th>\n",
       "      <th>4/22/20</th>\n",
       "      <th>4/23/20</th>\n",
       "      <th>4/24/20</th>\n",
       "      <th>4/25/20</th>\n",
       "      <th>4/26/20</th>\n",
       "      <th>4/27/20</th>\n",
       "      <th>4/28/20</th>\n",
       "      <th>4/29/20</th>\n",
       "      <th>4/30/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>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>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",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Northern 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>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Queensland</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>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>1026</td>\n",
       "      <td>1026</td>\n",
       "      <td>1026</td>\n",
       "      <td>1030</td>\n",
       "      <td>1033</td>\n",
       "      <td>1034</td>\n",
       "      <td>1033</td>\n",
       "      <td>1033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>South Australia</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>437</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Tasmania</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>201</td>\n",
       "      <td>205</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>212</td>\n",
       "      <td>214</td>\n",
       "      <td>218</td>\n",
       "      <td>219</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1336</td>\n",
       "      <td>1336</td>\n",
       "      <td>1337</td>\n",
       "      <td>1343</td>\n",
       "      <td>1346</td>\n",
       "      <td>1349</td>\n",
       "      <td>1349</td>\n",
       "      <td>1354</td>\n",
       "      <td>1361</td>\n",
       "      <td>1364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 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",
       "10            Northern Territory            Australia        0        0   \n",
       "11                    Queensland            Australia        0        0   \n",
       "12               South Australia            Australia        0        0   \n",
       "13                      Tasmania            Australia        0        0   \n",
       "14                      Victoria            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",
       "10        0        0        0        0        0        0  ...       28   \n",
       "11        0        0        0        0        0        1  ...     1024   \n",
       "12        0        0        0        0        0        0  ...      437   \n",
       "13        0        0        0        0        0        0  ...      201   \n",
       "14        0        0        1        1        1        1  ...     1336   \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",
       "10       28       28       28       28       28       28       28       28   \n",
       "11     1024     1026     1026     1026     1030     1033     1034     1033   \n",
       "12      438      438      438      438      438      438      438      438   \n",
       "13      205      207      207      207      212      214      218      219   \n",
       "14     1336     1337     1343     1346     1349     1349     1354     1361   \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",
       "10       28  \n",
       "11     1033  \n",
       "12      438  \n",
       "13      221  \n",
       "14     1364  \n",
       "\n",
       "[15 rows x 102 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use *cor_data_csv.drop([\"Lat\",\"Long\"],axis=1,inplace=True)* this will refelect the changes in the actual dataset and we dont have to create another dataframe to store the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.3: Aggregating the rows by the country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df.groupby(\"Country/Region\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</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>23</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3031</td>\n",
       "      <td>3144</td>\n",
       "      <td>3435</td>\n",
       "      <td>3607</td>\n",
       "      <td>3780</td>\n",
       "      <td>3892</td>\n",
       "      <td>4003</td>\n",
       "      <td>4127</td>\n",
       "      <td>4285</td>\n",
       "      <td>4428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</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>1401</td>\n",
       "      <td>1473</td>\n",
       "      <td>1523</td>\n",
       "      <td>1596</td>\n",
       "      <td>1677</td>\n",
       "      <td>1746</td>\n",
       "      <td>1808</td>\n",
       "      <td>1867</td>\n",
       "      <td>1932</td>\n",
       "      <td>2066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>6645</td>\n",
       "      <td>6652</td>\n",
       "      <td>6662</td>\n",
       "      <td>6677</td>\n",
       "      <td>6694</td>\n",
       "      <td>6714</td>\n",
       "      <td>6721</td>\n",
       "      <td>6744</td>\n",
       "      <td>6752</td>\n",
       "      <td>6766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</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>14873</td>\n",
       "      <td>14925</td>\n",
       "      <td>15002</td>\n",
       "      <td>15071</td>\n",
       "      <td>15148</td>\n",
       "      <td>15225</td>\n",
       "      <td>15274</td>\n",
       "      <td>15357</td>\n",
       "      <td>15402</td>\n",
       "      <td>15452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  \\\n",
       "Country/Region                                                              \n",
       "Afghanistan                0        0        0        0        0        0   \n",
       "Albania                    0        0        0        0        0        0   \n",
       "Algeria                    0        0        0        0        0        0   \n",
       "Andorra                    0        0        0        0        0        0   \n",
       "Angola                     0        0        0        0        0        0   \n",
       "Antigua and Barbuda        0        0        0        0        0        0   \n",
       "Argentina                  0        0        0        0        0        0   \n",
       "Armenia                    0        0        0        0        0        0   \n",
       "Australia                  0        0        0        0        4        5   \n",
       "Austria                    0        0        0        0        0        0   \n",
       "\n",
       "                     1/28/20  1/29/20  1/30/20  1/31/20  ...  4/21/20  \\\n",
       "Country/Region                                           ...            \n",
       "Afghanistan                0        0        0        0  ...     1092   \n",
       "Albania                    0        0        0        0  ...      609   \n",
       "Algeria                    0        0        0        0  ...     2811   \n",
       "Andorra                    0        0        0        0  ...      717   \n",
       "Angola                     0        0        0        0  ...       24   \n",
       "Antigua and Barbuda        0        0        0        0  ...       23   \n",
       "Argentina                  0        0        0        0  ...     3031   \n",
       "Armenia                    0        0        0        0  ...     1401   \n",
       "Australia                  5        6        9        9  ...     6645   \n",
       "Austria                    0        0        0        0  ...    14873   \n",
       "\n",
       "                     4/22/20  4/23/20  4/24/20  4/25/20  4/26/20  4/27/20  \\\n",
       "Country/Region                                                              \n",
       "Afghanistan             1176     1279     1351     1463     1531     1703   \n",
       "Albania                  634      663      678      712      726      736   \n",
       "Algeria                 2910     3007     3127     3256     3382     3517   \n",
       "Andorra                  723      723      731      738      738      743   \n",
       "Angola                    25       25       25       25       26       27   \n",
       "Antigua and Barbuda       24       24       24       24       24       24   \n",
       "Argentina               3144     3435     3607     3780     3892     4003   \n",
       "Armenia                 1473     1523     1596     1677     1746     1808   \n",
       "Australia               6652     6662     6677     6694     6714     6721   \n",
       "Austria                14925    15002    15071    15148    15225    15274   \n",
       "\n",
       "                     4/28/20  4/29/20  4/30/20  \n",
       "Country/Region                                  \n",
       "Afghanistan             1828     1939     2171  \n",
       "Albania                  750      766      773  \n",
       "Algeria                 3649     3848     4006  \n",
       "Andorra                  743      743      745  \n",
       "Angola                    27       27       27  \n",
       "Antigua and Barbuda       24       24       24  \n",
       "Argentina               4127     4285     4428  \n",
       "Armenia                 1867     1932     2066  \n",
       "Australia               6744     6752     6766  \n",
       "Austria                15357    15402    15452  \n",
       "\n",
       "[10 rows x 100 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After aggregation the indes gets changes to name of thew country comparew to the actual 0,1,2,... index for each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 100)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.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": [
       "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",
       "           ...  \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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc[\"China\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d69672c240>"
      ]
     },
     "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": [
    "df1.loc[\"China\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d6967cdf28>"
      ]
     },
     "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": [
    "df1.loc[\"China\"].plot()\n",
    "df1.loc[\"Italy\"].plot()\n",
    "df1.loc[\"Spain\"].plot()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task3: Calculating a good measure \n",
    "we need to find a good measure reperestend as a number, describing the spread of the virus in a country. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d69685b7f0>"
      ]
     },
     "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": [
    "df1.loc[\"China\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d6968c99b0>"
      ]
     },
     "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": [
    "df1.loc[\"China\"][:3].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### task 3.1: calculating the first derivative of the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d69692feb8>"
      ]
     },
     "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": [
    "df1.loc[\"China\"].diff().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### task 3.2: find maxmimum infection rate for China"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15136.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc[\"China\"].diff().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6557.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc[\"Italy\"].diff().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9630.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[232.0, 34.0, 199.0, 43.0, 5.0, 6.0, 291.0, 134.0, 497.0, 1321.0, 105.0, 7.0, 301.0, 641.0, 12.0, 1485.0, 2454.0, 4.0, 19.0, 1.0, 104.0, 92.0, 7.0, 7502.0, 26.0, 137.0, 41.0, 21.0, 6.0, 45.0, 31.0, 203.0, 2778.0, 31.0, 21.0, 1138.0, 15136.0, 353.0, 1.0, 57.0, 81.0, 37.0, 113.0, 96.0, 63.0, 58.0, 381.0, 391.0, 99.0, 156.0, 5.0, 371.0, 11536.0, 269.0, 32.0, 130.0, 7.0, 134.0, 20.0, 9.0, 5.0, 267.0, 26849.0, 38.0, 5.0, 42.0, 6933.0, 403.0, 156.0, 6.0, 68.0, 167.0, 132.0, 12.0, 10.0, 3.0, 72.0, 210.0, 99.0, 1893.0, 436.0, 3186.0, 91.0, 1515.0, 1131.0, 6557.0, 52.0, 1161.0, 40.0, 264.0, 29.0, 851.0, 289.0, 300.0, 69.0, 3.0, 48.0, 61.0, 17.0, 13.0, 21.0, 90.0, 234.0, 7.0, 14.0, 10.0, 235.0, 190.0, 58.0, 52.0, 2.0, 41.0, 1425.0, 222.0, 12.0, 13.0, 30.0, 281.0, 19.0, 3.0, 14.0, 1346.0, 89.0, 2.0, 69.0, 208.0, 107.0, 386.0, 144.0, 1292.0, 357.0, 5.0, 27.0, 3683.0, 538.0, 545.0, 1516.0, 957.0, 523.0, 7099.0, 22.0, 5.0, 6.0, 4.0, 54.0, 6.0, 1351.0, 87.0, 2379.0, 2.0, 20.0, 1426.0, 114.0, 70.0, 73.0, 354.0, 28.0, 9630.0, 65.0, 67.0, 3.0, 812.0, 1321.0, 6.0, 27.0, 15.0, 181.0, 188.0, 10.0, 14.0, 40.0, 82.0, 5138.0, 36188.0, 11.0, 578.0, 552.0, 8733.0, 48.0, 167.0, 29.0, 19.0, 66.0, 4.0, 5.0, 9.0, 8.0]\n"
     ]
    }
   ],
   "source": [
    "Countries=list(df1.index)\n",
    "max_infection=[]\n",
    "for i in Countries:\n",
    "    max_infection.append(df1.loc[i].diff().max())\n",
    "print(max_infection)"
   ]
  },
  {
   "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</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  \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": [
    "df1[\"Max_Infection\"]=max_infection\n",
    "df1.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": [
    "df2=pd.DataFrame(df1[\"Max_Infection\"])"
   ]
  },
  {
   "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</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\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": [
    "df2.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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "happy_csv=pd.read_csv(\"worldwide_happiness_report.csv\")"
   ]
  },
  {
   "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>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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happy_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.2: let's drop the useless columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "useless_cols=[\"Overall rank\",\"Score\",\"Generosity\",\"Perceptions of corruption\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=happy_csv.drop(useless_cols,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.3: changing the indices of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.set_index(\"Country or region\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country or region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   GDP per capita  Social support  Healthy life expectancy  \\\n",
       "Country or region                                                            \n",
       "Finland                     1.340           1.587                    0.986   \n",
       "Denmark                     1.383           1.573                    0.996   \n",
       "Norway                      1.488           1.582                    1.028   \n",
       "Iceland                     1.380           1.624                    1.026   \n",
       "Netherlands                 1.396           1.522                    0.999   \n",
       "\n",
       "                   Freedom to make life choices  \n",
       "Country or region                                \n",
       "Finland                                   0.596  \n",
       "Denmark                                   0.592  \n",
       "Norway                                    0.603  \n",
       "Iceland                                   0.591  \n",
       "Netherlands                               0.557  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Max_Infection</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bank and Gaza</th>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Sahara</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>187 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Max_Infection\n",
       "Country/Region                   \n",
       "Afghanistan                 232.0\n",
       "Albania                      34.0\n",
       "Algeria                     199.0\n",
       "Andorra                      43.0\n",
       "Angola                        5.0\n",
       "...                           ...\n",
       "West Bank and Gaza           66.0\n",
       "Western Sahara                4.0\n",
       "Yemen                         5.0\n",
       "Zambia                        9.0\n",
       "Zimbabwe                      8.0\n",
       "\n",
       "[187 rows x 1 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 1)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### wolrd happiness report Dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country or region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rwanda</th>\n",
       "      <td>0.359</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tanzania</th>\n",
       "      <td>0.476</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.499</td>\n",
       "      <td>0.417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\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>Central African Republic</th>\n",
       "      <td>0.026</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Sudan</th>\n",
       "      <td>0.306</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          GDP per capita  Social support  \\\n",
       "Country or region                                          \n",
       "Finland                            1.340           1.587   \n",
       "Denmark                            1.383           1.573   \n",
       "Norway                             1.488           1.582   \n",
       "Iceland                            1.380           1.624   \n",
       "Netherlands                        1.396           1.522   \n",
       "...                                  ...             ...   \n",
       "Rwanda                             0.359           0.711   \n",
       "Tanzania                           0.476           0.885   \n",
       "Afghanistan                        0.350           0.517   \n",
       "Central African Republic           0.026           0.000   \n",
       "South Sudan                        0.306           0.575   \n",
       "\n",
       "                          Healthy life expectancy  \\\n",
       "Country or region                                   \n",
       "Finland                                     0.986   \n",
       "Denmark                                     0.996   \n",
       "Norway                                      1.028   \n",
       "Iceland                                     1.026   \n",
       "Netherlands                                 0.999   \n",
       "...                                           ...   \n",
       "Rwanda                                      0.614   \n",
       "Tanzania                                    0.499   \n",
       "Afghanistan                                 0.361   \n",
       "Central African Republic                    0.105   \n",
       "South Sudan                                 0.295   \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  \n",
       "...                                                ...  \n",
       "Rwanda                                           0.555  \n",
       "Tanzania                                         0.417  \n",
       "Afghanistan                                      0.000  \n",
       "Central African Republic                         0.225  \n",
       "South Sudan                                      0.010  \n",
       "\n",
       "[156 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.960</td>\n",
       "      <td>1.427</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0.741</td>\n",
       "      <td>1.346</td>\n",
       "      <td>0.851</td>\n",
       "      <td>0.543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.287</td>\n",
       "      <td>1.163</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.578</td>\n",
       "      <td>1.058</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.366</td>\n",
       "      <td>1.114</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Max_Infection  GDP per capita  Social support  \\\n",
       "Afghanistan          232.0           0.350           0.517   \n",
       "Albania               34.0           0.947           0.848   \n",
       "Algeria              199.0           1.002           1.160   \n",
       "Argentina            291.0           1.092           1.432   \n",
       "Armenia              134.0           0.850           1.055   \n",
       "...                    ...             ...             ...   \n",
       "Venezuela             29.0           0.960           1.427   \n",
       "Vietnam               19.0           0.741           1.346   \n",
       "Yemen                  5.0           0.287           1.163   \n",
       "Zambia                 9.0           0.578           1.058   \n",
       "Zimbabwe               8.0           0.366           1.114   \n",
       "\n",
       "             Healthy life expectancy  Freedom to make life choices  \n",
       "Afghanistan                    0.361                         0.000  \n",
       "Albania                        0.874                         0.383  \n",
       "Algeria                        0.785                         0.086  \n",
       "Argentina                      0.881                         0.471  \n",
       "Armenia                        0.815                         0.283  \n",
       "...                              ...                           ...  \n",
       "Venezuela                      0.805                         0.154  \n",
       "Vietnam                        0.851                         0.543  \n",
       "Yemen                          0.463                         0.143  \n",
       "Zambia                         0.426                         0.431  \n",
       "Zimbabwe                       0.433                         0.361  \n",
       "\n",
       "[143 rows x 5 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=df2.join(df3,how=\"inner\")\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.5: correlation matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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</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</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  GDP per capita  Social support  \\\n",
       "Max_Infection                      1.000000        0.250118        0.191958   \n",
       "GDP per capita                     0.250118        1.000000        0.759468   \n",
       "Social support                     0.191958        0.759468        1.000000   \n",
       "Healthy life expectancy            0.289263        0.863062        0.765286   \n",
       "Freedom to make life choices       0.078196        0.394603        0.456246   \n",
       "\n",
       "                              Healthy life expectancy  \\\n",
       "Max_Infection                                0.289263   \n",
       "GDP per capita                               0.863062   \n",
       "Social support                               0.765286   \n",
       "Healthy life expectancy                      1.000000   \n",
       "Freedom to make life choices                 0.427892   \n",
       "\n",
       "                              Freedom to make life choices  \n",
       "Max_Infection                                     0.078196  \n",
       "GDP per capita                                    0.394603  \n",
       "Social support                                    0.456246  \n",
       "Healthy life expectancy                           0.427892  \n",
       "Freedom to make life choices                      1.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5: Visualization of the results\n",
    "our Analysis is not finished unless we visualize the results in terms figures and graphs so that everyone can understand what you get out of our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Max_Infection</th>\n",
       "      <th>GDP per capita</th>\n",
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       "      <th>Freedom to make life choices</th>\n",
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       "  </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  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": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.1: Plotting GDP vs maximum Infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d6969c1278>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x =data[\"GDP per capita\"]\n",
    "y= data[\"Max_Infection\"]\n",
    "sns.scatterplot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that, there is an scaling issuein the above mentioned scatter plot to solve this issue this problem we use **log scaling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d696a27b00>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6vtW77yWr13PxqQuqsicdgJB0kEMRf4NRGvU1ZvbXwBcop6X4MLA2FalEy+TVLpwmSc/yjetwbbR2cH9ftg7cuGsah1GOadS3Rvc9bt507rvslFTMWEkGORT1NxjFpzAAXAG8Idj1PeCT7r47JdnGIJ9CNIqQWydJan+Ag9OncPGpC5g/d4BpU3qZOxDP5NLKj7qRbyDrRiKO4pzIR2IYI6P7MTPeecP9ida3otfhvMifWu6joPH/mJlNd/ddsaQTbSOPseZp5uupmBoGp0/ho29amEg+nFZ6jY1yArXaC221DOPkKmo0wti1Z7QaFbX6wtclXt/ijmzyQh5/g2EIrRTMbCnlENLpwNFm9ipghbtflJZwIj55izVPeyhd+QFeccaiuvlw4vbOkk741sr1sspaWk859vbAmZ//ZS+4MqEuyfpW9PkqefsNhiWKo/la4E2UF9fB3X9EOamdyCFpzwqO6kBLO3VC5Qc4q39yIXtnYWhWhmnmLBofATU8MrYXnMYiRPXuWxSFAMWdmR81dfZGszEvpfi/tA4lzV5WnF5/2kPpyg/w+R17Ctk7C0OzMmy2fGaSjO8Fr9u4na/84Cd8fcXrcPfC9erToKgjnSgjhY2BCcnNrM/MPgo8kZJcIgEa9bJaDZOL0+tPO/678gN81VEzueGcxU17Z0UMFWxWhu20YdfrBX/ktIUcetDUQvbq06KII50oI4ULKS+McwTlhHbfAz6YhlAiPZKw7cdpfNrhNOzpMQ4emMKs/r4Je2dFDRVsVobtsGHX+iwOOWhKInmHRL5oGpJqZle7+2Vm9g53/+c2yXUACklNhiTC5OJeIy/LHOYlVDAOE5Vh2squqMq020kjzcWbzWwykFgyPJEdSZgY4jrQ8jKULmqoIExchvXWaEiywS7KOguiNcKYj74LvAgMBKmzjfKMZgO8hdXXRAYkYWIoqgOtQtJmlryMgKB5yGsrskZRpnkqExGNMMtxXuLuM4Hba5fhbHE5TpERSYXJ5aXXH4ckQwWLsHRlhVZlDRssUKQyEQcSOs1F1sinkBzqxSVXBkXyT7Qqa1ifQpHKpBtILc2Fmb0NuBqYR9l0JPNRQUl6lm4RSaoMiuSfaFXWsGbDyn1OPGoWF558HLP6J7N9eB+lUimxZxHpESUk9a+At7q75iYIEZB2GGiSo7qk/EnNlGnfpF7euGge5y2dPyYH1Q3nLmZwxtSuG5UWjSiT116QQhCdTJwJbWmmMohqm28mf7vSLswZ6ON/vuXAHFQrVq1VpFIBiLqewteAbwB7Kzvd/ZbEpRKizcSNwU8zEitK2oow8keVNe4opafH6O2x1M1q8o2lQxSlcBDwMvDGmn0OSCmIwtNK3qAk/BP1GrgoPoCtu0e49s4NXHHGoqoN/9o7N/DJs08YI1tYWVudqNYOs5om0qVDlPUUzk9TEJFfuqFHlqXDuFEDN2d6X+iGtVQqHWDDv3r5CbGdu60m15vdP5kbzl3MilVrm6Y1iVO/2pn8r9toqhTM7HOURwR1cfeLE5VI5Ipu6ZG1M/f9+Eawt4e6DdxtH1oWOl/UfucAG/5lN6/n6yteF0vGVpRkqeQ8ObSLz/zrf3HFGYuYM9DHvBlTOHxm/wF1Jm79KlLUV9EIM1LQ5AC6o7dcj27pkbVrla96jeAN5yxmcPqUMY3cpm3DDI/sD+0DcPe6jWTceUitKMnaOvO9x7dUz61XZ+LWr6IuYFMEmioFd/9KmAuZ2efc/cOti5Q/uqW3XI9u6ZG1K3VHvUZwxU1rueqs4zn/7x+uHldp4ML6AJJuJFtRklHqTNz6VfSlOvNMpEV2mrAswWslSqu9/G7pLdcjTz2ytEdr7ZjU16gRnD93oFrOcRq4pBvJVpRklDoTt34VPf9WnklSKeSSdqwf0MmmpSQbm1bKKavRWtLvtlEjOG1Kb0sNXBqNZFwlOb7OvHHRPP7nWxYxMrqfoZ17x8jVSv3SzPx0SCz3kZn90N1fncjF6hA391Ha6wfMGejreNNSEg1jq416Fvl00lBEja65YHA624b3dUzHolJnSqUSL+4eOSAKqbYMO7lTlQfSWE8h9L0TvFZipL1+QDfkmE8iI2qr5ZSFbyONd1tvzYMFg9N5cmhXw5nLRVw6tFJnenp6qgoB6pdhkTPudiJREuJNdfc94/bNdfcXg83PJCpZQqS9fkARHbFZ9MxaLaeJ3mNaz5PWux1v9hjaubehz6roI9Ei/j66nSgjhYfN7LWVDTNbDvygsu3ufx/15mbWa2brzOxbUc8NS9rrB6S9IH3SZJXrvtVyavQeZ/dPTu152vVuJ2o4iz4SbVSG+0teiBFPNxJFKfwe8Dkz+7SZ/QPwAeC3Wrz/HwGpJtlLe4nCdiUZS4qsGplWy6nRe9w2vC+155lIESVpzplI+RS9pz1noI8bzl08pgyvXn4Cf3H749V3VETzWCcTJc3Fo2b2SWAVsBN4vbtvintjMzsSeAvwSeBP4l4nDGlGKRQtNC6rRiaJcqr3HtN8nnoyz+6fzJNDuxI150wUgbN190huQoLj0NNjzB3oG5OT6Zo7NrBu43Y+8db9XT0HKK9E8Sn8LXAccALwq8C/mNnn3f0LMe/9N8ClwIwJ7nkBcAHA0UcfHfM26VOk0Lgs5x2kUU5pP08U+3/cZ5tIYbZzpnVYv0xUH05PTw9Xfevxuu+om+cA5ZUo5qPHgFPc/SfufgfwWiBWCKqZnQFscfe1Ex3n7ivdfYm7LxkcHIxzKzGOopm7mtHu50nb+TzeZ5W2+ROi+Zni+KQmekdFN491Ipms0WxmnwLOBUaBqZTTct/i7uc0OkdrNCdHp8WFt/N5osyXKEo5R3mmuPNFGpWF1nNOnzTXaF4AfApYRLkhB8Ddj40kYfmcjwMfD657MvDRiRSCCE+YhqhI5q4wtPN5Kr3ea+/cwPLFR1UzgM7unzzmuCLZytuRq6jRO1IOo/wRJc3F3wGfAK4FTgHOJ6cT1rqVIjVERaWnx1gwOJ0/esOvTjhLN2tbedhRSqnk7C956rmKGlG0QI1uIIpPod/d76JscnrW3a+k9ZBU3P0edz+j1euI7MJNu41tw/uaztJtp618fEjn6GgptN1/6+4R/uL2x7l6+QljbP43nLu4bm89DR+OZjTniygjhT1m1gM8aWYfAn4OzEtHLBEHOe3aQ5hyjtqjjut/qDc6/Mc/+I3Qo5SR0f187/EtDO0cGRM2OrfB/dWz73yiKIU/BqYBFwNXUR4lnJeGUCIeeUpz3cmEKecotvJWzH71Rodbdu4N3TmoPMu6jdtZsWpt9VluvahxJvxO80mJsYQ2H7n7w+6+y903ufv57v42d38gTeHSolNnUBYt3DTv76GRfGHKOUooaStmv3qjlsqEt1oadQ6KVmdE+oRZo/m2ib539zOTEyd9OtkZW6Shfd7fQzP5wpRz2B51K2a/8aOWE4+axcz+yax6/0n89MWX+exdTzK0a2/Dhr5IdUa0h6bzFMxsCNgIfBV4kHERR+7+b6lJV0NS8xQ6KS46j3HwYWXK+3top3y19zrxqFlcePJxzBno4/BZ/Rx60NQJ32mt8hqcPoVLT1/IJavXVxXZDecs5rBZU5nVn33dENmQxjyFQ4HTgN+lnBTvduCr7v6f8UTMlk5xxuaxpx1Fpry/hyjytaqca+c+nLd0PpfdvD70Ox3f03/XygfGmKFW3LSWWy9aJoUgQtPUp+Du+939u+5+HuXUFk8B95jZh1OXLgWKluq6EXkMP40iU97fQ1j5kkhFXmnYrzzz+KpCgPDvtNZMlWdFK4pBKEezmU0xs7cBNwEfBD4L3JKmYGnRKY61PPa0o8iU9/cQVr6klHNPj+HuiSxEVEueFK0oBmEczV8Bjge+A/y5uz+WulQp0imOtTyGn0aRKe/vIax8SSrnVt+pUkaIJAjjaC4Bu4PN2oMNcHc/KCXZxqCEeGOJYr9vl0M6j36OtEnSIZ1E+eUx+EBkS1RHcyZZUuMgpXAgzRqAUsnZPjzC5u17WHFT4zw97ZSp00haEXZb+Yn0kVLoQOI0FJXG6vkde7jim4/lNvSzE1BDLvJMaqmzRXJEXeUqTk+04gD9v+94VUObd7c2Zkk/t9I+iE5CSqHNRG3k46ZgrjhAtw/vq+u8nDypp+vs/9Cdfg8hohAldXZXkVZenqghjHGjWyqRLNff8/QBaZG/+J4lTOqx3M1zaAd5nN8hRJ7QSKEOafYmozbyccMUa8MTr7ljA1eddTzz5w4wbUovcwemsHnHcO7mObSDPM7vECJPaKRQhzR7k1EnGMWd5FUbZ//53zuR44+YydEHT2PejHIunSQnOuU922ktmuAlxMRIKdQhzd5k1EY+Sgrmeuc2WtEqqRnFSaR5aCd5n0ktRNYoJLUOaWfIjBP9MjpaYsuuvezbX2Jybw/zpk9h0qTWdHoYOZodk/dsp/Xo1qgr0Z0oJDUB0k4XEDWEcXS0xI9f2MmFNRPQrj9nMb92yIyWFEMzOcL4Vopoo89jCGkSikrKTiSBlEId8paXZ8uuvVWFAOVG98Kb1vL1Fa/j8Fn9Tc6OT5hw2DzmYCoaSaW3UKitSAL5FBowkT2+3ezbX6rbGx/dX0r1vmFGAbLRt04SgQ0KtRVJoZFCAZjc21O3Nz6p90CdnqQJIcwoIG+jqiKShAmuiGY8kU80UigA86ZP4fpzFo/pjV9/zmLmTR9rF086EijsKCBPo6oikkSYrEJtRVIo+qiNtNKLr0Qfje4vMalO9FGp5Dz/0h7eecP9iUYCyXmZPvIpiDRR9FFOafVHO2lST0OncuXau/eOJm5CyGOkTqeRhAlOZjyRFDIftYk0HYGVa2/dPVLXhDC5xfkMaVKk2dBpkoQJTmY8kQT5bS06jDQdgZVr10t+9+m3n8CuPaO5bGyLNhtaiG5A5qM2kWY8f+Xa6zZu55o7NnDFGYuYM9DHzP7JXLp6PUO79nLbh5axv0SuTAtx04ILIdJDI4U2kWY8f+21123czlXfepy9oyUuXb2edRu3Mzh9Cpu378ldj1xhlELkj0xGCmZ2FHAjcChQAla6+2eykKVdpOkIrL328L79PL1lF9fcsYF1G7cDcPGpC6prNEN+euSaDS1E/shqpDAK/Km7vwJ4LfBBM1uUkSxtI01HYOXaR87q59CZUxnatRcoN7Lz5w7kskeu2dDJIqe9SIJMRgruvhnYHHzeaWZPAEcAj2chTydRb0TieC575AqjTA7NUxBJkblPwcyOAU4EHqzz3QVmtsbM1gwNDbVbtMIyfkQyd2BKbnvkCqNMBuU+EkmRafSRmU0Hbgb+2N1fGv+9u68EVkJ5RnNacnT6rN2i9sg7/b0kiZz2IikyUwpmNpmyQvgHd78lKzm6ZdhdtJnJ3fJekkJOe5EUmZiPzMyAvwWecPe/zkKGChp25xO9l2jIaS+SIquRwjLgXOBRM3sk2Pdn7v7tdguiYXc+0XuJRlFNhCJ/ZBV99B9ALmpr0Ybd4+3ss/sns214X8c1BEV7L3mgaCZCkU8yjz7KmiINu+vlCvrxCzu5/Nb1uZqpnARFei9CdBJaT4HiRLkM7dzL2dfdd0Dv+YozFrFi1drqdtYzlZOiKO9FiDyj9RRiUJRhdyM7+6z+yWO2O8XuXpT3IkQn0fXmoyLRaMnF7cP7xmzL7i6EiIuUQoGoZ2e//pzF3Lx2Y3VbdnchRCvIp1AwuiX6SAiRDPIpdDj17OyyuwshkkLmIyGEEFU0UhBdTxqhrwqnFUVFSkF0NWkk3lMyP1FkZD4SXU0aifeUzE8UGSkF0dWkkXhPyfxEkZFSEF1NowmBrUwATOOaQrQLKQXR1aSReE/J/ESR0eQ10fUo+kh0Mpq8JkRE0ki8p2R+oqhIKYjMSKs3rV66EPGRUhCZkFYsv+YICNEacjSLTEgrll9zBIRoDSkFkQlpxfJrjoAQrSGlIDIhrVh+zREQojWkFEQmpBXLrzkCQrSG5imIzFD0kRDpo3kKojCkFcuvOQJCxEfmIyGEEFWkFIQQQlSRUhBCCFFFSkEIIUQVKQUhhBBVMlMKZna6mW0ws6fM7GNZySGEEOKXZBKSama9wBeA04BNwMNmdpu7P56FPHIH3QYAAAmoSURBVEVAsffZovIX3UJW8xROAp5y92cAzOyfgLMAKYU6KPNntqj8RTeRlfnoCGBjzfamYJ+ogzJ/ZovKX3QTWSmFet2rA/JtmNkFZrbGzNYMDQ21Qax8osyf2aLyF91EVkphE3BUzfaRwHPjD3L3le6+xN2XDA4Otk24vKHMn9mi8hfdRFZK4WFggZnNN7M+4N3AbRnJknuU+TNbVP6im8gsS6qZvRn4G6AX+LK7f3Ki47s9S6qiX7JF5S+KSmGypLr7t4FvZ3X/oqHMn9mi8hfdgmY0CyGEqCKlIIQQooqUghBCiCpSCkIIIapIKQghhKiSWUhqVMxsCHi2xcvMBV5MQJx2UTR5QTK3g6LJC5K5HTSS91fcPfTs38IohSQwszVR4nWzpmjygmRuB0WTFyRzO0hKXpmPhBBCVJFSEEIIUaXblMLKrAWISNHkBcncDoomL0jmdpCIvF3lUxBCCDEx3TZSEEIIMQFSCkIIIap0hFIws9PNbIOZPWVmH6vz/RQz+1rw/YNmdkzNdx8P9m8wszflSOY/MbPHzWy9md1lZr9S891+M3sk+GvbOhQhZH6vmQ3VyPYHNd+dZ2ZPBn/n5UTea2tk/S8z217zXdvL2My+bGZbzOyxBt+bmX02eJ71Zvbqmu/aXr4hZf79QNb1ZvYDM3tVzXc/NbNHgzJuW178EDKfbGY7at7//6r5bsI6lZG8l9TI+lhQdw8Ovotexu5e6D/K6zE8DRwL9AE/AhaNO+Yi4Prg87uBrwWfFwXHTwHmB9fpzYnMpwDTgs9/WJE52N6V03J+L/D5OuceDDwT/J8dfJ6dtbzjjv8w5XU9sizj1wOvBh5r8P2bge9QXs72tcCDWZVvBJmXVmQBfrsic7D9U2BuDsv5ZOBbrdapdsk77ti3At9vpYw7YaRwEvCUuz/j7iPAPwFnjTvmLOArwefVwKlmZsH+f3L3ve7+E+Cp4HqZy+zud7v7y8HmA5SXLM2SMOXciDcBd7r7L9x9G3AncHpKclaIKu/vAl9NWaYJcfd7gV9McMhZwI1e5gFglpkdRjblCzSX2d1/EMgE+ajHYcq5Ea38BmITUd6W63EnKIUjgI0125uCfXWPcfdRYAcwJ+S5aRD1vu+n3EOsMNXM1pjZA2b2O2kIWIewMi8PTAWrzayyDncW5Rz6noFpbj7w/ZrdWZRxMxo9U1b1OCrj67ED3zOztWZ2QUYyNeJ1ZvYjM/uOmb0y2JfrcjazaZQ7AzfX7I5cxpmtvJYg9dZEHB9n2+iYMOemQej7mtk5wBLgf9TsPtrdnzOzY4Hvm9mj7v50CnKOEaXOvvEy/wvwVXffa2YXUh6d/VbIc5Mmyj3fDax29/01+7Io42bkrR6HxsxOoawUfrNm97KgjOcBd5rZj4Necdb8kHK+oF1WXjb4G8AC8l/ObwXuc/faUUXkMu6EkcIm4Kia7SOB5xodY2aTgJmUh2Nhzk2DUPc1szcAlwNnuvveyn53fy74/wxwD3BimsIGNJXZ3bfWyPlFYHHYc1Mgyj3fzbghd0Zl3IxGz5RVPQ6FmZ0AfAk4y923VvbXlPEW4FbaY7ptiru/5O67gs/fBiab2VxyXs5MXI/Dl3HaTpI2OGEmUXaszeeXzp9Xjjvmg4x1NH89+PxKxjqan6E9juYwMp9I2am1YNz+2cCU4PNc4Ena4+wKI/NhNZ/PBh4IPh8M/CSQfXbw+eCs5Q2OW0jZGWdZl3Fwv2No7AB9C2MdzQ9lVb4RZD6asq9u6bj9A8CMms8/AE7PicyHVuoD5Ub0Z0GZh6pT7ZY3+L7S0R1otYzb8gLaUGBvBv4raEQvD/b9b8o9bICpwD8HlfMh4Niacy8PztsA/HaOZP5X4AXgkeDvtmD/UuDRoEI+Crw/RzJ/CvjPQLa7gV+rOfd9Qfk/BZyfB3mD7SuBvxx3XiZlTLmXtxnYR7lX+n7gQuDC4HsDvhA8z6PAkizLN6TMXwK21dTjNcH+Y4Py/VFQZy7PkcwfqqnHD1Cj0OrVqazlDY55L+WgmdrzYpWx0lwIIYSo0gk+BSGEEAkhpSCEEKKKlIIQQogqUgpCCCGqSCkIIYSoIqUgCoeZHWJm/2hmzwTT9+83s7OD7yoZLtcF2SzvNbMzas690sx+XpNR8szsniQaZvZtM5sV/F2UtTyiM5FSEIUiSGT4DeBedz/W3RdTnpBYm2jt3939RHdfCFwMfN7MTq35/lp3/3XgHcCXzSyx30GQ3jqV35W7v9ndtwOzKGf+FSJxpBRE0fgtYMTdr6/scPdn3f1z9Q5290coT1j7UJ3vngBGKc9arhKMJlaZ2feD9Qk+UPPdJWb2cJD078+DfceY2RNmdh3lvDlHjbvea4K1BH5kZg+Z2YzgnH83sx8Gf0uDY08ORje3Wnk9jesrSibIjT8X+EvguGC082kzm27lNTd+GOTOTz1zp+hcOiEhnuguXkm54Y3CD4FLxu80s98ASsBQnXNOoJxKYgBYZ2a3A8dTTox2EuXZxbeZ2espp0FYSHkm8ZgevJn1AV8D3uXuD5vZQcAwsAU4zd33mNkCyrNWlwSnnUR5rY9nge8Cb6Oc8r3Cx4Djg9FOJZ/X2e7+UqA0HjCz21wzU0UMpBREoTGzL1DOvDni7q9pdNi47Y8E2Wd3Um6s6zWe33T3YWDYzO6m3FD/JvBGYF1wzHTKSuJnwLNeXuNgPAuBze7+MJSTrQVyD1A2a/06sB/41ZpzHvJyIj7M7KvBfVfTGAP+T6CgSpTTOR8CPD/BOULURUpBFI3/BJZXNtz9g0HveKKlBk8EnqjZvtbdr2lyn/GKopKi+lPufkPtF1Ze3nV3g+tYnWsBfIRybqtXUTbj7mly74n4fWAQWOzu+8zsp5TzfQkRGfkURNH4PuUFcP6wZt+0RgcHaZuvoJxILgpnmdlUM5tDeXnGh4E7gPeZ2fTg2kcEeeon4sfA4Wb2muCcGTXp2ze7ewk4l/JSjxVOMrP5gS/hXcB/jLvmTmBGzfZMYEugEE4BfgUhYqKRgigU7u7BSmjXmtmllP0Bu4HLag7772a2jrKy2AJc7O53RbzVQ8DtlFM/X+XlvPTPmdkrgPvLQVDsAs6hbP5pJO+Imb0L+JyZ9VP2J7wBuA642czeQTmjbO1I437KzuT/BtxLOQ9+7TW3mtl9Vl7I/TvA1cC/WHlh9kcoKyIhYqEsqUKMw8yuBHaFMDGlce+TgY+6+xnNjhUiDWQ+EkIIUUUjBSGEEFU0UhBCCFFFSkEIIUQVKQUhhBBVpBSEEEJUkVIQQghR5f8D9cwCwfyupeQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d6969a0048>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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58ySUolDrikgizfLeMO/6wuNzBOHKi3vZtnGQrTUWhOzYgRfroNUpRxyeB5YDJ+s0FmURoF1UlWwSKacmIZYilalsNrUt65bye5nL+PIPXubYeJRE2pDOGM4dHp9dp16C4FYlF4sdtDPliMMA8FMReQKIuwuNMTfXfFTKgqWdu6iqxVMb0hkzW6QWr6ImIZ0xPOu4jB49cIaJaDLn/Ssutl1G2zbUThBE7MB3R9BHZ9DOLFqolCMOH6/XIJTFQzt2UXVnGDswNk3AsrioN6QWTxZeRTPiFKnNlOk2yiadMTx39Dy7igjC5StsC2Hb+gGGesMVHQNyW1dcvKSD37h+DW+64iI6Ar4FZR3Mh2dxMMbsFpGLgOucRU8YY0brMyxlodJuXVRdN9joVMyeahM4ORHn4r4wAZ+0hcVTLfPd/Eu5CZPpDFOx6txGriDsHhnjkYKC0MO2jUNVC4LLEy+f428ePkDQb7GsK8hkLMmnvztCb0dgwX/X2ZSTrfQrwF8Cu7Azvz4rIncbY3bWaWzKAqQV8rfLwXWDpTMGn9PtM4NhbCrO2oGulrZ4akGpm3+x+Zb/dtdLbFzRQzRRmdsonTE8d8xOO330wBnOFxCEtQPdvDw2w9mZOI8fPMsl/Z0Vi4PfsmZdRd945rjzt/uZrLZxfdaSctxKHwWuc60FERkEvgeoOCieaYX87XJw3WD2ZDL2jGoikEhnWtriqRWlYkTZbsKMsQPBPsviyLmZsoVhVhBGxnh0ZK4gbFrew40bB9m6YZDDZyJVVUUDhAO2GHQEfYT8F9Jgj52Ptp3rsx6UIw5WnhvpLLBwozFK3Wh2/nY5uG6wge4QJyaikAGDbUVUa/G0Q4C7VIxoVV8HpyZjhPy+2ThCrMDkOcVIZww/PmbHEB47cIbxSK4gvGpFj1OYNsjyLKvgUw/uL7sq2i1Ec60CX5GOpu3m+qwX5YjDt0XkP4H7ndfvAb5V6YFFpA/4EnAldjHdbxpjHq90f4pSD1w3WMAnXLwkzOmpOKk0XJo1kUwltEtKb6EbZSSRYsWSDkYnY7z9mpXc+9AB0mXMt+wKwu6RMzx6YMyzIGTjtSq6klTTdnN91otyAtJ3i8g7gddjxxx2GGP+tYpj3wt82xjzLhEJAotLlpWitNITdb4b7Jrh/pqMp11SerNvlCG/xUwiTSKV4R3XrGQ6nmLL2qWeJs9JZww/OT7B7v1jPFJAEDYu72G7k3a6fEnpuEF2VfR0PMV4JEEilaEj4OO5I+e56fKLKko1da+9mXiSZNoQ9FusH+ppSauu3kgzCp5FpBd4DljnteJ68+bNZu/evfUdmNJ0sp+os5/a7rn5igX147zhUw/T1xHIeZI1xjARTfLoh24quX2jBDSZzvCfz5/i7x97mZMT5c2cls4Ynj8+wa6RMR4ZKSwI2zYMst2jIGTjdmJNptIXYhMGBnqCBP3+iq6XhXrtichTxpjN5W5X0nIQkceMMTeIyBS2+2f2LcAYY3rLPSiwDhgD/qeIXA08BdxljJmpYF/KAqJdnqirpZhfuyvo47Yde3Ju+kCOELxu3VJ2Pn28bi6pZDrDTDzF9356mn/cc2R2joTff+OGkqKQzhiePzHBLifL6NxMIuf9jRf1sG3DANs2DrJiibe4RD4iwrZNg/SE/Xz46z/GAGG/j8GeED3hQMXXy2K59rxSUhyMMTc4//fU+LjXAh80xvxIRO4FPgz8afZKInI7cDvA6tWra3h4pVVpxyK5Sijk156IJhEgmTGzN/27dz6HAZZ0BGaXfX7XSyztCrCkw37arsVNLL9quZw5EhohCG4wuTPkpzNgT4/5tqs7+MS393FJngVW6fWyWK49r5TVldUY895SyzxyDDhmjPmR83ontjjkYIzZAewA261UwXGUNmOxZIoUSukN+iwS6UzOk+vx8SgIszfVzqCfdMYwEUky0H3BFVPpTSySSDlzJORWLZeaI8EVBDuGMFcQNlzUzXYnqHxxX2WCkN3VNJzVcTWbWl4vi+Xa80o52UpXZL8QET/wmkoOaow5JSJHRWSjMWY/8Ebgp5XsS1lYLKZMkfyUXjcOkU0qk5mTYRPyW8RSuTUE5dzEUk7V8tQ8VcuFsoFCfuHIuRk++/BBHhkZ42yeIKwf6mb7RjuoXKkghAM+uoJ+OkPFg8nZ8ZbuoI9JJ+ZQ7fWSfe2l0hlOT8ZJZjIELGHXvtFF51ryEnP4CPDHQIeITLqLgQTOU32FfBD4X06m0iHgN6rYl7JAaLciuWJUEjAu9OTqtyz715ZFT9hPKmLKElC3JfZ3nj/FfY8fLjnXspsNFPZbRJN259SpWJK0gX995vjsepcNdXNjFYLgNrLrDNmiUKz2wCU/BTiaTGOAoM9iIpqs6npxr71PfXsfr5yNEPAJq/o6SGZMS6YZ1xvP2Uoi8gljzEfqPJ6iaLaS0gwqucnPl/UCeOpT5G43GU3Oxhyy9/Wua1fy+KFzJQU0nkrPzrf8+MGzs3GE7JqEu27KjSNkjGHnk8f4yo8OE0+mSefdIi4b6p5NO13ZX74gFIofeOW2HXsK1l0M9YS5//bXlj2WZh2jkdQtWymLJ0RkiTFmwjlgH7DdGPONcg+qKO1ApYVqxbJePvXtfcwk0kX3V8hq+tO3XT67z3whuLPI8VPpzGxwOZG64DaaL46weW0/Pz0xOZt2emY612XUEfBx+YoeIok05yIJ9r4yzmWD3Z7FwWfZ56Mr5Cu7s2m2QI9NxVnem9t+u9ZBYw1M25QjDh/LLnozxpwXkY8BKg7KgqTS1MZiN5cDo9Os6u+Yd3/FWouUslaMuZBtVKynUX4cwRiDwTAyOsWtO/bMEYTLBi/EEI6PR8vuZeRWJ3eF/EUDyqXIF+gzU3GOn48BQq9zjmsdNNbAtE1ZvZWq3F5pIK1UZdyuVPoEWezm4m5f7v7mI5a0p9aciadIZ+Z3Ea/o7eDMdAwRsdtox1OknG0ijqBcOthlF6ZtHMy5Gf7Vd0Y89TJ65vA49z95lJMTUVYv7ar6ussX6OVLwhwbj3J6KkZP2F+XhIV6JEW04++xnJv7XhH5a+Dz2MVwH8QuXlNajHbp21Nrav0DrPQJstjNZe2yTqLJdNVPpK4gROJpT3MkZIzhxZOTdId8/Ph4jHwNWbEkzFuuXM62DYMMLy08lmK9jE5PRukO+ekI+njy0DnuffggAZ/Q3xmsyXWXL9A94QAr+wynJuNVB6CLUeukiHb9PZYjDh/ELlL7mvP6O8Cf1HxEStUsxkrP7B+gT+CZo+O8/yt72TDUXXGDvEqfIIvdXICKn0hjyTQz8RQzZQrC7pExdu8/w9h0POd9vyUMdIf41S2redvVK0ruL7uXkYhgCcRSGfo6g9z1wLMcHY8wGU3SFfLVtDivYAaXz+La1f11DQ7XsnNwu/4ey2m8NwN8WES6jTHTdRyTUiWtGFCrt1nt/gBTacOJiRgWtki8fGam4qe0ap4gi91cytmfKwiRRJpk2haE7Okr81NRjTHsOzXFrv1j7B4ZY3QqVxDWDXTNxhCKWQiFEBHe97pL+KvvjZDK2EV60aSdBTVFikQ6Q19HgFMTMaKJNCG/j56wff1Ve90thLqXVvw9eqGcCunrsVtsdwOrnZ5Idxhjfrdeg1Mqo9UCao0wq90f4MsTM1gIlmVP6ZnOmKqm86z13BOl9jefhVCopcV/f2iEd5xbxdh0vKAghP0Wb1g/yK++djWryxAEnyWz8x50BnysHeiivyuYI2wBS0hmzOx1FvLbFd5jU/FZcaj2ulsIdS+t9nv0Sjlupc8AvwB8E8AY85yIbK3LqJSqqPfTVrlWQCPMavcHmEhn8DlpksbYxVGt/pRWyEIohJuKGvbbNQrT8RSTsRR/u/ulnPX8ll1H0N8ZIGPg+RMTnDofKykOfsuaLUYrNPdBqYpud0KkWMpuxVGr666dJocqRLtaP2VlGxljjuZdMJVNEKvUlXo+bVViBTTCrHZ/gD5LyGSM0zIYBntCLfmUVm4MwRjDkfEZMgZOTlzIMnJZO9DFtg0D7HnpHDPOTchlvhnS/JZFV6iydNP8J+LejgDxVJpIIl23YHE70q7WTznicNRxLRmn5cWdwIv1GZZSLcWetqr1/VdiBTTCrHZ/gJ988EUOjE0TEGH5khA+q/B0ns1ILfRqIbgYY9h/eord+8fYPXKGczO58yEEfRbhgMXFSzr421+7FoAHnz9Vcoa0gM+iKzR/QzsvFHoiDvp9fPIdV7X8ja/RtKP1U444/Db27G0rsbuqfgf4QD0GpdSHWvj+K7ECGmVWuz9A98Z/bDzCUE94zo2/kamF5aadGmMYOT3Nrv2j7B45w6nJWM77rsuorzOAMZDKGH79+jWz72dnFV0YQ4YVSzpY0hGoqiAN5oqq1zYeSvvhpfHep4wxHwJuNMb8agPGpNSJWvj+K7ECGm1Wl3pKq3cMxO1nVI7LaOT0NLtHxti1f2yOIFyyrJNtTi+jscn4vFNy3nrdMPc+fMCpp/ARd9pn3PXG9Szrzm07US6FRHXn08fbfqY0pTBeLIe3isifAB8B/rnO41HqSC18/9Xk/rfKDaQeMZB0xtipnfFkTj+jYmQLwu6RMU5O5AnCUkcQNg6ydqBrdvnaga6i7SpEhO2bhljSEeC+x1/h+PloTYW4XfP1lcrwIg7fBs4AXU7LbsGukK5mmlClCdTC99+uwbVsahUDSWcMM4kU33vhNF/x0AbbGMOB0enZOgSvgjAfbsvrLifLyLKEt1y1grdcVbiwrZpYSzmi2o7tIpRcvEwTejdwt4j8mzHmlgaMSakTtfL9t5IVUAnVnIdEKkM0kWYmkSKeyvCjl87O25DOFQTXZZQvCMP9Hdy4cYhtGwdZs6zTc7fSDqehnZc5EFyqjbV4FdV2bReh5FJOhbQKQ5uzEJ76a0G55yGVzjATTxd0GRVqgx1JpPjyD17muePn2T0yxonzcwXBrVReO9BVliB0Bv10BX34i8ySNh/VuoW8iqq6nxYG5VRIvwP4FDCE7VJSt1Ib0u5P/bWi1HlwXUYz87TAhgsN6YwxxFPOjGnxFMl0jJHRC11mhvs72LZxkO1lCEK5s6SVotpYi1dRzT7OZDTJmek4iXSGY+PRRTndZrtSTirr/wf8kjFGaxuUBUkqnSHi1CLEkhlKzZJojGFJOMDJyRgx5yk6m1VZFsI6j4JQaJa0WvnvaxVz8jrdqd3nKoqFzD5NqnupfShHHE6rMCgLjUQqQySRYiaR5tH9Y0Wb2rkYY3hpbGY2hnD8fDTnfXcKzt98/VpuefXFngTBnRSns0DbinL896VEpFH1Ju5xRqdi9vTXAhhh+ZIwPqvyPldKYyl3PoevYc/8Ntvdyxjz9ZqPSlHqSLFup8UCy9et7efQ2Ay7nLTTY+O5gjDQFcRnCalMhlV9ndy2ZXXRdFOXoN+iK+inM+Qj5C9elObVf+9FRCqJOVVitbjHueMfnyJjDCGfxWBPiJ5wAGNMTdumaFZU/ShHHHqBCPDzWcsMoOKgtDTGGGLJDDOJ4pXK+YHlsN9iMpbiM98bIeC35gjCyj7bZbR9wyDrBr25jEIBH11OllEgL6Bc7CbnNU7wxUcOkUilOTttt9AO+ix6wv45IlJOzKmarKPtm4a4dnV/XdumaFZUfSknW+k36jkQpTVp1yezTMYQSaaJOBZCpkT84ORklJ6Qj3gqzVTMDSrnbrOyr4NtGwbYtmGQy4a6PQlC0G/REwrQGfLNEQSX+W5yXuMEI6cnmYylnHks7Hktzs4kSKUnS46xGNVmHb1u3VI+v+slUpkMIZ/Fks4AAZ+voBurkutMs6Lqi5f2GZ/FthAKYoy5s6YjUlqGdnsyS6UzzCTSRBLeAspgWxUvn5kBA6+cjc7pdhr0Wbzj2pXcuNG7IAR8Ft0hP10hP0H/XEHIvxGOz8SL3uS8xglcIbOcjCYRWyAT6dLnoBjVZDft2jfKzqeP098ZYCpm14Scm0nyge2rC8ZKKrnO2nUSnXbBi+Wwt+6jaGHa9cm5FrTDk1kynSESTzOdSBFPeusg7wqCG1Q+mucyClhCOOgj5Lf4f960gZ+9dFnJfbqCUCqGUOhG+MrZGVb1deSs597kvLaj9QgAACAASURBVMYJgn6LqGMhidjtyjEUFCevVJPd5F47SzrCDPbYyyKJFI8fOkf+02Sl11m7TqLTLnipkL7Py45E5LPGmA9WP6TWod2enGtNqz6ZxZL2nAEz8ZSn1teQKwi7R85w5FzuZ1ixJMyGoR5OTkSZiCZYsaSzaBsMF58ldIX8dJfR6bTQjTBgWZyeitPbEZxdL/sm5yVOsH6oh1fOTjMZvRBz6O0KsGZZt6dxFaKa7KZyrp1Kr7N2nUSnXShrsp8SvL6G+6oJzZi7YCHRKk9mmYw9q9h3XzjNfY+/womJ+XsYZfPymRl27x9j18hYQUHYtmGQ7RsHWe/RZSQidAV9dIf9dAR8nqubXQrdCC/qDXHsfKyqm5x7o1y+xF+zG2U1FfXlXDuVXmda8V9faikOLUWj5i5YyG6nWj+ZlXOu8uMHpXoYZXPBQhjj8NlcQVjeG54tTNtwUWlBeOLQOR7Ye5TTkzFW9XfwO9su5aZXXVTR54fCN0K/z2LDUDd9ncGKb3L1ulFWWlGff+2cmY4zHkkyEU1y2449OWOr5jrTiv/6sWDFoRFzFyx0t1MtbzhezpXrLookUp56GGVPf/nK2ZnZbqeFBGHbhgG2bRxk40U9np/2nzkyzue+f5CgX1jWFeTcTIKP//tPsZzW2JVQ7EZ4y9XLefzQueKZHx5opRtl9rVz4PQkU/E0S7sCLOsKzfnu1QJoTcRLRoenHYk8Y4y5piY7K8DmzZvN3r3eY+Pu5OfZNwJjDBPRJI9+6CZP+8i+oWX/kN3JTW7bsWeOeEQSKYZ6wtx/+2u9f7hFQLFzNdAV4u/e+5qS8YPb/sceesN+pxGDTSyV5txMgmXdoTmCcFFviO1O++tyBMENLHeH/fxff/9EXb7f7JnqVvV38rp1S9n59PGi11m7W6f6O2kuIvKUMWZzuduV03gvbIyJ5S0bMMaccV7eW+7B60kj5i5o1YBtKZpxs3HPlTGGjIGMMfhEOHxuhvORRMnt3ekvLRG7uV3MDrwCTMft831Rb2g2hlCOIFhiB5Z7wrmB5Xp9v/lP+Lft2FPUygXa3jp1z+NULMnYlN2EL2AJE9Fk6Y2VplFOntuTIjIr8yLyTuCH7mtjzD+Ue3AR8YnIMyLyf8rdthR3bF1HMm2IJFIYY/9f6dwF99/+Wh790E3cf/trc36Qw/2dRPPSJ1s9lc61hkanYjk3m137Rut2zGQ6w4reMFMxu+V1Kp2ZDTIv7+0ouf2RsxEGeoKcnIhx+FyEszOJWWHo6wjwhssGWD/Ubc+udmqayUjKkzB0Bv0M9Ya5ZFkngz2hORlHjfp+j45HcuZ8hgsilO0eFbH/D/hkVjjageH+Ts7OxDlxPkYqY/BZQjJjmIql6nrdKdVRTszhvwBfFpFdwMXAMsCbf6Y4dwEvYrfmqCmN8GO2YypdozKw8tNN33ntKu59+AAZkyYcsIglM6QyhluvGy64/ZFzkdkso5fPzOS8Zwks6wrx7tesYri/g7/5/kH8lrCkIzBvoBogHHAnySk9J0Kx7/d165Zy2449NbO85rNy29U6zeaOreu44x+fwmCwEIzjPezvDMxed+3uOluIlNM+4yci8hfAV4EpYKsx5lilBxaRVcDbgL8A/rDS/cxHvQN07RhIq+fNJpZMMx0v3L9oy7ql3MV6HnjyKKcmoywvkIrqCsLukTEO5QnCUM8Fl9Gm5RdcRn/4tefmDVSDHUfoCdv1COVMklPo+82OD9TKzTPfQ8YXHznUEunE1bB90xDdIR+xZGa2BmOgO0xP2M+x8ciCT+xoV8qJOfw9cClwFbAB+HcR+Zwx5vMVHvu/A/8V6JnnmLcDtwOsXr26wsPUl1bKEPFCLWsXjLFdQzNxO8MonZk/uWHLuqVznuaPnovMdjs9NFZYELZtGORVKwrHENzJdrIJByxOT0bpCQfmxBHKpZz4QKXXQamHjEZYp+U8uVfylL/hot6CQelV/Z2Lvp6oVSnHrfQ88FvGTm962Yk//HUlBxWRXwRGjTFPicj2YusZY3YAO8DOVqrkWEou1brCym1oV4ij5yKzdQgv5QnCYHeIbRsH2L5hiE0rerBKxA7cQHVHwAdiB5fjqQxrBroZ7AmVPbaSY29QkDp7eb2t03LnjKjkKX++6+5P/u35tnedLUTKcSt9Ju/1BPD+Co/7euBmEXkrEAZ6ReQfjTG/VuH+FI9UcrNJZ+yA/kw8TTSZ9tTQLp9SgrDV6XZ6+cW9JQUhm1u3DPM3Dx8gmcnQGfARS2VIZ6hb3Kccy6tWfvR6W6flPLlX+pQ/33U3/EhrVOIruZTjVloPfAK4HPuGDoAxpuxfoTHmI8BHnP1uB/5IhaF6vN6MvNxssiuU55tDeT6OjUdmm9vlC8JAd3DWZVSuIIDTCjsc4N2bh1neG25Y3Md9Ah6bis12G/VZwi1XX5yzXjv50Q+MThGJp0hmDEFnYp7ukL+mfZCg+HXXjokdi4Fy3Er/E/gY8BngRuA3gPJ+0UrdqMXNKJZME02kiSTTnjuc5nN8PGoLwsgYB0enc94b6A6ydYM9QU4lguCzZLZALbvzaSPjPts3DfGuY+fnzFOw8+njXLWqb3Yczfaje31Q2LVvlKlYyq47sYRUxnDifIxl3YWb9tWj31Y7JnYsBsoRhw5jzEMiIsaYw8DHReRRbMGoGGPMLmBXNftQKrsZue6iaMJ2F5UKKBfj+PnobNppviAs6w6ybb2dZVSJINi5/T56Kmx0Vw8eP3SOVf0dc4Kr2ee60Smo2WLQHfRxdiZBb0eg5IPCFx85RH9ngLMzCUzGmQcCw7mZJJ/45blP7vV6ym+3xI7FQDniEBMRCzggIr8HHAf022wRvN6MamEdQGlB2LrethCuWFm+IIA9pWa30w7bZzVfELLxcq4b2dE232o8ODZNKm3oCl0onCv2oHB0PMJAd4iQ326O56aadgSspgXIldagHHH4faATuBP4c+wCuPfVY1BK+RS7Ga3s67BrDxwLoVLrAC4Iwu6RMQ7kC0LXBZdRpYIQ8Fmz8yNUM0lNvfFy4y/3Cbua4HW+1ZjOGCyBsak4PWFbxIpZLe5n6e0I0OsIntv3qBj6lL84KCdb6Unnz2nseENbslArMbNvRiG/RSSRJpHK8PZrVjI6GSu9gyKcOB+dzTIaOV1YELZtGODKlUvKFoTsdtjD/Z387vZLW+a7mO868XLjL+cJu9p4Ub4lE/RZJNOZ2RYjUNxq0WCwUoySXVlF5JvzvW+MubmmIypCuV1ZC1Gqy2q7kkpniCbTPPTT09z3+GFOThSuQPbKyQnXQjjD/tNTOe8t7Qqydf0A2zcOViQILs8eOc9ff2+EoPPE20rfhZfrJL+zajUPGdV2Lc3f/tRElDPTCQzQ5cRrgn5f0XNby8+itB717Mr6OuAocD/wI9o4Q6nZGSS15DvPn2LHo/YPOlsI/mq4r6L9lRKEN7iCcPGSeWMATxw6xwNPHuXk5NzZ2vyW08Yi7Oej//o8Ib/Vkt+Fl+uklq6V7Cf/yWhy1vd/bDzKrn2jJY+T/fSfSmcYjySxBPyWEEulSUUMH9i+uuh+1E2kFMKLOCwHfg64Dbv53n8A9xtjXqjnwOpBOzcxm80sSqbZ9eIon3nInhWtp8SsaPNxaiJmt67YP1ZcEDbYFoKXoPATh84VnK3tQ4GN/PyVy3OejFv5uyh3bNW6Kl2/fyptODERxXJmrRDw5F7KdmE9fWQcvyUsX9IxG2+IJFI8fugcd3oekaJ4EAdjTBr4NvBtEQlhi8QuEbnHGPPZeg+wlrTKnMheKZZZ9L+fmH9WtPk4NRmbDSrvO5UrCP2dAbautyfI+RmPgpBN9mxtIkJP2EcsmeJre4/x9mtX5azbyt9FuVXQ1daXuE/+o1Mx2ywXwAjLl4TxWeLJmnKf/gtNctUqoqu0F54C0o4ovA1bGNYAfwN8vX7Dqg+tHnxz5ziYKZFZVKzZ3KnJaMH1T03GeMSpVC4kCG9YbweVr1rVV1Xa6KnJKH0dAXw+azYW0RksXGnbyt9FOWOrhavSffK/4x+fImMMIadKuSdsT45Uzo29lUVXaS9KioOI3AdcCTwI/DdjzPN1H1WdaMUc7XgqPVuEFktmPPUtymk25xBLZnImziklCDc4LqNqBcESoTPkoycUYO1AN6NTMQL+C/srdmNqxe+ikrHVyj22fdMQ167ur/rG3sqiq7QXXrKVMoDbGCd7ZQGMMabmE/UUohbZSq2Am1lUTVVytm8/e+Kc9732EiZiSXaNjPHiyVxBsAT6O4O845qV/Mp1w1UXlnUE7SK1rqAfy9nXQs0Gm49azo9cq/On2UdKNpVmK5UUh1ahXcXBGEMsmSGSsFtcJ9OZ0ht5wM0KOn4+QsBn4beEI+O5biVLIGPAb8FgdxC/z0cqY7jrpvIC1y5eJs1ZbDemWgviYjt/Sv1RcWghkukMkcQF66DSc1wsLXR0MsbuA2fYvX+Un+ZZCEs6Amy6qIcDY1NMxZK2rScCBoZ6Q1giLOsK8dfvudrTGHyWzFYtVzNpzkJGb+hKK1PPOgelBO6MaK4gzGcdzFcHkL9edlro6akof/Hgi/R3BjlyLtefvaQjMJt2evVwH3f/84/pCvqZjCaxRBARp5laglX9HZyajM47DrfZXXfIT2ewNZrd1ZJaV8lrnYCyEFFxqJBEKuOkmaY8B5KL1QEUqk944MmjgJ3OOjYVJ5ayBWcqlgIuCMK2DYO8ejg3qOxmMwV8Fqm0QcQ2HpLpDLFkhs6gv+A4/si3gTddsbwlm93VinaaZ0FRmomKQx7FnirdNFPXOkhlyo8dZNcBQOH6hLGpOI8cGOOFkxMk07mCYwmE/Bb33HLlHEHIxs1m6u8MMjoVwx6qwRK7X3/AMjn1CN0hi3gqzc6nj/POzcNlf652YiFVyStKPVFxyCL/qfL0ZJSPfuN5/vDn1nPtJUsrjh24FKtPOH4+wtefPsau/WM8f2Iy531LoDvkpyfkRwQGusO85pL+eY9z63XDs5bBUE+IMzMJUmlYs7ST29+wjs88NEJfRwC/Zc1mGvksWRSFUq1cma0orYSKQxZf2P0SPrGzcpJpg9+ySEqarz5+hGtWz39D9kJ2fUIynWE6nmIimiKRzvC57780u15v2M+Gi3o4NDZNZ9BHR9A3m65663Wln+y3rFvKXazngSePcmoyyuXLl3DrdcNs3ThId9jPN549zth0nKCHeoRStFuXWy0SUxRvLHpxyG5R8fLZGXrDfjJZtQfzVR6Xy1uvXM7ndh3k9FScRCrXLdUb9nPDZQNs2zjINcN9+H3WbND41GT5XVa3rFvKlnVLCfis2ak1A0766W9vu7QmhVLt6L/XIjFF8caiE4fsBnb5LSq8VB6Xy9npOLtHzrB7ZIznj0/kVBH6LOGa4T7evXnVrCBk497gv/rDV/inp47xx9/4CR0BH7/ymlW89/o1RY9piZ1+2hMunH5a7lwDxSyDdvTft3JltqK0EotCHGJuILnE1Jiurz6aTOdUHntx5WRzdjrOIwfO2DGEPEHocSyE7VkWwnx89YevcN+ew1gCPstut3HfnsMAcwSiM2hbCF0e0k+9pF+Wsgza1X/fiqmntXLPtZubT2ldFqQ4pNIZIsk0sYQtChmPgeR8X305rhxXEHaPjPGTY3MF4fWXOoKwum/WveOFf3rqmCMMzjYCZDL801PHeO/1azxVLVdKKctA/fe1oVbuuXZ08ymty4IQB7dFhZ1qmprjzy8H15XjhXMzCbu53TyCsG3jANeu7i9LELKJJtPkb2qJvfzivo66Vi2XsgzUf18bauWea0c3n9K6tK04uC0q3ICyV+ugWlxB2D0yxo/zBKE75AaVqxOEbDoCPuKpNMiFKfgyCN0h3xxhqLVLoZRloP772lAr91y7uvmU1qStxCGaSNe8gZ0Xzs0kePSALQjPHc0VhK6QzxaEDYO85pLaCEI279m8in94/DBkMvgsIWPsZnq/dcPanPXq4VLwYhm0ov++3aiVe07dfEotaRtxiKcynJyoTUqpF2xBOMPukVF+fGyC7M7alQiC155KLqGAj96wnz/9pSvo6wzypcdeZiaRpivo47duWMudb9owu+6ufaPc+cAzzCRShP2+2YliqnUpqGXQGGrlnlM3n1JL2qYr68+8+lrzb999pK7HGI/YgrBr/xg/PnZ+jiC4QeVrV/cT9Hu3EIrNv5DfOttvWXQ7wWWv+3cthhPno05sQjAGLu4L0x3yMxFN8uiHbvI8VqU51Kqzq3aIVfLRrqwVMh5J8NiBM+waGeO5o4UFwbUQyhGEbEr1VHJrErLdAV5xg5Ahv91kz2DHYw6fjRDyW6wd6KpozI1A0y4vUCv3nLr5lFqxKMXhvGshFBKEoI/XXbqMGzcOVSUI2RTqqdQR8DE6FWP10s6qUlDdIORAd4jj56Oksj5MKmMYm46za99oy90wNO1SUVqbRSMOriDsHhnj2TxB6Az6uP7SZWzfOMjmS5bWRBCyya68tizBEiGeSnPJsq6qaxPcIGRvR4BTE1HSAsbY6a4r+zrw+6QlUxk17VJRWpsFLQ7nIwkeO2jHEPIFwbUQtm0Y5Lo1tReEbP7LlmHuffggqUyGTr+/poHC7CBk2hgClmCAi5d00NsRwBjDgdOT3LZjT0u5bzTtUlFam6aIg4gMA18BlgMZYIcx5t5a7HsikuTRg/YUms8UsRAaIQgA4YCP3o4A775umKHecF0ChbkZRVFE4KKeML3OjffMdJypeJrRqVhLuW807VJRWpumZCuJyApghTHmaRHpAZ4C3m6M+WmxbebLVpoVhJExnjkyniMIHQEfr7+scYJgidAd9tMbDtT9WPkUmuz+2HiUpV0BBrrDs+tFEimGesLcf/trGzq+UmNNpg333HxF062adkUD/Eoh2ipbyRhzEjjp/D0lIi8CK4Gi4pDPRDQ5m2VUSBBcC2HL2voLAthzQPR2BOgJ+Wcn0Gk0heoSJqJJlnWFctZrBfeN1lDUFg3wK7Wm6TEHEVkDXAP8qMB7twO3A1y8anhWEHaPjPF0AUFwYwhb1vQTqmPPoazx0RXy0RsO1LXHUTnkpzLetmNPy7pvNO2ydmiAX6k1TRUHEekG/gX4fWPMZP77xpgdwA6ApZdsMu/8ux/WVBDKrVp2CfotekIBusP+ovM4twrtXDWrbhLvaIBfqTVNEwcRCWALw/8yxny91PoziTS9xp6Z7XXrlrF941BVFkJ21XJv2M/ZmTj3PnyAu1hfUCB81oUJdEL+1rASvNCu7ht1k5SHBviVWtOsbCUB/h540Rjz11626Qn5+fjNl/Oza5bWxGVUqmrZpZwJdFqVdnTfqJukPNrZQlRak2ZZDq8H3gv8RESedZb9sTHmW8U2WNHXwdb1gzUbQKGqZXe+6HpOoKN4Q90k5dGuFqLSujQrW+kxLkxP0BTmzBctkEgZLlnaxfDS1jTF833wr1u3lMcPnVuQPnl1k5RPO1qISuuyaB+Lb71umFTGEE+l8VlCKp3BGPid7Zc2e2gFcX3wbjHbK2enuffhg7x8ZjrHJ79r32izh1oT7ti6jmTaEEmkMMb+X90kitI4FqU4BHwWv3Dlcv78liu4uK+T6XiKi3o7WroAK9sHLyJMRlNYAlOxFCL28oDTR2khsH3TEPfcfAVDPWEmokmGesIt/f0oykKj6XUOjUJE6Ar66AkH6AjarqQ3Xb6cN12+vMkj80a+Dz6RzmCJ/b/LQvPJq5tEUZrHgheHgM+iN9weNQnzke+DD/osEukMwayAufrkFUWpFQvSrSROf6OL+zoYXtrJks5AWwsDzPXB93b4yRjoCfvVJ68oSs1ZUJbDQrESCpGfqrhmWTe3XWdnK2nqoqIotabtxcESobPF+hvVi0I++DubNBZFURY2bSsO4YCP7rCf7mDzuqAqiqIsVNpKHPyWZQtCyN/wuRIURVEWE20jDgGfMLy0o237GymtSz26v2pHWaXdaZvHb0tEhUGpOfmV57WoNK/HPhWl0bSNOChKPcivPK9FpXk99qkojUbFQVnUHB2PXGi+6FBtpXk99qkojUbFQVnUDPd3Ek2mc5ZVW2lej30qSqNRcVAWNfXo/qodZZWFgIqDsqipR/dX7SirLATEGNPsMXhi8+bNZu/evc0ehlIj6pXqqSmkipKLiDxljNlc7nZqOSgNp16pnppCqii1Q8VBaTj1SvXUFFJFqR0qDkrDqVeqp6aQKkrtUHFQGk69Uj01hVRRaoeKg9Jw6pXqqSmkilI7VByUhlOvVE9NIVWU2qGprIqiKAsYTWVVFEVRaoaKg6IoijIHFQdFURRlDioOiqIoyhxUHBRFUZQ5qDgoiqIoc/A368Ai8mbgXsAHfMkY88lmjaXV0U6jzUe/A2Wx0RTLQUR8wOeBtwCXA7eJyOXNGEuro51Gm49+B8pipFlupS3AQWPMIWNMAngAuKVJY2lptNNo89HvQFmMNEscVgJHs14fc5blICK3i8heEdk7NjbWsMG1EtpptPnod6AsRpolDlJg2Zw+HsaYHcaYzcaYzYODgw0YVuuhnUabj34HymKkWeJwDBjOer0KONGksbQ02mm0+eh3oCxGmiUOTwLrRWStiASBW4FvNmksLY12Gm0++h0oi5GmpLIaY1Ii8nvAf2Knsn7ZGPNCM8bSDmzfNKQ3oiaj34Gy2GhanYMx5lvAt5p1fEVRFKU4WiGtKIqizEHFQVEURZmDioOiKIoyBxUHRVEUZQ5tM4e0iIwBhyvcfAA4U8PhNAIdc+Nox3HrmBvDQhjzJcaYsquI20YcqkFE9lYywXYz0TE3jnYct465MSzmMatbSVEURZmDioOiKIoyh8UiDjuaPYAK0DE3jnYct465MSzaMS+KmIOiKIpSHovFclAURVHKQMVBURRFmUPbi4OIvFlE9ovIQRH5cIH3QyLyNef9H4nImqz3PuIs3y8iv9BCY/5DEfmpiPxYRB4SkUuy3kuLyLPOv4a1Ofcw5l8XkbGssf1W1nvvE5EDzr/3tdCYP5M13hEROZ/1XrPO85dFZFREni/yvojI3zif6ccicm3We806z6XG/KvOWH8sIj8Ukauz3ntFRH7inOe9LTTm7SIykXUN/FnWe/NeV00c891Z433euYaXOu+Vf56NMW37D7vd90vAOiAIPAdcnrfO7wJfcP6+Ffia8/flzvohYK2zH1+LjPlGoNP5+3fcMTuvp1v0PP868LkC2y4FDjn/9zt/97fCmPPW/yB26/imnWfnuFuBa4Hni7z/VuBB7NkUXwv8qJnn2eOYr3fHArzFHbPz+hVgoAXP83bg/1R7XTVyzHnr/hLwcDXnud0thy3AQWPMIWNMAngAuCVvnVuA+5y/dwJvFBFxlj9gjIkbY14GDjr7a/qYjTHfN8a4ExTvwZ4pr5l4Oc/F+AXgu8aYc8aYceC7wJvrNM5syh3zbcD9DRjXvBhjHgHOzbPKLcBXjM0eoE9EVtC881xyzMaYHzpjgta4nr2c52JU81uoijLHXPX13O7isBI4mvX6mLOs4DrGmBQwASzzuG09KPe478d+UnQJi8heEdkjIm+vxwAL4HXM73RcBztFxJ0GtuXPs+O2Wws8nLW4GefZC8U+V7POc7nkX88G+I6IPCUitzdpTMV4nYg8JyIPisgVzrKWP88i0on9YPAvWYvLPs9Nm+ynRkiBZfm5ucXW8bJtPfB8XBH5NWAzsC1r8WpjzAkRWQc8LCI/Mca8VIdx5gylwLL8Mf87cL8xJi4iv41trd3kcdt6UM5xbwV2GmPSWcuacZ690GrXs2dE5EZscbgha/HrnfM8BHxXRPY5T8jN5mnsnkTTIvJW4BvAetrgPGO7lH5gjMm2Mso+z+1uORwDhrNerwJOFFtHRPzAEmzTzMu29cDTcUXkTcBHgZuNMXF3uTHmhPP/IWAXcE09B+tQcszGmLNZ4/wfwGu8blsnyjnureSZ4E06z14o9rmadZ49ISJXAV8CbjHGnHWXZ53nUeBfaYxrtyTGmEljzLTz97eAgIgM0OLn2WG+69n7eW5EIKWOARo/duBtLReCQ1fkrfMBcgPS/+T8fQW5AelDNCYg7WXM12AHvdbnLe8HQs7fA8ABGhAM8zjmFVl//zKwx/l7KfCyM/Z+5++lrTBmZ72N2ME6afZ5zjr+GooHSt9GbkD6iWaeZ49jXo0d07s+b3kX0JP19w+BN7fImJe71wT2jfSIc849XVfNGLPzvvvw21XteW7IB6rzyXorMOLcTD/qLLsH+4kbIAz8s3NxPgGsy9r2o852+4G3tNCYvwecBp51/n3TWX498BPngvwJ8P4WGvMngBecsX0f2JS17W865/8g8ButMmbn9ceBT+Zt18zzfD9wEkhiP6W+H/ht4Led9wX4vPOZfgJsboHzXGrMXwLGs67nvc7ydc45fs65dj7aQmP+vazreQ9ZwlboumqFMTvr/Dp2ok32dhWdZ22foSiKosyh3WMOiqIoSh1QcVAURVHmoOKgKIqizEHFQVEURZmDioOiKIoyBxUHpe0QkYtE5H+LyCGnHcDjIvLLzntuN81nnM6Zj4jIL2Zt+3EROZ7VufLm5n2S8hCRb4lIn/Pvd5s9HmVho+KgtBVO08RvAI8YY9YZY16DXdyY3cztUWPMNcaYjcCdwOdE5I1Z73/GGPNq4N3Al0WkZr8Dp6V2XX5Xxpi3GmPOA33Y3YYVpW6oOCjtxk1AwhjzBXeBMeawMeazhVY2xjyLXfj2ewXeexFIYVdBz+JYF18VkYeduRH+76z37haRJ50Gg//NWbZGRF4Ukb/F7skznLe/65x5DJ4TkSdEpMfZ5lERedr5d72z7nbH2vlXsef0+IIrNk5P/gHgk8CljvXzlyLSLfa8H087Pfsb0iVUWdi0e+M9ZfFxBfYNuByeBu7OXygi4I9KfwAAAnFJREFUPwtkgLEC21yF3Z6iC3hGRP4DuBK7+doW7Erlb4rIVuzWChuxq5JznuhFJAh8DXiPMeZJEekFosAo8HPGmJiIrMeuft3sbLYFe76Rw8C3gXdgt5t3+TBwpWP9uD3DftkYM+mIxx4R+abRClelClQclLZGRD6P3eUzYYy5rthqea//wOl4O4V90y50E/03Y0wUiIrI97Fv2DcAPw8846zTjS0WR4DDxp5fIZ+NwEljzJNgN3Rzxt2F7e56NZAGNmRt84SxG/4hIvc7x91JcQT4fx2hymC3kL4IODXPNooyLyoOSrvxAvBO94Ux5gPO0/J8Ux9eA7yY9fozxphPlzhOvmC4bbE/YYz5YvYbYk89O1NkP1JgXwB/gN0/62ps926sxLHn41eBQeA1xpikiLyC3VNMUSpGYw5Ku/Ew9kQ8v5O1rLPYyk6r6D/FblZXDreISFhElmFPGfkk8J/Ab4pIt7PvlU5//PnYB1wsItc52/RktY4/aYzJAO/Fnn7SZYuIrHViDe8BHsvb5xTQk/V6CTDqCMONwCUoSpWo5aC0FcYY48zM9hkR+a/Y8YIZ4ENZq71BRJ7BFo1R4E5jzENlHuoJ4D+w203/ubH74Z8QkVcBj9tJU0wDv4btFio23oSIvAf4rIh0YMcb3gT8LfAvIvJu7C622ZbH49hB558BHsHuv5+9z7Mi8gOxJ5p/EPgU8O9iTxz/LLYgKUpVaFdWRclDRD4OTHtwPdXj2NuBPzLG/GKpdRWlnqhbSVEURZmDWg6KoijKHNRyUBRFUeag4qAoiqLMQcVBURRFmYOKg6IoijIHFQdFURRlDv8/aByHSpZtMAcAAAAASUVORK5CYII=\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": [
    "We have found a very interesting insight from this analysis that is people living developed countries are prone to getting the infection compared to less developed countries, this might be because of the less testing in less developed countires."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.2: Plotting Social support vs maximum Infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d696afb710>"
      ]
     },
     "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": [
    "x =data[\"Social support\"]\n",
    "y= data[\"Max_Infection\"]\n",
    "sns.scatterplot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d696a94fd0>"
      ]
     },
     "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": [
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d696bbb5c0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.3: Plotting Healthy life expectancy vs maximum Infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d696c28198>"
      ]
     },
     "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[\"Healthy life expectancy\"]\n",
    "y= data[\"Max_Infection\"]\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d696c81ac8>"
      ]
     },
     "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": [
    "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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d696ce5a90>"
      ]
     },
     "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[\"Freedom to make life choices\"]\n",
    "y= data[\"Max_Infection\"]\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d696d4b4a8>"
      ]
     },
     "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": [
    "sns.regplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4=pd.read_csv(\"covid19_deaths_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>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>36</td>\n",
       "      <td>40</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>47</td>\n",
       "      <td>50</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "      <td>60</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.153300</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>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>26</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.033900</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>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>392</td>\n",
       "      <td>402</td>\n",
       "      <td>407</td>\n",
       "      <td>415</td>\n",
       "      <td>419</td>\n",
       "      <td>425</td>\n",
       "      <td>432</td>\n",
       "      <td>437</td>\n",
       "      <td>444</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>24.215500</td>\n",
       "      <td>-12.885800</td>\n",
       "      <td>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>0.186360</td>\n",
       "      <td>6.613081</td>\n",
       "      <td>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>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>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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Comoros</td>\n",
       "      <td>-11.645500</td>\n",
       "      <td>43.333300</td>\n",
       "      <td>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>38.861034</td>\n",
       "      <td>71.276093</td>\n",
       "      <td>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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266 rows × 104 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State         Country/Region        Lat       Long  1/22/20  \\\n",
       "0              NaN            Afghanistan  33.000000  65.000000        0   \n",
       "1              NaN                Albania  41.153300  20.168300        0   \n",
       "2              NaN                Algeria  28.033900   1.659600        0   \n",
       "3              NaN                Andorra  42.506300   1.521800        0   \n",
       "4              NaN                 Angola -11.202700  17.873900        0   \n",
       "..             ...                    ...        ...        ...      ...   \n",
       "261            NaN         Western Sahara  24.215500 -12.885800        0   \n",
       "262            NaN  Sao Tome and Principe   0.186360   6.613081        0   \n",
       "263            NaN                  Yemen  15.552727  48.516388        0   \n",
       "264            NaN                Comoros -11.645500  43.333300        0   \n",
       "265            NaN             Tajikistan  38.861034  71.276093        0   \n",
       "\n",
       "     1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  4/21/20  4/22/20  \\\n",
       "0          0        0        0        0        0  ...       36       40   \n",
       "1          0        0        0        0        0  ...       26       27   \n",
       "2          0        0        0        0        0  ...      392      402   \n",
       "3          0        0        0        0        0  ...       37       37   \n",
       "4          0        0        0        0        0  ...        2        2   \n",
       "..       ...      ...      ...      ...      ...  ...      ...      ...   \n",
       "261        0        0        0        0        0  ...        0        0   \n",
       "262        0        0        0        0        0  ...        0        0   \n",
       "263        0        0        0        0        0  ...        0        0   \n",
       "264        0        0        0        0        0  ...        0        0   \n",
       "265        0        0        0        0        0  ...        0        0   \n",
       "\n",
       "     4/23/20  4/24/20  4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \n",
       "0         42       43       47       50       57       58       60       64  \n",
       "1         27       27       27       28       28       30       30       31  \n",
       "2        407      415      419      425      432      437      444      450  \n",
       "3         37       40       40       40       40       41       42       42  \n",
       "4          2        2        2        2        2        2        2        2  \n",
       "..       ...      ...      ...      ...      ...      ...      ...      ...  \n",
       "261        0        0        0        0        0        0        0        0  \n",
       "262        0        0        0        0        0        0        0        0  \n",
       "263        0        0        0        0        0        0        0        2  \n",
       "264        0        0        0        0        0        0        0        0  \n",
       "265        0        0        0        0        0        0        0        0  \n",
       "\n",
       "[266 rows x 104 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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