covid19 data analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COVID-19 Data Analysis using Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing 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 COVID-19 dataset\n",
    "importing \"Covid19_Confirmed_dataset.csv\" from \"./Dataset\" folder. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
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       "      <th>4/24/20</th>\n",
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       "      <th>4/27/20</th>\n",
       "      <th>4/28/20</th>\n",
       "      <th>4/29/20</th>\n",
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       "      <th>0</th>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <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",
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       "      <th>1</th>\n",
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       "      <td>Albania</td>\n",
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       "      <td>20.1683</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>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",
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       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2811</td>\n",
       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>717</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>745</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>25</td>\n",
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       "      <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",
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       "  </tbody>\n",
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       "<p>5 rows × 104 columns</p>\n",
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       "  Province/State Country/Region      Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
       "0            NaN    Afghanistan  33.0000  65.0000        0        0        0   \n",
       "1            NaN        Albania  41.1533  20.1683        0        0        0   \n",
       "2            NaN        Algeria  28.0339   1.6596        0        0        0   \n",
       "3            NaN        Andorra  42.5063   1.5218        0        0        0   \n",
       "4            NaN         Angola -11.2027  17.8739        0        0        0   \n",
       "\n",
       "   1/25/20  1/26/20  1/27/20  ...  4/21/20  4/22/20  4/23/20  4/24/20  \\\n",
       "0        0        0        0  ...     1092     1176     1279     1351   \n",
       "1        0        0        0  ...      609      634      663      678   \n",
       "2        0        0        0  ...     2811     2910     3007     3127   \n",
       "3        0        0        0  ...      717      723      723      731   \n",
       "4        0        0        0  ...       24       25       25       25   \n",
       "\n",
       "   4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \n",
       "0     1463     1531     1703     1828     1939     2171  \n",
       "1      712      726      736      750      766      773  \n",
       "2     3256     3382     3517     3649     3848     4006  \n",
       "3      738      738      743      743      743      745  \n",
       "4       25       26       27       27       27       27  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv=pd.read_csv(\"Dataset/covid19_Confirmed_dataset.csv\")\n",
    "corona_dataset_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking the shape of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(266, 104)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.2: Deleting the unwanted columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=corona_dataset_csv.drop([\"Lat\",\"Long\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>3256</td>\n",
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       "      <td>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>0</td>\n",
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       "      <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",
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       "  Province/State Country/Region  1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  \\\n",
       "0            NaN    Afghanistan        0        0        0        0        0   \n",
       "1            NaN        Albania        0        0        0        0        0   \n",
       "2            NaN        Algeria        0        0        0        0        0   \n",
       "3            NaN        Andorra        0        0        0        0        0   \n",
       "4            NaN         Angola        0        0        0        0        0   \n",
       "\n",
       "   1/27/20  1/28/20  1/29/20  ...  4/21/20  4/22/20  4/23/20  4/24/20  \\\n",
       "0        0        0        0  ...     1092     1176     1279     1351   \n",
       "1        0        0        0  ...      609      634      663      678   \n",
       "2        0        0        0  ...     2811     2910     3007     3127   \n",
       "3        0        0        0  ...      717      723      723      731   \n",
       "4        0        0        0  ...       24       25       25       25   \n",
       "\n",
       "   4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \n",
       "0     1463     1531     1703     1828     1939     2171  \n",
       "1      712      726      736      750      766      773  \n",
       "2     3256     3382     3517     3649     3848     4006  \n",
       "3      738      738      743      743      743      745  \n",
       "4       25       26       27       27       27       27  \n",
       "\n",
       "[5 rows x 102 columns]"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.3: Aggregating the rows by the country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_dataset_aggregated=corona_dataset_csv.groupby(\"Country/Region\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1092</td>\n",
       "      <td>1176</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1463</td>\n",
       "      <td>1531</td>\n",
       "      <td>1703</td>\n",
       "      <td>1828</td>\n",
       "      <td>1939</td>\n",
       "      <td>2171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>609</td>\n",
       "      <td>634</td>\n",
       "      <td>663</td>\n",
       "      <td>678</td>\n",
       "      <td>712</td>\n",
       "      <td>726</td>\n",
       "      <td>736</td>\n",
       "      <td>750</td>\n",
       "      <td>766</td>\n",
       "      <td>773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2811</td>\n",
       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>717</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan           0        0        0        0        0        0        0   \n",
       "Albania               0        0        0        0        0        0        0   \n",
       "Algeria               0        0        0        0        0        0        0   \n",
       "Andorra               0        0        0        0        0        0        0   \n",
       "Angola                0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  4/21/20  4/22/20  4/23/20  \\\n",
       "Country/Region                             ...                              \n",
       "Afghanistan           0        0        0  ...     1092     1176     1279   \n",
       "Albania               0        0        0  ...      609      634      663   \n",
       "Algeria               0        0        0  ...     2811     2910     3007   \n",
       "Andorra               0        0        0  ...      717      723      723   \n",
       "Angola                0        0        0  ...       24       25       25   \n",
       "\n",
       "                4/24/20  4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \n",
       "Country/Region                                                                 \n",
       "Afghanistan        1351     1463     1531     1703     1828     1939     2171  \n",
       "Albania             678      712      726      736      750      766      773  \n",
       "Algeria            3127     3256     3382     3517     3649     3848     4006  \n",
       "Andorra             731      738      738      743      743      743      745  \n",
       "Angola               25       25       26       27       27       27       27  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 100)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.4: Visualizing data related to a country\n",
    "visualization always helps for better understanding of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26824659a90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"China\"].plot()\n",
    "corona_dataset_aggregated.loc[\"Italy\"].plot()\n",
    "corona_dataset_aggregated.loc[\"Spain\"].plot()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3: Calculating a good measure \n",
    "we need to find a good measure reperestend as a number, describing the spread of the virus in a country. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268246db588>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc['China'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268243b94a8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"China\"][:3].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3.1: Calculating the first derivative of the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268247adc50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OeShtzcE1K3XYRDhjTAYsOOShaNzrNTsaoDTib93R0hEbjSIZY8YZCw55KO5pr9nRAGWFYQCaOmzDH2PM4HIKDiJSISKPiMh2EdkmIheJyGQRWS8iu9zvSpdXROR2EakRkc0ick7S46xx+XeJyJr+/6LJRDSuvRbdAygv9GsOzVZzMMZkINeaw78Cv1TVpcCZwDbgi8BTqroEeMrdB7gKWOJ+1gJ3AIjIZPytRi/A31705kRAMUMT9/o2KyVqDhYcjDGZGHJwEJFy4N3A3QCq2qWqJ4DVwL0u273ANe72auA+9b0IVIjIDGAlsF5VG1T1OLAeWDXUchl/Elxqs1JhOEAoIDRbs5IxJgO51BwWAvXAf4jIqyJyl4iUANNU9RCA+z3V5Z8FHEg6v9al9ZduhiiWpllJRCgrDFnNwRiTkVyCQwg4B7hDVc8GWulpQkpH0qTpAOl9H0BkrYhsFJGN9fX12ZZ3woilaVYCv2nJag7GmEzkEhxqgVpV3eDuP4IfLI645iLc77qk/HOSzp8NHBwgvQ9VvVNVV6jqiurq6hyKnt/Sra0EWM3BGJOxIQcHVT0MHBCRU13S5cBW4HEgMeJoDfCYu/048HE3aulCoNE1O60DrhSRStcRfaVLM0MUSzNDGiw4GGMyF8rx/L8E7heRAmAP8An8gPOwiNwAvAV82OV9AngfUAO0ubyoaoOIfAN42eX7uqo25FiuCa3/mkOYAw1to1AiY8x4k1NwUNXXgBVpDl2eJq8CN/bzOPcA9+RSFtMjFvcIp+1zsJqDMSYzNkM6D/lrK/X915Zbh7QxJkMWHPJQ1Ou7Exz4NYeWzhh+Jc4YY/pnwSEPxeP9j1byFFq7bGVWY8zALDjkoainhNKOVnKL77Vb05IxZmAWHPJQulVZwa85gK2vZIwZnAWHPBSNe/0OZQWsU9oYMygLDnko7mm/Q1nBag7GmMFZcMhDsXh/Q1n94GAb/hhjBmPBIQ/FvP4mwdmeDsaYzFhwyDOep3hKv0NZwYKDMWZwFhyGyf0b9vPMjrrBM55kMc+f4JZu4b2icJCgbfhjjMmABYdhctuvdnHPc3tHuxjEPA9IX3OwDX+MMZnKdVVWgz866FhLJ7vr0u1bNLISNYd08xwgsfie1RyMMQOzmsMwONbSiadwsLGD1s7R/VYeiw8cHPzF96zmYIwZmAWHYVDX3Nl9e+/R1lEsSU+zUrrlM8CW7TbGZMaCwzCoa+7ovr27vmUUSzJ4zaGsMGzzHIwxg7LgMAzqmnpqDjV1oxsc4ok+B6s5GGNykHNwEJGgiLwqIj939xeIyAYR2SUiD7ktRBGRiLtf447PT3qMm1z6DhFZmWuZRlqiWWlWRdGo1xyicdesNGCfg9UcjDEDG46aw18D25Lu3wLcqqpLgOPADS79BuC4qi4GbnX5EJFlwHXAcmAV8AMRCQ5DuUZMXXMHFcVhTptRzu660e1z6Kk59D9ayTb8McYMJqfgICKzgfcDd7n7AlwGPOKy3Atc426vdvdxxy93+VcDD6pqp6ruBWqA83Mp10ira+pkalmERVNL2Hu0tfsDejREB+1zsA1/jDGDy7XmcBvwd4Dn7lcBJ1Q10ahdC8xyt2cBBwDc8UaXvzs9zTnjQl1zJ1PLCllUXUpX3KP2eFv3sdbOGB/54Qs8+NJbI1KW7ppDmoX3wJbtNsZkZsjBQUSuBupUdVNycpqsOsixgc5J/ZtrRWSjiGysr6/PqrwnU32zqzlUlwK9Ryz9dtdRXtrXwBcffYMfv7j/pJclmpghPUCzEtj6SsaYgeVSc7gY+KCI7AMexG9Oug2oEJHEzOvZwEF3uxaYA+COTwIaktPTnNOLqt6pqitUdUV1dXUORR8+qkp9cyfV5REWVZcA9Op3+M3OOkojIS5fOpWv/Peb/PiFfSe1PImaQ9hqDsaYHAw5OKjqTao6W1Xn43coP62qHwOeAa512dYAj7nbj7v7uONPq98r+jhwnRvNtABYArw01HKNtBNtUbriHlPLCqkoLmBKaUF3zUFV+fWOei5eXMUP/ugc3nvaNL7y2Bb+a8PJa2JKjFZKt7YS9NQcmqzmYIwZwMmY5/AF4HMiUoPfp3C3S78bqHLpnwO+CKCqW4CHga3AL4EbVXXc9JYmhrFOLYsAsLC6tDs47DzSwqHGDt5z6lQioSA/+Ng5vGvJFL71xLaT9s29u+bQT7NSuTUrGWMyMCzBQVV/rapXu9t7VPV8VV2sqh9W1U6X3uHuL3bH9ySd/01VXaSqp6rqL4ajTCMlMTs6ERwWVZeyu95vVvq1W8L79071m8AKQgH+buVSWjpjPPTygTSPlrvEDOn+aw7WrGSMGZzNkM5RYnb01PJCABZVl9DQ2kVDaxfP7Khj6fQyZkwq6s7/jtmTOH/+ZH70/L5eQ16jcY/PPfQav3zzUE7lGWg/B7AOaWNMZiw45Ci1WWnRVH/E0usHTrBx3/HuWkOyP71kAbXH23lyy+HutB88s5tHX32bO5/d0yd/NmKD9DnYhj/GmExYcMhRXXMHJQVBSiL+N/LFbjjrfS/sI+Ypl54ytc85VyybxtzJxdztNgfaXHuC7z69i0lFYV556wR1TR19zslUbJA+B9vwxxiTCQsOOapr7uxuUgKYWVFEJBTgmR31lEZCrJhf2eecYED4k3fOZ+P+47y45xiffeg1ppRGuGvNCgCe3HpkyOXp2Qmu/3+tBQdjzGAsOOSovqmTatekBP4H/4Ip/nyHSxZP6bft/yPnzaEsEuLP7t3I7vpWvvPhM1gxr5L5VcWsS2puytZgS3YDlEVs8T1jzMAsOOSorrmju78hIdHvcGma/oaE0kiIj543h5bOGB+/aB7vWlKNiLBy+XRe2H2MxvahfXgPtvAe+DWHpnarORhj+mfBIUeJdZWSnTqtDBHSdkYn+4vLFvP371vKTVed1p125fLpxDztHgabkOkGPdFB1lYC2/DHGDM4Cw45aOmM0dYVZ2p575rDn1w8n4c/eVGvIazpVBQXsPbdiygq6Fmh/Ow5FUwti/RqWnrstbc562tP8ugrtYOWKT7Ifg7gT4SzPgdjzEAsOOQgMaootVmpvDDMefMnD+kxAwHhimXT+PWOejqicV596zh/+8hmPIV/WbeDjujAk8djGTYrWZ+DMWYgFhxy0DPHoXCQnNlZuXw6bV1xfrKplrU/3sS08gjfvf5sDjZ2cP8g6zLFMmxWsg1/jDEDseCQg+7gkNKslKsLF1ZRVhjiK//9Ju1dce5ecx4fOHMmFy+u4vvP1NDS2X+TUGIS3GA1B9vwxxgzEAsOQHtXfNDmmnT6a1bKVUEowBWnTSMg8N3rz+aUaWUA/O3KpTS0dnH3b/f2e25PzWGg4GDrKxljBmbBAfjLB17hsw+9lvV59c2dFIQCTCoKD3uZbv7gch678RLes7RnhvVZcypYtXw6//7bPTS0dqU9LxZXggHB34E1PVtfyRgzGAsOwObaRrYcbMr6vLrmTqpLIwN+EA/VpKIw75g9qU/651eeQltXjB88U5P2vJin/a6rlNATHKzmYIxJb8IHh7auGHXNnRw80d5rldRM1DV3DHt/w2AWTy3jD86ZzX0v7udQY3uf47G4N2CTEvQ0K9mGP8aY/kz44LD/WBvgf+M+kuWCd3VNncPe35CJv758CarK7U/t6nMs5umgwcE2/DHGDGbCB4d9R3v2e6493veb+EDSzY4eCXMmF/OxC+bx8MZa9rhd5xJinkeon/WcEqxD2hgzmCEHBxGZIyLPiMg2EdkiIn/t0ieLyHoR2eV+V7p0EZHbRaRGRDaLyDlJj7XG5d8lImv6+5snwz5XcwA40NA2QM7eOqJxGtujo1JzALjxPYspCAa49Ve9aw/xDGoO1iFtjBlMLjWHGPA3qnoacCFwo4gsw98b+ilVXQI85e4DXAUscT9rgTvADybAzcAFwPnAzYmAMhL2H2vtbmbJpuZQf5LmOGSquizCn14yn/95/SBbDjZ2p0fjgweH4oKeDX9UlUON7bx9IrtakzEmvw05OKjqIVV9xd1uBrYBs4DVwL0u273ANe72auA+9b0IVIjIDGAlsF5VG1T1OLAeWDXUcmVr37FWlkwrY1p5hNrjfWsOdz67u9eHb8L2w80ATCsf+WalhLXvWkR5YYj/++TO7rS4p4M2K4kIpZEQD7x0gDO/9iQX/dPTvOuWp/nnX26nK+ad7GIbY8aBYelzEJH5wNnABmCaqh4CP4AAiYH6s4ADSafVurT+0kfEvqNtzKsqZnZlMQdSgkNDaxffemI7n77/FdqTZhN3xTy+/YttzKsq5sKFVSNV1D4mFYe54ZKFPL29jsONfmd6NIPRSgDvP2MGi6pL+MCZM/nG6uV86JzZ/ODXu7nm+79jhwt8xpiJK5TrA4hIKfBT4DOq2jTAmP90B3SA9HR/ay1+kxRz587NvrAp2rviHG7qYEFVCZ6nbNx/vNfxxIfk/mNtfGfdDv7hA8sAuOu5Peyub+U/PnEeheFgn8cdSee5neZq6lqYPqnQ1RwGDw7f+v139Em7Ytk0bnr0DT7wvef45LsX8snfW0RpJOeXiDFmHMqp5iAiYfzAcL+qPuqSj7jmItzvxMYEtcCcpNNnAwcHSO9DVe9U1RWquqK6euC9EjLxluuAnjelhNmVxRxq7Ohemwhgx2F/Ytyq5dP5j+f3sml/A2+faOe7T9Wwcvk03nNq3/2hR1piY6HdbtRSNK4DbhE6kCuXT2fdZ9/NquXT+e7TNVz6nV/zXxve6nVNjDETQy6jlQS4G9imqv8v6dDjQGLE0RrgsaT0j7tRSxcCja7ZaR1wpYhUuo7oK13aSbfvmD+MdX5VMbMri4h7yqHGnrkOO440U1Ec5l8+ciYzJxXxt49s5ubH3kRRvnL1spEo4qCmlkUojYS6h7TGPY9wBjWH/kwpjXD79Wfzs0+/kwVTivn7n73BF376xnAV1xgzTuRSc7gY+GPgMhF5zf28D/g2cIWI7AKucPcBngD2ADXAvwOfBlDVBuAbwMvu5+su7aTb74LDvCq/5gC9RyxtP9zMqdPKKI2EuOVDZ7CnvpVfbavjLy9b0p1/tIkIi6pL2F3vP5dMls/IxNlzK3n4kxdx9Rkz+PWOOlve25gJZsgNyqr6HOn7CwAuT5NfgRv7eax7gHuGWpah2nu0jcklBUwqCjNnsr9rmz9iqQrPU3Yebubac2cDcMmSKax990I27T/On79r4UgXdUCLqkt5Yc8xwF94LzzEZqVUIsKKeZX8fPMh6po7R3VkljFmZE3o3sb9x1qZV+XXAGZMKkKkp+bw9ol2WrvinDq9vDv/37/vNFT1pCy0l4tFU0t59NW3aemM+TOkhyk4ACyb6S/+t+VgowUHYyaQCb18xv5jbcyvKgH8PRSmlxd2D2dNzGM4dXpZr3PGWmAAWFTtP4e99a3+2ko59DmkOm2G//y3ZrFqbWN7lM899Fr3REFjzPgzYYNDRzTOwcb27poDwJzK4u6aQ2KkUmpwGIsWVfeMWIplMEM6G2WFYeZVFWe1pPn6rUd49NW3eXr7kWErhzFmZE3Y4HCgoQ1VWDClpDttdmURbyeCw5EWZlcWjYtx/nOrigkGxA8OGcyQztbymeVsPZR5cPhdzVEAth2yyXTGjFcTNjgkFtybV9U7OBxqbCca99hxuIml46DWABAJBZk7udjVHDKbIZ2NZTPK2X+sjaYMVnFVVZ7rDg7Zb6BkjBkbJmxw2J80xyFhdmUxnvp9EXvqW7v3bh4PFlWXsLuuNaO1lbK1bKbfKb89g5rArroW6ps7KS8Msf1wsw2BNWacmrDBYd+xViYVhakoLuhOm+2Gs/5mZz0xT8dFf0PCoupS9h5tpTM2/DWH5W7E0tY0CxCmSjQpXX/BXBrboxzOcgMlY8zYMGGDw/5jbcxP6m8Av0Ma4Kltfkfq0qRhrGPdoupSuuIehxrbhz04TC2LUFVSkFGn9O9qjjK/qpj3njYNsKYlY8arCRsc9h5t7dWkBDB9UiEBgZf2NhAOCgurS/o5e+xZNNUvq6cM61BW8IfvLsugUzoa93hxTwMXL57SXeuyTmljxqcJGRy6Yh4HT7T36owGCAcDzJhURMwKB0/dAAARWklEQVRTFlWXEh7mtvuTaeGU0u7bwzkJLmHZzHJ2HmkecL+HzbUnaOmMccniKZQXhpldWWQ1B2PGqfHz6TcMVJVndtRx7Q+fx1N/FE6q2ZV+v8N46m8AqCwpoKrE7z8ZjrWVUi2bUU40rtTUtfSb57ldxxCBixb5e1wsnV7ePZnQGDO+TJjg8PqBE1z7wxf4xH+8TENrF/987RmsXD6tT77EgnrjLThAz2S4XFZl7c/ypGU0+vO73Uc5feak7k7+ZTPK2FPfQkc03u85xpixKe+DQyzu8d2ndvEHdzxP7fE2/vGa03n6by7lIyvmpF0KI1FzGC9zHJIl+h2Gup/DQBZMKaEoHOy336G1M8arbx3n4sVTutOWzijHU9h1pP/aRq48T9m4r8H2nDBmmI396b85ONDQxmcfeo2N+4/zwTNn8o1rTmdSUXjAc06fNYlIKMDp7pvyeHIyaw7BgLB0Rlm/ayy9tK+BaFy5JDk4JDqlDzfxjtl9r6fnKT/ZdICF1aWsmFeZ9bpVG/Yc4+s/38qWg03ccMmCMbPHhjH5IG+DwzM76virB14Fhds+ehbXnJ3ZttTvPW0qG7/8XsoKBw4iY1EiOJyMPgfw+x0ef/0g//3q22w52MjWQ00cbe6iuSNKQ1sXBaEAK9y2peDPPi8KB/udPHfLL7fzb8/uAWBhdQnXnTeHc+dV0tAapaG1k86YxxXLpjFjUlGv83Yeaea2X+3kiTcOM3NSIZeeWs3dz+3l8qVTeWdScDLGDF3eBQdV5Y7f7OY763awdHo5d/7xucyZnPnGPCIyLgMDJNccTk5r4RmzJ3H/hrf4zEOvURAKsHR6GfOqiikrDFNWGOLceZW99tQOBoRTppelHbF07/P7+Ldn9/CHF8zlrDkVPPTyAb71xPY++b72P1u5fOlUrj9/LoebOnjo5QO8duAEReEgn7viFP78XQtRlKtvf47P/+R1fvGZdw9aOzRmOHTFPNZtOcx7T5tGUcHo7iV/MuRVcGjtjPGFn27m55sPcfUZM/jOtWfm5T+tP7Mqi1h91kwuWDD5pDz+6rNmUVFcwLyq4oyH+p42vYx1Ww732gdj3ZbDfPV/tvDe06bxjdWnEwwIH1kxh5q6Fg40tFFVWsDkkgK6Yh4/2VTLwy8f4Mmt/sTEJVNL+fL7T+P3z55FVWmk++/8v4+exYfueJ6bH3uT2647+6Q8f2MSVJUv/ewNfrKplg+cOZPbrztrTC7nn4sxExxEZBXwr0AQuEtVvz3IKb288tZxPvvQa7zV0MYXVi3l//zewrz7Zw0mGBD+9SR+MBaGg6xcPj2rc06bUc6DLx/o3knudzVH+asHXuWM2RV89/qzezWBLZ5ayuKppb3O/8KqpXzmvUt4dudRppQWcNacirT/17PmVPBXly3h1l/t5PRZk/joeXPGbQ3QjH13/XYvP9lUy5lzKvif1w9y1pwKbrhkwWgXa1iNieAgIkHg+/h7TtcCL4vI46q6dbBzo3GP7z1dw/eeqWF6eSEPrb2I80/SN2eTvUSn9NaDTTyyqZb/++QOFlWXcveaFRnX6iKhIFcs6zvsONWN71nE72qO8o//u41bfrmdCxdWcfHiKRSG/BqOp3CirYsjTZ0cae6guSOGp+rPKg8I586r5PdOqWbF/EoiofFT4/Q8RaT3RlSep7zxdiMv72tgUXUp5y2Y3Gf5+XS7Gp5o6+LNt5s43NRBOCgUBAMUhAIUFQQpjYQoiYTojHocb+viWGsXsbjHKdPKWDy1tFeT4lgWjXvsqW9ld30L1WURFlWXMrmkoFeegXZ8fGrbEb71i228/x0zuP36s/nUf27iW09s4x2zJuXVZ4+MhVUzReQi4KuqutLdvwlAVf+pv3NWrFihGzdu5M/u3civth3hD86ZxVc/uJxy+7Y4pjS2RTnz608yuaSAhtYuPnDmTL79B++g5CTtkxH3lFfeOs6vth5h/bYj7Klv7XU8IDClNMLU8giTisIERAiI0N4V59UDx4nGleKCIKdMK2N6eSHTyiNUl/l5y4v8vpWicIiigiBF4SABgc6YR1fcQ1WpLC5gSlmEskioz4d1c0eM421dNHVECYhQEAp0N83FPY+Yp3iev/xJKCCEAgFaOmOcaOvieFuUls4o7V1x2qMeTR1R9h1tZU99K3uPtRIJBVhYXcoit17Ys7vqOdrS1f33QwHhzDkVVJUUcLCxnUMnOjje1kVFcQFTSguoLC7g7RPt3ZtdZSsYEOZXFRMJBYl7StTz8Dw/8CqKIBSFgxQVBCkuCOKp+tct5nX3Xy2dXs7iqaU0d8Q40tTB4aYOWjtj7rooqlAYDlBYECQSDHC4qYN9R9vYc7SVls4ok4rC3T9lhWHKC0OUFYaJq3/tWzqiHGnqpKauha6Uoc+TSwooLwzR0hmjuSNG3FPmVRVzyrQylkwtpbwoTDAgxD3l1vU7WVhdysOfvIiigiBNHVGu+d7vaOqI8eMbzqeqtIDCcJBIKEA4ECDgascd0Tj1zZ3Ut3QS95TJJQVMLvb3sA8EpNcKxkNt9YjFPVo740TCgX6DtYhsUtUVgz3WWAkO1wKrVPXP3P0/Bi5Q1b/o75xEcPjNznqaO6JcfcbMkSquydK7/vlpDjd28A9XL+OPLpw3os19jW1RvKTXeFlhqN8lzVs7Y7y45xi/2VnPnvrW7g+o5o5Y1n+3IBSgIBhwNROlK+bhDeNbLRgQ5k0uZmF1CQumlNAejbPXBYvOmMcli6fwnqXVXLCgir1HW3l+91Ge332Mlo4YMyuKmFlRRGVxmBPtUY42d9LQ2sX0SYWcPmsS75g1idmV/jIyXe4DvLUrRltnnNauGOFggMluRr4I7DjcwvbDTew80uwvGR8IEAwKQRECrkajqrRH47R1xWnvihMICJFQgEjID4DbDzdzoq33fiGhgFBaGCIUkO7mx86YR3tXnM6YR3VZhAVV/vMvLwrR1B6jsT1KY3uU5s4oTe0xmjqihAL+IJPSSIiq0gJOnV7GshnlLKoupb6lk911Leyub6G1M05ZYYjSwhCCsKe+hV11Lew/1trrfzerooiffuqdTJ/Us6f6ziPNXPP939HW1XfCZ6L8nQMsPdOfgPjL4YSCQjjof5lIXDfE/9IRVyUWdwGws+e1WhgOMKkoTCQUpCvm0Rnzr9u2b1w1roLDh4GVKcHhfFX9y5R8a4G1AHPnzj13//79I15Wk70tBxspCAZYMo72x0jWEY3T3OF/0DS1R2mPxumIxmnv8vBU/UAQCiDA8bYujjZ3cbS1k1hcCQgExH9jVxSHqSwuoLwojKoSjStd8TiC/+ERCggiEPP8N3s07lEaCVFRXEBlif9tuNh9+46EAnnVp6aqHGnqZE99C+VFYaaVF1JVUtD9rTtd/pF6/l0xj45Y3P8g9pSywjAFob5fMGrqmnntQCMd7vXRGfOIxv2fWFwpLwpTXerXRIMB8ZvmWrpobI+igAAikPhIVvwP/6jnnx+L+zXURI1LgaD0vHbKi8KUF4YpLQzRGYvT2BblRFuUzlicSChIJOx/YfnKB5aPq+Aw5GYlY4wxmcu0WWmsLJ/xMrBERBaISAFwHfD4KJfJGGMmrDExWklVYyLyF8A6/KGs96jqllEuljHGTFhjIjgAqOoTwBOjXQ5jjDFjp1nJGGPMGGLBwRhjTB8WHIwxxvRhwcEYY0wfFhyMMcb0MSYmwQ2FiDQDO0a7HGPEFODoaBdiDLHr0Ztdjx52LWCeqlYPlmnMDGUdgh2ZzPKbCERko12LHnY9erPr0cOuReasWckYY0wfFhyMMcb0MZ6Dw52jXYAxxK5Fb3Y9erPr0cOuRYbGbYe0McaYk2c81xyMMcacJCMeHETkHhGpE5E3U9IvEpF/F5ErRGSTiLzhfl/mjheLyP+KyHYR2SIi3045f4aIPCkiZ4nICy7PZhH5aFKeBSKyQUR2ichDbnnwUSMic0TkGRHZ5sr710nHEtejyuVpEZHvpZz/TRE5ICItaR573F2PZCJSKCIvicjrruxfSzp2vYh8SUQ+5p7TZhF5XkTOTHmMfxORi0XkO+51s1lEfiYiFUl5bhKRGhHZISIrR/I5DiSX5z/Q68odH/C95vKc69JrROR2GQM7C4lIUEReFZGfJ6UlrsVS9zrvFJHPp5yX9jPHHRuX12JEqOqI/gDvBs4B3kxJ/xrwIeBsYKZLOx14290uBt7jbhcAvwWuSjr/E8DfAKcAS1zaTOAQUOHuPwxc527/EPjUSD//lOc8AzjH3S4DdgLLUq5HCXAJ8H+A76Wcf6F7jJY0jz3urkdK+QUodbfDwAbgQnf/XuBc4J1ApUu7CtiQ8hiv4S8BfyUQcmm3ALe428uA14EIsADYDQRH+7nn+vwHel2lvLbSvtfc/ZeAi1w5fpH8XhvFa/I54L+AnyelJa7FVOA84JvA51POS/uZM56vxYhc71H6J89P/UcBzwGTUtIEOAZE0jzGvwJ/nnT/oeQ3QFL668AS91hHkz4kLgLWjfY/IKWsjwFXpLsewJ+QEhySjqULDuP+eiSVuRh4BbjAlft1XH9ZUp7KlDf0acDDaR7r94H73e2bgJuSjq0DLhrt5zscz7+/11W615ZL636v4QeX7UnHrgf+bZSvwWzgKeCyRHBIdy2Ar6YGB5fe5zNnvF6LkfoZE30OIjIFiKpqY8qhDwGvqmpnSv4K4AP4LxZEJAicqqpbU/Kdj1/L2A1UASdUNbEDdy0wa7ify1CJyHz8by8bBrgemT7WuL8e0N2M8BpQB6xX1Q341+h1de/UJDfgf6tLuAr4ZZqH/dOkfLOAA0nHxtQ1yPH5Jx5jvjtng7ufyXttFv61SBgL1+U24O8ALymtv2uRkXF8LUbEWJkhfSXwZHKCiCzHbwK4MiU9BDwA3K6qe1zyBbgXf1K+GcCPgTWq6vXTTjgmhmqJSCnwU+AzqtokIn9IyvXI0ri+HgmqGgfOcl8GfiYipwOrSPkQFJH34H84XpKUvBK/aS0535eAGHB/Iindnx2e0ucux+ff53XlkjN5r42p6yIiVwN1qrpJRC5NOtTnWmRp3F2LkTQmag6kfMsTkdnAz4CPq+rulLx3ArtU9bYBzi8H/hf4sqq+6JKPAhUuuIBfTT04rM9iCEQkjP8Gvl9VH3XJ/X3rzdS4vR7pqOoJ4Nf4Hwa93tAicgZwF7BaVY+5tGL8fpWDSfnWAFcDH0v6plkLzEn6U2PyGmT7/F16utcVZPZeq8W/FgmjfV0uBj4oIvuAB4HLROQ/SfPhnqXxeC1Gzmi0ZZHU/kdKuyFQ4e5/KM15/4j/gg+kpD8PlLvbBfjNTZ9Jc/5P6N0B++nRbNNzz/0+4LaUtHRtyn9Chn0O4/V6pJStmp6O8yL8AQirgeeS8swFaoB3ppz7fuDbSfdXAVuB6pR8y+ndIb2HsdMhncvz7/O6SvfaGuS99jL+gIdEJ+z7RvuauHJdCvwcmJR8LZKOf5UM+hzy4Vqc9Gs9Cv/cB/BHzETxo/IXgB8lHf8y0Io/0iTxMxU/YiuwLSn9z9yb6Omk8//IPXby+We5YwvxRx7UuA/GPh3dI3wtLnHPaXNSWf8h+Xq4fPuABqDFXbPEiKZ/dvc99/ur4/l6pDznM4BX3bV5012Xa4GvJuW5Czie9Lw2uvTvAZcm5avB71tI5Pth0rEv4ffB7GAMjULJ8fmne129D1iRyXvNHVvh/u5udz1lpJ77INflUvzgkHotprv3QBNwwt1OfEFK/cy5IR+uxcn+GfUZ0iLyZaBGVR8c4vl/BMxW1W8PmnkcsOvRPxG5C7hLe5rG+sv3CnCBqkZHpmQjI9PnP8D5Ob22xhK7FiffqAcHY4wxY89Y6ZA2xhgzhlhwMMYY04cFB2OMMX1YcDDGGNOHBQdjjDF9WHAwxhjThwUHY4wxffx/12dh7Mu/QhYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"China\"].diff().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3.2: Finding maxmimum infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15136.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"China\"].diff().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6557.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"Italy\"].diff().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9630.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"Spain\"].diff().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3.3: Finding the maximum infection rate for all countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries=list(corona_dataset_aggregated.index)\n",
    "max_infection_rates=[]\n",
    "for c in countries:\n",
    "    max_infection_rates.append(corona_dataset_aggregated.loc[c].diff().max())\n",
    "corona_dataset_aggregated[\"max_infection_rate\"]=max_infection_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>1/30/20</th>\n",
       "      <th>1/31/20</th>\n",
       "      <th>...</th>\n",
       "      <th>4/22/20</th>\n",
       "      <th>4/23/20</th>\n",
       "      <th>4/24/20</th>\n",
       "      <th>4/25/20</th>\n",
       "      <th>4/26/20</th>\n",
       "      <th>4/27/20</th>\n",
       "      <th>4/28/20</th>\n",
       "      <th>4/29/20</th>\n",
       "      <th>4/30/20</th>\n",
       "      <th>max_infection_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1176</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1463</td>\n",
       "      <td>1531</td>\n",
       "      <td>1703</td>\n",
       "      <td>1828</td>\n",
       "      <td>1939</td>\n",
       "      <td>2171</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>634</td>\n",
       "      <td>663</td>\n",
       "      <td>678</td>\n",
       "      <td>712</td>\n",
       "      <td>726</td>\n",
       "      <td>736</td>\n",
       "      <td>750</td>\n",
       "      <td>766</td>\n",
       "      <td>773</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>745</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan           0        0        0        0        0        0        0   \n",
       "Albania               0        0        0        0        0        0        0   \n",
       "Algeria               0        0        0        0        0        0        0   \n",
       "Andorra               0        0        0        0        0        0        0   \n",
       "Angola                0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  4/22/20  4/23/20  4/24/20  \\\n",
       "Country/Region                             ...                              \n",
       "Afghanistan           0        0        0  ...     1176     1279     1351   \n",
       "Albania               0        0        0  ...      634      663      678   \n",
       "Algeria               0        0        0  ...     2910     3007     3127   \n",
       "Andorra               0        0        0  ...      723      723      731   \n",
       "Angola                0        0        0  ...       25       25       25   \n",
       "\n",
       "                4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \\\n",
       "Country/Region                                                         \n",
       "Afghanistan        1463     1531     1703     1828     1939     2171   \n",
       "Albania             712      726      736      750      766      773   \n",
       "Algeria            3256     3382     3517     3649     3848     4006   \n",
       "Andorra             738      738      743      743      743      745   \n",
       "Angola               25       26       27       27       27       27   \n",
       "\n",
       "                max_infection_rate  \n",
       "Country/Region                      \n",
       "Afghanistan                  232.0  \n",
       "Albania                       34.0  \n",
       "Algeria                      199.0  \n",
       "Andorra                       43.0  \n",
       "Angola                         5.0  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3.4: Creating new dataframe with only needed column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_data=pd.DataFrame(corona_dataset_aggregated[\"max_infection_rate\"])"
   ]
  },
  {
   "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>max_infection_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                max_infection_rate\n",
       "Country/Region                    \n",
       "Afghanistan                  232.0\n",
       "Albania                       34.0\n",
       "Algeria                      199.0\n",
       "Andorra                       43.0\n",
       "Angola                         5.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.1 : Importing the dataset\n",
    "importing \"WorldHappinessReport.csv\" from \"./Dataset\" folder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv=pd.read_csv(\"Dataset/worldwide_happiness_report.csv\")"
   ]
  },
  {
   "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>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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.2: Dropping the useless columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "useless_cols=[\"Overall rank\",\"Score\",\"Generosity\",\"Perceptions of corruption\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country or region</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norway</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country or region  GDP per capita  Social support  Healthy life expectancy  \\\n",
       "0           Finland           1.340           1.587                    0.986   \n",
       "1           Denmark           1.383           1.573                    0.996   \n",
       "2            Norway           1.488           1.582                    1.028   \n",
       "3           Iceland           1.380           1.624                    1.026   \n",
       "4       Netherlands           1.396           1.522                    0.999   \n",
       "\n",
       "   Freedom to make life choices  \n",
       "0                         0.596  \n",
       "1                         0.592  \n",
       "2                         0.603  \n",
       "3                         0.591  \n",
       "4                         0.557  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.drop(useless_cols,axis=1,inplace=True)\n",
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.3: Changing the indices of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv.set_index(\"Country or region\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task4.4: Joining the two dataset that we have prepared  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### COVID-19 Dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                max_infection_rate\n",
       "Country/Region                    \n",
       "Afghanistan                  232.0\n",
       "Albania                       34.0\n",
       "Algeria                      199.0\n",
       "Andorra                       43.0\n",
       "Angola                         5.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wolrd Happiness Report Dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country or region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   GDP per capita  Social support  Healthy life expectancy  \\\n",
       "Country or region                                                            \n",
       "Finland                     1.340           1.587                    0.986   \n",
       "Denmark                     1.383           1.573                    0.996   \n",
       "Norway                      1.488           1.582                    1.028   \n",
       "Iceland                     1.380           1.624                    1.026   \n",
       "Netherlands                 1.396           1.522                    0.999   \n",
       "\n",
       "                   Freedom to make life choices  \n",
       "Country or region                                \n",
       "Finland                                   0.596  \n",
       "Denmark                                   0.592  \n",
       "Norway                                    0.603  \n",
       "Iceland                                   0.591  \n",
       "Netherlands                               0.557  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156, 4)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rate</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>232.0</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0</td>\n",
       "      <td>1.002</td>\n",
       "      <td>1.160</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>291.0</td>\n",
       "      <td>1.092</td>\n",
       "      <td>1.432</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>134.0</td>\n",
       "      <td>0.850</td>\n",
       "      <td>1.055</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             max_infection_rate  GDP per capita  Social support  \\\n",
       "Afghanistan               232.0           0.350           0.517   \n",
       "Albania                    34.0           0.947           0.848   \n",
       "Algeria                   199.0           1.002           1.160   \n",
       "Argentina                 291.0           1.092           1.432   \n",
       "Armenia                   134.0           0.850           1.055   \n",
       "\n",
       "             Healthy life expectancy  Freedom to make life choices  \n",
       "Afghanistan                    0.361                         0.000  \n",
       "Albania                        0.874                         0.383  \n",
       "Algeria                        0.785                         0.086  \n",
       "Argentina                      0.881                         0.471  \n",
       "Armenia                        0.815                         0.283  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=corona_data.join(happiness_report_csv,how=\"inner\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.5: Correlation matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rate</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max_infection_rate</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250118</td>\n",
       "      <td>0.191958</td>\n",
       "      <td>0.289263</td>\n",
       "      <td>0.078196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP per capita</th>\n",
       "      <td>0.250118</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.759468</td>\n",
       "      <td>0.863062</td>\n",
       "      <td>0.394603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social support</th>\n",
       "      <td>0.191958</td>\n",
       "      <td>0.759468</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.765286</td>\n",
       "      <td>0.456246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <td>0.289263</td>\n",
       "      <td>0.863062</td>\n",
       "      <td>0.765286</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.427892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <td>0.078196</td>\n",
       "      <td>0.394603</td>\n",
       "      <td>0.456246</td>\n",
       "      <td>0.427892</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              max_infection_rate  GDP per capita  \\\n",
       "max_infection_rate                      1.000000        0.250118   \n",
       "GDP per capita                          0.250118        1.000000   \n",
       "Social support                          0.191958        0.759468   \n",
       "Healthy life expectancy                 0.289263        0.863062   \n",
       "Freedom to make life choices            0.078196        0.394603   \n",
       "\n",
       "                              Social support  Healthy life expectancy  \\\n",
       "max_infection_rate                  0.191958                 0.289263   \n",
       "GDP per capita                      0.759468                 0.863062   \n",
       "Social support                      1.000000                 0.765286   \n",
       "Healthy life expectancy             0.765286                 1.000000   \n",
       "Freedom to make life choices        0.456246                 0.427892   \n",
       "\n",
       "                              Freedom to make life choices  \n",
       "max_infection_rate                                0.078196  \n",
       "GDP per capita                                    0.394603  \n",
       "Social support                                    0.456246  \n",
       "Healthy life expectancy                           0.427892  \n",
       "Freedom to make life choices                      1.000000  "
      ]
     },
     "execution_count": 31,
     "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rate</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>232.0</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0</td>\n",
       "      <td>1.002</td>\n",
       "      <td>1.160</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>291.0</td>\n",
       "      <td>1.092</td>\n",
       "      <td>1.432</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>134.0</td>\n",
       "      <td>0.850</td>\n",
       "      <td>1.055</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             max_infection_rate  GDP per capita  Social support  \\\n",
       "Afghanistan               232.0           0.350           0.517   \n",
       "Albania                    34.0           0.947           0.848   \n",
       "Algeria                   199.0           1.002           1.160   \n",
       "Argentina                 291.0           1.092           1.432   \n",
       "Armenia                   134.0           0.850           1.055   \n",
       "\n",
       "             Healthy life expectancy  Freedom to make life choices  \n",
       "Afghanistan                    0.361                         0.000  \n",
       "Albania                        0.874                         0.383  \n",
       "Algeria                        0.785                         0.086  \n",
       "Argentina                      0.881                         0.471  \n",
       "Armenia                        0.815                         0.283  "
      ]
     },
     "execution_count": 32,
     "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26824847c18>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=data[\"GDP per capita\"]\n",
    "y=data[\"max_infection_rate\"]\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268247f9b70>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.2: Plotting Social support vs maximum Infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268248d91d0>"
      ]
     },
     "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[\"Social support\"]\n",
    "y=data[\"max_infection_rate\"]\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2682497a470>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.3: Plotting Healthy life expectancy vs maximum Infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268249d29e8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=data[\"Healthy life expectancy\"]\n",
    "y=data[\"max_infection_rate\"]\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26824a3f5c0>"
      ]
     },
     "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": [
    "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26824a9b0f0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=data[\"Freedom to make life choices\"]\n",
    "y=data[\"max_infection_rate\"]\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2682480d470>"
      ]
     },
     "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))"
   ]
  }
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