05_02_Projet2_Analyse_de_levolution_du_covid19.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans ce projet, nous allons analyser l'évolution du Covid 19 dans le monde\n",
    "Source des données: https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici quelques questions qui peuvent guider notre analyse\n",
    "* Quels sont les pays avec les plus grand nombres de cas ?\n",
    "* Quels sont les pays avec un taux de mortalité élevé ?\n",
    "* Le confinement a-t-il eu un effet sur le nombre de cas ?\n",
    "* Comparer la situation par continent "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importer les données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install xlrd==1.2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateRep</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>countriesAndTerritories</th>\n",
       "      <th>geoId</th>\n",
       "      <th>countryterritoryCode</th>\n",
       "      <th>popData2019</th>\n",
       "      <th>continentExp</th>\n",
       "      <th>Cumulative_number_for_14_days_of_COVID-19_cases_per_100000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>746</td>\n",
       "      <td>6</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>9.013779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-13</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>298</td>\n",
       "      <td>9</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7.052776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>113</td>\n",
       "      <td>11</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.868768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-12-11</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>63</td>\n",
       "      <td>10</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7.134266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-12-10</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>202</td>\n",
       "      <td>16</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.968658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     dateRep  day  month  year  cases  deaths countriesAndTerritories geoId  \\\n",
       "0 2020-12-14   14     12  2020    746       6             Afghanistan    AF   \n",
       "1 2020-12-13   13     12  2020    298       9             Afghanistan    AF   \n",
       "2 2020-12-12   12     12  2020    113      11             Afghanistan    AF   \n",
       "3 2020-12-11   11     12  2020     63      10             Afghanistan    AF   \n",
       "4 2020-12-10   10     12  2020    202      16             Afghanistan    AF   \n",
       "\n",
       "  countryterritoryCode  popData2019 continentExp  \\\n",
       "0                  AFG   38041757.0         Asia   \n",
       "1                  AFG   38041757.0         Asia   \n",
       "2                  AFG   38041757.0         Asia   \n",
       "3                  AFG   38041757.0         Asia   \n",
       "4                  AFG   38041757.0         Asia   \n",
       "\n",
       "   Cumulative_number_for_14_days_of_COVID-19_cases_per_100000  \n",
       "0                                           9.013779           \n",
       "1                                           7.052776           \n",
       "2                                           6.868768           \n",
       "3                                           7.134266           \n",
       "4                                           6.968658           "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_excel('datasets/COVID-19-geographic-disbtribution-worldwide-2020-12-14.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 61900 entries, 0 to 61899\n",
      "Data columns (total 12 columns):\n",
      "dateRep                                                       61900 non-null datetime64[ns]\n",
      "day                                                           61900 non-null int64\n",
      "month                                                         61900 non-null int64\n",
      "year                                                          61900 non-null int64\n",
      "cases                                                         61900 non-null int64\n",
      "deaths                                                        61900 non-null int64\n",
      "countriesAndTerritories                                       61900 non-null object\n",
      "geoId                                                         61625 non-null object\n",
      "countryterritoryCode                                          61777 non-null object\n",
      "popData2019                                                   61777 non-null float64\n",
      "continentExp                                                  61900 non-null object\n",
      "Cumulative_number_for_14_days_of_COVID-19_cases_per_100000    59021 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(2), int64(5), object(4)\n",
      "memory usage: 5.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>popData2019</th>\n",
       "      <th>Cumulative_number_for_14_days_of_COVID-19_cases_per_100000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>61900.000000</td>\n",
       "      <td>61900.000000</td>\n",
       "      <td>61900.000000</td>\n",
       "      <td>61900.000000</td>\n",
       "      <td>61900.000000</td>\n",
       "      <td>6.177700e+04</td>\n",
       "      <td>59021.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.628934</td>\n",
       "      <td>7.067157</td>\n",
       "      <td>2019.998918</td>\n",
       "      <td>1155.147237</td>\n",
       "      <td>26.055460</td>\n",
       "      <td>4.098770e+07</td>\n",
       "      <td>66.320586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.841582</td>\n",
       "      <td>2.954776</td>\n",
       "      <td>0.032882</td>\n",
       "      <td>6779.224479</td>\n",
       "      <td>131.227055</td>\n",
       "      <td>1.531294e+08</td>\n",
       "      <td>162.329240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>-8261.000000</td>\n",
       "      <td>-1918.000000</td>\n",
       "      <td>8.150000e+02</td>\n",
       "      <td>-147.419587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.293120e+06</td>\n",
       "      <td>0.757526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.169456e+06</td>\n",
       "      <td>6.724045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>273.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.851583e+07</td>\n",
       "      <td>52.572719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>234633.000000</td>\n",
       "      <td>4928.000000</td>\n",
       "      <td>1.433784e+09</td>\n",
       "      <td>1900.836210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                day         month          year          cases        deaths  \\\n",
       "count  61900.000000  61900.000000  61900.000000   61900.000000  61900.000000   \n",
       "mean      15.628934      7.067157   2019.998918    1155.147237     26.055460   \n",
       "std        8.841582      2.954776      0.032882    6779.224479    131.227055   \n",
       "min        1.000000      1.000000   2019.000000   -8261.000000  -1918.000000   \n",
       "25%        8.000000      5.000000   2020.000000       0.000000      0.000000   \n",
       "50%       15.000000      7.000000   2020.000000      15.000000      0.000000   \n",
       "75%       23.000000     10.000000   2020.000000     273.000000      4.000000   \n",
       "max       31.000000     12.000000   2020.000000  234633.000000   4928.000000   \n",
       "\n",
       "        popData2019  \\\n",
       "count  6.177700e+04   \n",
       "mean   4.098770e+07   \n",
       "std    1.531294e+08   \n",
       "min    8.150000e+02   \n",
       "25%    1.293120e+06   \n",
       "50%    7.169456e+06   \n",
       "75%    2.851583e+07   \n",
       "max    1.433784e+09   \n",
       "\n",
       "       Cumulative_number_for_14_days_of_COVID-19_cases_per_100000  \n",
       "count                                       59021.000000           \n",
       "mean                                           66.320586           \n",
       "std                                           162.329240           \n",
       "min                                          -147.419587           \n",
       "25%                                             0.757526           \n",
       "50%                                             6.724045           \n",
       "75%                                            52.572719           \n",
       "max                                          1900.836210           "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['dateRep', 'day', 'month', 'year', 'cases', 'deaths',\n",
       "       'countriesAndTerritories', 'geoId', 'countryterritoryCode',\n",
       "       'popData2019', 'continentExp',\n",
       "       'Cumulative_number_for_14_days_of_COVID-19_cases_per_100000'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05492730210016155"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the percentage of null values:\n",
    "# CODE:\n",
    "df.isna().sum().sum()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pays avec les plus grand nombres de cas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['dateRep', 'day', 'month', 'year', 'cases', 'deaths',\n",
       "       'countriesAndTerritories', 'geoId', 'countryterritoryCode',\n",
       "       'popData2019', 'continentExp',\n",
       "       'Cumulative_number_for_14_days_of_COVID-19_cases_per_100000'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>countriesAndTerritories</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United_States_of_America</th>\n",
       "      <td>16256754</td>\n",
       "      <td>299177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>9884100</td>\n",
       "      <td>143355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>6901952</td>\n",
       "      <td>181402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>2653928</td>\n",
       "      <td>46941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2376852</td>\n",
       "      <td>57911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United_Kingdom</th>\n",
       "      <td>1849403</td>\n",
       "      <td>64170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1843712</td>\n",
       "      <td>64520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1730575</td>\n",
       "      <td>47624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>1498063</td>\n",
       "      <td>40764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>1425539</td>\n",
       "      <td>39051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1337078</td>\n",
       "      <td>21975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>1250044</td>\n",
       "      <td>113953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>1135438</td>\n",
       "      <td>22859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>1108269</td>\n",
       "      <td>52196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>993038</td>\n",
       "      <td>16140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peru</th>\n",
       "      <td>984739</td>\n",
       "      <td>36675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>900553</td>\n",
       "      <td>15243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South_Africa</th>\n",
       "      <td>860814</td>\n",
       "      <td>23276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>617820</td>\n",
       "      <td>18819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>612746</td>\n",
       "      <td>10034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>608001</td>\n",
       "      <td>17951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechia</th>\n",
       "      <td>581079</td>\n",
       "      <td>9609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iraq</th>\n",
       "      <td>574634</td>\n",
       "      <td>12579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>571763</td>\n",
       "      <td>15886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>556335</td>\n",
       "      <td>13385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>490485</td>\n",
       "      <td>7047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>460743</td>\n",
       "      <td>13431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>449400</td>\n",
       "      <td>8733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>440787</td>\n",
       "      <td>8832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>399513</td>\n",
       "      <td>6621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bermuda</th>\n",
       "      <td>399</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mauritius</th>\n",
       "      <td>372</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cambodia</th>\n",
       "      <td>359</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isle_of_Man</th>\n",
       "      <td>305</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cayman_Islands</th>\n",
       "      <td>284</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint_Lucia</th>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbados</th>\n",
       "      <td>259</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guernsey</th>\n",
       "      <td>229</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seychelles</th>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bonaire, Saint Eustatius and Saba</th>\n",
       "      <td>169</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua_and_Barbuda</th>\n",
       "      <td>141</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern_Mariana_Islands</th>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint_Vincent_and_the_Grenadines</th>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>British_Virgin_Islands</th>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dominica</th>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brunei_Darussalam</th>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grenada</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fiji</th>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timor_Leste</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laos</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Holy_See</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New_Caledonia</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint_Kitts_and_Nevis</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greenland</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solomon_Islands</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montserrat</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falkland_Islands_(Malvinas)</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anguilla</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marshall_Islands</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanuatu</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      cases  deaths\n",
       "countriesAndTerritories                            \n",
       "United_States_of_America           16256754  299177\n",
       "India                               9884100  143355\n",
       "Brazil                              6901952  181402\n",
       "Russia                              2653928   46941\n",
       "France                              2376852   57911\n",
       "United_Kingdom                      1849403   64170\n",
       "Italy                               1843712   64520\n",
       "Spain                               1730575   47624\n",
       "Argentina                           1498063   40764\n",
       "Colombia                            1425539   39051\n",
       "Germany                             1337078   21975\n",
       "Mexico                              1250044  113953\n",
       "Poland                              1135438   22859\n",
       "Iran                                1108269   52196\n",
       "Turkey                               993038   16140\n",
       "Peru                                 984739   36675\n",
       "Ukraine                              900553   15243\n",
       "South_Africa                         860814   23276\n",
       "Indonesia                            617820   18819\n",
       "Netherlands                          612746   10034\n",
       "Belgium                              608001   17951\n",
       "Czechia                              581079    9609\n",
       "Iraq                                 574634   12579\n",
       "Chile                                571763   15886\n",
       "Romania                              556335   13385\n",
       "Bangladesh                           490485    7047\n",
       "Canada                               460743   13431\n",
       "Philippines                          449400    8733\n",
       "Pakistan                             440787    8832\n",
       "Morocco                              399513    6621\n",
       "...                                     ...     ...\n",
       "Bermuda                                 399       9\n",
       "Mauritius                               372       7\n",
       "Cambodia                                359       0\n",
       "Isle_of_Man                             305      25\n",
       "Cayman_Islands                          284       1\n",
       "Saint_Lucia                             272       4\n",
       "Barbados                                259       7\n",
       "Guernsey                                229      13\n",
       "Seychelles                              180       0\n",
       "Bonaire, Saint Eustatius and Saba       169       3\n",
       "Antigua_and_Barbuda                     141       4\n",
       "Northern_Mariana_Islands                102       0\n",
       "Saint_Vincent_and_the_Grenadines         91       0\n",
       "British_Virgin_Islands                   73       1\n",
       "Dominica                                 73       0\n",
       "Brunei_Darussalam                        64       3\n",
       "Grenada                                  57       0\n",
       "Fiji                                     41       2\n",
       "Timor_Leste                              30       0\n",
       "Laos                                     30       0\n",
       "Holy_See                                 21       0\n",
       "New_Caledonia                            20       0\n",
       "Saint_Kitts_and_Nevis                    16       0\n",
       "Greenland                                 9       0\n",
       "Solomon_Islands                           9       0\n",
       "Montserrat                                8       1\n",
       "Falkland_Islands_(Malvinas)               8       0\n",
       "Anguilla                                  7       0\n",
       "Marshall_Islands                          3       0\n",
       "Vanuatu                                   0       0\n",
       "\n",
       "[211 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_by_country=df.groupby('countriesAndTerritories')['cases', 'deaths'].sum().sort_values('cases',ascending=False)\n",
    "df_by_country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pays avec Taux de mortalité élevé"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_by_country['mortality_rate']=df_by_country['deaths']/df_by_country['cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Countries with highest mortality rates')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JLk9yYVWd293vXpjtHUmO6O7PVNXTkrwgyaPnaZ/t7nsvqz4AAIDri2W2+B2Z5OLuvqS7P5/knCQnLM7Q3X/d3Z+ZB9+S5A5LrAcAAOB6aZnB78Akly0MXz6P254nJ/mzheEbV9WWqnpLVX3HthaoqpPnebZceeWV171iAACAAS2tq+euqKrHJTkiyYMXRh/S3VdU1R2TvLGq/rm737e4XHefleSsJDniiCN6txUMAACwB1lmi98VSQ5aGL7DPO4aquqhSZ6V5Pju/tzW8d19xfz/JUnelOQ+S6wVAABgWMsMfhcmObyqDquqfZKcmOQad+esqvskeUmm0PffC+P3q6obzY/3T/LAJIs3hQEAAGCdltbVs7uvqqpTkrw+yV5Jzu7ui6rq9CRbuvvcJC9Msm+S36+qJPlgdx+f5K5JXlJVX8wUTs9YczdQAAAA1mmp1/h19/lJzl8z7rSFxw/dznL/kOSey6wNAADg+mKpP+AOAADA6gl+AAAAgxP8AAAABif4AQAADE7wAwAAGJzgBwAAMDjBDwAAYHCCHwAAwOAEPwAAgMEJfgAAAIMT/AAAAAYn+AEAAAxO8AMAABic4AcAADA4wQ8AAGBwgh8AAMDgBD8AAIDBCX4AAACDE/wAAAAGJ/gBAAAMTvADAAAYnOAHAAAwOMEPAABgcIIfAADA4AQ/AACAwQl+AAAAgxP8AAAABif4AQAADE7wAwAAGJzgBwAAMDjBDwAAYHCCHwAAwOAEPwAAgMEJfgAAAIMT/AAAAAYn+AEAAAxO8AMAABic4AcAADA4wQ8AAGBwgh8AAMDgBD8AAIDBCX4AAACDE/wAAAAGJ/gBAAAMTvADAAAYnOAHAAAwOMEPAABgcIIfAADA4AQ/AACAwQl+AAAAgxP8AAAABif4AQAADE7wAwAAGJzgBwAAMDjBDwAAYHCCHwAAwOAEPwAAgMEJfgAAAIMT/AAAAAYn+AEAAAxO8AMAABic4AcAADA4wQ8AAGBwgh8AAMDgBD8AAIDBCX4AAACDE/wAAAAGJ/gBAAAMTvADAAAYnOAHAAAwOMEPAABgcIIfAADA4AQ/AACAwQl+AAAAgxP8AAAABif4AQAADE7wAwAAGJzgBwAAMDjBDwAAYHCCHwAAwOCWGvyq6tiqem9VXVxVp25j+jOr6t1V9a6q+quqOmRh2hOq6t/nf09YZp0AAAAjW1rwq6q9kpyZ5BFJ7pbkpKq625rZ3pHkiO7+uiSvS/KCedlbJXlOkvsnOTLJc6pqv2XVCgAAMLJltvgdmeTi7r6kuz+f5JwkJyzO0N1/3d2fmQffkuQO8+Njkryhuz/a3R9L8oYkxy6xVgAAgGEtM/gdmOSyheHL53Hb8+Qkf7Yry1bVyVW1paq2XHnlldexXAAAgDFtipu7VNXjkhyR5IW7slx3n9XdR3T3EQcccMByigMAANjDLTP4XZHkoIXhO8zjrqGqHprkWUmO7+7P7cqyAAAA7Nwyg9+FSQ6vqsOqap8kJyY5d3GGqrpPkpdkCn3/vTDp9UkeXlX7zTd1efg8DgAAgF2097JW3N1XVdUpmQLbXknO7u6Lqur0JFu6+9xMXTv3TfL7VZUkH+zu47v7o1X1s5nCY5Kc3t0fXVatAAAAI1ta8EuS7j4/yflrxp228PihO1j27CRnL686AACA64dNcXMXAAAAlkfwAwAAGJzgBwAAMDjBDwAAYHCCHwAAwOAEPwAAgMEJfgAAAIMT/AAAAAYn+AEAAAxO8AMAABic4AcAADA4wQ8AAGBwgh8AAMDgBD8AAIDBCX4AAACDE/wAAAAGJ/gBAAAMTvADAAAYnOAHAAAwOMEPAABgcIIfAADA4AQ/AACAwQl+AAAAgxP8AAAABif4AQAADE7wAwAAGJzgBwAAMDjBDwAAYHCCHwAAwOAEPwAAgMEJfgAAAIMT/AAAAAYn+AEAAAxO8AMAABic4AcAADA4wQ8AAGBwgh8AAMDgBD8AAIDBCX4AAACDE/wAAAAGJ/gBAAAMTvADAAAYnOAHAAAwOMEPAABgcIIfAADA4AQ/AACAwa07+FXVTZZZCAAAAMux0+BXVQ+oqncnec88fK+q+o2lVwYAAMCGWE+L34uTHJPkI0nS3e9McvQyiwIAAGDjrKurZ3dftmbU1UuoBQAAgCXYex3zXFZVD0jSVXXDJM9I8q/LLQsAAICNsp4Wv6cmeXqSA5NckeTeSX5gmUUBAACwcdbT4vc13f3YxRFV9cAkf7+ckgAAANhI62nx+7V1jgMAAGAT2m6LX1UdleQBSQ6oqmcuTLp5kr2WXRgAAAAbY0ddPfdJsu88z80Wxn8yySOXWRQAAAAbZ7vBr7svSHJBVf1Od39gN9YEAADABlrPzV0+U1UvTHL3JDfeOrK7v2VpVQEAALBh1nNzl99N8p4khyV5XpJLk1y4xJoAAADYQOsJfrfu7pcm+UJ3X9Dd35dEax8AAMAeYj1dPb8w//+hqjouyX8kudXySgIAAGAjrSf4/VxV3SLJj2b6/b6bJ/mRpVYFAADAhtlh8KuqvZIc3t3/L8knknzzbqkKAACADbPDa/y6++okJ+2mWgAAAFiC9XT1/Puq+vUkr0ny6a0ju/sfl1YVAAAAG2Y9we/e8/+nL4zruLMnAADAHmGnwa+7XdcHAACwB1vP7/gBAACwBxP8AAAABif4AQAADG6nwa+q3l5VT6+q/XZHQQAAAGys9bT4PTrJ7ZNcWFXnVNUxVVVLrgsAAIANstPg190Xd/ezktwlye8lOTvJB6rqeVV1q2UXCAAAwHWzrmv8qurrkvxSkhcm+YMkj0ryySRvXF5pAAAAbISd/o5fVb09yceTvDTJqd39uXnSW6vqgcssDgAAgOtup8EvyaO6+5LFEVV1WHe/v7u/a0l1AQAAsEHW09XzdescBwAAwCa03Ra/qvraJHdPcouqWmzZu3mSGy+7MAAAADbGjrp6fk2Sb0tyyyTfvjD+f5J8/zKLAgAAYONsN/h1958k+ZOqOqq737wbawIAAGADbfcav6r6ifnhY6rqV9f+W8/Kq+rYqnpvVV1cVaduY/rRVfWPVXVVVT1yzbSrq+qf5n/n7tKrAgAA4Et21NXzX+f/t1ybFVfVXknOTPKwJJcnubCqzu3udy/M9sEkT0zyY9tYxWe7+97X5rkBAAD4sh119fzT+f+XX8t1H5nk4q0/BVFV5yQ5IcmXgl93XzpP++K1fA4AAAB2Ykd39fzTJL296d19/E7WfWCSyxaGL09y/12o7cZVtSXJVUnO6O4/3kaNJyc5OUkOPvjgXVg1AADA9ceOunr+391WxbYd0t1XVNUdk7yxqv65u9+3OEN3n5XkrCQ54ogjthtSAQAArs921NXzguu47iuSHLQwfId53Lp09xXz/5dU1ZuS3CfJ+3a4EAAAAF9hu3f13KqqDq+q11XVu6vqkq3/1rHuC5McXlWHVdU+SU5Msq67c1bVflV1o/nx/kkemIVrAwEAAFi/nQa/JC9L8puZrrX75iSvSPKqnS3U3VclOSXJ6zPdIfS13X1RVZ1eVccnSVXdr6ouT/KoJC+pqovmxe+aZEtVvTPJX2e6xk/wAwAAuBZ2dI3fVl/V3X9VVdXdH0jy3Kp6e5LTdrZgd5+f5Pw1405beHxhpi6ga5f7hyT3XEdtAAAA7MR6gt/nquoGSf69qk7JdJ3evsstCwAAgI2ynq6ez0hykyQ/lOTrkzwuyfcusygAAAA2znqC36Hd/anuvry7n9Td353Ej+YBAADsIdYT/H5qneMAAADYhLZ7jV9VPSLJtyY5sKp+dWHSzTPd4RMAAIA9wI5u7vIfSbYkOT7J2xfG/0+SH1lmUQAAAGyc7Qa/7n5nVf1LkmO6++W7sSYAAAA20A6v8evuq5McVFX77KZ6AAAA2GDr+R2/9yf5+6o6N8mnt47s7hctrSoAAAA2zHqC3/vmfzdIcrPllgMAAMBG22nw6+7nJUlV7TsPf2rZRQEAALBxdvo7flV1j6p6R5KLklxUVW+vqrsvvzQAAAA2wnp+wP2sJM/s7kO6+5AkP5rkt5ZbFgAAABtlPcHvpt3911sHuvtNSW66tIoAAADYUOu5ucslVfUzSV45Dz8uySXLKwkAAICNtJ4Wv+9LckCSP5z/HTCPAwAAYA+wnrt6fizJD+2GWgAAAFiC7Qa/+Qfbt6u7j9/4cgAAANhoO2rxOyrJZUleneStSWq3VAQAAMCG2lHwu22ShyU5KcljkpyX5NXdfdHuKAwAAICNsd2bu3T31d395939hCTfkOTiJG+qqlN2W3UAAABcZzu8uUtV3SjJcZla/Q5N8qtJ/mj5ZQEAALBRdnRzl1ckuUeS85M8r7v/ZbdVBQAAwIbZUYvf45J8OskzkvxQ1Zfu7VJJurtvvuTaAAAA2ADbDX7dvZ4fdwcAAGCTE+4AAAAGJ/gBAAAMTvADAAAYnOAHAAAwOMEPAABgcIIfAADA4AQ/AACAwQl+AAAAgxP8AAAABif4AQAADE7wAwAAGJzgBwAAMDjBDwAAYHCCHwAAwOD2XnUBbN+hp5634eu89IzjNnydAADA5qbFDwAAYHCCHwAAwOAEPwAAgMEJfgAAAIMT/AAAAAYn+AEAAAxO8AMAABic4AcAADA4wQ8AAGBwgh8AAMDgBD8AAIDBCX4AAACDE/wAAAAGJ/gBAAAMTvADAAAYnOAHAAAwOMEPAABgcIIfAADA4AQ/AACAwQl+AAAAgxP8AAAABif4AQAADE7wAwAAGJzgBwAAMDjBDwAAYHCCHwAAwOAEPwAAgMEJfgAAAIMT/AAAAAYn+AEAAAxO8AMAABic4AcAADA4wQ8AAGBwgh8AAMDgBD8AAIDBCX4AAACDE/wAAAAGJ/gBAAAMTvADAAAYnOAHAAAwuKUGv6o6tqreW1UXV9Wp25h+dFX9Y1VdVVWPXDPtCVX17/O/JyyzTgAAgJEtLfhV1V5JzkzyiCR3S3JSVd1tzWwfTPLEJL+3ZtlbJXlOkvsnOTLJc6pqv2XVCgAAMLJltvgdmeTi7r6kuz+f5JwkJyzO0N2Xdve7knxxzbLHJHlDd3+0uz+W5A1Jjl1irQAAAMNaZvA7MMllC8OXz+M2bNmqOrmqtlTVliuvvPJaFwoAADCyPfrmLt19Vncf0d1HHHDAAasuBwAAYFNaZvC7IslBC8N3mMcte1kAAAAWLDP4XZjk8Ko6rKr2SXJiknPXuezrkzy8qvabb+ry8HkcAAAAu2hpwa+7r0pySqbA9q9JXtvdF1XV6VV1fJJU1f2q6vIkj0rykqq6aF72o0l+NlN4vDDJ6fM4AAAAdtHey1x5d5+f5Pw1405beHxhpm6c21r27CRnL7M+AACA64M9+uYuAAAA7JzgBwAAMDjBDwAAYHCCHwAAwOAEPwAAgMEJfgAAAIMT/AAAAAYn+AEAAAxO8AMAABic4AcAADC4vVddAHu+Q089b8PXeekZx234OgEA4PpKix8AAMDgBD8AAIDBCX4AAACDE/wAAAAGJ/gBAAAMTvADAAAYnOAHAAAwOMEPAABgcIIfAADA4AQ/AACAwQl+AAAAgxP8AAAABif4AQAADE7wAwAAGJzgBwAAMLi9V10A7A6Hnnrehq/z0jOO2/B1AgDAMmjxAwAAGJzgBwAAMDjBDwAAYHCCHwAAwOAEPwAAgMEJfgAAAIMT/AAAAAYn+AEAAAxO8AMAABic4AcAADA4wQ8AAGBwgh8AAMDgBD8AAIDBCX4AAACDE/wAAAAGJ/gBAAAMTvADAAAYnOAHAAAwOMEPAABgcIIfAADA4AQ/AACAwQl+AAAAgxP8AAAABrf3qgsAvuzQU8/b8HVeesZxG75OAAD2LFr8AAAABif4AQAADE7wAwAAGJzgBwAAMDjBDwAAYHCCHwAAwOAEPwAAgMEJfgAAAIMT/AAAAAa396oLAPY8h5563oav89IzjtvwdQIAMNHiBwAAMDjBDwAAYHC6egLD0iUVAGCixQ8AAGBwgh8AAMDgBD8AAIDBCX4AAACDE/wAAAAG566eACvm7qMAwLJp8QMAABicFj8A1mVPaZnc6Dq1ngIwAsEPAHazPSVEAzAOXT0BAAAGJ/gBAAAMTvADAH4sT+sAACAASURBVAAYnOAHAAAwOMEPAABgcIIfAADA4AQ/AACAwS31d/yq6tgkv5JkryS/3d1nrJl+oySvSPL1ST6S5NHdfWlVHZrkX5O8d571Ld391GXWCgBck98bBBjH0oJfVe2V5MwkD0tyeZILq+rc7n73wmxPTvKx7r5zVZ2Y5BeTPHqe9r7uvvey6gMAALi+WGZXzyOTXNzdl3T355Ock+SENfOckOTl8+PXJXlIVdUSawIAALjeWWbwOzDJZQvDl8/jtjlPd1+V5BNJbj1PO6yq3lFVF1TVg7b1BFV1clVtqaotV1555cZWDwAAMIilXuN3HXwoycHd/ZGq+vokf1xVd+/uTy7O1N1nJTkrSY444oheQZ0AwIq5FhFg55YZ/K5IctDC8B3mcdua5/Kq2jvJLZJ8pLs7yeeSpLvfXlXvS3KXJFuWWC8AwNIIqMAqLbOr54VJDq+qw6pqnyQnJjl3zTznJnnC/PiRSd7Y3V1VB8w3h0lV3THJ4UkuWWKtAAAAw1pai193X1VVpyR5faafczi7uy+qqtOTbOnuc5O8NMkrq+riJB/NFA6T5Ogkp1fVF5J8MclTu/ujy6oVAABgZEu9xq+7z09y/ppxpy08/t8kj9rGcn+Q5A+WWRsAAF9Jl1QY02a9uQsAAGyXgAq7ZpnX+AEAALAJCH4AAACDE/wAAAAGJ/gBAAAMTvADAAAYnOAHAAAwOMEPAABgcIIfAADA4AQ/AACAwQl+AAAAg9t71QUAAMCoDj31vA1d36VnHLeh6+P6Q/ADAIDrsY0Op4mAuhnp6gkAADA4wQ8AAGBwgh8AAMDgBD8AAIDBubkLAACw6bkJzXWjxQ8AAGBwgh8AAMDgBD8AAIDBucYPAABgg2zWaxG1+AEAAAxO8AMAABic4AcAADA4wQ8AAGBwgh8AAMDgBD8AAIDBCX4AAACDE/wAAAAGJ/gBAAAMTvADAAAYnOAHAAAwOMEPAABgcIIfAADA4AQ/AACAwQl+AAAAgxP8AAAABif4AQAADE7wAwAAGJzgBwAAMDjBDwAAYHCCHwAAwOAEPwAAgMEJfgAAAIMT/AAAAAYn+AEAAAxO8AMAABic4AcAADA4wQ8AAGBwgh8AAMDgBD8AAIDBCX4AAACDE/wAAAAGJ/gBAAAMTvADAAAYnOAHAAAwOMEPAABgcIIfAADA4AQ/AACAwQl+AAAAgxP8AAAABif4AQAADE7wAwAAGJzgBwAAMDjBDwAAYHCCHwAAwOAEPwAAgMEJfgAAAIMT/AAAAAYn+AEAAAxO8AMAABic4AcAADA4wQ8AAGBwgh8AAMDgBD8AAIDBCX4AAACDE/wAAAAGJ/gBAAAMTvADAAAY3FKDX1UdW1XvraqLq+rUbUy/UVW9Zp7+1qo6dGHaT83j31tVxyyzTgAAgJEtLfhV1V5JzkzyiCR3S3JSVd1tzWxPTvKx7r5zkhcn+cV52bslOTHJ3ZMcm+Q35vUBAACwi5bZ4ndkkou7+5Lu/nySc5KcsGaeE5K8fH78uiQPqaqax5/T3Z/r7vcnuXheHwAAALuouns5K656ZJJju/sp8/Djk9y/u09ZmOdf5nkun4ffl+T+SZ6b5C3d/ap5/EuT/Fl3v27Nc5yc5OR58GuSvHeDX8b+ST68wetcBnVuLHVurD2hzj2hxkSdG02dG0udG2dPqDFR50ZT58a6vtZ5SHcfsK0Je2/gk+x23X1WkrOWtf6q2tLdRyxr/RtFnRtLnRtrT6hzT6gxUedGU+fGUufG2RNqTNS50dS5sdT5lZbZ1fOKJActDN9hHrfNeapq7yS3SPKRdS4LAADAOiwz+F2Y5PCqOqyq9sl0s5Zz18xzbpInzI8fmeSNPfU9PTfJifNdPw9LcniSty2xVgAAgGEtratnd19VVackeX2SvZKc3d0XVdXpSbZ097lJXprklVV1cZKPZgqHmed7bZJ3J7kqydO7++pl1boDS+tGusHUubHUubH2hDr3hBoTdW40dW4sdW6cPaHGRJ0bTZ0bS51rLO3mLgAAAGwOS/0BdwAAAFZP8AMAABic4AewCVRVLf4PALCRrrfBb085yKqqvVZdA7vf4vuyqvZdZS3sNgclSW/yC68Xv5P2pO+nzf5dz/VHVd2mqo5ZdR2wLXvK8THXzvUy+FVVLRxc3XClxexAVd0yyb3nxw+qqkNXWtB2VNUe+z7arF9sW9+fVfW0JM/fjAfY29p2m217brZ6tmf+rP9JVf3YqmvZkaq6UZKjq2q/qvq6JMdu9s9/VX1tVe272QN1snnfr9s6ENxstW6vnk36/nxAkvdX1S0243d7svn2L8u3sM9vkWz+k5BJUlUnVNUDVl3HzlTVfarqHvPjlX4nbcYvxKVaDH1VdXKSV1fVE6vqoJ0sugoHJ3no/NMWZyb5rxXXs03d/cUkqarvqarnVNVJVXXHVde11sLByx2r6q7J9MW26g/h9lTV0Um+K8nPrOjnTHZo4XN016rat6puOG/PTXHAsOaz/t1V9W2b8Q9EVd2guz+e5ClJHl1VT1mYtim25YJ9k9w2yTlJ/iTJv239/G9GVXWzJE9N8nXz8Kb6rC98J92uqm62GQ+01pwovc38u7zZxJ/1U+a/Qy9Kpr9Pm2W/z/v5m7v7j5JcmeTnkzx2s4W/tcdJVfXcqnp0Vd101bWttfAZuntVfXVV7bfqmtZrs3x+tpo/09+a5I+r6sVVdb+qWtrPvl0ba04+3TDJnZK8oKqOXF1V6/LoJL+UfPmYeVU2xZfh7rR4IJjkUUn+X6aD68dsDQObRXe/K8ltknxrkt/r7s8mm+/LIkmq6vuTnJbpj9mjkzy5qo5abVXXNH+pHZfkjUl+uar+bB6/aQ4MtqqqQ5J8X6b9v/+Ky7mGxW1V0291/lmSX07yU1V1k81yQLjwWX9mkmckuW+mGp+60sLWWPgjcLsk70zy0/NJqU13xrW7P5LkE0m+Kck7kvxPsjm/k2afztSr41HJ6v/gLtp6cF1VJyR5ZZKXVdUTqur2q65t0cLn6OlJfjfJz1bVM7ZO2wz7fqHGZyR5ZKY6H1VV583TN8t3/H2S/GdV3by7P5bpt4rvm6nWTRP+FrbnQ5KcnOlY8cFJnjOfTNk05vfgQ5L8RZJfS/KTW1tWNpOFgHqfqrpvVd1ls514nsPTKUlemOl78zFJHrY1/G2mz/r8+AtJfjvT5/2Mqrr/ygpbYxv79aeSXLH1uHiV23LTvOF2p6q6X6aQckZ3vyzJ6UkOS3L8qr8wtvFmeHGSH0yyX1U9df6D0VV1ixWU9yVrzrrcKMn9kzylu38jyY8luTrJN66ovG2qqsOTnJTku7r7mCQ3qqrXJ6s/MFi737v7A0n+b5J/TPLdtYlapBdaeB+R6bq0hyZ5bZJbZjowWGn4q6lF93bz4/snObq7j06yd5L9khxVUxfaTWNu5Xthpj9gv5Lk5DlUbwoLBy3V3ecn+YYkf53ktKq677y/D6iqG6+00FlV3amq7je/V38myZ2r6ltWXVfy5esi5212dJJnJTkx00mzU5OcVFUHrrDEr1BVT8p0Qu+JSQ5N8oSq+oVkteGvqo6uqgfNj/dPcq8k35nkO5K8OclNqupv5jpXFvqr6qCqeuD82flQkl+rqhO7+8wkFyV5YKbv+U0T/qrq+5I8O8ljuvu0JK/K9B367Kq6+UqLyzW+k26R5KhM+/05ST6Z5GlVdc8VlncNVbXXQmvaq5N8c5K/3PodtRnC3/w38xcz9eI4L8lPZPpOeniSR1TV3qs8EbnmmPORVfU7SdLdn8x04uzcTCel7reaCr9U26FVddy8X7+xqh5fVUfN2+6/M33WV3pSd+Vvtt1hG3+UPp3k8kxnhm7W3VuSvCRTd6CH1dyVZXfbegZ4fvyEqnpykiPncPrWJPdM8p1V9YNJfmST1HlAd38uUyvA46rqpt19caZWoEdskj8QN6iqWyf52SSHZAql6e5vSbJXVf3dPLySA4M12/MHqurMqnp1pm36m5nC1aNqagVcuXl73jbJeUnuOO/vv0nyh5kODF5QVV+1ii+2eT9/Z5LP1tQN5N1JnlFVJyY5OslxmQ68vr+mFozN4uZJntfdf535rHWmg5dNUeNCy9RrampF2TvJPyS5LFPr/g9kCqy3WmGZSZI5fJ6U5Heq6keSfG2m9+fN5+krO9NaU2vej88ny5LkgEwn9o7K1PLzc0m+J8n/qao7rabKrzjIukmSz2f6XJ2Q6fqfH8x0refPJys9iNknyaVVdbvu/nCSH01y9yTf3d3fk+l98I01XS6xEvO2vFuST9V0renHM70fH1JV39Xdv5Wp9fxhmcL1Kutc9J5MrXwnzsNvTfL7mU7w/fgqP0fJNXrwnJbk2CSf6O73ZOrFdXmSH66qe62yxqq6TZJ099U19Sh7fpJvS/L+eZYLqupBmyT8/W+mVtNvq6qHdPdnkrwoU6+OYzLt95Wo6XKIrcdI35nkP5Pco6p+M0m6+1OZenLdONOJiRttd2XLd9ckL5lPjH8y0/f6aVV1WqbP0FOq6u4rrC/p7qH/JamFxw/M9EW2b5KvydSiclaSm83T75Xktpug5mcmuSDJ4zMdXD1vHn98pg/ivyS55ybYns9M8ltJbp3poPoFSZ42T/v2JH+aZN/NsO/n4W/IdG3SDyQ5dGH832YK2Kve749L8pZMF/6fMe/nO2W6wc/ZmQ629l71ttz6OMlDMp1EOWke3jvTmcxfSPLVK9yON8l0Eue5SQ6ax/1wkictPP75JLdb9bZcGHdqkjcvDN88U5B+S6Y/uF+xzG6u+Z5JLpz3748n+fNMZ4IPztQ16M1JTlhhfVvfk4dnatW9daaTPD+X6bqKD2c6kL3TirfjPpmCye23vv/mcWcnuc88/NtJXpfkkFVuy22M3z/JH239bGcKAX+c5IBV1jh/Pj6Y5Nvn4fsmeen8Pnh05hbfFW3Lu8zfkzfIFJj/PMnx87THJ3lZph4oSfJ/ktxj1fs8U5fzW8+Pj8p04P+UeXivJEcmuc0q6lxT85FJ3papG/ebk/zuwrR7ZwqEd19hfXtnOhl2l3n4hplCwYOSvGMe95wkX0zygFXt8/m7/R4Ln+unZwrP3zIP33jV35sLNR+d5A0L2/fvMp0c3ydTt9QXZ3Mcwx+f6fjtIfPwLTOd0P25JJ9K8r3z+L1WUt+qN9Bu3BE/kukA/3fmN8vXZToL90uZmt43RUDJFEhfPr+pn5XpDMxrkzx/YZ5bboLtedL8ZXvgPLz3/Ef2ZZm6gL0l/3975x1u2Xj98c+ayswYg9FFZ8YQ0Tujlxm9zei9l+idEGL0Hi2EIEQweu8kEkJ0QpCESKL8dFFn1u+PtbazXXcwk5nznnvn+3me+5y999nn3nX32ft939VhgdLXFFgROIoI/ekLLEKE0+1CeKtKXsMFgR1q+8cAu9b2DwSeymu7YqnJts39uR6R8/Gj3F+e9PbW7oNJC1/XaYgw43OIsONpCCvr50Q45d8ptxCsX8t1CWv6nLl/EXAnscDeMveLKdA1OQcClwO/qB3bBPhr7fnv0/b/KyDnOoSh7CxCkZqFWBD0IHI8LwY2y3O7FJCvW75OnWP6xbXrdz6h7A0m5qclCl3DLrXt3YFL8nVAPtv3E4rM1oQS2L+AjPVnaDfCi78R4TUbAkxGhH5dTnik5y50LecGnge2yv3JgG2IkLQ18lil/K1XQsZ2ZN6fKNr0QCUTkcbxFrBHaflqcs4MXEOk60Ao1ncBl9TOKbamq8nQkwiNPqt2bDfgp7m9DhE5s0oh+dYgDGJH5Hg+Sx7fmVjHrVz6GqY8RjhuRgP71I73yut3OfAKZRX9to6GDYCngY1zv2vORXsRkUi9isla+gtt0heyEHBdbu9Dw2JghPX1eFrA+k8UmVmXsLgNzQVAV2IR+A/ghILX8IfAarX9Q4Cdc7ta9PXIgW4O0mJY+HsfCjxBKCq3EuG80xMK10hiQTNJ2we2SbJ1I5SRO4Bt89jewEltzrsUmLz0tUxZdiOU/b2IcMlN8/iyOSAPKyxfl3yeXyaK4ixCWFwPrMm5EzCgBa5lZYg6nvACbA5MCpxLeFIeBuZvATkHEIrKGcDthAegS753MTC4tIwpy+yE0teXsKLfT3h8utfO2QK4oJB8lSFqecKjNx1h4Dsnx6RZcnz6HekRKnw9lyGMELsQ0QcXEAaU4Tl2PkRBw17KuE6On5XndCNiobVEjgXzUM5rOohQRNepvn9gxnwdTixWV6vdl+eSHt8S92Vu7wzcldu3Ai/U5qZlCINZ8eiDlGdOIrLkD8DytWv8B+A3peVrI+ss+VyflvvrE8bnw4n8zvnbfhfN+M7zHq2iitYH/g94F5gr398dWKTgdWsvMuZ0wggxSe1YtxxDmx550M41nT9/KoNepfyt3+b835LG8yLylvrDzbxhiLCaw4GzicVL9zy+Tt403ZstYzsyr0BY2Xrl/kbAwbm9PbGYmamQbN2J0M2paYTOHQKc0+a8tYBBBa/hD8iQTWLRdyFhbRuSD9+JRGhqf2BhCi1c2ky22wF3E0rqVEToyv7EomUzospjkQGt/l0CixIW1d5EqOwzxOJ6u3x/CQopVDnh16/pscAhub0qER59FC3gPUuZlgKuzu2DiQXi+WTIbB7vXVjGaiJ7iPCW9SRCVY4nvBZLEnk0C5W+nilnFb2xcco8Rx5fFOib2xsThqC+hWRciVi4rJ77k9Ao5lONq1WIXVMX14Ti3Du3NyQW+cvk/nxEkY/za3KWvj8HEMrTFW2Ob5j35Wol5KrJsQwwurZ/G+mpIIwTwwiPytAcv64ANmyyjPUxszthiJydMI5fTyior5OePmqL7QLXsxqP5iMUqf45Jh1IKM3LVecBixf+7itZpybnbmJtcjtpvCciJkaQ4clNkmvS2vgyK7EunpOIMngsj/8c+ITy0VD1e3MDwmC7dO6fRRglqhStpkdvjEHmynt6GGF8rkJ81ydyOjfI/fmI9ejMxWQtfbEm8A0zJRFXPymhYd9Xu/G3IhYBrRA+N4Swrh5ZO7Zi3iw/z8F3rkJyVtb9rkR4xZU0EvxfIQqmzEZ4Jf9KLXeuyXL2JrxQ99KIq56RWBD+KWVfigi9+RXQswXu1b2JcJXbCaV/KOGpupRGyGzTQxdy8uxGWE/roTPTE7Hrd+f+nkTy8vrNljH//sy17QVq20sCv6rtr0kog0W80G0nJsJzMjOxSL2bCFc5lVAAt6ONItvs777N/uLAmbk9I6Gk/Inw9lXhaiXCJqvF1Y8II09vIvTwZWC2fG9VwjgxQ+4vVuJ5qq4RYSwbTS2Mk1D+RhIGqaYbIPNe60N4HifPY73yOl5fO29QPkNnAj1a4L7sk8/KfWSYee29tSm8cE051iDmyK/N7fleXyInafG8N77KBWvSd15ff+wJ3JDb0xH5XVPk/s1EXvxkBa9j13wdQnjIDieiJWbPe3V/IiR5+dLfeU3mtQmj7T00DPgzEwaA09t+H036zhchjGO7Euu4yoizN3BEbm8I/B5YodB1a/uc70Osg/YDHqXhQT+TyNsubYCq5qF5+Lr39C1ibTRfvr8RsGxuT0GBEPmvyV3yj0/gL+SgvGHuILwR8wK3EIrUWfkllVoE1AfdDQlL9JVEjscMtfcWy8mjSD5SG5l/RCgD2xFhP8sTivX1hGftzlLXsybjSTlpHQEMyWML0PCuLEoofQNb4HrOSRgeJiMUgaFEeM0qtXOK5HJSSzgmPJCn1va3J5XBHOCurd+zTZRxakJZ6kcsAl/M778qLjQSGFE7v0g8fZtnfQnC0lpNFvvQ8EzuQiy+i3sliVyK2fLa9s77cvnadT+DCP+bp7Cc1cJ6YWJhszmxIDiVsBI/Q6PgR8ncwzmAi3P7HMKg16v2/iSUD5vslt/7MbnfizDkXVA7ZyAFjCdtnqEtCQ/0cEJ52orwRA4vef2+RfaVgC9qz3w9RK0+zjYtL5pagTDCKHYLjXDZroThcQTRR/YGyoXLTlHbXohQpGYHdiAMEy/kPdmHiJwoUhinHbnnItZFixMKwX+Aw/K9WQhjXwmD7qREhMG7NPJOu+T3/EvCMPU4WTiwxJjJ10PzpwQuze19ch7qScMZcRKFis7kteyf27PS8J4uR8N7ejpRV2Ce2ueKh0m7dyLFr83k0I+wUs1OuNTfJhb909AoTtEKFsElgTtyuyeh+J1Cxge3wvXMiWAaYhHVlwix2IrwSK1cO79U+FR98tyIsAgeRRRKWTXlfykH4teANUtez9r+PIQFq7JmTkXk+DxL5s6V/iEWz5cQlTsvz2ODiMXATUThmaZ7ommEz/QiFqvDcyBenTCgXEVYNe+n0KKldq1Oz+1tCW/zH4nqrNMTFthPCG/PixQqQtFGZiP6N11LLAAHE8aeG2h4hKYlDChHUCj8K6/t86TClDLNn/IeS1R0rUIqS3lPqwXK3DleVmH8F+R4WsyLUpevtr9APjOVh6JXPuNXlpSzJt8ORK7UcoTndN2cj7Yg5vumhkqOhdxDCCW6WijWF7fNDuntT4TxTpn7PwH+SyNFohvRl/UsIuKjVJXRSQiDclXRfAbCeL880du2H2GAepVQ/lol3G8WIopnJKnME4affwJHV/9bk2Wqr+X2JdYZV9BQ8CYj8vmOo2xl5lVSroNoRJNcRFQOvolGitZWZFRHQVmXINbquxPRhJX39MfAT3J7fSKSa8WSsrYrf2kBxtOXUFf6tsjB7MzasWHAGxRa9LcnL5ED8BZpYc3jlcJ6Hi1QkjZlmiRf761kIhZZW+QNv1Hb76CJsg0kPLgb1Y7tTcRYH5yT14K1yWyhErK2uT/717YvJBaB1YC2L7Fo/UELfO9rEhbVaQlL1qMpr9GwuhZR+gjluKqUtQ7hbd6qds4+eV+8R7lQ7kUIA9Pd+R1fkRPvMkSu8QGEIWU+omhOkVDu+v2ZslxKWNAnITxq9xIelX9SU6IJY1CxMYrwSJ5AKPhHAY8RSn/l6S+yECQWqLPndt1jcQnp9cv9XxFeizLlvL/pia5knpOIkjk093sThYamLzHGV7IS4VG/IsKNt8lnvqqSOilh4C1SoO17/g+rEWuQKVpAlrWIXKTKkDOCMOxUikBljCxizM2/3Z3wll4O7F87fgiNqI4dCKVgcOHr2daoux0RIrsmGbWTz9X/5WvTxqba2L5uPj89c945mDDuTU8YnYfXzi2xlludiC7ag0gfupiIeNuVbHGV521NzP9F8uNy3qlyxn9DeE+rNlFdUr6LiIr8T1DQe/qt/0dpAcbzlzKERqGEu4gY+srKujkRYtOnmQ9eTbb2KhQdTVh+p60dmyJv+lbok7MKodytTxRM2K52PfuUnmyJBf5nhAWz8vDtRCilvQhF6nwKlUpuR949icXpGUToyjzAaYRSVSUEF7Vk1WRdGfh5bX9SImTlklIy1WTZgLD6VmF8qxMLlz1q53QnrdoF5FuDsJZvQ4Rynw08XXt/uTz2k9LPOQ2v1GqEwem5HH9mzuMzEArhbcBVeazEwqBalExFWKh7ELk+VxNRHNMQXr59mi1bTcaBxKJkA8I7cSNRfGISwmBxCl8P+ynlTWnPE/0wUa23L2HYuQU4tuC1bG++PIpQBOrW/yMoXMxjLP6nobRIHhqxVnqJhvJ3COGpKhpy3EbGHkQl5quBg/LYXsTCekfCG13lUBXNiSYMepvTMC5vQxjRhtLIlyzS5ijnxydo1D7okuPlfkS+9ssl70sipHM0jfn8B8Q6aSiRF3kYofxdRMEUrZRtZULZq3KMLyAUwKq9VW/CkPszCnpPv/P/KC3AePxCtibcqrPm/q5EjG1d+SsSWsPXraubEe7gDXP/RCKZdpr2zi8lZ+4vlgPY7kQ7iTdo9BXcuZScNfmmzO/9OmKxOiwng4+IQi79c0IrmouUsm5PhFH1IxbX19CoKrs1sQBrBTmrym4DCW/PrLX3jibyLJqeh9bOvbkeURmraoa8Rl7TfQtfv8FEWNfitWMDCQvw6bVjKxG5aKUKztRzzOYnPHpL5z35s3zGZ2rzmUtLjaH596uCCVfRpqcYkYP8GLm4KSDbrMSipKpyW3lzryY8KjcTyvOw2mdKKNDf5Yk+kFCs58rnqUSfvnoO2sJ5f3bNOedvNOb4DfJ+KOYtH8f/ryWs/zRCUCcnvKrHAJdRoHhP22vD13Mhl817cTdCGTyIqOTZKr0P1yIMkT8hjBJ75fXcMuVei5jnm+JwIAr07FfbPzLH9VmIdJjr89mZgqgmv2QLXMOhhNGs8qb9GtgxtyfLsXTJtnNSIVnXSFmrqqKHEWvQGYk15yYU9J5+r/+htAD/w8VvuxBcFhhFI0SlR04UF9IICyutqOyTE+5WOVBUTa9PI5SB4n1IcnswEUtfD1UaQnis5qBg6f525J6OUJpuJMI6f0QoKItU90ELXM+eOfjOSCj9dxCW6gdyUig20baRuScR5387sXDdgzBKbEuELtxEgfzTNtdy8tqAuxah6FeVvtYhPAJFiuKkDPsAP87tyivRhfCynE+tFyflLMADCK9zVe1yKeCimqxz5Xd9CY3w7jWIRWIpL+qchMK3MuEt/zONQiSLE0aKdQt+79vQ8KJZjkVrE1bhuYniPQ8T4UxF8sv5fp7oc3KsmowyVUYXpdE+YA/CKzUy5Z6aMJRemz9/IEOp9DPO13sI4fGtQhKL9d+lsVgeSignp9EogV8pf3u395kScub2LDlWzkAU6XuOKJRyQL6/LU3u15Zj5cCUyYh18E1Efuz+RIj8ZSXnyTHIXc0xZ+UzXzlsWk55ouYxzznzMBre0yIVUcdK/tICjMMFb1uKeDEa+QkLEW7Yquloj5x4WyFssg/wi9zenwilqVcoGkGhVght5NydWOyfRHgBBubxwYSi0hLNxNvIPHXKfRMZBlC7wsITGQAAIABJREFUrqWV/XnzPjQihOHW2nvPEopWKU/0NypdEharI3Pi7UMoU4cTClXRqmlE6O51RNjKJoTSP4RQAioPepHyzjQWLWfSUEisdtwIb/9dwHH1zzRZzrmJcPhta8emz0lseO3YsYTV9bCc2BZq9vhUu3aDiLLoF9We6zlyoq2u9ez1zxS4roNz3FyNWPhdkc/3hUTxni4p88nU2jk0Wb6W9kQT4dnzEwryYfl9V9UmTyQiJibNcXReWjinryP9EPlfj5WeK1OW1XJ8XzTH0teB7fO9FXJeKlLJsSZjNS7Nmq8DU94/E0rXDoQCeERBGXvm81MZo+an0dt0DsIAVbxafDtyr0yEfU6T+8V6R34PWb/ymOf+CiXG9nGSvbQA43CxZ6pt70t40M7Lm7wXYRn6N7BbYTnbNketqnbeSCxee+R729Fki1AbOduWTL6BCEvYNa9tleg9I5EcXKos/kC+JYmbUFgqmVshdKELMFMuVnYmLOj9iXCwdQlv1a0UCl0grKqnEVbBzfh664MpiYX/lTQ8PkWKUNRkWjUnq6r58XnATvnecGLR3acFvvcVCeVu4dp9UD1DexN5IEWKohAK1OOkZyxl253IS1iL8PTulWPooynvmSVkrcm8MhEhcRjRu2tJGkU95iYWWEUXgilLr7x2T+Q4vywRSvXDHDer8f4Ksrpfk+VraU90ju875/YCOY4/RC2fhzBEHFL6u+6MP60wdqYcu+Y9OZTwUG1AeFGqAhpFi+PQUPpWJBSU9XJ/nWoOJXLqzqfJhlK+GQU3H2GEOrkm9/rEGqR1888aoZTFWxt9D1lXJ1KgikTCjLPcpQUYy4s8NVHNa8pcANyexy8kFqnVhLZcDhaTt30YmiRnXenbCdgstzcjQtNWzf2tc+EyS6HruWou8FbJ/f5Ewu/pRDjiV+Vz87VUPtJ0RL+uIdTyP8Zwf+xBhnmW/KHhmViVsFLuSCwONyK8F3+iUJgSkcf3JI0QyVmJdgKH1M4ZTFgwryAMF00tiEQU8agbJTYne/rk/iop84Dcb5WFS2/CY3oCqfzl8U2IBPUi1chShmWA0bX9O4ETc7s7oazcTuT3/Yjw/txGGC1KjKPzEnmFVcuGAwkP1RI0lL8ihqhvkXnKNvuDiQXszESRlxto4oKQjuOJni/nn0GEMWoOQvnbjYbx6VCy1YR+OsdP7T6cj4iMmZQwmNxBo8XEtYQxv3ibq5RnTcLQeD2NSJOFCEXwJKJtVJGCcoShbBca6U1zE8rfKbm/IdleoMRzPhb/xzq5/ujSynKmrEPpAOGdX5O5tABjeYFnzMVT/1yYnJSLgVuAnnnO8vlaJH+mjby7EYpVFYY0E+HhqyrnPU3ZCkV7AJ/mILse4ZW8ifCsVErfpoSSULJ0+wJEef5V87X3mAYDavlypQYMwvBwdO2eXIlYZO+RsvehQNGElGU6Ih9q0dzvXZP5FWD33N+ECENtejgVoeA/Qiggx+axhYics3qo2oXA0iWu43fIPyORw3k/YW09hiifXjwfiUbT87sYQygSoQysQOT+NF1mopDHVESPrtuo5aIQhql7yfLerfpDKNJDcuwcWjs+RsPVBJan5TzRbcdnovDV2US0QX9C8b+HyO08gloVR/10nh9CkXqkPiflWL9UjkMXkCknpX8Ig8TjRA7vEODC2ns/JAz9yzdZpkp5XowofnQkma6Tx+ciKk+eVPr6jeX/1RLG3LH9HjrCT3XDdBjM7DQi5OcuIqSmH7EI+MLMdiFCA9Z39w8KiomZ9SEsQgcQC6j1iFyaJ4j+aJMB77n7vwrK2J+wor5GWPvPJAaOq4hr3JsYTDZ392dKyQlgZvcTi/8t3f3aMZzT1d1HmVl3d/+iibKZV0++WRfC/b8WsXA9zd0/MbMtCKV1F3f/dbNka0fWKQjv+L5EfPpBRCGfNwilcFoixGplogH2c02Wb3UirO9EopLsvkSC/JdEOfdJiX5I/yFyD5d399eaKeP3wcwmJSoSrkxYq+919xfLShWY2UqEZ6+Hu4/OY0sSYZ/7EHnSGwMPu/tfmyjXV89R7i9NhPAfVX9mzOwg4G53/1OzZBsbzKw7MW4eReTY3GhmVr3vBSZdM+tN5Jb3IhqyP5bHNyEKTm3s7q82WaZu7v5l9ZrHliDmyk+J+ajqbfsI8DN3f72ZMooJi5nNR6w3NnD358xsauB9opDcgkRk175jmvObJGN9fu9KeB5fNbN1gE3dfZiZrUAYqIrIaWaLEsbaB9z9OjObgSiQ8qC7729mA4h8uSdLyCdai5ZX/MxsOcLt64TLel/gJXcfYWbbEYNDV8Kivi0RVtl0JSWVqIXd/XYzGwq8Qyz6liMaSb8PfAB86O4/abZ8NTnnB3D3p1JJGUFY168iPFLHEmGyixNW1/vc/ZVC4mJmXdx9tJmdQCQoPw6c3XaxX1P6+hGeoJ3d/a0myFefFPYiLP1nEpbK1YC33f2YXGzvSuTZ/HNCy/Vt8hKL+1UJi/pdRCja80QVwueB+4DPm22UMLMpgbeJRcC1ZrYYYTy5DviQ8ECuQITP9SbCV55tpoydBTMbApzh7nOaWVW6/yB3vyXf/5oS1gR5zN3dzFYmxvvniEiE6YgKdPu7+9XNkud/JZW/qdz9P82+lt8i04xExMlKRCTKJ0To10bu/nSTZemfMizk7u+YWQ93/zzfWzzl+ojIleoNfOHu/2imjGLCk+u7PYlWCBsQ89JURFj650ThjH+WfoZSsVuFiIh6xt0/MLMfEEaTS4n800Pd/fomy1WNmzsTnvtfEsbmz8xseiLS6G53/3Ez5RKtTUdQ/AYQ8d+rEArUhkS1yT2IxcG8RN7Ue8At7v58ITknJ8JUZiWaig/J7bmAx3Pw2p4Ia9iomR6pmoxTAW8RlbL2JjwqjxM5fTcQsfWbEaXdRzZbvjq1AW0OYoHysbu/b2aXEErAzyrFpKb0TU5YuY5y9weaLO8uRN+e7d39WTPrRoRXbUrE2U9GKDTFvT7pjf4hUR3venf/LI9fDNxZ2CM5lAiN3JoI5X6IUOSvIUrPb5vn9azkFuNGeldHEl7+/dz91pILrJTnOMJTtgPwT3ff0cxWIarK7uHuvykhW2ehlTzRZrYW4dlf0t3frUdqmNmPiHvgVeBkdx9VQkYxfqnN60sQSt3tZnY7EV5+ORHefwrwO3e/rKSsFWa2FNFi4A/EXH4VkV40mjCSv0kUGrutWeNn7TpO4+5v5rFNCMPOMcDvMwpuemA2d39oQsskOg4tr/i1JT1WQ4kQkGvc/ZHCIn2Fme1EeMyuc/ft8lgV4rMV4a0cXtJLYWZVrscxwBfAPIQi+KS7X2Zm2xCeqh2Ajwpb2dYFDiYUv78Sg+3NhBX4c0LBez3PnYII/T3S3R9sgmwLE3mQf8z9c4Hz3P1xM5vE3T9N5a8n4fV9rpUt1ma2ERH2ubG7v1xYltWJ7/oQdz8uj1Wh08Pd/a3SFuDOQnqi+5YMparJchDxDM9EKATrV5799ASOcvd7C4ooxjNmtgaxqF4klb8e7v55jq8LEXPpBI/cEM0jFf4TgF3bPs9mtgDhPdumFdZ26Xg4j1hr3GtmaxORMY8Sa5G9iCKDdxSQbU3C4/gM8Ad3/62ZbUkYm08hjDpNdzCI1qdLaQG+D5XylIu9p4jFwSfAFha5KUXlqnEDoTTNaGanw1f5HEsRiuqw0qFp7n4P4T3divBQ3k/ko6xuZj2Ia7uDu39YWOmrKoxuTZQgvp3InZuHKJozJVG1tYq7P4aoWtcMpW8NQvn8b+3vz0WEUOHun+apy7j7x+5+a6sqfWY2fYaoHklUby2q9AG4+23Ec7RNhu5CePUnJXJ/iuRJdUbc/e4Mq207ljWN9EJVXEkYz9Zx99fMbKiZbe3ud+XCq5icYvzj7rcSuaWPmtkUqfTtTuT13Sqlr3OR4cYHA2vl87yQmW1lZt3MbCFi/XFQiyh9RqS7TEmkEeHuNxBVRpcjorpOdPc7mj0uZYjsz4hq4TMAe5nZPu5+CREdczARZSTEN+hwHr8KMxtIJIFfUGJyqHsczGwHosrok+7+iwxPPI+o6PYAsYg91N3fbbacY8Iix+d4IszmIzObzd3/VlquCovk5BuANTNPpmos/jd3P7kK78xzuxFN0Cf49U1v1OGEBfCODJ/9nAhH/ilwsbv/2sw2IwrnrOTu/57Qco0rueheEXjB3V8qLU+dVLBPJAwUwwkLcdEiQ2L8YmY/JMKTTiNytc8CHnX3w81sGSLMdzd3v6ugmGICk8/68US16x2ATdz9iaJCifFKGkiNeKa7EvncMxEG3GeJ9JP53f3JEhEdlfKWIZT9gE88cuUWIaK1XvSsz5DRSC9783NjuxDXcAeiwv10hNH7fCIN6k4iRWIGVyEkMQY6rOIHkUBf2pVtZhsT1dJOIfJTznH34zK2+lwiUXmn0p6+9kjl72SiJP47eaxlQujM7KTcPMHd3zSzbYkCL/sR4/OoZsprjeIj63tUzpqDWKgcTnhOVycqd/6eyKHb1JtcFbOzkeEsI4EFW/EZEv8bGaJ9PtFi4jIi73Rnwrs7FdFy4qZyEopmYZHfeyPxrKv6YCciczY3An4B9CVCFH9N5M0tDqzr7nuXk7BBhqLuR9SSeMLdT7QoOLQ78C93P7CATFUF3O6Zu9eVKCR3EXCgR5XRm4iK10e2khFftB7dSgvwv9ACSt9ShCfisExS/jNwWSojI4B1zGxyd3+/pJxjwt1vsag+d1datbxVlL7kCiLM8zqLwiMHE2GoX1YnNFNej+pzawFHm9krhNJ8nbvfl6fcmrkpPVK0N5slW2fF3W8ys37u/t/Ssoj/DTObjMiLfScjNrq7+9NmtiNRYGozwnCyLtEL0b0FKvqJ5uDuN5tZHz3rnZIPiZzNLQjj+PbwVe7uiURVzyKY2WzATO7+YMpzNOE9GwYcnyHIh6Sy9WMzm8ub1ObGzKYDPnX39zLaaHMzu5Pob/k6MDOwpZldSxTnO0xKn/guOrTHr9m0XYCY2QZEjPVLZJVJi9LotxKFPk4sJOpYkZPtR6XlaA+LSp3DCWXq2cxRLIq1KT5Ss8YNJaqP3ldWQiFaiyyScCLRj+02orrxTESfu6fT83cbUWlyv1YLOxZCjD353H/m7n83s9mJcN7nCC9/19z/jbtfXyi8cwDRLH5ERvEMJTx9PyB6MO9D1Bc4O8PPm2bIzxSWI4i6BucSqS7XEn13HyVaN/QhvKhdiQbtRauxi46BFL/vSZucvvkJC9arRJjCcKLq5G/d/Y0MARwty8vY830G/1bwAFiUmD8TWCKtcVsTffqG6XsXooGZDQJ+RSxersnnZW5gLWAWIi/2z2a2BVG4aTNvgSJDQoixp5qf05O2EdHW6rgMR5yNyN3/PVHZ8/882jSVUvpGEsan8/PYJETRw0uJ+hG3mtlZRE7doGaPSxkZsQXRu/Y8d78015/bE2GdlxHr0CkyHab42ki0Ph2iqmcrUFP6dicGhROJHmMvEQPZXMBWFn1VXtbi/9upEqnNbCkz29bMljaz/u0NWhli8RWtMLC5+51EMvqDFj38tiNKUOt7FyLJ8M4zgXPd/UJ3fy/fWhCYhugjeoSZ7QvsBOwrpU+IjksqfasSNQ+eBd4F9rRGAblfAAsQ7Vnerz7TTBnNbB6igvCUwJtm1ivl+JSoHP03YOYs4tIXWKCZ41IWccHd/0KMn68T13BKj8r25xMRE1sTIfNv5vnF10ai9ZHi9x1kCFK1vQjRBmENd98QuI/oLfYQ0RtvSqI3nvgOcnJYi8jpmRoYAWxUKYQV1mjO3s/M1q8GxFbAoxT5wUR+0s4qPiLEN/iECJ26ujqQRZoOBdYBFiEqIC9MhMv/voSQQojxQ3rS9gGOdvebiX53nwDHWfQJXg/Yxwu1OMoibecTVbhXJXrxbWLZVsbdRxO98QYBRxF5/M/nZydo24bq97v7aDMbbGbXuvt/iIJXfwBOTeXvGeAMIkxWObFirGiZRXQrklarOzOsD+A/wPPAxwBZ3ekVomrnDcTCpWVaNrQaaf2vb68LrAw8BfQmwi6w6CdYV/omJ3Lq3spBuWXwqDjYT0qfEF8nFzF9iKIOS9eOTUrkqSxHtMF5ENgyw6rUp0+IDogFfYkwxFmJPDnc/SEaFTzXJXLRHiolJ7Hu3c3dR3q0Yzga2BwYbma9Adz9YqJa+wruPrKmkE1Qj1otsmww0WZpDTMbmevKo4F3gPNS+Xta6w4xLkjx+3YGEP3Z9k+X/9tEs8yhtXOeARzA3T9suoQdhLSm3ZfWfohwio+BU4HDgA3d/Q3CAjeojdI3EjjAm9CcfVyQxU2Ib+LBe0Rvvg3NbKFc2Jzr0T5mduB9oJdnpV6FKgnRsagrRe7+ARHFcwuwrEVTdtz9L+5+GjDco3prMQOPu7+d4ZKVcfluwvu3JTFO9cnzPs9xaoKPS2Y2h0UvSywqg18J3AEsAUxhZrd79Ks+AXiDVKqFGBc6dDuHJnAFsTh5jYilfhfYk2jZsCDRSHMlYNNSAnYU3P0TMzsUOMPMvnT3S8zsYcKq9lN3/5tFw+Yzgc1T6etDlC3+sbv/rqD4QohxZyRRdnxHM/st8EA+66cDh7rangjRIakVclkNGEIUubuJMOjuQbS06uLuj+ZHPoVyBp62xU/cfVS+3mtmTihW3czsomZFF1n0fB4JbJP1DL4ErqqFva9gZi+Y2ZXuPozoJyjEOCOPXxvMbP6smgThVv+ciPU+h1BSJidKkT9BVFUaVsV/i/apJSrfRlTHOtLMhhHl268BdjWzC4mk773d/ZH86OfAtlL6hOi4pKX6DKLQw1lEcayTiByg20rKJoQYd1LpGwocC9xPhCeeB0wGnEb0lluvSvMoofCZ2Q/N7KSavO16Gz3aMB1EtI1qZkrJXIRC7ETNgGmAVbIATcU5wIJmdmYT5RKdFLVzqGFmUwFvERWU9gb+QVSdO52o3DkF0WT4Qne/rpScHYmaRXBloG/Gy69EKHn7AtcB8wLTAW+6+1M5MHf1WqN2IUTHx8ymBUYBPd39dZUfF6LjkjlxxxAGnXmIvnM3Ezm9uwP/BfpkdcoS8vUhPJDnAzcCH7XieGNmjxCVjldN7+P+xPpoB6AXUTX8bCI3eu9W/B9Ex0GKXxvMbEWiQucxRIXOeQhF8El3vyyrUq1GPJAtOYi0CtZobL46YfHfMa1qmNlyhPJ3grtfWFBMIYQQQnwHZjYTMJDwSr3o7o9mQZdpCU/+FsAHRDTPh0QF9I8LyjslUTH8ZUJp2sndXxvDuV0qT199ewLJNYBQ9P7k7i+b2RlEZNkUwOK5btqRKIw1A3A4ES6/D7BmyWsqOj7K8WuDu9+TVTx/STx0GxI5fNNnfsrVwNUq5DJmLJq0vuPRmHVqYtDa2d3vS4VvTmJi2Ab4jZndBvxLSrQQQgjRelij993dwOLAG2b2trvvYNEH72/u/lczWxx4GDijtILi7u+Y2b+Itgw/+xalryom1ys+5p9MKJkyomlHol7ENWb2PHBg1kE4H3jKzOZ19/OzwvloYFngZGD90tdUdHzk8RsDZjYEOB5Y0t0/skbzUfEdZFjnVcDs7v6umR1NFMnpkj9fAq+6+8EWTdvfLiiuEEIIIcaAmQ0CLgTOdPfLM8TzB0Qe3+vuvp2Z3U2kygym0eKqOFktcxCwCpF/eLO7f157/6tewUSRld3d/bkJLNMqwE+IooEnE1Fl/yFaNpwHLAnMn2kykwArAK+4+wsTUi4xcSDF71tI5e9kYOmqrK9yUr4fGd75c2ABIixkTSKs4SEzWxvYFhgGfFlV1hJCCCFE65CKx3VAN3dfuc17A4ATidy+Z4m+nJ+4+7Ol1kq1ugKLEAbnl939sVzPHUCs6W5z9y/aaRt1pDepbZSZXQc85u5Hm9mWRB7iw0S02UlESOfD9f+pGXKJzo8Uv+/AzNYhLDOLkK1qCovUYciB9lQiZv29PLYskQh+iLvfXFI+IYQQQoyZbDGwHOGdesHdj62914NIf7nHo09fS2BmaxIK6R+JCqP/BI4EliFST0YA16eCODnRd/DAZlQQrymmiwJrE9fv18ApwL8JY/mN7v6MFD4xIZDi9z0wsz7u/lFpOToiqfydCSxMlCw+HrjL3W/UoCaEEEK0NmbWncjr24ModFdX/g4HXnL3K0rJVydlPRu42N1/b9FzeSjwgbufkd61F9z94cy324YoVNPUtlFmNg1REGc5YC93Py+P91Yen5iQSPETE5wM+7wIGAB87u6fSukTQgghOgZtlL+n3P1nGU55GbBVFZZYmlTmridSS47OY8OBjdx9g3bO7+nunzVZzOpvL0pUPF/f3f89oauJCgFq4C6agEeT5u2BBd390zwmpU8IIYToALj7F0QO2pnA3GZ2GeFZ279VlD74am1xAjCLmQ3Lwy8CPbNXc9vziyh9yRNEbuSyUvpEs5DHTzQVefqEEEKI1sPMZgfmTmNtu/N1ev6WItI2Rrj79a02r6eCtxrwY6KH36LAfu5+fVHB2iG9ft3d/aHSsoiJAyl+QgghhBATOWa2FDAK+Iu7v5/H2lP+ugJ9s11TSyl9dcxsWqLh/NslK40K0UpI8RNCCCGEEJjZZEQ1zBPd/eI89jWFqW1YYrMVqlplzHmAL9z9pTGd813HhJjYUI6fEEIIIcREShZEAcDdPwQOBvYws03zmFfnZO+70WbWz8z2r95vpqwpz1rAb4C+ZvaNtWye0y0/06XZcgrRqkjxE0IIIYSYCKkpUkua2RZmtjhwI3AQcGBVIKVSpLLheT/gGsIz2Cw5e9TkmAs4BNjW3f/cXlGUVFC/NLMpgJPNrHezZBWilZHiJ4QQQggxEZKK1GpEy6XJiGbmm7n7ncABwLFmtlme+2Uqfb8FjnT3B5shYxZrGWFmvfLQl8CrwAtm1i1zDqvzqlDUUdmc/SqiIbp64wmBFD8hhBBCiIkOC/oBWwBrAY8BrwN3Arj77cCehJKFmU0CXAv8rFlKX/IO0TpiajP7IfA20A8Y4O5fppK3FLCTmU1WhaISSt9R7n5PE2UVoqVRcRchhBBCiIkUMzsAmBNYANjY3f+eIZ7Pu/tTtfNmBCZx95ebJNc0hMK3rbt/YGaHAYOBzYEVgH2BK4BPgb2Avd395mw5cTdwuLvf3wxZhegodCstgBBCCCGEmPDUcvqmBUa7+1vAJ0Rvvp1S6VsQOArYof5Zd3+9yeK+B3wIXGlmGxKN2UcD56Rs/waWAWYFdnH3u/P/+8LMNnL3N5osrxAtjzx+QgghhBATCVkR80TgacKrd4SZnQtMAkwKDACOcPcbCooJfFXU5TTCI7ke8DlReGZ+4BB3/2u9TUMWdRlVTGAhWhwpfkIIIYQQEwFZEfMI4ELgH8ClwIPufrCZDQJmAf7l7k+W7ntnZt3d/YvcPgOYB1gX+Az4CRGauinwXyl7Qnw/pPgJIYQQQnRyMmfuBqJYyzbu/rGZ9QdGAi+6+/aF5ZsKmM7dnzWzIcAQoCvhnXwHOBwYBGxEKH8zNyvfUIjOgqp6CiGEEEJ0YsxsNuBdIk9uRmAZM+vh7m8DGwDzmtkP683cmyxfT2B3YAsz2xg4DridaDGxH7AY0Vj+38D1wCgpfUKMPfL4CSGEEEJ0UtKrdzjwf8CxhKK3I3A8cJ+7f14PqyyFmS0NrEkopq+5+6F5fF9gsLuvnT37Brr7swVFFaLDIo+fEEIIIUQnou65S6/evcDkRAuEq4HzgZ8CK1aVMIsIytdkfQg4C/gYWNrMFgVw95OByc1sMXcfJaVPiHFHip8QQgghRCciWzYsamaH5v51RG+76YEfu/uVwBnAuyULuKRsbmazAvcQjdl/QjSTX9fMVjWzgcAMRHsHIcT/gPr4CSGEEEJ0IszsR0QPvOFm9qm7n+zut5jZnMAOmd93XGEx67wD3AqcAuxJtHDYH/g58AKwr7u/WE48IToH8vgJIYQQQnRwqpBJM1uKqN75GbANsLGZHZCn/QF4niiQUhwzmwfA3T8AzgVuBs4GehD5iDcD+7v7DaUKzwjRmVBxFyGEEEKITkAqUr8ETnT3kXlsQeBaQulbAtjZ3W8vJ2U0Ws/NPwLPuPs2ebwfcBLRtmFL4J/u/mkZKYXofMjjJ4QQQgjRATGzOcxsfTNbNw91AUYTVTsBcPfHgUWB64BhJZW+mteuZzZdXx6YyczOA3D394j8vr8BU0npE2L8Io+fEEIIIUQHw8zmJpqv3w4sCVzg7r80swHAYUT7hr1LF29pi5mtAewKPEdU8rwZuAV4DXiY6Oe3pbs/UUxIITop8vgJIYQQQnQgzGwQcCVwsLvvS+TFTWJm87n7C0R+XG/gnIJifgMzW4Qo3vIb4FVgOLAzMDT35wUOl9InxIRBHj8hhBBCiA6EmS0DPODuXXL/KeB1ou3B4+6+tZnNC+xH5Ps9V07awMxmBu4DrnD3Q82sNzAAOAA4wN1fzZ6CXr2WlFeIzog8fkIIIYQQHQh3/x0w1MxeMbO7gKvdfQ0il29JMzsgG53v2QpKH4C7v0o0j9/JzGZz94/d/c9AT2DWPMfrr0KI8Yv6+AkhhBBCdDDc/VYz24HI8Vs1j31uZicAU+T+h6Xkq3nvFgXmAJ4AjiIasV9vZrsTeYjzAZ+UklOIiQl5/IQQQgghOiDufjewNvAiQDZo3x94qqRcEF47M1uTaC8xCLgcWMvdjwVuA+4AfgoMd/c/qU+fEBMeefyEEEIIIToo7n6LmY02s/8SbRD2cvc7SslT8/T1BzYBVgTmB9YB7snTDgL+DQwjiroIIZqAirsIIYQQQnRwzGwloK+7X1vo7/cHviScfe9nk/ajgakIxW9Td/+bmQ0B/uLur5jZccAiwBDgC+X2CTFhkeInhBBCCNFJKFER08wGAr8C3iL68V3glogSAAAH5klEQVTk7o+Y2Y+BLYED3f0uM1sKuBjY3N0fyc/2d/e3mymvEBMrUvyEEEIIIcQ4kW0jfgGMIIq1rAq85e4/z/d2Jbx+HxON5g9w95tKySvExIwUPyGEEEIIMU6Y2W+B5dx9utxfA9iUaNT+Xub7LQZMB7zu7o+pT58QZZDiJ4QQQgghxhkz+x3wjruvbWY7Erl9fyfaNNwI3Noq/QSFmJhRVU8hhBBCCDHWmFk3d//S3Zcxsz+a2UvAv4ABwEzAD4CdgDtLyimECOTxE0IIIYQQ40Sl/OX2HUB3d1+h9n5Pd/+smIBCiK+Q4ieEEEIIIcaZNsrf74BP3X3l3Fc+nxAtQpfSAgghhBBCiNbGzGYzs53be8/dvzSzbrm9DNDXzBbOfSl9QrQIyvETQgghhBDfxaTA0WbWxd3Pbvtmpfxlzt9iBeQTQnwH8vgJIYQQQogxkgrdc8COwCFmtk9756Xy17250gkhvi9S/IQQQgghxBhJhW4tYDPgOuBYMzuw7Xlm1tXdvzCzKczsLDPr0XRhhRBjRIqfEEIIIYRoFwsmBw4ELnX33YGFgD3qyl8qfaPMrB/wW+Bad/+8jNRCiPZQjp8QQgghhGiXLM7yvpk9DXyQOX7PmdmewNVm9r67n5tK3xTAlcBP3f3BooILIb6BPH5CCCGEEOIrzMzydXYzmz0PvwLsCvTJ/VeBa4C/5LndyTBQKX1CtCbq4yeEEEIIIb6GmQ0BjgPuB5YElgPOJ6p7vgmsAmzj7g9WvfrMbBp3f7OY0EKIb0WKnxBCCCGE+AozGwD8EtiUyOc7BZgri7wMBqYC3qo8e2rSLkTHQIqfEEIIIcRETIZpeip2PYB+wDrAZ8DuwCbu/rKZrQDc7+6jC4orhBhHpPgJIYQQQkykmFlXYDAwmlD4BgE3Ab8CegODUiFcEjgB2Nbd/1pKXiHEuKOqnkIIIYQQEyFm1t/d306P3/7A3MAO7v6UmR1AVOjczswmA7YEDpPSJ0THRVU9hRBCCCEmMsxsEmBvM5se+CPwOfAkMKmZ9XX3O4H1gWmJSp57ufsNVcVPIUTHQ6GeQgghhBATGRni2QuYEtgGGAGsBqwOPOruvzSz3kBfd/93OUmFEOMLefyEEEIIISYy3H2Uu38ITAcsAuzp7jcAvwMWMrOzgceB/gXFFEKMR+TxE0IIIYSYSKj13JsS+MTdPzGzhYCDgMfdfYSZLQ2sCDzm7rcUFVgIMd6Q4ieEEEIIMRFhZmsDRwEvAg+5++lmtjCwL/Cyux9eO1c9+oToJCjUUwghhBCiE2NmU5rZPLk9J7AVcCBwKlG180B3fww4DRhoZnNXn5XSJ0TnQR4/IYQQQohOipn1BA4mevI9AOwKvO7u2+f78wBXACPd/adZ0fODYgILISYY8vgJIYQQQnRS3P0z4E6iXcOcwDvADGa2qJl1d/fngc2BTc1sduDDctIKISYk8vgJIYQQQnQyzKyPu39U218KGEIofosDDpwEPOHuX5pZb3f/uIy0QohmII+fEEIIIUQnwsx6AbeY2VbVMXd/CLgF6AfcAbwEnAAsmO9L6ROik9OttABCCCGEEGL84e7/NbNTgcPM7FN3vzKPP5Q5f0cAWwAfE54/IcREgBQ/IYQQQohOhrtfa2afAceZGe5+pZl1cfd7zWwYMMDdR5SWUwjRPKT4CSGEEEJ0Qtz9FjMzQvnr4e6XmtkSwGDg/MLiCSGajBQ/IYQQQohOirvfbGYfApel0rc0sJ+7/7mwaEKIJqOqnkIIIYQQnRwz+wHQA+jm7i+UlkcI0Xyk+AkhhBBCCCFEJ0ftHIQQQgghhBCikyPFTwghhBBCCCE6OVL8hBBCCCGEEKKTI8VPCCGEEEIIITo5UvyEEEIIIYQQopMjxU8IIcREj5lNZ2a/MbOXzewxM7vFzOYej79/eTNbanz9PiGEEGJskeInhBBiosbMDLgWuM/d53D3hYGDgWnH459ZHmhX8TOzbuPx7wghhBDtIsVPCCHExM4KwBfufm51wN2fBH5nZiea2TNm9rSZDYOvvHc3Veea2VlmtnVu/93MjjKzP+dnBprZrMDOwN5m9oSZLWtmF5vZuWb2MHCCmf3VzKbO39HFzF6q9oUQQojxgayMQgghJnbmAx5r5/j6wALAj4D+wJ/M7IHv8fvedveFzGxXYD93397MzgU+cveTAMxsO2AmYCl3H2Vm7wObAacBKwNPuvtb//N/JoQQQiTy+AkhhBDtswxwhbuPcvc3gPuBRb/H50bm62PArN9y3lXuPiq3fwlsmdvbAheNvbhCCCHEmJHiJ4QQYmLnWWDhsTj/S74+f07S5v3P8nUU3x5Z83G14e6vAW+Y2YrAYsCtYyGPEEII8Z1I8RNCCDGxcw/Q08x2rA6Y2fzAe8AwM+ua+XbLAY8A/wAGmVlPM+sHrPQ9/saHwGTfcc4FwGV83RMohBBCjBek+AkhhJiocXcH1gNWznYOzwIjgMuBp4AnCeXwAHf/T3rnfgs8k6+Pf48/cyOwXlXcZQzn3AD0QWGeQgghJgAW850QQgghSmJmiwCnuvuYFEMhhBBinFFVTyGEEKIwZnYQsAtR2VMIIYQY78jjJ4QQQgghhBCdHOX4CSGEEEIIIUQnR4qfEEIIIYQQQnRypPgJIYQQQgghRCdHip8QQgghhBBCdHKk+AkhhBBCCCFEJ+f/AVMiDmyU/lMfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Sorting the values for the mortality rate in the descending order\n",
    "plt.figure(figsize=(15,10))\n",
    "ax = df_by_country['mortality_rate'].sort_values(ascending=False).head(20).plot.bar()\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
    "ax.set_xlabel(\"Country\")\n",
    "ax.set_ylabel(\"Mortality rate\")\n",
    "ax.set_title(\"Countries with highest mortality rates\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pays avec le plus de morts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of deaths')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sorting the number of deaths in the descending order\n",
    "plt.figure(figsize=(10,6))\n",
    "ax=df_by_country['deaths'].sort_values(ascending=False).head(5).plot(kind='bar')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n",
    "ax.set_title(\"Countries suffering the most fatalities from COVID-19\")\n",
    "ax.set_xlabel(\"Countries\")\n",
    "ax.set_ylabel(\"Number of deaths\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Effet du confinement sur le nombre de cas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_by_month = df.groupby('month')['cases','deaths'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9767</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74717</td>\n",
       "      <td>2702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>714475</td>\n",
       "      <td>35668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2338739</td>\n",
       "      <td>189142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2891247</td>\n",
       "      <td>140118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cases  deaths\n",
       "month                 \n",
       "1         9767     212\n",
       "2        74717    2702\n",
       "3       714475   35668\n",
       "4      2338739  189142\n",
       "5      2891247  140118"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_by_month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Nombre de morts')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(15,10))\n",
    "ax1=fig.add_subplot(1,2,1)\n",
    "ax2=fig.add_subplot(1,2,2)\n",
    "\n",
    "df_by_month['cases'].plot(kind='line',ax=ax1)\n",
    "ax1.set_title(\"Nombre total de cas de covid par mois\")\n",
    "ax1.set_xlabel(\"Mois\")\n",
    "ax1.set_ylabel(\"Nombre de cas\")\n",
    "df_by_month['deaths'].plot(kind='line',ax=ax2)\n",
    "ax2.set_title(\"Nombre total de morts par mois\")\n",
    "ax2.set_xlabel(\"Mois\")\n",
    "ax2.set_ylabel(\"Nombre de morts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quand on regarde de façon globale on a pas l'impression d'une diminution. Essayons de voir le cas de certains pays en particulier, qui ont un confinement strict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_germany = df[df.countriesAndTerritories == 'Germany']\n",
    "df_germany_monthwise = df_germany.groupby('month')['cases','deaths'].sum()\n",
    "df_germany_grouped = df_germany_monthwise.reset_index()\n",
    "\n",
    "\n",
    "df_uk = df[df.countriesAndTerritories == 'United_Kingdom']\n",
    "df_uk_monthwise = df_uk.groupby('month')['cases','deaths'].sum()\n",
    "df_uk_grouped = df_uk_monthwise.reset_index()\n",
    "\n",
    "\n",
    "df_france = df[df.countriesAndTerritories == 'France']\n",
    "df_france_monthwise = df_france.groupby('month')['cases','deaths'].sum()\n",
    "df_france_grouped = df_france_monthwise.reset_index()\n",
    "\n",
    "\n",
    "df_italy = df[df.countriesAndTerritories == 'Italy']\n",
    "df_italy_monthwise = df_italy.groupby('month')['cases','deaths'].sum()\n",
    "df_italy_grouped = df_italy_monthwise.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(20,15))\n",
    "\n",
    "ax1=fig.add_subplot(2,2,1)\n",
    "df_uk_grouped.plot(kind='line',x='month',y='cases',ax=ax1)\n",
    "ax1.set_title(\"Evolution du covid en UK\")\n",
    "\n",
    "ax2=fig.add_subplot(2,2,2)\n",
    "df_france_grouped.plot(kind='line',x='month',y='cases',ax=ax2)\n",
    "ax2.set_title(\"Evolution du covid en France\")\n",
    "\n",
    "ax3=fig.add_subplot(2,2,3)\n",
    "df_italy_grouped.plot(kind='line',x='month',y='cases',ax=ax3)\n",
    "ax3.set_title(\"Evolution du covid en Italy\")\n",
    "\n",
    "ax4=fig.add_subplot(2,2,4)\n",
    "df_germany_grouped.plot(kind='line',x='month',y='cases',ax=ax4)\n",
    "ax4.set_title(\"Evolution du covid en Allemagne\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Situation par continent ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['dateRep', 'day', 'month', 'year', 'cases', 'deaths',\n",
       "       'countriesAndTerritories', 'geoId', 'countryterritoryCode',\n",
       "       'popData2019', 'continentExp',\n",
       "       'Cumulative_number_for_14_days_of_COVID-19_cases_per_100000'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Europe     16657\n",
       "Africa     14004\n",
       "America    13105\n",
       "Asia       12695\n",
       "Oceania     2298\n",
       "Name: continentExp, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['continentExp'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continentExp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>53298</td>\n",
       "      <td>1151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>2361775</td>\n",
       "      <td>56134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>16780431</td>\n",
       "      <td>290097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>21395058</td>\n",
       "      <td>479706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America</th>\n",
       "      <td>30884895</td>\n",
       "      <td>785370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 cases  deaths\n",
       "continentExp                  \n",
       "Oceania          53298    1151\n",
       "Africa         2361775   56134\n",
       "Asia          16780431  290097\n",
       "Europe        21395058  479706\n",
       "America       30884895  785370"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('continentExp')['cases', 'deaths'].sum().sort_values('cases')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continentExp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>53298</td>\n",
       "      <td>1151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>2361775</td>\n",
       "      <td>56134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>16780431</td>\n",
       "      <td>290097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>21395058</td>\n",
       "      <td>479706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America</th>\n",
       "      <td>30884895</td>\n",
       "      <td>785370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 cases  deaths\n",
       "continentExp                  \n",
       "Oceania          53298    1151\n",
       "Africa         2361775   56134\n",
       "Asia          16780431  290097\n",
       "Europe        21395058  479706\n",
       "America       30884895  785370"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('continentExp')['cases', 'deaths'].sum().sort_values('deaths')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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