{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Sujet 7 : Autour du SARS-CoV-2 (Covid-19)\n",
    "\n",
    "## Objectifs\n",
    "\n",
    "Le but est ici de reproduire des graphes semblables à ceux du [South China Morning Post (SCMP)](https://www.scmp.com/), sur la page The Coronavirus Pandemic et qui montrent pour différents pays le nombre cumulé (c'est-à-dire le nombre total de cas depuis le début de l'épidémie) de personnes atteintes de la maladie à coronavirus 2019.\n",
    "\n",
    "1. Télécharger les données depuis le site et vérifier leur intégrité;\n",
    "\n",
    "2. Afficher un graphe montrant l’évolution du nombre de cas cumulé au cours du temps pour les pays suivants : la Belgique (Belgium), la Chine - toutes les provinces sauf Hong-Kong (China), Hong Kong (China, Hong-Kong), la France métropolitaine (France), l’Allemagne (Germany), l’Iran (Iran), l’Italie (Italy), le Japon (Japan), la Corée du Sud (Korea, South), la Hollande sans les colonies (Netherlands), le Portugal (Portugal), l’Espagne (Spain), le Royaume-Unis sans les colonies (United Kingdom), les États-Unis (US);\n",
    "\n",
    "3. Afficher un graphe avec la date en abscisse et le nombre cumulé de cas à cette date en ordonnée. Deux versions de ce graphe seront proposées, une avec une échelle linéaire et une avec une échelle logarithmique.\n",
    "\n",
    "## Première partie : récupération et nettoyage des données \n",
    "\n",
    "On déclare les librairies qui seront utilisées ici, puis on importe les données : \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    " %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import isoweek\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <td>6</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
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       "      <td>6.613081</td>\n",
       "      <td>0</td>\n",
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       "      <td>16</td>\n",
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       "      <td>Yemen</td>\n",
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       "      <td>Comoros</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Tajikistan</td>\n",
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       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>76</td>\n",
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      ],
      "text/plain": [
       "    Province/State         Country/Region        Lat       Long  1/22/20  \\\n",
       "0              NaN            Afghanistan  33.000000  65.000000        0   \n",
       "1              NaN                Albania  41.153300  20.168300        0   \n",
       "2              NaN                Algeria  28.033900   1.659600        0   \n",
       "3              NaN                Andorra  42.506300   1.521800        0   \n",
       "4              NaN                 Angola -11.202700  17.873900        0   \n",
       "..             ...                    ...        ...        ...      ...   \n",
       "261            NaN         Western Sahara  24.215500 -12.885800        0   \n",
       "262            NaN  Sao Tome and Principe   0.186360   6.613081        0   \n",
       "263            NaN                  Yemen  15.552727  48.516388        0   \n",
       "264            NaN                Comoros -11.645500  43.333300        0   \n",
       "265            NaN             Tajikistan  38.861034  71.276093        0   \n",
       "\n",
       "     1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  4/23/20  4/24/20  \\\n",
       "0          0        0        0        0        0  ...     1279     1351   \n",
       "1          0        0        0        0        0  ...      663      678   \n",
       "2          0        0        0        0        0  ...     3007     3127   \n",
       "3          0        0        0        0        0  ...      723      731   \n",
       "4          0        0        0        0        0  ...       25       25   \n",
       "..       ...      ...      ...      ...      ...  ...      ...      ...   \n",
       "261        0        0        0        0        0  ...        6        6   \n",
       "262        0        0        0        0        0  ...        4        4   \n",
       "263        0        0        0        0        0  ...        1        1   \n",
       "264        0        0        0        0        0  ...        0        0   \n",
       "265        0        0        0        0        0  ...        0        0   \n",
       "\n",
       "     4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  5/1/20  5/2/20  \n",
       "0       1463     1531     1703     1828     1939     2171    2335    2469  \n",
       "1        712      726      736      750      766      773     782     789  \n",
       "2       3256     3382     3517     3649     3848     4006    4154    4295  \n",
       "3        738      738      743      743      743      745     745     747  \n",
       "4         25       26       27       27       27       27      30      35  \n",
       "..       ...      ...      ...      ...      ...      ...     ...     ...  \n",
       "261        6        6        6        6        6        6       6       6  \n",
       "262        4        4        4        8        8       14      16      16  \n",
       "263        1        1        1        1        6        6       7      10  \n",
       "264        0        0        0        0        0        1       1       3  \n",
       "265        0        0        0        0        0       15      15      76  \n",
       "\n",
       "[266 rows x 106 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "data_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n",
    "raw_data = pd.read_csv(data_url, skiprows=0)\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On regarde maintenant si il y a des données manquantes dans l'une des colonnes, qui ne soit pas la province (certains pays n'en ont pas). Pour ça, on va créer une copie du tableau en supprimant la colonne province puis regarder si il y a des null :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "Empty DataFrame\n",
       "Columns: [Country/Region, Lat, Long, 1/22/20, 1/23/20, 1/24/20, 1/25/20, 1/26/20, 1/27/20, 1/28/20, 1/29/20, 1/30/20, 1/31/20, 2/1/20, 2/2/20, 2/3/20, 2/4/20, 2/5/20, 2/6/20, 2/7/20, 2/8/20, 2/9/20, 2/10/20, 2/11/20, 2/12/20, 2/13/20, 2/14/20, 2/15/20, 2/16/20, 2/17/20, 2/18/20, 2/19/20, 2/20/20, 2/21/20, 2/22/20, 2/23/20, 2/24/20, 2/25/20, 2/26/20, 2/27/20, 2/28/20, 2/29/20, 3/1/20, 3/2/20, 3/3/20, 3/4/20, 3/5/20, 3/6/20, 3/7/20, 3/8/20, 3/9/20, 3/10/20, 3/11/20, 3/12/20, 3/13/20, 3/14/20, 3/15/20, 3/16/20, 3/17/20, 3/18/20, 3/19/20, 3/20/20, 3/21/20, 3/22/20, 3/23/20, 3/24/20, 3/25/20, 3/26/20, 3/27/20, 3/28/20, 3/29/20, 3/30/20, 3/31/20, 4/1/20, 4/2/20, 4/3/20, 4/4/20, 4/5/20, 4/6/20, 4/7/20, 4/8/20, 4/9/20, 4/10/20, 4/11/20, 4/12/20, 4/13/20, 4/14/20, 4/15/20, 4/16/20, 4/17/20, 4/18/20, 4/19/20, 4/20/20, 4/21/20, 4/22/20, 4/23/20, 4/24/20, 4/25/20, 4/26/20, 4/27/20, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 105 columns]"
      ]
     },
     "execution_count": 3,
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    }
   ],
   "source": [
    "check_null_data = raw_data.drop(columns=['Province/State'])\n",
    "check_null_data[check_null_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il n'y a pas de données manquantes. \n",
    "\n",
    "## Seconde partie : formattage du dataset pour l'affichage du graphe par pays\n",
    "\n",
    "Pour formater le dataset, nous allons créer une fonction faisant office de filtre : si le pays est défini dans le tableau (qui représente tous les pays demandés dans l'énoncé) alors on le garde dans le dataset; sinon on le retire. Certains pays devant être affichés sans leurs colonies, le filtre le prendra en compte et retirera les lignes ou le champ *Province/State* est défini.\n",
    "\n",
    "La Chine est un cas particulier : on va récupérer toutes les rows de Chine dans un second dataset et les fusionner (sauf Hong Kong) pour obtenir le nombre de cas en Chine, puis on ira la fusionner avec le premier dataset plus tard.\n",
    "\n",
    "Voici le résultat :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>...</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>5/1/20</th>\n",
       "      <th>5/2/20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1061.0367</td>\n",
       "      <td>3570.2197</td>\n",
       "      <td>548</td>\n",
       "      <td>641</td>\n",
       "      <td>918</td>\n",
       "      <td>1401</td>\n",
       "      <td>2067</td>\n",
       "      <td>2869</td>\n",
       "      <td>5501</td>\n",
       "      <td>6077</td>\n",
       "      <td>...</td>\n",
       "      <td>82849</td>\n",
       "      <td>82864</td>\n",
       "      <td>82872</td>\n",
       "      <td>82875</td>\n",
       "      <td>82881</td>\n",
       "      <td>82903</td>\n",
       "      <td>82907</td>\n",
       "      <td>82919</td>\n",
       "      <td>82920</td>\n",
       "      <td>82920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 104 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Lat       Long  1/22/20  1/23/20  1/24/20  1/25/20  \\\n",
       "Country/Region                                                             \n",
       "China           1061.0367  3570.2197      548      641      918     1401   \n",
       "\n",
       "                1/26/20  1/27/20  1/28/20  1/29/20  ...  4/23/20  4/24/20  \\\n",
       "Country/Region                                      ...                     \n",
       "China              2067     2869     5501     6077  ...    82849    82864   \n",
       "\n",
       "                4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  5/1/20  \\\n",
       "Country/Region                                                                 \n",
       "China             82872    82875    82881    82903    82907    82919   82920   \n",
       "\n",
       "                5/2/20  \n",
       "Country/Region          \n",
       "China            82920  \n",
       "\n",
       "[1 rows x 104 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def filter_countries(row):\n",
    "    countries = ['Belgium', 'France', 'China', 'Germany', 'Iran', 'Italy', 'Japan', 'Korea', 'Netherlands', 'Portugal', 'Spain', 'United Kingdom', 'US']\n",
    "    if row['Country/Region'] in countries:\n",
    "        if str(row['Country/Region']) in ['France','Netherlands','United Kingdom']:\n",
    "            if str(row['Province/State']) == \"nan\":\n",
    "                return True\n",
    "            else:\n",
    "                return False\n",
    "        if str(row['Country/Region']) == \"China\":\n",
    "            if str(row['Province/State']) == \"Hong Kong\":\n",
    "                return True\n",
    "            else:\n",
    "                return False\n",
    "        return True\n",
    "\n",
    "    return False\n",
    "\n",
    "only_china = raw_data.loc[raw_data['Country/Region'] == 'China']\n",
    "only_china = only_china.loc[only_china['Province/State'] != 'Hong Kong']\n",
    "only_china = only_china.groupby(['Country/Region']).sum()\n",
    "only_china"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le calcul suivant va appliquer le filtre sur le dataset, renommer la ligne pour la Chine (qui représente seulement Hong Kong) en Hong Kong (on le considèrera comme une région), fusionner la ligne pour la Chine du second dataset (qui est la somme de toutes les régions) avec le premier dataset puis enfin nettoyer le dataset des colonnes devenues obsolètes et transposer le dataset pour que les rows soient maintenant des dates.\n",
    "\n",
    "Seulement, ces dates sont en format texte : on utilise une fonction lambda pour les convertir en format datetime, puis on les réindexent pour qu'elles soient triées par temps. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country/Region</th>\n",
       "      <th>Belgium</th>\n",
       "      <th>Hong Kong</th>\n",
       "      <th>France</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Iran</th>\n",
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       "      <th>Japan</th>\n",
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       "      <th>Spain</th>\n",
       "      <th>United Kingdom</th>\n",
       "      <th>US</th>\n",
       "      <th>China</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-28</th>\n",
       "      <td>47334</td>\n",
       "      <td>1037</td>\n",
       "      <td>167605</td>\n",
       "      <td>159912</td>\n",
       "      <td>92584</td>\n",
       "      <td>201505</td>\n",
       "      <td>13736</td>\n",
       "      <td>38416</td>\n",
       "      <td>24322</td>\n",
       "      <td>210773</td>\n",
       "      <td>161145</td>\n",
       "      <td>1012582</td>\n",
       "      <td>82903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-29</th>\n",
       "      <td>47859</td>\n",
       "      <td>1037</td>\n",
       "      <td>165093</td>\n",
       "      <td>161539</td>\n",
       "      <td>93657</td>\n",
       "      <td>203591</td>\n",
       "      <td>13895</td>\n",
       "      <td>38802</td>\n",
       "      <td>24505</td>\n",
       "      <td>212917</td>\n",
       "      <td>165221</td>\n",
       "      <td>1039909</td>\n",
       "      <td>82907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-30</th>\n",
       "      <td>48519</td>\n",
       "      <td>1037</td>\n",
       "      <td>165764</td>\n",
       "      <td>163009</td>\n",
       "      <td>94640</td>\n",
       "      <td>205463</td>\n",
       "      <td>14088</td>\n",
       "      <td>39316</td>\n",
       "      <td>25045</td>\n",
       "      <td>213435</td>\n",
       "      <td>171253</td>\n",
       "      <td>1069424</td>\n",
       "      <td>82919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-01</th>\n",
       "      <td>49032</td>\n",
       "      <td>1039</td>\n",
       "      <td>165764</td>\n",
       "      <td>164077</td>\n",
       "      <td>95646</td>\n",
       "      <td>207428</td>\n",
       "      <td>14305</td>\n",
       "      <td>39791</td>\n",
       "      <td>25351</td>\n",
       "      <td>213435</td>\n",
       "      <td>177454</td>\n",
       "      <td>1103461</td>\n",
       "      <td>82920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-02</th>\n",
       "      <td>49517</td>\n",
       "      <td>1039</td>\n",
       "      <td>166976</td>\n",
       "      <td>164967</td>\n",
       "      <td>96448</td>\n",
       "      <td>209328</td>\n",
       "      <td>14571</td>\n",
       "      <td>40236</td>\n",
       "      <td>25190</td>\n",
       "      <td>216582</td>\n",
       "      <td>182260</td>\n",
       "      <td>1132539</td>\n",
       "      <td>82920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country/Region  Belgium  Hong Kong  France  Germany   Iran   Italy  Japan  \\\n",
       "2020-01-22            0          0       0        0      0       0      2   \n",
       "2020-01-23            0          2       0        0      0       0      2   \n",
       "2020-01-24            0          2       2        0      0       0      2   \n",
       "2020-01-25            0          5       3        0      0       0      2   \n",
       "2020-01-26            0          8       3        0      0       0      4   \n",
       "...                 ...        ...     ...      ...    ...     ...    ...   \n",
       "2020-04-28        47334       1037  167605   159912  92584  201505  13736   \n",
       "2020-04-29        47859       1037  165093   161539  93657  203591  13895   \n",
       "2020-04-30        48519       1037  165764   163009  94640  205463  14088   \n",
       "2020-05-01        49032       1039  165764   164077  95646  207428  14305   \n",
       "2020-05-02        49517       1039  166976   164967  96448  209328  14571   \n",
       "\n",
       "Country/Region  Netherlands  Portugal   Spain  United Kingdom       US  China  \n",
       "2020-01-22                0         0       0               0        1    548  \n",
       "2020-01-23                0         0       0               0        1    641  \n",
       "2020-01-24                0         0       0               0        2    918  \n",
       "2020-01-25                0         0       0               0        2   1401  \n",
       "2020-01-26                0         0       0               0        5   2067  \n",
       "...                     ...       ...     ...             ...      ...    ...  \n",
       "2020-04-28            38416     24322  210773          161145  1012582  82903  \n",
       "2020-04-29            38802     24505  212917          165221  1039909  82907  \n",
       "2020-04-30            39316     25045  213435          171253  1069424  82919  \n",
       "2020-05-01            39791     25351  213435          177454  1103461  82920  \n",
       "2020-05-02            40236     25190  216582          182260  1132539  82920  \n",
       "\n",
       "[102 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if len(raw_data.index) > 0:\n",
    "    new_data = raw_data[raw_data.apply(filter_countries, axis=1)]\n",
    "\n",
    "new_data = new_data.set_index('Country/Region')\n",
    "new_data = new_data.rename(index={\"China\": \"Hong Kong\"})\n",
    "new_data = new_data.append(only_china, sort='False')\n",
    "new_data = new_data.drop(columns=['Lat', 'Long','Province/State'])\n",
    "new_data = new_data.T\n",
    "new_data.index = [pd.to_datetime(datetext) for datetext in new_data.index]\n",
    "new_data = new_data.sort_index()\n",
    "new_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Troisième partie : affichage du graphe par pays\n",
    "\n",
    "Nos données sont prêtes : on les affiche en utilisant la fonction plot() fournie par matplotlib :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fec29c81668>"
      ]
     },
     "execution_count": 6,
     "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": [
    "new_data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quatrième partie : formattage des données pour les graphes cumulés sur le temps\n",
    "\n",
    "Pour ces graphes, on a juste à sommer toutes les lignes et se débarasser des données inutiles, puis formatter les champs temps string en datetime :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-22        555\n",
       "2020-01-23        654\n",
       "2020-01-24        941\n",
       "2020-01-25       1434\n",
       "2020-01-26       2118\n",
       "               ...   \n",
       "2020-04-28    3097190\n",
       "2020-04-29    3172287\n",
       "2020-04-30    3256853\n",
       "2020-05-01    3343777\n",
       "2020-05-02    3427343\n",
       "Length: 102, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumul_data = raw_data.copy()\n",
    "cumul_data = cumul_data.sum()\n",
    "cumul_data = cumul_data[3:]\n",
    "cumul_data.index = [pd.to_datetime(datetext) for datetext in cumul_data.index]\n",
    "cumul_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cinquième partie : affichage des graphes montrant le nombre de contaminations cumulées dans le temps\n",
    "\n",
    "Maintenant, on affiche le plot sous format linéaire et logarithmique : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fec23ac7ba8>"
      ]
     },
     "execution_count": 8,
     "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": [
    "cumul_data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fec23a42470>"
      ]
     },
     "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": [
    "cumul_data.plot(logy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si l'on devait faire une petite analyse de ces graphes, on voit que le nombre de contaminations va fortement à la hausse sur le temps ... néanmoins, la courbe n'est pas exponentielle mais droite. Les mesures de confinement qui ont commencées en moyene dans le mois de Mars sont efficaces pour empêcher la propagation exponentielle du virus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}