"* Graphique exemple de [South Chine Morning Post](https://www.scmp.com/coronavirus?src=homepage_covid_widget). Datant du 20 Mai 2020.\n",
"* Graphique exemple de [South Chine Morning Post](https://www.scmp.com/coronavirus?src=homepage_covid_widget). Datant du 20 Mai 2020.\n",
"* Données brutes utilisées dans ce document : [time_series_covid19_confirmed_global.csv](https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv)\n",
"* Données brutes utilisées dans ce document : [time_series_covid19_confirmed_global.csv](https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv)\n",
"\n"
"\n",
"\n",
"On procède à un test afin de savoir si les données sont disponibles en local ou si l'ont doit utiliser l'URL."
"| Province/State | gives data for a specific regions |\n",
"| Country/Region | the country or the region to which the data are corresponding |\n",
"| Lat | latitude |\n",
"| Long | longitude |\n",
"| 1/22/20 | from here it gives the number citizens having the covid19 |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Les données manquantes corresponde aux pays qui ne sont pas représenté à travers différentes provinces et états les composants.\n",
"Cependant, nous ne sommes pas dépendant de ces données, seul les données relatives au pays suivant nous intéresse. \n",
"\n",
"* Belgique \n",
"* Chine - toutes les provinces sauf Hong-Kong (China), Hong Kong (China, Hong-Kong)\n",
"* France métropolitaine\n",
"* Allemagne\n",
"* Iran\n",
"* Italie\n",
"* Japon\n",
"* Corée du Sud\n",
"* Hollande\n",
"* Portugal \n",
"* Espagne\n",
"* Royaume-Unis\n",
"* États-Unis\n",
"\n",
"## Regroupement des données à inclure dans l'étude\n",
"\n",
"Ici nous utilisons la méthode [*loc*](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html) de pandas pour extraire des données brutes les lignes correspondantes aux pays cités ci-dessus.\n",
"\n",
"Afin de ne pas rendre le *code* illisible le processus est divisé en de multiples étapes. (toutes ces étapes peuvent être regroupé en une expression logique."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Province/State</th>\n",
" <th>Country/Region</th>\n",
" <th>Lat</th>\n",
" <th>Long</th>\n",
" <th>1/22/20</th>\n",
" <th>1/23/20</th>\n",
" <th>1/24/20</th>\n",
" <th>1/25/20</th>\n",
" <th>1/26/20</th>\n",
" <th>1/27/20</th>\n",
" <th>...</th>\n",
" <th>5/19/20</th>\n",
" <th>5/20/20</th>\n",
" <th>5/21/20</th>\n",
" <th>5/22/20</th>\n",
" <th>5/23/20</th>\n",
" <th>5/24/20</th>\n",
" <th>5/25/20</th>\n",
" <th>5/26/20</th>\n",
" <th>5/27/20</th>\n",
" <th>5/28/20</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>NaN</td>\n",
" <td>Belgium</td>\n",
" <td>50.8333</td>\n",
" <td>4.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>55791</td>\n",
" <td>55983</td>\n",
" <td>56235</td>\n",
" <td>56511</td>\n",
" <td>56810</td>\n",
" <td>57092</td>\n",
" <td>57342</td>\n",
" <td>57455</td>\n",
" <td>57592</td>\n",
" <td>57849</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 132 columns</p>\n",
"</div>"
],
"text/plain": [
" Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n",