jour1 chargement des données

parent 1ebfa6cf
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sujet 7 : Autour du SARS-CoV-2 (Covid-19) *(Evaluation par pairs)*\n",
"\n",
"## Résumé de l'énoncé\n",
"\n",
"Le but est ici de reproduire des graphes semblables à ceux du South China Morning Post (SCMP), sur la page [The Coronavirus Pandemic](https://www.scmp.com/coronavirus?src=homepage_covid_widget) 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. Les données sont disponibles à https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv.\n",
"Nous créerons un graphe montrant l’évolution du nombre de cas cumulé au cours du temps pour les pays suivants (14 au total): \n",
"+ la Belgique (Belgium) \n",
"+ la Chine - toutes les provinces sauf Hong-Kong (China), \n",
"+ Hong Kong (China, Hong-Kong), \n",
"+ la France métropolitaine (France), \n",
"+ l’Allemagne (Germany), \n",
"+ l’Iran (Iran), \n",
"+ l’Italie (Italy), \n",
"+ le Japon (Japan), \n",
"+ la Corée du Sud (Korea, South), \n",
"+ la Hollande sans les colonies (Netherlands), \n",
"+ le Portugal (Portugal), \n",
"+ l’Espagne (Spain), \n",
"+ le Royaume-Unis sans les colonies (United Kingdom), \n",
"+ les États-Unis (US).\n",
"\n",
"Les graphes auront la date en abscisse et le nombre cumulé de cas à cette date en ordonnée. Nous aurons deux versions de ce graphe, une avec une échelle linéaire et une avec une échelle logarithmique."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chargement des données"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous importons les librairies necessaires pour l'analyse des données dans un premier temps."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous chargeons dans un second temps les données dans une variable Python (utilisant la structure de données *panda*)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"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)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb744d815f8>"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"raw_data.loc[(raw_data[\"Country/Region\"]==\"France\") & raw_data[\"Province/State\"].isna()].iloc[:,4:].isnull().values.any()\n",
"raw_data.loc[(raw_data[\"Country/Region\"]==\"France\") & raw_data[\"Province/State\"].isna()].iloc[:,4:].transpose().plot()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous notons la date de début et la date de fin. \n",
"Ensuite nous chargeons les données des 14 pays.\n",
"Nous vérifions qu'il n'existe pas d'entrées eronnées dans les données de ces pays."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_fr = raw_data.loc[(raw_data[\"Country/Region\"]==\"France\") & raw_data[\"Province/State\"].isna()]\n",
"data_be = raw_data.loc[(raw_data[\"Country/Region\"]==\"Belgium\") & raw_data[\"Province/State\"].isna()]\n",
"data_de = raw_data.loc[(raw_data[\"Country/Region\"]==\"Germany\") & raw_data[\"Province/State\"].isna()]\n",
"data_ir = raw_data.loc[(raw_data[\"Country/Region\"]==\"Iran\") & raw_data[\"Province/State\"].isna()]\n",
"data_it = raw_data.loc[(raw_data[\"Country/Region\"]==\"Italy\") & raw_data[\"Province/State\"].isna()]\n",
"data_jp = raw_data.loc[(raw_data[\"Country/Region\"]==\"Japan\") & raw_data[\"Province/State\"].isna()]\n",
"data_kr = raw_data.loc[(raw_data[\"Country/Region\"]==\"Korea, South\") & raw_data[\"Province/State\"].isna()]\n",
"data_nl = raw_data.loc[(raw_data[\"Country/Region\"]==\"Netherlands\") & raw_data[\"Province/State\"].isna()]\n",
"data_pt = raw_data.loc[(raw_data[\"Country/Region\"]==\"Portugal\") & raw_data[\"Province/State\"].isna()]\n",
"data_es = raw_data.loc[(raw_data[\"Country/Region\"]==\"Spain\") & raw_data[\"Province/State\"].isna()]\n",
"data_uk = raw_data.loc[(raw_data[\"Country/Region\"]==\"United Kingdom\") & raw_data[\"Province/State\"].isna()]\n",
"data_us = raw_data.loc[(raw_data[\"Country/Region\"]==\"US\") & raw_data[\"Province/State\"].isna()]\n",
"\n",
"data_hg = raw_data.loc[(raw_data[\"Country/Region\"]==\"China\") & (raw_data[\"Province/State\"]==\"Hong Kong\")]\n",
"data_ch = pd.DataFrame([raw_data.loc[(raw_data[\"Country/Region\"]==\"China\") & (raw_data[\"Province/State\"]!=\"Hong Kong\")].sum(axis=0)])\n",
"\n",
"\n",
"\n"
]
}
],
"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": 4
}
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