{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Document Computationnel : Sujet 7 - Autour du SARS-CoV-2 (Covid-19)\n", "- Dernière modification : *30/05/2020*\n", "- Langage utilisé : *Python*\n", "\n", "## Table des matières \n", "\n", "1. [Résumé / *abstract*](#résumé)\n", "2. [Importation des données](#importation-des-données)\n", "3. [Formatage des données](#formatage-des-données)\n", "4. [Traitement des données](#traitement-des-données)\n", "5. Visualisation\n", "6. Conclusion\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Résumé\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Importation des données\n", "\n", "## Sources :\n", "\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", "\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." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", "#import isoweek not needed here\n", "\n", "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\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Local data \n", "localData = \"time_series_covid19_confirmed_global.csv\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Local File Selected\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...5/19/205/20/205/21/205/22/205/23/205/24/205/25/205/26/205/27/205/28/20
0NaNAfghanistan33.00000065.000000000000...765381458676921699981058211173118311245613036
1NaNAlbania41.15330020.168300000000...9499649699819899981004102910501076
2NaNAlgeria28.0339001.659600000000...7377754277287918811383068503869788578997
3NaNAndorra42.5063001.521800000000...761762762762762762763763763763
4NaNAngola-11.20270017.873900000000...52525860616970707174
5NaNAntigua and Barbuda17.060800-61.796400000000...25252525252525252525
6NaNArgentina-38.416100-63.616700000000...88099283993110649113531207612628132281393314702
7NaNArmenia40.06910045.038200000000...5041527156065928630266617113740277748216
8Australian Capital TerritoryAustralia-35.473500149.012400000000...107107107107107107107107107107
9New South WalesAustralia-33.868800151.209300000034...3081308230843086308730903092308930903092
10Northern TerritoryAustralia-12.463400130.845600000000...29292929292929292929
11QueenslandAustralia-28.016700153.400000000000...1058105810581060106110561057105810581058
12South AustraliaAustralia-34.928500138.600700000000...439439439439439439439440440440
13TasmaniaAustralia-41.454500145.970700000000...228228228228228228228228228228
14VictoriaAustralia-37.813600144.963100000011...1573158115931593160316051610161816281634
15Western AustraliaAustralia-31.950500115.860500000000...557557557557560560564570570577
16NaNAustria47.51620014.550100000000...16321163531640416436164861650316539165571659116628
17NaNAzerbaijan40.14310047.576900000000...3518363137493855398241224271440345684759
18NaNBahamas25.034300-77.396300000000...96979797100100100100100101
19NaNBahrain26.02750050.550000000000...75327888817484148802913891719366969210052
20NaNBangladesh23.68500090.356300000000...25121267382851130205320783361035585367513829240321
21NaNBarbados13.193900-59.543200000000...90909090929292929292
22NaNBelarus53.70980027.953400000000...31508324263337134303352443619837144380593895639858
23NaNBelgium50.8333004.000000000000...55791559835623556511568105709257342574555759257849
24NaNBenin9.3077002.315800000000...130130135135135191191208210210
25NaNBhutan27.51420090.433600000000...21212121242427272831
26NaNBolivia-16.290200-63.588700000000...4481491951875579591562636660713677688387
27NaNBosnia and Herzegovina43.91590017.679100000000...2321233823502372239124012406241624352462
28NaNBrazil-14.235000-51.925300000000...271885291579310087330890347398363211374898391222411821438238
29NaNBrunei4.535300114.727700000000...141141141141141141141141141141
..................................................................
236NaNTimor-Leste-8.874217125.727539000000...24242424242424242424
237NaNBelize13.193900-59.543200000000...18181818181818181818
238NaNLaos19.856270102.495496000000...19191919191919191919
239NaNLibya26.33510017.228331000000...686971727575757799105
240NaNWest Bank and Gaza31.95220035.233200000000...391398423423423423423429434446
241NaNGuinea-Bissau11.803700-15.180400000000...1038108911091114111411141178117811951195
242NaNMali17.570692-3.996166000000...901931947969101510301059107711161194
243NaNSaint Kitts and Nevis17.357822-62.782998000000...15151515151515151515
244Northwest TerritoriesCanada64.825500-124.845700000000...5555555555
245YukonCanada64.282300-135.000000000000...11111111111111111111
246NaNKosovo42.60263620.902977000000...98998910031004102510321038103810471048
247NaNBurma21.91620095.956000000000...193199199199201201203206206206
248AnguillaUnited Kingdom18.220600-63.068600000000...3333333333
249British Virgin IslandsUnited Kingdom18.420700-64.640000000000...8888888888
250Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...12121212121212121212
251NaNMS Zaandam0.0000000.000000000000...9999999999
252NaNBotswana-22.32850024.684900000000...25252930303535353535
253NaNBurundi-3.37310029.918900000000...42424242424242424242
254NaNSierra Leone8.460555-11.779889000000...534570585606621707735754782812
255Bonaire, Sint Eustatius and SabaNetherlands12.178400-68.238500000000...6666666666
256NaNMalawi-13.25430834.301525000000...707172828283101101101203
257Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...13131313131313131313
258Saint Pierre and MiquelonFrance46.885200-56.315900000000...1111111111
259NaNSouth Sudan6.87700031.307000000000...290290481563655655806806994994
260NaNWestern Sahara24.215500-12.885800000000...6666699999
261NaNSao Tome and Principe0.1863606.613081000000...251251251251251251299441443458
262NaNYemen15.55272748.516388000000...167184197209212222233249256278
263NaNComoros-11.64550043.333300000000...11343478788787878787
264NaNTajikistan38.86103471.276093000000...1936214023502551273829293100326634243563
265NaNLesotho-29.60998828.233608000000...1112222222
\n", "

266 rows × 132 columns

\n", "
" ], "text/plain": [ " Province/State Country/Region Lat \\\n", "0 NaN Afghanistan 33.000000 \n", "1 NaN Albania 41.153300 \n", "2 NaN Algeria 28.033900 \n", "3 NaN Andorra 42.506300 \n", "4 NaN Angola -11.202700 \n", "5 NaN Antigua and Barbuda 17.060800 \n", "6 NaN Argentina -38.416100 \n", "7 NaN Armenia 40.069100 \n", "8 Australian Capital Territory Australia -35.473500 \n", "9 New South Wales Australia -33.868800 \n", "10 Northern Territory Australia -12.463400 \n", "11 Queensland Australia -28.016700 \n", "12 South Australia Australia -34.928500 \n", "13 Tasmania Australia -41.454500 \n", "14 Victoria Australia -37.813600 \n", "15 Western Australia Australia -31.950500 \n", "16 NaN Austria 47.516200 \n", "17 NaN Azerbaijan 40.143100 \n", "18 NaN Bahamas 25.034300 \n", "19 NaN Bahrain 26.027500 \n", "20 NaN Bangladesh 23.685000 \n", "21 NaN Barbados 13.193900 \n", "22 NaN Belarus 53.709800 \n", "23 NaN Belgium 50.833300 \n", "24 NaN Benin 9.307700 \n", "25 NaN Bhutan 27.514200 \n", "26 NaN Bolivia -16.290200 \n", "27 NaN Bosnia and Herzegovina 43.915900 \n", "28 NaN Brazil -14.235000 \n", "29 NaN Brunei 4.535300 \n", ".. ... ... ... \n", "236 NaN Timor-Leste -8.874217 \n", "237 NaN Belize 13.193900 \n", "238 NaN Laos 19.856270 \n", "239 NaN Libya 26.335100 \n", "240 NaN West Bank and Gaza 31.952200 \n", "241 NaN Guinea-Bissau 11.803700 \n", "242 NaN Mali 17.570692 \n", "243 NaN Saint Kitts and Nevis 17.357822 \n", "244 Northwest Territories Canada 64.825500 \n", "245 Yukon Canada 64.282300 \n", "246 NaN Kosovo 42.602636 \n", "247 NaN Burma 21.916200 \n", "248 Anguilla United Kingdom 18.220600 \n", "249 British Virgin Islands United Kingdom 18.420700 \n", "250 Turks and Caicos Islands United Kingdom 21.694000 \n", "251 NaN MS Zaandam 0.000000 \n", "252 NaN Botswana -22.328500 \n", "253 NaN Burundi -3.373100 \n", "254 NaN Sierra Leone 8.460555 \n", "255 Bonaire, Sint Eustatius and Saba Netherlands 12.178400 \n", "256 NaN Malawi -13.254308 \n", "257 Falkland Islands (Malvinas) United Kingdom -51.796300 \n", "258 Saint Pierre and Miquelon France 46.885200 \n", "259 NaN South Sudan 6.877000 \n", "260 NaN Western Sahara 24.215500 \n", "261 NaN Sao Tome and Principe 0.186360 \n", "262 NaN Yemen 15.552727 \n", "263 NaN Comoros -11.645500 \n", "264 NaN Tajikistan 38.861034 \n", "265 NaN Lesotho -29.609988 \n", "\n", " Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 \\\n", "0 65.000000 0 0 0 0 0 0 \n", "1 20.168300 0 0 0 0 0 0 \n", "2 1.659600 0 0 0 0 0 0 \n", "3 1.521800 0 0 0 0 0 0 \n", "4 17.873900 0 0 0 0 0 0 \n", "5 -61.796400 0 0 0 0 0 0 \n", "6 -63.616700 0 0 0 0 0 0 \n", "7 45.038200 0 0 0 0 0 0 \n", "8 149.012400 0 0 0 0 0 0 \n", "9 151.209300 0 0 0 0 3 4 \n", "10 130.845600 0 0 0 0 0 0 \n", "11 153.400000 0 0 0 0 0 0 \n", "12 138.600700 0 0 0 0 0 0 \n", "13 145.970700 0 0 0 0 0 0 \n", "14 144.963100 0 0 0 0 1 1 \n", "15 115.860500 0 0 0 0 0 0 \n", "16 14.550100 0 0 0 0 0 0 \n", "17 47.576900 0 0 0 0 0 0 \n", "18 -77.396300 0 0 0 0 0 0 \n", "19 50.550000 0 0 0 0 0 0 \n", "20 90.356300 0 0 0 0 0 0 \n", "21 -59.543200 0 0 0 0 0 0 \n", "22 27.953400 0 0 0 0 0 0 \n", "23 4.000000 0 0 0 0 0 0 \n", "24 2.315800 0 0 0 0 0 0 \n", "25 90.433600 0 0 0 0 0 0 \n", "26 -63.588700 0 0 0 0 0 0 \n", "27 17.679100 0 0 0 0 0 0 \n", "28 -51.925300 0 0 0 0 0 0 \n", "29 114.727700 0 0 0 0 0 0 \n", ".. ... ... ... ... ... ... ... \n", "236 125.727539 0 0 0 0 0 0 \n", "237 -59.543200 0 0 0 0 0 0 \n", "238 102.495496 0 0 0 0 0 0 \n", "239 17.228331 0 0 0 0 0 0 \n", "240 35.233200 0 0 0 0 0 0 \n", "241 -15.180400 0 0 0 0 0 0 \n", "242 -3.996166 0 0 0 0 0 0 \n", "243 -62.782998 0 0 0 0 0 0 \n", "244 -124.845700 0 0 0 0 0 0 \n", "245 -135.000000 0 0 0 0 0 0 \n", "246 20.902977 0 0 0 0 0 0 \n", "247 95.956000 0 0 0 0 0 0 \n", "248 -63.068600 0 0 0 0 0 0 \n", "249 -64.640000 0 0 0 0 0 0 \n", "250 -71.797900 0 0 0 0 0 0 \n", "251 0.000000 0 0 0 0 0 0 \n", "252 24.684900 0 0 0 0 0 0 \n", "253 29.918900 0 0 0 0 0 0 \n", "254 -11.779889 0 0 0 0 0 0 \n", "255 -68.238500 0 0 0 0 0 0 \n", "256 34.301525 0 0 0 0 0 0 \n", "257 -59.523600 0 0 0 0 0 0 \n", "258 -56.315900 0 0 0 0 0 0 \n", "259 31.307000 0 0 0 0 0 0 \n", "260 -12.885800 0 0 0 0 0 0 \n", "261 6.613081 0 0 0 0 0 0 \n", "262 48.516388 0 0 0 0 0 0 \n", "263 43.333300 0 0 0 0 0 0 \n", "264 71.276093 0 0 0 0 0 0 \n", "265 28.233608 0 0 0 0 0 0 \n", "\n", " ... 5/19/20 5/20/20 5/21/20 5/22/20 5/23/20 5/24/20 5/25/20 \\\n", "0 ... 7653 8145 8676 9216 9998 10582 11173 \n", "1 ... 949 964 969 981 989 998 1004 \n", "2 ... 7377 7542 7728 7918 8113 8306 8503 \n", "3 ... 761 762 762 762 762 762 763 \n", "4 ... 52 52 58 60 61 69 70 \n", "5 ... 25 25 25 25 25 25 25 \n", "6 ... 8809 9283 9931 10649 11353 12076 12628 \n", "7 ... 5041 5271 5606 5928 6302 6661 7113 \n", "8 ... 107 107 107 107 107 107 107 \n", "9 ... 3081 3082 3084 3086 3087 3090 3092 \n", "10 ... 29 29 29 29 29 29 29 \n", "11 ... 1058 1058 1058 1060 1061 1056 1057 \n", "12 ... 439 439 439 439 439 439 439 \n", "13 ... 228 228 228 228 228 228 228 \n", "14 ... 1573 1581 1593 1593 1603 1605 1610 \n", "15 ... 557 557 557 557 560 560 564 \n", "16 ... 16321 16353 16404 16436 16486 16503 16539 \n", "17 ... 3518 3631 3749 3855 3982 4122 4271 \n", "18 ... 96 97 97 97 100 100 100 \n", "19 ... 7532 7888 8174 8414 8802 9138 9171 \n", "20 ... 25121 26738 28511 30205 32078 33610 35585 \n", "21 ... 90 90 90 90 92 92 92 \n", "22 ... 31508 32426 33371 34303 35244 36198 37144 \n", "23 ... 55791 55983 56235 56511 56810 57092 57342 \n", "24 ... 130 130 135 135 135 191 191 \n", "25 ... 21 21 21 21 24 24 27 \n", "26 ... 4481 4919 5187 5579 5915 6263 6660 \n", "27 ... 2321 2338 2350 2372 2391 2401 2406 \n", "28 ... 271885 291579 310087 330890 347398 363211 374898 \n", "29 ... 141 141 141 141 141 141 141 \n", ".. ... ... ... ... ... ... ... ... \n", "236 ... 24 24 24 24 24 24 24 \n", "237 ... 18 18 18 18 18 18 18 \n", "238 ... 19 19 19 19 19 19 19 \n", "239 ... 68 69 71 72 75 75 75 \n", "240 ... 391 398 423 423 423 423 423 \n", "241 ... 1038 1089 1109 1114 1114 1114 1178 \n", "242 ... 901 931 947 969 1015 1030 1059 \n", "243 ... 15 15 15 15 15 15 15 \n", "244 ... 5 5 5 5 5 5 5 \n", "245 ... 11 11 11 11 11 11 11 \n", "246 ... 989 989 1003 1004 1025 1032 1038 \n", "247 ... 193 199 199 199 201 201 203 \n", "248 ... 3 3 3 3 3 3 3 \n", "249 ... 8 8 8 8 8 8 8 \n", "250 ... 12 12 12 12 12 12 12 \n", "251 ... 9 9 9 9 9 9 9 \n", "252 ... 25 25 29 30 30 35 35 \n", "253 ... 42 42 42 42 42 42 42 \n", "254 ... 534 570 585 606 621 707 735 \n", "255 ... 6 6 6 6 6 6 6 \n", "256 ... 70 71 72 82 82 83 101 \n", "257 ... 13 13 13 13 13 13 13 \n", "258 ... 1 1 1 1 1 1 1 \n", "259 ... 290 290 481 563 655 655 806 \n", "260 ... 6 6 6 6 6 9 9 \n", "261 ... 251 251 251 251 251 251 299 \n", "262 ... 167 184 197 209 212 222 233 \n", "263 ... 11 34 34 78 78 87 87 \n", "264 ... 1936 2140 2350 2551 2738 2929 3100 \n", "265 ... 1 1 1 2 2 2 2 \n", "\n", " 5/26/20 5/27/20 5/28/20 \n", "0 11831 12456 13036 \n", "1 1029 1050 1076 \n", "2 8697 8857 8997 \n", "3 763 763 763 \n", "4 70 71 74 \n", "5 25 25 25 \n", "6 13228 13933 14702 \n", "7 7402 7774 8216 \n", "8 107 107 107 \n", "9 3089 3090 3092 \n", "10 29 29 29 \n", "11 1058 1058 1058 \n", "12 440 440 440 \n", "13 228 228 228 \n", "14 1618 1628 1634 \n", "15 570 570 577 \n", "16 16557 16591 16628 \n", "17 4403 4568 4759 \n", "18 100 100 101 \n", "19 9366 9692 10052 \n", "20 36751 38292 40321 \n", "21 92 92 92 \n", "22 38059 38956 39858 \n", "23 57455 57592 57849 \n", "24 208 210 210 \n", "25 27 28 31 \n", "26 7136 7768 8387 \n", "27 2416 2435 2462 \n", "28 391222 411821 438238 \n", "29 141 141 141 \n", ".. ... ... ... \n", "236 24 24 24 \n", "237 18 18 18 \n", "238 19 19 19 \n", "239 77 99 105 \n", "240 429 434 446 \n", "241 1178 1195 1195 \n", "242 1077 1116 1194 \n", "243 15 15 15 \n", "244 5 5 5 \n", "245 11 11 11 \n", "246 1038 1047 1048 \n", "247 206 206 206 \n", "248 3 3 3 \n", "249 8 8 8 \n", "250 12 12 12 \n", "251 9 9 9 \n", "252 35 35 35 \n", "253 42 42 42 \n", "254 754 782 812 \n", "255 6 6 6 \n", "256 101 101 203 \n", "257 13 13 13 \n", "258 1 1 1 \n", "259 806 994 994 \n", "260 9 9 9 \n", "261 441 443 458 \n", "262 249 256 278 \n", "263 87 87 87 \n", "264 3266 3424 3563 \n", "265 2 2 2 \n", "\n", "[266 rows x 132 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "import urllib.request\n", "\n", "if os.path.exists(localData):\n", " raw_data = pd.read_csv(localData)\n", " print(\"Local File Selected\")\n", "else :\n", " urllib.request.urlretrieve(data_url, data_data)\n", " raw_data = pd.read_csv(data_url)\n", " print(\"Online File Selected\")\n", " \n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données ci-dessus sont les données brutes provenant du fichier CSV de gauche à droite elles correspondent à :\n", "\n", "| Column's Name | Meaning |\n", "| ---------------|:------------------------------------------------------------------------------:|\n", "| ID | unique identity for the row |\n", "| 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),\n", "* 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", "---\n", "\n", "# Formatage des données\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 regroupées)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...5/19/205/20/205/21/205/22/205/23/205/24/205/25/205/26/205/27/205/28/20
23NaNBelgium50.83334.0000000...55791559835623556511568105709257342574555759257849
\n", "

1 rows × 132 columns

\n", "
" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n", "23 NaN Belgium 50.8333 4.0 0 0 0 \n", "\n", " 1/25/20 1/26/20 1/27/20 ... 5/19/20 5/20/20 5/21/20 5/22/20 \\\n", "23 0 0 0 ... 55791 55983 56235 56511 \n", "\n", " 5/23/20 5/24/20 5/25/20 5/26/20 5/27/20 5/28/20 \n", "23 56810 57092 57342 57455 57592 57849 \n", "\n", "[1 rows x 132 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# let's create a new variable to store our new data frame\n", "# starting with Belgium\n", "dataCountries = raw_data.loc[(raw_data['Country/Region'] == 'Belgium')]\n", "\n", "dataCountries" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...5/19/205/20/205/21/205/22/205/23/205/24/205/25/205/26/205/27/205/28/20
23NaNBelgium50.83334.0000000000...55791559835623556511568105709257342574555759257849
116NaNFrance46.22762.2137002333...178428179069179306179645179964179859180166179887180044183309
\n", "

2 rows × 132 columns

\n", "
" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n", "23 NaN Belgium 50.8333 4.0000 0 0 0 \n", "116 NaN France 46.2276 2.2137 0 0 2 \n", "\n", " 1/25/20 1/26/20 1/27/20 ... 5/19/20 5/20/20 5/21/20 5/22/20 \\\n", "23 0 0 0 ... 55791 55983 56235 56511 \n", "116 3 3 3 ... 178428 179069 179306 179645 \n", "\n", " 5/23/20 5/24/20 5/25/20 5/26/20 5/27/20 5/28/20 \n", "23 56810 57092 57342 57455 57592 57849 \n", "116 179964 179859 180166 179887 180044 183309 \n", "\n", "[2 rows x 132 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# now let's add to dataCountries the rest of the countries needed \n", "# Here with & Province/State.isnull we are only including metropolitan France's row and not the specific regions from France detailed in the data.\n", "\n", "dataCountries = dataCountries.append(raw_data.loc[(raw_data['Country/Region'] == 'France') & (raw_data['Province/State'].isnull())])\n", "\n", "dataCountries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les mêmes étapes sont utilisées pour le reste des pays manquants, sauf pour la Chine qui nécessite une opération spécial. (Voir ci-dessous)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [], "source": [ "countries_list= list(['Germany', 'Iran', 'Italy', 'Japan', 'Korea, South', 'Netherlands', 'Portugal', 'Spain', 'United Kingdom', 'US'])\n", "#print(countries_list)\n", "\n", "for country in countries_list : \n", " dataCountries = dataCountries.append(raw_data.loc[(raw_data['Country/Region'] == country) & (raw_data['Province/State'].isnull())])\n", "\n", "# Manualy adding Hong-Kong \n", "dataCountries = dataCountries.append(raw_data.loc[(raw_data['Country/Region'] == 'China') & (raw_data['Province/State'] == 'Hong Kong')]) \n", "\n", "#Uncomment to see the dataframe\n", "#dataCountries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour éviter que deux lignes correspondent au même pays, la Chine. On renome *Hong Kong, China* en *Hong Kong, Hong Kong*. Ainsi Nous pourrons ajouter toute les régions de Chine dans une même ligne nommée *China*. Nous utilisons donc la méthode [replace](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.replace.html) pour remplacer le nom du pays." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...5/19/205/20/205/21/205/22/205/23/205/24/205/25/205/26/205/27/205/28/20
23NaNBelgium50.83334.0000000000...55791559835623556511568105709257342574555759257849
116NaNFrance46.22762.2137002333...178428179069179306179645179964179859180166179887180044183309
120NaNGermany51.00009.0000000001...177778178473179021179710179986180328180600181200181524182196
133NaNIran32.000053.0000000000...124603126949129341131652133521135701137724139511141591143849
137NaNItaly43.000012.0000000000...226699227364228006228658229327229858230158230555231139231732
139NaNJapan36.0000138.0000222244...16367163671642416513165361655016581166231665116598
143NaNKorea, South36.0000128.0000112234...11110111221114211165111901120611225112651134411402
169NaNNetherlands52.13265.2913000000...44249444474470044888450644523645445455784576845950
184NaNPortugal39.3999-8.2245000000...29432296602991230200304713062330788310073129231596
201NaNSpain40.0000-4.0000000000...232037232555233037234824235290235772235400236259236259237906
223NaNUnited Kingdom55.3781-3.4360000000...248818248293250908254195257154259559261184265227267240269127
225NaNUS37.0902-95.7129112255...1528568155185315771471600937162261216432461662302168091316991761721753
61Hong KongHong Kong22.3000114.2000022588...1055105510551065106510651065106510661066
\n", "

13 rows × 132 columns

\n", "
" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", "23 NaN Belgium 50.8333 4.0000 0 0 \n", "116 NaN France 46.2276 2.2137 0 0 \n", "120 NaN Germany 51.0000 9.0000 0 0 \n", "133 NaN Iran 32.0000 53.0000 0 0 \n", "137 NaN Italy 43.0000 12.0000 0 0 \n", "139 NaN Japan 36.0000 138.0000 2 2 \n", "143 NaN Korea, South 36.0000 128.0000 1 1 \n", "169 NaN Netherlands 52.1326 5.2913 0 0 \n", "184 NaN Portugal 39.3999 -8.2245 0 0 \n", "201 NaN Spain 40.0000 -4.0000 0 0 \n", "223 NaN United Kingdom 55.3781 -3.4360 0 0 \n", "225 NaN US 37.0902 -95.7129 1 1 \n", "61 Hong Kong Hong Kong 22.3000 114.2000 0 2 \n", "\n", " 1/24/20 1/25/20 1/26/20 1/27/20 ... 5/19/20 5/20/20 5/21/20 \\\n", "23 0 0 0 0 ... 55791 55983 56235 \n", "116 2 3 3 3 ... 178428 179069 179306 \n", "120 0 0 0 1 ... 177778 178473 179021 \n", "133 0 0 0 0 ... 124603 126949 129341 \n", "137 0 0 0 0 ... 226699 227364 228006 \n", "139 2 2 4 4 ... 16367 16367 16424 \n", "143 2 2 3 4 ... 11110 11122 11142 \n", "169 0 0 0 0 ... 44249 44447 44700 \n", "184 0 0 0 0 ... 29432 29660 29912 \n", "201 0 0 0 0 ... 232037 232555 233037 \n", "223 0 0 0 0 ... 248818 248293 250908 \n", "225 2 2 5 5 ... 1528568 1551853 1577147 \n", "61 2 5 8 8 ... 1055 1055 1055 \n", "\n", " 5/22/20 5/23/20 5/24/20 5/25/20 5/26/20 5/27/20 5/28/20 \n", "23 56511 56810 57092 57342 57455 57592 57849 \n", "116 179645 179964 179859 180166 179887 180044 183309 \n", "120 179710 179986 180328 180600 181200 181524 182196 \n", "133 131652 133521 135701 137724 139511 141591 143849 \n", "137 228658 229327 229858 230158 230555 231139 231732 \n", "139 16513 16536 16550 16581 16623 16651 16598 \n", "143 11165 11190 11206 11225 11265 11344 11402 \n", "169 44888 45064 45236 45445 45578 45768 45950 \n", "184 30200 30471 30623 30788 31007 31292 31596 \n", "201 234824 235290 235772 235400 236259 236259 237906 \n", "223 254195 257154 259559 261184 265227 267240 269127 \n", "225 1600937 1622612 1643246 1662302 1680913 1699176 1721753 \n", "61 1065 1065 1065 1065 1065 1066 1066 \n", "\n", "[13 rows x 132 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "dataCountries[\"Country/Region\"].replace({\"China\": \"Hong Kong\"}, inplace=True)\n", "\n", "dataCountries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La chine est composée de plusieurs provinces. Pour étudier l'ensemble de la Chine moins Hong-kong (voir consigne) nous additionnons le nombre de contaminés par jour dans une nouvelle ligne nommée China avec l'index 1 car non utilisé (orignellement utilisé par l'Afghanistan). Pour se faire nous utilisons les méthodes [at](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.at.html) et [sum](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sum.html).\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py:2035: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " self.loc[index, col] = value\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...5/19/205/20/205/21/205/22/205/23/205/24/205/25/205/26/205/27/205/28/20
23NaNBelgium50.83334.00000.00.00.00.00.00.0...55791.055983.056235.056511.056810.057092.057342.057455.057592.057849.0
116NaNFrance46.22762.21370.00.02.03.03.03.0...178428.0179069.0179306.0179645.0179964.0179859.0180166.0179887.0180044.0183309.0
120NaNGermany51.00009.00000.00.00.00.00.01.0...177778.0178473.0179021.0179710.0179986.0180328.0180600.0181200.0181524.0182196.0
133NaNIran32.000053.00000.00.00.00.00.00.0...124603.0126949.0129341.0131652.0133521.0135701.0137724.0139511.0141591.0143849.0
137NaNItaly43.000012.00000.00.00.00.00.00.0...226699.0227364.0228006.0228658.0229327.0229858.0230158.0230555.0231139.0231732.0
139NaNJapan36.0000138.00002.02.02.02.04.04.0...16367.016367.016424.016513.016536.016550.016581.016623.016651.016598.0
143NaNKorea, South36.0000128.00001.01.02.02.03.04.0...11110.011122.011142.011165.011190.011206.011225.011265.011344.011402.0
169NaNNetherlands52.13265.29130.00.00.00.00.00.0...44249.044447.044700.044888.045064.045236.045445.045578.045768.045950.0
184NaNPortugal39.3999-8.22450.00.00.00.00.00.0...29432.029660.029912.030200.030471.030623.030788.031007.031292.031596.0
201NaNSpain40.0000-4.00000.00.00.00.00.00.0...232037.0232555.0233037.0234824.0235290.0235772.0235400.0236259.0236259.0237906.0
223NaNUnited Kingdom55.3781-3.43600.00.00.00.00.00.0...248818.0248293.0250908.0254195.0257154.0259559.0261184.0265227.0267240.0269127.0
225NaNUS37.0902-95.71291.01.02.02.05.05.0...1528568.01551853.01577147.01600937.01622612.01643246.01662302.01680913.01699176.01721753.0
61Hong KongHong Kong22.3000114.20000.02.02.05.08.08.0...1055.01055.01055.01065.01065.01065.01065.01065.01066.01066.0
1NaNChinaNaNNaN548.0641.0918.01401.02067.02869.0...83008.083008.083008.083016.083019.083030.083037.083038.083040.083040.0
\n", "

14 rows × 132 columns

\n", "
" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", "23 NaN Belgium 50.8333 4.0000 0.0 0.0 \n", "116 NaN France 46.2276 2.2137 0.0 0.0 \n", "120 NaN Germany 51.0000 9.0000 0.0 0.0 \n", "133 NaN Iran 32.0000 53.0000 0.0 0.0 \n", "137 NaN Italy 43.0000 12.0000 0.0 0.0 \n", "139 NaN Japan 36.0000 138.0000 2.0 2.0 \n", "143 NaN Korea, South 36.0000 128.0000 1.0 1.0 \n", "169 NaN Netherlands 52.1326 5.2913 0.0 0.0 \n", "184 NaN Portugal 39.3999 -8.2245 0.0 0.0 \n", "201 NaN Spain 40.0000 -4.0000 0.0 0.0 \n", "223 NaN United Kingdom 55.3781 -3.4360 0.0 0.0 \n", "225 NaN US 37.0902 -95.7129 1.0 1.0 \n", "61 Hong Kong Hong Kong 22.3000 114.2000 0.0 2.0 \n", "1 NaN China NaN NaN 548.0 641.0 \n", "\n", " 1/24/20 1/25/20 1/26/20 1/27/20 ... 5/19/20 5/20/20 \\\n", "23 0.0 0.0 0.0 0.0 ... 55791.0 55983.0 \n", "116 2.0 3.0 3.0 3.0 ... 178428.0 179069.0 \n", "120 0.0 0.0 0.0 1.0 ... 177778.0 178473.0 \n", "133 0.0 0.0 0.0 0.0 ... 124603.0 126949.0 \n", "137 0.0 0.0 0.0 0.0 ... 226699.0 227364.0 \n", "139 2.0 2.0 4.0 4.0 ... 16367.0 16367.0 \n", "143 2.0 2.0 3.0 4.0 ... 11110.0 11122.0 \n", "169 0.0 0.0 0.0 0.0 ... 44249.0 44447.0 \n", "184 0.0 0.0 0.0 0.0 ... 29432.0 29660.0 \n", "201 0.0 0.0 0.0 0.0 ... 232037.0 232555.0 \n", "223 0.0 0.0 0.0 0.0 ... 248818.0 248293.0 \n", "225 2.0 2.0 5.0 5.0 ... 1528568.0 1551853.0 \n", "61 2.0 5.0 8.0 8.0 ... 1055.0 1055.0 \n", "1 918.0 1401.0 2067.0 2869.0 ... 83008.0 83008.0 \n", "\n", " 5/21/20 5/22/20 5/23/20 5/24/20 5/25/20 5/26/20 \\\n", "23 56235.0 56511.0 56810.0 57092.0 57342.0 57455.0 \n", "116 179306.0 179645.0 179964.0 179859.0 180166.0 179887.0 \n", "120 179021.0 179710.0 179986.0 180328.0 180600.0 181200.0 \n", "133 129341.0 131652.0 133521.0 135701.0 137724.0 139511.0 \n", "137 228006.0 228658.0 229327.0 229858.0 230158.0 230555.0 \n", "139 16424.0 16513.0 16536.0 16550.0 16581.0 16623.0 \n", "143 11142.0 11165.0 11190.0 11206.0 11225.0 11265.0 \n", "169 44700.0 44888.0 45064.0 45236.0 45445.0 45578.0 \n", "184 29912.0 30200.0 30471.0 30623.0 30788.0 31007.0 \n", "201 233037.0 234824.0 235290.0 235772.0 235400.0 236259.0 \n", "223 250908.0 254195.0 257154.0 259559.0 261184.0 265227.0 \n", "225 1577147.0 1600937.0 1622612.0 1643246.0 1662302.0 1680913.0 \n", "61 1055.0 1065.0 1065.0 1065.0 1065.0 1065.0 \n", "1 83008.0 83016.0 83019.0 83030.0 83037.0 83038.0 \n", "\n", " 5/27/20 5/28/20 \n", "23 57592.0 57849.0 \n", "116 180044.0 183309.0 \n", "120 181524.0 182196.0 \n", "133 141591.0 143849.0 \n", "137 231139.0 231732.0 \n", "139 16651.0 16598.0 \n", "143 11344.0 11402.0 \n", "169 45768.0 45950.0 \n", "184 31292.0 31596.0 \n", "201 236259.0 237906.0 \n", "223 267240.0 269127.0 \n", "225 1699176.0 1721753.0 \n", "61 1066.0 1066.0 \n", "1 83040.0 83040.0 \n", "\n", "[14 rows x 132 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# For china the data have to be summed between region in order to get the results for the whole country.\n", "dataChina = raw_data.loc[((raw_data['Country/Region'] == 'China') & (raw_data['Province/State'] != 'Hong Kong' ))]\n", "\n", "#print(dataChina)\n", "\n", "#We want to sum per date and not the regions or the latitude so we remove them from our temporary list of column.\n", "col_list= list(dataChina)\n", "col_list.remove(\"Province/State\")\n", "col_list.remove(\"Country/Region\")\n", "col_list.remove(\"Lat\")\n", "col_list.remove(\"Long\")\n", "\n", "\n", "#let's use df.sum() to sum rows \n", "for col in col_list: \n", " dataChina.at['1', col] = dataChina[col].sum()\n", "\n", "#Rename the Country in the column we have just created above.\n", "dataChina.at['1', \"Country/Region\"] = \"China\"\n", "\n", "dataChina\n", "#Now add the data to Data Countries\n", "dataCountries= dataCountries.append(dataChina.loc[(dataChina['Country/Region'] == 'China') & (dataChina['Province/State'].isnull())])\n", "\n", "dataCountries\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous avons donc un dataFrame regroupant l'ensemble des données necéssaire nous pouvons encore supprimer les données que nous n'utiliserons pas telles que les provinces et régions ou la latitude et la longitude. Nous utilisons la méthode [drop](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Country/Region1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/201/30/20...5/19/205/20/205/21/205/22/205/23/205/24/205/25/205/26/205/27/205/28/20
23Belgium0.00.00.00.00.00.00.00.00.0...55791.055983.056235.056511.056810.057092.057342.057455.057592.057849.0
116France0.00.02.03.03.03.04.05.05.0...178428.0179069.0179306.0179645.0179964.0179859.0180166.0179887.0180044.0183309.0
120Germany0.00.00.00.00.01.04.04.04.0...177778.0178473.0179021.0179710.0179986.0180328.0180600.0181200.0181524.0182196.0
133Iran0.00.00.00.00.00.00.00.00.0...124603.0126949.0129341.0131652.0133521.0135701.0137724.0139511.0141591.0143849.0
137Italy0.00.00.00.00.00.00.00.00.0...226699.0227364.0228006.0228658.0229327.0229858.0230158.0230555.0231139.0231732.0
139Japan2.02.02.02.04.04.07.07.011.0...16367.016367.016424.016513.016536.016550.016581.016623.016651.016598.0
143Korea, South1.01.02.02.03.04.04.04.04.0...11110.011122.011142.011165.011190.011206.011225.011265.011344.011402.0
169Netherlands0.00.00.00.00.00.00.00.00.0...44249.044447.044700.044888.045064.045236.045445.045578.045768.045950.0
184Portugal0.00.00.00.00.00.00.00.00.0...29432.029660.029912.030200.030471.030623.030788.031007.031292.031596.0
201Spain0.00.00.00.00.00.00.00.00.0...232037.0232555.0233037.0234824.0235290.0235772.0235400.0236259.0236259.0237906.0
223United Kingdom0.00.00.00.00.00.00.00.00.0...248818.0248293.0250908.0254195.0257154.0259559.0261184.0265227.0267240.0269127.0
225US1.01.02.02.05.05.05.05.05.0...1528568.01551853.01577147.01600937.01622612.01643246.01662302.01680913.01699176.01721753.0
61Hong Kong0.02.02.05.08.08.08.010.010.0...1055.01055.01055.01065.01065.01065.01065.01065.01066.01066.0
1China548.0641.0918.01401.02067.02869.05501.06077.08131.0...83008.083008.083008.083016.083019.083030.083037.083038.083040.083040.0
\n", "

14 rows × 129 columns

\n", "
" ], "text/plain": [ " Country/Region 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 \\\n", "23 Belgium 0.0 0.0 0.0 0.0 0.0 0.0 \n", "116 France 0.0 0.0 2.0 3.0 3.0 3.0 \n", "120 Germany 0.0 0.0 0.0 0.0 0.0 1.0 \n", "133 Iran 0.0 0.0 0.0 0.0 0.0 0.0 \n", "137 Italy 0.0 0.0 0.0 0.0 0.0 0.0 \n", "139 Japan 2.0 2.0 2.0 2.0 4.0 4.0 \n", "143 Korea, South 1.0 1.0 2.0 2.0 3.0 4.0 \n", "169 Netherlands 0.0 0.0 0.0 0.0 0.0 0.0 \n", "184 Portugal 0.0 0.0 0.0 0.0 0.0 0.0 \n", "201 Spain 0.0 0.0 0.0 0.0 0.0 0.0 \n", "223 United Kingdom 0.0 0.0 0.0 0.0 0.0 0.0 \n", "225 US 1.0 1.0 2.0 2.0 5.0 5.0 \n", "61 Hong Kong 0.0 2.0 2.0 5.0 8.0 8.0 \n", "1 China 548.0 641.0 918.0 1401.0 2067.0 2869.0 \n", "\n", " 1/28/20 1/29/20 1/30/20 ... 5/19/20 5/20/20 5/21/20 \\\n", "23 0.0 0.0 0.0 ... 55791.0 55983.0 56235.0 \n", "116 4.0 5.0 5.0 ... 178428.0 179069.0 179306.0 \n", "120 4.0 4.0 4.0 ... 177778.0 178473.0 179021.0 \n", "133 0.0 0.0 0.0 ... 124603.0 126949.0 129341.0 \n", "137 0.0 0.0 0.0 ... 226699.0 227364.0 228006.0 \n", "139 7.0 7.0 11.0 ... 16367.0 16367.0 16424.0 \n", "143 4.0 4.0 4.0 ... 11110.0 11122.0 11142.0 \n", "169 0.0 0.0 0.0 ... 44249.0 44447.0 44700.0 \n", "184 0.0 0.0 0.0 ... 29432.0 29660.0 29912.0 \n", "201 0.0 0.0 0.0 ... 232037.0 232555.0 233037.0 \n", "223 0.0 0.0 0.0 ... 248818.0 248293.0 250908.0 \n", "225 5.0 5.0 5.0 ... 1528568.0 1551853.0 1577147.0 \n", "61 8.0 10.0 10.0 ... 1055.0 1055.0 1055.0 \n", "1 5501.0 6077.0 8131.0 ... 83008.0 83008.0 83008.0 \n", "\n", " 5/22/20 5/23/20 5/24/20 5/25/20 5/26/20 5/27/20 \\\n", "23 56511.0 56810.0 57092.0 57342.0 57455.0 57592.0 \n", "116 179645.0 179964.0 179859.0 180166.0 179887.0 180044.0 \n", "120 179710.0 179986.0 180328.0 180600.0 181200.0 181524.0 \n", "133 131652.0 133521.0 135701.0 137724.0 139511.0 141591.0 \n", "137 228658.0 229327.0 229858.0 230158.0 230555.0 231139.0 \n", "139 16513.0 16536.0 16550.0 16581.0 16623.0 16651.0 \n", "143 11165.0 11190.0 11206.0 11225.0 11265.0 11344.0 \n", "169 44888.0 45064.0 45236.0 45445.0 45578.0 45768.0 \n", "184 30200.0 30471.0 30623.0 30788.0 31007.0 31292.0 \n", "201 234824.0 235290.0 235772.0 235400.0 236259.0 236259.0 \n", "223 254195.0 257154.0 259559.0 261184.0 265227.0 267240.0 \n", "225 1600937.0 1622612.0 1643246.0 1662302.0 1680913.0 1699176.0 \n", "61 1065.0 1065.0 1065.0 1065.0 1065.0 1066.0 \n", "1 83016.0 83019.0 83030.0 83037.0 83038.0 83040.0 \n", "\n", " 5/28/20 \n", "23 57849.0 \n", "116 183309.0 \n", "120 182196.0 \n", "133 143849.0 \n", "137 231732.0 \n", "139 16598.0 \n", "143 11402.0 \n", "169 45950.0 \n", "184 31596.0 \n", "201 237906.0 \n", "223 269127.0 \n", "225 1721753.0 \n", "61 1066.0 \n", "1 83040.0 \n", "\n", "[14 rows x 129 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataCountries = dataCountries.drop(['Province/State','Lat', 'Long'], axis=1)\n", "dataCountries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Traitement des données \n", "\n", "Ici le traitement des données consiste uniquement à représenter des données temporelles dans un graphique. \n" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataTransposed = dataCountries.transpose()\n", "\n", "header_row = 0\n", "dataTransposed.columns = dataTransposed.iloc[header_row]\n", "dataTransposed = dataTransposed.drop(['Country/Region'], axis=0)\n", "\n", "fig, ax = plt.subplots()\n", "#ax = fig.add_subplot(1, 1, 1)\n", "\n", "for col in dataTransposed.columns :\n", " dataTransposed.plot(kind='line',x=dataTransposed.index,y=col,ax=ax)\n", "\n", " \n", "#plt.style.use('seaborn')\n", "ax.set_xlabel(\"Days\")\n", "ax.set_ylabel(\"Coronavirus Cases\")\n", "fig.set_size_inches(20, 20)\n", "\n", "# To specify the number of ticks on both or any single axes\n", "plt.locator_params(axis='y', nbins=20)\n", "\n", "plt.text(0.80, 0.95, ('US May 28 '+'\\n'+str(dataTransposed.iloc[dataTransposed.index.size-1]['US'])+' cases'),\n", " horizontalalignment='center',\n", " verticalalignment='center',\n", " transform=ax.transAxes,\n", " fontsize=20)\n", "\n", "\n", "plt.text(0.85, 0.20, ('China May 28 '+'\\n'+str(dataTransposed.iloc[dataTransposed.index.size-1]['China'])+' cases'),\n", " horizontalalignment='center',\n", " verticalalignment='center',\n", " transform=ax.transAxes,\n", " fontsize=20)\n", "\n", "\n", "\n", "#ax.xaxis.set_major_locator(plt.MaxNLocator(3))\n", "#plt.locator_params(axis=\"x\",steps=[1,2,4,5,6,8,10])\n", "#plt.xticks(np.arange(dataTransposed.index.size),dataTransposed.index) # Set text labels.\n", "#ax.xaxis.set_major_locator(plt.MaxN,Locator(20))\n", "ax.set_title('Cumulative coronavirus cases')\n", "ax.legend(loc = 'upper left',fontsize='x-large')\n", "plt.show()\n", "\n", "#dataTransposed.plot()\n", "\n" ] }, { "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 }