{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sujet 7 : Autour du SARS-CoV-2 (Covid-19)\n", "\n", "\n", "## Consignes :\n", "\n", "### Prérequis\n", "\n", "Techniques de présentation graphique. Cet exercice peut être réalisé indifféremment en R ou en Python.\n", "\n", "### Sujet\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 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", "Les données que nous utiliserons dans un premier temps sont compilées par le Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) et sont mises à disposition sur GitHub. C'est plus particulièrement sur les données time_series_covid19_confirmed_global.csv (des suites chronologiques au format csv) disponibles à l'adresse : https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv, que nous allons nous concentrer.\n", "\n", "Vous commencerez par télécharger les données pour créer 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", "Le nom entre parenthèses est le nom du « pays » tel qu’il apparaît dans le fichier time_series_covid19_confirmed_global.csv. Les données de la Chine apparaissent par province et nous avons séparé Hong-Kong, non pour prendre parti dans les différences entre cette province et l'état chinois, mais parce que c'est ainsi qu'apparaissent les données sur le site du SCMP. Les données pour la France, la Hollande et le Royaume-Uni excluent les territoires d'outre-mer et autres « résidus coloniaux ».\n", "\n", "Ensuite vous ferez un graphe avec la date en abscisse et le nombre cumulé de cas à cette date en ordonnée. Nous vous proposons de faire deux versions de ce graphe, une avec une échelle linéaire et une avec une échelle logarithmique.\n", "\n", "### Question subsidiaire à faire quand on sera sorti du « merdier »\n", "\n", "Vous pourrez également utiliser les données de décès (timeseriescovid19deathsglobal.csv) et refaire les courbes, mais là encore, faites attention lors de l'interprétation. Ces courbes, même si elles paraissent effrayantes, doivent être comparées à la mortalité « normale ». Pour la France des données sont disponibles sur le site de l'INSEE : https://www.insee.fr/fr/information/4470857, ainsi que dans les « Points hebdomadaires » de surveillance de la mortalité diffusés par Santé publique France, comme celui de la semaine 12 (le site étant très mal conçu pour quiconque souhaite une information spécifique, le plus simple est de passer par un moteur de recherche généraliste…).\n", "\n", "Pour atténuer les effets dus aux méthodes de comptage, etc., vous pourrez, une fois l'épidémie terminée, prendre les données du nombre total de décès et les normaliser pour 1000 habitants du pays concerné. Vous irez ensuite chercher les données sur le nombre de lits d'hôpital pour 1000 habitants sur le site de l'OCDE et vous pourrez corréler les deux (c'est-à-dire, faire un graphe avec le nombre de lits en abscisse et le nombre de décès en ordonnée)…\n", "\n", "---\n", "\n", "# Pour commencer\n", "\n", "Nous allons tout d'abord importer les outils necéssaires pour réaliser ce travail.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import os\n", "import urllib.request\n", "\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons maintenant télécharger les [données](https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv) si elles ne sont pas déjà téléchargés." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/23/212/24/212/25/212/26/212/27/212/28/213/1/213/2/213/3/213/4/21
0NaNAfghanistan33.93911067.709953000000...55646556645568055696557075571455733557595577055775
1NaNAlbania41.15330020.168300000000...102306103327104313105229106215107167107931108823109674110521
2NaNAlgeria28.0339001.659600000000...112279112461112622112805112960113092113255113430113593113761
3NaNAndorra42.5063001.521800000000...10739107751079910822108491086610889109081094810976
4NaNAngola-11.20270017.873900000000...20584206402069520759207822080720854208822092320981
5NaNAntigua and Barbuda17.060800-61.796400000000...636646701701726730769769769813
6NaNArgentina-38.416100-63.616700000000...2077228208541120936452098728210419721073652112023211867621265312133963
7NaNArmenia40.06910045.038200000000...170672170945171227171510171793172058172216172456172816173307
8Australian Capital TerritoryAustralia-35.473500149.012400000000...118118118118118118118120120120
9New South WalesAustralia-33.868800151.209300000034...5155516251665172517751805183518951935205
10Northern TerritoryAustralia-12.463400130.845600000000...104104104105105105105105105105
11QueenslandAustralia-27.469800153.025100000000...1323132413281329132913311335133513421344
12South AustraliaAustralia-34.928500138.600700000000...613613613613613616617618618618
13TasmaniaAustralia-42.882100147.327200000000...234234234234234234234234234234
14VictoriaAustralia-37.813600144.963100000011...20479204792048120481204812048120481204812048120481
15Western AustraliaAustralia-31.950500115.860500000000...913913913913913913913914914914
16NaNAustria47.51620014.550100000000...448371450376452767454860457317459440460849462769465322467646
17NaNAzerbaijan40.14310047.576900000000...233424233644233770233989234267234537234662235014235333235647
18NaNBahamas25.025885-78.035889000000...8477849684968519851985198519851985738573
19NaNBahrain26.02750050.550000000000...119205119858120495121127121778122394123039123531124269124857
20NaNBangladesh23.68500090.356300000000...544116544544544954545424545831546216546801547316547930548549
21NaNBarbados13.193900-59.543200000000...2852290729492994303830683115314031633186
22NaNBelarus53.70980027.953400000000...280428281707282898284500285959287306288267289136290447291621
23NaNBelgium50.8333004.469936000000...757696760809763885766654769414771511772294774344777608780251
24NaNBelize17.189900-88.497600000000...12264122711228012293122931229312313123201232812329
25NaNBenin9.3077002.315800000000...5434543454345434543454345434543460716071
26NaNBhutan27.51420090.433600000000...867867867867867867867867867867
27NaNBolivia-16.290200-63.588700000000...244380245719246822247891248547249010249767250557251391252360
28NaNBosnia and Herzegovina43.91590017.679100000000...129176129753130510130979130979130979130979133088133982134892
29NaNBotswana-22.32850024.684900000000...27721277212837128371283712837130727307273072731746
..................................................................
244NaNTimor-Leste-8.874217125.727539000000...107109110110111113113113113113
245NaNTogo8.6195000.824800000000...6466654866376730685169016933708671887323
246NaNTrinidad and Tobago10.691800-61.222500000000...7686769076977704770577137716771777237727
247NaNTunisia33.8869179.537499000000...229781230443231298231964232615233277233669234231235008235643
248NaNTurkey38.96370035.243300000000...2655633266519426747662683971269316427015882711479272331627348362746158
249NaNUS40.000000-100.000000112255...28234656283090852838649228463190285273442857854828637313286940712875998028827144
250NaNUganda1.37333332.290275000000...40243402614030040322403354035740367403954040840426
251NaNUkraine48.37940031.165600000000...1358871136486113731391381273138957013955281399934140539414127481422927
252NaNUnited Arab Emirates23.42407653.847818000000...375535378637381662385160388594391524394050396771399463402205
253AnguillaUnited Kingdom18.220600-63.068600000000...18181818181818181818
254BermudaUnited Kingdom32.307800-64.750500000000...703703705705705705707713713718
255British Virgin IslandsUnited Kingdom18.420700-64.640000000000...114114114153153153153153153153
256Cayman IslandsUnited Kingdom19.313300-81.254600000000...431431438444444444447447447448
257Channel IslandsUnited Kingdom49.372300-2.364400000000...4030403140324033403440384038403940404041
258Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...54545454545454545454
259GibraltarUnited Kingdom36.140800-5.353600000000...4234423442354236423842394239424342434243
260Isle of ManUnited Kingdom54.236100-4.548100000000...462470475475484484486494511546
261MontserratUnited Kingdom16.742498-62.187366000000...20202020202020202020
262Saint Helena, Ascension and Tristan da CunhaUnited Kingdom-7.946700-14.355900000000...4444444444
263Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...2051205120702088209921142114211521322161
264NaNUnited Kingdom55.378100-3.436000000000...4134639414457741545624163085417051941765544182009418840041947854201358
265NaNUruguay-32.522800-55.765800000000...53973548745569556542573625799458589591716007460945
266NaNUzbekistan41.37749164.585262000000...79717797497977379804798367992679926799618003580035
267NaNVanuatu-15.376700166.959200000000...1111111111
268NaNVenezuela6.423800-66.589700000000...136986137445137871138295138739139116139545139934140383140960
269NaNVietnam14.058324108.277199022222...2403241224212426243224482448247524822488
270NaNWest Bank and Gaza31.95220035.233200000000...176377177768179293180848181909183612185336187309189326191203
271NaNYemen15.55272748.516388000000...2187222122552267226922852310234223632375
272NaNZambia-13.13389727.849332000000...75582764847717177639782027853479002795578009080687
273NaNZimbabwe-19.01543829.154857000000...35910359603599436044360583608936115361483617936223
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274 rows × 412 columns

\n", "
" ], "text/plain": [ " Province/State Country/Region \\\n", "0 NaN Afghanistan \n", "1 NaN Albania \n", "2 NaN Algeria \n", "3 NaN Andorra \n", "4 NaN Angola \n", "5 NaN Antigua and Barbuda \n", "6 NaN Argentina \n", "7 NaN Armenia \n", "8 Australian Capital Territory Australia \n", "9 New South Wales Australia \n", "10 Northern Territory Australia \n", "11 Queensland Australia \n", "12 South Australia Australia \n", "13 Tasmania Australia \n", "14 Victoria Australia \n", "15 Western Australia Australia \n", "16 NaN Austria \n", "17 NaN Azerbaijan \n", "18 NaN Bahamas \n", "19 NaN Bahrain \n", "20 NaN Bangladesh \n", "21 NaN Barbados \n", "22 NaN Belarus \n", "23 NaN Belgium \n", "24 NaN Belize \n", "25 NaN Benin \n", "26 NaN Bhutan \n", "27 NaN Bolivia \n", "28 NaN Bosnia and Herzegovina \n", "29 NaN Botswana \n", ".. ... ... \n", "244 NaN Timor-Leste \n", "245 NaN Togo \n", "246 NaN Trinidad and Tobago \n", "247 NaN Tunisia \n", "248 NaN Turkey \n", "249 NaN US \n", "250 NaN Uganda \n", "251 NaN Ukraine \n", "252 NaN United Arab Emirates \n", "253 Anguilla United Kingdom \n", "254 Bermuda United Kingdom \n", "255 British Virgin Islands United Kingdom \n", "256 Cayman Islands United Kingdom \n", "257 Channel Islands United Kingdom \n", "258 Falkland Islands (Malvinas) United Kingdom \n", "259 Gibraltar United Kingdom \n", "260 Isle of Man United Kingdom \n", "261 Montserrat United Kingdom \n", "262 Saint Helena, Ascension and Tristan da Cunha United Kingdom \n", "263 Turks and Caicos Islands United Kingdom \n", "264 NaN United Kingdom \n", "265 NaN Uruguay \n", "266 NaN Uzbekistan \n", "267 NaN Vanuatu \n", "268 NaN Venezuela \n", "269 NaN Vietnam \n", "270 NaN West Bank and Gaza \n", "271 NaN Yemen \n", "272 NaN Zambia \n", "273 NaN Zimbabwe \n", "\n", " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n", "0 33.939110 67.709953 0 0 0 0 0 \n", "1 41.153300 20.168300 0 0 0 0 0 \n", "2 28.033900 1.659600 0 0 0 0 0 \n", "3 42.506300 1.521800 0 0 0 0 0 \n", "4 -11.202700 17.873900 0 0 0 0 0 \n", "5 17.060800 -61.796400 0 0 0 0 0 \n", "6 -38.416100 -63.616700 0 0 0 0 0 \n", "7 40.069100 45.038200 0 0 0 0 0 \n", "8 -35.473500 149.012400 0 0 0 0 0 \n", "9 -33.868800 151.209300 0 0 0 0 3 \n", "10 -12.463400 130.845600 0 0 0 0 0 \n", "11 -27.469800 153.025100 0 0 0 0 0 \n", "12 -34.928500 138.600700 0 0 0 0 0 \n", "13 -42.882100 147.327200 0 0 0 0 0 \n", "14 -37.813600 144.963100 0 0 0 0 1 \n", "15 -31.950500 115.860500 0 0 0 0 0 \n", "16 47.516200 14.550100 0 0 0 0 0 \n", "17 40.143100 47.576900 0 0 0 0 0 \n", "18 25.025885 -78.035889 0 0 0 0 0 \n", "19 26.027500 50.550000 0 0 0 0 0 \n", "20 23.685000 90.356300 0 0 0 0 0 \n", "21 13.193900 -59.543200 0 0 0 0 0 \n", "22 53.709800 27.953400 0 0 0 0 0 \n", "23 50.833300 4.469936 0 0 0 0 0 \n", "24 17.189900 -88.497600 0 0 0 0 0 \n", "25 9.307700 2.315800 0 0 0 0 0 \n", "26 27.514200 90.433600 0 0 0 0 0 \n", "27 -16.290200 -63.588700 0 0 0 0 0 \n", "28 43.915900 17.679100 0 0 0 0 0 \n", "29 -22.328500 24.684900 0 0 0 0 0 \n", ".. ... ... ... ... ... ... ... \n", "244 -8.874217 125.727539 0 0 0 0 0 \n", "245 8.619500 0.824800 0 0 0 0 0 \n", "246 10.691800 -61.222500 0 0 0 0 0 \n", "247 33.886917 9.537499 0 0 0 0 0 \n", "248 38.963700 35.243300 0 0 0 0 0 \n", "249 40.000000 -100.000000 1 1 2 2 5 \n", "250 1.373333 32.290275 0 0 0 0 0 \n", "251 48.379400 31.165600 0 0 0 0 0 \n", "252 23.424076 53.847818 0 0 0 0 0 \n", "253 18.220600 -63.068600 0 0 0 0 0 \n", "254 32.307800 -64.750500 0 0 0 0 0 \n", "255 18.420700 -64.640000 0 0 0 0 0 \n", "256 19.313300 -81.254600 0 0 0 0 0 \n", "257 49.372300 -2.364400 0 0 0 0 0 \n", "258 -51.796300 -59.523600 0 0 0 0 0 \n", "259 36.140800 -5.353600 0 0 0 0 0 \n", "260 54.236100 -4.548100 0 0 0 0 0 \n", "261 16.742498 -62.187366 0 0 0 0 0 \n", "262 -7.946700 -14.355900 0 0 0 0 0 \n", "263 21.694000 -71.797900 0 0 0 0 0 \n", "264 55.378100 -3.436000 0 0 0 0 0 \n", "265 -32.522800 -55.765800 0 0 0 0 0 \n", "266 41.377491 64.585262 0 0 0 0 0 \n", "267 -15.376700 166.959200 0 0 0 0 0 \n", "268 6.423800 -66.589700 0 0 0 0 0 \n", "269 14.058324 108.277199 0 2 2 2 2 \n", "270 31.952200 35.233200 0 0 0 0 0 \n", "271 15.552727 48.516388 0 0 0 0 0 \n", "272 -13.133897 27.849332 0 0 0 0 0 \n", "273 -19.015438 29.154857 0 0 0 0 0 \n", "\n", " 1/27/20 ... 2/23/21 2/24/21 2/25/21 2/26/21 2/27/21 \\\n", "0 0 ... 55646 55664 55680 55696 55707 \n", "1 0 ... 102306 103327 104313 105229 106215 \n", "2 0 ... 112279 112461 112622 112805 112960 \n", "3 0 ... 10739 10775 10799 10822 10849 \n", "4 0 ... 20584 20640 20695 20759 20782 \n", "5 0 ... 636 646 701 701 726 \n", "6 0 ... 2077228 2085411 2093645 2098728 2104197 \n", "7 0 ... 170672 170945 171227 171510 171793 \n", "8 0 ... 118 118 118 118 118 \n", "9 4 ... 5155 5162 5166 5172 5177 \n", "10 0 ... 104 104 104 105 105 \n", "11 0 ... 1323 1324 1328 1329 1329 \n", "12 0 ... 613 613 613 613 613 \n", "13 0 ... 234 234 234 234 234 \n", "14 1 ... 20479 20479 20481 20481 20481 \n", "15 0 ... 913 913 913 913 913 \n", "16 0 ... 448371 450376 452767 454860 457317 \n", "17 0 ... 233424 233644 233770 233989 234267 \n", "18 0 ... 8477 8496 8496 8519 8519 \n", "19 0 ... 119205 119858 120495 121127 121778 \n", "20 0 ... 544116 544544 544954 545424 545831 \n", "21 0 ... 2852 2907 2949 2994 3038 \n", "22 0 ... 280428 281707 282898 284500 285959 \n", "23 0 ... 757696 760809 763885 766654 769414 \n", "24 0 ... 12264 12271 12280 12293 12293 \n", "25 0 ... 5434 5434 5434 5434 5434 \n", "26 0 ... 867 867 867 867 867 \n", "27 0 ... 244380 245719 246822 247891 248547 \n", "28 0 ... 129176 129753 130510 130979 130979 \n", "29 0 ... 27721 27721 28371 28371 28371 \n", ".. ... ... ... ... ... ... ... \n", "244 0 ... 107 109 110 110 111 \n", "245 0 ... 6466 6548 6637 6730 6851 \n", "246 0 ... 7686 7690 7697 7704 7705 \n", "247 0 ... 229781 230443 231298 231964 232615 \n", "248 0 ... 2655633 2665194 2674766 2683971 2693164 \n", "249 5 ... 28234656 28309085 28386492 28463190 28527344 \n", "250 0 ... 40243 40261 40300 40322 40335 \n", "251 0 ... 1358871 1364861 1373139 1381273 1389570 \n", "252 0 ... 375535 378637 381662 385160 388594 \n", "253 0 ... 18 18 18 18 18 \n", "254 0 ... 703 703 705 705 705 \n", "255 0 ... 114 114 114 153 153 \n", "256 0 ... 431 431 438 444 444 \n", "257 0 ... 4030 4031 4032 4033 4034 \n", "258 0 ... 54 54 54 54 54 \n", "259 0 ... 4234 4234 4235 4236 4238 \n", "260 0 ... 462 470 475 475 484 \n", "261 0 ... 20 20 20 20 20 \n", "262 0 ... 4 4 4 4 4 \n", "263 0 ... 2051 2051 2070 2088 2099 \n", "264 0 ... 4134639 4144577 4154562 4163085 4170519 \n", "265 0 ... 53973 54874 55695 56542 57362 \n", "266 0 ... 79717 79749 79773 79804 79836 \n", "267 0 ... 1 1 1 1 1 \n", "268 0 ... 136986 137445 137871 138295 138739 \n", "269 2 ... 2403 2412 2421 2426 2432 \n", "270 0 ... 176377 177768 179293 180848 181909 \n", "271 0 ... 2187 2221 2255 2267 2269 \n", "272 0 ... 75582 76484 77171 77639 78202 \n", "273 0 ... 35910 35960 35994 36044 36058 \n", "\n", " 2/28/21 3/1/21 3/2/21 3/3/21 3/4/21 \n", "0 55714 55733 55759 55770 55775 \n", "1 107167 107931 108823 109674 110521 \n", "2 113092 113255 113430 113593 113761 \n", "3 10866 10889 10908 10948 10976 \n", "4 20807 20854 20882 20923 20981 \n", "5 730 769 769 769 813 \n", "6 2107365 2112023 2118676 2126531 2133963 \n", "7 172058 172216 172456 172816 173307 \n", "8 118 118 120 120 120 \n", "9 5180 5183 5189 5193 5205 \n", "10 105 105 105 105 105 \n", "11 1331 1335 1335 1342 1344 \n", "12 616 617 618 618 618 \n", "13 234 234 234 234 234 \n", "14 20481 20481 20481 20481 20481 \n", "15 913 913 914 914 914 \n", "16 459440 460849 462769 465322 467646 \n", "17 234537 234662 235014 235333 235647 \n", "18 8519 8519 8519 8573 8573 \n", "19 122394 123039 123531 124269 124857 \n", "20 546216 546801 547316 547930 548549 \n", "21 3068 3115 3140 3163 3186 \n", "22 287306 288267 289136 290447 291621 \n", "23 771511 772294 774344 777608 780251 \n", "24 12293 12313 12320 12328 12329 \n", "25 5434 5434 5434 6071 6071 \n", "26 867 867 867 867 867 \n", "27 249010 249767 250557 251391 252360 \n", "28 130979 130979 133088 133982 134892 \n", "29 28371 30727 30727 30727 31746 \n", ".. ... ... ... ... ... \n", "244 113 113 113 113 113 \n", "245 6901 6933 7086 7188 7323 \n", "246 7713 7716 7717 7723 7727 \n", "247 233277 233669 234231 235008 235643 \n", "248 2701588 2711479 2723316 2734836 2746158 \n", "249 28578548 28637313 28694071 28759980 28827144 \n", "250 40357 40367 40395 40408 40426 \n", "251 1395528 1399934 1405394 1412748 1422927 \n", "252 391524 394050 396771 399463 402205 \n", "253 18 18 18 18 18 \n", "254 705 707 713 713 718 \n", "255 153 153 153 153 153 \n", "256 444 447 447 447 448 \n", "257 4038 4038 4039 4040 4041 \n", "258 54 54 54 54 54 \n", "259 4239 4239 4243 4243 4243 \n", "260 484 486 494 511 546 \n", "261 20 20 20 20 20 \n", "262 4 4 4 4 4 \n", "263 2114 2114 2115 2132 2161 \n", "264 4176554 4182009 4188400 4194785 4201358 \n", "265 57994 58589 59171 60074 60945 \n", "266 79926 79926 79961 80035 80035 \n", "267 1 1 1 1 1 \n", "268 139116 139545 139934 140383 140960 \n", "269 2448 2448 2475 2482 2488 \n", "270 183612 185336 187309 189326 191203 \n", "271 2285 2310 2342 2363 2375 \n", "272 78534 79002 79557 80090 80687 \n", "273 36089 36115 36148 36179 36223 \n", "\n", "[274 rows x 412 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# URL des données\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\"\n", "\n", "# Nom du fichier csv\n", "data_file = \"data_covid.csv\"\n", "\n", "# Téléchargement des données si elles ne sont pas déjà présentes dans le répertoire\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)\n", "\n", "# Affichage des données\n", "raw_data = pd.read_csv(data_file)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ces données sont organisés sur 274 lignes pour chaque pays et (pour le moment) 412 colonnes présentant la province, le pays, les latitudes et longitudes suivi du nombre de cas par jour du 22 Janvier 2020 au 4 Mars 2021 au moment de l'écriture de ce rapport.\n", "\n", "Filtrons les données en choisant les colonnes des dates la regex `\\d{1,2}\\/\\d{1,2}\\/\\d{2}` sélectionne les colonnes dont le nom commence par :\n", "un ou deux chiffres suivi d'un \"/\", deux fois, puis se terminent par deux chiffres\n", "c'est à dire les colonnes des dates (vous pouvez inverser la regex avec `[^\\d{1,2}\\/\\d{1,2}\\/\\d{2}]` pour constater que la commande fait bien l'inverse et nous rend des données avec des NaN)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/23/212/24/212/25/212/26/212/27/212/28/213/1/213/2/213/3/213/4/21
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0 rows × 412 columns

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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Province/State, 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, ...]\n", "Index: []\n", "\n", "[0 rows x 412 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.filter(regex=\"\\d{1,2}\\/\\d{1,2}\\/\\d{2}\").isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le tableau en sortie est bien vide, il ne manque donc aucune donnée importante.\n", "\n", "Listons les pays que nous allons analyser, ils sont tous représentés dans les données par le nom du pays dans la colonne `Country/Region` et `Nan` dans la colonne `Province/State` sauf dans le cas de la Chine ou nous allons devoir faire une somme de toutes les provinces d'un coté et de récupérer Hong-Knog de l'autre." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "countries = [ \n", " \"Belgium\",\n", " \"France\",\n", " \"Germany\",\n", " \"Iran\",\n", " \"Italy\",\n", " \"Japan\",\n", " \"Korea, South\",\n", " \"Netherlands\",\n", " \"Portugal\",\n", " \"Spain\",\n", " \"United Kingdom\",\n", " \"US\",\n", "# \"China\",\n", "# \"China, Hong-Kong\",\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enregistrons les lignes consernées dans un nouveau tableau :" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[nan, 'France', 46.2276, 2.2137, 0, 0, 2, 3, 3, 3, 4, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 14, 18, 38, 57, 100, 130, 191, 212, 285, 423, 613, 938, 1115, 1401, 1773, 2281, 2281, 3661, 4469, 4499, 6633, 7652, 9043, 10871, 12612, 14282, 16533, 19856, 22054, 24967, 28856, 32609, 37162, 39761, 44029, 51579, 56362, 58404, 63588, 46483, 47299, 49934, 46400, 50242, 54003, 55538, 56972, 107712, 110836, 128272, 131476, 144035, 145896, 149683, 150752, 153197, 155821, 157603, 155881, 158653, 160295, 160907, 164592, 167650, 166193, 167326, 167846, 168637, 168887, 169405, 170467, 174596, 175218, 175743, 176132, 176338, 176651, 177330, 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