{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Autour du SARS-CoV-2 (Covid-19)\n", "\n", "Le but est ici de reproduire des graphes semblables à ceux du [South China Morning Post](https://www.scmp.com/) (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](https://fr.wikipedia.org/wiki/Maladie_%C3%A0_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)](https://systems.jhu.edu/) et sont mises à disposition sur [GitHub](https://github.com/CSSEGISandData/COVID-19). C'est plus particulièrement sur les données `time_series_covid19_confirmed_global.csv` (des suites chronologiques au format csv) disponibles [ici](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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Téléchargement des données\n", "\n", "Les données relevées sont stockées dans un fichier. Celles-ci sont à la date du 22 juin 2021." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'time_series_covid19_confirmed_global.csv'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_file = \"time_series_covid19_confirmed_global.csv\"\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", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)\n", "data_file" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...6/12/216/13/216/14/216/15/216/16/216/17/216/18/216/19/216/20/216/21/21
119French GuianaFrance3.933900-53.125800000000...25506255062550625788259502614326143261432614326450
120French PolynesiaFrance-17.679700149.406800000000...18930189301893918947189521895718963189631896318972
121GuadeloupeFrance16.265000-61.551000000000...17108172881728817288172881728817288172881728817427
122MartiniqueFrance14.641500-61.024200000000...12060121301213012130121301213012130121301213012199
123MayotteFrance-12.82750045.166244000000...19373193731937819378193781937819389193891938919389
124New CaledoniaFrance-20.904305165.618042000000...128128128128128128128128129129
125ReunionFrance-21.11510055.536400000000...27235272352723527235284412844128441284412844128441
126Saint BarthelemyFrance17.900000-62.833300000000...1005100510051005100510051005100510051005
127Saint Pierre and MiquelonFrance46.885200-56.315900000000...25252525262626262626
128St MartinFrance18.070800-63.050100000000...2040204020402040204021332133213321332133
129Wallis and FutunaFrance-14.293800-178.116500000000...445445445445445445445445445445
130NaNFrance46.2276002.213700002333...5675604567820956788935681846568353656853875688557569118156929965692968
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12 rows × 521 columns

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" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 \\\n", "119 French Guiana France 3.933900 -53.125800 0 \n", "120 French Polynesia France -17.679700 149.406800 0 \n", "121 Guadeloupe France 16.265000 -61.551000 0 \n", "122 Martinique France 14.641500 -61.024200 0 \n", "123 Mayotte France -12.827500 45.166244 0 \n", "124 New Caledonia France -20.904305 165.618042 0 \n", "125 Reunion France -21.115100 55.536400 0 \n", "126 Saint Barthelemy France 17.900000 -62.833300 0 \n", "127 Saint Pierre and Miquelon France 46.885200 -56.315900 0 \n", "128 St Martin France 18.070800 -63.050100 0 \n", "129 Wallis and Futuna France -14.293800 -178.116500 0 \n", "130 NaN France 46.227600 2.213700 0 \n", "\n", " 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... 6/12/21 6/13/21 \\\n", "119 0 0 0 0 0 ... 25506 25506 \n", "120 0 0 0 0 0 ... 18930 18930 \n", "121 0 0 0 0 0 ... 17108 17288 \n", "122 0 0 0 0 0 ... 12060 12130 \n", "123 0 0 0 0 0 ... 19373 19373 \n", "124 0 0 0 0 0 ... 128 128 \n", "125 0 0 0 0 0 ... 27235 27235 \n", "126 0 0 0 0 0 ... 1005 1005 \n", "127 0 0 0 0 0 ... 25 25 \n", "128 0 0 0 0 0 ... 2040 2040 \n", "129 0 0 0 0 0 ... 445 445 \n", "130 0 2 3 3 3 ... 5675604 5678209 \n", "\n", " 6/14/21 6/15/21 6/16/21 6/17/21 6/18/21 6/19/21 6/20/21 6/21/21 \n", "119 25506 25788 25950 26143 26143 26143 26143 26450 \n", "120 18939 18947 18952 18957 18963 18963 18963 18972 \n", "121 17288 17288 17288 17288 17288 17288 17288 17427 \n", "122 12130 12130 12130 12130 12130 12130 12130 12199 \n", "123 19378 19378 19378 19378 19389 19389 19389 19389 \n", "124 128 128 128 128 128 128 129 129 \n", "125 27235 27235 28441 28441 28441 28441 28441 28441 \n", "126 1005 1005 1005 1005 1005 1005 1005 1005 \n", "127 25 25 26 26 26 26 26 26 \n", "128 2040 2040 2040 2133 2133 2133 2133 2133 \n", "129 445 445 445 445 445 445 445 445 \n", "130 5678893 5681846 5683536 5685387 5688557 5691181 5692996 5692968 \n", "\n", "[12 rows x 521 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "raw_data = pd.read_csv(data_file, sep=',')\n", "raw_data[raw_data['Country/Region'] == 'France']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On s'intéressera ici spécifiquement aux données de la **Belgique**, la **Chine** (en traitant **Hong-Kong** à part), la **France métroploitaine**, l'**Allemagne**, l'**Iran**, l'**Italie**, le **Japon**, la **Corée du Sud**, les **Pays-Bas** (*hors colonies*), le **Portugal**, l'**Espagne**, le **Royaume-Uni** (*hors colonies*) et les **États-Unis**.\n", "\n", "[//]: # \"Initialement, il est demandé de tenir compte également de la **Chine** (en traitant **Hong-Kong** à part). Cependant, et comme on peut le voir juste au dessus, le format utilisé pour le fichier `.csv` traite chacune des 34 provinces chinoises à part, avec aucune donnée générale sur la Chine. Plusieurs choix s'offrent à nous : reconstituer une ligne *globale* pour ce pays en mélangeant **toutes** ses provinces, faire la même chose en gardant de côté **Hong-Kong** pour coller à la consigne ou se simplifier la vie en mettant de côté les données chinoises.\"\n", "\n", "[//]: # \"Je choisis cette dernière options pour plusieurs raisons. La première, et plus évidente, est la facilité : je ne pense pas parvenir à mélanger toutes les provinces de la Chine efficacement/élégamment, et suis presque certain d'effectuer une erreur en m'y frottant. Par ailleurs, on remarque en lisant l'énoncé de cet exercice :\"\n", "\n", "[//]: # \"> 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.\"\n", "\n", "[//]: # \"Ce qui laisse penser que cette difficulté n'est pas initialement prévue, et que la consigne initiale est tournée de manière à ne pas devoir réaliser de fusion de lignes. Pour toutes ces raisons, laisser de côté les données pour la **Chine** me semble à la fois bien plus judicieux en terme de temps, mais aussi plus proche de l'intention initiale de la consigne.\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "selectedCountries = ['Belgium', 'France', 'China', 'Germany', 'Iran', 'Italy',\n", " 'Japan', 'Korea,South', 'Netherlands', 'Portugal', 'Spain',\n", " 'United Kingdom', 'US']\n", "\n", "data = raw_data[raw_data['Country/Region'].isin(selectedCountries)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour tous les pays - sauf la Chine - les données hors provinces/colonies présentent `NaN` dans leur colonne `Province/State`. On peut donc récupérer d'une part toutes les données chinoises, et d'autre part les données des autres pays." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...6/12/216/13/216/14/216/15/216/16/216/17/216/18/216/19/216/20/216/21/21
58AnhuiChina31.8257117.22641915396070...1004100410041004100410041004100410041004
59BeijingChina40.1824116.4142142236416880...1069107010711071107210721073107310751075
60ChongqingChina30.0572107.874069275775110...598598598598598598598598598598
61FujianChina26.0789117.98741510183559...637637638638641646650651652659
62GansuChina35.7518104.28610224714...194194194194194194194194194194
63GuangdongChina23.3417113.424426325378111151...2618262526352650265726662680269226992706
64GuangxiChina23.8298108.78812523233646...275275275275275275275275275275
65GuizhouChina26.8154106.8748133457...147147147147147147147147147147
66HainanChina19.1959109.7453458192233...188188188188188188188188188188
67HebeiChina39.5490116.130611281318...1317131713171317131713171317131713171317
68HeilongjiangChina47.8620127.761502491521...1612161216121612161216121612161216121612
69HenanChina37.8957114.90425593283128...1316131613161316131613161316131613171317
70Hong KongChina22.3000114.2000022588...11877118771187811880118811188111884118851188611889
71HubeiChina30.9756112.270744444454976110581423...68159681596815968159681606816068160681606816068160
72HunanChina27.6104111.708849244369100...1051105110511051105110511051105110511051
73Inner MongoliaChina44.0935113.94480017711...390393393393393393393393393394
74JiangsuChina32.9711119.4550159183347...735736736738739739739739740740
75JiangxiChina27.6140115.72212718183672...937937937937937937937937937937
76JilinChina43.6661126.1923013446...573573573573573573573573573573
77LiaoningChina41.2956122.6085234172127...426426426426426426426426426426
78MacauChina22.1667113.5500122256...52525252525253535353
79NingxiaChina37.2692106.1655112347...76767676767676767676
80QinghaiChina35.745295.9956000116...18181818181818181818
81ShaanxiChina35.1917108.8701035152235...622622622622622622624624624624
82ShandongChina36.3427118.14982615274675...883883883883883883883883883883
83ShanghaiChina31.2020121.449191620334053...2155216021652168217021732179218221832184
84ShanxiChina37.5777112.29221116913...253253253253253253253253253253
85SichuanChina30.6171102.71035815284469...1050105410551056105710571057105810591064
86TianjinChina39.3054117.3230448101423...398398398398398399399399399399
87TibetChina31.692788.0924000000...1111111111
88UnknownChinaNaNNaN000000...0000000000
89XinjiangChina41.112985.2401022345...980980980980980980980980980980
90YunnanChina24.9740101.4870125111626...374376377377380382384388391391
91ZhejiangChina29.1832120.093410274362104128...1372137213731373137313761377137913791383
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34 rows × 521 columns

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" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", "58 Anhui China 31.8257 117.2264 1 9 \n", "59 Beijing China 40.1824 116.4142 14 22 \n", "60 Chongqing China 30.0572 107.8740 6 9 \n", "61 Fujian China 26.0789 117.9874 1 5 \n", "62 Gansu China 35.7518 104.2861 0 2 \n", "63 Guangdong China 23.3417 113.4244 26 32 \n", "64 Guangxi China 23.8298 108.7881 2 5 \n", "65 Guizhou China 26.8154 106.8748 1 3 \n", "66 Hainan China 19.1959 109.7453 4 5 \n", "67 Hebei China 39.5490 116.1306 1 1 \n", "68 Heilongjiang China 47.8620 127.7615 0 2 \n", "69 Henan China 37.8957 114.9042 5 5 \n", "70 Hong Kong China 22.3000 114.2000 0 2 \n", "71 Hubei China 30.9756 112.2707 444 444 \n", "72 Hunan China 27.6104 111.7088 4 9 \n", "73 Inner Mongolia China 44.0935 113.9448 0 0 \n", "74 Jiangsu China 32.9711 119.4550 1 5 \n", "75 Jiangxi China 27.6140 115.7221 2 7 \n", "76 Jilin China 43.6661 126.1923 0 1 \n", "77 Liaoning China 41.2956 122.6085 2 3 \n", "78 Macau China 22.1667 113.5500 1 2 \n", "79 Ningxia China 37.2692 106.1655 1 1 \n", "80 Qinghai China 35.7452 95.9956 0 0 \n", "81 Shaanxi China 35.1917 108.8701 0 3 \n", "82 Shandong China 36.3427 118.1498 2 6 \n", "83 Shanghai China 31.2020 121.4491 9 16 \n", "84 Shanxi China 37.5777 112.2922 1 1 \n", "85 Sichuan China 30.6171 102.7103 5 8 \n", "86 Tianjin China 39.3054 117.3230 4 4 \n", "87 Tibet China 31.6927 88.0924 0 0 \n", "88 Unknown China NaN NaN 0 0 \n", "89 Xinjiang China 41.1129 85.2401 0 2 \n", "90 Yunnan China 24.9740 101.4870 1 2 \n", "91 Zhejiang China 29.1832 120.0934 10 27 \n", "\n", " 1/24/20 1/25/20 1/26/20 1/27/20 ... 6/12/21 6/13/21 6/14/21 \\\n", "58 15 39 60 70 ... 1004 1004 1004 \n", "59 36 41 68 80 ... 1069 1070 1071 \n", "60 27 57 75 110 ... 598 598 598 \n", "61 10 18 35 59 ... 637 637 638 \n", "62 2 4 7 14 ... 194 194 194 \n", "63 53 78 111 151 ... 2618 2625 2635 \n", "64 23 23 36 46 ... 275 275 275 \n", "65 3 4 5 7 ... 147 147 147 \n", "66 8 19 22 33 ... 188 188 188 \n", "67 2 8 13 18 ... 1317 1317 1317 \n", "68 4 9 15 21 ... 1612 1612 1612 \n", "69 9 32 83 128 ... 1316 1316 1316 \n", "70 2 5 8 8 ... 11877 11877 11878 \n", "71 549 761 1058 1423 ... 68159 68159 68159 \n", "72 24 43 69 100 ... 1051 1051 1051 \n", "73 1 7 7 11 ... 390 393 393 \n", "74 9 18 33 47 ... 735 736 736 \n", "75 18 18 36 72 ... 937 937 937 \n", "76 3 4 4 6 ... 573 573 573 \n", "77 4 17 21 27 ... 426 426 426 \n", "78 2 2 5 6 ... 52 52 52 \n", "79 2 3 4 7 ... 76 76 76 \n", "80 0 1 1 6 ... 18 18 18 \n", "81 5 15 22 35 ... 622 622 622 \n", "82 15 27 46 75 ... 883 883 883 \n", "83 20 33 40 53 ... 2155 2160 2165 \n", "84 1 6 9 13 ... 253 253 253 \n", "85 15 28 44 69 ... 1050 1054 1055 \n", "86 8 10 14 23 ... 398 398 398 \n", "87 0 0 0 0 ... 1 1 1 \n", "88 0 0 0 0 ... 0 0 0 \n", "89 2 3 4 5 ... 980 980 980 \n", "90 5 11 16 26 ... 374 376 377 \n", "91 43 62 104 128 ... 1372 1372 1373 \n", "\n", " 6/15/21 6/16/21 6/17/21 6/18/21 6/19/21 6/20/21 6/21/21 \n", "58 1004 1004 1004 1004 1004 1004 1004 \n", "59 1071 1072 1072 1073 1073 1075 1075 \n", "60 598 598 598 598 598 598 598 \n", "61 638 641 646 650 651 652 659 \n", "62 194 194 194 194 194 194 194 \n", "63 2650 2657 2666 2680 2692 2699 2706 \n", "64 275 275 275 275 275 275 275 \n", "65 147 147 147 147 147 147 147 \n", "66 188 188 188 188 188 188 188 \n", "67 1317 1317 1317 1317 1317 1317 1317 \n", "68 1612 1612 1612 1612 1612 1612 1612 \n", "69 1316 1316 1316 1316 1316 1317 1317 \n", "70 11880 11881 11881 11884 11885 11886 11889 \n", "71 68159 68160 68160 68160 68160 68160 68160 \n", "72 1051 1051 1051 1051 1051 1051 1051 \n", "73 393 393 393 393 393 393 394 \n", "74 738 739 739 739 739 740 740 \n", "75 937 937 937 937 937 937 937 \n", "76 573 573 573 573 573 573 573 \n", "77 426 426 426 426 426 426 426 \n", "78 52 52 52 53 53 53 53 \n", "79 76 76 76 76 76 76 76 \n", "80 18 18 18 18 18 18 18 \n", "81 622 622 622 624 624 624 624 \n", "82 883 883 883 883 883 883 883 \n", "83 2168 2170 2173 2179 2182 2183 2184 \n", "84 253 253 253 253 253 253 253 \n", "85 1056 1057 1057 1057 1058 1059 1064 \n", "86 398 398 399 399 399 399 399 \n", "87 1 1 1 1 1 1 1 \n", "88 0 0 0 0 0 0 0 \n", "89 980 980 980 980 980 980 980 \n", "90 377 380 382 384 388 391 391 \n", "91 1373 1373 1376 1377 1379 1379 1383 \n", "\n", "[34 rows x 521 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataChina = data[data['Country/Region'] == 'China']\n", "dataChina" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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149NaNIran32.42790853.688046000000...3020522302871730394323049648306013530704263080526308697430951353105620
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155NaNJapan36.204824138.252924222244...774240775624776565777979779696781241782877784384785702786566
197NaNNetherlands52.1326005.291300000000...1671703167274416735961674628167564416767081677596167828216789831679542
214NaNPortugal39.399900-8.224500000000...856740857447858072859045860395861628862926864109865050865806
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11 rows × 521 columns

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