{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Autour du SARS-CoV-2 (Covid-19)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data_url=\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"" ] }, { "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...11/15/2111/16/2111/17/2111/18/2111/19/2111/20/2111/21/2111/22/2111/23/2111/24/21
0NaNAfghanistan33.93911067.709953000000...156610156649156739156739156812156864156896156911157015157032
1NaNAlbania41.15330020.168300000000...193269193856194472195021195523195988195988196611197167197776
2NaNAlgeria28.0339001.659600000000...208104208245208380208532208695208839208952209111209283209463
3NaNAndorra42.5063001.521800000000...15907159291597216035160861608616086162991634216426
4NaNAngola-11.20270017.873900000000...64913649406496864985649976501165024650336506165080
5NaNAntigua and Barbuda17.060800-61.796400000000...4118412241294129413141354135413641384138
6NaNArgentina-38.416100-63.616700000000...5307159530878153103345312089531360753147025315348531598953176335319867
7NaNArmenia40.06910045.038200000000...329341329913330895331914332713333583334075334347334878335738
8Australian Capital TerritoryAustralia-35.473500149.012400000000...1965197119962013202720422053207220872095
9New South WalesAustralia-33.868800151.209300000034...78766789947925479468796467982179997801638040880681
10Northern TerritoryAustralia-12.463400130.845600000000...243250245253252262265266280278
11QueenslandAustralia-27.469800153.025100000000...2110211021112112211221122112211321152117
12South AustraliaAustralia-34.928500138.600700000000...920921921921922922922922922922
13TasmaniaAustralia-42.882100147.327200000000...238238238238238238238239239239
14VictoriaAustralia-37.813600144.963100000011...106262107245108231109495110662111926112942113755114943116189
15Western AustraliaAustralia-31.950500115.860500000000...1113111611161117111811191120112111211121
16NaNAustria47.51620014.550100000000...9715419819049963201011465102727410425711056613107041910799321095297
17NaNAzerbaijan40.14310047.576900000000...561925563940565965568089570322572085573974574799576703578627
18NaNBahamas25.025885-78.035889000000...22601226132262922640226552265522655226652266522696
19NaNBahrain26.02750050.550000000000...277246277262277282277304277322277348277389277421277444277481
20NaNBangladesh23.68500090.356300000000...1572735157294815732141573458157371115738891574088157435215746361574948
21NaNBarbados13.193900-59.543200000000...22316226392292323175234262362323795239442423424432
22NaNBelarus53.70980027.953400000000...629271631025632887634731636543638402640206641919643372645069
23NaNBelgium50.8333004.469936000000...1512474152486215432991559998158150015815001581500162356016356751659025
24NaNBelize17.189900-88.497600000000...29105292692938629501295012950129501296822986129967
25NaNBenin9.3077002.315800000000...24833248332483324846248462484624846248462484624850
26NaNBhutan27.51420090.433600000000...2625262526292629263226322632263226332633
27NaNBolivia-16.290200-63.588700000000...524261525187526306527380527380528407529075530369531428531428
28NaNBosnia and Herzegovina43.91590017.679100000000...265149265942266994267851268572268572268572269904270498271323
29NaNBotswana-22.32850024.684900000000...194129194129194129194445194445194445194445194652194652194652
..................................................................
250NaNTogo8.6195000.824800000000...26167261672617826185261992619926207262102621926219
251NaNTonga-21.179000-175.198200000000...1111111111
252NaNTrinidad and Tobago10.691800-61.222500000000...62670630846386564402648936548565889662936697567664
253NaNTunisia33.8869179.537499000000...715716715818715911715988716074716183716262716298716609716702
254NaNTurkey38.96370035.243300000000...8433988845908984829568505190852900085523478573524859838086265508654142
255NaNUS40.000000-100.000000112255...47222751473103374742174147531319476603794770187247730591478881714798078048092052
256NaNUganda1.37333332.290275000000...126965127002127046127073127128127128127177127237127268127305
257NaNUkraine48.37940031.165600000000...3381399339891334187923440602346187334813473493203350181535156413530969
258NaNUnited Arab Emirates23.42407653.847818000000...741006741074741148741214741291741370741433741500741570741643
259AnguillaUnited Kingdom18.220600-63.068600000000...1137117511751175126812681268126813341334
260BermudaUnited Kingdom32.307800-64.750500000000...5708571357145714571557155715572457245724
261British Virgin IslandsUnited Kingdom18.420700-64.640000000000...2745274527652765276527652765276527652765
262Cayman IslandsUnited Kingdom19.313300-81.254600000000...4203420342035156536255325532609160916283
263Channel IslandsUnited Kingdom49.372300-2.364400000000...15045151801538515518156921569215692162791662916815
264Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...79797979797979797979
265GibraltarUnited Kingdom36.140800-5.353600000000...6561663467216780684368916920694469967049
266Isle of ManUnited Kingdom54.236100-4.548100000000...10296103541041210455105041055810610106501072010797
267MontserratUnited Kingdom16.742498-62.187366000000...41414141444444444444
268Saint Helena, Ascension and Tristan da CunhaUnited Kingdom-7.946700-14.355900000000...4444444444
269Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...3045304530603067307630763085308530853092
270NaNUnited Kingdom55.378100-3.436000000000...9600369963719096750589721916976615398060349845492988992699324089974843
271NaNUruguay-32.522800-55.765800000000...396677396888397143397318397549397712397841397990398213398436
272NaNUzbekistan41.37749164.585262000000...189915190104190332190584190850191122191334191576191750191956
273NaNVanuatu-15.376700166.959200000000...6666666666
274NaNVenezuela6.423800-66.589700000000...420500420500422229422229423633424441425213426014426014426799
275NaNVietnam14.058324108.277199022222...1035138104539710552461065469107509410846251094514110483511439671155778
276NaNWest Bank and Gaza31.95220035.233200000000...457154457390457477457729457950457950457950458448458698458956
277NaNYemen15.55272748.516388000000...9936993699479950995499559955996199679972
278NaNZambia-13.13389727.849332000000...209996210008210020210036210043210057210070210073210090210099
279NaNZimbabwe-19.01543829.154857000000...133438133505133557133593133593133615133647133674133674133747
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280 rows × 677 columns

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" ], "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", "250 NaN Togo \n", "251 NaN Tonga \n", "252 NaN Trinidad and Tobago \n", "253 NaN Tunisia \n", "254 NaN Turkey \n", "255 NaN US \n", "256 NaN Uganda \n", "257 NaN Ukraine \n", "258 NaN United Arab Emirates \n", "259 Anguilla United Kingdom \n", "260 Bermuda United Kingdom \n", "261 British Virgin Islands United Kingdom \n", "262 Cayman Islands United Kingdom \n", "263 Channel Islands United Kingdom \n", "264 Falkland Islands (Malvinas) United Kingdom \n", "265 Gibraltar United Kingdom \n", "266 Isle of Man United Kingdom \n", "267 Montserrat United Kingdom \n", "268 Saint Helena, Ascension and Tristan da Cunha United Kingdom \n", "269 Turks and Caicos Islands United Kingdom \n", "270 NaN United Kingdom \n", "271 NaN Uruguay \n", "272 NaN Uzbekistan \n", "273 NaN Vanuatu \n", "274 NaN Venezuela \n", "275 NaN Vietnam \n", "276 NaN West Bank and Gaza \n", "277 NaN Yemen \n", "278 NaN Zambia \n", "279 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", "250 8.619500 0.824800 0 0 0 0 0 \n", "251 -21.179000 -175.198200 0 0 0 0 0 \n", "252 10.691800 -61.222500 0 0 0 0 0 \n", "253 33.886917 9.537499 0 0 0 0 0 \n", "254 38.963700 35.243300 0 0 0 0 0 \n", "255 40.000000 -100.000000 1 1 2 2 5 \n", "256 1.373333 32.290275 0 0 0 0 0 \n", "257 48.379400 31.165600 0 0 0 0 0 \n", "258 23.424076 53.847818 0 0 0 0 0 \n", "259 18.220600 -63.068600 0 0 0 0 0 \n", "260 32.307800 -64.750500 0 0 0 0 0 \n", "261 18.420700 -64.640000 0 0 0 0 0 \n", "262 19.313300 -81.254600 0 0 0 0 0 \n", "263 49.372300 -2.364400 0 0 0 0 0 \n", "264 -51.796300 -59.523600 0 0 0 0 0 \n", "265 36.140800 -5.353600 0 0 0 0 0 \n", "266 54.236100 -4.548100 0 0 0 0 0 \n", "267 16.742498 -62.187366 0 0 0 0 0 \n", "268 -7.946700 -14.355900 0 0 0 0 0 \n", "269 21.694000 -71.797900 0 0 0 0 0 \n", "270 55.378100 -3.436000 0 0 0 0 0 \n", "271 -32.522800 -55.765800 0 0 0 0 0 \n", "272 41.377491 64.585262 0 0 0 0 0 \n", "273 -15.376700 166.959200 0 0 0 0 0 \n", "274 6.423800 -66.589700 0 0 0 0 0 \n", "275 14.058324 108.277199 0 2 2 2 2 \n", "276 31.952200 35.233200 0 0 0 0 0 \n", "277 15.552727 48.516388 0 0 0 0 0 \n", "278 -13.133897 27.849332 0 0 0 0 0 \n", "279 -19.015438 29.154857 0 0 0 0 0 \n", "\n", " 1/27/20 ... 11/15/21 11/16/21 11/17/21 11/18/21 11/19/21 \\\n", "0 0 ... 156610 156649 156739 156739 156812 \n", "1 0 ... 193269 193856 194472 195021 195523 \n", "2 0 ... 208104 208245 208380 208532 208695 \n", "3 0 ... 15907 15929 15972 16035 16086 \n", "4 0 ... 64913 64940 64968 64985 64997 \n", "5 0 ... 4118 4122 4129 4129 4131 \n", "6 0 ... 5307159 5308781 5310334 5312089 5313607 \n", "7 0 ... 329341 329913 330895 331914 332713 \n", "8 0 ... 1965 1971 1996 2013 2027 \n", "9 4 ... 78766 78994 79254 79468 79646 \n", "10 0 ... 243 250 245 253 252 \n", "11 0 ... 2110 2110 2111 2112 2112 \n", "12 0 ... 920 921 921 921 922 \n", "13 0 ... 238 238 238 238 238 \n", "14 1 ... 106262 107245 108231 109495 110662 \n", "15 0 ... 1113 1116 1116 1117 1118 \n", "16 0 ... 971541 981904 996320 1011465 1027274 \n", "17 0 ... 561925 563940 565965 568089 570322 \n", "18 0 ... 22601 22613 22629 22640 22655 \n", "19 0 ... 277246 277262 277282 277304 277322 \n", "20 0 ... 1572735 1572948 1573214 1573458 1573711 \n", "21 0 ... 22316 22639 22923 23175 23426 \n", "22 0 ... 629271 631025 632887 634731 636543 \n", "23 0 ... 1512474 1524862 1543299 1559998 1581500 \n", "24 0 ... 29105 29269 29386 29501 29501 \n", "25 0 ... 24833 24833 24833 24846 24846 \n", "26 0 ... 2625 2625 2629 2629 2632 \n", "27 0 ... 524261 525187 526306 527380 527380 \n", "28 0 ... 265149 265942 266994 267851 268572 \n", "29 0 ... 194129 194129 194129 194445 194445 \n", ".. ... ... ... ... ... ... ... \n", "250 0 ... 26167 26167 26178 26185 26199 \n", "251 0 ... 1 1 1 1 1 \n", "252 0 ... 62670 63084 63865 64402 64893 \n", "253 0 ... 715716 715818 715911 715988 716074 \n", "254 0 ... 8433988 8459089 8482956 8505190 8529000 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... 9936 9936 9947 9950 9954 \n", "278 0 ... 209996 210008 210020 210036 210043 \n", "279 0 ... 133438 133505 133557 133593 133593 \n", "\n", " 11/20/21 11/21/21 11/22/21 11/23/21 11/24/21 \n", "0 156864 156896 156911 157015 157032 \n", "1 195988 195988 196611 197167 197776 \n", "2 208839 208952 209111 209283 209463 \n", "3 16086 16086 16299 16342 16426 \n", "4 65011 65024 65033 65061 65080 \n", "5 4135 4135 4136 4138 4138 \n", "6 5314702 5315348 5315989 5317633 5319867 \n", "7 333583 334075 334347 334878 335738 \n", "8 2042 2053 2072 2087 2095 \n", "9 79821 79997 80163 80408 80681 \n", "10 262 265 266 280 278 \n", "11 2112 2112 2113 2115 2117 \n", "12 922 922 922 922 922 \n", "13 238 238 239 239 239 \n", "14 111926 112942 113755 114943 116189 \n", "15 1119 1120 1121 1121 1121 \n", "16 1042571 1056613 1070419 1079932 1095297 \n", "17 572085 573974 574799 576703 578627 \n", "18 22655 22655 22665 22665 22696 \n", "19 277348 277389 277421 277444 277481 \n", "20 1573889 1574088 1574352 1574636 1574948 \n", "21 23623 23795 23944 24234 24432 \n", "22 638402 640206 641919 643372 645069 \n", "23 1581500 1581500 1623560 1635675 1659025 \n", "24 29501 29501 29682 29861 29967 \n", "25 24846 24846 24846 24846 24850 \n", "26 2632 2632 2632 2633 2633 \n", "27 528407 529075 530369 531428 531428 \n", "28 268572 268572 269904 270498 271323 \n", "29 194445 194445 194652 194652 194652 \n", ".. ... ... ... ... ... \n", "250 26199 26207 26210 26219 26219 \n", "251 1 1 1 1 1 \n", "252 65485 65889 66293 66975 67664 \n", "253 716183 716262 716298 716609 716702 \n", "254 8552347 8573524 8598380 8626550 8654142 \n", "255 47701872 47730591 47888171 47980780 48092052 \n", "256 127128 127177 127237 127268 127305 \n", "257 3481347 3493203 3501815 3515641 3530969 \n", "258 741370 741433 741500 741570 741643 \n", "259 1268 1268 1268 1334 1334 \n", "260 5715 5715 5724 5724 5724 \n", "261 2765 2765 2765 2765 2765 \n", "262 5532 5532 6091 6091 6283 \n", "263 15692 15692 16279 16629 16815 \n", "264 79 79 79 79 79 \n", "265 6891 6920 6944 6996 7049 \n", "266 10558 10610 10650 10720 10797 \n", "267 44 44 44 44 44 \n", "268 4 4 4 4 4 \n", "269 3076 3085 3085 3085 3092 \n", "270 9806034 9845492 9889926 9932408 9974843 \n", "271 397712 397841 397990 398213 398436 \n", "272 191122 191334 191576 191750 191956 \n", "273 6 6 6 6 6 \n", "274 424441 425213 426014 426014 426799 \n", "275 1084625 1094514 1104835 1143967 1155778 \n", "276 457950 457950 458448 458698 458956 \n", "277 9955 9955 9961 9967 9972 \n", "278 210057 210070 210073 210090 210099 \n", "279 133615 133647 133674 133674 133747 \n", "\n", "[280 rows x 677 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=0)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## les données" ] }, { "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 }