diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..a48d49479bba5ac2c5cc667134e68322c3738453 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -1,5 +1,3724 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sujet 7 : Autour du SARS-CoV-2 (Covid-19)\n", + "\n", + "## Objectifs\n", + "\n", + "Le but est ici de reproduire des graphes semblables à ceux du [South China Morning Post (SCMP)](https://www.scmp.com/), 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", + "1. Télécharger les données depuis le site \n", + "\n", + "2. 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", + "3. Faire 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", + "## Première partie : récupération et nettoyage des données \n", + "\n", + "On déclare les librairies qui seront utilisées ici, puis on importe les données : \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + " %matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2NaNAlgeria28.0339001.659600000000...2910300731273256338235173649384840064154
3NaNAndorra42.5063001.521800000000...723723731738738743743743745745
4NaNAngola-11.20270017.873900000000...25252525262727272730
5NaNAntigua and Barbuda17.060800-61.796400000000...24242424242424242425
6NaNArgentina-38.416100-63.616700000000...3144343536073780389240034127428544284532
7NaNArmenia40.06910045.038200000000...1473152315961677174618081867193220662148
8Australian Capital TerritoryAustralia-35.473500149.012400000000...104104105106106106106106106106
9New South WalesAustralia-33.868800151.209300000034...2971297629822994300230043016301630253030
10Northern TerritoryAustralia-12.463400130.845600000000...28282828282828282827
11QueenslandAustralia-28.016700153.400000000000...1024102610261026103010331034103310331034
12South AustraliaAustralia-34.928500138.600700000000...438438438438438438438438438438
13TasmaniaAustralia-41.454500145.970700000000...205207207207212214218219221221
14VictoriaAustralia-37.813600144.963100000011...1336133713431346134913491354136113641371
15Western AustraliaAustralia-31.950500115.860500000000...546546548549549549550551551551
16NaNAustria47.51620014.550100000000...14925150021507115148152251527415357154021545215531
17NaNAzerbaijan40.14310047.576900000000...1518154815921617164516781717176618041854
18NaNBahamas25.034300-77.396300000000...65727378808080808181
19NaNBahrain26.02750050.550000000000...2027221725182588264727232811292130403170
20NaNBangladesh23.68500090.356300000000...3772418646894998541659136462710376678238
21NaNBarbados13.193900-59.543200000000...75767779798080808181
22NaNBelarus53.70980027.953400000000...7281802287739590104631128912208131811402714917
23NaNBelgium50.8333004.000000000000...41889427974429345325461344668747334478594851949032
24NaNBenin9.3077002.315800000000...54545454646464646490
25NaNBhutan27.51420090.433600000000...6777777777
26NaNBolivia-16.290200-63.588700000000...60970380786695010141014111011101229
27NaNBosnia and Herzegovina43.91590017.679100000000...1368141314211486151615651585167717571781
28NaNBrazil-14.235000-51.925300000000...45757500365404359324631006744673235796858718792202
29NaNBrunei4.535300114.727700000000...138138138138138138138138138138
..................................................................
236NaNTimor-Leste-8.874217125.727539000000...23232424242424242424
237NaNBelize13.193900-59.543200000000...18181818181818181818
238RecoveredCanada0.0000000.000000000000...0000000000
239NaNLaos19.856270102.495496000000...19191919191919191919
240NaNLibya26.33510017.228331000000...59606161616161616163
241NaNWest Bank and Gaza31.95220035.233200000000...474480484342342342343344344353
242NaNGuinea-Bissau11.803700-15.180400000000...50505252537373205205257
243NaNMali17.570692-3.996166000000...293309325370389408424482490508
244NaNSaint Kitts and Nevis17.357822-62.782998000000...15151515151515151515
245Northwest TerritoriesCanada64.825500-124.845700000000...5555555555
246YukonCanada64.282300-135.000000000000...11111111111111111111
247NaNKosovo42.60263620.902977000000...630669703731763780790799806806
248NaNBurma21.91620095.956000000000...123139144146146146150150151151
249AnguillaUnited Kingdom18.220600-63.068600000000...3333333333
250British Virgin IslandsUnited Kingdom18.420700-64.640000000000...5556666666
251Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...11111111111212121212
252NaNMS Zaandam0.0000000.000000000000...9999999999
253NaNBotswana-22.32850024.684900000000...22222222222223232323
254NaNBurundi-3.37310029.918900000000...11111111111111111111
255NaNSierra Leone8.460555-11.779889000000...616482829393104104124136
256Bonaire, Sint Eustatius and SabaNetherlands12.178400-68.238500000000...5555555556
257NaNMalawi-13.25430834.301525000000...23333333343636363737
258Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...11121313131313131313
259Saint Pierre and MiquelonFrance46.885200-56.315900000000...1111111111
260NaNSouth Sudan6.87700031.307000000000...45556634343545
261NaNWestern Sahara24.215500-12.885800000000...6666666666
262NaNSao Tome and Principe0.1863606.613081000000...444444881416
263NaNYemen15.55272748.516388000000...1111111667
264NaNComoros-11.64550043.333300000000...0000000011
265NaNTajikistan38.86103471.276093000000...000000001515
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266 rows × 105 columns

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"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 Recovered Canada 0.000000 \n", + "239 NaN Laos 19.856270 \n", + "240 NaN Libya 26.335100 \n", + "241 NaN West Bank and Gaza 31.952200 \n", + "242 NaN Guinea-Bissau 11.803700 \n", + "243 NaN Mali 17.570692 \n", + "244 NaN Saint Kitts and Nevis 17.357822 \n", + "245 Northwest Territories Canada 64.825500 \n", + "246 Yukon Canada 64.282300 \n", + "247 NaN Kosovo 42.602636 \n", + "248 NaN Burma 21.916200 \n", + "249 Anguilla United Kingdom 18.220600 \n", + "250 British Virgin Islands United Kingdom 18.420700 \n", + "251 Turks and Caicos Islands United Kingdom 21.694000 \n", + "252 NaN MS Zaandam 0.000000 \n", + "253 NaN Botswana -22.328500 \n", + "254 NaN Burundi -3.373100 \n", + "255 NaN Sierra Leone 8.460555 \n", + "256 Bonaire, Sint Eustatius and Saba Netherlands 12.178400 \n", + "257 NaN Malawi -13.254308 \n", + "258 Falkland Islands (Malvinas) United Kingdom -51.796300 \n", + "259 Saint Pierre and Miquelon France 46.885200 \n", + "260 NaN South Sudan 6.877000 \n", + "261 NaN Western Sahara 24.215500 \n", + "262 NaN Sao Tome and Principe 0.186360 \n", + "263 NaN Yemen 15.552727 \n", + "264 NaN Comoros -11.645500 \n", + "265 NaN Tajikistan 38.861034 \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 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regarde maintenant si il y a des données manquantes dans l'une des colonnes, qui ne soit pas la province (certains pays n'en ont pas). Pour ça, on va créer une copie du tableau en supprimant la colonne province puis regarder si il y a des null :" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...4/22/204/23/204/24/204/25/204/26/204/27/204/28/204/29/204/30/205/1/20
23NaNBelgium50.83334.0000000000...41889427974429345325461344668747334478594851949032
49AnhuiChina31.8257117.22641915396070...991991991991991991991991991991
50BeijingChina40.1824116.4142142236416880...593593593593593593593593593593
51ChongqingChina30.0572107.874069275775110...579579579579579579579579579579
52FujianChina26.0789117.98741510183559...355355355355355355355355356356
53GansuChina37.8099101.05830224714...139139139139139139139139139139
54GuangdongChina23.3417113.424426325378111151...1582158515851586158715871588158815881588
55GuangxiChina23.8298108.78812523233646...254254254254254254254254254254
56GuizhouChina26.8154106.8748133457...147147147147147147147147147147
57HainanChina19.1959109.7453458192233...168168168168168168168168168168
58HebeiChina39.5490116.130611281318...328328328328328328328328328328
59HeilongjiangChina47.8620127.761502491521...921928930935936939939939944944
60HenanChina33.8820113.61405593283128...1276127612761276127612761276127612761276
61Hong KongChina22.3000114.2000022588...1033103510351037103710371037103710371039
62HubeiChina30.9756112.270744444454976110581423...68128681286812868128681286812868128681286812868128
63HunanChina27.6104111.708849244369100...1019101910191019101910191019101910191019
64Inner MongoliaChina44.0935113.94480017711...194194197198198199199200201201
65JiangsuChina32.9711119.4550159183347...653653653653653653653653653653
66JiangxiChina27.6140115.72212718183672...937937937937937937937937937937
67JilinChina43.6661126.1923013446...106108109109110110110111111112
68LiaoningChina41.2956122.6085234172127...146146146146146146146146146146
69MacauChina22.1667113.5500122256...45454545454545454545
70NingxiaChina37.2692106.1655112347...75757575757575757575
71QinghaiChina35.745295.9956000116...18181818181818181818
72ShaanxiChina35.1917108.8701035152235...279279286286286286306306306306
73ShandongChina36.3427118.14982615274675...787787787787787787787787787787
74ShanghaiChina31.2020121.449191620334053...639641641642642644645647652652
75ShanxiChina37.5777112.29221116913...197197197197197197197197197197
76SichuanChina30.6171102.71035815284469...561561561561561561561561561561
77TianjinChina39.3054117.3230448101423...189189190190190190190190190190
..................................................................
111New CaledoniaFrance-20.9043165.6180000000...18181818181818181818
112ReunionFrance-21.135155.2471000000...410412412417417418418420420422
113Saint BarthelemyFrance17.9000-62.8333000000...6666666666
114St MartinFrance18.0708-63.0501000000...38383838383838383838
115MartiniqueFrance14.6415-61.0242000000...164164170175175175175175178179
116NaNFrance46.22762.2137002333...154715157026158636160292160847164589167605165093165764165764
120NaNGermany51.00009.0000000001...150648153129154999156513157770158758159912161539163009164077
133NaNIran32.000053.0000000000...85996870268819489328904819147292584936579464095646
137NaNItaly43.000012.0000000000...187327189973192994195351197675199414201505203591205463207428
139NaNJapan36.0000138.0000222244...11512123681282913231134411415313736138951408814305
166ArubaNetherlands12.5186-70.0358000000...100100100100100100100100100100
167CuracaoNetherlands12.1696-68.9900000000...14141616161616161616
168Sint MaartenNetherlands18.0425-63.0548000000...71737373747475757576
169NaNNetherlands52.13265.2913000000...34842357293653537190378453824538416388023931639791
184NaNPortugal39.3999-8.2245000000...21982223532279723392238642402724322245052504525351
201NaNSpain40.0000-4.0000000000...208389213024202990205905207634209465210773212917213435213435
217BermudaUnited Kingdom32.3078-64.7505000000...999999109109110110111114114
218Cayman IslandsUnited Kingdom19.3133-81.2546000000...66667070707070737374
219Channel IslandsUnited Kingdom49.3723-2.3644000000...498521523525525525530537537538
220GibraltarUnited Kingdom36.1408-5.3536000000...132133133136141141141141144144
221Isle of ManUnited Kingdom54.2361-4.5481000000...307307308308308308309313315316
222MontserratUnited Kingdom16.7425-62.1874000000...11111111111111111111
223NaNUnited Kingdom55.3781-3.4360000000...133495138078143464148377152840157149161145165221171253177454
225NaNUS37.0902-95.7129112255...8403518691709053589381549657859881971012582103990910694241103461
249AnguillaUnited Kingdom18.2206-63.0686000000...3333333333
250British Virgin IslandsUnited Kingdom18.4207-64.6400000000...5556666666
251Turks and Caicos IslandsUnited Kingdom21.6940-71.7979000000...11111111111212121212
256Bonaire, Sint Eustatius and SabaNetherlands12.1784-68.2385000000...5555555556
258Falkland Islands (Malvinas)United Kingdom-51.7963-59.5236000000...11121313131313131313
259Saint Pierre and MiquelonFrance46.8852-56.3159000000...1111111111
\n", + "

68 rows × 105 columns

\n", + "
" + ], + "text/plain": [ + " Province/State Country/Region Lat Long \\\n", + "23 NaN Belgium 50.8333 4.0000 \n", + "49 Anhui China 31.8257 117.2264 \n", + "50 Beijing China 40.1824 116.4142 \n", + "51 Chongqing China 30.0572 107.8740 \n", + "52 Fujian China 26.0789 117.9874 \n", + "53 Gansu China 37.8099 101.0583 \n", + "54 Guangdong China 23.3417 113.4244 \n", + "55 Guangxi China 23.8298 108.7881 \n", + "56 Guizhou China 26.8154 106.8748 \n", + "57 Hainan China 19.1959 109.7453 \n", + "58 Hebei China 39.5490 116.1306 \n", + "59 Heilongjiang China 47.8620 127.7615 \n", + "60 Henan China 33.8820 113.6140 \n", + "61 Hong Kong China 22.3000 114.2000 \n", + "62 Hubei China 30.9756 112.2707 \n", + "63 Hunan China 27.6104 111.7088 \n", + "64 Inner Mongolia China 44.0935 113.9448 \n", + "65 Jiangsu China 32.9711 119.4550 \n", + "66 Jiangxi China 27.6140 115.7221 \n", + "67 Jilin China 43.6661 126.1923 \n", + "68 Liaoning China 41.2956 122.6085 \n", + "69 Macau China 22.1667 113.5500 \n", + "70 Ningxia China 37.2692 106.1655 \n", + "71 Qinghai China 35.7452 95.9956 \n", + "72 Shaanxi China 35.1917 108.8701 \n", + "73 Shandong China 36.3427 118.1498 \n", + "74 Shanghai China 31.2020 121.4491 \n", + "75 Shanxi China 37.5777 112.2922 \n", + "76 Sichuan China 30.6171 102.7103 \n", + "77 Tianjin China 39.3054 117.3230 \n", + ".. ... ... ... ... \n", + "111 New Caledonia France -20.9043 165.6180 \n", + "112 Reunion France -21.1351 55.2471 \n", + "113 Saint Barthelemy France 17.9000 -62.8333 \n", + "114 St Martin France 18.0708 -63.0501 \n", + "115 Martinique France 14.6415 -61.0242 \n", + "116 NaN France 46.2276 2.2137 \n", + "120 NaN Germany 51.0000 9.0000 \n", + "133 NaN Iran 32.0000 53.0000 \n", + "137 NaN Italy 43.0000 12.0000 \n", + "139 NaN Japan 36.0000 138.0000 \n", + "166 Aruba Netherlands 12.5186 -70.0358 \n", + "167 Curacao Netherlands 12.1696 -68.9900 \n", + "168 Sint Maarten Netherlands 18.0425 -63.0548 \n", + "169 NaN Netherlands 52.1326 5.2913 \n", + "184 NaN Portugal 39.3999 -8.2245 \n", + "201 NaN Spain 40.0000 -4.0000 \n", + "217 Bermuda United Kingdom 32.3078 -64.7505 \n", + "218 Cayman Islands United Kingdom 19.3133 -81.2546 \n", + "219 Channel Islands United Kingdom 49.3723 -2.3644 \n", + "220 Gibraltar United Kingdom 36.1408 -5.3536 \n", + "221 Isle of Man United Kingdom 54.2361 -4.5481 \n", + "222 Montserrat United Kingdom 16.7425 -62.1874 \n", + "223 NaN United Kingdom 55.3781 -3.4360 \n", + "225 NaN US 37.0902 -95.7129 \n", + "249 Anguilla United Kingdom 18.2206 -63.0686 \n", + "250 British Virgin Islands United Kingdom 18.4207 -64.6400 \n", + "251 Turks and Caicos Islands United Kingdom 21.6940 -71.7979 \n", + "256 Bonaire, Sint Eustatius and Saba Netherlands 12.1784 -68.2385 \n", + "258 Falkland Islands (Malvinas) United Kingdom -51.7963 -59.5236 \n", + "259 Saint Pierre and Miquelon France 46.8852 -56.3159 \n", + "\n", + " 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... 4/22/20 \\\n", + "23 0 0 0 0 0 0 ... 41889 \n", + "49 1 9 15 39 60 70 ... 991 \n", + "50 14 22 36 41 68 80 ... 593 \n", + "51 6 9 27 57 75 110 ... 579 \n", + "52 1 5 10 18 35 59 ... 355 \n", + "53 0 2 2 4 7 14 ... 139 \n", + "54 26 32 53 78 111 151 ... 1582 \n", + "55 2 5 23 23 36 46 ... 254 \n", + "56 1 3 3 4 5 7 ... 147 \n", + "57 4 5 8 19 22 33 ... 168 \n", + "58 1 1 2 8 13 18 ... 328 \n", + "59 0 2 4 9 15 21 ... 921 \n", + "60 5 5 9 32 83 128 ... 1276 \n", + "61 0 2 2 5 8 8 ... 1033 \n", + "62 444 444 549 761 1058 1423 ... 68128 \n", + "63 4 9 24 43 69 100 ... 1019 \n", + "64 0 0 1 7 7 11 ... 194 \n", + "65 1 5 9 18 33 47 ... 653 \n", + "66 2 7 18 18 36 72 ... 937 \n", + "67 0 1 3 4 4 6 ... 106 \n", + "68 2 3 4 17 21 27 ... 146 \n", + "69 1 2 2 2 5 6 ... 45 \n", + "70 1 1 2 3 4 7 ... 75 \n", + "71 0 0 0 1 1 6 ... 18 \n", + "72 0 3 5 15 22 35 ... 279 \n", + "73 2 6 15 27 46 75 ... 787 \n", + "74 9 16 20 33 40 53 ... 639 \n", + "75 1 1 1 6 9 13 ... 197 \n", + "76 5 8 15 28 44 69 ... 561 \n", + "77 4 4 8 10 14 23 ... 189 \n", + ".. ... 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111 \n", + "68 146 146 146 146 146 146 146 146 \n", + "69 45 45 45 45 45 45 45 45 \n", + "70 75 75 75 75 75 75 75 75 \n", + "71 18 18 18 18 18 18 18 18 \n", + "72 279 286 286 286 286 306 306 306 \n", + "73 787 787 787 787 787 787 787 787 \n", + "74 641 641 642 642 644 645 647 652 \n", + "75 197 197 197 197 197 197 197 197 \n", + "76 561 561 561 561 561 561 561 561 \n", + "77 189 190 190 190 190 190 190 190 \n", + ".. ... ... ... ... ... ... ... ... \n", + "111 18 18 18 18 18 18 18 18 \n", + "112 412 412 417 417 418 418 420 420 \n", + "113 6 6 6 6 6 6 6 6 \n", + "114 38 38 38 38 38 38 38 38 \n", + "115 164 170 175 175 175 175 175 178 \n", + "116 157026 158636 160292 160847 164589 167605 165093 165764 \n", + "120 153129 154999 156513 157770 158758 159912 161539 163009 \n", + "133 87026 88194 89328 90481 91472 92584 93657 94640 \n", + "137 189973 192994 195351 197675 199414 201505 203591 205463 \n", + "139 12368 12829 13231 13441 14153 13736 13895 14088 \n", + "166 100 100 100 100 100 100 100 100 \n", + "167 14 16 16 16 16 16 16 16 \n", + "168 73 73 73 74 74 75 75 75 \n", + "169 35729 36535 37190 37845 38245 38416 38802 39316 \n", + "184 22353 22797 23392 23864 24027 24322 24505 25045 \n", + "201 213024 202990 205905 207634 209465 210773 212917 213435 \n", + "217 99 99 109 109 110 110 111 114 \n", + "218 66 70 70 70 70 70 73 73 \n", + "219 521 523 525 525 525 530 537 537 \n", + "220 133 133 136 141 141 141 141 144 \n", + "221 307 308 308 308 308 309 313 315 \n", + "222 11 11 11 11 11 11 11 11 \n", + "223 138078 143464 148377 152840 157149 161145 165221 171253 \n", + "225 869170 905358 938154 965785 988197 1012582 1039909 1069424 \n", + "249 3 3 3 3 3 3 3 3 \n", + "250 5 5 6 6 6 6 6 6 \n", + "251 11 11 11 11 12 12 12 12 \n", + "256 5 5 5 5 5 5 5 5 \n", + "258 12 13 13 13 13 13 13 13 \n", + "259 1 1 1 1 1 1 1 1 \n", + "\n", + " 5/1/20 \n", + "23 49032 \n", + "49 991 \n", + "50 593 \n", + "51 579 \n", + "52 356 \n", + "53 139 \n", + "54 1588 \n", + "55 254 \n", + "56 147 \n", + "57 168 \n", + "58 328 \n", + "59 944 \n", + "60 1276 \n", + "61 1039 \n", + "62 68128 \n", + "63 1019 \n", + "64 201 \n", + "65 653 \n", + "66 937 \n", + "67 112 \n", + "68 146 \n", + "69 45 \n", + "70 75 \n", + "71 18 \n", + "72 306 \n", + "73 787 \n", + "74 652 \n", + "75 197 \n", + "76 561 \n", + "77 190 \n", + ".. ... \n", + "111 18 \n", + "112 422 \n", + "113 6 \n", + "114 38 \n", + "115 179 \n", + "116 165764 \n", + "120 164077 \n", + "133 95646 \n", + "137 207428 \n", + "139 14305 \n", + "166 100 \n", + "167 16 \n", + "168 76 \n", + "169 39791 \n", + "184 25351 \n", + "201 213435 \n", + "217 114 \n", + "218 74 \n", + "219 538 \n", + "220 144 \n", + "221 316 \n", + "222 11 \n", + "223 177454 \n", + "225 1103461 \n", + "249 3 \n", + "250 6 \n", + "251 12 \n", + "256 6 \n", + "258 13 \n", + "259 1 \n", + "\n", + "[68 rows x 105 columns]" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def filter_countries(row):\n", + " countries = ['Belgium', 'China', 'France', 'Germany', 'Iran', 'Italy', 'Japan', 'Korea', 'Netherlands', 'Portugal', 'Spain', 'United Kingdom', 'US']\n", + " if any(str(row['Country/Region']) in s for s in countries):\n", + " return True\n", + "\n", + " return False\n", + "\n", + "if len(raw_data.index) > 0:\n", + " new_data = raw_data[raw_data.apply(filter_countries, axis=1)]\n", + " \n", + "new_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On va déjà essayer d'afficher le graphe avec toutes ces données, pour voir le résultat :" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +3735,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -