{ "cells": [ { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "# Analyse statistique des données liées à l'épidemie de COVID 19" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os\n", "import urllib" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Dans ce fichier, on s'intéresse à l'évolution du nombres de cas de COVID 19 dans les pays suivants :\n", " - la Belgique (Belgium),\n", " - la Chine -sans Hong-Kong (China),\n", " - Hong Kong (China, Hong-Kong),\n", " - France métropolitaine (France),\n", " - Allemagne (Germany),\n", " - Iran (Iran), l’Italie (Italy), \n", " - Japon (Japan),\n", " - Corée du Sud (Korea, South), -\n", " - Pays-Bas métropolitain (Netherlands), \n", " - Portugal (Portugal),\n", " - Espagne (Spain), \n", " - Royaume-Unis métropolitain (United Kingdom), \n", " - États-Unis (US)." ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "hideCode": false, "hidePrompt": false, "scrolled": true }, "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": 159, "metadata": { "hideCode": false, "hidePrompt": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...11/30/2012/1/2012/2/2012/3/2012/4/2012/5/2012/6/2012/7/2012/8/2012/9/20
0NaNAfghanistan33.93911067.709953000000...46274465164671846837468374707247306475164771647851
1NaNAlbania41.15330020.168300000000...38182390143971940501413024214842988436834443645188
2NaNAlgeria28.0339001.659600000000...83199841528508485927867308750288252888258941690014
3NaNAndorra42.5063001.521800000000...6745679068426904695570057050708471277162
4NaNAngola-11.20270017.873900000000...15139152511531915361154931553615591156481572915804
5NaNAntigua and Barbuda17.060800-61.796400000000...141142144144144144144146146146
6NaNArgentina-38.416100-63.616700000000...1424533143257014401031447732145463114598321463110146630914699191475222
7NaNArmenia40.06910045.038200000000...135124135967137231138508139692140959141937142344142928144066
8Australian Capital TerritoryAustralia-35.473500149.012400000000...117117117117117117117117117117
9New South WalesAustralia-33.868800151.209300000034...4582458845974603460546104614462046224624
10Northern TerritoryAustralia-12.463400130.845600000000...53535959595959616161
11QueenslandAustralia-27.469800153.025100000000...1202120512061208121012121215122112211225
12South AustraliaAustralia-34.928500138.600700000000...562562562562562562562562562562
13TasmaniaAustralia-42.882100147.327200000000...230230230230230230230230233234
14VictoriaAustralia-37.813600144.963100000011...20345203452034520345203452034520345203452034520345
15Western AustraliaAustralia-31.950500115.860500000000...821823823825828830830831832832
16NaNAustria47.51620014.550100000000...282456285489289461293430297245300689303430305693308070311002
17NaNAzerbaijan40.14310047.576900000000...121176125602129544133733138000142323146679149765154152158555
18NaNBahamas25.025885-78.035889000000...7541754375437549756575707570757975797585
19NaNBahrain26.02750050.550000000000...86956871378727087432876008773287930881118829488495
20NaNBangladesh23.68500090.356300000000...464932467225469423471739473991475789477545479743481945484104
21NaNBarbados13.193900-59.543200000000...276278279281284285285287289290
22NaNBelarus53.70980027.953400000000...136647138219139908141609143383145279147157148953150602152453
23NaNBelgium50.8333004.469936000000...577345579212582252584857587439589942591756592615594572597643
24NaNBelize17.189900-88.497600000000...5854585458547236738376017769795380138519
25NaNBenin9.3077002.315800000000...3015301530553055305530553055307330733090
26NaNBhutan27.51420090.433600000000...410414415418424426430432433433
27NaNBolivia-16.290200-63.588700000000...144708144810144994145186145325145492145560145657145846146060
28NaNBosnia and Herzegovina43.91590017.679100000000...87901890859037891539927939376894507949449602197317
29NaNBotswana-22.32850024.684900000000...10742107421074211531115311153111531120581205812058
..................................................................
241NaNThailand15.870032100.992541235788...4008402640264053407240864107412641514169
242NaNTimor-Leste-8.874217125.727539000000...30303131313131313131
243NaNTogo8.6195000.824800000000...2974299730143039305130513095311131323151
244NaNTrinidad and Tobago10.691800-61.222500000000...6669668567046725673567516767677567826808
245NaNTunisia33.8869179.537499000000...96769967699928099280101900102991104002104329105445106856
246NaNTurkey38.96370035.243300000000...638847668957700880733261765997797893828295860432893630925342
247NaNUS40.000000-100.000000112255...13541185137218221392192914139609143674371458131814756982149494171516529515386562
248NaNUganda1.37333332.290275000000...20459210352140921612218982218822499232002320025059
249NaNUkraine48.37940031.165600000000...752343765117778560793372808828822985834913843898855054867991
250NaNUnited Arab Emirates23.42407653.847818000000...168860170149171434172751174062175276176429177577178837180150
251AnguillaUnited Kingdom18.220600-63.068600000000...6666777101010
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257GibraltarUnited Kingdom36.140800-5.353600000000...1020103010351038104210481053105510581065
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259MontserratUnited Kingdom16.742498-62.187366000000...13131313131313131313
260Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...748750750751755756756756761761
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"26 NaN Bhutan 27.514200 \n", "27 NaN Bolivia -16.290200 \n", "28 NaN Bosnia and Herzegovina 43.915900 \n", "29 NaN Botswana -22.328500 \n", ".. ... ... ... \n", "241 NaN Thailand 15.870032 \n", "242 NaN Timor-Leste -8.874217 \n", "243 NaN Togo 8.619500 \n", "244 NaN Trinidad and Tobago 10.691800 \n", "245 NaN Tunisia 33.886917 \n", "246 NaN Turkey 38.963700 \n", "247 NaN US 40.000000 \n", "248 NaN Uganda 1.373333 \n", "249 NaN Ukraine 48.379400 \n", "250 NaN United Arab Emirates 23.424076 \n", "251 Anguilla United Kingdom 18.220600 \n", "252 Bermuda United Kingdom 32.307800 \n", "253 British Virgin Islands United Kingdom 18.420700 \n", "254 Cayman Islands United Kingdom 19.313300 \n", "255 Channel Islands United Kingdom 49.372300 \n", "256 Falkland Islands (Malvinas) United Kingdom -51.796300 \n", "257 Gibraltar United Kingdom 36.140800 \n", "258 Isle of Man United Kingdom 54.236100 \n", "259 Montserrat United Kingdom 16.742498 \n", "260 Turks and Caicos Islands United Kingdom 21.694000 \n", "261 NaN United Kingdom 55.378100 \n", "262 NaN Uruguay -32.522800 \n", "263 NaN Uzbekistan 41.377491 \n", "264 NaN Vanuatu -15.376700 \n", "265 NaN Venezuela 6.423800 \n", "266 NaN Vietnam 14.058324 \n", "267 NaN West Bank and Gaza 31.952200 \n", "268 NaN Yemen 15.552727 \n", "269 NaN Zambia -13.133897 \n", "270 NaN Zimbabwe -19.015438 \n", "\n", " Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 \\\n", "0 67.709953 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", "11 153.025100 0 0 0 0 0 0 \n", "12 138.600700 0 0 0 0 0 0 \n", "13 147.327200 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 -78.035889 0 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102992 \n", "268 2337 2383 2078 2079 \n", "269 17916 17931 17963 18062 \n", "270 10718 10839 10912 11007 \n", "\n", "[271 rows x 327 columns]" ] }, "execution_count": 159, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path_to_file = \"../Sujet7_StatCOVID19/DonneeBrute\"\n", "csv_file = \"donnee_JohnHopkins.csv\"\n", "if os.path.exists(os.path.join(path_to_file,csv_file)):\n", " raw_data = pd.read_csv(os.path.join(path_to_file,csv_file), skiprows=0)\n", "else: \n", " if not(os.path.isdir(path_to_file)):\n", " os.mkdir(path_to_file)\n", " urllib.request.urlretrieve(data_url, os.path.join(path_to_file,csv_file))\n", " raw_data = pd.read_csv(os.path.join(path_to_file,csv_file), skiprows=0)\n", " \n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Selection des données des pays d'intérêt\n", "\n", "On crée une liste des pays qui nous intéresse." ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "hideCode": false, "hidePrompt": false, "scrolled": true }, "outputs": [], "source": [ "country_list = ['Belgium','China','France','Germany','Iran','Italy','Japan','Korea, South','Netherlands','Portugal','Spain','United Kingdom','US']\n", "data = raw_data[raw_data['Country/Region'].isin(country_list)]" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "hideCode": false, "hidePrompt": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...11/30/2012/1/2012/2/2012/3/2012/4/2012/5/2012/6/2012/7/2012/8/2012/9/20
23NaNBelgium50.8333004.469936000000...577345579212582252584857587439589942591756592615594572597643
58AnhuiChina31.825700117.2264001915396070...992992992992992992992992992992
59BeijingChina40.182400116.414200142236416880...951951951951952952952952952952
60ChongqingChina30.057200107.87400069275775110...590590590590590590590590590590
61FujianChina26.078900117.9874001510183559...489490490492493499500500500501
62GansuChina35.751800104.2861000224714...182182182182182182182182182182
63GuangdongChina23.341700113.42440026325378111151...1989199219961997200020022004200720092010
64GuangxiChina23.829800108.7881002523233646...263263263263263263263263264264
65GuizhouChina26.815400106.874800133457...147147147147147147147147147147
66HainanChina19.195900109.745300458192233...171171171171171171171171171171
67HebeiChina39.549000116.13060011281318...373373373373373373373373373373
68HeilongjiangChina47.862000127.76150002491521...949949949949949949949949949949
69HenanChina37.895700114.9042005593283128...1289129012901292129512951295129512951295
70Hong KongChina22.300000114.200000022588...6314639664996589670168026897697570757179
71HubeiChina30.975600112.27070044444454976110581423...68148681486814968149681496814968149681496814968149
72HunanChina27.610400111.70880049244369100...1020102010201020102010201020102010201020
73Inner MongoliaChina44.093500113.9448000017711...327329330333333333336336336337
74JiangsuChina32.971100119.455000159183347...680680680680680680680680681681
75JiangxiChina27.614000115.7221002718183672...935935935935935935935935935935
76JilinChina43.666100126.192300013446...157157157157157157157157157157
77LiaoningChina41.295600122.608500234172127...289289289289289289289289289289
78MacauChina22.166700113.550000122256...46464646464646464646
79NingxiaChina37.269200106.165500112347...75757575757575757575
80QinghaiChina35.74520095.995600000116...18181818181818181818
81ShaanxiChina35.191700108.870100035152235...499500501501501502502502502502
82ShandongChina36.342700118.1498002615274675...854854856857857857857857857857
83ShanghaiChina31.202000121.44910091620334053...1332133313411350135313591366137013761385
84ShanxiChina37.577700112.2922001116913...221221221221222222222222222222
85SichuanChina30.617100102.7103005815284469...808808808808811812812820822822
86TianjinChina39.305400117.323000448101423...300300300300300301301301301301
..................................................................
123New CaledoniaFrance-20.904305165.618042000000...33333334353535353536
124ReunionFrance-21.11510055.536400000000...8054805481028102820082008200829482948345
125Saint BarthelemyFrance17.900000-62.833300000000...147147147147147147147147162162
126Saint Pierre and MiquelonFrance46.885200-56.315900000000...16161616161616161616
127St MartinFrance18.070800-63.050100000000...717717717717717717717717801801
128NaNFrance46.2276002.213700002333...2221421222927822432512255912226701922798692290891229415723078702321996
132NaNGermany51.16569110.451526000001...1069912109467811179531141544115751411836401194550120000612292691254358
147NaNIran32.42790853.688046000000...9620709759519895721003494101683510289861040547105137410623971072620
151NaNItaly41.87194012.567380000000...1601554162090116416101664829168893917099911728878174255717573941770149
153NaNJapan36.204824138.252924222244...148962150976153432155938158387160870162917164434166592169429
157NaNKorea, South35.907757127.766922112234...34652351633570336332369153754638161387553943240098
189ArubaNetherlands12.521100-69.968300000000...4845487249024923494249574965496649844996
190Bonaire, Sint Eustatius and SabaNetherlands12.178400-68.238500000000...162162162164164164164164169172
191CuracaoNetherlands12.169600-68.990000000000...2379250326582740289030113136317432463325
192Sint MaartenNetherlands18.042500-63.054800000000...1066106910721105112011391139115511611185
193NaNNetherlands52.1326005.291300000000...523478527523532444538050543924550445557224564291570437576965
208NaNPortugal39.399900-8.224500000000...298061300462303846307618312553318640322474325071327976332073
231NaNSpain40.463667-3.749220000000...1648187165644416657751675902168464716846471684647170232817023281712101
247NaNUS40.000000-100.000000112255...13541185137218221392192914139609143674371458131814756982149494171516529515386562
251AnguillaUnited Kingdom18.220600-63.068600000000...6666777101010
252BermudaUnited Kingdom32.307800-64.750500000000...260262267272278288288306330340
253British Virgin IslandsUnited Kingdom18.420700-64.640000000000...71727272727272727272
254Cayman IslandsUnited Kingdom19.313300-81.254600000000...274285285288288288288290291292
255Channel IslandsUnited Kingdom49.372300-2.364400000000...1299134113971458152615491645167617481786
256Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...16161717171717171717
257GibraltarUnited Kingdom36.140800-5.353600000000...1020103010351038104210481053105510581065
258Isle of ManUnited Kingdom54.236100-4.548100000000...369369369369370370370370370370
259MontserratUnited Kingdom16.742498-62.187366000000...13131313131313131313
260Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...748750750751755756756756761761
261NaNUnited Kingdom55.378100-3.436000000000...1629657164308616592561674134169043217059711723242173796017502411766819
\n", "

69 rows × 327 columns

\n", "
" ], "text/plain": [ " Province/State Country/Region Lat Long \\\n", "23 NaN Belgium 50.833300 4.469936 \n", "58 Anhui China 31.825700 117.226400 \n", "59 Beijing China 40.182400 116.414200 \n", "60 Chongqing China 30.057200 107.874000 \n", "61 Fujian China 26.078900 117.987400 \n", "62 Gansu China 35.751800 104.286100 \n", "63 Guangdong China 23.341700 113.424400 \n", "64 Guangxi China 23.829800 108.788100 \n", "65 Guizhou China 26.815400 106.874800 \n", "66 Hainan China 19.195900 109.745300 \n", "67 Hebei China 39.549000 116.130600 \n", "68 Heilongjiang China 47.862000 127.761500 \n", "69 Henan China 37.895700 114.904200 \n", "70 Hong Kong China 22.300000 114.200000 \n", "71 Hubei China 30.975600 112.270700 \n", "72 Hunan China 27.610400 111.708800 \n", "73 Inner Mongolia China 44.093500 113.944800 \n", "74 Jiangsu China 32.971100 119.455000 \n", "75 Jiangxi China 27.614000 115.722100 \n", "76 Jilin China 43.666100 126.192300 \n", "77 Liaoning China 41.295600 122.608500 \n", "78 Macau China 22.166700 113.550000 \n", "79 Ningxia China 37.269200 106.165500 \n", "80 Qinghai China 35.745200 95.995600 \n", "81 Shaanxi China 35.191700 108.870100 \n", "82 Shandong China 36.342700 118.149800 \n", "83 Shanghai China 31.202000 121.449100 \n", "84 Shanxi China 37.577700 112.292200 \n", "85 Sichuan China 30.617100 102.710300 \n", "86 Tianjin China 39.305400 117.323000 \n", ".. ... ... ... ... \n", "123 New Caledonia France -20.904305 165.618042 \n", "124 Reunion France -21.115100 55.536400 \n", "125 Saint Barthelemy France 17.900000 -62.833300 \n", "126 Saint Pierre and Miquelon France 46.885200 -56.315900 \n", "127 St Martin France 18.070800 -63.050100 \n", "128 NaN France 46.227600 2.213700 \n", "132 NaN Germany 51.165691 10.451526 \n", "147 NaN Iran 32.427908 53.688046 \n", "151 NaN Italy 41.871940 12.567380 \n", "153 NaN Japan 36.204824 138.252924 \n", "157 NaN Korea, South 35.907757 127.766922 \n", "189 Aruba Netherlands 12.521100 -69.968300 \n", "190 Bonaire, Sint 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58AnhuiChina31.825700117.2264001915396070...992992992992992992992992992992
59BeijingChina40.182400116.414200142236416880...951951951951952952952952952952
60ChongqingChina30.057200107.87400069275775110...590590590590590590590590590590
61FujianChina26.078900117.9874001510183559...489490490492493499500500500501
62GansuChina35.751800104.2861000224714...182182182182182182182182182182
63GuangdongChina23.341700113.42440026325378111151...1989199219961997200020022004200720092010
64GuangxiChina23.829800108.7881002523233646...263263263263263263263263264264
65GuizhouChina26.815400106.874800133457...147147147147147147147147147147
66HainanChina19.195900109.745300458192233...171171171171171171171171171171
67HebeiChina39.549000116.13060011281318...373373373373373373373373373373
68HeilongjiangChina47.862000127.76150002491521...949949949949949949949949949949
69HenanChina37.895700114.9042005593283128...1289129012901292129512951295129512951295
70NaNChina, Hong Kong22.300000114.200000022588...6314639664996589670168026897697570757179
71HubeiChina30.975600112.27070044444454976110581423...68148681486814968149681496814968149681496814968149
72HunanChina27.610400111.70880049244369100...1020102010201020102010201020102010201020
73Inner MongoliaChina44.093500113.9448000017711...327329330333333333336336336337
74JiangsuChina32.971100119.455000159183347...680680680680680680680680681681
75JiangxiChina27.614000115.7221002718183672...935935935935935935935935935935
76JilinChina43.666100126.192300013446...157157157157157157157157157157
77LiaoningChina41.295600122.608500234172127...289289289289289289289289289289
78MacauChina22.166700113.550000122256...46464646464646464646
79NingxiaChina37.269200106.165500112347...75757575757575757575
80QinghaiChina35.74520095.995600000116...18181818181818181818
81ShaanxiChina35.191700108.870100035152235...499500501501501502502502502502
82ShandongChina36.342700118.1498002615274675...854854856857857857857857857857
83ShanghaiChina31.202000121.44910091620334053...1332133313411350135313591366137013761385
84ShanxiChina37.577700112.2922001116913...221221221221222222222222222222
85SichuanChina30.617100102.7103005815284469...808808808808811812812820822822
86TianjinChina39.305400117.323000448101423...300300300300300301301301301301
87TibetChina31.69270088.092400000000...1111111111
88XinjiangChina41.11290085.240100022345...980980980980980980980980980980
89YunnanChina24.974000101.487000125111626...219219219219219220221221221221
90ZhejiangChina29.183200120.09340010274362104128...1294129412941294129412941295129512951295
128NaNFrance46.2276002.213700002333...2221421222927822432512255912226701922798692290891229415723078702321996
132NaNGermany51.16569110.451526000001...1069912109467811179531141544115751411836401194550120000612292691254358
147NaNIran32.42790853.688046000000...9620709759519895721003494101683510289861040547105137410623971072620
151NaNItaly41.87194012.567380000000...1601554162090116416101664829168893917099911728878174255717573941770149
153NaNJapan36.204824138.252924222244...148962150976153432155938158387160870162917164434166592169429
157NaNKorea, South35.907757127.766922112234...34652351633570336332369153754638161387553943240098
193NaNNetherlands52.1326005.291300000000...523478527523532444538050543924550445557224564291570437576965
208NaNPortugal39.399900-8.224500000000...298061300462303846307618312553318640322474325071327976332073
231NaNSpain40.463667-3.749220000000...1648187165644416657751675902168464716846471684647170232817023281712101
247NaNUS40.000000-100.000000112255...13541185137218221392192914139609143674371458131814756982149494171516529515386562
261NaNUnited Kingdom55.378100-3.436000000000...1629657164308616592561674134169043217059711723242173796017502411766819
\n", "

45 rows × 327 columns

\n", "
" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 \\\n", "23 NaN Belgium 50.833300 4.469936 0 \n", "58 Anhui China 31.825700 117.226400 1 \n", "59 Beijing China 40.182400 116.414200 14 \n", "60 Chongqing China 30.057200 107.874000 6 \n", "61 Fujian China 26.078900 117.987400 1 \n", "62 Gansu China 35.751800 104.286100 0 \n", "63 Guangdong China 23.341700 113.424400 26 \n", "64 Guangxi China 23.829800 108.788100 2 \n", "65 Guizhou China 26.815400 106.874800 1 \n", "66 Hainan China 19.195900 109.745300 4 \n", "67 Hebei China 39.549000 116.130600 1 \n", "68 Heilongjiang China 47.862000 127.761500 0 \n", "69 Henan China 37.895700 114.904200 5 \n", "70 NaN China, Hong Kong 22.300000 114.200000 0 \n", "71 Hubei China 30.975600 112.270700 444 \n", "72 Hunan China 27.610400 111.708800 4 \n", "73 Inner Mongolia China 44.093500 113.944800 0 \n", "74 Jiangsu China 32.971100 119.455000 1 \n", "75 Jiangxi China 27.614000 115.722100 2 \n", "76 Jilin China 43.666100 126.192300 0 \n", "77 Liaoning China 41.295600 122.608500 2 \n", "78 Macau China 22.166700 113.550000 1 \n", "79 Ningxia China 37.269200 106.165500 1 \n", "80 Qinghai China 35.745200 95.995600 0 \n", "81 Shaanxi China 35.191700 108.870100 0 \n", "82 Shandong China 36.342700 118.149800 2 \n", "83 Shanghai China 31.202000 121.449100 9 \n", "84 Shanxi China 37.577700 112.292200 1 \n", "85 Sichuan China 30.617100 102.710300 5 \n", "86 Tianjin China 39.305400 117.323000 4 \n", "87 Tibet China 31.692700 88.092400 0 \n", "88 Xinjiang China 41.112900 85.240100 0 \n", "89 Yunnan China 24.974000 101.487000 1 \n", "90 Zhejiang China 29.183200 120.093400 10 \n", "128 NaN France 46.227600 2.213700 0 \n", "132 NaN Germany 51.165691 10.451526 0 \n", "147 NaN Iran 32.427908 53.688046 0 \n", "151 NaN Italy 41.871940 12.567380 0 \n", "153 NaN Japan 36.204824 138.252924 2 \n", "157 NaN Korea, South 35.907757 127.766922 1 \n", "193 NaN Netherlands 52.132600 5.291300 0 \n", "208 NaN Portugal 39.399900 -8.224500 0 \n", "231 NaN Spain 40.463667 -3.749220 0 \n", "247 NaN US 40.000000 -100.000000 1 \n", "261 NaN United Kingdom 55.378100 -3.436000 0 \n", "\n", " 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... 11/30/20 \\\n", "23 0 0 0 0 0 ... 577345 \n", "58 9 15 39 60 70 ... 992 \n", "59 22 36 41 68 80 ... 951 \n", "60 9 27 57 75 110 ... 590 \n", "61 5 10 18 35 59 ... 489 \n", "62 2 2 4 7 14 ... 182 \n", "63 32 53 78 111 151 ... 1989 \n", "64 5 23 23 36 46 ... 263 \n", "65 3 3 4 5 7 ... 147 \n", "66 5 8 19 22 33 ... 171 \n", "67 1 2 8 13 18 ... 373 \n", "68 2 4 9 15 21 ... 949 \n", "69 5 9 32 83 128 ... 1289 \n", "70 2 2 5 8 8 ... 6314 \n", "71 444 549 761 1058 1423 ... 68148 \n", "72 9 24 43 69 100 ... 1020 \n", "73 0 1 7 7 11 ... 327 \n", "74 5 9 18 33 47 ... 680 \n", "75 7 18 18 36 72 ... 935 \n", "76 1 3 4 4 6 ... 157 \n", "77 3 4 17 21 27 ... 289 \n", "78 2 2 2 5 6 ... 46 \n", "79 1 2 3 4 7 ... 75 \n", "80 0 0 1 1 6 ... 18 \n", "81 3 5 15 22 35 ... 499 \n", "82 6 15 27 46 75 ... 854 \n", "83 16 20 33 40 53 ... 1332 \n", "84 1 1 6 9 13 ... 221 \n", "85 8 15 28 44 69 ... 808 \n", "86 4 8 10 14 23 ... 300 \n", "87 0 0 0 0 0 ... 1 \n", "88 2 2 3 4 5 ... 980 \n", "89 2 5 11 16 26 ... 219 \n", "90 27 43 62 104 128 ... 1294 \n", "128 0 2 3 3 3 ... 2221421 \n", "132 0 0 0 0 1 ... 1069912 \n", "147 0 0 0 0 0 ... 962070 \n", "151 0 0 0 0 0 ... 1601554 \n", "153 2 2 2 4 4 ... 148962 \n", "157 1 2 2 3 4 ... 34652 \n", "193 0 0 0 0 0 ... 523478 \n", "208 0 0 0 0 0 ... 298061 \n", "231 0 0 0 0 0 ... 1648187 \n", "247 1 2 2 5 5 ... 13541185 \n", "261 0 0 0 0 0 ... 1629657 \n", "\n", " 12/1/20 12/2/20 12/3/20 12/4/20 12/5/20 12/6/20 12/7/20 \\\n", "23 579212 582252 584857 587439 589942 591756 592615 \n", "58 992 992 992 992 992 992 992 \n", "59 951 951 951 952 952 952 952 \n", "60 590 590 590 590 590 590 590 \n", "61 490 490 492 493 499 500 500 \n", "62 182 182 182 182 182 182 182 \n", "63 1992 1996 1997 2000 2002 2004 2007 \n", "64 263 263 263 263 263 263 263 \n", "65 147 147 147 147 147 147 147 \n", "66 171 171 171 171 171 171 171 \n", "67 373 373 373 373 373 373 373 \n", "68 949 949 949 949 949 949 949 \n", "69 1290 1290 1292 1295 1295 1295 1295 \n", "70 6396 6499 6589 6701 6802 6897 6975 \n", "71 68148 68149 68149 68149 68149 68149 68149 \n", "72 1020 1020 1020 1020 1020 1020 1020 \n", "73 329 330 333 333 333 336 336 \n", "74 680 680 680 680 680 680 680 \n", "75 935 935 935 935 935 935 935 \n", "76 157 157 157 157 157 157 157 \n", "77 289 289 289 289 289 289 289 \n", "78 46 46 46 46 46 46 46 \n", "79 75 75 75 75 75 75 75 \n", "80 18 18 18 18 18 18 18 \n", "81 500 501 501 501 502 502 502 \n", "82 854 856 857 857 857 857 857 \n", "83 1333 1341 1350 1353 1359 1366 1370 \n", "84 221 221 221 222 222 222 222 \n", "85 808 808 808 811 812 812 820 \n", "86 300 300 300 300 301 301 301 \n", "87 1 1 1 1 1 1 1 \n", "88 980 980 980 980 980 980 980 \n", "89 219 219 219 219 220 221 221 \n", "90 1294 1294 1294 1294 1294 1295 1295 \n", "128 2229278 2243251 2255912 2267019 2279869 2290891 2294157 \n", "132 1094678 1117953 1141544 1157514 1183640 1194550 1200006 \n", "147 975951 989572 1003494 1016835 1028986 1040547 1051374 \n", "151 1620901 1641610 1664829 1688939 1709991 1728878 1742557 \n", "153 150976 153432 155938 158387 160870 162917 164434 \n", "157 35163 35703 36332 36915 37546 38161 38755 \n", "193 527523 532444 538050 543924 550445 557224 564291 \n", "208 300462 303846 307618 312553 318640 322474 325071 \n", "231 1656444 1665775 1675902 1684647 1684647 1684647 1702328 \n", "247 13721822 13921929 14139609 14367437 14581318 14756982 14949417 \n", "261 1643086 1659256 1674134 1690432 1705971 1723242 1737960 \n", "\n", " 12/8/20 12/9/20 \n", "23 594572 597643 \n", "58 992 992 \n", "59 952 952 \n", "60 590 590 \n", "61 500 501 \n", "62 182 182 \n", "63 2009 2010 \n", "64 264 264 \n", "65 147 147 \n", "66 171 171 \n", "67 373 373 \n", "68 949 949 \n", "69 1295 1295 \n", "70 7075 7179 \n", "71 68149 68149 \n", "72 1020 1020 \n", "73 336 337 \n", "74 681 681 \n", "75 935 935 \n", "76 157 157 \n", "77 289 289 \n", "78 46 46 \n", "79 75 75 \n", "80 18 18 \n", "81 502 502 \n", "82 857 857 \n", "83 1376 1385 \n", "84 222 222 \n", "85 822 822 \n", "86 301 301 \n", "87 1 1 \n", "88 980 980 \n", "89 221 221 \n", "90 1295 1295 \n", "128 2307870 2321996 \n", "132 1229269 1254358 \n", "147 1062397 1072620 \n", "151 1757394 1770149 \n", "153 166592 169429 \n", "157 39432 40098 \n", "193 570437 576965 \n", "208 327976 332073 \n", "231 1702328 1712101 \n", "247 15165295 15386562 \n", "261 1750241 1766819 \n", "\n", "[45 rows x 327 columns]" ] }, "execution_count": 162, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = data[data['Province/State'].isna() | data['Country/Region'].isin(['China'])] #Suppression des \"colonnies\" \n", "hkdata = data[data['Province/State'].isin(['Hong Kong']) & data['Country/Region'].isin(['China'])] # récupération des données de Hong Kong\n", "hkdata = hkdata.replace(['China','Hong Kong'],['China, Hong Kong', 'NaN']) # modification des champs pour coincider avec le SCMP\n", "data = data[~data['Province/State'].isin(['Hong Kong'])] # suppression de l'ancienne ligne HK\n", "data = data.append(hkdata) # ajout de la ligne modifiée\n", "data.sort_index().copy() # tri des lignes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "hide_code_all_hidden": false, "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 }