{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Subject 7: The SARS-CoV-2 (Covid-19) epidemic\n", "\n", "The goal is to produce plots similar to the one of the South China Morning Post (SCMP), on The Coronavirus Pandemic page, which shows for different countries the cumulative number (i.e. the total number since the beginning of the pandemic) of people with coronavirus 2019 disease." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data we are going to use in a first step are compiled by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) and are freely available on GitHub." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Firstly, download the data to make a graph showing the evolution of the cumulative number of cases over time for: Belgium, China (all provinces except Hong Kong), China, Hong-Kong, France except Dom/Tom, Germany, Iran, Italy, Japan, Korea South, Netherlands without the colonies, Portugal, Spain, United Kingdom without the colonies, US." ] }, { "cell_type": "code", "execution_count": 28, "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": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/14/212/15/212/16/212/17/212/18/212/19/212/20/212/21/212/22/212/23/21
0NaNAfghanistan33.93911067.709953000000...55492555145551855540555575557555580556045561755646
1NaNAlbania41.15330020.168300000000...93075938509465195726968389790999062100246101285102306
2NaNAlgeria28.0339001.659600000000...110711110894111069111247111418111600111764111917112094112279
3NaNAndorra42.5063001.521800000000...10503105381055510583106101064510672106991071210739
4NaNAngola-11.20270017.873900000000...20366203812038920400204522047820499205192054820584
5NaNAntigua and Barbuda17.060800-61.796400000000...427443443525548548598598614636
6NaNArgentina-38.416100-63.616700000000...2025798202905720330602039124204679520546812060625206433420697512077228
7NaNArmenia40.06910045.038200000000...169167169255169391169597169820170011170234170402170506170672
8Australian Capital TerritoryAustralia-35.473500149.012400000000...118118118118118118118118118118
9New South WalesAustralia-33.868800151.209300000034...5138513951435143514551465149515051545155
10Northern TerritoryAustralia-12.463400130.845600000000...103103103103103104104104104104
11QueenslandAustralia-27.469800153.025100000000...1320132013201320132113211321132313231323
12South AustraliaAustralia-34.928500138.600700000000...606606608608608608610610612613
13TasmaniaAustralia-42.882100147.327200000000...234234234234234234234234234234
14VictoriaAustralia-37.813600144.963100000011...20471204752047520476204792047920479204792047920479
15Western AustraliaAustralia-31.950500115.860500000000...910910910910910910911912913913
16NaNAustria47.51620014.550100000000...433487434712436139437874439841441659443536445374446644448371
17NaNAzerbaijan40.14310047.576900000000...232123232197232337232491232636232829232973233129233201233424
18NaNBahamas25.025885-78.035889000000...8311831183838383840384038403840384718477
19NaNBahrain26.02750050.550000000000...112742113590114361115057115705116482117234117809118530119205
20NaNBangladesh23.68500090.356300000000...540592541038541434541877542268542674543024543351543717544116
21NaNBarbados13.193900-59.543200000000...2061226823312457264726772715277227912852
22NaNBelarus53.70980027.953400000000...268687269787270921272273273659275322276990278312279456280428
23NaNBelgium50.8333004.469936000000...738631739488741205743882746302749739752379754473755594757696
24NaNBelize17.189900-88.497600000000...12145121751218812195122071222712244122441225512264
25NaNBenin9.3077002.315800000000...4560503950395143514351435143543454345434
26NaNBhutan27.51420090.433600000000...864866866866866866866866866867
27NaNBolivia-16.290200-63.588700000000...236732237144237706238495239524240676241771242292243176244380
28NaNBosnia and Herzegovina43.91590017.679100000000...125402126139126413126781127135127537127537127537128661129176
29NaNBotswana-22.32850024.684900000000...24926258022580225802265242652426524265242772127721
..................................................................
244NaNTimor-Leste-8.874217125.727539000000...102102102102103103103103103107
245NaNTogo8.6195000.824800000000...5874588259536007608561826268631963486466
246NaNTrinidad and Tobago10.691800-61.222500000000...7642764676567663766676667676768076827686
247NaNTunisia33.8869179.537499000000...223244223549224329225116226015226740227643228362228937229781
248NaNTurkey38.96370035.243300000000...2586183259412826020342609359261660026240192631876263842226465262655633
249NaNUS40.000000-100.000000112255...27644213276981902776066027830489278997552800611028077620281341152819015928261595
250NaNUganda1.37333332.290275000000...40019400554006340102401544016840199402134022140243
251NaNUkraine48.37940031.165600000000...1316520131906013224061326891133333213400541346527135119013545451358871
252NaNUnited Arab Emirates23.42407653.847818000000...348772351895355131358583361877365017368175370425372530375535
253AnguillaUnited Kingdom18.220600-63.068600000000...18181818181818181818
254BermudaUnited Kingdom32.307800-64.750500000000...694695695697699699699699699703
255British Virgin IslandsUnited Kingdom18.420700-64.640000000000...114114114114114114114114114114
256Cayman IslandsUnited Kingdom19.313300-81.254600000000...416416419419425428428428431431
257Channel IslandsUnited Kingdom49.372300-2.364400000000...3999400440094013401640234025402640304030
258Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...53535454545454545454
259GibraltarUnited Kingdom36.140800-5.353600000000...4219422342244226422742284228422842324234
260Isle of ManUnited Kingdom54.236100-4.548100000000...436436437437444449450450456462
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...1873187418741909192819842028202820292051
264NaNUnited Kingdom55.378100-3.436000000000...4038078404784340584684071185408324240952694105675411550941261504134639
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266NaNUzbekistan41.37749164.585262000000...79416794427946179497795487959879632796547968179717
267NaNVanuatu-15.376700166.959200000000...1111111111
268NaNVenezuela6.423800-66.589700000000...133218133577133927134319134781135114135603136068136545136986
269NaNVietnam14.058324108.277199022222...2228226923112329234723622368238323922403
270NaNWest Bank and Gaza31.95220035.233200000000...167604168444169487170527171154171717172315173635174969176377
271NaNYemen15.55272748.516388000000...2145214521482151215421572157216521762187
272NaNZambia-13.13389727.849332000000...69437702487082371677724677320373894745037502775582
273NaNZimbabwe-19.01543829.154857000000...35172352223531535423355433571035768357963586235910
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274 rows × 403 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", "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 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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/14/212/15/212/16/212/17/212/18/212/19/212/20/212/21/212/22/212/23/21
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1 rows × 403 columns

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" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", "23 NaN Belgium 50.8333 4.469936 0 0 \n", "\n", " 1/24/20 1/25/20 1/26/20 1/27/20 ... 2/14/21 2/15/21 2/16/21 \\\n", "23 0 0 0 0 ... 738631 739488 741205 \n", "\n", " 2/17/21 2/18/21 2/19/21 2/20/21 2/21/21 2/22/21 2/23/21 \n", "23 743882 746302 749739 752379 754473 755594 757696 \n", "\n", "[1 rows x 403 columns]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_Belgium = raw_data[raw_data[\"Country/Region\"].str.contains('Belgium')]\n", "data_Belgium" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/14/212/15/212/16/212/17/212/18/212/19/212/20/212/21/212/22/212/23/21
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59BeijingChina40.1824116.4142142236416880...1046104610461046104610461046104710471047
60ChongqingChina30.0572107.874069275775110...591591591591591591591591591591
61FujianChina26.0789117.98741510183559...548548548548548548548549549549
62GansuChina35.7518104.28610224714...187187187187187187187187187187
63GuangdongChina23.3417113.424426325378111151...2163217121772180218021832184218721962196
64GuangxiChina23.8298108.78812523233646...267267267267267267267267267267
65GuizhouChina26.8154106.8748133457...147147147147147147147147147147
66HainanChina19.1959109.7453458192233...171171171171171171171171171171
67HebeiChina39.5490116.130611281318...1317131713171317131713171317131713171317
68HeilongjiangChina47.8620127.761502491521...1609160916091610161016101610161016101610
69HenanChina37.8957114.90425593283128...1304130413041304130413041304130413041304
71HubeiChina30.9756112.270744444454976110581423...68150681506815068151681516815168151681516815168151
72HunanChina27.6104111.708849244369100...1033103310331033103410351035103610361036
73Inner MongoliaChina44.0935113.94480017711...367367367367367367367367367367
74JiangsuChina32.9711119.4550159183347...703703703703703703703703703704
75JiangxiChina27.6140115.72212718183672...935935935935935935935935935935
76JilinChina43.6661126.1923013446...573573573573573573573573573573
77LiaoningChina41.2956122.6085234172127...404404404404404405406406406406
78MacauChina22.1667113.5500122256...48484848484848484848
79NingxiaChina37.2692106.1655112347...75757575757575757575
80QinghaiChina35.745295.9956000116...18181818181818181818
81ShaanxiChina35.1917108.8701035152235...545547547547547547547547547549
82ShandongChina36.3427118.14982615274675...867867867867867867867867867868
83ShanghaiChina31.2020121.449191620334053...1760176517651769177617781781178317831786
84ShanxiChina37.5777112.29221116913...239239239239239239239239240240
85SichuanChina30.6171102.71035815284469...878879880882882883885887887889
86TianjinChina39.3054117.3230448101423...349349349349351351351352352353
87TibetChina31.692788.0924000000...1111111111
88XinjiangChina41.112985.2401022345...980980980980980980980980980980
89YunnanChina24.9740101.4870125111626...231231231231231231231231231231
90ZhejiangChina29.1832120.093410274362104128...1320132013201320132013201320132013201321
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32 rows × 403 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", "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 Xinjiang China 41.1129 85.2401 0 2 \n", "89 Yunnan China 24.9740 101.4870 1 2 \n", "90 Zhejiang China 29.1832 120.0934 10 27 \n", "\n", " 1/24/20 1/25/20 1/26/20 1/27/20 ... 2/14/21 2/15/21 2/16/21 \\\n", "58 15 39 60 70 ... 994 994 994 \n", "59 36 41 68 80 ... 1046 1046 1046 \n", "60 27 57 75 110 ... 591 591 591 \n", "61 10 18 35 59 ... 548 548 548 \n", "62 2 4 7 14 ... 187 187 187 \n", "63 53 78 111 151 ... 2163 2171 2177 \n", "64 23 23 36 46 ... 267 267 267 \n", "65 3 4 5 7 ... 147 147 147 \n", "66 8 19 22 33 ... 171 171 171 \n", "67 2 8 13 18 ... 1317 1317 1317 \n", "68 4 9 15 21 ... 1609 1609 1609 \n", "69 9 32 83 128 ... 1304 1304 1304 \n", "71 549 761 1058 1423 ... 68150 68150 68150 \n", "72 24 43 69 100 ... 1033 1033 1033 \n", "73 1 7 7 11 ... 367 367 367 \n", "74 9 18 33 47 ... 703 703 703 \n", "75 18 18 36 72 ... 935 935 935 \n", "76 3 4 4 6 ... 573 573 573 \n", "77 4 17 21 27 ... 404 404 404 \n", "78 2 2 5 6 ... 48 48 48 \n", "79 2 3 4 7 ... 75 75 75 \n", "80 0 1 1 6 ... 18 18 18 \n", "81 5 15 22 35 ... 545 547 547 \n", "82 15 27 46 75 ... 867 867 867 \n", "83 20 33 40 53 ... 1760 1765 1765 \n", "84 1 6 9 13 ... 239 239 239 \n", "85 15 28 44 69 ... 878 879 880 \n", "86 8 10 14 23 ... 349 349 349 \n", "87 0 0 0 0 ... 1 1 1 \n", "88 2 3 4 5 ... 980 980 980 \n", "89 5 11 16 26 ... 231 231 231 \n", "90 43 62 104 128 ... 1320 1320 1320 \n", "\n", " 2/17/21 2/18/21 2/19/21 2/20/21 2/21/21 2/22/21 2/23/21 \n", "58 994 994 994 994 994 994 994 \n", "59 1046 1046 1046 1046 1047 1047 1047 \n", "60 591 591 591 591 591 591 591 \n", "61 548 548 548 548 549 549 549 \n", "62 187 187 187 187 187 187 187 \n", "63 2180 2180 2183 2184 2187 2196 2196 \n", "64 267 267 267 267 267 267 267 \n", "65 147 147 147 147 147 147 147 \n", "66 171 171 171 171 171 171 171 \n", "67 1317 1317 1317 1317 1317 1317 1317 \n", "68 1610 1610 1610 1610 1610 1610 1610 \n", "69 1304 1304 1304 1304 1304 1304 1304 \n", "71 68151 68151 68151 68151 68151 68151 68151 \n", "72 1033 1034 1035 1035 1036 1036 1036 \n", "73 367 367 367 367 367 367 367 \n", "74 703 703 703 703 703 703 704 \n", "75 935 935 935 935 935 935 935 \n", "76 573 573 573 573 573 573 573 \n", "77 404 404 405 406 406 406 406 \n", "78 48 48 48 48 48 48 48 \n", "79 75 75 75 75 75 75 75 \n", "80 18 18 18 18 18 18 18 \n", "81 547 547 547 547 547 547 549 \n", "82 867 867 867 867 867 867 868 \n", "83 1769 1776 1778 1781 1783 1783 1786 \n", "84 239 239 239 239 239 240 240 \n", "85 882 882 883 885 887 887 889 \n", "86 349 351 351 351 352 352 353 \n", "87 1 1 1 1 1 1 1 \n", "88 980 980 980 980 980 980 980 \n", "89 231 231 231 231 231 231 231 \n", "90 1320 1320 1320 1320 1320 1320 1321 \n", "\n", "[32 rows x 403 columns]" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_China = raw_data[raw_data[\"Country/Region\"].str.contains('China')]\n", "data_HK = data_China[data_China[\"Province/State\"].str.contains('Hong Kong')]\n", "data_HK\n", "data_China_exHK = data_China.drop(index=[70])\n", "data_China_exHK" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/14/212/15/212/16/212/17/212/18/212/19/212/20/212/21/212/22/212/23/21
129NaNFrance46.22762.2137002333...3447518345189434712683495775351717735412823562707358432635889723608271
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1 rows × 403 columns

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" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n", "129 NaN France 46.2276 2.2137 0 0 2 \n", "\n", " 1/25/20 1/26/20 1/27/20 ... 2/14/21 2/15/21 2/16/21 2/17/21 \\\n", "129 3 3 3 ... 3447518 3451894 3471268 3495775 \n", "\n", " 2/18/21 2/19/21 2/20/21 2/21/21 2/22/21 2/23/21 \n", "129 3517177 3541282 3562707 3584326 3588972 3608271 \n", "\n", "[1 rows x 403 columns]" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_France = raw_data[raw_data[\"Country/Region\"].str.contains('France')]\n", "data_France\n", "data_France_ex = data_France.loc[[129],:]\n", "data_France_ex" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "data_Germany = raw_data[raw_data[\"Country/Region\"].str.contains('Germany')]\n", "data_Iran = raw_data[raw_data[\"Country/Region\"].str.contains('Iran')]\n", "data_Italy = raw_data[raw_data[\"Country/Region\"].str.contains('Italy')]\n", "data_Japan = raw_data[raw_data[\"Country/Region\"].str.contains('Japan')]\n", "data_KS = raw_data[raw_data[\"Country/Region\"].str.contains('Korea South')]" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/14/212/15/212/16/212/17/212/18/212/19/212/20/212/21/212/22/212/23/21
195NaNNetherlands52.13265.2913000000...1029284103209410347951038156104267410474001051965105663910608011064598
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1 rows × 403 columns

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" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n", "195 NaN Netherlands 52.1326 5.2913 0 0 0 \n", "\n", " 1/25/20 1/26/20 1/27/20 ... 2/14/21 2/15/21 2/16/21 2/17/21 \\\n", "195 0 0 0 ... 1029284 1032094 1034795 1038156 \n", "\n", " 2/18/21 2/19/21 2/20/21 2/21/21 2/22/21 2/23/21 \n", "195 1042674 1047400 1051965 1056639 1060801 1064598 \n", "\n", "[1 rows x 403 columns]" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_Netherlands = raw_data[raw_data[\"Country/Region\"].str.contains('Netherlands')]\n", "data_Netherlands\n", "data_Netherlands_ex = data_Netherlands.loc[[195],:]\n", "data_Netherlands_ex" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "data_Portugal = raw_data[raw_data[\"Country/Region\"].str.contains('Portugal')]\n", "data_Spain = raw_data[raw_data[\"Country/Region\"].str.contains('Spain')]\n", "data_US = raw_data[raw_data[\"Country/Region\"].str.contains('US')]" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/14/212/15/212/16/212/17/212/18/212/19/212/20/212/21/212/22/212/23/21
264NaNUnited Kingdom55.3781-3.436000000...4038078404784340584684071185408324240952694105675411550941261504134639
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1 rows × 403 columns

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" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n", "264 NaN United Kingdom 55.3781 -3.436 0 0 0 \n", "\n", " 1/25/20 1/26/20 1/27/20 ... 2/14/21 2/15/21 2/16/21 2/17/21 \\\n", "264 0 0 0 ... 4038078 4047843 4058468 4071185 \n", "\n", " 2/18/21 2/19/21 2/20/21 2/21/21 2/22/21 2/23/21 \n", "264 4083242 4095269 4105675 4115509 4126150 4134639 \n", "\n", "[1 rows x 403 columns]" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_UK = raw_data[raw_data[\"Country/Region\"].str.contains('United Kingdom')]\n", "data_UK\n", "data_UK_ex = data_UK.loc[[264],:]\n", "data_UK_ex" ] }, { "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 }