{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import isoweek\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "importation des modules" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "y = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\",index_col=1)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateLatLong1/22/201/23/201/24/201/25/201/26/201/27/201/28/20...6/3/206/4/206/5/206/6/206/7/206/8/206/9/206/10/206/11/206/12/20
Country/Region
AfghanistanNaN33.00000065.0000000000000...17267180541896919551203422091721459221422289023546
AlbaniaNaN41.15330020.1683000000000...1184119712121232124612631299134113851416
AlgeriaNaN28.0339001.6596000000000...97339831993510050101541026510382104841058910698
AndorraNaN42.5063001.5218000000000...851852852852852852852852852853
AngolaNaN-11.20270017.8739000000000...86868688919296113118130
Antigua and BarbudaNaN17.060800-61.7964000000000...26262626262626262626
ArgentinaNaN-38.416100-63.6167000000000...19268201972103722020227942362024761259872737328764
ArmeniaNaN40.06910045.0382000000000...10524112211181712364131301332513675141031466915281
AustraliaAustralian Capital Territory-35.473500149.0124000000000...107107107108108108108108108108
AustraliaNew South Wales-33.868800151.2093000000344...3106311031103109311231143117311731153119
AustraliaNorthern Territory-12.463400130.8456000000000...29292929292929292929
AustraliaQueensland-28.016700153.4000000000000...1060106010611061106210621062106310641065
AustraliaSouth Australia-34.928500138.6007000000000...440440440440440440440440440440
AustraliaTasmania-41.454500145.9707000000000...228228228228228228228228228228
AustraliaVictoria-37.813600144.9631000000111...1678168116811685168716871691169917031703
AustraliaWestern Australia-31.950500115.8605000000000...592592596599599599599601602602
AustriaNaN47.51620014.5501000000000...16771168051684316898169021696816979170051703417064
AzerbaijanNaN40.14310047.5769000000000...6260652268607239755378768191853088829218
BahamasNaN25.034300-77.3963000000000...102102102103103103103103103103
BahrainNaN26.02750050.5500000000000...12815132961383514383147631541715731162001666717269
BangladeshNaN23.68500090.3563000000000...55140575636039163026657696850471675748657805281523
BarbadosNaN13.193900-59.5432000000000...92929292929292969696
BelarusNaN53.70980027.9534000000000...45116459814686847751486304945350265510665181652520
BelgiumNaN50.8333004.0000000000000...58685587675890759072592265934859437595695971159819
BeninNaN9.3077002.3158000000000...244261261261261288305305305388
BhutanNaN27.51420090.4336000000000...47474848595959596262
BoliviaNaN-16.290200-63.5887000000000...11638122451272813358136431394914644152811616516929
Bosnia and HerzegovinaNaN43.91590017.6791000000000...2551259426062606260627042728277528322893
BrazilNaN-14.235000-51.9253000000000...584016614941645771672846691758707412739503772416802828828810
BruneiNaN4.535300114.7277000000000...141141141141141141141141141141
..................................................................
Timor-LesteNaN-8.874217125.7275390000000...24242424242424242424
BelizeNaN13.193900-59.5432000000000...18181919191920202020
LaosNaN19.856270102.4954960000000...19191919191919191919
LibyaNaN26.33510017.2283310000000...196209239256256332359378393409
West Bank and GazaNaN31.95220035.2332000000000...457464464464472473481485487489
Guinea-BissauNaN11.803700-15.1804000000000...1339133913681368136813891389138913891460
MaliNaN17.570692-3.9961660000000...1386146114851523153315471586166717221752
Saint Kitts and NevisNaN17.357822-62.7829980000000...15151515151515151515
CanadaNorthwest Territories64.825500-124.8457000000000...5555555555
CanadaYukon64.282300-135.0000000000000...11111111111111111111
KosovoNaN42.60263620.9029770000000...1142114211421142114212631263129813261326
BurmaNaN21.91620095.9560000000000...233236236240242244246248260261
United KingdomAnguilla18.220600-63.0686000000000...3333333333
United KingdomBritish Virgin Islands18.420700-64.6400000000000...8888888888
United KingdomTurks and Caicos Islands21.694000-71.7979000000000...12121212121212121212
MS ZaandamNaN0.0000000.0000000000000...9999999999
BotswanaNaN-22.32850024.6849000000000...40404040404242484848
BurundiNaN-3.37310029.9189000000000...63636383838383838585
Sierra LeoneNaN8.460555-11.7798890000000...90991492994696910011025106210851103
NetherlandsBonaire, Sint Eustatius and Saba12.178400-68.2385000000000...7777777777
MalawiNaN-13.25430834.3015250000000...369393409409438443455455481481
United KingdomFalkland Islands (Malvinas)-51.796300-59.5236000000000...13131313131313131313
FranceSaint Pierre and Miquelon46.885200-56.3159000000000...1111111111
South SudanNaN6.87700031.3070000000000...994994994994131716041604160416701670
Western SaharaNaN24.215500-12.8858000000000...9999999999
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LesothoNaN-29.60998828.2336080000000...4444444444
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108 108 108 108 \n", "Australia 3110 3109 3112 3114 3117 3117 \n", "Australia 29 29 29 29 29 29 \n", "Australia 1061 1061 1062 1062 1062 1063 \n", "Australia 440 440 440 440 440 440 \n", "Australia 228 228 228 228 228 228 \n", "Australia 1681 1685 1687 1687 1691 1699 \n", "Australia 596 599 599 599 599 601 \n", "Austria 16843 16898 16902 16968 16979 17005 \n", "Azerbaijan 6860 7239 7553 7876 8191 8530 \n", "Bahamas 102 103 103 103 103 103 \n", "Bahrain 13835 14383 14763 15417 15731 16200 \n", "Bangladesh 60391 63026 65769 68504 71675 74865 \n", "Barbados 92 92 92 92 92 96 \n", "Belarus 46868 47751 48630 49453 50265 51066 \n", "Belgium 58907 59072 59226 59348 59437 59569 \n", "Benin 261 261 261 288 305 305 \n", "Bhutan 48 48 59 59 59 59 \n", "Bolivia 12728 13358 13643 13949 14644 15281 \n", "Bosnia and Herzegovina 2606 2606 2606 2704 2728 2775 \n", "Brazil 645771 672846 691758 707412 739503 772416 \n", "Brunei 141 141 141 141 141 141 \n", "... ... ... ... ... ... ... \n", "Timor-Leste 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141 141 141 162 \n", "Tajikistan 4370 4453 4529 4609 4690 4763 \n", "Lesotho 4 4 4 4 4 4 \n", "\n", " 6/11/20 6/12/20 \n", "Country/Region \n", "Afghanistan 22890 23546 \n", "Albania 1385 1416 \n", "Algeria 10589 10698 \n", "Andorra 852 853 \n", "Angola 118 130 \n", "Antigua and Barbuda 26 26 \n", "Argentina 27373 28764 \n", "Armenia 14669 15281 \n", "Australia 108 108 \n", "Australia 3115 3119 \n", "Australia 29 29 \n", "Australia 1064 1065 \n", "Australia 440 440 \n", "Australia 228 228 \n", "Australia 1703 1703 \n", "Australia 602 602 \n", "Austria 17034 17064 \n", "Azerbaijan 8882 9218 \n", "Bahamas 103 103 \n", "Bahrain 16667 17269 \n", "Bangladesh 78052 81523 \n", "Barbados 96 96 \n", "Belarus 51816 52520 \n", "Belgium 59711 59819 \n", "Benin 305 388 \n", "Bhutan 62 62 \n", "Bolivia 16165 16929 \n", "Bosnia and Herzegovina 2832 2893 \n", "Brazil 802828 828810 \n", "Brunei 141 141 \n", "... ... ... \n", "Timor-Leste 24 24 \n", "Belize 20 20 \n", "Laos 19 19 \n", "Libya 393 409 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Province/State1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/201/30/20...6/3/206/4/206/5/206/6/206/7/206/8/206/9/206/10/206/11/206/12/20
Country/Region
AfghanistanNaN000000000...17267180541896919551203422091721459221422289023546
AlbaniaNaN000000000...1184119712121232124612631299134113851416
AlgeriaNaN000000000...97339831993510050101541026510382104841058910698
AndorraNaN000000000...851852852852852852852852852853
AngolaNaN000000000...86868688919296113118130
Antigua and BarbudaNaN000000000...26262626262626262626
ArgentinaNaN000000000...19268201972103722020227942362024761259872737328764
ArmeniaNaN000000000...10524112211181712364131301332513675141031466915281
AustraliaAustralian Capital Territory000000000...107107107108108108108108108108
AustraliaNew South Wales000034444...3106311031103109311231143117311731153119
AustraliaNorthern Territory000000000...29292929292929292929
AustraliaQueensland000000013...1060106010611061106210621062106310641065
AustraliaSouth Australia000000000...440440440440440440440440440440
AustraliaTasmania000000000...228228228228228228228228228228
AustraliaVictoria000011112...1678168116811685168716871691169917031703
AustraliaWestern Australia000000000...592592596599599599599601602602
AustriaNaN000000000...16771168051684316898169021696816979170051703417064
AzerbaijanNaN000000000...6260652268607239755378768191853088829218
BahamasNaN000000000...102102102103103103103103103103
BahrainNaN000000000...12815132961383514383147631541715731162001666717269
BangladeshNaN000000000...55140575636039163026657696850471675748657805281523
BarbadosNaN000000000...92929292929292969696
BelarusNaN000000000...45116459814686847751486304945350265510665181652520
BelgiumNaN000000000...58685587675890759072592265934859437595695971159819
BeninNaN000000000...244261261261261288305305305388
BhutanNaN000000000...47474848595959596262
BoliviaNaN000000000...11638122451272813358136431394914644152811616516929
Bosnia and HerzegovinaNaN000000000...2551259426062606260627042728277528322893
BrazilNaN000000000...584016614941645771672846691758707412739503772416802828828810
BruneiNaN000000000...141141141141141141141141141141
..................................................................
Timor-LesteNaN000000000...24242424242424242424
BelizeNaN000000000...18181919191920202020
LaosNaN000000000...19191919191919191919
LibyaNaN000000000...196209239256256332359378393409
West Bank and GazaNaN000000000...457464464464472473481485487489
Guinea-BissauNaN000000000...1339133913681368136813891389138913891460
MaliNaN000000000...1386146114851523153315471586166717221752
Saint Kitts and NevisNaN000000000...15151515151515151515
CanadaNorthwest Territories000000000...5555555555
CanadaYukon000000000...11111111111111111111
KosovoNaN000000000...1142114211421142114212631263129813261326
BurmaNaN000000000...233236236240242244246248260261
United KingdomAnguilla000000000...3333333333
United KingdomBritish Virgin Islands000000000...8888888888
United KingdomTurks and Caicos Islands000000000...12121212121212121212
MS ZaandamNaN000000000...9999999999
BotswanaNaN000000000...40404040404242484848
BurundiNaN000000000...63636383838383838585
Sierra LeoneNaN000000000...90991492994696910011025106210851103
NetherlandsBonaire, Sint Eustatius and Saba000000000...7777777777
MalawiNaN000000000...369393409409438443455455481481
United KingdomFalkland Islands (Malvinas)000000000...13131313131313131313
FranceSaint Pierre and Miquelon000000000...1111111111
South SudanNaN000000000...994994994994131716041604160416701670
Western SaharaNaN000000000...9999999999
Sao Tome and PrincipeNaN000000000...484485499499513513514611632639
YemenNaN000000000...419453469482484496524560591632
ComorosNaN000000000...132132132141141141141162162163
TajikistanNaN000000000...4191428943704453452946094690476348344902
LesothoNaN000000000...4444444444
\n", "

266 rows × 144 columns

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" ], "text/plain": [ " Province/State 1/22/20 1/23/20 \\\n", "Country/Region \n", "Afghanistan NaN 0 0 \n", "Albania NaN 0 0 \n", "Algeria NaN 0 0 \n", "Andorra NaN 0 0 \n", "Angola NaN 0 0 \n", "Antigua and Barbuda NaN 0 0 \n", "Argentina NaN 0 0 \n", "Armenia NaN 0 0 \n", "Australia Australian Capital Territory 0 0 \n", "Australia New South Wales 0 0 \n", "Australia Northern Territory 0 0 \n", "Australia Queensland 0 0 \n", "Australia South Australia 0 0 \n", "Australia Tasmania 0 0 \n", "Australia Victoria 0 0 \n", "Australia Western Australia 0 0 \n", "Austria NaN 0 0 \n", "Azerbaijan NaN 0 0 \n", "Bahamas NaN 0 0 \n", "Bahrain NaN 0 0 \n", "Bangladesh NaN 0 0 \n", "Barbados NaN 0 0 \n", "Belarus NaN 0 0 \n", "Belgium NaN 0 0 \n", "Benin NaN 0 0 \n", "Bhutan NaN 0 0 \n", "Bolivia NaN 0 0 \n", "Bosnia and Herzegovina NaN 0 0 \n", "Brazil NaN 0 0 \n", "Brunei NaN 0 0 \n", "... ... ... ... \n", "Timor-Leste NaN 0 0 \n", "Belize NaN 0 0 \n", "Laos NaN 0 0 \n", "Libya NaN 0 0 \n", "West Bank and Gaza NaN 0 0 \n", "Guinea-Bissau NaN 0 0 \n", "Mali NaN 0 0 \n", "Saint Kitts and Nevis NaN 0 0 \n", "Canada Northwest Territories 0 0 \n", "Canada Yukon 0 0 \n", "Kosovo NaN 0 0 \n", "Burma NaN 0 0 \n", "United Kingdom Anguilla 0 0 \n", "United Kingdom British Virgin Islands 0 0 \n", "United Kingdom Turks and Caicos Islands 0 0 \n", "MS Zaandam NaN 0 0 \n", "Botswana NaN 0 0 \n", "Burundi NaN 0 0 \n", "Sierra Leone NaN 0 0 \n", "Netherlands Bonaire, Sint Eustatius and Saba 0 0 \n", "Malawi NaN 0 0 \n", "United Kingdom Falkland Islands (Malvinas) 0 0 \n", "France Saint Pierre and Miquelon 0 0 \n", "South Sudan NaN 0 0 \n", "Western Sahara NaN 0 0 \n", "Sao Tome and Principe NaN 0 0 \n", "Yemen NaN 0 0 \n", "Comoros NaN 0 0 \n", "Tajikistan NaN 0 0 \n", "Lesotho NaN 0 0 \n", "\n", " 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 \\\n", "Country/Region \n", "Afghanistan 0 0 0 0 0 0 \n", "Albania 0 0 0 0 0 0 \n", "Algeria 0 0 0 0 0 0 \n", "Andorra 0 0 0 0 0 0 \n", "Angola 0 0 0 0 0 0 \n", "Antigua and Barbuda 0 0 0 0 0 0 \n", "Argentina 0 0 0 0 0 0 \n", "Armenia 0 0 0 0 0 0 \n", "Australia 0 0 0 0 0 0 \n", "Australia 0 0 3 4 4 4 \n", "Australia 0 0 0 0 0 0 \n", "Australia 0 0 0 0 0 1 \n", "Australia 0 0 0 0 0 0 \n", "Australia 0 0 0 0 0 0 \n", "Australia 0 0 1 1 1 1 \n", "Australia 0 0 0 0 0 0 \n", "Austria 0 0 0 0 0 0 \n", "Azerbaijan 0 0 0 0 0 0 \n", "Bahamas 0 0 0 0 0 0 \n", "Bahrain 0 0 0 0 0 0 \n", "Bangladesh 0 0 0 0 0 0 \n", "Barbados 0 0 0 0 0 0 \n", "Belarus 0 0 0 0 0 0 \n", "Belgium 0 0 0 0 0 0 \n", "Benin 0 0 0 0 0 0 \n", "Bhutan 0 0 0 0 0 0 \n", "Bolivia 0 0 0 0 0 0 \n", "Bosnia and Herzegovina 0 0 0 0 0 0 \n", "Brazil 0 0 0 0 0 0 \n", "Brunei 0 0 0 0 0 0 \n", "... ... ... ... ... ... ... \n", "Timor-Leste 0 0 0 0 0 0 \n", "Belize 0 0 0 0 0 0 \n", "Laos 0 0 0 0 0 0 \n", "Libya 0 0 0 0 0 0 \n", "West Bank and Gaza 0 0 0 0 0 0 \n", "Guinea-Bissau 0 0 0 0 0 0 \n", "Mali 0 0 0 0 0 0 \n", "Saint Kitts and Nevis 0 0 0 0 0 0 \n", "Canada 0 0 0 0 0 0 \n", "Canada 0 0 0 0 0 0 \n", "Kosovo 0 0 0 0 0 0 \n", "Burma 0 0 0 0 0 0 \n", "United Kingdom 0 0 0 0 0 0 \n", "United Kingdom 0 0 0 0 0 0 \n", "United Kingdom 0 0 0 0 0 0 \n", "MS Zaandam 0 0 0 0 0 0 \n", "Botswana 0 0 0 0 0 0 \n", "Burundi 0 0 0 0 0 0 \n", "Sierra Leone 0 0 0 0 0 0 \n", "Netherlands 0 0 0 0 0 0 \n", "Malawi 0 0 0 0 0 0 \n", "United Kingdom 0 0 0 0 0 0 \n", "France 0 0 0 0 0 0 \n", "South Sudan 0 0 0 0 0 0 \n", "Western Sahara 0 0 0 0 0 0 \n", "Sao Tome and Principe 0 0 0 0 0 0 \n", "Yemen 0 0 0 0 0 0 \n", "Comoros 0 0 0 0 0 0 \n", "Tajikistan 0 0 0 0 0 0 \n", "Lesotho 0 0 0 0 0 0 \n", "\n", " 1/30/20 ... 6/3/20 6/4/20 6/5/20 6/6/20 \\\n", "Country/Region ... \n", "Afghanistan 0 ... 17267 18054 18969 19551 \n", "Albania 0 ... 1184 1197 1212 1232 \n", "Algeria 0 ... 9733 9831 9935 10050 \n", "Andorra 0 ... 851 852 852 852 \n", "Angola 0 ... 86 86 86 88 \n", "Antigua and Barbuda 0 ... 26 26 26 26 \n", "Argentina 0 ... 19268 20197 21037 22020 \n", "Armenia 0 ... 10524 11221 11817 12364 \n", "Australia 0 ... 107 107 107 108 \n", "Australia 4 ... 3106 3110 3110 3109 \n", "Australia 0 ... 29 29 29 29 \n", "Australia 3 ... 1060 1060 1061 1061 \n", "Australia 0 ... 440 440 440 440 \n", "Australia 0 ... 228 228 228 228 \n", "Australia 2 ... 1678 1681 1681 1685 \n", "Australia 0 ... 592 592 596 599 \n", "Austria 0 ... 16771 16805 16843 16898 \n", "Azerbaijan 0 ... 6260 6522 6860 7239 \n", "Bahamas 0 ... 102 102 102 103 \n", "Bahrain 0 ... 12815 13296 13835 14383 \n", "Bangladesh 0 ... 55140 57563 60391 63026 \n", "Barbados 0 ... 92 92 92 92 \n", "Belarus 0 ... 45116 45981 46868 47751 \n", "Belgium 0 ... 58685 58767 58907 59072 \n", "Benin 0 ... 244 261 261 261 \n", "Bhutan 0 ... 47 47 48 48 \n", "Bolivia 0 ... 11638 12245 12728 13358 \n", "Bosnia and Herzegovina 0 ... 2551 2594 2606 2606 \n", "Brazil 0 ... 584016 614941 645771 672846 \n", "Brunei 0 ... 141 141 141 141 \n", "... ... ... ... ... ... ... \n", "Timor-Leste 0 ... 24 24 24 24 \n", "Belize 0 ... 18 18 19 19 \n", "Laos 0 ... 19 19 19 19 \n", "Libya 0 ... 196 209 239 256 \n", "West Bank and Gaza 0 ... 457 464 464 464 \n", "Guinea-Bissau 0 ... 1339 1339 1368 1368 \n", "Mali 0 ... 1386 1461 1485 1523 \n", "Saint Kitts and Nevis 0 ... 15 15 15 15 \n", "Canada 0 ... 5 5 5 5 \n", "Canada 0 ... 11 11 11 11 \n", "Kosovo 0 ... 1142 1142 1142 1142 \n", "Burma 0 ... 233 236 236 240 \n", "United Kingdom 0 ... 3 3 3 3 \n", "United Kingdom 0 ... 8 8 8 8 \n", "United Kingdom 0 ... 12 12 12 12 \n", "MS Zaandam 0 ... 9 9 9 9 \n", "Botswana 0 ... 40 40 40 40 \n", "Burundi 0 ... 63 63 63 83 \n", "Sierra Leone 0 ... 909 914 929 946 \n", "Netherlands 0 ... 7 7 7 7 \n", "Malawi 0 ... 369 393 409 409 \n", "United Kingdom 0 ... 13 13 13 13 \n", "France 0 ... 1 1 1 1 \n", "South Sudan 0 ... 994 994 994 994 \n", "Western Sahara 0 ... 9 9 9 9 \n", "Sao Tome and Principe 0 ... 484 485 499 499 \n", "Yemen 0 ... 419 453 469 482 \n", "Comoros 0 ... 132 132 132 141 \n", "Tajikistan 0 ... 4191 4289 4370 4453 \n", "Lesotho 0 ... 4 4 4 4 \n", "\n", " 6/7/20 6/8/20 6/9/20 6/10/20 6/11/20 6/12/20 \n", "Country/Region \n", "Afghanistan 20342 20917 21459 22142 22890 23546 \n", "Albania 1246 1263 1299 1341 1385 1416 \n", "Algeria 10154 10265 10382 10484 10589 10698 \n", "Andorra 852 852 852 852 852 853 \n", "Angola 91 92 96 113 118 130 \n", "Antigua and Barbuda 26 26 26 26 26 26 \n", "Argentina 22794 23620 24761 25987 27373 28764 \n", "Armenia 13130 13325 13675 14103 14669 15281 \n", "Australia 108 108 108 108 108 108 \n", "Australia 3112 3114 3117 3117 3115 3119 \n", "Australia 29 29 29 29 29 29 \n", "Australia 1062 1062 1062 1063 1064 1065 \n", "Australia 440 440 440 440 440 440 \n", "Australia 228 228 228 228 228 228 \n", "Australia 1687 1687 1691 1699 1703 1703 \n", "Australia 599 599 599 601 602 602 \n", "Austria 16902 16968 16979 17005 17034 17064 \n", "Azerbaijan 7553 7876 8191 8530 8882 9218 \n", "Bahamas 103 103 103 103 103 103 \n", "Bahrain 14763 15417 15731 16200 16667 17269 \n", "Bangladesh 65769 68504 71675 74865 78052 81523 \n", "Barbados 92 92 92 96 96 96 \n", "Belarus 48630 49453 50265 51066 51816 52520 \n", "Belgium 59226 59348 59437 59569 59711 59819 \n", "Benin 261 288 305 305 305 388 \n", "Bhutan 59 59 59 59 62 62 \n", "Bolivia 13643 13949 14644 15281 16165 16929 \n", "Bosnia and Herzegovina 2606 2704 2728 2775 2832 2893 \n", "Brazil 691758 707412 739503 772416 802828 828810 \n", "Brunei 141 141 141 141 141 141 \n", "... ... ... ... ... ... ... \n", "Timor-Leste 24 24 24 24 24 24 \n", "Belize 19 19 20 20 20 20 \n", "Laos 19 19 19 19 19 19 \n", "Libya 256 332 359 378 393 409 \n", "West Bank and Gaza 472 473 481 485 487 489 \n", "Guinea-Bissau 1368 1389 1389 1389 1389 1460 \n", "Mali 1533 1547 1586 1667 1722 1752 \n", "Saint Kitts and Nevis 15 15 15 15 15 15 \n", "Canada 5 5 5 5 5 5 \n", "Canada 11 11 11 11 11 11 \n", "Kosovo 1142 1263 1263 1298 1326 1326 \n", "Burma 242 244 246 248 260 261 \n", "United Kingdom 3 3 3 3 3 3 \n", "United Kingdom 8 8 8 8 8 8 \n", "United Kingdom 12 12 12 12 12 12 \n", "MS Zaandam 9 9 9 9 9 9 \n", "Botswana 40 42 42 48 48 48 \n", "Burundi 83 83 83 83 85 85 \n", "Sierra Leone 969 1001 1025 1062 1085 1103 \n", "Netherlands 7 7 7 7 7 7 \n", "Malawi 438 443 455 455 481 481 \n", "United Kingdom 13 13 13 13 13 13 \n", "France 1 1 1 1 1 1 \n", "South Sudan 1317 1604 1604 1604 1670 1670 \n", "Western Sahara 9 9 9 9 9 9 \n", "Sao Tome and Principe 513 513 514 611 632 639 \n", "Yemen 484 496 524 560 591 632 \n", "Comoros 141 141 141 162 162 163 \n", "Tajikistan 4529 4609 4690 4763 4834 4902 \n", "Lesotho 4 4 4 4 4 4 \n", "\n", "[266 rows x 144 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "Belgique=y.loc[['Belgium'],:]\n", "Chine=y.loc[['China'],:]\n", "France=y.loc[['France'],:]\n", "Allemagne=y.loc[['Germany'],:]\n", "Iran=y.loc[['Iran'],:]\n", "Italie=y.loc[['Italy'],:]\n", "Japon=y.loc[['Japan'],:]\n", "Hollande_et_colonies=y.loc[['Netherlands'],:]\n", "Portugal=y.loc[['Portugal'],:]\n", "Espagne=y.loc[['Spain'],:]\n", "RoyaumeUni_et_colonies=y.loc[['United Kingdom'],:]\n", "CoréeduSud=y.loc[['Korea, South'],:]\n", "EtatsUnis=y.loc[['US'],:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "récupération des régions d'intérêt sauf pour Hong Kong" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "France_metropolitaine=France[France.isnull().any(axis=1)]\n", "RoyaumeUnis=RoyaumeUni_et_colonies[RoyaumeUni_et_colonies.isnull().any(axis=1)]\n", "Hollande=Hollande_et_colonies[Hollande_et_colonies.isnull().any(axis=1)]\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/State1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/201/30/20...6/3/206/4/206/5/206/6/206/7/206/8/206/9/206/10/206/11/206/12/20
Country/Region
FranceNaN002333455...188836185986186538187067187360187458187599187996188354188918
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1 rows × 144 columns

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" ], "text/plain": [ " Province/State 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n", "Country/Region \n", "France NaN 0 0 2 3 3 \n", "\n", " 1/27/20 1/28/20 1/29/20 1/30/20 ... 6/3/20 6/4/20 \\\n", "Country/Region ... \n", "France 3 4 5 5 ... 188836 185986 \n", "\n", " 6/5/20 6/6/20 6/7/20 6/8/20 6/9/20 6/10/20 6/11/20 \\\n", "Country/Region \n", "France 186538 187067 187360 187458 187599 187996 188354 \n", "\n", " 6/12/20 \n", "Country/Region \n", "France 188918 \n", "\n", "[1 rows x 144 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "France_metropolitaine" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "HongKong=Chine.iloc[[12],:]\n", "Chine=Chine.drop(index='Hong Kong')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "suppression des colonies et isolation de Hong Kong de la Chine" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "Belgique=Belgique.drop(columns=['Province/State'])\n", "Chine=Chine.drop(columns=['Province/State'])\n", "France_metropolitaine=France_metropolitaine.drop(columns=['Province/State'])\n", "Allemagne=Allemagne.drop(columns=['Province/State'])\n", "Iran=Iran.drop(columns=['Province/State'])\n", "Italie=Italie.drop(columns=['Province/State'])\n", "Japon=Japon.drop(columns=['Province/State'])\n", "Hollande=Hollande.drop(columns=['Province/State'])\n", "Portugal=Portugal.drop(columns=['Province/State'])\n", "Espagne=Espagne.drop(columns=['Province/State'])\n", "RoyaumeUnis=RoyaumeUnis.drop(columns=['Province/State'])\n", "CoréeduSud=CoréeduSud.drop(columns=['Province/State'])\n", "EtatsUnis=EtatsUnis.drop(columns=['Province/State'])\n" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Suppression des colonnes province/etat sauf pour Hong Kong" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "SommeChine=Chine.sum()\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1/22/20 548\n", "1/23/20 643\n", "1/24/20 920\n", "1/25/20 1406\n", "1/26/20 2075\n", "1/27/20 2877\n", "1/28/20 5509\n", "1/29/20 6087\n", "1/30/20 8141\n", "1/31/20 9802\n", "2/1/20 11891\n", "2/2/20 16630\n", "2/3/20 19716\n", "2/4/20 23707\n", "2/5/20 27440\n", "2/6/20 30587\n", "2/7/20 34110\n", "2/8/20 36814\n", "2/9/20 39829\n", "2/10/20 42354\n", "2/11/20 44386\n", "2/12/20 44759\n", "2/13/20 59895\n", "2/14/20 66358\n", "2/15/20 68413\n", "2/16/20 70513\n", "2/17/20 72434\n", "2/18/20 74211\n", "2/19/20 74619\n", "2/20/20 75077\n", " ... \n", "5/14/20 84029\n", "5/15/20 84038\n", "5/16/20 84044\n", "5/17/20 84054\n", "5/18/20 84063\n", "5/19/20 84063\n", "5/20/20 84063\n", "5/21/20 84063\n", "5/22/20 84081\n", "5/23/20 84084\n", "5/24/20 84095\n", "5/25/20 84102\n", "5/26/20 84103\n", "5/27/20 84106\n", "5/28/20 84106\n", "5/29/20 84123\n", "5/30/20 84128\n", "5/31/20 84146\n", "6/1/20 84154\n", "6/2/20 84161\n", "6/3/20 84160\n", "6/4/20 84171\n", "6/5/20 84177\n", "6/6/20 84186\n", "6/7/20 84191\n", "6/8/20 84195\n", "6/9/20 84198\n", "6/10/20 84209\n", "6/11/20 84216\n", "6/12/20 84228\n", "Length: 143, dtype: int64\n" ] } ], "source": [ "print(SommeChine)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "HongKong=HongKong.drop(columns='Province/State')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'str' object is not callable", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mHongKong\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Conutry/Region'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mPY2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mrename\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3025\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axis'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3026\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mapper'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3027\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3028\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3029\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fillna'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0m_shared_doc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mrename\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 882\u001b[0m \u001b[0mlevel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_level_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 883\u001b[0m result._data = result._data.rename_axis(f, axis=baxis, copy=copy,\n\u001b[0;32m--> 884\u001b[0;31m level=level)\n\u001b[0m\u001b[1;32m 885\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 886\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mrename_axis\u001b[0;34m(self, mapper, axis, copy, level)\u001b[0m\n\u001b[1;32m 3089\u001b[0m \"\"\"\n\u001b[1;32m 3090\u001b[0m \u001b[0mobj\u001b[0m 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"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36m_transform_index\u001b[0;34m(index, func, level)\u001b[0m\n\u001b[1;32m 5078\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_tuples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5079\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5080\u001b[0;31m \u001b[0mitems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5081\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5082\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 5078\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_tuples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5079\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5080\u001b[0;31m \u001b[0mitems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5081\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5082\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: 'str' object is not callable" ] } ], "source": [ "HongKong.rename('Conutry/Region')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "Belgique=Belgique.T\n", "France=France_metropolitaine.T\n", "Allemagne=Allemagne.T\n", "Iran=Iran.T\n", "Italie=Italie.T\n", "Japon=Japon.T\n", "Hollande=Hollande.T\n", "Portugal=Portugal.T\n", "Espagne=Espagne.T\n", "RoyaumeUnis=RoyaumeUnis.T\n", "CoreeduSud=CoréeduSud.T\n", "EtatsUnis=EtatsUnis.T\n", "HongKong=HongKong.T" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html#pandas.DataFrame.drop\n", "https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#other-plots\n", "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-with-list-with-missing-labels-is-deprecated\n", "https://pandas.pydata.org/pandas-docs/stable/user_guide/index.html#user-guide" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pd.concat([Belgique,France,Allemagne,Iran,Italie,Japon,Hollande,Portugal,Espagne,RoyaumeUnis,CoreeduSud,EtatsUnis,HongKong,SommeChine],axis=1).plot(logy=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "plotter en log,\n", "SommeChine=0\n", "HongKong=China" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pd.concat([Belgique,SommeChine],axis=1).plot(logy=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "La suite c est pas encore important" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "df=pd.concat([Belgique],axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "plt.figure();\n", "\n", "Belgique.plot(label='dates').set(y_label(\"nombredemalades\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure();\n", "\n", "Belgique.plot(style='k--', label='Series');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a=pd.concat([Belgique,France,Allemagne,Iran,Italie,Japon,Hollande,Portugal,Espagne,RoyaumeUnis,CoreeduSud,EtatsUnis],axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a.plot().set_xlabel('dates')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "axes = pyplot.gca()\n", "axes.set_xlabel('axe des x')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\",index_col=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df=df.drop(columns=['Lat','Long'])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Belgique1=df.loc[['Belgium'],:]\n", "Chine1=df.loc[['China'],:]\n", "France1=df.loc[['France'],:]\n", "Allemagne1=df.loc[['Germany'],:]\n", "Iran1=df.loc[['Iran'],:]\n", "Italie1=df.loc[['Italy'],:]\n", "Japon1=df.loc[['Japan'],:]\n", "Hollande_et_colonies1=df.loc[['Netherlands'],:]\n", "Portugal1=df.loc[['Portugal'],:]\n", "Espagne1=df.loc[['Spain'],:]\n", "RoyaumeUni_et_colonies1=df.loc[['United Kingdom'],:]\n", "CoréeduSud1=df.loc[['Korea, South'],:]\n", "EtatsUnis1=df.loc[['US'],:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "France1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "France_metropolitaine=France1[France1.isnull().any(axis=1)]\n", "RoyaumeUnis=RoyaumeUni_et_colonies1[RoyaumeUni_et_colonies1.isnull().any(axis=1)]\n", "Hollande=Hollande_et_colonies1[Hollande_et_colonies1.isnull().any(axis=1)]\n", "HongKong1=Chine1.iloc[[12],:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "France1=France1.T" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "F=France1.groupby(level=0, axis=1).sum()" ] }, { "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 }