{ "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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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", 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"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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ChinaChongqing69275775110132147182...579579579579579579579579579580
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ChinaGuangdong26325378111151207277354...1598160116021602160416041607160716081625
ChinaGuangxi2523233646515878...254254254254254254254254254254
ChinaGuizhou1334579912...147147147147147147147147147147
ChinaHainan458192233404346...169169170170170170170170171171
ChinaHebei11281318334865...328328328328328328328328328328
ChinaHeilongjiang02491521333844...947947947947947947947947947947
ChinaHenan5593283128168206278...1276127612761276127612761276127612761276
ChinaHong Kong02258881010...1099110211051106110711071107110711081109
ChinaHubei44444454976110581423355435544903...68135681356813568135681356813568135681356813568135
ChinaHunan49244369100143221277...1019101910191019101910191019101910191019
ChinaInner Mongolia0017711151619...235235235235235237237237237237
ChinaJiangsu1591833477099129...653653653653653653653653653653
ChinaJiangxi2718183672109109162...932932932932932932932932932932
ChinaJilin0134468914...155155155155155155155155155155
ChinaLiaoning234172127343941...149149149149149149149149149151
ChinaMacau122256777...45454545454545454545
ChinaNingxia112347111217...75757575757575757575
ChinaQinghai000116668...18181818181818181818
ChinaShaanxi035152235465663...309309311311311311311311311311
ChinaShandong261527467595130158...792792792792792792792792792792
ChinaShanghai916203340536696112...677677677678678678684689690691
ChinaShanxi1116913272735...198198198198198198198198198198
ChinaSichuan581528446990108142...578578578581582582582582583583
ChinaTianjin448101423242731...192192193193193194195195196196
ChinaTibet000000001...1111111111
ChinaXinjiang022345101314...76767676767676767676
ChinaYunnan125111626445570...185185185185185185185185185185
ChinaZhejiang10274362104128173296428...1268126812681268126812681268126812681268
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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", "China Anhui 1 9 15 39 60 \n", "China Beijing 14 22 36 41 68 \n", "China Chongqing 6 9 27 57 75 \n", "China Fujian 1 5 10 18 35 \n", "China Gansu 0 2 2 4 7 \n", "China Guangdong 26 32 53 78 111 \n", "China Guangxi 2 5 23 23 36 \n", "China Guizhou 1 3 3 4 5 \n", "China Hainan 4 5 8 19 22 \n", "China Hebei 1 1 2 8 13 \n", "China Heilongjiang 0 2 4 9 15 \n", "China Henan 5 5 9 32 83 \n", "China Hong Kong 0 2 2 5 8 \n", "China Hubei 444 444 549 761 1058 \n", "China Hunan 4 9 24 43 69 \n", "China Inner Mongolia 0 0 1 7 7 \n", "China Jiangsu 1 5 9 18 33 \n", "China Jiangxi 2 7 18 18 36 \n", "China Jilin 0 1 3 4 4 \n", "China Liaoning 2 3 4 17 21 \n", "China Macau 1 2 2 2 5 \n", "China Ningxia 1 1 2 3 4 \n", "China Qinghai 0 0 0 1 1 \n", "China Shaanxi 0 3 5 15 22 \n", "China Shandong 2 6 15 27 46 \n", "China Shanghai 9 16 20 33 40 \n", "China Shanxi 1 1 1 6 9 \n", "China Sichuan 5 8 15 28 44 \n", "China Tianjin 4 4 8 10 14 \n", "China Tibet 0 0 0 0 0 \n", "China Xinjiang 0 2 2 3 4 \n", "China Yunnan 1 2 5 11 16 \n", "China Zhejiang 10 27 43 62 104 \n", "\n", " 1/27/20 1/28/20 1/29/20 1/30/20 ... 6/4/20 6/5/20 \\\n", "Country/Region ... \n", "China 70 106 152 200 ... 991 991 \n", "China 80 91 111 114 ... 594 594 \n", "China 110 132 147 182 ... 579 579 \n", "China 59 80 84 101 ... 358 358 \n", "China 14 19 24 26 ... 139 139 \n", "China 151 207 277 354 ... 1598 1601 \n", "China 46 51 58 78 ... 254 254 \n", "China 7 9 9 12 ... 147 147 \n", "China 33 40 43 46 ... 169 169 \n", "China 18 33 48 65 ... 328 328 \n", "China 21 33 38 44 ... 947 947 \n", "China 128 168 206 278 ... 1276 1276 \n", "China 8 8 10 10 ... 1099 1102 \n", "China 1423 3554 3554 4903 ... 68135 68135 \n", "China 100 143 221 277 ... 1019 1019 \n", "China 11 15 16 19 ... 235 235 \n", "China 47 70 99 129 ... 653 653 \n", "China 72 109 109 162 ... 932 932 \n", "China 6 8 9 14 ... 155 155 \n", "China 27 34 39 41 ... 149 149 \n", "China 6 7 7 7 ... 45 45 \n", "China 7 11 12 17 ... 75 75 \n", "China 6 6 6 8 ... 18 18 \n", "China 35 46 56 63 ... 309 309 \n", "China 75 95 130 158 ... 792 792 \n", "China 53 66 96 112 ... 677 677 \n", "China 13 27 27 35 ... 198 198 \n", "China 69 90 108 142 ... 578 578 \n", "China 23 24 27 31 ... 192 192 \n", "China 0 0 0 1 ... 1 1 \n", "China 5 10 13 14 ... 76 76 \n", "China 26 44 55 70 ... 185 185 \n", "China 128 173 296 428 ... 1268 1268 \n", "\n", " 6/6/20 6/7/20 6/8/20 6/9/20 6/10/20 6/11/20 6/12/20 \\\n", "Country/Region \n", "China 991 991 991 991 991 991 991 \n", "China 594 594 594 594 594 595 601 \n", "China 579 579 579 579 579 579 579 \n", "China 359 359 359 359 360 361 361 \n", "China 139 139 139 139 139 139 139 \n", "China 1602 1602 1604 1604 1607 1607 1608 \n", "China 254 254 254 254 254 254 254 \n", "China 147 147 147 147 147 147 147 \n", "China 170 170 170 170 170 170 171 \n", "China 328 328 328 328 328 328 328 \n", "China 947 947 947 947 947 947 947 \n", "China 1276 1276 1276 1276 1276 1276 1276 \n", "China 1105 1106 1107 1107 1107 1107 1108 \n", "China 68135 68135 68135 68135 68135 68135 68135 \n", "China 1019 1019 1019 1019 1019 1019 1019 \n", "China 235 235 235 237 237 237 237 \n", "China 653 653 653 653 653 653 653 \n", "China 932 932 932 932 932 932 932 \n", "China 155 155 155 155 155 155 155 \n", "China 149 149 149 149 149 149 149 \n", "China 45 45 45 45 45 45 45 \n", "China 75 75 75 75 75 75 75 \n", "China 18 18 18 18 18 18 18 \n", "China 311 311 311 311 311 311 311 \n", "China 792 792 792 792 792 792 792 \n", "China 677 678 678 678 684 689 690 \n", "China 198 198 198 198 198 198 198 \n", "China 578 581 582 582 582 582 583 \n", "China 193 193 193 194 195 195 196 \n", "China 1 1 1 1 1 1 1 \n", "China 76 76 76 76 76 76 76 \n", "China 185 185 185 185 185 185 185 \n", "China 1268 1268 1268 1268 1268 1268 1268 \n", "\n", " 6/13/20 \n", "Country/Region \n", "China 991 \n", "China 637 \n", "China 580 \n", "China 361 \n", "China 139 \n", "China 1625 \n", "China 254 \n", "China 147 \n", "China 171 \n", "China 328 \n", "China 947 \n", "China 1276 \n", "China 1109 \n", "China 68135 \n", "China 1019 \n", "China 237 \n", "China 653 \n", "China 932 \n", "China 155 \n", "China 151 \n", "China 45 \n", "China 75 \n", "China 18 \n", "China 311 \n", "China 792 \n", "China 691 \n", "China 198 \n", "China 583 \n", "China 196 \n", "China 1 \n", "China 76 \n", "China 185 \n", "China 1268 \n", "\n", "[33 rows x 145 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Chine" ] }, { "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 \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3091\u001b[0;31m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_transform_index\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[0mmapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3092\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3093\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_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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yZcsICAhg//79jBkzhqSkJKxWK19++SVXX311qSnxPfKCiS0Q6lxdJZ4zKU3VDpdCiCovL/VKZGQkgwYN8pRv2rSJefPmsXbtWvz9/VmyZAnbtm1j3bp1PP30057EkPHx8YwdO5bffvuNK664gsWLFwPwwAMPMHbsWHbs2MEvv/xCw4YNC6TEt9vtbN26lfXr1xfskNaQfsJIp2ILgJBm1SKYgIxQhBDecgEjCW8o7pLXbbfdRp06dQAjx9fzzz/P+vXrsVgsHDt2jBMnTgAQHh7uyQXWoUMHEhISSEtL49ixY54A5e9vrJZYakp87YaUI5B1GvyvMFZarCbBBCSgCCGqqMDAv5IsLliwgKSkJLZu3YrNZiMsLMyThj5/ynmr1UpWVlaxae1LTInvchqjktx0CGoANRtU6Qn4osglLyFElZeamkq9evWw2WysW7eOQ4cOlbh/cHAwjRs3ZunSpQDk5OSQmZlZfEp8RxYk74PcDCMvV3DDahdMQEYoQohq4IEHHqB///507NiRyMhIWrRoUWqdTz75hNGjR/Piiy9is9n48ssvi06JP2sG9VwWI4CENq/U6ecvlqSvF0JcsKqQvv6iZJ6GlEPgE2AsjOXjW949umiSvl4IIS63jGRIPQK+QUYwqUaT78WRgCKEEOcr/aSxMJZfTajdDCwyHQ0SUIQQ4vykHYe0RGNhrGrw9Pv5kIAihBBllXbCCCYBtY27uarhnVwlkdAqhBBlkXkK0v4EfwkmxZGAIoQQpck+azwB7xsEta+SYFIMCShCiErvxIkT3H///TRr1owOHTpwww03sGTJEu80npthPAHv42/czSVzJsWSv4wQolLTWjNw4EC6d+/OH3/8wdatW/niiy84evRomeq7XK7iNzqz4fQfYPGBkOqRMfhiyKS8EMIr3tjyBr+f/t2rbbao04JnOz9b4j5r167F19eXMWPGeMqaNm3Kk08+icvl4rnnniM6OpqcnBzGjh3L6NGjiY6OZvLkyTRs2BC73c53331H37596dq1K7/++ivt2rXjkWEP8NIL/+Jk8mkWfPopnevb2LJlC+PHjycrK4uAgADmzJnDddddx9y5c1m+fDmZmZkcOHCAQYMG8eabbzJr1ix2797N1KlTAfjoo4/Yu3cv77zzjlf/ThWFBBQhRKX222+/0b59+yK3zZo1i1q1ahETE0NOTg433XQTvXv3BmDLli3s3r2b8PBwEhIS2L9/P19++SUzZ86kU8eOfDb3IzYum8fyX37n1Tf/w9Kl3WjRogXr16/Hx8eH1atX8/zzz3vS3dvtdrZv346fnx/XXXcdTz75JPfeey8RERG8+eab2Gw25syZw4cffnjZ/jaXmwQUIYRXlDaSuFzGjh3Lxo0b8fX1pWnTpuzcuZOvvvoKMJJExsfH4+vrS+fOnQkPD/fUCw8PN5YSdmTR+upG3NI1ChXanLbXB5Hw71c99YcPH058fDxKKRwOh6f+LbfcQq1atQBo1aoVhw4dokmTJvTq1YsVK1bQsmVLHA5HgeWKq5pS51CUUrOVUieVUrvzlU1SSh1TStnN1x35tk1USu1XSu1TSvXJV95BKbXL3DZNKeM2CaWUn1JqoVm+WSkVlq/OcKVUvPkanq883Nw33qxb+RPoCCEuSOvWrdm2bZvn+/vvv8+aNWtISkpCa817772H3W7Hbrdz8OBBzwglf3p7MNPYO7Lg1H4sFgt+IVeBLQCLxYLT6QTghRdeoGfPnuzevZtvvvnGkwLfU99ktVo9dUaOHMncuXOZM2cOjzzyyCX7O1QEZZmUnwv0LaJ8qtY60nx9B6CUagXcC7Q26/xPKZU3izUDGAU0N195bT4KnNFaXwNMBd4w26oDvAREAZ2Bl5RStc06b5jHbw6cMdsQQlRDvXr1Ijs7mxkzZnjKMjMzAejTpw8zZszwjCTi4uLIyMgouiHthuR4QIFfcJGJHlNTU2nUqBFgLDNcFlFRURw5coTPPvuM++67r+w/rBIqNaBordcDp8vY3gDgC611jtb6ILAf6KyUaggEa603aSO98XxgYL4688zPXwG3mKOXPsAqrfVprfUZYBXQ19zWy9wXs25eW0KIakYpxdKlS/npp58IDw+nc+fODB8+nDfeeIORI0fSqlUr2rdvT5s2bRg9erRn5FBAbia4co27uEKbF3s31z//+U8mTpzITTfdVPLdYYXcc8893HTTTdSuXbv0nSszrXWpLyAM2J3v+yQgAdgJzAZqm+XTgQfz7TcLGAx0BFbnK+8GrDA/7wYa59t2AAgF/g/4f/nKXzDLQoH9+cqb5O9bEX0fBcQCsVdddZUWQnjPnj17yrsLFy8rVes/d2h9fLfWjuxLcog777xTr169+pK07W1F/TcFYnUZYsWFPocyA7gaiAQSgf+Y5UU9PqpLKL+QOiW1de4GrWdqrTtqrTvWrVu3uN2EENVRRjKcPgBWG4Q0Bx+/0uuch5SUFK699loCAgK45ZZbvNp2RXRBd3lprU/kfVZKfQSsML8exRgx5GkM/GmWNy6iPH+do0opH6AWxiW2o0CPQnWigWTgCqWUj9baWagtIYQom4wkSD1qpqAPvyQPLV5xxRXExcV5vd2K6oJGKOacSJ5BGJetAJYD95p3boVjTL5v0VonAmlKqS7mHMgwYFm+Onl3cA0G1ppDrB+B3kqp2uZkfG/gR3PbOnNfzLp5bQkhROmyUsxgEgx15Al4byl1hKKU+hxjpBCqlDqKcedVD6VUJMalpgRgNIDW+jel1CJgD+AExmqt82auHse4YywA+N58gTHP8olSaj/GyORes63TSql/AzHmfi9rrfNuDngW+EIp9Qqw3WxDCCFKl5sBZxLAVsNcz0QSPXpLqQFFa13UfW7FnsC11lOAKUWUxwJtiijPBoYU09ZsjEn/wuV/YNxKLIQQZefIhlMHwOory/ZeApIcUghRPbgcxgS8UhDSzJiIF14lAUUIUakFBQWVvpN2w+mD4HIaIxMf/0vfsWpIAooQosop8NCh1sYEvCPDWBzLN7D4iuKiSHJIIYRXHH/1VXL2ejd9vV/LFjR4/vky7Vs4Jf2ePXsYOHAgRw4dJDsznaeeGM2op/4JGKOap556ihUrVhAQEMCyZcuoX7++V/teHckIRQhRZWzZsoUpU6awZ88eAGZPe52t384ldu03TPtoPqdOnQIgIyODLl26sGPHDrp3785HH31Unt2uMmSEIoTwirKOJC4lT0p6rSEtkWnvTmXJj+vB6suRI0eIj48nJCQEX19f+vXrB0CHDh1YtWpVOfe8apCAIoSoMgIDA805kyNEr/6R1T9vY9PmWGoEBtKjRw9PunmbzYa5gkaBVPPi4khAEUJUHVobd3PlpJLq8KF23QbUCAzk999/59dffy3v3lV5ElCEEFWDywWOTMhJhVqN6Xt3Kz6Yv4iIiAiuu+46unTpUt49rPKUkRqreujYsaOOjY0t724IUWXs3buXli1blnc3wJlrPLTozIHaTSGgiq87cgkV9d9UKbVVa92xtLoyQhFCVG6OLCOdinZDyNVG9mBRLiSgCCEqL0eWsWyvUhB6jZHwUZQbCShCiMrJlWuMTJTFWLbXy4tjifMnDzYKISoftwtO/QHaZSR6lGBSIUhAEUJULlpDyiFwZhkrLcplrgpDAooQonJJPw7ZqRDcCPyDy7s3Ih8JKEKIyiMrBdKOQ0AdCKwL/JW+PiEhgc8++6zUJhISEmjT5py1/oQXSEARQlQOjizjUpetBtRqcs7SvWUNKOLSkbu8hBBesWFRHMlH0r3aZmiTILrdc62xMNbpP4w7uuqEg+Xcfws/99xz7N27l8jISIYPH86gQYN46KGHyMjIAGD69OnceOONBep069aN9957j8jISABuuukmZsyYQUREhFd/R3UhIxQhRMWm3XDmoLGEb51mxnrwRXj99dfp1q0bdrudCRMmUK9ePVatWsW2bdtYuHAhf//738+pM3LkSObOnQtAXFwcOTk5EkwugoxQhBBe0e2ea73fqNZwJgFy0+GKpue12qLD4WDcuHHY7XasVitxcXHn7DNkyBD+/e9/89ZbbzF79mwefvhh7/W9GpKAIoSomLSGs0chOwWCr4Qadc6r+tSpU6lfvz47duzA7Xbj73/uOvI1atTgtttuY9myZSxatAjJ9XdxJKAIISqm9BOQkQyB9SCo9OV5a9asSVpamud7amoqjRs3xmKxMG/evILrzOczcuRI+vfvT7du3ahT5/yClihI5lCEEBVPxilISzSyBgdfWaYqERER+Pj40K5dO6ZOncoTTzzBvHnz6NKlC3FxccbiW0Xo0KEDwcHBPPLII978BdWSjFCEEBVL5mlIPWxkDb7iqnNuDy4sPd24s8xms7FmzZoC23bu3On5/NprrwEQFhbG7t27PeV//vknbreb3r17e+sXVFsyQhFCVByZp41nTXyDjLQq6tKeoubPn09UVBRTpkzBUsStyOL8yAhFCFExZJ/9K5jUaQYW6yU/5LBhwxg2bNglP051ISFZCFH+cjONZ018Ai5bMBHeJwFFCFG+XLnmU/BWIxW9BJNKSwKKEKL8aLfx4KJ2Gcv3FvMUvKgcJKAIIcrP2UTIzTCSPdoCyrs34iJJQBFClI/ss5BxEmqEnvdT8IXlpbAX5UsCihDi8nO7IOUw+PgbC2WJKkFuGxZCeMW6uTM5eeiPsu3szDGyB9tqFJmKPk+9ps3o+fCoMjWZnp7OgAEDOHPmDA6Hg1deeYUBAwaQkJBA3759iYqKYvv27Vx77bXMnz+fGjVq8PLLL/PNN9+QlZXFjTfeyIcffohSih49ehAVFcW6detISUlh1qxZdOvWrWy/rRqTEYoQ4vJyu4xgYvUtMZicL39/f5YsWcK2bdtYt24dTz/9NFprAPbt28eoUaPYuXMnwcHB/O9//wNg3LhxxMTEsHv3brKyslixYoWnPafTyZYtW/jvf//L5MmTvdbPqkxGKEIIryjTSMLlgKR9xhPwda/z6i3CWmuef/551q9fj8Vi4dixY5w4cQKAJk2acNNNNwHw4IMPMm3aNP7v//6PdevW8eabb5KZmcnp06dp3bo1/fv3B+Duu+8GjFxfCQkJXutnVSYBRQhxeWhtPAnvdkLotV5/3mTBggUkJSWxdetWbDYbYWFhZGdnA6AK5QNTSpGdnc0TTzxBbGwsTZo0YdKkSZ79Afz8/ACwWq04nU6v9rWqkkteQojLI/0E5KRBrcbgW8PrzaemplKvXj1sNhvr1q3j0KFDnm2HDx9m06ZNAHz++ed07drVEzxCQ0NJT0/nq6++8nqfqhsJKEKISy879a909DVCvNq00+nEz8+PBx54gNjYWDp27MiCBQto0aKFZ5+WLVsyb948IiIiOH36NI8//jhXXHEFjz32GG3btmXgwIF06tTJq/2qlrTWJb6A2cBJYHe+sjrAKiDefK+db9tEYD+wD+iTr7wDsMvcNg1QZrkfsNAs3wyE5asz3DxGPDA8X3m4uW+8Wde3tN+htaZDhw5aCOE9e/bsKX2n3Cyt/7RrfXKv1i6n1/tgt9t1p06dit1+8OBB3bp1a68ft6oq6r8pEKvLcI4tywhlLtC3UNlzwBqtdXNgjfkdpVQr4F6gtVnnf0qpvAulM4BRQHPzldfmo8AZrfU1wFTgDbOtOsBLQBTQGXhJKVXbrPMGMNU8/hmzDSFEReNywOkDxiR8be/n6frggw+47777eOWVV7zarrgwpQYUrfV64HSh4gHAPPPzPGBgvvIvtNY5WuuDGKOOzkqphkCw1nqTGe3mF6qT19ZXwC3KmEHrA6zSWp/WWp/BGAn1Nbf1MvctfHwhREXhdsGpA8YkfJ1m4OP9PF1jxoxhz549JS6OVXhBLXHpXOgcSn2tdSKA+V7PLG8EHMm331GzrJH5uXB5gTpaayeQCoSU0FYIkGLuW7itcyilRimlYpVSsUlJSef5M4UQF8TtNjIIO7OMhbJ8i15+V1Qt3p6UL2qtTl1C+YXUKamtczdoPVNr3VFr3bFu3brF7SaE8Ja8YJKbDlc0Bf/g8u6RuEwuNKCcMC9jYb6fNMuPAk3y7dcY+NMsb1xEeYE6SikfoBbGJbY6JzOyAAAgAElEQVTi2koGrjD3LdyWEKI8aTec+QNy04z14C8y6aOoXC40oCzHuAML831ZvvJ7lVJ+SqlwjMn3LeZlsTSlVBdzDmRYoTp5bQ0G1przLD8CvZVStc3J+N7Aj+a2dea+hY8vhChPqcfMZ02aeP32YFHxlRpQlFKfA5uA65RSR5VSjwKvA7cppeKB28zvaK1/AxYBe4AfgLFaa5fZ1OPAxxgT9QeA783yWUCIUmo/8A/MO8a01qeBfwMx5utlswzgWeAfZp0Qsw0hRHnKSIbMZAisB4Ghl+2wSimefvppz/e3336bSZMmlVgnOjqaX375xfP94YcfvugHG8PCwkhOTr6oNvJU1nT8paZe0VrfV8ymW4rZfwowpYjyWKBNEeXZwJBi2pqN8RxM4fI/MG4lFkJUBI5MSD0KvjUh+MrLemg/Pz++/vprJk6cSGho2QJZdHQ0QUFB3HjjjRd9fP3X83HVnuTyEkJcHK0h5TAp63PITVUYzy97h++VgVzR/+oS9/Hx8WHUqFFMnTqVKVMK/ls2KSmJMWPGcPjwYQD++9//0qhRIz744AOsViuffvop7733HgDr16/nnXfe4fjx47z55psMHmxcVX/rrbdYtGgROTk5DBo0iMmTJ5OQkMDtt99Oz5492bRpE0uXLi1w3IEDB3LkyBGys7N56qmnGDXKSJwZFBTEU089xYoVKwgICGDZsmXUr1+fgwcPcv/99+N0Ounb96/H/hITExk6dChnz57F6XQyY8aMCp1GX1KvCCEuTvpJcGSBX02Kvgnz0hs7diwLFiwgNTW1QPlTTz3FhAkTiImJYfHixYwcOZKwsDDGjBnDhAkTsNvtnhN0YmIiGzduZMWKFTz33HMArFy5kvj4eLZs2YLdbmfr1q2sX78eMFLiDxs2jO3bt9O0adMCx509ezZbt24lNjaWadOmcerUKQAyMjLo0qULO3bsoHv37nz00Ueefj7++OPExMTQoEEDTzufffYZffr0wW63s2PHDiIjIy/NH9BLZIQihLhwLoeRo8u/FlcMDAdVPgElODiYYcOGMW3aNAIC/lqbfvXq1ezZs8fz/ezZs6SlpRXZxsCBA7FYLLRq1cqT9n7lypWsXLmS66+/HjAW8YqPj+eqq66iadOmdOnSpci2pk2bxpIlSwA4cuQI8fHxhISE4OvrS79+/QAjLf6qVasA+Pnnn1m8eDEADz30EM8++ywAnTp1YsSIETgcDgYOHCgBRQhRRbndkHUGVG0jg3A5BZM848ePp3379jzyyCOeMrfbzaZNmwoEmeLkpasHPHMiWmsmTpzI6NGjC+ybkJBAYGDRD2tGR0ezevVqNm3aRI0aNejRo4cns7HNZvOk0i+cFr9win2A7t27s379er799lseeughnnnmGYYNG1bqbykvcslLCHFhts0DZ7YxCW/1flqV81WnTh3uueceZs3666bP3r17M336dM93u90OQM2aNYsdqeTXp08fZs+eTXp6OgDHjh3j5MmTJdZJTU2ldu3a1KhRg99//51ff/211OPcdNNNfPHFF4CxrkueQ4cOUa9ePR577DEeffRRtm3bVmpb5UkCihDi/J39E1a9CD7+Fep5k6effrrArbvTpk0jNjaWiIgIWrVqxQcffABA//79WbJkCZGRkWzYsKHY9nr37s3999/PDTfcQNu2bRk8eHCpgahv3744nU4iIiJ44YUXir0slt+7777L+++/T6dOnQrMA0VHRxMZGcn111/P4sWLeeqpp0ptqzyp6nS7W8eOHXVsbGx5d0OIyk1r+OJ+OLCOvYNW0bJ12/LukfCivXv30rJlywJlSqmtWuuOpdWVEYoQ4vz8tgT2fQe9/gUWmYYVf5GAIoQou8zT8N0zcOX1EPV4efdGVDDyzwshRNloDd/+A7JT4K5lYJXThyhIRihCiLLZ9L5xuavnv6DBOVmUhJCAIoQog4Prjbu6WvSDrhPKuzeigpKAIoQo2emD8OXDEHI1DJxR7g8wiopLAooQonhZKfDZPcbCWfd9USFXX7RarURGRtKmTRuGDBlCZmbmedV/9dVXL0m/EhISaNOmel0alIAihCiaywFfDjdGKEM/NUYoFVBAQAB2u53du3fj6+vreXixNFpr3G73JQso1ZHcpiGEOJfWxu3Bf0TDgPchrGupVb7//nuOHz/u1W40aNCA22+/vcz7d+vWjZ07dwLwzjvvMHu2sZzSyJEjGT9+/Dlp5yMjI8nKyiIyMpLWrVszZcoU+vXrx+7duwFjsa709HQmTZpETEwMjz76KIGBgXTt2pXvv/+e3bt3k5CQwEMPPURGRgYA06dP98o6K5WRBBQhxLl+nQFb58BN4+H6B8u7N2XidDr5/vvv6du3L1u3bmXOnDls3rwZrTVRUVHcfPPN1K5dm3379jFnzhz+97//AfDll196cnwlJCQU2/4jjzzCzJkzufHGGz3p7QHq1avHqlWr8Pf3Jz4+nvvuu4/qmpFDAooQoqB9P8CPzxt3dN3yUpmrnc9IwpvyRhhgjFAeffRRZsyYwaBBgzwZge+++242bNjAXXfdVWLa+eKkpKSQlpbmGXncf//9rFixAgCHw8G4ceOw2+1YrVbi4uK8+OsqFwkoQoi/HN8Nix+FhhFw90ywVPxp1rw5lPxKylFYXNp5MFZ/dLvdnu95aedLam/q1KnUr1+fHTt24Ha78ff3L2vXq5yK/38tQojLIz0JPhsKfsFw30LwLf7EW9F1796dpUuXkpmZSUZGBkuWLCl26VybzYbD4QCgfv36nDx5klOnTpGTk+MZhdSuXZuaNWt6UtHnpZoHI119w4YNsVgsfPLJJ7hcrkv86youCShCCGMSftkTkJEE930OwQ3Lu0cXpX379jz88MN07tyZqKgoRo4c6Vl1sbBRo0YRERHBAw88gM1m48UXXyQqKop+/frRokULz36zZs1i1KhR3HDDDWitqVWrFgBPPPEE8+bNo0uXLsTFxZU4AqrqJH29EMKYhP/hObjjbej8WJmrFZXqvKpKT08nKCgIgNdff53ExETefffdcu6V911M+nqZQxGiukvcaaRVue4O6DSyvHtTYX377be89tprOJ1OmjZtyty5c8u7SxWOBBQhqrPcDPhqhLHq4l3TJa1KCYYOHcrQoUPLuxsVmgQUIaqzH56DU/th2DIIrDhL+YrKSSblhaiuflsK2+ZD1/HQ7Oby7o2oAiSgCFEdpRyBb/4OjToY65sI4QUSUISoblxO+PoxcLvhbx+D1VbePRJVhAQUIaqb9W/B4U1w53+gTrPy7o1XTJkyhdatWxMREUFkZCSbN28+7zaWL1/O66+/fgl6V33IpLwQ1cmBtfDTG9DuPmhXNe5Y2rRpEytWrGDbtm34+fmRnJxMbm7uebdz1113cdddd12CHlYfElCEqC5Sj8HikVC3hTE68bK4uH+Tlr7Xq23WDGrJtde+UOI+iYmJhIaG4ufnB0BoaCgAYWFhDB06lHXr1gHw2Wefcc011/DNN9/wyiuvkJubS0hICAsWLKB+/frMnTuX2NhYpk+fzsMPP0xwcDCxsbEcP36cN998k8GDB3v1t1VFcslLiOrAkW0sluXMgaGfVOo8XYX17t2bI0eOcO211/LEE0/w008/ebYFBwezZcsWxo0bx/jx4wHo2rUrv/76K9u3b+fee+/lzTffLLLdxMRENm7cyIoVKwqkqxfFkxGKEFVdXp6uozFwz3wIbX5JDlPaSOJSCQoKYuvWrWzYsIF169YxdOhQz1zIfffd53mfMGECAEePHmXo0KEkJiaSm5tLeHh4ke0OHDgQi8VCq1atOHHixOX5MZWcjFCEqOqiX4Pdi+HWSdBqQHn35pKwWq306NGDyZMnM336dBYvXgyAyvfkf97nJ598knHjxrFr1y4+/PBDT4r6wvIuoUHJ6evFXySgCFGV7VhoTMJf/6Cx+mIVtG/fPuLj4z3f7XY7TZs2BWDhwoWe9xtuuAEw0s03atQIgHnz5l3m3lZtcslLiKrq0C+wfByEdYM7p1bZPF3p6ek8+eSTpKSk4OPjwzXXXMPMmTNZsWIFOTk5REVF4Xa7+fzzzwGYNGkSQ4YMoVGjRnTp0oWDBw+W8y+oOiR9vRBVUcph+PBmI+njyFUQUPuSHKYip68PCwsjNjbWc9eXKJuLSV8vl7yEqGqcufDlI+B2wv0LL1kwEaIwueQlRFWz6gU4Fmvc0RVydXn3ptwkJCSUdxeqnYsaoSilEpRSu5RSdqVUrFlWRym1SikVb77Xzrf/RKXUfqXUPqVUn3zlHcx29iulpinzdgyllJ9SaqFZvlkpFZavznDzGPFKqeEX8zuEqDJ+WwqbP4Cox6vsHV2i4vLGJa+eWuvIfNfXngPWaK2bA2vM7yilWgH3Aq2BvsD/lFJWs84MYBTQ3Hz1NcsfBc5ora8BpgJvmG3VAV4CooDOwEv5A5cQ1dKpA7BsHDTuBLe9XN69EdXQpZhDGQDk3Ys3DxiYr/wLrXWO1vogsB/orJRqCARrrTdp4w6B+YXq5LX1FXCLOXrpA6zSWp/WWp8BVvFXEBKi+nFkwaLhYPWBwXPAx7e8eySqoYsNKBpYqZTaqpQaZZbV11onApjv9czyRsCRfHWPmmWNzM+FywvU0Vo7gVQgpIS2zqGUGqWUilVKxSYlJV3QjxSiwlvzbzixCwbNhCualHdvRDV1sQHlJq11e+B2YKxSqnsJ+xZ1E7wuofxC6xQs1Hqm1rqj1rpj3bp1S+ieEJXUkRj49X/Q8VG4tnd59+aySkhIoE2bNgXKJk2axNtvv11ivdjYWP7+978DEB0dzS+//HLexw4LCyM5ObnE8q1btxIeHs727du9mh4/Ojqafv36eaUtb7qou7y01n+a7yeVUksw5jNOKKUaaq0TzctZJ83djwL5/+nUGPjTLG9cRHn+OkeVUj5ALeC0Wd6jUJ3oi/ktQlRKzhxYNhaCGxmpVUSZdOzYkY4djWnf6OhogoKCuPHGG716jJ07dzJ48GAWLlzI9ddfz/XXX1/l0+NfcEBRSgUCFq11mvm5N/AysBwYDrxuvi8zqywHPlNKvQNciTH5vkVr7VJKpSmlugCbgWHAe/nqDAc2AYOBtVprrZT6EXg130R8b2Dihf4WISqt9W9B8j544CvwDy7XrrwQf5Td6VlebbNNUAD/bt649B2L0aNHD6Kioli3bh0pKSnMmjWLbt26ER0dzdtvv8306dP54IMPsFqtfPrpp7z33nu0aNGCMWPGcPjwYQD++9//ctNNN3Hq1Cnuu+8+kpKS6Ny5c4n5vfbu3cvw4cP55JNP6Ny5M0CZ0uO73W7GjRvHTz/9RHh4OG63mxEjRjB48GB++OEHxo8fT2hoKO3bt/cc6/Tp04wYMYI//viDGjVqMHPmTCIiIpg0aRIHDx4kMTGRuLg43nnnHX799Ve+//57GjVqxDfffIPN5t3VOi/mkld9YKNSagewBfhWa/0DRiC5TSkVD9xmfkdr/RuwCNgD/ACM1Vq7zLYeBz7GmKg/AHxvls8CQpRS+4F/YN4xprU+DfwbiDFfL5tlQlQfx3fBxqkQcS80v628e1NhOZ1OtmzZwn//+18mT55cYFtYWBhjxoxhwoQJ2O12unXrxlNPPcWECROIiYlh8eLFjBw5EoDJkyfTtWtXtm/fzl133eUJOEUZMGAA06dPp2vXrsXuU1R6/K+//pqEhAR27drFxx9/zKZNmwDIzs7mscce45tvvmHDhg0cP37c085LL73E9ddfz86dO3n11VcZNmyYZ9uBAwf49ttvWbZsGQ8++CA9e/Zk165dBAQE8O23357/H7MUFzxC0Vr/AbQrovwUcEsxdaYAU4oojwXaFFGeDQwppq3ZwOzz67UQVYTLaVzqCqgNfV8r794AXNRI4kKpYvKT5S+/++67AejQoUOZHnZcvXo1e/bs8Xw/e/YsaWlprF+/nq+//hqAO++8k9q1i39S4dZbb+Xjjz+mT58+WK3WIvcpKj3+xo0bGTJkCBaLhQYNGtCzZ08Afv/9d8LDw2ne3Fh64MEHH2TmzJmeOnnZlXv16sWpU6dITU0F4Pbbb8dms9G2bVtcLhd9+xo3w7Zt2/aSPPgpqVeEqIx+eRcSd8Adb0ONOuXdm3ITEhLCmTNnCpSdPn26QP6uvDT0VqsVp9NZaptut5tNmzZht9ux2+0cO3aMmjVrAsUHsMKmT58OwBNPPFHsPkWlxy/pMlpxxy6qTt6+ecewWCzYbDZPucViKdPf4nxJQBGisvlzO6x7DVreVe2fhg8KCqJhw4asWbMGMILJDz/8UOKlpsJq1qxJWlqa53vv3r09AQGMdPgA3bt3Z8GCBQB8//335wSy/CwWC59//jn79u3jxRdfLHNfunbtyuLFi3G73Zw4cYLo6GgAWrRowcGDBzlw4ACAJ3Ny4X5FR0cTGhpKcHD5zKdJQBGiMsnNMNaFD6wL/d+tsinpz8f8+fN55ZVXiIyMpFevXrz00ktcfXXZc5j179+fJUuWEBkZyYYNG5g2bRqxsbFERETQqlUrPvjgA8CYq1i/fj3t27dn5cqVXHXVVSW26+fnx7Jly1i+fDnvv/9+mfryt7/9jcaNG9OmTRtGjx5NVFQUtWrVwt/fn5kzZ3LnnXfStWtXz3ovYNwmndff5557rlzXeJH09UJUJt88BVvnwbBl0Ozm8u5NhU5fX1mlp6cTFBTEqVOn6Ny5Mz///DMNGjS4bMe/mPT1km1YiMpi7wrYOhdueqpCBBNxafTr14+UlBRyc3N54YUXLmswuVgSUISoDM4mwvInoWE76Pn/yrs34hLKmzepjGQORYiKzu2CpWOMBJB3fyyJH0WFJSMUISq6n96AP6KNSfi615Z3b4QoloxQhKjI4lYaAaXd/dBe1pETFZsEFCEqqjOH4OvHoH4buPM/couwqPAkoAhRETmyYdEw0NpYG963Rnn3qMIKCgryfP7uu+9o3rx5iXm2LqXZs2fTtm1bIiIiaNOmDcuWLSu9UhHsdjvfffed53tZUvJXBDKHIkRF9MOzkGiHez+DkLI/pFedrVmzhieffLJMDx3mcTqd+Ph45zR49OhRpkyZwrZt26hVqxbp6elc6KJ+drud2NhY7rjjDq/07XKRgCJERbP7a+N5k64ToMWd5d2bMpv8zW/s+fOsV9tsdWUwL/VvXep+GzZs4LHHHuO7777zPCV/6NAhRowYQVJSEnXr1mXOnDlcddVVPPzww9SpU4ft27fTvn17Xn75ZZ588kl27dqF0+lk0qRJDBgwgISEBB566CEyMjIAIz9XSWumnDx5kpo1a3pGTEFBQZ7PdrudMWPGkJmZydVXX83s2bOpXbs2PXr04O2336Zjx44kJyfTsWNH4uLiePHFF8nKymLjxo1MnGiszLFnzx569OjB4cOHGT9+vGeBsIpELnkJUZGkn4Rvn4Yr28vzJmWUk5PDgAEDWLp0KS1atPCUjxs3jmHDhrFz504eeOCBAifguLg4Vq9ezX/+8x+mTJlCr169iImJYd26dTzzzDNkZGRQr149Vq1axbZt21i4cGGpJ/B27dpRv359wsPDeeSRR/jmm28824YNG8Ybb7zBzp07adu27Tlp9PPz9fXl5ZdfZujQodjtdoYOHQoYGYd//PFHtmzZwuTJk3E4HICRHNLp1mS73GQ4XZx1ujjjcJKc6+BEjoM/s3M5kpWDw+2+oL/v+ZARihAVhdawYgLkpsPAGWCtXP/vWZaRxKVgs9m48cYbmTVrFu+++66nfNOmTZ508w899BD//Oc/PduGDBniSSu/cuVKli9f7pmjyM7O5vDhw1x55ZWMGzcOu92O1WolLi6uxH5YrVZ++OEHYmJiWLNmDRMmTGDr1q1MmDCBlJQUbr7ZyG4wfPhwhgwpuCqH1hqXmQYry+Um2+Um1+3mVK4Tl9akOV107dOXRDe4agRxRWhdNvyRQN0rG+EuJXuWUmBViroavLuc1rkq1//FClGV7VwEv6+AWydDvRal7y8AI7PvokWLuPXWW3n11Vd5/vnni9wvf/r3wMBAz2etNYsXL+a6664rsP+kSZOoX78+O3bswO124+/vX2pflFJ07tyZTp060evWW3l0xAhGPfl3NHAq14lTa47n5JLr1vyRmU2usrA/PRPf9CyOJ6eQqzVxGdmczHVw1unmaHYuAJkuN4EBNrJdbqxKYbVa8dNu6th8jO8KfJQyPoOnzKoUlst4d6Bc8hKiIjiTYFzqatIFbnyyvHtT6dSoUYMVK1awYMECZs2aBcCNN97IF198AcCCBQuKTWnfp08f3nvvPc+6Itu3bwcgNTWVhg0bYrFY+OSTT3C5XJ46LVq0wKWNy0xpThencp3YDyaw4pdf2Zuexa70LL77NYY6jRqTZPOnRq1aLF+7juM5Dj795FPad+2KU0OTpk05sGMHoTYfNn+7HB+laBrgS7OQOvhkZ9IyyJ82NQOo5+tDAz8bLYICaB7oj69F0cjfj0b+vjTws1HX10Ztmw/BPlYCfaz4Wy3YLJbLGkxARihClD+XE74eZVybuHsmWIpe4U+UrE6dOvzwww90796d0NBQpk2bxogRI3jrrbc8k/JFeeGFFxg/fjwRERForbmqaVO+WLacBx8bxbCh9/D5okXc2P1magQGcjAzhxNJSeS43OxOyyrQTmJGNq9MfI6k44n4+/sTGlqXqdPfJ7yGH3PnzmX82LFkZWbSrFkz5syZQ+1AfyY/9yz33HMP3y78nF69emEBrrD5cMett/DuW2/SuX17Jk6cWOaFvcqbpK8XorxFvw7Rrxl5uiKKXPG6wqqM6eu11uRqTY5bk+N2k+vO91lrKOaUaFHga1Fs+OEH/kw4yOhxT2KzKHyVwmZR2BQo8lZQdAPa/Gy8NNqYJ/N8dhdTnvfZ7fmMWYLnfF10mc63/1/fjc8BAVdhtf61SmRxJH29EJXV4c1GapWIoZUumFQ0Wmu0duJ256K1C4eGHLcmVytyNca7W5GrFcap36CUxhc3vspFoHJiUy5sOLHg9pygfXBiwQUuzdDbWqJpATnxgMaNJgdNziX5VQo0aE9/lRkvzO/aeNf5PhfeZnxXuH01xSxv7zUSUIQoL9lnjdQqtZoYa8NXI3knaq1daO0C3GhtvDyfcUPhsrzv2mWcyrUbJxZytZVcbORiw4Evufii800RK9zYcOCDgwAc2MyXj3Zi1ebttHmBRivjBJ534ja/O7EUvw9/lRf4TsGTOihUgW1GH41LWpa/tpuXuPIudSnzfxnbzBY9TRst5v1c9dfOnnoA1stw16AEFCHKg9ttrG+SehQe+R78y2cN8LJyux1kZOwnLf030tJ+Iy1tD9qdS0DAv/Kd6B1o7fQEiXNfBbcVe22pCFobJ3MnNrLxJ5cAcpWNXOWLO1/gsGg3Pm5NDZcLH7cTmxtsGqwolLKilA9KBaKUMl4WhVIY73mflXkyLvDZ2EbhMvKVCQkoQpSLtf+GPUvhtpfhqqjy7o2H1pqMjDhOnYomJ+ckDkcKGZnxpKfHo7VxC6vFEkDNoBa4dS4ORwppab+V0KICbUVrC9ptQbutoG3Gd22BfO94TvoWLMqCslhxKwvZVkW2BbIsGke+lv2UoqZS+FsUARYL/hYLPvkDhJzkLzsJKEJcblvnwcZ3oMMjcGP5pc9wuXJISdnC6TM/k3Z2Fy5XJjm5SeTkJAJgtQZh8wkmoEYYja58CKtujjsznMwzoZw9kMvZ5EwadnTgygnG7bKi3T4FgofCisVqwWJVWKwKq8WCxccYCVis5rvnMzgxHurLdLvJcmmy3W4cbuPSmEVBoNVKqNVCkI8RPC73LbEXRZsT7NoNbqexaFreCE1rPFP3ygJuJ9qZZVzas9jQFh/P5Hve/3g+ey4d6hL30WiCazbGx6f0SfmLIQFFiMtp/xrjafhrbjXmTS7jSdHhSCUjcz8ZGfs5c2YTyclrcbkyUMqXmjVbY7XWxt92JYHqIZxnO5GWFEBSUhapSVmkp+SY57+zwFls/laCQwNQyg8fa32sfqpA8LBYLVgsxf82t9Zkutyku9xk5rrJcrtx5nvk28+qCLRaCLBZqGE1Xhal/rrrSbvBnff53HfjJFpw3sXtKdPmHI3Grd2eE27eu9vcpiFfWd5JXxfahueFZ3uh7zrvjqy/5sn/2kedx4W/olnc4OMGq1tjdZmf871bze3Ophn4BElAEaJqOPEbLBoO9VrC4DmXLLWKy5VJevrvpKXtJSMj3nhlHiA396/MtxZqYXHcjOtUJ1KPNuPgCU12Rv4LSikE1MygVt0aNLq2NsF1A6iV7+UfZEMpxd69e6lZp9AT5NoNbgfkOsGVC64ccrGQpXzJxEqmhkytcJtnV19cBODEVzvwxYGPdoDLjVu7caPJcLtIcztxmydrt/nSCtwoDh8+xugHx7F8wxLzRK2Y/ub71AisQfuo9rz+r9fJzcklNzeXvgP7MvafY8/r71l4it1SRJk5Xe6ZzflrmwUsxuW7vMl2pSwoZfHUUSpvsl6jNKAsKKvN2M/tQjmcWBwulNONcua9u1EuF7jybi0uxGIBHyvK5gM+PigfH2x+Qefu52USUIS4HE7+Dp8OBr8guH+R1ybhtXaTkRHPmZQtnE3dztm038jM/APjlAu4a+DOaUzO2VZkJNUj60x9cs9eiSMzBIWFoDr+1KobQLP2BQNGcIg/vu5USD0CqX8YNw+gILsB/OmLTjtBevoxnDV7kHn6AC63E6fbhRMXDizkWvzItfjjsPjhsgSgPacaDdqB0tlY3FkodzZu3GQBWUX/RONEbbGglMICWFDmZ4WPUvj5+GFBEexjTrZjIdDqR6CPPy8++QKz5n9I24g2aJdmf/wfNAls8Nc8DRbPCd4oM0/45jHy3i8VrTXa6UTnOtCOXHRuLtrhQEHRercAABOGSURBVOemez4X/GMolK8vyvb/2zvz4Lqq+45/zr33Pb1Vy9OCZFmShbBlyQu4GMKaEhgSSBpoWiB0CAM0iZtmoEkmSUtIwpB0miFNQ5t0sgBOGxIG2mQSKJ2EYicT3CaAsZXgVdh4lW1J1pPe09v01ntP/7hvl7xgS7aFz2fm6p177jnn/nSld773bL/jRLhtoRCGgXA4isIhDAMx1/ODj4ESFIVirhnaCM/cAUYN3P0c1LWfclGWlSUW28FkZBOT4dcJT27GNPMu480A6Ug38dEPkAx1kprsQGabqG3yUNfspr3VTe0KN3Utbuqa3PhdCfShDZAIYk2FiJlpQiMRhrbsIBzaQ0hmCGs6YV0jpNufYU0npGuEdZ2sEPxL/xUIaSF1D62//Qbu8V24Ka+Apf3mjUQrhguzo0pTaAtTXSumxSKgdQXi5q8f95nIiANDd7KgvrsY562pw1fjY2I8RF/PSgLeFgAaL2075Wf/dpGWZYtFLgfZbDEsczlbNLJZZCYzrYUhDAPhdKJ5vbZwOB22iDid9rVzeOxICYpCMZfsehF+eh/ULoC7fw4Ni95WdtNMEYn8geDR1whNvM5UeiuIFACZeCtTYyuZCi4mGVyCrrXS3FlLR6eflqv8NHf68QdciPxYhpSSYDLIgfHtbBx4loP7f80BTXLQYXDEMMiJYk0Ojf6iDV7dRYOrgYaaenxGCzVGF35nF0GtFcuow3TYAqlpDnShoQu7FaEX1k2cFqeX/zOf+Qy9vb1cd9113HTTTdxzzz0n5eTxWEgpoSAUeZGgKBI5ZK4srsz3VzmFVoRWU4Pw+Upi4cgLhzZ/XSwqQVEo5oo/PA0v/A20XQx3/RS8TSfMYlk5hodeZ/jwOuKJASx9F0IzkVKQnmxnavwqstGluB2X0NCykPYeL4FrvdS3ugiJIMOJYUbi+3kjMcrwm0cYiRxgNH6EyUyUuJWhvIpz+dx0+RbSW9/DjfU9NLqbbOFwNRBwBbC0WvaknWyLZ9gaS/JGfIpQ1i7BkNDncuPTM3S6nXh1Dect35yjB3l8jvXGLoTg4Ycf5q677mLdunU888wzPPvss7z88svT0kopS8JQ1oqoiMvmWxtyhn1FhEAYjpJQeL2lLiiHoyginOMtjNNFCYpCMdtIaU8L/vVXoed6uOPH9tjJDMTDaY4eiDB6eCOx1P8gvK9guKJISycV60KkP4C7ZhWBwGqalrUSaPPi9GkcjB5kMDTIaxM72LlvJ29ufpNkrjQKIRA0mxat2Qy9uRwBS+J3N9Lsb2dR/YUsWnQDFyx5P5oovQ0fTmXYOBnnuUiC1w7FeWtqDABDwFKvm5ub6ljp97DS76HP68KlawwODtLgOLvVSGNjI+FwuCIuFAqxqKsLK5Ohe8EC1tx9N395xx209vQwOjhIoLbWFotiV9TMCy2FrheFQfO4bdEoG+gujl/kx3jOd5SgKBSziWXBS1+Ajd+HFXfArd8BwwnY4hEcijI2FCM4FGNyYhBH4HfUdm7E6ZvAcDsQ6cup029g4YU30tzRikmOtybfYnBiJ78I/ZzBvYPsDu8mZdrdXm7DTW9DLx+66EMsDSxloX8hrbqH1mc/giOVgOu/CM190LocakrdWFlLsjWWZHM0wUAkwaZogsMpewC41tC4rNbHHa0Brqj3scLnxqWf+W4YKSWYJtKy7G4m07LXb1iW3Z1kWcicidPM0drYyItPP831V1zBxPgEL77wAn918808v3YtN117LUII3ty3D10IfJkMVixmtxgcDjS3uyQO+bji+TzufjobKEFRKGaLVBSe+wTs+gXm5Z9kuOuzHF03zNEDUcYORpmKZDA8E9R1vk59z2ba+oYADZ/rXbR3/Cmtbe9jMpvm1eFXeeHI99m5dSd7J/eSkzkAfA4fSwNLuW3JbfQ39tMX6KO7rhu94O4+HbNnk730BZg8BPf8N3RdCUAwk2UgGGFTXkC2xKZI5td9tDodXFrn4RMdPq6o89Lnc6Of4tt2YYwByyoTArN0XhCIvDgUwrJaKPJ5T4xA6Dprv/51Pv2Vr/Dg174GQvClz3+eJatX89Unn+TBxx7D4/FgOBw8/fTTeFeuVK2JOUK5r1coZgE5vg/z6Q+jT+5lm+uT/O7wDVg5+7sV6EjS1LsVZ+A1cmwHoK52FRe03kJ94w1sDw/x6vCrvDL8CrvCu+zrNXUsb1zO0sBS+hr76A/00+5vt7uozCyMvwVjO+3j6E4Y2wGTQwCY6Oz6k8fZ1PYeNkcTbI4k2J+03aY4hGC5z83qOg+X1npZXeelvcbeGFamUpiRCGYkihWNYEajmNEYViyGNZXASpQOM/8Zve8+Fre1VQrEySCE/favaXa3UlVYaDro2vQ0uj4tTonD7KLc1ysUZxhpSUIjCUb2RkhtfYkVwS/bW8JPPkyq+SpWvMfC37GZNL8hFn8DAJdvKU3Nn2aqZjm/Dw+xdvAVNh/9NmkzjaEZrGpZxaf+6FNcueBK+gJ9aNkkxI9CcDds+UleOAZhfLe9cBBA6NC0mMHO9/GLS67kdUc7v8+6iMeA2GEapcWqbJI/S0xycShI7+gwRmQSMxrBikRJR6O8FY1iRSLT1zxUIRwONK+34kDT0GpclZV//rNU+esIvUoYhPK19U5ECYpCcRJk0yZH90cY3RdhZG+E0X1RrFScVd7nWe39KVPORYxe+c9c3DVCOPqvhMOvko5LajwXYTZ8kMGMh4GJwwzu/lFx/KO7rpvbltzGVW1XstqzAM/oDjiyGTb9DEa3QSYGgDQhl9LJam3kjIXkxHsZc7SwzbOAHZ5GftvUxvaGBWimRfehQ1y/dzfL9+1m2b7dtI2PlSbeCkGqtha9cNTVYrS2FsNabS16bR16nX1dy4c1nw/d60U4ndOey+DgIM7OjjP0Vzj/KO9BklWB8iuyMmLa9AIpwdDnfn95JSgKxQxYliR4MMahwQkODYYZ3RvBsiQIaG7V+eOL/pcLIz/EyIyTuuhqDvZ3MBb9a6yDGaTRzAGtn1+Oj/PWoWFgmBq9hr5AH7ct/nNW4GRVdIK20Z3I3/4Yc/wxMlGLSNwgO+UkSzO5XA/ZuEluMkVQGuzu7LaP1m52d15IMNBYtLUnFOTTWzfywdgEzX4vxoWt6Kv70Rvq0evqbYGoq0Pz+c6pQeaiLyxZFUbm48rChfRlYbDdec2UFwrrBUuVbdG/lizGltIW00yPL/fFVVFO/qS83Bnjiz/K7KkqqypJZbpZQACLL/DjcsztCnolKAoFdqURGk5wZHeYI7smObI7THrKHgxv7vRzyY2dLOzM0Tb+nxhv/DsEQyRbutje6WHCswsrepi9ZisvBifYl45T69S5pv1a7mxYyvKsTtehI8jtW8kM/hvZUJJM3GBfyksmBjJTEoeQv449/cvZ07uMXR2L2NV0AUddnuL1C3XBVX43lwRqWVnrZYXfQ60x+5WEaUni6RyJ/GGHTeLpLPG0WYy7tC7LkckklmU7UDQtOb2Sr6ydi2HJzFtQHStctR9hRViUxVTHFfOLmcovSy+m55v5HtPzlucvXZ3hmpwpfVW5Yoa4qryla1WR5eVXpdXOwHC5EhTFeUcmlSM8OkV4JEFoJEF4JMHRA1GSMXsMwd/oovuSZjr7Aizsrcc1+Qbm7x9F/+VzCDNLsMnLwSV1RGrj7M/5+fWok/Exk6XpLB9OddEfztE8HCIX/C8ykZ+Tm9IZKn69dYSzAWdHB9EVS9nT28eutg4G6wJs152MmgUftNDjqeFqv4eVPjcr/R5W+N34T0I8MjmLWCpLNJUjmswSTWVJZS2ypkUonmY8lCQWy5BL5ewjbWKlTWTGhKwFWQvdkrgAF6L4WYPAATgAH4IA4Lylntp4rqLCFoCQ5RXxTBIwd0yvN6u6hETVtWOZJApZZuh2Kssjp/2sjJfHST9TaKYyquMpa4mVxVSdV/4M4AJUC+WYCCFuAr6F/ZTWSikfPcsmKc4RLNMiHk4TnUgRDSYJjSaKAhIPlXb/1nRB/QUeOpY20LbIRWPLJKnUVhIHXqJm01bkr8YQ6RxCg+GWGvY21hMad2INaPh2aqw6kOXqKbCdMU7kD0h4NLSWBqJXLSbY00+wu4/Rlg6OenwcETpvJlKMpG0BK4jHVXnxWOZ10WEYWGmTWCxDPJQhMTTBbxIZ0lNZ0sks2aRJLpXDSueQGQsyFlrWQs9ZOCV4ELgpfArq8mFXvmazN9i1yGFhYmGJUtjEwtRMTCyywiKng6lLTE1iaRZSk0gNhC5A92M6CoP5sqLCndaPP71KLAuXfhxrbECWApWp5mqm6vGKnaNblk9UOGFYVF+rnOigVeURx9lOYLaYt4IihNCB7wA3AoeBTUKIF6SUO8+uZYq5QkpJOpkjOpkkFkowFZoiGUmSjKdJxjJMxTLEoibxuEUiCVKWOgSElsbvCONxjtHQMIpDBNG1cXQtTI2WxB1Jou+cwjGQ5IKpJBqQE4Ixw8c2s57hYAPWJgdm1CDjrSHndjLhd7HnGj/xxhYmG9uZqG8j7G0kaLgI5izC0rJ9/krABEayeMwQtVlJIC1ZOmVyQSJLSzJHTdZESHtfkD8g2SIkGrb7dhOJKQrhUpyFZV9DYgo7janbYUtIWzSEna4gIJa0u6asGd9mRdV52Umu/O9QCn/C6iaUy3E83l41VugIO05J1a/8QHBsjL9/5Mts3fIGzhon7Qs7eO/7buJX69fxgx/+eJoAPPi3n+WjH1vD4iW9Ffcuv/s0zTgpESmUcIzEJyxjxlGVk0p9IjweL4Yxt1X+vBUU4HJgj5RyH4AQ4j+AW4FZF5QvfOtepoiXIo75H2f3acrKiIq00//VjvsvXFVOVdqyU1E+uDhj/mP3tVaeVFs405ejuu+g/J1SVL5Vynxc2c5CsnC5rMKvGNisiBdlg5eVlU11/7FAYnhMHB4THRODHAYmhrBwYJLFJEYOQ5h4SVFHArfI2K7TLRhJ6QyJdvZ72thvtbJPtpHFAA/2sXDagysRG7GPMjwzpySeP4YKETWAa646uOei3FKZ9wmThHb86cZzjZSSj6+5l1tuv52vPf4dAN7cvoMN69djCklcn+6k8UuP/SMAcaqvnYGBhtnkbah17gz8bvNZUNqBQ2Xnh4Fpm3MLIdYAawA6OztP6Ub7nVvY4Tn+W5hivqEB/hniM8BB4CAG8/sLciYQ+nVojkkAntrxFAeiB2a1/EW1i7hn2T3HTbPx/zbicAju/OgHAduW/lXtxKf62fi7l/nsmvvY8+Ye+i/u59HvPYoQgntvvZfPfeVzLL9kOZd1XcZH1nyEDes34HK5+PaPvk1TSxMvv/Qyjz/2ONlMlvpAPY9+71GaWk7s4PNcxWE0z/k95vP3ZSZtnt5KlfIJ4AmwV8qfyo2uNm7h4lik6u7lb+eiIkqggcyfC/st294HopCjMEyZ3wuiwuz8yt9pv51EQ0BFP2hhP4my5o9dQr5orbKY4s5wZflFyfaKwdOyHeUKZRdLE6I0Y6bwI38vXRPomkTXSr+jQCsr1u7LFYZeXPAmCg4KRUXKfJT9PEp25u8lDHThQDdqQHOiYa+ulrrTLpfSBknlz8bQBYYmcBgahqEXN2wyCr6qyh6Bll+kZ+h6VR922cK8/Kcd1OxHgVZp7xzwdtzCn6wNp1Lm6P5RlgSWAFDvqseTPFa77NRocDXQG+g9bpp1Q+u4+l1XT0s36h9l9/bdbNm2hQULFvDua9/N+OA411xzDR6Hh67aLnoDvSSnkrz/uvfz3W9+lwf/7kE2/GwDD33xIVpuamHNnWsQQvCDtT/g+bXP841/+sas/n7H4vTd/k+n3BHoXDGfBeUwUL6iaiEwPBc3euDjX5mLYhWKeU9QBHFotuuWh9710FmxQRc6mtAwtMrqTNd0Lr/8chZ1LgJg1SWrODx0GEMzEAgMzcDQDJxOJ7fecitCCC5bfRnr16/H0AxGh0e56y/uYmRkhEwmQ3d397R7KCo5d1Y5vX02AYuFEN1CCCdwJ/DCWbZJoVCcYZYtW8bAwMCM12pqaophXdfJzTCBwOFwFFtc5WkeeOAB7r//frZt28bjjz9OKpWaA+vfWcxbQZFS5oD7gZeAQeAnUsodZ9cqhUJxprn++utJp9M8+eSTxbhNmzaxYcOG0yo3EonQ3m7vRvnUU0+dVlnnC/NWUACklL+UUi6RUvZIKf/hbNujUCjOPEIInnvuOdavX09PTw/Lli3jkUceYcGCBadV7iOPPMLtt9/OtddeS1PT/B2MP5Mo9/UKheKUmcnVuWJ+czru6+d1C0WhUCgU5w5KUBQKhUIxKyhBUSgUp8X51G3+Tud0/5bn1aTqgYGBcSHEwVPM3gSMz6Y9c4iydW5QtlbxxBNPXJRMJl0Oh8M8lUWcpmkauq7PCzcU73RbpZRks1l9YGAg1d/fv6fqctfJlHFeDcqfDkKIzSczKHUuoGydG5St0xkYGGgxDGMtsJxT6PEIBoNtzc3NIydOefY5D2y1gO25XO5jl1566dip3Pe8aqEoFIrZJV/x3HKq+ZVIzw1ny1Y1hqJQKBSKWUEJysnzxNk24G2gbJ0blK2zz3yxE5StJ0SNoSgUCoViVlAtFIVCoVDMCkpQFAqFQjErKEFRKBQKxaygBEWhUCgUs4ISFIVCoVDMCv8Pg2oFza5ZOUQAAAAASUVORK5CYII=\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 }