diff --git a/module3/exo1/Covid-19 analysis.ipynb b/module3/exo1/Covid-19 analysis.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..120712d46e91acb9540528f3bf3a01b861a9be6e --- /dev/null +++ b/module3/exo1/Covid-19 analysis.ipynb @@ -0,0 +1,3087 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "# The SARS-CoV-2 (Covid-19) epidemic analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek\n", + "import os.path\n", + "from os import path" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "The data on the Covid-19 incidence are available [here](https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv). We download them as a file in CSV format." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "data_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Data downloaded on 09.06.2020\n", + "\n", + "This is the documentation of the data from [the download site](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", + "\n", + "| Column name | Description |\n", + "|--------------|---------------------------------------------------------------------------------------------------------------------------|\n", + "| `Province/State` | Province/State |\n", + "| `Country/Region` | Country/Region |\n", + "| `Lat` | Latitude |\n", + "| `Long` | Longitude |\n", + "| `1/22/20` | Dates |" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...5/30/205/31/206/1/206/2/206/3/206/4/206/5/206/6/206/7/206/8/20
0NaNAfghanistan33.00000065.000000000000...14525152051575016509172671805418969195512034220917
1NaNAlbania41.15330020.168300000000...1122113711431164118411971212123212461263
2NaNAlgeria28.0339001.659600000000...9267939495139626973398319935100501015410265
3NaNAndorra42.5063001.521800000000...764764765844851852852852852852
4NaNAngola-11.20270017.873900000000...84868686868686889192
5NaNAntigua and Barbuda17.060800-61.796400000000...25262626262626262626
6NaNArgentina-38.416100-63.616700000000...16214168511741518319192682019721037220202279423620
7NaNArmenia40.06910045.038200000000...89279282949210009105241122111817123641313013325
8Australian Capital TerritoryAustralia-35.473500149.012400000000...107107107107107107107108108108
9New South WalesAustralia-33.868800151.209300000034...3095309831043104310631103110310931123114
10Northern TerritoryAustralia-12.463400130.845600000000...29292929292929292929
11QueenslandAustralia-28.016700153.400000000000...1058105810591059106010601061106110621062
12South AustraliaAustralia-34.928500138.600700000000...440440440440440440440440440440
13TasmaniaAustralia-41.454500145.970700000000...228228228228228228228228228228
14VictoriaAustralia-37.813600144.963100000011...1649165316631670167816811681168516871687
15Western AustraliaAustralia-31.950500115.860500000000...586589591592592592596599599599
16NaNAustria47.51620014.550100000000...16685167311673316759167711680516843168981690216968
17NaNAzerbaijan40.14310047.576900000000...5246549456625935626065226860723975537876
18NaNBahamas25.034300-77.396300000000...102102102102102102102103103103
19NaNBahrain26.02750050.550000000000...10793113981187112311128151329613835143831476315417
20NaNBangladesh23.68500090.356300000000...44608471534953452445551405756360391630266576968504
21NaNBarbados13.193900-59.543200000000...92929292929292929292
22NaNBelarus53.70980027.953400000000...41658425564340344255451164598146868477514863049453
23NaNBelgium50.8333004.000000000000...58186583815851758615586855876758907590725922659348
24NaNBenin9.3077002.315800000000...224232243244244261261261261288
25NaNBhutan27.51420090.433600000000...33434347474748485959
26NaNBolivia-16.290200-63.588700000000...959299821053110991116381224512728133581364313949
27NaNBosnia and Herzegovina43.91590017.679100000000...2494251025242535255125942606260626062704
28NaNBrazil-14.235000-51.925300000000...498440514849526447555383584016614941645771672846691758707412
29NaNBrunei4.535300114.727700000000...141141141141141141141141141141
..................................................................
236NaNTimor-Leste-8.874217125.727539000000...24242424242424242424
237NaNBelize13.193900-59.543200000000...18181818181819191919
238NaNLaos19.856270102.495496000000...19191919191919191919
239NaNLibya26.33510017.228331000000...130156168182196209239256256332
240NaNWest Bank and Gaza31.95220035.233200000000...447448449451457464464464472473
241NaNGuinea-Bissau11.803700-15.180400000000...1256125613391339133913391368136813681389
242NaNMali17.570692-3.996166000000...1250126513151351138614611485152315331547
243NaNSaint Kitts and Nevis17.357822-62.782998000000...15151515151515151515
244Northwest TerritoriesCanada64.825500-124.845700000000...5555555555
245YukonCanada64.282300-135.000000000000...11111111111111111111
246NaNKosovo42.60263620.902977000000...1064106410641064114211421142114211421263
247NaNBurma21.91620095.956000000000...224224228232233236236240242244
248AnguillaUnited Kingdom18.220600-63.068600000000...3333333333
249British Virgin IslandsUnited Kingdom18.420700-64.640000000000...8888888888
250Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...12121212121212121212
251NaNMS Zaandam0.0000000.000000000000...9999999999
252NaNBotswana-22.32850024.684900000000...35353840404040404042
253NaNBurundi-3.37310029.918900000000...63636363636363838383
254NaNSierra Leone8.460555-11.779889000000...8528618658969099149299469691001
255Bonaire, Sint Eustatius and SabaNetherlands12.178400-68.238500000000...6677777777
256NaNMalawi-13.25430834.301525000000...279284336358369393409409438443
257Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...13131313131313131313
258Saint Pierre and MiquelonFrance46.885200-56.315900000000...1111111111
259NaNSouth Sudan6.87700031.307000000000...99499499499499499499499413171604
260NaNWestern Sahara24.215500-12.885800000000...9999999999
261NaNSao Tome and Principe0.1863606.613081000000...479483484484484485499499513513
262NaNYemen15.55272748.516388000000...310323354399419453469482484496
263NaNComoros-11.64550043.333300000000...106106106132132132132141141141
264NaNTajikistan38.86103471.276093000000...3807393040134100419142894370445345294609
265NaNLesotho-29.60998828.233608000000...2222444444
\n", + "

266 rows × 143 columns

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" + ], + "text/plain": [ + " Province/State Country/Region Lat \\\n", + "0 NaN Afghanistan 33.000000 \n", + "1 NaN Albania 41.153300 \n", + "2 NaN Algeria 28.033900 \n", + "3 NaN Andorra 42.506300 \n", + "4 NaN Angola -11.202700 \n", + "5 NaN Antigua and Barbuda 17.060800 \n", + "6 NaN Argentina -38.416100 \n", + "7 NaN Armenia 40.069100 \n", + "8 Australian Capital Territory Australia -35.473500 \n", + "9 New South Wales Australia -33.868800 \n", + "10 Northern Territory Australia -12.463400 \n", + "11 Queensland Australia -28.016700 \n", + "12 South Australia Australia -34.928500 \n", + "13 Tasmania Australia -41.454500 \n", + "14 Victoria Australia -37.813600 \n", + "15 Western Australia Australia -31.950500 \n", + "16 NaN Austria 47.516200 \n", + "17 NaN Azerbaijan 40.143100 \n", + "18 NaN Bahamas 25.034300 \n", + "19 NaN Bahrain 26.027500 \n", + "20 NaN Bangladesh 23.685000 \n", + "21 NaN Barbados 13.193900 \n", + "22 NaN Belarus 53.709800 \n", + "23 NaN Belgium 50.833300 \n", + "24 NaN Benin 9.307700 \n", + "25 NaN Bhutan 27.514200 \n", + "26 NaN Bolivia -16.290200 \n", + "27 NaN Bosnia and Herzegovina 43.915900 \n", + "28 NaN Brazil -14.235000 \n", + "29 NaN Brunei 4.535300 \n", + ".. ... ... ... \n", + "236 NaN Timor-Leste -8.874217 \n", + "237 NaN Belize 13.193900 \n", + "238 NaN Laos 19.856270 \n", + "239 NaN Libya 26.335100 \n", + "240 NaN West Bank and Gaza 31.952200 \n", + "241 NaN Guinea-Bissau 11.803700 \n", + "242 NaN Mali 17.570692 \n", + "243 NaN Saint Kitts and Nevis 17.357822 \n", + "244 Northwest Territories Canada 64.825500 \n", + "245 Yukon Canada 64.282300 \n", + "246 NaN Kosovo 42.602636 \n", + "247 NaN Burma 21.916200 \n", + "248 Anguilla United Kingdom 18.220600 \n", + "249 British Virgin Islands United Kingdom 18.420700 \n", + "250 Turks and Caicos Islands United Kingdom 21.694000 \n", + "251 NaN MS Zaandam 0.000000 \n", + "252 NaN Botswana -22.328500 \n", + "253 NaN Burundi -3.373100 \n", + "254 NaN Sierra Leone 8.460555 \n", + "255 Bonaire, Sint Eustatius and Saba Netherlands 12.178400 \n", + "256 NaN Malawi -13.254308 \n", + "257 Falkland Islands (Malvinas) United Kingdom -51.796300 \n", + "258 Saint Pierre and Miquelon France 46.885200 \n", + "259 NaN South Sudan 6.877000 \n", + "260 NaN Western Sahara 24.215500 \n", + "261 NaN Sao Tome and Principe 0.186360 \n", + "262 NaN Yemen 15.552727 \n", + "263 NaN Comoros -11.645500 \n", + "264 NaN Tajikistan 38.861034 \n", + "265 NaN Lesotho -29.609988 \n", + "\n", + " Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... \\\n", + "0 65.000000 0 0 0 0 0 0 ... \n", + "1 20.168300 0 0 0 0 0 0 ... \n", + "2 1.659600 0 0 0 0 0 0 ... \n", + "3 1.521800 0 0 0 0 0 0 ... \n", + "4 17.873900 0 0 0 0 0 0 ... \n", + "5 -61.796400 0 0 0 0 0 0 ... \n", + "6 -63.616700 0 0 0 0 0 0 ... \n", + "7 45.038200 0 0 0 0 0 0 ... \n", + "8 149.012400 0 0 0 0 0 0 ... \n", + "9 151.209300 0 0 0 0 3 4 ... \n", + "10 130.845600 0 0 0 0 0 0 ... \n", + "11 153.400000 0 0 0 0 0 0 ... \n", + "12 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"8 108 \n", + "9 3114 \n", + "10 29 \n", + "11 1062 \n", + "12 440 \n", + "13 228 \n", + "14 1687 \n", + "15 599 \n", + "16 16968 \n", + "17 7876 \n", + "18 103 \n", + "19 15417 \n", + "20 68504 \n", + "21 92 \n", + "22 49453 \n", + "23 59348 \n", + "24 288 \n", + "25 59 \n", + "26 13949 \n", + "27 2704 \n", + "28 707412 \n", + "29 141 \n", + ".. ... \n", + "236 24 \n", + "237 19 \n", + "238 19 \n", + "239 332 \n", + "240 473 \n", + "241 1389 \n", + "242 1547 \n", + "243 15 \n", + "244 5 \n", + "245 11 \n", + "246 1263 \n", + "247 244 \n", + "248 3 \n", + "249 8 \n", + "250 12 \n", + "251 9 \n", + "252 42 \n", + "253 83 \n", + "254 1001 \n", + "255 7 \n", + "256 443 \n", + "257 13 \n", + "258 1 \n", + "259 1604 \n", + "260 9 \n", + "261 513 \n", + "262 496 \n", + "263 141 \n", + "264 4609 \n", + "265 4 \n", + "\n", + "[266 rows x 143 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Remove Long and Lat columns (just for convenience) and make a spared copy in df_total for the \"world\" graph" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Province/State Country/Region 1/22/20 \\\n", + "0 NaN Afghanistan 0 \n", + "1 NaN Albania 0 \n", + "2 NaN Algeria 0 \n", + "3 NaN Andorra 0 \n", + "4 NaN Angola 0 \n", + "5 NaN Antigua and Barbuda 0 \n", + "6 NaN Argentina 0 \n", + "7 NaN Armenia 0 \n", + "8 Australian Capital Territory Australia 0 \n", + "9 New South Wales Australia 0 \n", + "10 Northern Territory Australia 0 \n", + "11 Queensland Australia 0 \n", + "12 South Australia Australia 0 \n", + "13 Tasmania Australia 0 \n", + "14 Victoria Australia 0 \n", + "15 Western Australia Australia 0 \n", + "16 NaN Austria 0 \n", + "17 NaN Azerbaijan 0 \n", + "18 NaN Bahamas 0 \n", + "19 NaN Bahrain 0 \n", + "20 NaN Bangladesh 0 \n", + "21 NaN Barbados 0 \n", + "22 NaN Belarus 0 \n", + "23 NaN Belgium 0 \n", + "24 NaN Benin 0 \n", + "25 NaN Bhutan 0 \n", + "26 NaN Bolivia 0 \n", + "27 NaN Bosnia and Herzegovina 0 \n", + "28 NaN Brazil 0 \n", + "29 NaN Brunei 0 \n", + ".. ... ... ... \n", + "236 NaN Timor-Leste 0 \n", + "237 NaN Belize 0 \n", + "238 NaN Laos 0 \n", + "239 NaN Libya 0 \n", + "240 NaN West Bank and Gaza 0 \n", + "241 NaN Guinea-Bissau 0 \n", + "242 NaN Mali 0 \n", + "243 NaN Saint Kitts and Nevis 0 \n", + "244 Northwest Territories Canada 0 \n", + "245 Yukon Canada 0 \n", + "246 NaN Kosovo 0 \n", + "247 NaN Burma 0 \n", + "248 Anguilla United Kingdom 0 \n", + "249 British Virgin Islands United Kingdom 0 \n", + "250 Turks and Caicos Islands United Kingdom 0 \n", + "251 NaN MS Zaandam 0 \n", + "252 NaN Botswana 0 \n", + "253 NaN Burundi 0 \n", + "254 NaN Sierra Leone 0 \n", + "255 Bonaire, Sint Eustatius and Saba Netherlands 0 \n", + "256 NaN Malawi 0 \n", + "257 Falkland Islands (Malvinas) United Kingdom 0 \n", + "258 Saint Pierre and Miquelon France 0 \n", + "259 NaN South Sudan 0 \n", + "260 NaN Western Sahara 0 \n", + "261 NaN Sao Tome and Principe 0 \n", + "262 NaN Yemen 0 \n", + "263 NaN Comoros 0 \n", + "264 NaN Tajikistan 0 \n", + "265 NaN Lesotho 0 \n", + "\n", + " 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 ... \\\n", + "0 0 0 0 0 0 0 0 ... \n", + "1 0 0 0 0 0 0 0 ... \n", + "2 0 0 0 0 0 0 0 ... \n", + "3 0 0 0 0 0 0 0 ... \n", + "4 0 0 0 0 0 0 0 ... \n", + "5 0 0 0 0 0 0 0 ... \n", + "6 0 0 0 0 0 0 0 ... \n", + "7 0 0 0 0 0 0 0 ... \n", + "8 0 0 0 0 0 0 0 ... \n", + "9 0 0 0 3 4 4 4 ... \n", + "10 0 0 0 0 0 0 0 ... \n", + "11 0 0 0 0 0 0 1 ... \n", + "12 0 0 0 0 0 0 0 ... \n", + "13 0 0 0 0 0 0 0 ... \n", + "14 0 0 0 1 1 1 1 ... \n", + "15 0 0 0 0 0 0 0 ... \n", + "16 0 0 0 0 0 0 0 ... \n", + "17 0 0 0 0 0 0 0 ... \n", + "18 0 0 0 0 0 0 0 ... \n", + "19 0 0 0 0 0 0 0 ... \n", + "20 0 0 0 0 0 0 0 ... \n", + "21 0 0 0 0 0 0 0 ... \n", + "22 0 0 0 0 0 0 0 ... \n", + "23 0 0 0 0 0 0 0 ... \n", + "24 0 0 0 0 0 0 0 ... \n", + "25 0 0 0 0 0 0 0 ... \n", + "26 0 0 0 0 0 0 0 ... \n", + "27 0 0 0 0 0 0 0 ... \n", + "28 0 0 0 0 0 0 0 ... \n", + "29 0 0 0 0 0 0 0 ... \n", + ".. ... ... ... ... ... ... ... ... \n", + "236 0 0 0 0 0 0 0 ... \n", + "237 0 0 0 0 0 0 0 ... \n", + "238 0 0 0 0 0 0 0 ... \n", + "239 0 0 0 0 0 0 0 ... \n", + "240 0 0 0 0 0 0 0 ... \n", + "241 0 0 0 0 0 0 0 ... \n", + "242 0 0 0 0 0 0 0 ... \n", + "243 0 0 0 0 0 0 0 ... \n", + "244 0 0 0 0 0 0 0 ... \n", + "245 0 0 0 0 0 0 0 ... \n", + "246 0 0 0 0 0 0 0 ... \n", + "247 0 0 0 0 0 0 0 ... \n", + "248 0 0 0 0 0 0 0 ... \n", + "249 0 0 0 0 0 0 0 ... \n", + "250 0 0 0 0 0 0 0 ... \n", + "251 0 0 0 0 0 0 0 ... \n", + "252 0 0 0 0 0 0 0 ... \n", + "253 0 0 0 0 0 0 0 ... \n", + "254 0 0 0 0 0 0 0 ... \n", + "255 0 0 0 0 0 0 0 ... \n", + "256 0 0 0 0 0 0 0 ... \n", + "257 0 0 0 0 0 0 0 ... \n", + "258 0 0 0 0 0 0 0 ... \n", + "259 0 0 0 0 0 0 0 ... \n", + "260 0 0 0 0 0 0 0 ... \n", + "261 0 0 0 0 0 0 0 ... \n", + "262 0 0 0 0 0 0 0 ... \n", + "263 0 0 0 0 0 0 0 ... \n", + "264 0 0 0 0 0 0 0 ... \n", + "265 0 0 0 0 0 0 0 ... \n", + "\n", + " 5/30/20 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 \\\n", + "0 14525 15205 15750 16509 17267 18054 18969 19551 20342 \n", + "1 1122 1137 1143 1164 1184 1197 1212 1232 1246 \n", + "2 9267 9394 9513 9626 9733 9831 9935 10050 10154 \n", + "3 764 764 765 844 851 852 852 852 852 \n", + "4 84 86 86 86 86 86 86 88 91 \n", + "5 25 26 26 26 26 26 26 26 26 \n", + "6 16214 16851 17415 18319 19268 20197 21037 22020 22794 \n", + "7 8927 9282 9492 10009 10524 11221 11817 12364 13130 \n", + "8 107 107 107 107 107 107 107 108 108 \n", + "9 3095 3098 3104 3104 3106 3110 3110 3109 3112 \n", + "10 29 29 29 29 29 29 29 29 29 \n", + "11 1058 1058 1059 1059 1060 1060 1061 1061 1062 \n", + "12 440 440 440 440 440 440 440 440 440 \n", + "13 228 228 228 228 228 228 228 228 228 \n", + "14 1649 1653 1663 1670 1678 1681 1681 1685 1687 \n", + "15 586 589 591 592 592 592 596 599 599 \n", + "16 16685 16731 16733 16759 16771 16805 16843 16898 16902 \n", + "17 5246 5494 5662 5935 6260 6522 6860 7239 7553 \n", + "18 102 102 102 102 102 102 102 103 103 \n", + "19 10793 11398 11871 12311 12815 13296 13835 14383 14763 \n", + "20 44608 47153 49534 52445 55140 57563 60391 63026 65769 \n", + "21 92 92 92 92 92 92 92 92 92 \n", + "22 41658 42556 43403 44255 45116 45981 46868 47751 48630 \n", + "23 58186 58381 58517 58615 58685 58767 58907 59072 59226 \n", + "24 224 232 243 244 244 261 261 261 261 \n", + "25 33 43 43 47 47 47 48 48 59 \n", + "26 9592 9982 10531 10991 11638 12245 12728 13358 13643 \n", + "27 2494 2510 2524 2535 2551 2594 2606 2606 2606 \n", + "28 498440 514849 526447 555383 584016 614941 645771 672846 691758 \n", + "29 141 141 141 141 141 141 141 141 141 \n", + ".. ... ... ... ... ... ... ... ... ... \n", + "236 24 24 24 24 24 24 24 24 24 \n", + "237 18 18 18 18 18 18 19 19 19 \n", + "238 19 19 19 19 19 19 19 19 19 \n", + "239 130 156 168 182 196 209 239 256 256 \n", + "240 447 448 449 451 457 464 464 464 472 \n", + "241 1256 1256 1339 1339 1339 1339 1368 1368 1368 \n", + "242 1250 1265 1315 1351 1386 1461 1485 1523 1533 \n", + "243 15 15 15 15 15 15 15 15 15 \n", + "244 5 5 5 5 5 5 5 5 5 \n", + "245 11 11 11 11 11 11 11 11 11 \n", + "246 1064 1064 1064 1064 1142 1142 1142 1142 1142 \n", + "247 224 224 228 232 233 236 236 240 242 \n", + "248 3 3 3 3 3 3 3 3 3 \n", + "249 8 8 8 8 8 8 8 8 8 \n", + "250 12 12 12 12 12 12 12 12 12 \n", + "251 9 9 9 9 9 9 9 9 9 \n", + "252 35 35 38 40 40 40 40 40 40 \n", + "253 63 63 63 63 63 63 63 83 83 \n", + "254 852 861 865 896 909 914 929 946 969 \n", + "255 6 6 7 7 7 7 7 7 7 \n", + "256 279 284 336 358 369 393 409 409 438 \n", + "257 13 13 13 13 13 13 13 13 13 \n", + "258 1 1 1 1 1 1 1 1 1 \n", + "259 994 994 994 994 994 994 994 994 1317 \n", + "260 9 9 9 9 9 9 9 9 9 \n", + "261 479 483 484 484 484 485 499 499 513 \n", + "262 310 323 354 399 419 453 469 482 484 \n", + "263 106 106 106 132 132 132 132 141 141 \n", + "264 3807 3930 4013 4100 4191 4289 4370 4453 4529 \n", + "265 2 2 2 2 4 4 4 4 4 \n", + "\n", + " 6/8/20 \n", + "0 20917 \n", + "1 1263 \n", + "2 10265 \n", + "3 852 \n", + "4 92 \n", + "5 26 \n", + "6 23620 \n", + "7 13325 \n", + "8 108 \n", + "9 3114 \n", + "10 29 \n", + "11 1062 \n", + "12 440 \n", + "13 228 \n", + "14 1687 \n", + "15 599 \n", + "16 16968 \n", + "17 7876 \n", + "18 103 \n", + "19 15417 \n", + "20 68504 \n", + "21 92 \n", + "22 49453 \n", + "23 59348 \n", + "24 288 \n", + "25 59 \n", + "26 13949 \n", + "27 2704 \n", + "28 707412 \n", + "29 141 \n", + ".. ... \n", + "236 24 \n", + "237 19 \n", + "238 19 \n", + "239 332 \n", + "240 473 \n", + "241 1389 \n", + "242 1547 \n", + "243 15 \n", + "244 5 \n", + "245 11 \n", + "246 1263 \n", + "247 244 \n", + "248 3 \n", + "249 8 \n", + "250 12 \n", + "251 9 \n", + "252 42 \n", + "253 83 \n", + "254 1001 \n", + "255 7 \n", + "256 443 \n", + "257 13 \n", + "258 1 \n", + "259 1604 \n", + "260 9 \n", + "261 513 \n", + "262 496 \n", + "263 141 \n", + "264 4609 \n", + "265 4 \n", + "\n", + "[266 rows x 141 columns]\n" + ] + } + ], + "source": [ + "df = pd.DataFrame(raw_data)\n", + "\n", + "df_total=df.drop(columns=['Lat', 'Long'])\n", + "df=df_total\n", + "\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Remove \"not interesting\" countries" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Province/State Country/Region 1/22/20 1/23/20 \\\n", + "23 NaN Belgium 0 0 \n", + "49 Anhui China 1 9 \n", + "50 Beijing China 14 22 \n", + "51 Chongqing China 6 9 \n", + "52 Fujian China 1 5 \n", + "53 Gansu China 0 2 \n", + "54 Guangdong China 26 32 \n", + "55 Guangxi China 2 5 \n", + "56 Guizhou China 1 3 \n", + "57 Hainan China 4 5 \n", + "58 Hebei China 1 1 \n", + "59 Heilongjiang China 0 2 \n", + "60 Henan China 5 5 \n", + "61 Hong Kong China 0 2 \n", + "62 Hubei China 444 444 \n", + "63 Hunan China 4 9 \n", + "64 Inner Mongolia China 0 0 \n", + "65 Jiangsu China 1 5 \n", + "66 Jiangxi China 2 7 \n", + "67 Jilin China 0 1 \n", + "68 Liaoning China 2 3 \n", + "69 Macau China 1 2 \n", + "70 Ningxia China 1 1 \n", + "71 Qinghai China 0 0 \n", + "72 Shaanxi China 0 3 \n", + "73 Shandong China 2 6 \n", + "74 Shanghai China 9 16 \n", + "75 Shanxi China 1 1 \n", + "76 Sichuan China 5 8 \n", + "77 Tianjin China 4 4 \n", + ".. ... ... ... ... \n", + "111 New Caledonia France 0 0 \n", + "112 Reunion France 0 0 \n", + "113 Saint Barthelemy France 0 0 \n", + "114 St Martin France 0 0 \n", + "115 Martinique France 0 0 \n", + "116 NaN France 0 0 \n", + "120 NaN Germany 0 0 \n", + "133 NaN Iran 0 0 \n", + "137 NaN Italy 0 0 \n", + "139 NaN Japan 2 2 \n", + "166 Aruba Netherlands 0 0 \n", + "167 Curacao Netherlands 0 0 \n", + "168 Sint Maarten Netherlands 0 0 \n", + "169 NaN Netherlands 0 0 \n", + "184 NaN Portugal 0 0 \n", + "201 NaN Spain 0 0 \n", + "217 Bermuda United Kingdom 0 0 \n", + "218 Cayman Islands United Kingdom 0 0 \n", + "219 Channel Islands United Kingdom 0 0 \n", + "220 Gibraltar United Kingdom 0 0 \n", + "221 Isle of Man United Kingdom 0 0 \n", + "222 Montserrat United Kingdom 0 0 \n", + "223 NaN United Kingdom 0 0 \n", + "225 NaN US 1 1 \n", + "248 Anguilla United Kingdom 0 0 \n", + "249 British Virgin Islands United Kingdom 0 0 \n", + "250 Turks and Caicos Islands United Kingdom 0 0 \n", + "255 Bonaire, Sint Eustatius and Saba Netherlands 0 0 \n", + "257 Falkland Islands (Malvinas) United Kingdom 0 0 \n", + "258 Saint Pierre and Miquelon France 0 0 \n", + "\n", + " 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 ... 5/30/20 \\\n", + "23 0 0 0 0 0 0 ... 58186 \n", + "49 15 39 60 70 106 152 ... 991 \n", + "50 36 41 68 80 91 111 ... 593 \n", + "51 27 57 75 110 132 147 ... 579 \n", + "52 10 18 35 59 80 84 ... 358 \n", + "53 2 4 7 14 19 24 ... 139 \n", + "54 53 78 111 151 207 277 ... 1593 \n", + "55 23 23 36 46 51 58 ... 254 \n", + "56 3 4 5 7 9 9 ... 147 \n", + "57 8 19 22 33 40 43 ... 169 \n", + "58 2 8 13 18 33 48 ... 328 \n", + "59 4 9 15 21 33 38 ... 945 \n", + "60 9 32 83 128 168 206 ... 1276 \n", + "61 2 5 8 8 8 10 ... 1082 \n", + "62 549 761 1058 1423 3554 3554 ... 68135 \n", + "63 24 43 69 100 143 221 ... 1019 \n", + "64 1 7 7 11 15 16 ... 232 \n", + "65 9 18 33 47 70 99 ... 653 \n", + "66 18 18 36 72 109 109 ... 937 \n", + "67 3 4 4 6 8 9 ... 155 \n", + "68 4 17 21 27 34 39 ... 149 \n", + "69 2 2 5 6 7 7 ... 45 \n", + "70 2 3 4 7 11 12 ... 75 \n", + "71 0 1 1 6 6 6 ... 18 \n", + "72 5 15 22 35 46 56 ... 308 \n", + "73 15 27 46 75 95 130 ... 792 \n", + "74 20 33 40 53 66 96 ... 672 \n", + "75 1 6 9 13 27 27 ... 198 \n", + "76 15 28 44 69 90 108 ... 564 \n", + "77 8 10 14 23 24 27 ... 192 \n", + ".. ... ... ... ... ... ... ... ... \n", + "111 0 0 0 0 0 0 ... 19 \n", + "112 0 0 0 0 0 0 ... 471 \n", + "113 0 0 0 0 0 0 ... 6 \n", + "114 0 0 0 0 0 0 ... 41 \n", + "115 0 0 0 0 0 0 ... 200 \n", + "116 2 3 3 3 4 5 ... 185616 \n", + "120 0 0 0 1 4 4 ... 183189 \n", + "133 0 0 0 0 0 0 ... 148950 \n", + "137 0 0 0 0 0 0 ... 232664 \n", + "139 2 2 4 4 7 7 ... 16716 \n", + "166 0 0 0 0 0 0 ... 101 \n", + "167 0 0 0 0 0 0 ... 19 \n", + "168 0 0 0 0 0 0 ... 77 \n", + "169 0 0 0 0 0 0 ... 46257 \n", + "184 0 0 0 0 0 0 ... 32203 \n", + "201 0 0 0 0 0 0 ... 239228 \n", + "217 0 0 0 0 0 0 ... 140 \n", + "218 0 0 0 0 0 0 ... 141 \n", + "219 0 0 0 0 0 0 ... 560 \n", + "220 0 0 0 0 0 0 ... 169 \n", + "221 0 0 0 0 0 0 ... 336 \n", + "222 0 0 0 0 0 0 ... 11 \n", + "223 0 0 0 0 0 0 ... 272826 \n", + "225 2 2 5 5 5 5 ... 1770165 \n", + "248 0 0 0 0 0 0 ... 3 \n", + "249 0 0 0 0 0 0 ... 8 \n", + "250 0 0 0 0 0 0 ... 12 \n", + "255 0 0 0 0 0 0 ... 6 \n", + "257 0 0 0 0 0 0 ... 13 \n", + "258 0 0 0 0 0 0 ... 1 \n", + "\n", + " 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 \\\n", + "23 58381 58517 58615 58685 58767 58907 59072 59226 \n", + "49 991 991 991 991 991 991 991 991 \n", + "50 593 593 593 594 594 594 594 594 \n", + "51 579 579 579 579 579 579 579 579 \n", + "52 358 358 358 358 358 358 359 359 \n", + "53 139 139 139 139 139 139 139 139 \n", + "54 1595 1596 1597 1598 1598 1601 1602 1602 \n", + "55 254 254 254 254 254 254 254 254 \n", + "56 147 147 147 147 147 147 147 147 \n", + "57 169 169 169 169 169 169 170 170 \n", + "58 328 328 328 328 328 328 328 328 \n", + "59 945 945 945 947 947 947 947 947 \n", + "60 1276 1276 1276 1276 1276 1276 1276 1276 \n", + "61 1084 1087 1093 1093 1099 1102 1105 1106 \n", + "62 68135 68135 68135 68135 68135 68135 68135 68135 \n", + "63 1019 1019 1019 1019 1019 1019 1019 1019 \n", + "64 235 235 235 235 235 235 235 235 \n", + "65 653 653 653 653 653 653 653 653 \n", + "66 937 937 937 932 932 932 932 932 \n", + "67 155 155 155 155 155 155 155 155 \n", + "68 149 149 149 149 149 149 149 149 \n", + "69 45 45 45 45 45 45 45 45 \n", + "70 75 75 75 75 75 75 75 75 \n", + "71 18 18 18 18 18 18 18 18 \n", + "72 308 309 309 309 309 309 311 311 \n", + "73 792 792 792 792 792 792 792 792 \n", + "74 672 673 673 673 677 677 677 678 \n", + "75 198 198 198 198 198 198 198 198 \n", + "76 575 577 577 577 578 578 578 581 \n", + "77 192 192 192 192 192 192 193 193 \n", + ".. ... ... ... ... ... ... ... ... \n", + "111 19 20 20 20 20 20 20 20 \n", + "112 471 473 477 478 479 480 480 480 \n", + "113 6 6 6 6 6 6 6 6 \n", + "114 41 41 41 41 41 41 41 41 \n", + "115 200 200 200 200 200 202 202 202 \n", + "116 185851 185952 184980 188836 185986 186538 187067 187360 \n", + "120 183410 183594 183879 184121 184472 184924 185450 185750 \n", + "133 151466 154445 157562 160696 164270 167156 169425 171789 \n", + "137 232997 233197 233515 233836 234013 234531 234801 234998 \n", + "139 16751 16787 16837 16867 16911 16958 17000 17039 \n", + "166 101 101 101 101 101 101 101 101 \n", + "167 19 19 20 21 21 21 21 21 \n", + "168 77 77 77 77 77 77 77 77 \n", + "169 46442 46545 46647 46733 46942 47152 47335 47574 \n", + "184 32500 32700 32895 33261 33592 33969 34351 34693 \n", + "201 239479 239638 239932 240326 240660 240978 241310 241550 \n", + "217 140 141 141 141 141 141 141 141 \n", + "218 141 150 151 156 160 164 164 164 \n", + "219 560 560 560 561 561 561 563 563 \n", + "220 170 170 172 173 173 174 175 176 \n", + "221 336 336 336 336 336 336 336 336 \n", + "222 11 11 11 11 11 11 11 11 \n", + "223 274762 276332 277985 279856 281661 283311 284868 286194 \n", + "225 1790172 1811020 1831821 1851520 1872660 1897380 1920061 1943647 \n", + "248 3 3 3 3 3 3 3 3 \n", + "249 8 8 8 8 8 8 8 8 \n", + "250 12 12 12 12 12 12 12 12 \n", + "255 6 7 7 7 7 7 7 7 \n", + "257 13 13 13 13 13 13 13 13 \n", + "258 1 1 1 1 1 1 1 1 \n", + "\n", + " 6/8/20 \n", + "23 59348 \n", + "49 991 \n", + "50 594 \n", + "51 579 \n", + "52 359 \n", + "53 139 \n", + "54 1604 \n", + "55 254 \n", + "56 147 \n", + "57 170 \n", + "58 328 \n", + "59 947 \n", + "60 1276 \n", + "61 1107 \n", + "62 68135 \n", + "63 1019 \n", + "64 235 \n", + "65 653 \n", + "66 932 \n", + "67 155 \n", + "68 149 \n", + "69 45 \n", + "70 75 \n", + "71 18 \n", + "72 311 \n", + "73 792 \n", + "74 678 \n", + "75 198 \n", + "76 582 \n", + "77 193 \n", + ".. ... \n", + "111 20 \n", + "112 481 \n", + "113 6 \n", + "114 41 \n", + "115 202 \n", + "116 187458 \n", + "120 186109 \n", + "133 173832 \n", + "137 235278 \n", + "139 17060 \n", + "166 101 \n", + "167 21 \n", + "168 77 \n", + "169 47739 \n", + "184 34885 \n", + "201 241717 \n", + "217 141 \n", + "218 171 \n", + "219 564 \n", + "220 176 \n", + "221 336 \n", + "222 11 \n", + "223 287399 \n", + "225 1960897 \n", + "248 3 \n", + "249 8 \n", + "250 12 \n", + "255 7 \n", + "257 13 \n", + "258 1 \n", + "\n", + "[68 rows x 141 columns]\n" + ] + } + ], + "source": [ + "df=df.drop(df[(df['Country/Region'] != 'Belgium') & (df['Country/Region'] != 'China') & (df['Country/Region'] != 'France') & (df['Country/Region'] != 'Germany') & (df['Country/Region'] != 'Iran') & (df['Country/Region'] != 'Italy') & (df['Country/Region'] != 'Japan') & (df['Country/Region'] != 'Korea South') & (df['Country/Region'] != 'Netherlands') & (df['Country/Region'] != 'Portugal') & (df['Country/Region'] != 'Spain') & (df['Country/Region'] != 'United Kingdom') & (df['Country/Region'] != 'US')].index)\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "For convenience change China to Hong Kong in the Hong Kong line" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Province/State Country/Region 1/22/20 1/23/20 \\\n", + "23 NaN Belgium 0 0 \n", + "49 Anhui China 1 9 \n", + "50 Beijing China 14 22 \n", + "51 Chongqing China 6 9 \n", + "52 Fujian China 1 5 \n", + "53 Gansu China 0 2 \n", + "54 Guangdong China 26 32 \n", + "55 Guangxi China 2 5 \n", + "56 Guizhou China 1 3 \n", + "57 Hainan China 4 5 \n", + "58 Hebei China 1 1 \n", + "59 Heilongjiang China 0 2 \n", + "60 Henan China 5 5 \n", + "61 Hong Kong Hong Kong 0 2 \n", + "62 Hubei China 444 444 \n", + "63 Hunan China 4 9 \n", + "64 Inner Mongolia China 0 0 \n", + "65 Jiangsu China 1 5 \n", + "66 Jiangxi China 2 7 \n", + "67 Jilin China 0 1 \n", + "68 Liaoning China 2 3 \n", + "69 Macau China 1 2 \n", + "70 Ningxia China 1 1 \n", + "71 Qinghai China 0 0 \n", + "72 Shaanxi China 0 3 \n", + "73 Shandong China 2 6 \n", + "74 Shanghai China 9 16 \n", + "75 Shanxi China 1 1 \n", + "76 Sichuan China 5 8 \n", + "77 Tianjin China 4 4 \n", + ".. ... ... ... ... \n", + "111 New Caledonia France 0 0 \n", + "112 Reunion France 0 0 \n", + "113 Saint Barthelemy France 0 0 \n", + "114 St Martin France 0 0 \n", + "115 Martinique France 0 0 \n", + "116 NaN France 0 0 \n", + "120 NaN Germany 0 0 \n", + "133 NaN Iran 0 0 \n", + "137 NaN Italy 0 0 \n", + "139 NaN Japan 2 2 \n", + "166 Aruba Netherlands 0 0 \n", + "167 Curacao Netherlands 0 0 \n", + "168 Sint Maarten Netherlands 0 0 \n", + "169 NaN Netherlands 0 0 \n", + "184 NaN Portugal 0 0 \n", + "201 NaN Spain 0 0 \n", + "217 Bermuda United Kingdom 0 0 \n", + "218 Cayman Islands United Kingdom 0 0 \n", + "219 Channel Islands United Kingdom 0 0 \n", + "220 Gibraltar United Kingdom 0 0 \n", + "221 Isle of Man United Kingdom 0 0 \n", + "222 Montserrat United Kingdom 0 0 \n", + "223 NaN United Kingdom 0 0 \n", + "225 NaN US 1 1 \n", + "248 Anguilla United Kingdom 0 0 \n", + "249 British Virgin Islands United Kingdom 0 0 \n", + "250 Turks and Caicos Islands United Kingdom 0 0 \n", + "255 Bonaire, Sint Eustatius and Saba Netherlands 0 0 \n", + "257 Falkland Islands (Malvinas) United Kingdom 0 0 \n", + "258 Saint Pierre and Miquelon France 0 0 \n", + "\n", + " 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 ... 5/30/20 \\\n", + "23 0 0 0 0 0 0 ... 58186 \n", + "49 15 39 60 70 106 152 ... 991 \n", + "50 36 41 68 80 91 111 ... 593 \n", + "51 27 57 75 110 132 147 ... 579 \n", + "52 10 18 35 59 80 84 ... 358 \n", + "53 2 4 7 14 19 24 ... 139 \n", + "54 53 78 111 151 207 277 ... 1593 \n", + "55 23 23 36 46 51 58 ... 254 \n", + "56 3 4 5 7 9 9 ... 147 \n", + "57 8 19 22 33 40 43 ... 169 \n", + "58 2 8 13 18 33 48 ... 328 \n", + "59 4 9 15 21 33 38 ... 945 \n", + "60 9 32 83 128 168 206 ... 1276 \n", + "61 2 5 8 8 8 10 ... 1082 \n", + "62 549 761 1058 1423 3554 3554 ... 68135 \n", + "63 24 43 69 100 143 221 ... 1019 \n", + "64 1 7 7 11 15 16 ... 232 \n", + "65 9 18 33 47 70 99 ... 653 \n", + "66 18 18 36 72 109 109 ... 937 \n", + "67 3 4 4 6 8 9 ... 155 \n", + "68 4 17 21 27 34 39 ... 149 \n", + "69 2 2 5 6 7 7 ... 45 \n", + "70 2 3 4 7 11 12 ... 75 \n", + "71 0 1 1 6 6 6 ... 18 \n", + "72 5 15 22 35 46 56 ... 308 \n", + "73 15 27 46 75 95 130 ... 792 \n", + "74 20 33 40 53 66 96 ... 672 \n", + "75 1 6 9 13 27 27 ... 198 \n", + "76 15 28 44 69 90 108 ... 564 \n", + "77 8 10 14 23 24 27 ... 192 \n", + ".. ... ... ... ... ... ... ... ... \n", + "111 0 0 0 0 0 0 ... 19 \n", + "112 0 0 0 0 0 0 ... 471 \n", + "113 0 0 0 0 0 0 ... 6 \n", + "114 0 0 0 0 0 0 ... 41 \n", + "115 0 0 0 0 0 0 ... 200 \n", + "116 2 3 3 3 4 5 ... 185616 \n", + "120 0 0 0 1 4 4 ... 183189 \n", + "133 0 0 0 0 0 0 ... 148950 \n", + "137 0 0 0 0 0 0 ... 232664 \n", + "139 2 2 4 4 7 7 ... 16716 \n", + "166 0 0 0 0 0 0 ... 101 \n", + "167 0 0 0 0 0 0 ... 19 \n", + "168 0 0 0 0 0 0 ... 77 \n", + "169 0 0 0 0 0 0 ... 46257 \n", + "184 0 0 0 0 0 0 ... 32203 \n", + "201 0 0 0 0 0 0 ... 239228 \n", + "217 0 0 0 0 0 0 ... 140 \n", + "218 0 0 0 0 0 0 ... 141 \n", + "219 0 0 0 0 0 0 ... 560 \n", + "220 0 0 0 0 0 0 ... 169 \n", + "221 0 0 0 0 0 0 ... 336 \n", + "222 0 0 0 0 0 0 ... 11 \n", + "223 0 0 0 0 0 0 ... 272826 \n", + "225 2 2 5 5 5 5 ... 1770165 \n", + "248 0 0 0 0 0 0 ... 3 \n", + "249 0 0 0 0 0 0 ... 8 \n", + "250 0 0 0 0 0 0 ... 12 \n", + "255 0 0 0 0 0 0 ... 6 \n", + "257 0 0 0 0 0 0 ... 13 \n", + "258 0 0 0 0 0 0 ... 1 \n", + "\n", + " 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 \\\n", + "23 58381 58517 58615 58685 58767 58907 59072 59226 \n", + "49 991 991 991 991 991 991 991 991 \n", + "50 593 593 593 594 594 594 594 594 \n", + "51 579 579 579 579 579 579 579 579 \n", + "52 358 358 358 358 358 358 359 359 \n", + "53 139 139 139 139 139 139 139 139 \n", + "54 1595 1596 1597 1598 1598 1601 1602 1602 \n", + "55 254 254 254 254 254 254 254 254 \n", + "56 147 147 147 147 147 147 147 147 \n", + "57 169 169 169 169 169 169 170 170 \n", + "58 328 328 328 328 328 328 328 328 \n", + "59 945 945 945 947 947 947 947 947 \n", + "60 1276 1276 1276 1276 1276 1276 1276 1276 \n", + "61 1084 1087 1093 1093 1099 1102 1105 1106 \n", + "62 68135 68135 68135 68135 68135 68135 68135 68135 \n", + "63 1019 1019 1019 1019 1019 1019 1019 1019 \n", + "64 235 235 235 235 235 235 235 235 \n", + "65 653 653 653 653 653 653 653 653 \n", + "66 937 937 937 932 932 932 932 932 \n", + "67 155 155 155 155 155 155 155 155 \n", + "68 149 149 149 149 149 149 149 149 \n", + "69 45 45 45 45 45 45 45 45 \n", + "70 75 75 75 75 75 75 75 75 \n", + "71 18 18 18 18 18 18 18 18 \n", + "72 308 309 309 309 309 309 311 311 \n", + "73 792 792 792 792 792 792 792 792 \n", + "74 672 673 673 673 677 677 677 678 \n", + "75 198 198 198 198 198 198 198 198 \n", + "76 575 577 577 577 578 578 578 581 \n", + "77 192 192 192 192 192 192 193 193 \n", + ".. ... ... ... ... ... ... ... ... \n", + "111 19 20 20 20 20 20 20 20 \n", + "112 471 473 477 478 479 480 480 480 \n", + "113 6 6 6 6 6 6 6 6 \n", + "114 41 41 41 41 41 41 41 41 \n", + "115 200 200 200 200 200 202 202 202 \n", + "116 185851 185952 184980 188836 185986 186538 187067 187360 \n", + "120 183410 183594 183879 184121 184472 184924 185450 185750 \n", + "133 151466 154445 157562 160696 164270 167156 169425 171789 \n", + "137 232997 233197 233515 233836 234013 234531 234801 234998 \n", + "139 16751 16787 16837 16867 16911 16958 17000 17039 \n", + "166 101 101 101 101 101 101 101 101 \n", + "167 19 19 20 21 21 21 21 21 \n", + "168 77 77 77 77 77 77 77 77 \n", + "169 46442 46545 46647 46733 46942 47152 47335 47574 \n", + "184 32500 32700 32895 33261 33592 33969 34351 34693 \n", + "201 239479 239638 239932 240326 240660 240978 241310 241550 \n", + "217 140 141 141 141 141 141 141 141 \n", + "218 141 150 151 156 160 164 164 164 \n", + "219 560 560 560 561 561 561 563 563 \n", + "220 170 170 172 173 173 174 175 176 \n", + "221 336 336 336 336 336 336 336 336 \n", + "222 11 11 11 11 11 11 11 11 \n", + "223 274762 276332 277985 279856 281661 283311 284868 286194 \n", + "225 1790172 1811020 1831821 1851520 1872660 1897380 1920061 1943647 \n", + "248 3 3 3 3 3 3 3 3 \n", + "249 8 8 8 8 8 8 8 8 \n", + "250 12 12 12 12 12 12 12 12 \n", + "255 6 7 7 7 7 7 7 7 \n", + "257 13 13 13 13 13 13 13 13 \n", + "258 1 1 1 1 1 1 1 1 \n", + "\n", + " 6/8/20 \n", + "23 59348 \n", + "49 991 \n", + "50 594 \n", + "51 579 \n", + "52 359 \n", + "53 139 \n", + "54 1604 \n", + "55 254 \n", + "56 147 \n", + "57 170 \n", + "58 328 \n", + "59 947 \n", + "60 1276 \n", + "61 1107 \n", + "62 68135 \n", + "63 1019 \n", + "64 235 \n", + "65 653 \n", + "66 932 \n", + "67 155 \n", + "68 149 \n", + "69 45 \n", + "70 75 \n", + "71 18 \n", + "72 311 \n", + "73 792 \n", + "74 678 \n", + "75 198 \n", + "76 582 \n", + "77 193 \n", + ".. ... \n", + "111 20 \n", + "112 481 \n", + "113 6 \n", + "114 41 \n", + "115 202 \n", + "116 187458 \n", + "120 186109 \n", + "133 173832 \n", + "137 235278 \n", + "139 17060 \n", + "166 101 \n", + "167 21 \n", + "168 77 \n", + "169 47739 \n", + "184 34885 \n", + "201 241717 \n", + "217 141 \n", + "218 171 \n", + "219 564 \n", + "220 176 \n", + "221 336 \n", + "222 11 \n", + "223 287399 \n", + "225 1960897 \n", + "248 3 \n", + "249 8 \n", + "250 12 \n", + "255 7 \n", + "257 13 \n", + "258 1 \n", + "\n", + "[68 rows x 141 columns]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + } + ], + "source": [ + "df=df.set_value(df[(df['Province/State'] == 'Hong Kong')].index, 'Country/Region', 'Hong Kong')\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Remove colonies of France, Netherlands and UK" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Province/State Country/Region 1/22/20 1/23/20 1/24/20 1/25/20 \\\n", + "23 NaN Belgium 0 0 0 0 \n", + "49 Anhui China 1 9 15 39 \n", + "50 Beijing China 14 22 36 41 \n", + "51 Chongqing China 6 9 27 57 \n", + "52 Fujian China 1 5 10 18 \n", + "53 Gansu China 0 2 2 4 \n", + "54 Guangdong China 26 32 53 78 \n", + "55 Guangxi China 2 5 23 23 \n", + "56 Guizhou China 1 3 3 4 \n", + "57 Hainan China 4 5 8 19 \n", + "58 Hebei China 1 1 2 8 \n", + "59 Heilongjiang China 0 2 4 9 \n", + "60 Henan China 5 5 9 32 \n", + "61 Hong Kong Hong Kong 0 2 2 5 \n", + "62 Hubei China 444 444 549 761 \n", + "63 Hunan China 4 9 24 43 \n", + "64 Inner Mongolia China 0 0 1 7 \n", + "65 Jiangsu China 1 5 9 18 \n", + "66 Jiangxi China 2 7 18 18 \n", + "67 Jilin China 0 1 3 4 \n", + "68 Liaoning China 2 3 4 17 \n", + "69 Macau China 1 2 2 2 \n", + "70 Ningxia China 1 1 2 3 \n", + "71 Qinghai China 0 0 0 1 \n", + "72 Shaanxi China 0 3 5 15 \n", + "73 Shandong China 2 6 15 27 \n", + "74 Shanghai China 9 16 20 33 \n", + "75 Shanxi China 1 1 1 6 \n", + "76 Sichuan China 5 8 15 28 \n", + "77 Tianjin China 4 4 8 10 \n", + "78 Tibet China 0 0 0 0 \n", + "79 Xinjiang China 0 2 2 3 \n", + "80 Yunnan China 1 2 5 11 \n", + "81 Zhejiang China 10 27 43 62 \n", + "116 NaN France 0 0 2 3 \n", + "120 NaN Germany 0 0 0 0 \n", + "133 NaN Iran 0 0 0 0 \n", + "137 NaN Italy 0 0 0 0 \n", + "139 NaN Japan 2 2 2 2 \n", + "169 NaN Netherlands 0 0 0 0 \n", + "184 NaN Portugal 0 0 0 0 \n", + "201 NaN Spain 0 0 0 0 \n", + "223 NaN United Kingdom 0 0 0 0 \n", + "225 NaN US 1 1 2 2 \n", + "\n", + " 1/26/20 1/27/20 1/28/20 1/29/20 ... 5/30/20 5/31/20 6/1/20 \\\n", + "23 0 0 0 0 ... 58186 58381 58517 \n", + "49 60 70 106 152 ... 991 991 991 \n", + "50 68 80 91 111 ... 593 593 593 \n", + "51 75 110 132 147 ... 579 579 579 \n", + "52 35 59 80 84 ... 358 358 358 \n", + "53 7 14 19 24 ... 139 139 139 \n", + "54 111 151 207 277 ... 1593 1595 1596 \n", + "55 36 46 51 58 ... 254 254 254 \n", + "56 5 7 9 9 ... 147 147 147 \n", + "57 22 33 40 43 ... 169 169 169 \n", + "58 13 18 33 48 ... 328 328 328 \n", + "59 15 21 33 38 ... 945 945 945 \n", + "60 83 128 168 206 ... 1276 1276 1276 \n", + "61 8 8 8 10 ... 1082 1084 1087 \n", + "62 1058 1423 3554 3554 ... 68135 68135 68135 \n", + "63 69 100 143 221 ... 1019 1019 1019 \n", + "64 7 11 15 16 ... 232 235 235 \n", + "65 33 47 70 99 ... 653 653 653 \n", + "66 36 72 109 109 ... 937 937 937 \n", + "67 4 6 8 9 ... 155 155 155 \n", + "68 21 27 34 39 ... 149 149 149 \n", + "69 5 6 7 7 ... 45 45 45 \n", + "70 4 7 11 12 ... 75 75 75 \n", + "71 1 6 6 6 ... 18 18 18 \n", + "72 22 35 46 56 ... 308 308 309 \n", + "73 46 75 95 130 ... 792 792 792 \n", + "74 40 53 66 96 ... 672 672 673 \n", + "75 9 13 27 27 ... 198 198 198 \n", + "76 44 69 90 108 ... 564 575 577 \n", + "77 14 23 24 27 ... 192 192 192 \n", + "78 0 0 0 0 ... 1 1 1 \n", + "79 4 5 10 13 ... 76 76 76 \n", + "80 16 26 44 55 ... 185 185 185 \n", + "81 104 128 173 296 ... 1268 1268 1268 \n", + "116 3 3 4 5 ... 185616 185851 185952 \n", + "120 0 1 4 4 ... 183189 183410 183594 \n", + "133 0 0 0 0 ... 148950 151466 154445 \n", + "137 0 0 0 0 ... 232664 232997 233197 \n", + "139 4 4 7 7 ... 16716 16751 16787 \n", + "169 0 0 0 0 ... 46257 46442 46545 \n", + "184 0 0 0 0 ... 32203 32500 32700 \n", + "201 0 0 0 0 ... 239228 239479 239638 \n", + "223 0 0 0 0 ... 272826 274762 276332 \n", + "225 5 5 5 5 ... 1770165 1790172 1811020 \n", + "\n", + " 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 6/8/20 \n", + "23 58615 58685 58767 58907 59072 59226 59348 \n", + "49 991 991 991 991 991 991 991 \n", + "50 593 594 594 594 594 594 594 \n", + "51 579 579 579 579 579 579 579 \n", + "52 358 358 358 358 359 359 359 \n", + "53 139 139 139 139 139 139 139 \n", + "54 1597 1598 1598 1601 1602 1602 1604 \n", + "55 254 254 254 254 254 254 254 \n", + "56 147 147 147 147 147 147 147 \n", + "57 169 169 169 169 170 170 170 \n", + "58 328 328 328 328 328 328 328 \n", + "59 945 947 947 947 947 947 947 \n", + "60 1276 1276 1276 1276 1276 1276 1276 \n", + "61 1093 1093 1099 1102 1105 1106 1107 \n", + "62 68135 68135 68135 68135 68135 68135 68135 \n", + "63 1019 1019 1019 1019 1019 1019 1019 \n", + "64 235 235 235 235 235 235 235 \n", + "65 653 653 653 653 653 653 653 \n", + "66 937 932 932 932 932 932 932 \n", + "67 155 155 155 155 155 155 155 \n", + "68 149 149 149 149 149 149 149 \n", + "69 45 45 45 45 45 45 45 \n", + "70 75 75 75 75 75 75 75 \n", + "71 18 18 18 18 18 18 18 \n", + "72 309 309 309 309 311 311 311 \n", + "73 792 792 792 792 792 792 792 \n", + "74 673 673 677 677 677 678 678 \n", + "75 198 198 198 198 198 198 198 \n", + "76 577 577 578 578 578 581 582 \n", + "77 192 192 192 192 193 193 193 \n", + "78 1 1 1 1 1 1 1 \n", + "79 76 76 76 76 76 76 76 \n", + "80 185 185 185 185 185 185 185 \n", + "81 1268 1268 1268 1268 1268 1268 1268 \n", + "116 184980 188836 185986 186538 187067 187360 187458 \n", + "120 183879 184121 184472 184924 185450 185750 186109 \n", + "133 157562 160696 164270 167156 169425 171789 173832 \n", + "137 233515 233836 234013 234531 234801 234998 235278 \n", + "139 16837 16867 16911 16958 17000 17039 17060 \n", + "169 46647 46733 46942 47152 47335 47574 47739 \n", + "184 32895 33261 33592 33969 34351 34693 34885 \n", + "201 239932 240326 240660 240978 241310 241550 241717 \n", + "223 277985 279856 281661 283311 284868 286194 287399 \n", + "225 1831821 1851520 1872660 1897380 1920061 1943647 1960897 \n", + "\n", + "[44 rows x 141 columns]\n" + ] + } + ], + "source": [ + "fr=df[(df['Country/Region']=='France')]\n", + "fr=fr['Province/State']\n", + "fr=fr.dropna()\n", + "\n", + "ne=df[(df['Country/Region']=='Netherlands')]\n", + "ne=ne['Province/State']\n", + "ne=ne.dropna()\n", + "\n", + "uk=df[(df['Country/Region']=='United Kingdom')]\n", + "uk=uk['Province/State']\n", + "uk=uk.dropna()\n", + "\n", + "df=df.drop(fr.index)\n", + "df=df.drop(ne.index)\n", + "df=df.drop(uk.index)\n", + "\n", + "\n", + "print(df)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Remove Province/State column and compute total daily sum for China" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "hideCode": false, + "hidePrompt": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 \\\n", + "Country/Region \n", + "Belgium 0 0 0 0 0 0 0 \n", + "China 548 641 918 1401 2067 2869 5501 \n", + "France 0 0 2 3 3 3 4 \n", + "Germany 0 0 0 0 0 1 4 \n", + "Hong Kong 0 2 2 5 8 8 8 \n", + "Iran 0 0 0 0 0 0 0 \n", + "Italy 0 0 0 0 0 0 0 \n", + "Japan 2 2 2 2 4 4 7 \n", + "Netherlands 0 0 0 0 0 0 0 \n", + "Portugal 0 0 0 0 0 0 0 \n", + "Spain 0 0 0 0 0 0 0 \n", + "US 1 1 2 2 5 5 5 \n", + "United Kingdom 0 0 0 0 0 0 0 \n", + "\n", + " 1/29/20 1/30/20 1/31/20 ... 5/30/20 5/31/20 6/1/20 \\\n", + "Country/Region ... \n", + "Belgium 0 0 0 ... 58186 58381 58517 \n", + "China 6077 8131 9790 ... 83046 83062 83067 \n", + "France 5 5 5 ... 185616 185851 185952 \n", + "Germany 4 4 5 ... 183189 183410 183594 \n", + "Hong Kong 10 10 12 ... 1082 1084 1087 \n", + "Iran 0 0 0 ... 148950 151466 154445 \n", + "Italy 0 0 2 ... 232664 232997 233197 \n", + "Japan 7 11 15 ... 16716 16751 16787 \n", + "Netherlands 0 0 0 ... 46257 46442 46545 \n", + "Portugal 0 0 0 ... 32203 32500 32700 \n", + "Spain 0 0 0 ... 239228 239479 239638 \n", + "US 5 5 7 ... 1770165 1790172 1811020 \n", + "United Kingdom 0 0 2 ... 272826 274762 276332 \n", + "\n", + " 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 6/8/20 \n", + "Country/Region \n", + "Belgium 58615 58685 58767 58907 59072 59226 59348 \n", + "China 83068 83067 83072 83075 83081 83085 83088 \n", + "France 184980 188836 185986 186538 187067 187360 187458 \n", + "Germany 183879 184121 184472 184924 185450 185750 186109 \n", + "Hong Kong 1093 1093 1099 1102 1105 1106 1107 \n", + "Iran 157562 160696 164270 167156 169425 171789 173832 \n", + "Italy 233515 233836 234013 234531 234801 234998 235278 \n", + "Japan 16837 16867 16911 16958 17000 17039 17060 \n", + "Netherlands 46647 46733 46942 47152 47335 47574 47739 \n", + "Portugal 32895 33261 33592 33969 34351 34693 34885 \n", + "Spain 239932 240326 240660 240978 241310 241550 241717 \n", + "US 1831821 1851520 1872660 1897380 1920061 1943647 1960897 \n", + "United Kingdom 277985 279856 281661 283311 284868 286194 287399 \n", + "\n", + "[13 rows x 139 columns]\n" + ] + } + ], + "source": [ + "df.drop('Province/State', axis = 1, inplace = True)\n", + "grouped=df.groupby('Country/Region')\n", + "df=grouped.sum()\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hideOutput": true, + "hidePrompt": false + }, + "source": [ + "Construct graphs for the countries above" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax=df.transpose().plot()\n", + "ax.set_xlabel(\"date\")\n", + "ax.set_ylabel(\"confirmed cases\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we make the analogous graph for the Covid-19 incidence in the world" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df=df_total\n", + "df.drop('Province/State', axis = 1, inplace = True)\n", + "df.drop('Country/Region', axis = 1, inplace = True)\n", + "df=df.sum(axis=0)\n", + "\n", + "ax=df.transpose().plot()\n", + "ax.set_xlabel(\"date\")\n", + "ax.set_ylabel(\"confirmed cases\")\n", + "plt.show()\n" + ] + } + ], + "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": 1 +} diff --git a/module3/exo1/influenza-like-illness-analysis.ipynb b/module3/exo1/influenza-like-illness-analysis.ipynb deleted file mode 100644 index 43b9563eea254c068b283cc99d88342fe446bca1..0000000000000000000000000000000000000000 --- a/module3/exo1/influenza-like-illness-analysis.ipynb +++ /dev/null @@ -1,3433 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Incidence of influenza-like illness in France" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import isoweek\n", - "import os.path\n", - "from os import path" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The data on the incidence of influenza-like illness are available from the Web site of the [Réseau Sentinelles](http://www.sentiweb.fr/). We download them as a file in CSV format, in which each line corresponds to a week in the observation period. Only the complete dataset, starting in 1984 and ending with a recent week, is available for download." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is the documentation of the data from [the download site](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", - "\n", - "| Column name | Description |\n", - "|--------------|---------------------------------------------------------------------------------------------------------------------------|\n", - "| `week` | ISO8601 Yearweek number as numeric (year times 100 + week nubmer) |\n", - "| `indicator` | Unique identifier of the indicator, see metadata document https://www.sentiweb.fr/meta.json |\n", - "| `inc` | Estimated incidence value for the time step, in the geographic level |\n", - "| `inc_low` | Lower bound of the estimated incidence 95% Confidence Interval |\n", - "| `inc_up` | Upper bound of the estimated incidence 95% Confidence Interval |\n", - "| `inc100` | Estimated rate incidence per 100,000 inhabitants |\n", - "| `inc100_low` | Lower bound of the estimated incidence 95% Confidence Interval |\n", - "| `inc100_up` | Upper bound of the estimated rate incidence 95% Confidence Interval |\n", - "| `geo_insee` | Identifier of the geographic area, from INSEE https://www.insee.fr |\n", - "| `geo_name` | Geographic label of the area, corresponding to INSEE code. This label is not an id and is only provided for human reading |\n", - "\n", - "The first line of the CSV file is a comment, which we ignore with `skip=1`." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1846 rows × 10 columns

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" - ], - "text/plain": [ - " week indicator inc inc_low inc_up inc100 inc100_low \\\n", - "0 202011 3 101704 93652.0 109756.0 154 142.0 \n", - "1 202010 3 104977 96650.0 113304.0 159 146.0 \n", - "2 202009 3 110696 102066.0 119326.0 168 155.0 \n", - "3 202008 3 143753 133984.0 153522.0 218 203.0 \n", - "4 202007 3 183610 172812.0 194408.0 279 263.0 \n", - "5 202006 3 206669 195481.0 217857.0 314 297.0 \n", - "6 202005 3 187957 177445.0 198469.0 285 269.0 \n", - "7 202004 3 122331 113492.0 131170.0 186 173.0 \n", - "8 202003 3 78413 71330.0 85496.0 119 108.0 \n", - "9 202002 3 53614 47654.0 59574.0 81 72.0 \n", - "10 202001 3 36850 31608.0 42092.0 56 48.0 \n", - "11 201952 3 28135 23220.0 33050.0 43 36.0 \n", - "12 201951 3 29786 25042.0 34530.0 45 38.0 \n", - "13 201950 3 34223 29156.0 39290.0 52 44.0 \n", - "14 201949 3 25662 21414.0 29910.0 39 33.0 \n", - "15 201948 3 22367 18055.0 26679.0 34 27.0 \n", - "16 201947 3 18669 14759.0 22579.0 28 22.0 \n", - "17 201946 3 16030 12567.0 19493.0 24 19.0 \n", - "18 201945 3 10138 7160.0 13116.0 15 10.0 \n", - "19 201944 3 7822 5010.0 10634.0 12 8.0 \n", - "20 201943 3 9487 6448.0 12526.0 14 9.0 \n", - "21 201942 3 7747 5243.0 10251.0 12 8.0 \n", - "22 201941 3 7122 4720.0 9524.0 11 7.0 \n", - "23 201940 3 8505 5784.0 11226.0 13 9.0 \n", - "24 201939 3 7091 4462.0 9720.0 11 7.0 \n", - "25 201938 3 4897 2891.0 6903.0 7 4.0 \n", - "26 201937 3 3172 1367.0 4977.0 5 2.0 \n", - "27 201936 3 2295 728.0 3862.0 3 1.0 \n", - "28 201935 3 1010 2.0 2018.0 2 0.0 \n", - "29 201934 3 1672 279.0 3065.0 3 1.0 \n", - "... ... ... ... ... ... ... ... \n", - "1816 198521 3 26096 19621.0 32571.0 47 35.0 \n", - "1817 198520 3 27896 20885.0 34907.0 51 38.0 \n", - "1818 198519 3 43154 32821.0 53487.0 78 59.0 \n", - "1819 198518 3 40555 29935.0 51175.0 74 55.0 \n", - "1820 198517 3 34053 24366.0 43740.0 62 44.0 \n", - "1821 198516 3 50362 36451.0 64273.0 91 66.0 \n", - "1822 198515 3 63881 45538.0 82224.0 116 83.0 \n", - "1823 198514 3 134545 114400.0 154690.0 244 207.0 \n", - "1824 198513 3 197206 176080.0 218332.0 357 319.0 \n", - "1825 198512 3 245240 223304.0 267176.0 445 405.0 \n", - "1826 198511 3 276205 252399.0 300011.0 501 458.0 \n", - "1827 198510 3 353231 326279.0 380183.0 640 591.0 \n", - "1828 198509 3 369895 341109.0 398681.0 670 618.0 \n", - "1829 198508 3 389886 359529.0 420243.0 707 652.0 \n", - "1830 198507 3 471852 432599.0 511105.0 855 784.0 \n", - "1831 198506 3 565825 518011.0 613639.0 1026 939.0 \n", - "1832 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", - "1833 198504 3 424937 390794.0 459080.0 770 708.0 \n", - "1834 198503 3 213901 174689.0 253113.0 388 317.0 \n", - "1835 198502 3 97586 80949.0 114223.0 177 147.0 \n", - "1836 198501 3 85489 65918.0 105060.0 155 120.0 \n", - "1837 198452 3 84830 60602.0 109058.0 154 110.0 \n", - "1838 198451 3 101726 80242.0 123210.0 185 146.0 \n", - "1839 198450 3 123680 101401.0 145959.0 225 184.0 \n", - "1840 198449 3 101073 81684.0 120462.0 184 149.0 \n", - "1841 198448 3 78620 60634.0 96606.0 143 110.0 \n", - "1842 198447 3 72029 54274.0 89784.0 131 99.0 \n", - "1843 198446 3 87330 67686.0 106974.0 159 123.0 \n", - "1844 198445 3 135223 101414.0 169032.0 246 184.0 \n", - "1845 198444 3 68422 20056.0 116788.0 125 37.0 \n", - "\n", - " inc100_up geo_insee geo_name \n", - "0 166.0 FR France \n", - "1 172.0 FR France \n", - "2 181.0 FR France \n", - "3 233.0 FR France \n", - "4 295.0 FR France \n", - "5 331.0 FR France \n", - "6 301.0 FR France \n", - "7 199.0 FR France \n", - "8 130.0 FR France \n", - "9 90.0 FR France \n", - "10 64.0 FR France \n", - "11 50.0 FR France \n", - "12 52.0 FR France \n", - "13 60.0 FR France \n", - "14 45.0 FR France \n", - "15 41.0 FR France \n", - "16 34.0 FR France \n", - "17 29.0 FR France \n", - "18 20.0 FR France \n", - "19 16.0 FR France \n", - "20 19.0 FR France \n", - "21 16.0 FR France \n", - "22 15.0 FR France \n", - "23 17.0 FR France \n", - "24 15.0 FR France \n", - "25 10.0 FR France \n", - "26 8.0 FR France \n", - "27 5.0 FR France \n", - "28 4.0 FR France \n", - "29 5.0 FR France \n", - "... ... ... ... \n", - "1816 59.0 FR France \n", - "1817 64.0 FR France \n", - "1818 97.0 FR France \n", - "1819 93.0 FR France \n", - "1820 80.0 FR France \n", - "1821 116.0 FR France \n", - "1822 149.0 FR France \n", - "1823 281.0 FR France \n", - "1824 395.0 FR France \n", - "1825 485.0 FR France \n", - "1826 544.0 FR France \n", - "1827 689.0 FR France \n", - "1828 722.0 FR France \n", - "1829 762.0 FR France \n", - "1830 926.0 FR France \n", - "1831 1113.0 FR France \n", - "1832 1236.0 FR France \n", - "1833 832.0 FR France \n", - "1834 459.0 FR France \n", - "1835 207.0 FR France \n", - "1836 190.0 FR France \n", - "1837 198.0 FR France \n", - "1838 224.0 FR France \n", - "1839 266.0 FR France \n", - "1840 219.0 FR France \n", - "1841 176.0 FR France \n", - "1842 163.0 FR France \n", - "1843 195.0 FR France \n", - "1844 308.0 FR France \n", - "1845 213.0 FR France \n", - "\n", - "[1846 rows x 10 columns]" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raw_data = pd.read_csv(data_url, skiprows=1)\n", - "raw_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "We save the table to a local file" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Are there missing data points? Yes, week 19 of year 1989 does not have any observed values." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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.................................
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1845 rows × 10 columns

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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
160919891930NaNNaN0NaNNaNFRFrance
\n", - "
" - ], - "text/plain": [ - " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", - "1609 198919 3 0 NaN NaN 0 NaN NaN \n", - "\n", - " geo_insee geo_name \n", - "1609 FR France " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raw_data[raw_data.isnull().any(axis=1)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We delete this point, which does not have big consequence for our rather simple analysis." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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23201940385055784.011226.0139.017.0FRFrance
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26201937331721367.04977.052.08.0FRFrance
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.................................
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1845 rows × 10 columns

\n", - "
" - ], - "text/plain": [ - " week indicator inc inc_low inc_up inc100 inc100_low \\\n", - "0 202011 3 101704 93652.0 109756.0 154 142.0 \n", - "1 202010 3 104977 96650.0 113304.0 159 146.0 \n", - "2 202009 3 110696 102066.0 119326.0 168 155.0 \n", - "3 202008 3 143753 133984.0 153522.0 218 203.0 \n", - "4 202007 3 183610 172812.0 194408.0 279 263.0 \n", - "5 202006 3 206669 195481.0 217857.0 314 297.0 \n", - "6 202005 3 187957 177445.0 198469.0 285 269.0 \n", - "7 202004 3 122331 113492.0 131170.0 186 173.0 \n", - "8 202003 3 78413 71330.0 85496.0 119 108.0 \n", - "9 202002 3 53614 47654.0 59574.0 81 72.0 \n", - "10 202001 3 36850 31608.0 42092.0 56 48.0 \n", - "11 201952 3 28135 23220.0 33050.0 43 36.0 \n", - "12 201951 3 29786 25042.0 34530.0 45 38.0 \n", - "13 201950 3 34223 29156.0 39290.0 52 44.0 \n", - "14 201949 3 25662 21414.0 29910.0 39 33.0 \n", - "15 201948 3 22367 18055.0 26679.0 34 27.0 \n", - "16 201947 3 18669 14759.0 22579.0 28 22.0 \n", - "17 201946 3 16030 12567.0 19493.0 24 19.0 \n", - "18 201945 3 10138 7160.0 13116.0 15 10.0 \n", - "19 201944 3 7822 5010.0 10634.0 12 8.0 \n", - "20 201943 3 9487 6448.0 12526.0 14 9.0 \n", - "21 201942 3 7747 5243.0 10251.0 12 8.0 \n", - "22 201941 3 7122 4720.0 9524.0 11 7.0 \n", - "23 201940 3 8505 5784.0 11226.0 13 9.0 \n", - "24 201939 3 7091 4462.0 9720.0 11 7.0 \n", - "25 201938 3 4897 2891.0 6903.0 7 4.0 \n", - "26 201937 3 3172 1367.0 4977.0 5 2.0 \n", - "27 201936 3 2295 728.0 3862.0 3 1.0 \n", - "28 201935 3 1010 2.0 2018.0 2 0.0 \n", - "29 201934 3 1672 279.0 3065.0 3 1.0 \n", - "... ... ... ... ... ... ... ... \n", - "1816 198521 3 26096 19621.0 32571.0 47 35.0 \n", - "1817 198520 3 27896 20885.0 34907.0 51 38.0 \n", - "1818 198519 3 43154 32821.0 53487.0 78 59.0 \n", - "1819 198518 3 40555 29935.0 51175.0 74 55.0 \n", - "1820 198517 3 34053 24366.0 43740.0 62 44.0 \n", - "1821 198516 3 50362 36451.0 64273.0 91 66.0 \n", - "1822 198515 3 63881 45538.0 82224.0 116 83.0 \n", - "1823 198514 3 134545 114400.0 154690.0 244 207.0 \n", - "1824 198513 3 197206 176080.0 218332.0 357 319.0 \n", - "1825 198512 3 245240 223304.0 267176.0 445 405.0 \n", - "1826 198511 3 276205 252399.0 300011.0 501 458.0 \n", - "1827 198510 3 353231 326279.0 380183.0 640 591.0 \n", - "1828 198509 3 369895 341109.0 398681.0 670 618.0 \n", - "1829 198508 3 389886 359529.0 420243.0 707 652.0 \n", - "1830 198507 3 471852 432599.0 511105.0 855 784.0 \n", - "1831 198506 3 565825 518011.0 613639.0 1026 939.0 \n", - "1832 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", - "1833 198504 3 424937 390794.0 459080.0 770 708.0 \n", - "1834 198503 3 213901 174689.0 253113.0 388 317.0 \n", - "1835 198502 3 97586 80949.0 114223.0 177 147.0 \n", - "1836 198501 3 85489 65918.0 105060.0 155 120.0 \n", - "1837 198452 3 84830 60602.0 109058.0 154 110.0 \n", - "1838 198451 3 101726 80242.0 123210.0 185 146.0 \n", - "1839 198450 3 123680 101401.0 145959.0 225 184.0 \n", - "1840 198449 3 101073 81684.0 120462.0 184 149.0 \n", - "1841 198448 3 78620 60634.0 96606.0 143 110.0 \n", - "1842 198447 3 72029 54274.0 89784.0 131 99.0 \n", - "1843 198446 3 87330 67686.0 106974.0 159 123.0 \n", - "1844 198445 3 135223 101414.0 169032.0 246 184.0 \n", - "1845 198444 3 68422 20056.0 116788.0 125 37.0 \n", - "\n", - " inc100_up geo_insee geo_name \n", - "0 166.0 FR France \n", - "1 172.0 FR France \n", - "2 181.0 FR France \n", - "3 233.0 FR France \n", - "4 295.0 FR France \n", - "5 331.0 FR France \n", - "6 301.0 FR France \n", - "7 199.0 FR France \n", - "8 130.0 FR France \n", - "9 90.0 FR France \n", - "10 64.0 FR France \n", - "11 50.0 FR France \n", - "12 52.0 FR France \n", - "13 60.0 FR France \n", - "14 45.0 FR France \n", - "15 41.0 FR France \n", - "16 34.0 FR France \n", - "17 29.0 FR France \n", - "18 20.0 FR France \n", - "19 16.0 FR France \n", - "20 19.0 FR France \n", - "21 16.0 FR France \n", - "22 15.0 FR France \n", - "23 17.0 FR France \n", - "24 15.0 FR France \n", - "25 10.0 FR France \n", - "26 8.0 FR France \n", - "27 5.0 FR France \n", - "28 4.0 FR France \n", - "29 5.0 FR France \n", - "... ... ... ... \n", - "1816 59.0 FR France \n", - "1817 64.0 FR France \n", - "1818 97.0 FR France \n", - "1819 93.0 FR France \n", - "1820 80.0 FR France \n", - "1821 116.0 FR France \n", - "1822 149.0 FR France \n", - "1823 281.0 FR France \n", - "1824 395.0 FR France \n", - "1825 485.0 FR France \n", - "1826 544.0 FR France \n", - "1827 689.0 FR France \n", - "1828 722.0 FR France \n", - "1829 762.0 FR France \n", - "1830 926.0 FR France \n", - "1831 1113.0 FR France \n", - "1832 1236.0 FR France \n", - "1833 832.0 FR France \n", - "1834 459.0 FR France \n", - "1835 207.0 FR France \n", - "1836 190.0 FR France \n", - "1837 198.0 FR France \n", - "1838 224.0 FR France \n", - "1839 266.0 FR France \n", - "1840 219.0 FR France \n", - "1841 176.0 FR France \n", - "1842 163.0 FR France \n", - "1843 195.0 FR France \n", - "1844 308.0 FR France \n", - "1845 213.0 FR France \n", - "\n", - "[1845 rows x 10 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = raw_data.dropna().copy()\n", - "data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our dataset uses an uncommon encoding; the week number is attached\n", - "to the year number, leaving the impression of a six-digit integer.\n", - "That is how Pandas interprets it.\n", - "\n", - "A second problem is that Pandas does not know about week numbers.\n", - "It needs to be given the dates of the beginning and end of the week.\n", - "We use the library `isoweek` for that.\n", - "\n", - "Since the conversion is a bit lengthy, we write a small Python \n", - "function for doing it. Then we apply it to all points in our dataset. \n", - "The results go into a new column 'period'." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def convert_week(year_and_week_int):\n", - " year_and_week_str = str(year_and_week_int)\n", - " year = int(year_and_week_str[:4])\n", - " week = int(year_and_week_str[4:])\n", - " w = isoweek.Week(year, week)\n", - " return pd.Period(w.day(0), 'W')\n", - "\n", - "data['period'] = [convert_week(yw) for yw in data['week']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are two more small changes to make.\n", - "\n", - "First, we define the observation periods as the new index of\n", - "our dataset. That turns it into a time series, which will be\n", - "convenient later on.\n", - "\n", - "Second, we sort the points chronologically." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "sorted_data = data.set_index('period').sort_index()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We check the consistency of the data. Between the end of a period and\n", - "the beginning of the next one, the difference should be zero, or very small.\n", - "We tolerate an error of one second.\n", - "\n", - "This is OK except for one pair of consecutive periods between which\n", - "a whole week is missing.\n", - "\n", - "We recognize the dates: it's the week without observations that we\n", - "have deleted earlier!" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" - ] - } - ], - "source": [ - "periods = sorted_data.index\n", - "for p1, p2 in zip(periods[:-1], periods[1:]):\n", - " delta = p2.to_timestamp() - p1.end_time\n", - " if delta > pd.Timedelta('1s'):\n", - " print(p1, p2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A first look at the data!" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "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": [ - "sorted_data['inc'][-200:].plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Study of the annual incidence" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the peaks of the epidemic happen in winter, near the transition\n", - "between calendar years, we define the reference period for the annual\n", - "incidence from August 1st of year $N$ to August 1st of year $N+1$. We\n", - "label this period as year $N+1$ because the peak is always located in\n", - "year $N+1$. The very low incidence in summer ensures that the arbitrariness\n", - "of the choice of reference period has no impact on our conclusions.\n", - "\n", - "Our task is a bit complicated by the fact that a year does not have an\n", - "integer number of weeks. Therefore we modify our reference period a bit:\n", - "instead of August 1st, we use the first day of the week containing August 1st.\n", - "\n", - "A final detail: the dataset starts in October 1984, the first peak is thus\n", - "incomplete, We start the analysis with the first full peak." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", - " for y in range(1985,\n", - " sorted_data.index[-1].year)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Starting from this list of weeks that contain August 1st, we obtain intervals of approximately one year as the periods between two adjacent weeks in this list. We compute the sums of weekly incidences for all these periods.\n", - "\n", - "We also check that our periods contain between 51 and 52 weeks, as a safeguard against potential mistakes in our code." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "year = []\n", - "yearly_incidence = []\n", - "for week1, week2 in zip(first_august_week[:-1],\n", - " first_august_week[1:]):\n", - " one_year = sorted_data['inc'][week1:week2-1]\n", - " assert abs(len(one_year)-52) < 2\n", - " yearly_incidence.append(one_year.sum())\n", - " year.append(week2.year)\n", - "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And here are the annual incidences." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "yearly_incidence.plot(style='*')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A sorted list makes it easier to find the highest values (at the end)." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2014 1600941\n", - "1991 1659249\n", - "1995 1840410\n", - "2012 2175217\n", - "2003 2234584\n", - "2019 2254386\n", - "2006 2307352\n", - "2017 2321583\n", - "2001 2529279\n", - "1992 2574578\n", - "1993 2703886\n", - "2018 2705325\n", - "1988 2765617\n", - "2007 2780164\n", - "1987 2855570\n", - "2016 2856393\n", - "2011 2857040\n", - "2008 2973918\n", - "1998 3034904\n", - "2002 3125418\n", - "2009 3444020\n", - "1994 3514763\n", - "1996 3539413\n", - "2004 3567744\n", - "1997 3620066\n", - "2015 3654892\n", - "2000 3826372\n", - "2005 3835025\n", - "1999 3908112\n", - "2010 4111392\n", - "2013 4182691\n", - "1986 5115251\n", - "1990 5235827\n", - "1989 5466192\n", - "dtype: int64" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "yearly_incidence.sort_values()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, a histogram clearly shows the few very strong epidemics, which affect about 10% of the French population,\n", - "but are rare: there were three of them in the course of 35 years. The typical epidemic affects only half as many people." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "yearly_incidence.hist(xrot=20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb deleted file mode 100644 index 0bbbe371b01e359e381e43239412d77bf53fb1fb..0000000000000000000000000000000000000000 --- a/module3/exo3/exercice.ipynb +++ /dev/null @@ -1,25 +0,0 @@ -{ - "cells": [], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} - diff --git a/module3/exo3/exercise.ipynb b/module3/exo3/exercise.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a41d67703b5baf6cda230f544b053effceb13e30 --- /dev/null +++ b/module3/exo3/exercise.ipynb @@ -0,0 +1,3031 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The SARS-CoV-2 (Covid-19) epidemic analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek\n", + "import os.path\n", + "from os import path" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data on the Covid-19 incidence are available [here](https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv). We download them as a file in CSV format." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data downloaded on 09.06.2020\n", + "\n", + "This is the documentation of the data from [the download site](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", + "\n", + "| Column name | Description |\n", + "|--------------|---------------------------------------------------------------------------------------------------------------------------|\n", + "| `Province/State` | Province/State |\n", + "| `Country/Region` | Country/Region |\n", + "| `Lat` | Latitude |\n", + "| `Long` | Longitude |\n", + "| `1/22/20` | Dates |" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...5/30/205/31/206/1/206/2/206/3/206/4/206/5/206/6/206/7/206/8/20
0NaNAfghanistan33.00000065.000000000000...14525152051575016509172671805418969195512034220917
1NaNAlbania41.15330020.168300000000...1122113711431164118411971212123212461263
2NaNAlgeria28.0339001.659600000000...9267939495139626973398319935100501015410265
3NaNAndorra42.5063001.521800000000...764764765844851852852852852852
4NaNAngola-11.20270017.873900000000...84868686868686889192
5NaNAntigua and Barbuda17.060800-61.796400000000...25262626262626262626
6NaNArgentina-38.416100-63.616700000000...16214168511741518319192682019721037220202279423620
7NaNArmenia40.06910045.038200000000...89279282949210009105241122111817123641313013325
8Australian Capital TerritoryAustralia-35.473500149.012400000000...107107107107107107107108108108
9New South WalesAustralia-33.868800151.209300000034...3095309831043104310631103110310931123114
10Northern TerritoryAustralia-12.463400130.845600000000...29292929292929292929
11QueenslandAustralia-28.016700153.400000000000...1058105810591059106010601061106110621062
12South AustraliaAustralia-34.928500138.600700000000...440440440440440440440440440440
13TasmaniaAustralia-41.454500145.970700000000...228228228228228228228228228228
14VictoriaAustralia-37.813600144.963100000011...1649165316631670167816811681168516871687
15Western AustraliaAustralia-31.950500115.860500000000...586589591592592592596599599599
16NaNAustria47.51620014.550100000000...16685167311673316759167711680516843168981690216968
17NaNAzerbaijan40.14310047.576900000000...5246549456625935626065226860723975537876
18NaNBahamas25.034300-77.396300000000...102102102102102102102103103103
19NaNBahrain26.02750050.550000000000...10793113981187112311128151329613835143831476315417
20NaNBangladesh23.68500090.356300000000...44608471534953452445551405756360391630266576968504
21NaNBarbados13.193900-59.543200000000...92929292929292929292
22NaNBelarus53.70980027.953400000000...41658425564340344255451164598146868477514863049453
23NaNBelgium50.8333004.000000000000...58186583815851758615586855876758907590725922659348
24NaNBenin9.3077002.315800000000...224232243244244261261261261288
25NaNBhutan27.51420090.433600000000...33434347474748485959
26NaNBolivia-16.290200-63.588700000000...959299821053110991116381224512728133581364313949
27NaNBosnia and Herzegovina43.91590017.679100000000...2494251025242535255125942606260626062704
28NaNBrazil-14.235000-51.925300000000...498440514849526447555383584016614941645771672846691758707412
29NaNBrunei4.535300114.727700000000...141141141141141141141141141141
..................................................................
236NaNTimor-Leste-8.874217125.727539000000...24242424242424242424
237NaNBelize13.193900-59.543200000000...18181818181819191919
238NaNLaos19.856270102.495496000000...19191919191919191919
239NaNLibya26.33510017.228331000000...130156168182196209239256256332
240NaNWest Bank and Gaza31.95220035.233200000000...447448449451457464464464472473
241NaNGuinea-Bissau11.803700-15.180400000000...1256125613391339133913391368136813681389
242NaNMali17.570692-3.996166000000...1250126513151351138614611485152315331547
243NaNSaint Kitts and Nevis17.357822-62.782998000000...15151515151515151515
244Northwest TerritoriesCanada64.825500-124.845700000000...5555555555
245YukonCanada64.282300-135.000000000000...11111111111111111111
246NaNKosovo42.60263620.902977000000...1064106410641064114211421142114211421263
247NaNBurma21.91620095.956000000000...224224228232233236236240242244
248AnguillaUnited Kingdom18.220600-63.068600000000...3333333333
249British Virgin IslandsUnited Kingdom18.420700-64.640000000000...8888888888
250Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...12121212121212121212
251NaNMS Zaandam0.0000000.000000000000...9999999999
252NaNBotswana-22.32850024.684900000000...35353840404040404042
253NaNBurundi-3.37310029.918900000000...63636363636363838383
254NaNSierra Leone8.460555-11.779889000000...8528618658969099149299469691001
255Bonaire, Sint Eustatius and SabaNetherlands12.178400-68.238500000000...6677777777
256NaNMalawi-13.25430834.301525000000...279284336358369393409409438443
257Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...13131313131313131313
258Saint Pierre and MiquelonFrance46.885200-56.315900000000...1111111111
259NaNSouth Sudan6.87700031.307000000000...99499499499499499499499413171604
260NaNWestern Sahara24.215500-12.885800000000...9999999999
261NaNSao Tome and Principe0.1863606.613081000000...479483484484484485499499513513
262NaNYemen15.55272748.516388000000...310323354399419453469482484496
263NaNComoros-11.64550043.333300000000...106106106132132132132141141141
264NaNTajikistan38.86103471.276093000000...3807393040134100419142894370445345294609
265NaNLesotho-29.60998828.233608000000...2222444444
\n", + "

266 rows × 143 columns

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" + ], + "text/plain": [ + " Province/State Country/Region Lat \\\n", + "0 NaN Afghanistan 33.000000 \n", + "1 NaN Albania 41.153300 \n", + "2 NaN Algeria 28.033900 \n", + "3 NaN Andorra 42.506300 \n", + "4 NaN Angola -11.202700 \n", + "5 NaN Antigua and Barbuda 17.060800 \n", + "6 NaN Argentina -38.416100 \n", + "7 NaN Armenia 40.069100 \n", + "8 Australian Capital Territory Australia -35.473500 \n", + "9 New South Wales Australia -33.868800 \n", + "10 Northern Territory Australia -12.463400 \n", + "11 Queensland Australia -28.016700 \n", + "12 South Australia Australia -34.928500 \n", + "13 Tasmania Australia -41.454500 \n", + "14 Victoria Australia -37.813600 \n", + "15 Western Australia Australia -31.950500 \n", + "16 NaN Austria 47.516200 \n", + "17 NaN Azerbaijan 40.143100 \n", + "18 NaN Bahamas 25.034300 \n", + "19 NaN Bahrain 26.027500 \n", + "20 NaN Bangladesh 23.685000 \n", + "21 NaN Barbados 13.193900 \n", + "22 NaN Belarus 53.709800 \n", + "23 NaN Belgium 50.833300 \n", + "24 NaN Benin 9.307700 \n", + "25 NaN Bhutan 27.514200 \n", + "26 NaN Bolivia -16.290200 \n", + "27 NaN Bosnia and Herzegovina 43.915900 \n", + "28 NaN Brazil -14.235000 \n", + "29 NaN Brunei 4.535300 \n", + ".. ... ... ... \n", + "236 NaN Timor-Leste -8.874217 \n", + "237 NaN Belize 13.193900 \n", + "238 NaN Laos 19.856270 \n", + "239 NaN Libya 26.335100 \n", + "240 NaN West Bank and Gaza 31.952200 \n", + "241 NaN Guinea-Bissau 11.803700 \n", + "242 NaN Mali 17.570692 \n", + "243 NaN Saint Kitts and Nevis 17.357822 \n", + "244 Northwest Territories Canada 64.825500 \n", + "245 Yukon Canada 64.282300 \n", + "246 NaN Kosovo 42.602636 \n", + "247 NaN Burma 21.916200 \n", + "248 Anguilla United Kingdom 18.220600 \n", + "249 British Virgin Islands United Kingdom 18.420700 \n", + "250 Turks and Caicos Islands United Kingdom 21.694000 \n", + "251 NaN MS Zaandam 0.000000 \n", + "252 NaN Botswana -22.328500 \n", + "253 NaN Burundi -3.373100 \n", + "254 NaN Sierra Leone 8.460555 \n", + "255 Bonaire, Sint Eustatius and Saba Netherlands 12.178400 \n", + "256 NaN Malawi -13.254308 \n", + "257 Falkland Islands (Malvinas) United Kingdom -51.796300 \n", + "258 Saint Pierre and Miquelon France 46.885200 \n", + "259 NaN South Sudan 6.877000 \n", + "260 NaN Western Sahara 24.215500 \n", + "261 NaN Sao Tome and Principe 0.186360 \n", + "262 NaN Yemen 15.552727 \n", + "263 NaN Comoros -11.645500 \n", + "264 NaN Tajikistan 38.861034 \n", + "265 NaN Lesotho -29.609988 \n", + "\n", + " Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... \\\n", + "0 65.000000 0 0 0 0 0 0 ... \n", + "1 20.168300 0 0 0 0 0 0 ... \n", + "2 1.659600 0 0 0 0 0 0 ... \n", + "3 1.521800 0 0 0 0 0 0 ... \n", + "4 17.873900 0 0 0 0 0 0 ... \n", + "5 -61.796400 0 0 0 0 0 0 ... \n", + "6 -63.616700 0 0 0 0 0 0 ... \n", + "7 45.038200 0 0 0 0 0 0 ... \n", + "8 149.012400 0 0 0 0 0 0 ... \n", + "9 151.209300 0 0 0 0 3 4 ... \n", + "10 130.845600 0 0 0 0 0 0 ... \n", + "11 153.400000 0 0 0 0 0 0 ... \n", + "12 138.600700 0 0 0 0 0 0 ... \n", + "13 145.970700 0 0 0 0 0 0 ... \n", + "14 144.963100 0 0 0 0 1 1 ... \n", + "15 115.860500 0 0 0 0 0 0 ... \n", + "16 14.550100 0 0 0 0 0 0 ... \n", + "17 47.576900 0 0 0 0 0 0 ... \n", + "18 -77.396300 0 0 0 0 0 0 ... \n", + "19 50.550000 0 0 0 0 0 0 ... \n", + "20 90.356300 0 0 0 0 0 0 ... \n", + "21 -59.543200 0 0 0 0 0 0 ... \n", + "22 27.953400 0 0 0 0 0 0 ... \n", + "23 4.000000 0 0 0 0 0 0 ... \n", + "24 2.315800 0 0 0 0 0 0 ... \n", + "25 90.433600 0 0 0 0 0 0 ... \n", + "26 -63.588700 0 0 0 0 0 0 ... \n", + "27 17.679100 0 0 0 0 0 0 ... \n", + "28 -51.925300 0 0 0 0 0 0 ... \n", + "29 114.727700 0 0 0 0 0 0 ... \n", + ".. ... ... ... ... ... ... ... ... \n", + "236 125.727539 0 0 0 0 0 0 ... \n", + "237 -59.543200 0 0 0 0 0 0 ... \n", + "238 102.495496 0 0 0 0 0 0 ... \n", + "239 17.228331 0 0 0 0 0 0 ... \n", + "240 35.233200 0 0 0 0 0 0 ... \n", + "241 -15.180400 0 0 0 0 0 0 ... \n", + "242 -3.996166 0 0 0 0 0 0 ... \n", + "243 -62.782998 0 0 0 0 0 0 ... \n", + "244 -124.845700 0 0 0 0 0 0 ... \n", + "245 -135.000000 0 0 0 0 0 0 ... \n", + "246 20.902977 0 0 0 0 0 0 ... \n", + "247 95.956000 0 0 0 0 0 0 ... \n", + "248 -63.068600 0 0 0 0 0 0 ... \n", + "249 -64.640000 0 0 0 0 0 0 ... \n", + "250 -71.797900 0 0 0 0 0 0 ... \n", + "251 0.000000 0 0 0 0 0 0 ... \n", + "252 24.684900 0 0 0 0 0 0 ... \n", + "253 29.918900 0 0 0 0 0 0 ... \n", + "254 -11.779889 0 0 0 0 0 0 ... \n", + "255 -68.238500 0 0 0 0 0 0 ... \n", + "256 34.301525 0 0 0 0 0 0 ... \n", + "257 -59.523600 0 0 0 0 0 0 ... \n", + "258 -56.315900 0 0 0 0 0 0 ... \n", + "259 31.307000 0 0 0 0 0 0 ... \n", + "260 -12.885800 0 0 0 0 0 0 ... \n", + "261 6.613081 0 0 0 0 0 0 ... \n", + "262 48.516388 0 0 0 0 0 0 ... \n", + "263 43.333300 0 0 0 0 0 0 ... \n", + "264 71.276093 0 0 0 0 0 0 ... \n", + "265 28.233608 0 0 0 0 0 0 ... \n", + "\n", + " 5/30/20 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 \\\n", + "0 14525 15205 15750 16509 17267 18054 18969 19551 20342 \n", + "1 1122 1137 1143 1164 1184 1197 1212 1232 1246 \n", + "2 9267 9394 9513 9626 9733 9831 9935 10050 10154 \n", + "3 764 764 765 844 851 852 852 852 852 \n", + "4 84 86 86 86 86 86 86 88 91 \n", + "5 25 26 26 26 26 26 26 26 26 \n", + "6 16214 16851 17415 18319 19268 20197 21037 22020 22794 \n", + "7 8927 9282 9492 10009 10524 11221 11817 12364 13130 \n", + "8 107 107 107 107 107 107 107 108 108 \n", + "9 3095 3098 3104 3104 3106 3110 3110 3109 3112 \n", + "10 29 29 29 29 29 29 29 29 29 \n", + "11 1058 1058 1059 1059 1060 1060 1061 1061 1062 \n", + "12 440 440 440 440 440 440 440 440 440 \n", + "13 228 228 228 228 228 228 228 228 228 \n", + "14 1649 1653 1663 1670 1678 1681 1681 1685 1687 \n", + "15 586 589 591 592 592 592 596 599 599 \n", + "16 16685 16731 16733 16759 16771 16805 16843 16898 16902 \n", + "17 5246 5494 5662 5935 6260 6522 6860 7239 7553 \n", + "18 102 102 102 102 102 102 102 103 103 \n", + "19 10793 11398 11871 12311 12815 13296 13835 14383 14763 \n", + "20 44608 47153 49534 52445 55140 57563 60391 63026 65769 \n", + "21 92 92 92 92 92 92 92 92 92 \n", + "22 41658 42556 43403 44255 45116 45981 46868 47751 48630 \n", + "23 58186 58381 58517 58615 58685 58767 58907 59072 59226 \n", + "24 224 232 243 244 244 261 261 261 261 \n", + "25 33 43 43 47 47 47 48 48 59 \n", + "26 9592 9982 10531 10991 11638 12245 12728 13358 13643 \n", + "27 2494 2510 2524 2535 2551 2594 2606 2606 2606 \n", + "28 498440 514849 526447 555383 584016 614941 645771 672846 691758 \n", + "29 141 141 141 141 141 141 141 141 141 \n", + ".. ... ... ... ... ... ... ... ... ... \n", + "236 24 24 24 24 24 24 24 24 24 \n", + "237 18 18 18 18 18 18 19 19 19 \n", + "238 19 19 19 19 19 19 19 19 19 \n", + "239 130 156 168 182 196 209 239 256 256 \n", + "240 447 448 449 451 457 464 464 464 472 \n", + "241 1256 1256 1339 1339 1339 1339 1368 1368 1368 \n", + "242 1250 1265 1315 1351 1386 1461 1485 1523 1533 \n", + "243 15 15 15 15 15 15 15 15 15 \n", + "244 5 5 5 5 5 5 5 5 5 \n", + "245 11 11 11 11 11 11 11 11 11 \n", + "246 1064 1064 1064 1064 1142 1142 1142 1142 1142 \n", + "247 224 224 228 232 233 236 236 240 242 \n", + "248 3 3 3 3 3 3 3 3 3 \n", + "249 8 8 8 8 8 8 8 8 8 \n", + "250 12 12 12 12 12 12 12 12 12 \n", + "251 9 9 9 9 9 9 9 9 9 \n", + "252 35 35 38 40 40 40 40 40 40 \n", + "253 63 63 63 63 63 63 63 83 83 \n", + "254 852 861 865 896 909 914 929 946 969 \n", + "255 6 6 7 7 7 7 7 7 7 \n", + "256 279 284 336 358 369 393 409 409 438 \n", + "257 13 13 13 13 13 13 13 13 13 \n", + "258 1 1 1 1 1 1 1 1 1 \n", + "259 994 994 994 994 994 994 994 994 1317 \n", + "260 9 9 9 9 9 9 9 9 9 \n", + "261 479 483 484 484 484 485 499 499 513 \n", + "262 310 323 354 399 419 453 469 482 484 \n", + "263 106 106 106 132 132 132 132 141 141 \n", + "264 3807 3930 4013 4100 4191 4289 4370 4453 4529 \n", + "265 2 2 2 2 4 4 4 4 4 \n", + "\n", + " 6/8/20 \n", + "0 20917 \n", + "1 1263 \n", + "2 10265 \n", + "3 852 \n", + "4 92 \n", + "5 26 \n", + "6 23620 \n", + "7 13325 \n", + "8 108 \n", + "9 3114 \n", + "10 29 \n", + "11 1062 \n", + "12 440 \n", + "13 228 \n", + "14 1687 \n", + "15 599 \n", + "16 16968 \n", + "17 7876 \n", + "18 103 \n", + "19 15417 \n", + "20 68504 \n", + "21 92 \n", + "22 49453 \n", + "23 59348 \n", + "24 288 \n", + "25 59 \n", + "26 13949 \n", + "27 2704 \n", + "28 707412 \n", + "29 141 \n", + ".. ... \n", + "236 24 \n", + "237 19 \n", + "238 19 \n", + "239 332 \n", + "240 473 \n", + "241 1389 \n", + "242 1547 \n", + "243 15 \n", + "244 5 \n", + "245 11 \n", + "246 1263 \n", + "247 244 \n", + "248 3 \n", + "249 8 \n", + "250 12 \n", + "251 9 \n", + "252 42 \n", + "253 83 \n", + "254 1001 \n", + "255 7 \n", + "256 443 \n", + "257 13 \n", + "258 1 \n", + "259 1604 \n", + "260 9 \n", + "261 513 \n", + "262 496 \n", + "263 141 \n", + "264 4609 \n", + "265 4 \n", + "\n", + "[266 rows x 143 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remove Long and Lat columns (just for convenience) and make a spared copy in df_total for the \"world\" graph" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Province/State Country/Region 1/22/20 \\\n", + "0 NaN Afghanistan 0 \n", + "1 NaN Albania 0 \n", + "2 NaN Algeria 0 \n", + "3 NaN Andorra 0 \n", + "4 NaN Angola 0 \n", + "5 NaN Antigua and Barbuda 0 \n", + "6 NaN Argentina 0 \n", + "7 NaN Armenia 0 \n", + "8 Australian Capital Territory Australia 0 \n", + "9 New South Wales Australia 0 \n", + "10 Northern Territory Australia 0 \n", + "11 Queensland Australia 0 \n", + "12 South Australia Australia 0 \n", + "13 Tasmania Australia 0 \n", + "14 Victoria Australia 0 \n", + "15 Western Australia Australia 0 \n", + "16 NaN Austria 0 \n", + "17 NaN Azerbaijan 0 \n", + "18 NaN Bahamas 0 \n", + "19 NaN Bahrain 0 \n", + "20 NaN Bangladesh 0 \n", + "21 NaN Barbados 0 \n", + "22 NaN Belarus 0 \n", + "23 NaN Belgium 0 \n", + "24 NaN Benin 0 \n", + "25 NaN Bhutan 0 \n", + "26 NaN Bolivia 0 \n", + "27 NaN Bosnia and Herzegovina 0 \n", + "28 NaN Brazil 0 \n", + "29 NaN Brunei 0 \n", + ".. ... ... ... \n", + "236 NaN Timor-Leste 0 \n", + "237 NaN Belize 0 \n", + "238 NaN Laos 0 \n", + "239 NaN Libya 0 \n", + "240 NaN West Bank and Gaza 0 \n", + "241 NaN Guinea-Bissau 0 \n", + "242 NaN Mali 0 \n", + "243 NaN Saint Kitts and Nevis 0 \n", + "244 Northwest Territories Canada 0 \n", + "245 Yukon Canada 0 \n", + "246 NaN Kosovo 0 \n", + "247 NaN Burma 0 \n", + "248 Anguilla United Kingdom 0 \n", + "249 British Virgin Islands United Kingdom 0 \n", + "250 Turks and Caicos Islands United Kingdom 0 \n", + "251 NaN MS Zaandam 0 \n", + "252 NaN Botswana 0 \n", + "253 NaN Burundi 0 \n", + "254 NaN Sierra Leone 0 \n", + "255 Bonaire, Sint Eustatius and Saba Netherlands 0 \n", + "256 NaN Malawi 0 \n", + "257 Falkland Islands (Malvinas) United Kingdom 0 \n", + "258 Saint Pierre and Miquelon France 0 \n", + "259 NaN South Sudan 0 \n", + "260 NaN Western Sahara 0 \n", + "261 NaN Sao Tome and Principe 0 \n", + "262 NaN Yemen 0 \n", + "263 NaN Comoros 0 \n", + "264 NaN Tajikistan 0 \n", + "265 NaN Lesotho 0 \n", + "\n", + " 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 ... \\\n", + "0 0 0 0 0 0 0 0 ... \n", + "1 0 0 0 0 0 0 0 ... \n", + "2 0 0 0 0 0 0 0 ... \n", + "3 0 0 0 0 0 0 0 ... \n", + "4 0 0 0 0 0 0 0 ... \n", + "5 0 0 0 0 0 0 0 ... \n", + "6 0 0 0 0 0 0 0 ... \n", + "7 0 0 0 0 0 0 0 ... \n", + "8 0 0 0 0 0 0 0 ... \n", + "9 0 0 0 3 4 4 4 ... \n", + "10 0 0 0 0 0 0 0 ... \n", + "11 0 0 0 0 0 0 1 ... \n", + "12 0 0 0 0 0 0 0 ... \n", + "13 0 0 0 0 0 0 0 ... \n", + "14 0 0 0 1 1 1 1 ... \n", + "15 0 0 0 0 0 0 0 ... \n", + "16 0 0 0 0 0 0 0 ... \n", + "17 0 0 0 0 0 0 0 ... \n", + "18 0 0 0 0 0 0 0 ... \n", + "19 0 0 0 0 0 0 0 ... \n", + "20 0 0 0 0 0 0 0 ... \n", + "21 0 0 0 0 0 0 0 ... \n", + "22 0 0 0 0 0 0 0 ... \n", + "23 0 0 0 0 0 0 0 ... \n", + "24 0 0 0 0 0 0 0 ... \n", + "25 0 0 0 0 0 0 0 ... \n", + "26 0 0 0 0 0 0 0 ... \n", + "27 0 0 0 0 0 0 0 ... \n", + "28 0 0 0 0 0 0 0 ... \n", + "29 0 0 0 0 0 0 0 ... \n", + ".. ... ... ... ... ... ... ... ... \n", + "236 0 0 0 0 0 0 0 ... \n", + "237 0 0 0 0 0 0 0 ... \n", + "238 0 0 0 0 0 0 0 ... \n", + "239 0 0 0 0 0 0 0 ... \n", + "240 0 0 0 0 0 0 0 ... \n", + "241 0 0 0 0 0 0 0 ... \n", + "242 0 0 0 0 0 0 0 ... \n", + "243 0 0 0 0 0 0 0 ... \n", + "244 0 0 0 0 0 0 0 ... \n", + "245 0 0 0 0 0 0 0 ... \n", + "246 0 0 0 0 0 0 0 ... \n", + "247 0 0 0 0 0 0 0 ... \n", + "248 0 0 0 0 0 0 0 ... \n", + "249 0 0 0 0 0 0 0 ... \n", + "250 0 0 0 0 0 0 0 ... \n", + "251 0 0 0 0 0 0 0 ... \n", + "252 0 0 0 0 0 0 0 ... \n", + "253 0 0 0 0 0 0 0 ... \n", + "254 0 0 0 0 0 0 0 ... \n", + "255 0 0 0 0 0 0 0 ... \n", + "256 0 0 0 0 0 0 0 ... \n", + "257 0 0 0 0 0 0 0 ... \n", + "258 0 0 0 0 0 0 0 ... \n", + "259 0 0 0 0 0 0 0 ... \n", + "260 0 0 0 0 0 0 0 ... \n", + "261 0 0 0 0 0 0 0 ... \n", + "262 0 0 0 0 0 0 0 ... \n", + "263 0 0 0 0 0 0 0 ... \n", + "264 0 0 0 0 0 0 0 ... \n", + "265 0 0 0 0 0 0 0 ... \n", + "\n", + " 5/30/20 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 \\\n", + "0 14525 15205 15750 16509 17267 18054 18969 19551 20342 \n", + "1 1122 1137 1143 1164 1184 1197 1212 1232 1246 \n", + "2 9267 9394 9513 9626 9733 9831 9935 10050 10154 \n", + "3 764 764 765 844 851 852 852 852 852 \n", + "4 84 86 86 86 86 86 86 88 91 \n", + "5 25 26 26 26 26 26 26 26 26 \n", + "6 16214 16851 17415 18319 19268 20197 21037 22020 22794 \n", + "7 8927 9282 9492 10009 10524 11221 11817 12364 13130 \n", + "8 107 107 107 107 107 107 107 108 108 \n", + "9 3095 3098 3104 3104 3106 3110 3110 3109 3112 \n", + "10 29 29 29 29 29 29 29 29 29 \n", + "11 1058 1058 1059 1059 1060 1060 1061 1061 1062 \n", + "12 440 440 440 440 440 440 440 440 440 \n", + "13 228 228 228 228 228 228 228 228 228 \n", + "14 1649 1653 1663 1670 1678 1681 1681 1685 1687 \n", + "15 586 589 591 592 592 592 596 599 599 \n", + "16 16685 16731 16733 16759 16771 16805 16843 16898 16902 \n", + "17 5246 5494 5662 5935 6260 6522 6860 7239 7553 \n", + "18 102 102 102 102 102 102 102 103 103 \n", + "19 10793 11398 11871 12311 12815 13296 13835 14383 14763 \n", + "20 44608 47153 49534 52445 55140 57563 60391 63026 65769 \n", + "21 92 92 92 92 92 92 92 92 92 \n", + "22 41658 42556 43403 44255 45116 45981 46868 47751 48630 \n", + "23 58186 58381 58517 58615 58685 58767 58907 59072 59226 \n", + "24 224 232 243 244 244 261 261 261 261 \n", + "25 33 43 43 47 47 47 48 48 59 \n", + "26 9592 9982 10531 10991 11638 12245 12728 13358 13643 \n", + "27 2494 2510 2524 2535 2551 2594 2606 2606 2606 \n", + "28 498440 514849 526447 555383 584016 614941 645771 672846 691758 \n", + "29 141 141 141 141 141 141 141 141 141 \n", + ".. ... ... ... ... ... ... ... ... ... \n", + "236 24 24 24 24 24 24 24 24 24 \n", + "237 18 18 18 18 18 18 19 19 19 \n", + "238 19 19 19 19 19 19 19 19 19 \n", + "239 130 156 168 182 196 209 239 256 256 \n", + "240 447 448 449 451 457 464 464 464 472 \n", + "241 1256 1256 1339 1339 1339 1339 1368 1368 1368 \n", + "242 1250 1265 1315 1351 1386 1461 1485 1523 1533 \n", + "243 15 15 15 15 15 15 15 15 15 \n", + "244 5 5 5 5 5 5 5 5 5 \n", + "245 11 11 11 11 11 11 11 11 11 \n", + "246 1064 1064 1064 1064 1142 1142 1142 1142 1142 \n", + "247 224 224 228 232 233 236 236 240 242 \n", + "248 3 3 3 3 3 3 3 3 3 \n", + "249 8 8 8 8 8 8 8 8 8 \n", + "250 12 12 12 12 12 12 12 12 12 \n", + "251 9 9 9 9 9 9 9 9 9 \n", + "252 35 35 38 40 40 40 40 40 40 \n", + "253 63 63 63 63 63 63 63 83 83 \n", + "254 852 861 865 896 909 914 929 946 969 \n", + "255 6 6 7 7 7 7 7 7 7 \n", + "256 279 284 336 358 369 393 409 409 438 \n", + "257 13 13 13 13 13 13 13 13 13 \n", + "258 1 1 1 1 1 1 1 1 1 \n", + "259 994 994 994 994 994 994 994 994 1317 \n", + "260 9 9 9 9 9 9 9 9 9 \n", + "261 479 483 484 484 484 485 499 499 513 \n", + "262 310 323 354 399 419 453 469 482 484 \n", + "263 106 106 106 132 132 132 132 141 141 \n", + "264 3807 3930 4013 4100 4191 4289 4370 4453 4529 \n", + "265 2 2 2 2 4 4 4 4 4 \n", + "\n", + " 6/8/20 \n", + "0 20917 \n", + "1 1263 \n", + "2 10265 \n", + "3 852 \n", + "4 92 \n", + "5 26 \n", + "6 23620 \n", + "7 13325 \n", + "8 108 \n", + "9 3114 \n", + "10 29 \n", + "11 1062 \n", + "12 440 \n", + "13 228 \n", + "14 1687 \n", + "15 599 \n", + "16 16968 \n", + "17 7876 \n", + "18 103 \n", + "19 15417 \n", + "20 68504 \n", + "21 92 \n", + "22 49453 \n", + "23 59348 \n", + "24 288 \n", + "25 59 \n", + "26 13949 \n", + "27 2704 \n", + "28 707412 \n", + "29 141 \n", + ".. ... \n", + "236 24 \n", + "237 19 \n", + "238 19 \n", + "239 332 \n", + "240 473 \n", + "241 1389 \n", + "242 1547 \n", + "243 15 \n", + "244 5 \n", + "245 11 \n", + "246 1263 \n", + "247 244 \n", + "248 3 \n", + "249 8 \n", + "250 12 \n", + "251 9 \n", + "252 42 \n", + "253 83 \n", + "254 1001 \n", + "255 7 \n", + "256 443 \n", + "257 13 \n", + "258 1 \n", + "259 1604 \n", + "260 9 \n", + "261 513 \n", + "262 496 \n", + "263 141 \n", + "264 4609 \n", + "265 4 \n", + "\n", + "[266 rows x 141 columns]\n" + ] + } + ], + "source": [ + "df = pd.DataFrame(raw_data)\n", + "\n", + "df_total=df.drop(columns=['Lat', 'Long'])\n", + "df=df_total\n", + "\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remove \"not interesting\" countries" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Province/State Country/Region 1/22/20 1/23/20 \\\n", + "23 NaN Belgium 0 0 \n", + "49 Anhui China 1 9 \n", + "50 Beijing China 14 22 \n", + "51 Chongqing China 6 9 \n", + "52 Fujian China 1 5 \n", + "53 Gansu China 0 2 \n", + "54 Guangdong China 26 32 \n", + "55 Guangxi China 2 5 \n", + "56 Guizhou China 1 3 \n", + "57 Hainan China 4 5 \n", + "58 Hebei China 1 1 \n", + "59 Heilongjiang China 0 2 \n", + "60 Henan China 5 5 \n", + "61 Hong Kong China 0 2 \n", + "62 Hubei China 444 444 \n", + "63 Hunan China 4 9 \n", + "64 Inner Mongolia China 0 0 \n", + "65 Jiangsu China 1 5 \n", + "66 Jiangxi China 2 7 \n", + "67 Jilin China 0 1 \n", + "68 Liaoning China 2 3 \n", + "69 Macau China 1 2 \n", + "70 Ningxia China 1 1 \n", + "71 Qinghai China 0 0 \n", + "72 Shaanxi China 0 3 \n", + "73 Shandong China 2 6 \n", + "74 Shanghai China 9 16 \n", + "75 Shanxi China 1 1 \n", + "76 Sichuan China 5 8 \n", + "77 Tianjin China 4 4 \n", + ".. ... ... ... ... \n", + "111 New Caledonia France 0 0 \n", + "112 Reunion France 0 0 \n", + "113 Saint Barthelemy France 0 0 \n", + "114 St Martin France 0 0 \n", + "115 Martinique France 0 0 \n", + "116 NaN France 0 0 \n", + "120 NaN Germany 0 0 \n", + "133 NaN Iran 0 0 \n", + "137 NaN Italy 0 0 \n", + "139 NaN Japan 2 2 \n", + "166 Aruba Netherlands 0 0 \n", + "167 Curacao Netherlands 0 0 \n", + "168 Sint Maarten Netherlands 0 0 \n", + "169 NaN Netherlands 0 0 \n", + "184 NaN Portugal 0 0 \n", + "201 NaN Spain 0 0 \n", + "217 Bermuda United Kingdom 0 0 \n", + "218 Cayman Islands United Kingdom 0 0 \n", + "219 Channel Islands United Kingdom 0 0 \n", + "220 Gibraltar United Kingdom 0 0 \n", + "221 Isle of Man United Kingdom 0 0 \n", + "222 Montserrat United Kingdom 0 0 \n", + "223 NaN United Kingdom 0 0 \n", + "225 NaN US 1 1 \n", + "248 Anguilla United Kingdom 0 0 \n", + "249 British Virgin Islands United Kingdom 0 0 \n", + "250 Turks and Caicos Islands United Kingdom 0 0 \n", + "255 Bonaire, Sint Eustatius and Saba Netherlands 0 0 \n", + "257 Falkland Islands (Malvinas) United Kingdom 0 0 \n", + "258 Saint Pierre and Miquelon France 0 0 \n", + "\n", + " 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 ... 5/30/20 \\\n", + "23 0 0 0 0 0 0 ... 58186 \n", + "49 15 39 60 70 106 152 ... 991 \n", + "50 36 41 68 80 91 111 ... 593 \n", + "51 27 57 75 110 132 147 ... 579 \n", + "52 10 18 35 59 80 84 ... 358 \n", + "53 2 4 7 14 19 24 ... 139 \n", + "54 53 78 111 151 207 277 ... 1593 \n", + "55 23 23 36 46 51 58 ... 254 \n", + "56 3 4 5 7 9 9 ... 147 \n", + "57 8 19 22 33 40 43 ... 169 \n", + "58 2 8 13 18 33 48 ... 328 \n", + "59 4 9 15 21 33 38 ... 945 \n", + "60 9 32 83 128 168 206 ... 1276 \n", + "61 2 5 8 8 8 10 ... 1082 \n", + "62 549 761 1058 1423 3554 3554 ... 68135 \n", + "63 24 43 69 100 143 221 ... 1019 \n", + "64 1 7 7 11 15 16 ... 232 \n", + "65 9 18 33 47 70 99 ... 653 \n", + "66 18 18 36 72 109 109 ... 937 \n", + "67 3 4 4 6 8 9 ... 155 \n", + "68 4 17 21 27 34 39 ... 149 \n", + "69 2 2 5 6 7 7 ... 45 \n", + "70 2 3 4 7 11 12 ... 75 \n", + "71 0 1 1 6 6 6 ... 18 \n", + "72 5 15 22 35 46 56 ... 308 \n", + "73 15 27 46 75 95 130 ... 792 \n", + "74 20 33 40 53 66 96 ... 672 \n", + "75 1 6 9 13 27 27 ... 198 \n", + "76 15 28 44 69 90 108 ... 564 \n", + "77 8 10 14 23 24 27 ... 192 \n", + ".. ... ... ... ... ... ... ... ... \n", + "111 0 0 0 0 0 0 ... 19 \n", + "112 0 0 0 0 0 0 ... 471 \n", + "113 0 0 0 0 0 0 ... 6 \n", + "114 0 0 0 0 0 0 ... 41 \n", + "115 0 0 0 0 0 0 ... 200 \n", + "116 2 3 3 3 4 5 ... 185616 \n", + "120 0 0 0 1 4 4 ... 183189 \n", + "133 0 0 0 0 0 0 ... 148950 \n", + "137 0 0 0 0 0 0 ... 232664 \n", + "139 2 2 4 4 7 7 ... 16716 \n", + "166 0 0 0 0 0 0 ... 101 \n", + "167 0 0 0 0 0 0 ... 19 \n", + "168 0 0 0 0 0 0 ... 77 \n", + "169 0 0 0 0 0 0 ... 46257 \n", + "184 0 0 0 0 0 0 ... 32203 \n", + "201 0 0 0 0 0 0 ... 239228 \n", + "217 0 0 0 0 0 0 ... 140 \n", + "218 0 0 0 0 0 0 ... 141 \n", + "219 0 0 0 0 0 0 ... 560 \n", + "220 0 0 0 0 0 0 ... 169 \n", + "221 0 0 0 0 0 0 ... 336 \n", + "222 0 0 0 0 0 0 ... 11 \n", + "223 0 0 0 0 0 0 ... 272826 \n", + "225 2 2 5 5 5 5 ... 1770165 \n", + "248 0 0 0 0 0 0 ... 3 \n", + "249 0 0 0 0 0 0 ... 8 \n", + "250 0 0 0 0 0 0 ... 12 \n", + "255 0 0 0 0 0 0 ... 6 \n", + "257 0 0 0 0 0 0 ... 13 \n", + "258 0 0 0 0 0 0 ... 1 \n", + "\n", + " 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 \\\n", + "23 58381 58517 58615 58685 58767 58907 59072 59226 \n", + "49 991 991 991 991 991 991 991 991 \n", + "50 593 593 593 594 594 594 594 594 \n", + "51 579 579 579 579 579 579 579 579 \n", + "52 358 358 358 358 358 358 359 359 \n", + "53 139 139 139 139 139 139 139 139 \n", + "54 1595 1596 1597 1598 1598 1601 1602 1602 \n", + "55 254 254 254 254 254 254 254 254 \n", + "56 147 147 147 147 147 147 147 147 \n", + "57 169 169 169 169 169 169 170 170 \n", + "58 328 328 328 328 328 328 328 328 \n", + "59 945 945 945 947 947 947 947 947 \n", + "60 1276 1276 1276 1276 1276 1276 1276 1276 \n", + "61 1084 1087 1093 1093 1099 1102 1105 1106 \n", + "62 68135 68135 68135 68135 68135 68135 68135 68135 \n", + "63 1019 1019 1019 1019 1019 1019 1019 1019 \n", + "64 235 235 235 235 235 235 235 235 \n", + "65 653 653 653 653 653 653 653 653 \n", + "66 937 937 937 932 932 932 932 932 \n", + "67 155 155 155 155 155 155 155 155 \n", + "68 149 149 149 149 149 149 149 149 \n", + "69 45 45 45 45 45 45 45 45 \n", + "70 75 75 75 75 75 75 75 75 \n", + "71 18 18 18 18 18 18 18 18 \n", + "72 308 309 309 309 309 309 311 311 \n", + "73 792 792 792 792 792 792 792 792 \n", + "74 672 673 673 673 677 677 677 678 \n", + "75 198 198 198 198 198 198 198 198 \n", + "76 575 577 577 577 578 578 578 581 \n", + "77 192 192 192 192 192 192 193 193 \n", + ".. ... ... ... ... ... ... ... ... \n", + "111 19 20 20 20 20 20 20 20 \n", + "112 471 473 477 478 479 480 480 480 \n", + "113 6 6 6 6 6 6 6 6 \n", + "114 41 41 41 41 41 41 41 41 \n", + "115 200 200 200 200 200 202 202 202 \n", + "116 185851 185952 184980 188836 185986 186538 187067 187360 \n", + "120 183410 183594 183879 184121 184472 184924 185450 185750 \n", + "133 151466 154445 157562 160696 164270 167156 169425 171789 \n", + "137 232997 233197 233515 233836 234013 234531 234801 234998 \n", + "139 16751 16787 16837 16867 16911 16958 17000 17039 \n", + "166 101 101 101 101 101 101 101 101 \n", + "167 19 19 20 21 21 21 21 21 \n", + "168 77 77 77 77 77 77 77 77 \n", + "169 46442 46545 46647 46733 46942 47152 47335 47574 \n", + "184 32500 32700 32895 33261 33592 33969 34351 34693 \n", + "201 239479 239638 239932 240326 240660 240978 241310 241550 \n", + "217 140 141 141 141 141 141 141 141 \n", + "218 141 150 151 156 160 164 164 164 \n", + "219 560 560 560 561 561 561 563 563 \n", + "220 170 170 172 173 173 174 175 176 \n", + "221 336 336 336 336 336 336 336 336 \n", + "222 11 11 11 11 11 11 11 11 \n", + "223 274762 276332 277985 279856 281661 283311 284868 286194 \n", + "225 1790172 1811020 1831821 1851520 1872660 1897380 1920061 1943647 \n", + "248 3 3 3 3 3 3 3 3 \n", + "249 8 8 8 8 8 8 8 8 \n", + "250 12 12 12 12 12 12 12 12 \n", + "255 6 7 7 7 7 7 7 7 \n", + "257 13 13 13 13 13 13 13 13 \n", + "258 1 1 1 1 1 1 1 1 \n", + "\n", + " 6/8/20 \n", + "23 59348 \n", + "49 991 \n", + "50 594 \n", + "51 579 \n", + "52 359 \n", + "53 139 \n", + "54 1604 \n", + "55 254 \n", + "56 147 \n", + "57 170 \n", + "58 328 \n", + "59 947 \n", + "60 1276 \n", + "61 1107 \n", + "62 68135 \n", + "63 1019 \n", + "64 235 \n", + "65 653 \n", + "66 932 \n", + "67 155 \n", + "68 149 \n", + "69 45 \n", + "70 75 \n", + "71 18 \n", + "72 311 \n", + "73 792 \n", + "74 678 \n", + "75 198 \n", + "76 582 \n", + "77 193 \n", + ".. ... \n", + "111 20 \n", + "112 481 \n", + "113 6 \n", + "114 41 \n", + "115 202 \n", + "116 187458 \n", + "120 186109 \n", + "133 173832 \n", + "137 235278 \n", + "139 17060 \n", + "166 101 \n", + "167 21 \n", + "168 77 \n", + "169 47739 \n", + "184 34885 \n", + "201 241717 \n", + "217 141 \n", + "218 171 \n", + "219 564 \n", + "220 176 \n", + "221 336 \n", + "222 11 \n", + "223 287399 \n", + "225 1960897 \n", + "248 3 \n", + "249 8 \n", + "250 12 \n", + "255 7 \n", + "257 13 \n", + "258 1 \n", + "\n", + "[68 rows x 141 columns]\n" + ] + } + ], + "source": [ + "df=df.drop(df[(df['Country/Region'] != 'Belgium') & (df['Country/Region'] != 'China') & (df['Country/Region'] != 'France') & (df['Country/Region'] != 'Germany') & (df['Country/Region'] != 'Iran') & (df['Country/Region'] != 'Italy') & (df['Country/Region'] != 'Japan') & (df['Country/Region'] != 'Korea South') & (df['Country/Region'] != 'Netherlands') & (df['Country/Region'] != 'Portugal') & (df['Country/Region'] != 'Spain') & (df['Country/Region'] != 'United Kingdom') & (df['Country/Region'] != 'US')].index)\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For convenience change China to Hong Kong in the Hong Kong line" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Province/State Country/Region 1/22/20 1/23/20 \\\n", + "23 NaN Belgium 0 0 \n", + "49 Anhui China 1 9 \n", + "50 Beijing China 14 22 \n", + "51 Chongqing China 6 9 \n", + "52 Fujian China 1 5 \n", + "53 Gansu China 0 2 \n", + "54 Guangdong China 26 32 \n", + "55 Guangxi China 2 5 \n", + "56 Guizhou China 1 3 \n", + "57 Hainan China 4 5 \n", + "58 Hebei China 1 1 \n", + "59 Heilongjiang China 0 2 \n", + "60 Henan China 5 5 \n", + "61 Hong Kong Hong Kong 0 2 \n", + "62 Hubei China 444 444 \n", + "63 Hunan China 4 9 \n", + "64 Inner Mongolia China 0 0 \n", + "65 Jiangsu China 1 5 \n", + "66 Jiangxi China 2 7 \n", + "67 Jilin China 0 1 \n", + "68 Liaoning China 2 3 \n", + "69 Macau China 1 2 \n", + "70 Ningxia China 1 1 \n", + "71 Qinghai China 0 0 \n", + "72 Shaanxi China 0 3 \n", + "73 Shandong China 2 6 \n", + "74 Shanghai China 9 16 \n", + "75 Shanxi China 1 1 \n", + "76 Sichuan China 5 8 \n", + "77 Tianjin China 4 4 \n", + ".. ... ... ... ... \n", + "111 New Caledonia France 0 0 \n", + "112 Reunion France 0 0 \n", + "113 Saint Barthelemy France 0 0 \n", + "114 St Martin France 0 0 \n", + "115 Martinique France 0 0 \n", + "116 NaN France 0 0 \n", + "120 NaN Germany 0 0 \n", + "133 NaN Iran 0 0 \n", + "137 NaN Italy 0 0 \n", + "139 NaN Japan 2 2 \n", + "166 Aruba Netherlands 0 0 \n", + "167 Curacao Netherlands 0 0 \n", + "168 Sint Maarten Netherlands 0 0 \n", + "169 NaN Netherlands 0 0 \n", + "184 NaN Portugal 0 0 \n", + "201 NaN Spain 0 0 \n", + "217 Bermuda United Kingdom 0 0 \n", + "218 Cayman Islands United Kingdom 0 0 \n", + "219 Channel Islands United Kingdom 0 0 \n", + "220 Gibraltar United Kingdom 0 0 \n", + "221 Isle of Man United Kingdom 0 0 \n", + "222 Montserrat United Kingdom 0 0 \n", + "223 NaN United Kingdom 0 0 \n", + "225 NaN US 1 1 \n", + "248 Anguilla United Kingdom 0 0 \n", + "249 British Virgin Islands United Kingdom 0 0 \n", + "250 Turks and Caicos Islands United Kingdom 0 0 \n", + "255 Bonaire, Sint Eustatius and Saba Netherlands 0 0 \n", + "257 Falkland Islands (Malvinas) United Kingdom 0 0 \n", + "258 Saint Pierre and Miquelon France 0 0 \n", + "\n", + " 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 ... 5/30/20 \\\n", + "23 0 0 0 0 0 0 ... 58186 \n", + "49 15 39 60 70 106 152 ... 991 \n", + "50 36 41 68 80 91 111 ... 593 \n", + "51 27 57 75 110 132 147 ... 579 \n", + "52 10 18 35 59 80 84 ... 358 \n", + "53 2 4 7 14 19 24 ... 139 \n", + "54 53 78 111 151 207 277 ... 1593 \n", + "55 23 23 36 46 51 58 ... 254 \n", + "56 3 4 5 7 9 9 ... 147 \n", + "57 8 19 22 33 40 43 ... 169 \n", + "58 2 8 13 18 33 48 ... 328 \n", + "59 4 9 15 21 33 38 ... 945 \n", + "60 9 32 83 128 168 206 ... 1276 \n", + "61 2 5 8 8 8 10 ... 1082 \n", + "62 549 761 1058 1423 3554 3554 ... 68135 \n", + "63 24 43 69 100 143 221 ... 1019 \n", + "64 1 7 7 11 15 16 ... 232 \n", + "65 9 18 33 47 70 99 ... 653 \n", + "66 18 18 36 72 109 109 ... 937 \n", + "67 3 4 4 6 8 9 ... 155 \n", + "68 4 17 21 27 34 39 ... 149 \n", + "69 2 2 5 6 7 7 ... 45 \n", + "70 2 3 4 7 11 12 ... 75 \n", + "71 0 1 1 6 6 6 ... 18 \n", + "72 5 15 22 35 46 56 ... 308 \n", + "73 15 27 46 75 95 130 ... 792 \n", + "74 20 33 40 53 66 96 ... 672 \n", + "75 1 6 9 13 27 27 ... 198 \n", + "76 15 28 44 69 90 108 ... 564 \n", + "77 8 10 14 23 24 27 ... 192 \n", + ".. ... ... ... ... ... ... ... ... \n", + "111 0 0 0 0 0 0 ... 19 \n", + "112 0 0 0 0 0 0 ... 471 \n", + "113 0 0 0 0 0 0 ... 6 \n", + "114 0 0 0 0 0 0 ... 41 \n", + "115 0 0 0 0 0 0 ... 200 \n", + "116 2 3 3 3 4 5 ... 185616 \n", + "120 0 0 0 1 4 4 ... 183189 \n", + "133 0 0 0 0 0 0 ... 148950 \n", + "137 0 0 0 0 0 0 ... 232664 \n", + "139 2 2 4 4 7 7 ... 16716 \n", + "166 0 0 0 0 0 0 ... 101 \n", + "167 0 0 0 0 0 0 ... 19 \n", + "168 0 0 0 0 0 0 ... 77 \n", + "169 0 0 0 0 0 0 ... 46257 \n", + "184 0 0 0 0 0 0 ... 32203 \n", + "201 0 0 0 0 0 0 ... 239228 \n", + "217 0 0 0 0 0 0 ... 140 \n", + "218 0 0 0 0 0 0 ... 141 \n", + "219 0 0 0 0 0 0 ... 560 \n", + "220 0 0 0 0 0 0 ... 169 \n", + "221 0 0 0 0 0 0 ... 336 \n", + "222 0 0 0 0 0 0 ... 11 \n", + "223 0 0 0 0 0 0 ... 272826 \n", + "225 2 2 5 5 5 5 ... 1770165 \n", + "248 0 0 0 0 0 0 ... 3 \n", + "249 0 0 0 0 0 0 ... 8 \n", + "250 0 0 0 0 0 0 ... 12 \n", + "255 0 0 0 0 0 0 ... 6 \n", + "257 0 0 0 0 0 0 ... 13 \n", + "258 0 0 0 0 0 0 ... 1 \n", + "\n", + " 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 \\\n", + "23 58381 58517 58615 58685 58767 58907 59072 59226 \n", + "49 991 991 991 991 991 991 991 991 \n", + "50 593 593 593 594 594 594 594 594 \n", + "51 579 579 579 579 579 579 579 579 \n", + "52 358 358 358 358 358 358 359 359 \n", + "53 139 139 139 139 139 139 139 139 \n", + "54 1595 1596 1597 1598 1598 1601 1602 1602 \n", + "55 254 254 254 254 254 254 254 254 \n", + "56 147 147 147 147 147 147 147 147 \n", + "57 169 169 169 169 169 169 170 170 \n", + "58 328 328 328 328 328 328 328 328 \n", + "59 945 945 945 947 947 947 947 947 \n", + "60 1276 1276 1276 1276 1276 1276 1276 1276 \n", + "61 1084 1087 1093 1093 1099 1102 1105 1106 \n", + "62 68135 68135 68135 68135 68135 68135 68135 68135 \n", + "63 1019 1019 1019 1019 1019 1019 1019 1019 \n", + "64 235 235 235 235 235 235 235 235 \n", + "65 653 653 653 653 653 653 653 653 \n", + "66 937 937 937 932 932 932 932 932 \n", + "67 155 155 155 155 155 155 155 155 \n", + "68 149 149 149 149 149 149 149 149 \n", + "69 45 45 45 45 45 45 45 45 \n", + "70 75 75 75 75 75 75 75 75 \n", + "71 18 18 18 18 18 18 18 18 \n", + "72 308 309 309 309 309 309 311 311 \n", + "73 792 792 792 792 792 792 792 792 \n", + "74 672 673 673 673 677 677 677 678 \n", + "75 198 198 198 198 198 198 198 198 \n", + "76 575 577 577 577 578 578 578 581 \n", + "77 192 192 192 192 192 192 193 193 \n", + ".. ... ... ... ... ... ... ... ... \n", + "111 19 20 20 20 20 20 20 20 \n", + "112 471 473 477 478 479 480 480 480 \n", + "113 6 6 6 6 6 6 6 6 \n", + "114 41 41 41 41 41 41 41 41 \n", + "115 200 200 200 200 200 202 202 202 \n", + "116 185851 185952 184980 188836 185986 186538 187067 187360 \n", + "120 183410 183594 183879 184121 184472 184924 185450 185750 \n", + "133 151466 154445 157562 160696 164270 167156 169425 171789 \n", + "137 232997 233197 233515 233836 234013 234531 234801 234998 \n", + "139 16751 16787 16837 16867 16911 16958 17000 17039 \n", + "166 101 101 101 101 101 101 101 101 \n", + "167 19 19 20 21 21 21 21 21 \n", + "168 77 77 77 77 77 77 77 77 \n", + "169 46442 46545 46647 46733 46942 47152 47335 47574 \n", + "184 32500 32700 32895 33261 33592 33969 34351 34693 \n", + "201 239479 239638 239932 240326 240660 240978 241310 241550 \n", + "217 140 141 141 141 141 141 141 141 \n", + "218 141 150 151 156 160 164 164 164 \n", + "219 560 560 560 561 561 561 563 563 \n", + "220 170 170 172 173 173 174 175 176 \n", + "221 336 336 336 336 336 336 336 336 \n", + "222 11 11 11 11 11 11 11 11 \n", + "223 274762 276332 277985 279856 281661 283311 284868 286194 \n", + "225 1790172 1811020 1831821 1851520 1872660 1897380 1920061 1943647 \n", + "248 3 3 3 3 3 3 3 3 \n", + "249 8 8 8 8 8 8 8 8 \n", + "250 12 12 12 12 12 12 12 12 \n", + "255 6 7 7 7 7 7 7 7 \n", + "257 13 13 13 13 13 13 13 13 \n", + "258 1 1 1 1 1 1 1 1 \n", + "\n", + " 6/8/20 \n", + "23 59348 \n", + "49 991 \n", + "50 594 \n", + "51 579 \n", + "52 359 \n", + "53 139 \n", + "54 1604 \n", + "55 254 \n", + "56 147 \n", + "57 170 \n", + "58 328 \n", + "59 947 \n", + "60 1276 \n", + "61 1107 \n", + "62 68135 \n", + "63 1019 \n", + "64 235 \n", + "65 653 \n", + "66 932 \n", + "67 155 \n", + "68 149 \n", + "69 45 \n", + "70 75 \n", + "71 18 \n", + "72 311 \n", + "73 792 \n", + "74 678 \n", + "75 198 \n", + "76 582 \n", + "77 193 \n", + ".. ... \n", + "111 20 \n", + "112 481 \n", + "113 6 \n", + "114 41 \n", + "115 202 \n", + "116 187458 \n", + "120 186109 \n", + "133 173832 \n", + "137 235278 \n", + "139 17060 \n", + "166 101 \n", + "167 21 \n", + "168 77 \n", + "169 47739 \n", + "184 34885 \n", + "201 241717 \n", + "217 141 \n", + "218 171 \n", + "219 564 \n", + "220 176 \n", + "221 336 \n", + "222 11 \n", + "223 287399 \n", + "225 1960897 \n", + "248 3 \n", + "249 8 \n", + "250 12 \n", + "255 7 \n", + "257 13 \n", + "258 1 \n", + "\n", + "[68 rows x 141 columns]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + } + ], + "source": [ + "df=df.set_value(df[(df['Province/State'] == 'Hong Kong')].index, 'Country/Region', 'Hong Kong')\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remove colonies of France, Netherlands and UK" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Province/State Country/Region 1/22/20 1/23/20 1/24/20 1/25/20 \\\n", + "23 NaN Belgium 0 0 0 0 \n", + "49 Anhui China 1 9 15 39 \n", + "50 Beijing China 14 22 36 41 \n", + "51 Chongqing China 6 9 27 57 \n", + "52 Fujian China 1 5 10 18 \n", + "53 Gansu China 0 2 2 4 \n", + "54 Guangdong China 26 32 53 78 \n", + "55 Guangxi China 2 5 23 23 \n", + "56 Guizhou China 1 3 3 4 \n", + "57 Hainan China 4 5 8 19 \n", + "58 Hebei China 1 1 2 8 \n", + "59 Heilongjiang China 0 2 4 9 \n", + "60 Henan China 5 5 9 32 \n", + "61 Hong Kong Hong Kong 0 2 2 5 \n", + "62 Hubei China 444 444 549 761 \n", + "63 Hunan China 4 9 24 43 \n", + "64 Inner Mongolia China 0 0 1 7 \n", + "65 Jiangsu China 1 5 9 18 \n", + "66 Jiangxi China 2 7 18 18 \n", + "67 Jilin China 0 1 3 4 \n", + "68 Liaoning China 2 3 4 17 \n", + "69 Macau China 1 2 2 2 \n", + "70 Ningxia China 1 1 2 3 \n", + "71 Qinghai China 0 0 0 1 \n", + "72 Shaanxi China 0 3 5 15 \n", + "73 Shandong China 2 6 15 27 \n", + "74 Shanghai China 9 16 20 33 \n", + "75 Shanxi China 1 1 1 6 \n", + "76 Sichuan China 5 8 15 28 \n", + "77 Tianjin China 4 4 8 10 \n", + "78 Tibet China 0 0 0 0 \n", + "79 Xinjiang China 0 2 2 3 \n", + "80 Yunnan China 1 2 5 11 \n", + "81 Zhejiang China 10 27 43 62 \n", + "116 NaN France 0 0 2 3 \n", + "120 NaN Germany 0 0 0 0 \n", + "133 NaN Iran 0 0 0 0 \n", + "137 NaN Italy 0 0 0 0 \n", + "139 NaN Japan 2 2 2 2 \n", + "169 NaN Netherlands 0 0 0 0 \n", + "184 NaN Portugal 0 0 0 0 \n", + "201 NaN Spain 0 0 0 0 \n", + "223 NaN United Kingdom 0 0 0 0 \n", + "225 NaN US 1 1 2 2 \n", + "\n", + " 1/26/20 1/27/20 1/28/20 1/29/20 ... 5/30/20 5/31/20 6/1/20 \\\n", + "23 0 0 0 0 ... 58186 58381 58517 \n", + "49 60 70 106 152 ... 991 991 991 \n", + "50 68 80 91 111 ... 593 593 593 \n", + "51 75 110 132 147 ... 579 579 579 \n", + "52 35 59 80 84 ... 358 358 358 \n", + "53 7 14 19 24 ... 139 139 139 \n", + "54 111 151 207 277 ... 1593 1595 1596 \n", + "55 36 46 51 58 ... 254 254 254 \n", + "56 5 7 9 9 ... 147 147 147 \n", + "57 22 33 40 43 ... 169 169 169 \n", + "58 13 18 33 48 ... 328 328 328 \n", + "59 15 21 33 38 ... 945 945 945 \n", + "60 83 128 168 206 ... 1276 1276 1276 \n", + "61 8 8 8 10 ... 1082 1084 1087 \n", + "62 1058 1423 3554 3554 ... 68135 68135 68135 \n", + "63 69 100 143 221 ... 1019 1019 1019 \n", + "64 7 11 15 16 ... 232 235 235 \n", + "65 33 47 70 99 ... 653 653 653 \n", + "66 36 72 109 109 ... 937 937 937 \n", + "67 4 6 8 9 ... 155 155 155 \n", + "68 21 27 34 39 ... 149 149 149 \n", + "69 5 6 7 7 ... 45 45 45 \n", + "70 4 7 11 12 ... 75 75 75 \n", + "71 1 6 6 6 ... 18 18 18 \n", + "72 22 35 46 56 ... 308 308 309 \n", + "73 46 75 95 130 ... 792 792 792 \n", + "74 40 53 66 96 ... 672 672 673 \n", + "75 9 13 27 27 ... 198 198 198 \n", + "76 44 69 90 108 ... 564 575 577 \n", + "77 14 23 24 27 ... 192 192 192 \n", + "78 0 0 0 0 ... 1 1 1 \n", + "79 4 5 10 13 ... 76 76 76 \n", + "80 16 26 44 55 ... 185 185 185 \n", + "81 104 128 173 296 ... 1268 1268 1268 \n", + "116 3 3 4 5 ... 185616 185851 185952 \n", + "120 0 1 4 4 ... 183189 183410 183594 \n", + "133 0 0 0 0 ... 148950 151466 154445 \n", + "137 0 0 0 0 ... 232664 232997 233197 \n", + "139 4 4 7 7 ... 16716 16751 16787 \n", + "169 0 0 0 0 ... 46257 46442 46545 \n", + "184 0 0 0 0 ... 32203 32500 32700 \n", + "201 0 0 0 0 ... 239228 239479 239638 \n", + "223 0 0 0 0 ... 272826 274762 276332 \n", + "225 5 5 5 5 ... 1770165 1790172 1811020 \n", + "\n", + " 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 6/8/20 \n", + "23 58615 58685 58767 58907 59072 59226 59348 \n", + "49 991 991 991 991 991 991 991 \n", + "50 593 594 594 594 594 594 594 \n", + "51 579 579 579 579 579 579 579 \n", + "52 358 358 358 358 359 359 359 \n", + "53 139 139 139 139 139 139 139 \n", + "54 1597 1598 1598 1601 1602 1602 1604 \n", + "55 254 254 254 254 254 254 254 \n", + "56 147 147 147 147 147 147 147 \n", + "57 169 169 169 169 170 170 170 \n", + "58 328 328 328 328 328 328 328 \n", + "59 945 947 947 947 947 947 947 \n", + "60 1276 1276 1276 1276 1276 1276 1276 \n", + "61 1093 1093 1099 1102 1105 1106 1107 \n", + "62 68135 68135 68135 68135 68135 68135 68135 \n", + "63 1019 1019 1019 1019 1019 1019 1019 \n", + "64 235 235 235 235 235 235 235 \n", + "65 653 653 653 653 653 653 653 \n", + "66 937 932 932 932 932 932 932 \n", + "67 155 155 155 155 155 155 155 \n", + "68 149 149 149 149 149 149 149 \n", + "69 45 45 45 45 45 45 45 \n", + "70 75 75 75 75 75 75 75 \n", + "71 18 18 18 18 18 18 18 \n", + "72 309 309 309 309 311 311 311 \n", + "73 792 792 792 792 792 792 792 \n", + "74 673 673 677 677 677 678 678 \n", + "75 198 198 198 198 198 198 198 \n", + "76 577 577 578 578 578 581 582 \n", + "77 192 192 192 192 193 193 193 \n", + "78 1 1 1 1 1 1 1 \n", + "79 76 76 76 76 76 76 76 \n", + "80 185 185 185 185 185 185 185 \n", + "81 1268 1268 1268 1268 1268 1268 1268 \n", + "116 184980 188836 185986 186538 187067 187360 187458 \n", + "120 183879 184121 184472 184924 185450 185750 186109 \n", + "133 157562 160696 164270 167156 169425 171789 173832 \n", + "137 233515 233836 234013 234531 234801 234998 235278 \n", + "139 16837 16867 16911 16958 17000 17039 17060 \n", + "169 46647 46733 46942 47152 47335 47574 47739 \n", + "184 32895 33261 33592 33969 34351 34693 34885 \n", + "201 239932 240326 240660 240978 241310 241550 241717 \n", + "223 277985 279856 281661 283311 284868 286194 287399 \n", + "225 1831821 1851520 1872660 1897380 1920061 1943647 1960897 \n", + "\n", + "[44 rows x 141 columns]\n" + ] + } + ], + "source": [ + "fr=df[(df['Country/Region']=='France')]\n", + "fr=fr['Province/State']\n", + "fr=fr.dropna()\n", + "\n", + "ne=df[(df['Country/Region']=='Netherlands')]\n", + "ne=ne['Province/State']\n", + "ne=ne.dropna()\n", + "\n", + "uk=df[(df['Country/Region']=='United Kingdom')]\n", + "uk=uk['Province/State']\n", + "uk=uk.dropna()\n", + "\n", + "df=df.drop(fr.index)\n", + "df=df.drop(ne.index)\n", + "df=df.drop(uk.index)\n", + "\n", + "\n", + "print(df)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remove Province/State column and compute total daily sum for China" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 \\\n", + "Country/Region \n", + "Belgium 0 0 0 0 0 0 0 \n", + "China 548 641 918 1401 2067 2869 5501 \n", + "France 0 0 2 3 3 3 4 \n", + "Germany 0 0 0 0 0 1 4 \n", + "Hong Kong 0 2 2 5 8 8 8 \n", + "Iran 0 0 0 0 0 0 0 \n", + "Italy 0 0 0 0 0 0 0 \n", + "Japan 2 2 2 2 4 4 7 \n", + "Netherlands 0 0 0 0 0 0 0 \n", + "Portugal 0 0 0 0 0 0 0 \n", + "Spain 0 0 0 0 0 0 0 \n", + "US 1 1 2 2 5 5 5 \n", + "United Kingdom 0 0 0 0 0 0 0 \n", + "\n", + " 1/29/20 1/30/20 1/31/20 ... 5/30/20 5/31/20 6/1/20 \\\n", + "Country/Region ... \n", + "Belgium 0 0 0 ... 58186 58381 58517 \n", + "China 6077 8131 9790 ... 83046 83062 83067 \n", + "France 5 5 5 ... 185616 185851 185952 \n", + "Germany 4 4 5 ... 183189 183410 183594 \n", + "Hong Kong 10 10 12 ... 1082 1084 1087 \n", + "Iran 0 0 0 ... 148950 151466 154445 \n", + "Italy 0 0 2 ... 232664 232997 233197 \n", + "Japan 7 11 15 ... 16716 16751 16787 \n", + "Netherlands 0 0 0 ... 46257 46442 46545 \n", + "Portugal 0 0 0 ... 32203 32500 32700 \n", + "Spain 0 0 0 ... 239228 239479 239638 \n", + "US 5 5 7 ... 1770165 1790172 1811020 \n", + "United Kingdom 0 0 2 ... 272826 274762 276332 \n", + "\n", + " 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 6/8/20 \n", + "Country/Region \n", + "Belgium 58615 58685 58767 58907 59072 59226 59348 \n", + "China 83068 83067 83072 83075 83081 83085 83088 \n", + "France 184980 188836 185986 186538 187067 187360 187458 \n", + "Germany 183879 184121 184472 184924 185450 185750 186109 \n", + "Hong Kong 1093 1093 1099 1102 1105 1106 1107 \n", + "Iran 157562 160696 164270 167156 169425 171789 173832 \n", + "Italy 233515 233836 234013 234531 234801 234998 235278 \n", + "Japan 16837 16867 16911 16958 17000 17039 17060 \n", + "Netherlands 46647 46733 46942 47152 47335 47574 47739 \n", + "Portugal 32895 33261 33592 33969 34351 34693 34885 \n", + "Spain 239932 240326 240660 240978 241310 241550 241717 \n", + "US 1831821 1851520 1872660 1897380 1920061 1943647 1960897 \n", + "United Kingdom 277985 279856 281661 283311 284868 286194 287399 \n", + "\n", + "[13 rows x 139 columns]\n" + ] + } + ], + "source": [ + "df.drop('Province/State', axis = 1, inplace = True)\n", + "grouped=df.groupby('Country/Region')\n", + "df=grouped.sum()\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Construct graphs for the countries above" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax=df.transpose().plot()\n", + "ax.set_xlabel(\"date\")\n", + "ax.set_ylabel(\"confirmed cases\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we make the analogous graph for the Covid-19 incidence in the world" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df=df_total\n", + "df.drop('Province/State', axis = 1, inplace = True)\n", + "df.drop('Country/Region', axis = 1, inplace = True)\n", + "df=df.sum(axis=0)\n", + "\n", + "ax=df.transpose().plot()\n", + "ax.set_xlabel(\"date\")\n", + "ax.set_ylabel(\"confirmed cases\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}