{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Autour du Covid19" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np \n", "import pandas as pd\n", "import isoweekk" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dowload data" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...9/6/209/7/209/8/209/9/209/10/209/11/209/12/209/13/209/14/209/15/20
0NaNAfghanistan33.93911067.709953000000...38398384943852038544385723860638641387163877238815
1NaNAlbania41.15330020.168300000000...10255104061055310704108601102111185113531152011672
2NaNAlgeria28.0339001.659600000000...46364466534693847216474884775248007482544849648734
3NaNAndorra42.5063001.521800000000...1215126112611301130113441344134414381438
4NaNAngola-11.20270017.873900000000...2965298130333092321732793335338834393569
5NaNAntigua and Barbuda17.060800-61.796400000000...95959595959595959595
6NaNArgentina-38.416100-63.616700000000...478792488007500034512293524198535705546481555537565446577338
7NaNArmenia40.06910045.038200000000...44783448454495345152453264550345675458624596946119
8Australian Capital TerritoryAustralia-35.473500149.012400000000...113113113113113113113113113113
9New South WalesAustralia-33.868800151.209300000034...4118412641354142415241574166417041774185
10Northern TerritoryAustralia-12.463400130.845600000000...33333333333333333333
11QueenslandAustralia-27.469800153.025100000000...1133113411431143114511491149114911501149
12South AustraliaAustralia-34.928500138.600700000000...464464465465465466466466466466
13TasmaniaAustralia-42.882100147.327200000000...230230230230230230230230230230
14VictoriaAustralia-37.813600144.963100000011...19574196151968819739197671980019835198721991119943
15Western AustraliaAustralia-31.950500115.860500000000...656658658659659659659659659659
16NaNAustria47.51620014.550100000000...29271295613008130583312473182732696331593354134305
17NaNAzerbaijan40.14310047.576900000000...37329374183755737732378743803738172383273840338517
18NaNBahamas25.025885-78.035889000000...2506254625852721272128142928292830083032
19NaNBahrain26.02750050.550000000000...55415560765677857450582075883959586603076096561643
20NaNBangladesh23.68500090.356300000000...325157327359329251331078332970334762336044337520339332341056
21NaNBarbados13.193900-59.543200000000...178179180180180180180181183184
22NaNBelarus53.70980027.953400000000...72859730317320873402735917378473975741737436074552
23NaNBelgium50.8333004.469936000000...88367887698914189691905689153792478934559430694795
24NaNBelize17.189900-88.497600000000...1194130713611365136514351458148015011528
25NaNBenin9.3077002.315800000000...2213221322132242224222422242226722672267
26NaNBhutan27.51420090.433600000000...228233234234238241244245245246
27NaNBolivia-16.290200-63.588700000000...120769121604122308123345124205125172125982126791127619128286
28NaNBosnia and Herzegovina43.91590017.679100000000...21560216602196122258225442283423138234652363523929
29NaNBotswana-22.32850024.684900000000...2002212621262126225222522252225224632463
..................................................................
236NaNThailand15.870032100.992541235788...3445344634473454346134613473347534753490
237NaNTimor-Leste-8.874217125.727539000000...27272727272727272727
238NaNTogo8.6195000.824800000000...1488149315131528153715481555157215781595
239NaNTrinidad and Tobago10.691800-61.222500000000...2250227723912588269828252993304231413223
240NaNTunisia33.8869179.537499000000...5041512454175417588262596635663573827623
241NaNTurkey38.96370035.243300000000...279806281509283270284943286455288126289635291162292878294620
242NaNUS40.000000-100.000000112255...6276365630062263270096360212639610064436526485123652012265536526593269
243NaNUganda1.37333332.290275000000...3667377639004101429143774703479949785123
244NaNUkraine48.37940031.165600000000...139171141424143914146511149146152373155558158122160679163678
245NaNUnited Arab Emirates23.42407653.847818000000...73984744547509875981769117784278849794898026680940
246AnguillaUnited Kingdom18.220600-63.068600000000...3333333333
247BermudaUnited Kingdom32.307800-64.750500000000...175175175177177177177177177177
248British Virgin IslandsUnited Kingdom18.420700-64.640000000000...63636363636366666666
249Cayman IslandsUnited Kingdom19.313300-81.254600000000...205205205207208208208208208208
250Channel IslandsUnited Kingdom49.372300-2.364400000000...626628629631631633633633633639
251Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...13131313131313131313
252GibraltarUnited Kingdom36.140800-5.353600000000...315315320322323323327330330334
253Isle of ManUnited Kingdom54.236100-4.548100000000...337337337337337337337337339339
254MontserratUnited Kingdom16.742498-62.187366000000...13131313131313131313
255Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...598599614628638641641646648650
256NaNUnited Kingdom55.378100-3.436000000000...347152350100352560355219358138361677365174368504371125374228
257NaNUruguay-32.522800-55.765800000000...1679169317121741175917731780180818121827
258NaNUzbekistan41.37749164.585262000000...43587438934428144930454734616046721472874783648429
259NaNVenezuela6.423800-66.589700000000...53289543505556356751578235866359630605406156962655
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"metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\")\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[datetime.date(2020, 1, 22), datetime.date(2020, 1, 23), datetime.date(2020, 1, 24), datetime.date(2020, 1, 25), datetime.date(2020, 1, 26), datetime.date(2020, 1, 27), datetime.date(2020, 1, 28), datetime.date(2020, 1, 29), datetime.date(2020, 1, 30), datetime.date(2020, 1, 31), datetime.date(2020, 2, 1), datetime.date(2020, 2, 2), datetime.date(2020, 2, 3), datetime.date(2020, 2, 4), datetime.date(2020, 2, 5), datetime.date(2020, 2, 6), datetime.date(2020, 2, 7), datetime.date(2020, 2, 8), datetime.date(2020, 2, 9), datetime.date(2020, 2, 10), datetime.date(2020, 2, 11), datetime.date(2020, 2, 12), datetime.date(2020, 2, 13), datetime.date(2020, 2, 14), datetime.date(2020, 2, 15), datetime.date(2020, 2, 16), datetime.date(2020, 2, 17), datetime.date(2020, 2, 18), datetime.date(2020, 2, 19), datetime.date(2020, 2, 20), datetime.date(2020, 2, 21), datetime.date(2020, 2, 22), datetime.date(2020, 2, 23), datetime.date(2020, 2, 24), datetime.date(2020, 2, 25), datetime.date(2020, 2, 26), datetime.date(2020, 2, 27), datetime.date(2020, 2, 28), datetime.date(2020, 2, 29), datetime.date(2020, 3, 1), datetime.date(2020, 3, 2), datetime.date(2020, 3, 3), datetime.date(2020, 3, 4), datetime.date(2020, 3, 5), datetime.date(2020, 3, 6), datetime.date(2020, 3, 7), datetime.date(2020, 3, 8), datetime.date(2020, 3, 9), datetime.date(2020, 3, 10), datetime.date(2020, 3, 11), datetime.date(2020, 3, 12), datetime.date(2020, 3, 13), datetime.date(2020, 3, 14), datetime.date(2020, 3, 15), datetime.date(2020, 3, 16), datetime.date(2020, 3, 17), datetime.date(2020, 3, 18), datetime.date(2020, 3, 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datetime.date(2020, 7, 6), datetime.date(2020, 7, 7), datetime.date(2020, 7, 8), datetime.date(2020, 7, 9), datetime.date(2020, 7, 10), datetime.date(2020, 7, 11), datetime.date(2020, 7, 12), datetime.date(2020, 7, 13), datetime.date(2020, 7, 14), datetime.date(2020, 7, 15), datetime.date(2020, 7, 16), datetime.date(2020, 7, 17), datetime.date(2020, 7, 18), datetime.date(2020, 7, 19), datetime.date(2020, 7, 20), datetime.date(2020, 7, 21), datetime.date(2020, 7, 22), datetime.date(2020, 7, 23), datetime.date(2020, 7, 24), datetime.date(2020, 7, 25), datetime.date(2020, 7, 26), datetime.date(2020, 7, 27), datetime.date(2020, 7, 28), datetime.date(2020, 7, 29), datetime.date(2020, 7, 30), datetime.date(2020, 7, 31), datetime.date(2020, 8, 1), datetime.date(2020, 8, 2), datetime.date(2020, 8, 3), datetime.date(2020, 8, 4), datetime.date(2020, 8, 5), datetime.date(2020, 8, 6), datetime.date(2020, 8, 7), datetime.date(2020, 8, 8), datetime.date(2020, 8, 9), datetime.date(2020, 8, 10), datetime.date(2020, 8, 11), datetime.date(2020, 8, 12), datetime.date(2020, 8, 13), datetime.date(2020, 8, 14), datetime.date(2020, 8, 15), datetime.date(2020, 8, 16), datetime.date(2020, 8, 17), datetime.date(2020, 8, 18), datetime.date(2020, 8, 19), datetime.date(2020, 8, 20), datetime.date(2020, 8, 21), datetime.date(2020, 8, 22), datetime.date(2020, 8, 23), datetime.date(2020, 8, 24), datetime.date(2020, 8, 25), datetime.date(2020, 8, 26), datetime.date(2020, 8, 27), datetime.date(2020, 8, 28), datetime.date(2020, 8, 29), datetime.date(2020, 8, 30), datetime.date(2020, 8, 31), datetime.date(2020, 9, 1), datetime.date(2020, 9, 2), datetime.date(2020, 9, 3), datetime.date(2020, 9, 4), datetime.date(2020, 9, 5), datetime.date(2020, 9, 6), datetime.date(2020, 9, 7), datetime.date(2020, 9, 8), datetime.date(2020, 9, 9), datetime.date(2020, 9, 10), datetime.date(2020, 9, 11), datetime.date(2020, 9, 12), datetime.date(2020, 9, 13), datetime.date(2020, 9, 14), datetime.date(2020, 9, 15)]\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "date1=[]\n", "date2=[]\n", "belgium_cumulated_list = []\n", "\n", "for i in range(4, len(raw_data.keys())):\n", " date1.append(raw_data.keys()[i])\n", " date2.append(pd.to_datetime(raw_data.keys()[i]).date())\n", "print(date2)\n", "b=0\n", "for date in date1:\n", " raw_data.loc[raw_data['Country/Region'] == 'Belgium'][date].values[0]\n", " belgium_cumulated_list.append(a)\n", " \n", "plt.plot(date2, belgium_cumulated_list);\n", " \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }