diff --git a/module3/exo3/module3_exo3_exercice_en_sujet7_SARS-COV2_bis.ipynb b/module3/exo3/module3_exo3_exercice_en_sujet7_SARS-COV2_bis.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e62a3c56ded2c55e52a6782f3abfec7a29128cc9 --- /dev/null +++ b/module3/exo3/module3_exo3_exercice_en_sujet7_SARS-COV2_bis.ipynb @@ -0,0 +1,12841 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sujet 7 : Autour du SARS-CoV-2 (Covid-19)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyse rapide des donnees recuperees\n", + "Imports des packages necessaires a l'analyse" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "## import\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.pyplot import cm\n", + "import pandas as pd\n", + "import numpy as np\n", + "import datetime\n", + "import os.path\n", + "from urllib.request import urlretrieve\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour cette analyse, nous utiliserons les données compilées par le Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) mises à disposition sur GitHub, plus particulièrement les données __time_series_covid19_confirmed_global.csv__\n", + "\n", + "Ces donnees sont disponibles aussi à l'adresse : 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", + "Nous commencons par verifier la presence d'une copie des donnees. Si elle n'existe pas, nous allons la recuperer sur le site et creons la copie locale.\n" + ] + }, + { + "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", + "data_local = \"time_series_covid19_confirmed_global.csv\"\n", + "if not os.path.isfile(data_local):\n", + " urlretrieve(data_url, data_local)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1ere visualisation des donnees : \n", + "Comptage rapide du nombre de donnees presentes :\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(289, 1147)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_local)\n", + "raw_data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le fichier contient 289 lignes pour 1147 colonnes\n", + "\n", + "On regarde le formattage des donnees a savoir \n", + "* chaque pays/province est indiquee en ligne \n", + "* les donnees des colonnes 1 a 4 correspondent aux infos du pays/region converne\n", + "* les donnees des colonnes 5 et suivantes correspondent aux nombres de cas cummule par jour (jour indique en etiquette de la colonne)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
0NaNAfghanistan33.93911067.709953000000...209322209340209358209362209369209390209406209436209451209451
1NaNAlbania41.15330020.168300000000...334391334408334408334427334427334427334427334427334443334457
2NaNAlgeria28.0339001.659600000000...271441271448271463271469271469271477271477271490271494271496
3NaNAndorra42.5063001.521800000000...47866478754787547875478754787547875478754789047890
4NaNAngola-11.20270017.873900000000...105255105277105277105277105277105277105277105277105288105288
5NaNAntarctica-71.94990023.347000000000...11111111111111111111
6NaNAntigua and Barbuda17.060800-61.796400000000...9106910691069106910691069106910691069106
7NaNArgentina-38.416100-63.616700000000...10044125100441251004412510044125100441251004412510044957100449571004495710044957
8NaNArmenia40.06910045.038200000000...446819446819446819446819446819446819446819446819447308447308
9Australian Capital TerritoryAustralia-35.473500149.012400000000...232018232018232619232619232619232619232619232619232619232974
10New South WalesAustralia-33.868800151.209300000034...3900969390096939081293908129390812939081293908129390812939081293915992
11Northern TerritoryAustralia-12.463400130.845600000000...104931104931105021105021105021105021105021105021105021105111
12QueenslandAustralia-27.469800153.025100000000...1796633179663318002361800236180023618002361800236180023618002361800236
13South AustraliaAustralia-34.928500138.600700000000...880207880207881911881911881911881911881911881911881911883620
14TasmaniaAustralia-42.882100147.327200000000...286264286264286264286897286897286897286897286897286897287507
15VictoriaAustralia-37.813600144.963100000011...2874262287426228772602877260287726028772602877260287726028772602880559
16Western AustraliaAustralia-31.950500115.860500000000...1291077129107712934611293461129346112934611293461129346112934611293461
17NaNAustria47.51620014.550100000000...5911294591961659261485931247593666659409355943417594941859558605961143
18NaNAzerbaijan40.14310047.576900000000...828548828588828628828648828682828721828730828783828819828825
19NaNBahamas25.025885-78.035889000000...37491374913749137491374913749137491374913749137491
20NaNBahrain26.02750050.550000000000...707480707828708061708532708768709230709230709858710306710693
21NaNBangladesh23.68500090.356300000000...2037773203782920378292037829203782920378292037829203782920378712037871
22NaNBarbados13.193900-59.543200000000...106645106645106645106645106645106645106645106645106645106798
23NaNBelarus53.70980027.953400000000...994037994037994037994037994037994037994037994037994037994037
24NaNBelgium50.8333004.469936000000...4717655471765547277954727795472779547277954727795472779547277954739365
25NaNBelize17.189900-88.497600000000...70757707577075770757707577075770757707577075770757
26NaNBenin9.3077002.315800000000...27990279902799027990279902799027990279992799927999
27NaNBhutan27.51420090.433600000000...62615626206262062620626206262062620626206262762627
28NaNBolivia-16.290200-63.588700000000...1193009119325611934181193650119381511939081193970119406911941871194277
29NaNBosnia and Herzegovina43.91590017.679100000000...401575401636401636401636401636401636401636401636401729401729
..................................................................
259NaNTuvalu-7.109500177.649300000000...2805280528052805280528052805280528052805
260NaNUS40.000000-100.000000112255...103443455103533872103589757103648690103650837103646975103655539103690910103755771103802702
261NaNUganda1.37333332.290275000000...170504170504170504170504170504170504170504170504170544170544
262NaNUkraine48.37940031.165600000000...5693846570124957013335701474570160257017435701855570195957118185711929
263NaNUnited Arab Emirates23.42407653.847818000000...1051998105212210522471052382105251910526641052664105292610530681053213
264AnguillaUnited Kingdom18.220600-63.068600000000...3904390439043904390439043904390439043904
265BermudaUnited Kingdom32.307800-64.750500000000...18799188141881418814188141881418814188141882818828
266British Virgin IslandsUnited Kingdom18.420700-64.640000000000...7305730573057305730573057305730573057305
267Cayman IslandsUnited Kingdom19.313300-81.254600000000...31472314723147231472314723147231472314723147231472
268Channel IslandsUnited Kingdom49.372300-2.364400000000...0000000000
269Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...1930193019301930193019301930193019301930
270GibraltarUnited Kingdom36.140800-5.353600000000...20423204232042320433204332043320433204332043320433
271GuernseyUnited Kingdom49.448196-2.589490000000...34867349293492934929349293492934929349293499134991
272Isle of ManUnited Kingdom54.236100-4.548100000000...38008380083800838008380083800838008380083800838008
273JerseyUnited Kingdom49.213800-2.135800000000...66391663916639166391663916639166391663916639166391
274MontserratUnited Kingdom16.742498-62.187366000000...1403140314031403140314031403140314031403
275Pitcairn IslandsUnited Kingdom-24.376800-128.324200000000...4444444444
276Saint Helena, Ascension and Tristan da CunhaUnited Kingdom-7.946700-14.355900000000...2166216621662166216621662166216621662166
277Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...6551655165516551655165516551655765576561
278NaNUnited Kingdom55.378100-3.436000000000...24370150243701502439653024396530243965302439653024396530243965302439653024425309
279NaNUruguay-32.522800-55.765800000000...1034303103430310343031034303103430310343031034303103430310343031034303
280NaNUzbekistan41.37749164.585262000000...250932251071251071251071251071251071251071251071251247251247
281NaNVanuatu-15.376700166.959200000000...12014120141201412014120141201412014120141201412014
282NaNVenezuela6.423800-66.589700000000...551981551986551986552014552051552051552125552157552157552162
283NaNVietnam14.058324108.277199022222...11526917115269261152693711526950115269621152696611526966115269861152699411526994
284NaNWest Bank and Gaza31.95220035.233200000000...703228703228703228703228703228703228703228703228703228703228
285NaNWinter Olympics 202239.904200116.407400000000...535535535535535535535535535535
286NaNYemen15.55272748.516388000000...11945119451194511945119451194511945119451194511945
287NaNZambia-13.13389727.849332000000...343012343012343079343079343079343135343135343135343135343135
288NaNZimbabwe-19.01543829.154857000000...263921264127264127264127264127264127264127264127264276264276
\n", + "

289 rows × 1147 columns

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" + ], + "text/plain": [ + " Province/State Country/Region \\\n", + "0 NaN Afghanistan \n", + "1 NaN Albania \n", + "2 NaN Algeria \n", + "3 NaN Andorra \n", + "4 NaN Angola \n", + "5 NaN Antarctica \n", + "6 NaN Antigua and Barbuda \n", + "7 NaN Argentina \n", + "8 NaN Armenia \n", + "9 Australian Capital Territory Australia \n", + "10 New South Wales Australia \n", + "11 Northern Territory Australia \n", + "12 Queensland Australia \n", + "13 South Australia Australia \n", + "14 Tasmania Australia \n", + "15 Victoria Australia \n", + "16 Western Australia Australia \n", + "17 NaN Austria \n", + "18 NaN Azerbaijan \n", + "19 NaN Bahamas \n", + "20 NaN Bahrain \n", + "21 NaN Bangladesh \n", + "22 NaN Barbados \n", + "23 NaN Belarus \n", + "24 NaN Belgium \n", + "25 NaN Belize \n", + "26 NaN Benin \n", + "27 NaN Bhutan \n", + "28 NaN Bolivia \n", + "29 NaN Bosnia and Herzegovina \n", + ".. ... ... \n", + "259 NaN Tuvalu \n", + "260 NaN US \n", + "261 NaN Uganda \n", + "262 NaN Ukraine \n", + "263 NaN United Arab Emirates \n", + "264 Anguilla United Kingdom \n", + "265 Bermuda United Kingdom \n", + "266 British Virgin Islands United Kingdom \n", + "267 Cayman Islands United Kingdom \n", + "268 Channel Islands United Kingdom \n", + "269 Falkland Islands (Malvinas) United Kingdom \n", + "270 Gibraltar United Kingdom \n", + "271 Guernsey United Kingdom \n", + "272 Isle of Man United Kingdom \n", + "273 Jersey United Kingdom \n", + "274 Montserrat United Kingdom \n", + "275 Pitcairn Islands United Kingdom \n", + "276 Saint Helena, Ascension and Tristan da Cunha United Kingdom \n", + "277 Turks and Caicos Islands United Kingdom \n", + "278 NaN United Kingdom \n", + "279 NaN Uruguay \n", + "280 NaN Uzbekistan \n", + "281 NaN Vanuatu \n", + "282 NaN Venezuela \n", + "283 NaN Vietnam \n", + "284 NaN West Bank and Gaza \n", + "285 NaN Winter Olympics 2022 \n", + "286 NaN Yemen \n", + "287 NaN Zambia \n", + "288 NaN Zimbabwe \n", + "\n", + " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n", + "0 33.939110 67.709953 0 0 0 0 0 \n", + "1 41.153300 20.168300 0 0 0 0 0 \n", + "2 28.033900 1.659600 0 0 0 0 0 \n", + "3 42.506300 1.521800 0 0 0 0 0 \n", + "4 -11.202700 17.873900 0 0 0 0 0 \n", + "5 -71.949900 23.347000 0 0 0 0 0 \n", + "6 17.060800 -61.796400 0 0 0 0 0 \n", + "7 -38.416100 -63.616700 0 0 0 0 0 \n", + "8 40.069100 45.038200 0 0 0 0 0 \n", + "9 -35.473500 149.012400 0 0 0 0 0 \n", + "10 -33.868800 151.209300 0 0 0 0 3 \n", + "11 -12.463400 130.845600 0 0 0 0 0 \n", + "12 -27.469800 153.025100 0 0 0 0 0 \n", + "13 -34.928500 138.600700 0 0 0 0 0 \n", + "14 -42.882100 147.327200 0 0 0 0 0 \n", + "15 -37.813600 144.963100 0 0 0 0 1 \n", + "16 -31.950500 115.860500 0 0 0 0 0 \n", + "17 47.516200 14.550100 0 0 0 0 0 \n", + "18 40.143100 47.576900 0 0 0 0 0 \n", + "19 25.025885 -78.035889 0 0 0 0 0 \n", + "20 26.027500 50.550000 0 0 0 0 0 \n", + "21 23.685000 90.356300 0 0 0 0 0 \n", + "22 13.193900 -59.543200 0 0 0 0 0 \n", + "23 53.709800 27.953400 0 0 0 0 0 \n", + "24 50.833300 4.469936 0 0 0 0 0 \n", + "25 17.189900 -88.497600 0 0 0 0 0 \n", + "26 9.307700 2.315800 0 0 0 0 0 \n", + "27 27.514200 90.433600 0 0 0 0 0 \n", + "28 -16.290200 -63.588700 0 0 0 0 0 \n", + "29 43.915900 17.679100 0 0 0 0 0 \n", + ".. ... ... ... ... ... ... ... \n", + "259 -7.109500 177.649300 0 0 0 0 0 \n", + "260 40.000000 -100.000000 1 1 2 2 5 \n", + "261 1.373333 32.290275 0 0 0 0 0 \n", + "262 48.379400 31.165600 0 0 0 0 0 \n", + "263 23.424076 53.847818 0 0 0 0 0 \n", + "264 18.220600 -63.068600 0 0 0 0 0 \n", + "265 32.307800 -64.750500 0 0 0 0 0 \n", + "266 18.420700 -64.640000 0 0 0 0 0 \n", + "267 19.313300 -81.254600 0 0 0 0 0 \n", + "268 49.372300 -2.364400 0 0 0 0 0 \n", + "269 -51.796300 -59.523600 0 0 0 0 0 \n", + "270 36.140800 -5.353600 0 0 0 0 0 \n", + "271 49.448196 -2.589490 0 0 0 0 0 \n", + "272 54.236100 -4.548100 0 0 0 0 0 \n", + "273 49.213800 -2.135800 0 0 0 0 0 \n", + "274 16.742498 -62.187366 0 0 0 0 0 \n", + "275 -24.376800 -128.324200 0 0 0 0 0 \n", + "276 -7.946700 -14.355900 0 0 0 0 0 \n", + "277 21.694000 -71.797900 0 0 0 0 0 \n", + "278 55.378100 -3.436000 0 0 0 0 0 \n", + "279 -32.522800 -55.765800 0 0 0 0 0 \n", + "280 41.377491 64.585262 0 0 0 0 0 \n", + "281 -15.376700 166.959200 0 0 0 0 0 \n", + "282 6.423800 -66.589700 0 0 0 0 0 \n", + "283 14.058324 108.277199 0 2 2 2 2 \n", + "284 31.952200 35.233200 0 0 0 0 0 \n", + "285 39.904200 116.407400 0 0 0 0 0 \n", + "286 15.552727 48.516388 0 0 0 0 0 \n", + "287 -13.133897 27.849332 0 0 0 0 0 \n", + "288 -19.015438 29.154857 0 0 0 0 0 \n", + "\n", + " 1/27/20 ... 2/28/23 3/1/23 3/2/23 3/3/23 \\\n", + "0 0 ... 209322 209340 209358 209362 \n", + "1 0 ... 334391 334408 334408 334427 \n", + "2 0 ... 271441 271448 271463 271469 \n", + "3 0 ... 47866 47875 47875 47875 \n", + "4 0 ... 105255 105277 105277 105277 \n", + "5 0 ... 11 11 11 11 \n", + "6 0 ... 9106 9106 9106 9106 \n", + "7 0 ... 10044125 10044125 10044125 10044125 \n", + "8 0 ... 446819 446819 446819 446819 \n", + "9 0 ... 232018 232018 232619 232619 \n", + "10 4 ... 3900969 3900969 3908129 3908129 \n", + "11 0 ... 104931 104931 105021 105021 \n", + "12 0 ... 1796633 1796633 1800236 1800236 \n", + "13 0 ... 880207 880207 881911 881911 \n", + "14 0 ... 286264 286264 286264 286897 \n", + "15 1 ... 2874262 2874262 2877260 2877260 \n", + "16 0 ... 1291077 1291077 1293461 1293461 \n", + "17 0 ... 5911294 5919616 5926148 5931247 \n", + "18 0 ... 828548 828588 828628 828648 \n", + "19 0 ... 37491 37491 37491 37491 \n", + "20 0 ... 707480 707828 708061 708532 \n", + "21 0 ... 2037773 2037829 2037829 2037829 \n", + "22 0 ... 106645 106645 106645 106645 \n", + "23 0 ... 994037 994037 994037 994037 \n", + "24 0 ... 4717655 4717655 4727795 4727795 \n", + "25 0 ... 70757 70757 70757 70757 \n", + "26 0 ... 27990 27990 27990 27990 \n", + "27 0 ... 62615 62620 62620 62620 \n", + "28 0 ... 1193009 1193256 1193418 1193650 \n", + "29 0 ... 401575 401636 401636 401636 \n", + ".. 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251071 251071 251071 251071 251247 251247 \n", + "281 12014 12014 12014 12014 12014 12014 \n", + "282 552051 552051 552125 552157 552157 552162 \n", + "283 11526962 11526966 11526966 11526986 11526994 11526994 \n", + "284 703228 703228 703228 703228 703228 703228 \n", + "285 535 535 535 535 535 535 \n", + "286 11945 11945 11945 11945 11945 11945 \n", + "287 343079 343135 343135 343135 343135 343135 \n", + "288 264127 264127 264127 264127 264276 264276 \n", + "\n", + "[289 rows x 1147 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Verification rapide de l'integrite des donnes\n", + "\n", + "#### Verification d'absence de donnees ou de donnees negatives\n", + "On regarde ici si on a une colonne de donnees (colonne 5 et suivante) dont le comptage serait negatif.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "columns_to_study = raw_data.iloc[:,4:].columns\n", + "\n", + "for i in raw_data.index : \n", + " for d in range(len(columns_to_study[:-1])):\n", + " if (pd.isna(raw_data.iloc[i,d+4]) or raw_data.iloc[i,d+4]<0):\n", + " print(raw_data.iloc[i,d+4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--> aucun retour donc aucune valeur manquante ou negative\n", + "\n", + "#### Est-ce qu'il y a des donnees qui sont superieures a celle du jour suivant ?\n", + "On peut aussi regarder si chaque valeur est inferieure a la valeur suivante." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Il y a 362 donnees superieurs a celle de la donnee suivante\n" + ] + } + ], + "source": [ + "flag = 0\n", + "table_of_errors = []\n", + "for i in raw_data.index : \n", + " for d in range(len(columns_to_study[:-1])):\n", + " if (int(raw_data.iloc[i,d+4]) > int(raw_data.iloc[i,d+4 +1])):\n", + " data_problem = (raw_data.iloc[i, 1:2], columns_to_study[d], columns_to_study[d+1])\n", + " table_of_errors.append((i,d+4))\n", + " table_of_errors.append((i,d+5))\n", + " flag = flag +1\n", + "print(\"Il y a %s donnees superieurs a celle de la donnee suivante\" % str(flag))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--> il y a donc bien des valeurs possiblement incoherentes avec des jours ou le taux cummule de cas decroit par rapport a la veille. Est-ce une rectification due a un mauvais diagnostique initial ? une modification de la methode de comptage ?\n", + "\n", + "Dans le doute, on \"supprime\" les 2 donnees en les fixant a na grace a la sauvegarde des couple (row, col) dans table_of_errors" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
0NaNAfghanistan33.93911067.709953000000...209322.0209340.0209358.0209362209369.0209390.0209406.0209436209451209451
1NaNAlbania41.15330020.168300000000...334391.0334408.0334408.0334427334427.0334427.0334427.0334427334443334457
2NaNAlgeria28.0339001.659600000000...271441.0271448.0271463.0271469271469.0271477.0271477.0271490271494271496
3NaNAndorra42.5063001.521800000000...47866.047875.047875.04787547875.047875.047875.0478754789047890
4NaNAngola-11.20270017.873900000000...105255.0105277.0105277.0105277105277.0105277.0105277.0105277105288105288
5NaNAntarctica-71.94990023.347000000000...11.011.011.01111.011.011.0111111
6NaNAntigua and Barbuda17.060800-61.796400000000...9106.09106.09106.091069106.09106.09106.0910691069106
7NaNArgentina-38.416100-63.616700000000...10044125.010044125.010044125.01004412510044125.010044125.010044957.0100449571004495710044957
8NaNArmenia40.06910045.038200000000...446819.0446819.0446819.0446819446819.0446819.0446819.0446819447308447308
9Australian Capital TerritoryAustralia-35.473500149.012400000000...232018.0232018.0232619.0232619232619.0232619.0232619.0232619232619232974
10New South WalesAustralia-33.868800151.209300000034...3900969.03900969.03908129.039081293908129.03908129.03908129.0390812939081293915992
11Northern TerritoryAustralia-12.463400130.845600000000...104931.0104931.0105021.0105021105021.0105021.0105021.0105021105021105111
12QueenslandAustralia-27.469800153.025100000000...1796633.01796633.01800236.018002361800236.01800236.01800236.0180023618002361800236
13South AustraliaAustralia-34.928500138.600700000000...880207.0880207.0881911.0881911881911.0881911.0881911.0881911881911883620
14TasmaniaAustralia-42.882100147.327200000000...286264.0286264.0286264.0286897286897.0286897.0286897.0286897286897287507
15VictoriaAustralia-37.813600144.963100000011...2874262.02874262.02877260.028772602877260.02877260.02877260.0287726028772602880559
16Western AustraliaAustralia-31.950500115.860500000000...1291077.01291077.01293461.012934611293461.01293461.01293461.0129346112934611293461
17NaNAustria47.51620014.550100000000...5911294.05919616.05926148.059312475936666.05940935.05943417.0594941859558605961143
18NaNAzerbaijan40.14310047.576900000000...828548.0828588.0828628.0828648828682.0828721.0828730.0828783828819828825
19NaNBahamas25.025885-78.035889000000...37491.037491.037491.03749137491.037491.037491.0374913749137491
20NaNBahrain26.02750050.550000000000...707480.0707828.0708061.0708532708768.0709230.0709230.0709858710306710693
21NaNBangladesh23.68500090.356300000000...2037773.02037829.02037829.020378292037829.02037829.02037829.0203782920378712037871
22NaNBarbados13.193900-59.543200000000...106645.0106645.0106645.0106645106645.0106645.0106645.0106645106645106798
23NaNBelarus53.70980027.953400000000...994037.0994037.0994037.0994037994037.0994037.0994037.0994037994037994037
24NaNBelgium50.8333004.469936000000...4717655.04717655.04727795.047277954727795.04727795.04727795.0472779547277954739365
25NaNBelize17.189900-88.497600000000...70757.070757.070757.07075770757.070757.070757.0707577075770757
26NaNBenin9.3077002.315800000000...27990.027990.027990.02799027990.027990.027990.0279992799927999
27NaNBhutan27.51420090.433600000000...62615.062620.062620.06262062620.062620.062620.0626206262762627
28NaNBolivia-16.290200-63.588700000000...1193009.01193256.01193418.011936501193815.01193908.01193970.0119406911941871194277
29NaNBosnia and Herzegovina43.91590017.679100000000...401575.0401636.0401636.0401636401636.0401636.0401636.0401636401729401729
..................................................................
259NaNTuvalu-7.109500177.649300000000...2805.02805.02805.028052805.02805.02805.0280528052805
260NaNUS40.000000-100.000000112255...103443455.0103533872.0103589757.0103648690NaNNaN103655539.0103690910103755771103802702
261NaNUganda1.37333332.290275000000...170504.0170504.0170504.0170504170504.0170504.0170504.0170504170544170544
262NaNUkraine48.37940031.165600000000...5693846.05701249.05701333.057014745701602.05701743.05701855.0570195957118185711929
263NaNUnited Arab Emirates23.42407653.847818000000...1051998.01052122.01052247.010523821052519.01052664.01052664.0105292610530681053213
264AnguillaUnited Kingdom18.220600-63.068600000000...3904.03904.03904.039043904.03904.03904.0390439043904
265BermudaUnited Kingdom32.307800-64.750500000000...18799.018814.018814.01881418814.018814.018814.0188141882818828
266British Virgin IslandsUnited Kingdom18.420700-64.640000000000...7305.07305.07305.073057305.07305.07305.0730573057305
267Cayman IslandsUnited Kingdom19.313300-81.254600000000...31472.031472.031472.03147231472.031472.031472.0314723147231472
268Channel IslandsUnited Kingdom49.372300-2.364400000000...0.00.00.000.00.00.0000
269Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...1930.01930.01930.019301930.01930.01930.0193019301930
270GibraltarUnited Kingdom36.140800-5.353600000000...20423.020423.020423.02043320433.020433.020433.0204332043320433
271GuernseyUnited Kingdom49.448196-2.589490000000...34867.034929.034929.03492934929.034929.034929.0349293499134991
272Isle of ManUnited Kingdom54.236100-4.548100000000...38008.038008.038008.03800838008.038008.038008.0380083800838008
273JerseyUnited Kingdom49.213800-2.135800000000...66391.066391.066391.06639166391.066391.066391.0663916639166391
274MontserratUnited Kingdom16.742498-62.187366000000...1403.01403.01403.014031403.01403.01403.0140314031403
275Pitcairn IslandsUnited Kingdom-24.376800-128.324200000000...4.04.04.044.04.04.0444
276Saint Helena, Ascension and Tristan da CunhaUnited Kingdom-7.946700-14.355900000000...2166.02166.02166.021662166.02166.02166.0216621662166
277Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...6551.06551.06551.065516551.06551.06551.0655765576561
278NaNUnited Kingdom55.378100-3.436000000000...24370150.024370150.024396530.02439653024396530.024396530.024396530.0243965302439653024425309
279NaNUruguay-32.522800-55.765800000000...1034303.01034303.01034303.010343031034303.01034303.01034303.0103430310343031034303
280NaNUzbekistan41.37749164.585262000000...250932.0251071.0251071.0251071251071.0251071.0251071.0251071251247251247
281NaNVanuatu-15.376700166.959200000000...12014.012014.012014.01201412014.012014.012014.0120141201412014
282NaNVenezuela6.423800-66.589700000000...551981.0551986.0551986.0552014552051.0552051.0552125.0552157552157552162
283NaNVietnam14.058324108.277199022222...11526917.011526926.011526937.01152695011526962.011526966.011526966.0115269861152699411526994
284NaNWest Bank and Gaza31.95220035.233200000000...703228.0703228.0703228.0703228703228.0703228.0703228.0703228703228703228
285NaNWinter Olympics 202239.904200116.407400000000...535.0535.0535.0535535.0535.0535.0535535535
286NaNYemen15.55272748.516388000000...11945.011945.011945.01194511945.011945.011945.0119451194511945
287NaNZambia-13.13389727.849332000000...343012.0343012.0343079.0343079343079.0343135.0343135.0343135343135343135
288NaNZimbabwe-19.01543829.154857000000...263921.0264127.0264127.0264127264127.0264127.0264127.0264127264276264276
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289 rows × 1147 columns

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" + ], + "text/plain": [ + " Province/State Country/Region \\\n", + "0 NaN Afghanistan \n", + "1 NaN Albania \n", + "2 NaN Algeria \n", + "3 NaN Andorra \n", + "4 NaN Angola \n", + "5 NaN Antarctica \n", + "6 NaN Antigua and Barbuda \n", + "7 NaN Argentina \n", + "8 NaN Armenia \n", + "9 Australian Capital Territory Australia \n", + "10 New South Wales Australia \n", + "11 Northern Territory Australia \n", + "12 Queensland Australia \n", + "13 South Australia Australia \n", + "14 Tasmania Australia \n", + "15 Victoria Australia \n", + "16 Western Australia Australia \n", + "17 NaN Austria \n", + "18 NaN Azerbaijan \n", + "19 NaN Bahamas \n", + "20 NaN Bahrain \n", + "21 NaN Bangladesh \n", + "22 NaN Barbados \n", + "23 NaN Belarus \n", + "24 NaN Belgium \n", + "25 NaN Belize \n", + "26 NaN Benin \n", + "27 NaN Bhutan \n", + "28 NaN Bolivia \n", + "29 NaN Bosnia and Herzegovina \n", + ".. ... ... \n", + "259 NaN Tuvalu \n", + "260 NaN US \n", + "261 NaN Uganda \n", + "262 NaN Ukraine \n", + "263 NaN United Arab Emirates \n", + "264 Anguilla United Kingdom \n", + "265 Bermuda United Kingdom \n", + "266 British Virgin Islands United Kingdom \n", + "267 Cayman Islands United Kingdom \n", + "268 Channel Islands United Kingdom \n", + "269 Falkland Islands (Malvinas) United Kingdom \n", + "270 Gibraltar United Kingdom \n", + "271 Guernsey United Kingdom \n", + "272 Isle of Man United Kingdom \n", + "273 Jersey United Kingdom \n", + "274 Montserrat United Kingdom \n", + "275 Pitcairn Islands United Kingdom \n", + "276 Saint Helena, Ascension and Tristan da Cunha United Kingdom \n", + "277 Turks and Caicos Islands United Kingdom \n", + "278 NaN United Kingdom \n", + "279 NaN Uruguay \n", + "280 NaN Uzbekistan \n", + "281 NaN Vanuatu \n", + "282 NaN Venezuela \n", + "283 NaN Vietnam \n", + "284 NaN West Bank and Gaza \n", + "285 NaN Winter Olympics 2022 \n", + "286 NaN Yemen \n", + "287 NaN Zambia \n", + "288 NaN Zimbabwe \n", + "\n", + " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n", + "0 33.939110 67.709953 0 0 0 0 0 \n", + "1 41.153300 20.168300 0 0 0 0 0 \n", + "2 28.033900 1.659600 0 0 0 0 0 \n", + "3 42.506300 1.521800 0 0 0 0 0 \n", + "4 -11.202700 17.873900 0 0 0 0 0 \n", + "5 -71.949900 23.347000 0 0 0 0 0 \n", + "6 17.060800 -61.796400 0 0 0 0 0 \n", + "7 -38.416100 -63.616700 0 0 0 0 0 \n", + "8 40.069100 45.038200 0 0 0 0 0 \n", + "9 -35.473500 149.012400 0 0 0 0 0 \n", + "10 -33.868800 151.209300 0 0 0 0 3 \n", + "11 -12.463400 130.845600 0 0 0 0 0 \n", + "12 -27.469800 153.025100 0 0 0 0 0 \n", + "13 -34.928500 138.600700 0 0 0 0 0 \n", + "14 -42.882100 147.327200 0 0 0 0 0 \n", + "15 -37.813600 144.963100 0 0 0 0 1 \n", + "16 -31.950500 115.860500 0 0 0 0 0 \n", + "17 47.516200 14.550100 0 0 0 0 0 \n", + "18 40.143100 47.576900 0 0 0 0 0 \n", + "19 25.025885 -78.035889 0 0 0 0 0 \n", + "20 26.027500 50.550000 0 0 0 0 0 \n", + "21 23.685000 90.356300 0 0 0 0 0 \n", + "22 13.193900 -59.543200 0 0 0 0 0 \n", + "23 53.709800 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4 \n", + "276 0 ... 2166.0 2166.0 2166.0 2166 \n", + "277 0 ... 6551.0 6551.0 6551.0 6551 \n", + "278 0 ... 24370150.0 24370150.0 24396530.0 24396530 \n", + "279 0 ... 1034303.0 1034303.0 1034303.0 1034303 \n", + "280 0 ... 250932.0 251071.0 251071.0 251071 \n", + "281 0 ... 12014.0 12014.0 12014.0 12014 \n", + "282 0 ... 551981.0 551986.0 551986.0 552014 \n", + "283 2 ... 11526917.0 11526926.0 11526937.0 11526950 \n", + "284 0 ... 703228.0 703228.0 703228.0 703228 \n", + "285 0 ... 535.0 535.0 535.0 535 \n", + "286 0 ... 11945.0 11945.0 11945.0 11945 \n", + "287 0 ... 343012.0 343012.0 343079.0 343079 \n", + "288 0 ... 263921.0 264127.0 264127.0 264127 \n", + "\n", + " 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", + "0 209369.0 209390.0 209406.0 209436 209451 209451 \n", + "1 334427.0 334427.0 334427.0 334427 334443 334457 \n", + "2 271469.0 271477.0 271477.0 271490 271494 271496 \n", + "3 47875.0 47875.0 47875.0 47875 47890 47890 \n", + "4 105277.0 105277.0 105277.0 105277 105288 105288 \n", + "5 11.0 11.0 11.0 11 11 11 \n", + "6 9106.0 9106.0 9106.0 9106 9106 9106 \n", + "7 10044125.0 10044125.0 10044957.0 10044957 10044957 10044957 \n", + "8 446819.0 446819.0 446819.0 446819 447308 447308 \n", + "9 232619.0 232619.0 232619.0 232619 232619 232974 \n", + "10 3908129.0 3908129.0 3908129.0 3908129 3908129 3915992 \n", + "11 105021.0 105021.0 105021.0 105021 105021 105111 \n", + "12 1800236.0 1800236.0 1800236.0 1800236 1800236 1800236 \n", + "13 881911.0 881911.0 881911.0 881911 881911 883620 \n", + "14 286897.0 286897.0 286897.0 286897 286897 287507 \n", + "15 2877260.0 2877260.0 2877260.0 2877260 2877260 2880559 \n", + "16 1293461.0 1293461.0 1293461.0 1293461 1293461 1293461 \n", + "17 5936666.0 5940935.0 5943417.0 5949418 5955860 5961143 \n", + "18 828682.0 828721.0 828730.0 828783 828819 828825 \n", + "19 37491.0 37491.0 37491.0 37491 37491 37491 \n", + "20 708768.0 709230.0 709230.0 709858 710306 710693 \n", + "21 2037829.0 2037829.0 2037829.0 2037829 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11945.0 11945.0 11945 11945 11945 \n", + "287 343079.0 343135.0 343135.0 343135 343135 343135 \n", + "288 264127.0 264127.0 264127.0 264127 264276 264276 \n", + "\n", + "[289 rows x 1147 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clean_data = raw_data.copy()\n", + "columns_to_study = clean_data.iloc[:,4:].columns\n", + "\n", + "for coord in table_of_errors : \n", + " clean_data.iloc[coord[0],coord[1]] = np.nan\n", + "clean_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Est-ce une rectification due a un mauvais diagnostique initial ? une modification de la methode de comptage ?\n", + "Quoi qu'il en soit, ce nombre d'incoherence (362) est tres faible par rapport au nombre de donnees total (288 lignes de donnees * 1142 comptage = 328896 donnees totales), et ne devrait pas impacter particulierement les conclusions qu'elles soient presentes ou non dans la table. \n", + "\n", + "\n", + "\n", + "## 1eres analyses realisees uniquement sur la France\n", + "\n", + "On commence par ne recuperer que la ligne de donnees correspondant a la France \n", + "\n", + "--> Country/Region = France ET Province/State = Nan" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n", + "131 NaN France 46.2276 2.2137 0 0 2 \n", + "\n", + " 1/25/20 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 \\\n", + "131 3 3 3 ... 38579269.0 38583794.0 38587990.0 \n", + "\n", + " 3/3/23 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 \\\n", + "131 38591184 38591184.0 38591184.0 38599330.0 38606393 38612201 \n", + "\n", + " 3/9/23 \n", + "131 38618509 \n", + "\n", + "[1 rows x 1147 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_france = clean_data.loc[(raw_data['Country/Region'] == \"France\") & (raw_data['Province/State'].isnull())]\n", + "df_france" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour le plot, on transpose les donnees en ne conservant que les lignes des incidences cummulees - a partir de la colonne 5 " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "df_france_final = df_france.transpose()[5:]\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour plus de clarte on change le nom de la colonne pour le nom du pays France" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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France
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" + ], + "text/plain": [ + " France\n", + "1/23/20 0\n", + "1/24/20 2\n", + "1/25/20 3\n", + "1/26/20 3\n", + "1/27/20 3" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_france_final.rename(columns={131: \"France\"}, inplace=True)\n", + "df_france_final.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On change les dates en un format interpretable par pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2020-01-23', '2020-01-24', '2020-01-25', '2020-01-26',\n", + " '2020-01-27', '2020-01-28', '2020-01-29', '2020-01-30',\n", + " '2020-01-31', '2020-02-01',\n", + " ...\n", + " '2023-02-28', '2023-03-01', '2023-03-02', '2023-03-03',\n", + " '2023-03-04', '2023-03-05', '2023-03-06', '2023-03-07',\n", + " '2023-03-08', '2023-03-09'],\n", + " dtype='datetime64[ns]', length=1142, freq=None)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_dates = pd.to_datetime(df_france_final.index)\n", + "all_dates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On reinitialise ces dates formattees comme index de la table de donnees " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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France
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2020-01-242
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" + ], + "text/plain": [ + " France\n", + "2020-01-23 0\n", + "2020-01-24 2\n", + "2020-01-25 3\n", + "2020-01-26 3\n", + "2020-01-27 3" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_france_final.index = all_dates\n", + "df_france_final.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On peut ploter l'incidence en France " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n", + "71 NaN Hong Kong 22.3 114.2 0 2 2 \n", + "\n", + " 1/25/20 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 \\\n", + "71 5 8 8 ... 2876106.0 2876106.0 2876106.0 \n", + "\n", + " 3/3/23 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", + "71 2876106 2876106.0 2876106.0 2876106.0 2876106 2876106 2876106 \n", + "\n", + "[1 rows x 1147 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_data= clean_data.copy()\n", + "new_data.loc[(new_data['Province/State'] == \"Hong Kong\"),'Country/Region'] = \"Hong Kong\"\n", + "new_data.loc[(new_data['Province/State'] == \"Hong Kong\"),'Province/State'] = np.nan\n", + "new_data.loc[(new_data['Country/Region'] == \"Hong Kong\")]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gestion particuliere de la Chine\n", + "La Chine apparait sous de multiples province que nous allons sommer en un unique pays.\n", + "\n", + "On commence par recuperer toutes les donnees de Chine\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(33, 1147)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_china = new_data.loc[(new_data['Country/Region'] == \"China\")]\n", + "df_china.shape\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On somme toutes les donnes pour les 33 provinces de Chine et on reinitialise les province, lattitude, longitude a NA, le pays a China.\n", + "\n", + "On travaille sur une Serie pandas, on la reformate en dataframe avec une tranposition. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 \\\n", + "0 NaN China NaN NaN 548 641 918 1401 \n", + "\n", + " 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 3/3/23 \\\n", + "0 2067 2869 ... 2.02742e+06 2.02742e+06 2.02742e+06 2027418 \n", + "\n", + " 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", + "0 2.02742e+06 2.02742e+06 2.02742e+06 2027418 2027418 2027418 \n", + "\n", + "[1 rows x 1147 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_China_combined = df_china.sum()\n", + "df_China_combined[\"Province/State\"] = np.nan\n", + "df_China_combined[\"Lat\"] = np.nan\n", + "df_China_combined[\"Long\"] = np.nan\n", + "df_China_combined[\"Country/Region\"] = \"China\"\n", + "df_China_combined = pd.DataFrame(df_China_combined)\n", + "df_China_combined = df_China_combined.transpose()\n", + "df_China_combined" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On ajoute les donnees China \"total\" dans un nouveau dataframe pandas \"newSet\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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China 41.2956 122.6085 2 3 4 \n", + "79 Macau China 22.1667 113.5500 1 2 2 \n", + "80 Ningxia China 37.2692 106.1655 1 1 2 \n", + "81 Qinghai China 35.7452 95.9956 0 0 0 \n", + "82 Shaanxi China 35.1917 108.8701 0 3 5 \n", + "83 Shandong China 36.3427 118.1498 2 6 15 \n", + "84 Shanghai China 31.2020 121.4491 9 16 20 \n", + "85 Shanxi China 37.5777 112.2922 1 1 1 \n", + "86 Sichuan China 30.6171 102.7103 5 8 15 \n", + "87 Tianjin China 39.3054 117.3230 4 4 8 \n", + "88 Tibet China 31.6927 88.0924 0 0 0 \n", + "89 Unknown China NaN NaN 0 0 0 \n", + "90 Xinjiang China 41.1129 85.2401 0 2 2 \n", + "91 Yunnan China 24.9740 101.4870 1 2 5 \n", + "92 Zhejiang China 29.1832 120.0934 10 27 43 \n", + "0 NaN China NaN NaN 548 641 918 \n", + "\n", + " 1/25/20 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 \\\n", + "59 39 60 70 ... 2275 2275 2275 \n", + "60 41 68 80 ... 40774 40774 40774 \n", + "61 57 75 110 ... 14715 14715 14715 \n", + "62 18 35 59 ... 17122 17122 17122 \n", + "63 4 7 14 ... 1742 1742 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1147 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "newSet = pd.concat([new_data,df_China_combined])\n", + "newSet.loc[(newSet['Country/Region'] == \"China\")]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Recuperation des donnees pour les pays d'interet listes ci dessus\n", + "\n", + "On cree une liste avec les pays d'interet \"interest_countries\".\n", + "\n", + "On recupere par la suite un sous jeu de donnees avec uniquement ces pays et \"NA\" en province. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
24NaNBelgium50.8333004.469936000000...4.71766e+064.71766e+064.7278e+0647277954.7278e+064.7278e+064.7278e+06472779547277954739365
71NaNHong Kong22.300000114.200000022588...2.87611e+062.87611e+062.87611e+0628761062.87611e+062.87611e+062.87611e+06287610628761062876106
131NaNFrance46.2276002.213700002333...3.85793e+073.85838e+073.8588e+07385911843.85912e+073.85912e+073.85993e+07386063933861220138618509
135NaNGermany51.16569110.451526000001...3.81689e+073.819e+073.82026e+07382108503.82108e+073.82109e+073.82109e+07382316103824123138249060
150NaNIran32.42790853.688046000000...7.56791e+067.5689e+067.56926e+0675694837.56977e+067.57023e+067.57074e+06757135275719967572311
154NaNItaly41.87194012.567380000000...2.55769e+072.55769e+072.55769e+07256035102.56035e+072.56035e+072.56035e+07256035102560351025603510
156NaNJapan36.204824138.252924222244...3.32272e+073.32412e+073.32527e+07332632083.32736e+073.32824e+073.32866e+07332987993331060433320438
162NaNKorea, South35.907757127.766922112234...3.0526e+073.05336e+073.0544e+07305551023.05551e+073.05692e+073.05815e+07305942973060518730615522
200NaNNetherlands52.1326005.291300000000...8.59616e+068.59616e+068.59616e+0685980438.59804e+068.59804e+068.59804e+06859998185999818599981
218NaNPortugal39.399900-8.224500000000...5.56671e+065.56808e+065.56808e+0655680845.56808e+065.56808e+065.56808e+06556808455704735570473
241NaNSpain40.463667-3.749220000000...1.37633e+071.37633e+071.37633e+07137704291.37704e+071.37704e+071.37704e+07137704291377042913770429
260NaNUS40.000000-100.000000112255...1.03443e+081.03534e+081.0359e+08103648690NaNNaN1.03656e+08103690910103755771103802702
278NaNUnited Kingdom55.378100-3.436000000000...2.43702e+072.43702e+072.43965e+07243965302.43965e+072.43965e+072.43965e+07243965302439653024425309
0NaNChinaNaNNaN548641918140120672869...2.02742e+062.02742e+062.02742e+0620274182.02742e+062.02742e+062.02742e+06202741820274182027418
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14 rows × 1147 columns

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" + ], + "text/plain": [ + "Country/Region Belgium Hong Kong France Germany Iran Italy Japan Korea, South \\\n", + "1/23/20 0 2 0 0 0 0 2 1 \n", + "1/24/20 0 2 2 0 0 0 2 2 \n", + "1/25/20 0 5 3 0 0 0 2 2 \n", + "1/26/20 0 8 3 0 0 0 4 3 \n", + "1/27/20 0 8 3 1 0 0 4 4 \n", + "\n", + "Country/Region Netherlands Portugal Spain US United Kingdom China \n", + "1/23/20 0 0 0 1 0 641 \n", + "1/24/20 0 0 0 2 0 918 \n", + "1/25/20 0 0 0 2 0 1401 \n", + "1/26/20 0 0 0 5 0 2067 \n", + "1/27/20 0 0 0 5 0 2869 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_allCountries_final = df_allCountries.transpose()[5:]\n", + "df_allCountries_final.columns = df_allCountries[\"Country/Region\"]\n", + "df_allCountries_final.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On reformatte les dates " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Country/RegionBelgiumHong KongFranceGermanyIranItalyJapanKorea, SouthNetherlandsPortugalSpainUSUnited KingdomChina
2020-01-230200002100010641
2020-01-240220002200020918
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2020-01-2608300043000502067
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" + ], + "text/plain": [ + "Country/Region Belgium Hong Kong France Germany Iran Italy Japan Korea, South \\\n", + "2020-01-23 0 2 0 0 0 0 2 1 \n", + "2020-01-24 0 2 2 0 0 0 2 2 \n", + "2020-01-25 0 5 3 0 0 0 2 2 \n", + "2020-01-26 0 8 3 0 0 0 4 3 \n", + "2020-01-27 0 8 3 1 0 0 4 4 \n", + "\n", + "Country/Region Netherlands Portugal Spain US United Kingdom China \n", + "2020-01-23 0 0 0 1 0 641 \n", + "2020-01-24 0 0 0 2 0 918 \n", + "2020-01-25 0 0 0 2 0 1401 \n", + "2020-01-26 0 0 0 5 0 2067 \n", + "2020-01-27 0 0 0 5 0 2869 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_dates = pd.to_datetime(df_allCountries_final.index)\n", + "df_allCountries_final.index = all_dates\n", + "df_allCountries_final.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On plot le graph en format classique avec le nombre de cas en fonction des jours, en utilisant un code couleur pour les pays consideres. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "color = cm.rainbow(np.linspace(0, 1, len(interest_countries)))\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "df_allCountries_final.plot(ax=ax, color=color)\n", + "ax.legend(bbox_to_anchor=(1, 1), loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On modifie l'echelle des y en log pour mieux voir les distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "plt.yscale(\"log\") \n", + "df_allCountries_final.plot(ax=ax, color=color)\n", + "ax.legend(bbox_to_anchor=(1, 1), loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "C'est donc les USA ayant eu le plus de cas recences, mais a normaliser par le nombre d'habitant global de chaque territoire et/ou du nombre de deces. \n", + "\n", + "## Question subsidiaire - utilisqtion des donnees de deces\n", + "\n", + "On recupere les donnees de deces en faisant une copie local au besoin. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "death_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\"\n", + "death_file = \"time_series_covid19_deaths_global.csv\"\n", + "\n", + "if not os.path.isfile(death_file):\n", + " urlretrieve(death_url, death_file) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comme precedemment, on se doit de regarder les donnees apres chargement." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(289, 1147)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "death_data = pd.read_csv(death_file)\n", + "death_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
0NaNAfghanistan33.93911067.709953000000...7896789678967896789678967896789678967896
1NaNAlbania41.15330020.168300000000...3598359835983598359835983598359835983598
2NaNAlgeria28.0339001.659600000000...6881688168816881688168816881688168816881
3NaNAndorra42.5063001.521800000000...165165165165165165165165165165
4NaNAngola-11.20270017.873900000000...1933193319331933193319331933193319331933
5NaNAntarctica-71.94990023.347000000000...0000000000
6NaNAntigua and Barbuda17.060800-61.796400000000...146146146146146146146146146146
7NaNArgentina-38.416100-63.616700000000...130463130463130463130463130463130463130472130472130472130472
8NaNArmenia40.06910045.038200000000...8721872187218721872187218721872187278727
9Australian Capital TerritoryAustralia-35.473500149.012400000000...224224228228228228228228228228
10New South WalesAustralia-33.868800151.209300000000...6464646464936493649364936493649364936529
11Northern TerritoryAustralia-12.463400130.845600000000...90909090909090909091
12QueenslandAustralia-27.469800153.025100000000...2760276027832783278327832783278327832783
13South AustraliaAustralia-34.928500138.600700000000...1322132213221322132213221322132213221365
14TasmaniaAustralia-42.882100147.327200000000...252252252253253253253253253256
15VictoriaAustralia-37.813600144.963100000000...7317731773387338733873387338733873387370
16Western AustraliaAustralia-31.950500115.860500000000...944944952952952952952952952952
17NaNAustria47.51620014.550100000000...21887218912189921907219212192221923219412194921970
18NaNAzerbaijan40.14310047.576900000000...10119101191012210126101271012910129101351013810138
19NaNBahamas25.025885-78.035889000000...833833833833833833833833833833
20NaNBahrain26.02750050.550000000000...1548154915501552155215521552155315531553
21NaNBangladesh23.68500090.356300000000...29445294452944529445294452944529445294452944529445
22NaNBarbados13.193900-59.543200000000...575575575575575575575575575579
23NaNBelarus53.70980027.953400000000...7118711871187118711871187118711871187118
24NaNBelgium50.8333004.469936000000...33717337173377533775337753377533775337753377533814
25NaNBelize17.189900-88.497600000000...688688688688688688688688688688
26NaNBenin9.3077002.315800000000...163163163163163163163163163163
27NaNBhutan27.51420090.433600000000...21212121212121212121
28NaNBolivia-16.290200-63.588700000000...22365223652236522365223652236522365223652236522365
29NaNBosnia and Herzegovina43.91590017.679100000000...16278162791627916279162791627916279162791628016280
..................................................................
259NaNTuvalu-7.109500177.649300000000...0000000000
260NaNUS40.000000-100.000000000000...1119917112089711216581122165112217211221341122181112251611232461123836
261NaNUganda1.37333332.290275000000...3630363036303630363036303630363036303630
262NaNUkraine48.37940031.165600000000...119149119209119210119211119212119213119216119217119281119283
263NaNUnited Arab Emirates23.42407653.847818000000...2349234923492349234923492349234923492349
264AnguillaUnited Kingdom18.220600-63.068600000000...12121212121212121212
265BermudaUnited Kingdom32.307800-64.750500000000...160160160160160160160160160160
266British Virgin IslandsUnited Kingdom18.420700-64.640000000000...64646464646464646464
267Cayman IslandsUnited Kingdom19.313300-81.254600000000...37373737373737373737
268Channel IslandsUnited Kingdom49.372300-2.364400000000...0000000000
269Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...0000000000
270GibraltarUnited Kingdom36.140800-5.353600000000...111111111111111111111111111111
271GuernseyUnited Kingdom49.448196-2.589490000000...66666666666666666666
272Isle of ManUnited Kingdom54.236100-4.548100000000...116116116116116116116116116116
273JerseyUnited Kingdom49.213800-2.135800000000...161161161161161161161161161161
274MontserratUnited Kingdom16.742498-62.187366000000...8888888888
275Pitcairn IslandsUnited Kingdom-24.376800-128.324200000000...0000000000
276Saint Helena, Ascension and Tristan da CunhaUnited Kingdom-7.946700-14.355900000000...0000000000
277Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...38383838383838383838
278NaNUnited Kingdom55.378100-3.436000000000...219948219948219948219948219948219948219948219948219948219948
279NaNUruguay-32.522800-55.765800000000...7617761776177617761776177617761776177617
280NaNUzbekistan41.37749164.585262000000...1637163716371637163716371637163716371637
281NaNVanuatu-15.376700166.959200000000...14141414141414141414
282NaNVenezuela6.423800-66.589700000000...5853585458545854585458545854585458545854
283NaNVietnam14.058324108.277199000000...43186431864318643186431864318643186431864318643186
284NaNWest Bank and Gaza31.95220035.233200000000...5708570857085708570857085708570857085708
285NaNWinter Olympics 202239.904200116.407400000000...0000000000
286NaNYemen15.55272748.516388000000...2159215921592159215921592159215921592159
287NaNZambia-13.13389727.849332000000...4057405740574057405740574057405740574057
288NaNZimbabwe-19.01543829.154857000000...5663566856685668566856685668566856715671
\n", + "

289 rows × 1147 columns

\n", + "
" + ], + "text/plain": [ + " Province/State Country/Region \\\n", + "0 NaN Afghanistan \n", + "1 NaN Albania \n", + "2 NaN Algeria \n", + "3 NaN Andorra \n", + "4 NaN Angola \n", + "5 NaN Antarctica \n", + "6 NaN Antigua and Barbuda \n", + "7 NaN Argentina \n", + "8 NaN Armenia \n", + "9 Australian Capital Territory Australia \n", + "10 New South Wales Australia \n", + "11 Northern Territory Australia \n", + "12 Queensland Australia \n", + "13 South Australia Australia \n", + "14 Tasmania Australia \n", + "15 Victoria Australia \n", + "16 Western Australia Australia \n", + "17 NaN Austria \n", + "18 NaN Azerbaijan \n", + "19 NaN Bahamas \n", + "20 NaN Bahrain \n", + "21 NaN Bangladesh \n", + "22 NaN Barbados \n", + "23 NaN Belarus \n", + "24 NaN Belgium \n", + "25 NaN Belize \n", + "26 NaN Benin \n", + "27 NaN Bhutan \n", + "28 NaN Bolivia \n", + "29 NaN Bosnia and Herzegovina \n", + ".. ... ... \n", + "259 NaN Tuvalu \n", + "260 NaN US \n", + "261 NaN Uganda \n", + "262 NaN Ukraine \n", + "263 NaN United Arab Emirates \n", + "264 Anguilla United Kingdom \n", + "265 Bermuda United Kingdom \n", + "266 British Virgin Islands United Kingdom \n", + "267 Cayman Islands United Kingdom \n", + "268 Channel Islands United Kingdom \n", + "269 Falkland Islands (Malvinas) United Kingdom \n", + "270 Gibraltar United Kingdom \n", + "271 Guernsey United Kingdom \n", + "272 Isle of Man United Kingdom \n", + "273 Jersey United Kingdom \n", + "274 Montserrat United Kingdom \n", + "275 Pitcairn Islands United Kingdom \n", + "276 Saint Helena, Ascension and Tristan da Cunha United Kingdom \n", + "277 Turks and Caicos Islands United Kingdom \n", + "278 NaN United Kingdom \n", + "279 NaN Uruguay \n", + "280 NaN Uzbekistan \n", + "281 NaN Vanuatu \n", + "282 NaN Venezuela \n", + "283 NaN Vietnam \n", + "284 NaN West Bank and Gaza \n", + "285 NaN Winter Olympics 2022 \n", + "286 NaN Yemen \n", + "287 NaN Zambia \n", + "288 NaN Zimbabwe \n", + "\n", + " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n", + "0 33.939110 67.709953 0 0 0 0 0 \n", + "1 41.153300 20.168300 0 0 0 0 0 \n", + "2 28.033900 1.659600 0 0 0 0 0 \n", + "3 42.506300 1.521800 0 0 0 0 0 \n", + "4 -11.202700 17.873900 0 0 0 0 0 \n", + "5 -71.949900 23.347000 0 0 0 0 0 \n", + "6 17.060800 -61.796400 0 0 0 0 0 \n", + "7 -38.416100 -63.616700 0 0 0 0 0 \n", + "8 40.069100 45.038200 0 0 0 0 0 \n", + "9 -35.473500 149.012400 0 0 0 0 0 \n", + "10 -33.868800 151.209300 0 0 0 0 0 \n", + "11 -12.463400 130.845600 0 0 0 0 0 \n", + "12 -27.469800 153.025100 0 0 0 0 0 \n", + "13 -34.928500 138.600700 0 0 0 0 0 \n", + "14 -42.882100 147.327200 0 0 0 0 0 \n", + "15 -37.813600 144.963100 0 0 0 0 0 \n", + "16 -31.950500 115.860500 0 0 0 0 0 \n", + "17 47.516200 14.550100 0 0 0 0 0 \n", + "18 40.143100 47.576900 0 0 0 0 0 \n", + "19 25.025885 -78.035889 0 0 0 0 0 \n", + "20 26.027500 50.550000 0 0 0 0 0 \n", + "21 23.685000 90.356300 0 0 0 0 0 \n", + "22 13.193900 -59.543200 0 0 0 0 0 \n", + "23 53.709800 27.953400 0 0 0 0 0 \n", + "24 50.833300 4.469936 0 0 0 0 0 \n", + "25 17.189900 -88.497600 0 0 0 0 0 \n", + "26 9.307700 2.315800 0 0 0 0 0 \n", + "27 27.514200 90.433600 0 0 0 0 0 \n", + "28 -16.290200 -63.588700 0 0 0 0 0 \n", + "29 43.915900 17.679100 0 0 0 0 0 \n", + ".. ... ... ... ... ... ... ... \n", + "259 -7.109500 177.649300 0 0 0 0 0 \n", + "260 40.000000 -100.000000 0 0 0 0 0 \n", + "261 1.373333 32.290275 0 0 0 0 0 \n", + "262 48.379400 31.165600 0 0 0 0 0 \n", + "263 23.424076 53.847818 0 0 0 0 0 \n", + "264 18.220600 -63.068600 0 0 0 0 0 \n", + "265 32.307800 -64.750500 0 0 0 0 0 \n", + "266 18.420700 -64.640000 0 0 0 0 0 \n", + "267 19.313300 -81.254600 0 0 0 0 0 \n", + "268 49.372300 -2.364400 0 0 0 0 0 \n", + "269 -51.796300 -59.523600 0 0 0 0 0 \n", + "270 36.140800 -5.353600 0 0 0 0 0 \n", + "271 49.448196 -2.589490 0 0 0 0 0 \n", + "272 54.236100 -4.548100 0 0 0 0 0 \n", + "273 49.213800 -2.135800 0 0 0 0 0 \n", + "274 16.742498 -62.187366 0 0 0 0 0 \n", + "275 -24.376800 -128.324200 0 0 0 0 0 \n", + "276 -7.946700 -14.355900 0 0 0 0 0 \n", + "277 21.694000 -71.797900 0 0 0 0 0 \n", + "278 55.378100 -3.436000 0 0 0 0 0 \n", + "279 -32.522800 -55.765800 0 0 0 0 0 \n", + "280 41.377491 64.585262 0 0 0 0 0 \n", + "281 -15.376700 166.959200 0 0 0 0 0 \n", + "282 6.423800 -66.589700 0 0 0 0 0 \n", + "283 14.058324 108.277199 0 0 0 0 0 \n", + "284 31.952200 35.233200 0 0 0 0 0 \n", + "285 39.904200 116.407400 0 0 0 0 0 \n", + "286 15.552727 48.516388 0 0 0 0 0 \n", + "287 -13.133897 27.849332 0 0 0 0 0 \n", + "288 -19.015438 29.154857 0 0 0 0 0 \n", + "\n", + " 1/27/20 ... 2/28/23 3/1/23 3/2/23 3/3/23 3/4/23 3/5/23 \\\n", + "0 0 ... 7896 7896 7896 7896 7896 7896 \n", + "1 0 ... 3598 3598 3598 3598 3598 3598 \n", + "2 0 ... 6881 6881 6881 6881 6881 6881 \n", + "3 0 ... 165 165 165 165 165 165 \n", + "4 0 ... 1933 1933 1933 1933 1933 1933 \n", + "5 0 ... 0 0 0 0 0 0 \n", + "6 0 ... 146 146 146 146 146 146 \n", + "7 0 ... 130463 130463 130463 130463 130463 130463 \n", + "8 0 ... 8721 8721 8721 8721 8721 8721 \n", + "9 0 ... 224 224 228 228 228 228 \n", + "10 0 ... 6464 6464 6493 6493 6493 6493 \n", + "11 0 ... 90 90 90 90 90 90 \n", + "12 0 ... 2760 2760 2783 2783 2783 2783 \n", + "13 0 ... 1322 1322 1322 1322 1322 1322 \n", + "14 0 ... 252 252 252 253 253 253 \n", + "15 0 ... 7317 7317 7338 7338 7338 7338 \n", + "16 0 ... 944 944 952 952 952 952 \n", + "17 0 ... 21887 21891 21899 21907 21921 21922 \n", + "18 0 ... 10119 10119 10122 10126 10127 10129 \n", + "19 0 ... 833 833 833 833 833 833 \n", + "20 0 ... 1548 1549 1550 1552 1552 1552 \n", + "21 0 ... 29445 29445 29445 29445 29445 29445 \n", + "22 0 ... 575 575 575 575 575 575 \n", + "23 0 ... 7118 7118 7118 7118 7118 7118 \n", + "24 0 ... 33717 33717 33775 33775 33775 33775 \n", + "25 0 ... 688 688 688 688 688 688 \n", + "26 0 ... 163 163 163 163 163 163 \n", + "27 0 ... 21 21 21 21 21 21 \n", + "28 0 ... 22365 22365 22365 22365 22365 22365 \n", + "29 0 ... 16278 16279 16279 16279 16279 16279 \n", + ".. ... ... ... ... ... ... ... ... \n", + "259 0 ... 0 0 0 0 0 0 \n", + "260 0 ... 1119917 1120897 1121658 1122165 1122172 1122134 \n", + "261 0 ... 3630 3630 3630 3630 3630 3630 \n", + "262 0 ... 119149 119209 119210 119211 119212 119213 \n", + "263 0 ... 2349 2349 2349 2349 2349 2349 \n", + "264 0 ... 12 12 12 12 12 12 \n", + "265 0 ... 160 160 160 160 160 160 \n", + "266 0 ... 64 64 64 64 64 64 \n", + "267 0 ... 37 37 37 37 37 37 \n", + "268 0 ... 0 0 0 0 0 0 \n", + "269 0 ... 0 0 0 0 0 0 \n", + "270 0 ... 111 111 111 111 111 111 \n", + "271 0 ... 66 66 66 66 66 66 \n", + "272 0 ... 116 116 116 116 116 116 \n", + "273 0 ... 161 161 161 161 161 161 \n", + "274 0 ... 8 8 8 8 8 8 \n", + "275 0 ... 0 0 0 0 0 0 \n", + "276 0 ... 0 0 0 0 0 0 \n", + "277 0 ... 38 38 38 38 38 38 \n", + "278 0 ... 219948 219948 219948 219948 219948 219948 \n", + "279 0 ... 7617 7617 7617 7617 7617 7617 \n", + "280 0 ... 1637 1637 1637 1637 1637 1637 \n", + "281 0 ... 14 14 14 14 14 14 \n", + "282 0 ... 5853 5854 5854 5854 5854 5854 \n", + "283 0 ... 43186 43186 43186 43186 43186 43186 \n", + "284 0 ... 5708 5708 5708 5708 5708 5708 \n", + "285 0 ... 0 0 0 0 0 0 \n", + "286 0 ... 2159 2159 2159 2159 2159 2159 \n", + "287 0 ... 4057 4057 4057 4057 4057 4057 \n", + "288 0 ... 5663 5668 5668 5668 5668 5668 \n", + "\n", + " 3/6/23 3/7/23 3/8/23 3/9/23 \n", + "0 7896 7896 7896 7896 \n", + "1 3598 3598 3598 3598 \n", + "2 6881 6881 6881 6881 \n", + "3 165 165 165 165 \n", + "4 1933 1933 1933 1933 \n", + "5 0 0 0 0 \n", + "6 146 146 146 146 \n", + "7 130472 130472 130472 130472 \n", + "8 8721 8721 8727 8727 \n", + "9 228 228 228 228 \n", + "10 6493 6493 6493 6529 \n", + "11 90 90 90 91 \n", + "12 2783 2783 2783 2783 \n", + "13 1322 1322 1322 1365 \n", + "14 253 253 253 256 \n", + "15 7338 7338 7338 7370 \n", + "16 952 952 952 952 \n", + "17 21923 21941 21949 21970 \n", + "18 10129 10135 10138 10138 \n", + "19 833 833 833 833 \n", + "20 1552 1553 1553 1553 \n", + "21 29445 29445 29445 29445 \n", + "22 575 575 575 579 \n", + "23 7118 7118 7118 7118 \n", + "24 33775 33775 33775 33814 \n", + "25 688 688 688 688 \n", + "26 163 163 163 163 \n", + "27 21 21 21 21 \n", + "28 22365 22365 22365 22365 \n", + "29 16279 16279 16280 16280 \n", + ".. ... ... ... ... \n", + "259 0 0 0 0 \n", + "260 1122181 1122516 1123246 1123836 \n", + "261 3630 3630 3630 3630 \n", + "262 119216 119217 119281 119283 \n", + "263 2349 2349 2349 2349 \n", + "264 12 12 12 12 \n", + "265 160 160 160 160 \n", + "266 64 64 64 64 \n", + "267 37 37 37 37 \n", + "268 0 0 0 0 \n", + "269 0 0 0 0 \n", + "270 111 111 111 111 \n", + "271 66 66 66 66 \n", + "272 116 116 116 116 \n", + "273 161 161 161 161 \n", + "274 8 8 8 8 \n", + "275 0 0 0 0 \n", + "276 0 0 0 0 \n", + "277 38 38 38 38 \n", + "278 219948 219948 219948 219948 \n", + "279 7617 7617 7617 7617 \n", + "280 1637 1637 1637 1637 \n", + "281 14 14 14 14 \n", + "282 5854 5854 5854 5854 \n", + "283 43186 43186 43186 43186 \n", + "284 5708 5708 5708 5708 \n", + "285 0 0 0 0 \n", + "286 2159 2159 2159 2159 \n", + "287 4057 4057 4057 4057 \n", + "288 5668 5668 5671 5671 \n", + "\n", + "[289 rows x 1147 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "death_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le format est donc identique aux donnees precedentes et peuvent etre traitees de la meme maniere apres les memes verifications\n", + "* presence de donnees manquantes " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "columns_to_study = death_data.iloc[:,4:].columns\n", + "\n", + "for i in raw_data.index : \n", + " for d in range(len(columns_to_study[:-1])):\n", + " if (pd.isna(death_data.iloc[i,d+4]) or death_data.iloc[i,d+4]<0):\n", + " print(death_data.iloc[i,d+4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--> pas de donnees manquantes\n", + "* incoherence avec un nombre de deces commule qui decroit ?" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Il y a 291 donnees superieurs a celle de la donnee suivante\n" + ] + } + ], + "source": [ + "flag = 0\n", + "table_of_errors = []\n", + "for i in death_data.index : \n", + " for d in range(len(columns_to_study[:-1])):\n", + " if (int(death_data.iloc[i,d+4]) > int(death_data.iloc[i,d+4 +1])):\n", + " data_problem = (death_data.iloc[i, 1:2], columns_to_study[d], columns_to_study[d+1])\n", + " table_of_errors.append((i,d+4))\n", + " table_of_errors.append((i,d+5))\n", + " flag = flag +1\n", + "print(\"Il y a %s donnees superieurs a celle de la donnee suivante\" % str(flag))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--> on a le meme type d'incoherence que precedemment, minoritaire comparee a la quantite de donnees, mais au'on choisit toutefois de supprimer en remettant ces valeurs aberrantes a na" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
0NaNAfghanistan33.93911067.709953000000...7896.07896789678967896.07896.07896789678967896
1NaNAlbania41.15330020.168300000000...3598.03598359835983598.03598.03598359835983598
2NaNAlgeria28.0339001.659600000000...6881.06881688168816881.06881.06881688168816881
3NaNAndorra42.5063001.521800000000...165.0165165165165.0165.0165165165165
4NaNAngola-11.20270017.873900000000...1933.01933193319331933.01933.01933193319331933
5NaNAntarctica-71.94990023.347000000000...0.00000.00.00000
6NaNAntigua and Barbuda17.060800-61.796400000000...146.0146146146146.0146.0146146146146
7NaNArgentina-38.416100-63.616700000000...130463.0130463130463130463130463.0130463.0130472130472130472130472
8NaNArmenia40.06910045.038200000000...8721.08721872187218721.08721.08721872187278727
9Australian Capital TerritoryAustralia-35.473500149.012400000000...224.0224228228228.0228.0228228228228
10New South WalesAustralia-33.868800151.209300000000...6464.06464649364936493.06493.06493649364936529
11Northern TerritoryAustralia-12.463400130.845600000000...90.090909090.090.090909091
12QueenslandAustralia-27.469800153.025100000000...2760.02760278327832783.02783.02783278327832783
13South AustraliaAustralia-34.928500138.600700000000...1322.01322132213221322.01322.01322132213221365
14TasmaniaAustralia-42.882100147.327200000000...252.0252252253253.0253.0253253253256
15VictoriaAustralia-37.813600144.963100000000...7317.07317733873387338.07338.07338733873387370
16Western AustraliaAustralia-31.950500115.860500000000...944.0944952952952.0952.0952952952952
17NaNAustria47.51620014.550100000000...21887.021891218992190721921.021922.021923219412194921970
18NaNAzerbaijan40.14310047.576900000000...10119.010119101221012610127.010129.010129101351013810138
19NaNBahamas25.025885-78.035889000000...833.0833833833833.0833.0833833833833
20NaNBahrain26.02750050.550000000000...1548.01549155015521552.01552.01552155315531553
21NaNBangladesh23.68500090.356300000000...29445.029445294452944529445.029445.029445294452944529445
22NaNBarbados13.193900-59.543200000000...575.0575575575575.0575.0575575575579
23NaNBelarus53.70980027.953400000000...7118.07118711871187118.07118.07118711871187118
24NaNBelgium50.8333004.469936000000...33717.033717337753377533775.033775.033775337753377533814
25NaNBelize17.189900-88.497600000000...688.0688688688688.0688.0688688688688
26NaNBenin9.3077002.315800000000...163.0163163163163.0163.0163163163163
27NaNBhutan27.51420090.433600000000...21.021212121.021.021212121
28NaNBolivia-16.290200-63.588700000000...22365.022365223652236522365.022365.022365223652236522365
29NaNBosnia and Herzegovina43.91590017.679100000000...16278.016279162791627916279.016279.016279162791628016280
..................................................................
259NaNTuvalu-7.109500177.649300000000...0.00000.00.00000
260NaNUS40.000000-100.000000000000...1119917.0112089711216581122165NaNNaN1122181112251611232461123836
261NaNUganda1.37333332.290275000000...3630.03630363036303630.03630.03630363036303630
262NaNUkraine48.37940031.165600000000...119149.0119209119210119211119212.0119213.0119216119217119281119283
263NaNUnited Arab Emirates23.42407653.847818000000...2349.02349234923492349.02349.02349234923492349
264AnguillaUnited Kingdom18.220600-63.068600000000...12.012121212.012.012121212
265BermudaUnited Kingdom32.307800-64.750500000000...160.0160160160160.0160.0160160160160
266British Virgin IslandsUnited Kingdom18.420700-64.640000000000...64.064646464.064.064646464
267Cayman IslandsUnited Kingdom19.313300-81.254600000000...37.037373737.037.037373737
268Channel IslandsUnited Kingdom49.372300-2.364400000000...0.00000.00.00000
269Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...0.00000.00.00000
270GibraltarUnited Kingdom36.140800-5.353600000000...111.0111111111111.0111.0111111111111
271GuernseyUnited Kingdom49.448196-2.589490000000...66.066666666.066.066666666
272Isle of ManUnited Kingdom54.236100-4.548100000000...116.0116116116116.0116.0116116116116
273JerseyUnited Kingdom49.213800-2.135800000000...161.0161161161161.0161.0161161161161
274MontserratUnited Kingdom16.742498-62.187366000000...8.08888.08.08888
275Pitcairn IslandsUnited Kingdom-24.376800-128.324200000000...0.00000.00.00000
276Saint Helena, Ascension and Tristan da CunhaUnited Kingdom-7.946700-14.355900000000...0.00000.00.00000
277Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...38.038383838.038.038383838
278NaNUnited Kingdom55.378100-3.436000000000...219948.0219948219948219948219948.0219948.0219948219948219948219948
279NaNUruguay-32.522800-55.765800000000...7617.07617761776177617.07617.07617761776177617
280NaNUzbekistan41.37749164.585262000000...1637.01637163716371637.01637.01637163716371637
281NaNVanuatu-15.376700166.959200000000...14.014141414.014.014141414
282NaNVenezuela6.423800-66.589700000000...5853.05854585458545854.05854.05854585458545854
283NaNVietnam14.058324108.277199000000...43186.043186431864318643186.043186.043186431864318643186
284NaNWest Bank and Gaza31.95220035.233200000000...5708.05708570857085708.05708.05708570857085708
285NaNWinter Olympics 202239.904200116.407400000000...0.00000.00.00000
286NaNYemen15.55272748.516388000000...2159.02159215921592159.02159.02159215921592159
287NaNZambia-13.13389727.849332000000...4057.04057405740574057.04057.04057405740574057
288NaNZimbabwe-19.01543829.154857000000...5663.05668566856685668.05668.05668566856715671
\n", + "

289 rows × 1147 columns

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" + ], + "text/plain": [ + " Province/State Country/Region \\\n", + "0 NaN Afghanistan \n", + "1 NaN Albania \n", + "2 NaN Algeria \n", + "3 NaN Andorra \n", + "4 NaN Angola \n", + "5 NaN Antarctica \n", + "6 NaN Antigua and Barbuda \n", + "7 NaN Argentina \n", + "8 NaN Armenia \n", + "9 Australian Capital Territory Australia \n", + "10 New South Wales Australia \n", + "11 Northern Territory Australia \n", + "12 Queensland Australia \n", + "13 South Australia Australia \n", + "14 Tasmania Australia \n", + "15 Victoria Australia \n", + "16 Western Australia Australia \n", + "17 NaN Austria \n", + "18 NaN Azerbaijan \n", + "19 NaN Bahamas \n", + "20 NaN Bahrain \n", + "21 NaN Bangladesh \n", + "22 NaN Barbados \n", + "23 NaN Belarus \n", + "24 NaN Belgium \n", + "25 NaN Belize \n", + "26 NaN Benin \n", + "27 NaN Bhutan \n", + "28 NaN Bolivia \n", + "29 NaN Bosnia and Herzegovina \n", + ".. ... ... \n", + "259 NaN Tuvalu \n", + "260 NaN US \n", + "261 NaN Uganda \n", + "262 NaN Ukraine \n", + "263 NaN United Arab Emirates \n", + "264 Anguilla United Kingdom \n", + "265 Bermuda United Kingdom \n", + "266 British Virgin Islands United Kingdom \n", + "267 Cayman Islands United Kingdom \n", + "268 Channel Islands United Kingdom \n", + "269 Falkland Islands (Malvinas) United Kingdom \n", + "270 Gibraltar United Kingdom \n", + "271 Guernsey United Kingdom \n", + "272 Isle of Man United Kingdom \n", + "273 Jersey United Kingdom \n", + "274 Montserrat United Kingdom \n", + "275 Pitcairn Islands United Kingdom \n", + "276 Saint Helena, Ascension and Tristan da Cunha United Kingdom \n", + "277 Turks and Caicos Islands United Kingdom \n", + "278 NaN United Kingdom \n", + "279 NaN Uruguay \n", + "280 NaN Uzbekistan \n", + "281 NaN Vanuatu \n", + "282 NaN Venezuela \n", + "283 NaN Vietnam \n", + "284 NaN West Bank and Gaza \n", + "285 NaN Winter Olympics 2022 \n", + "286 NaN Yemen \n", + "287 NaN Zambia \n", + "288 NaN Zimbabwe \n", + "\n", + " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n", + "0 33.939110 67.709953 0 0 0 0 0 \n", + "1 41.153300 20.168300 0 0 0 0 0 \n", + "2 28.033900 1.659600 0 0 0 0 0 \n", + "3 42.506300 1.521800 0 0 0 0 0 \n", + "4 -11.202700 17.873900 0 0 0 0 0 \n", + "5 -71.949900 23.347000 0 0 0 0 0 \n", + "6 17.060800 -61.796400 0 0 0 0 0 \n", + "7 -38.416100 -63.616700 0 0 0 0 0 \n", + "8 40.069100 45.038200 0 0 0 0 0 \n", + "9 -35.473500 149.012400 0 0 0 0 0 \n", + "10 -33.868800 151.209300 0 0 0 0 0 \n", + "11 -12.463400 130.845600 0 0 0 0 0 \n", + "12 -27.469800 153.025100 0 0 0 0 0 \n", + "13 -34.928500 138.600700 0 0 0 0 0 \n", + "14 -42.882100 147.327200 0 0 0 0 0 \n", + "15 -37.813600 144.963100 0 0 0 0 0 \n", + "16 -31.950500 115.860500 0 0 0 0 0 \n", + "17 47.516200 14.550100 0 0 0 0 0 \n", + "18 40.143100 47.576900 0 0 0 0 0 \n", + "19 25.025885 -78.035889 0 0 0 0 0 \n", + "20 26.027500 50.550000 0 0 0 0 0 \n", + "21 23.685000 90.356300 0 0 0 0 0 \n", + "22 13.193900 -59.543200 0 0 0 0 0 \n", + "23 53.709800 27.953400 0 0 0 0 0 \n", + "24 50.833300 4.469936 0 0 0 0 0 \n", + "25 17.189900 -88.497600 0 0 0 0 0 \n", + "26 9.307700 2.315800 0 0 0 0 0 \n", + "27 27.514200 90.433600 0 0 0 0 0 \n", + "28 -16.290200 -63.588700 0 0 0 0 0 \n", + "29 43.915900 17.679100 0 0 0 0 0 \n", + ".. ... ... ... ... ... ... ... \n", + "259 -7.109500 177.649300 0 0 0 0 0 \n", + "260 40.000000 -100.000000 0 0 0 0 0 \n", + "261 1.373333 32.290275 0 0 0 0 0 \n", + "262 48.379400 31.165600 0 0 0 0 0 \n", + "263 23.424076 53.847818 0 0 0 0 0 \n", + "264 18.220600 -63.068600 0 0 0 0 0 \n", + "265 32.307800 -64.750500 0 0 0 0 0 \n", + "266 18.420700 -64.640000 0 0 0 0 0 \n", + "267 19.313300 -81.254600 0 0 0 0 0 \n", + "268 49.372300 -2.364400 0 0 0 0 0 \n", + "269 -51.796300 -59.523600 0 0 0 0 0 \n", + "270 36.140800 -5.353600 0 0 0 0 0 \n", + "271 49.448196 -2.589490 0 0 0 0 0 \n", + "272 54.236100 -4.548100 0 0 0 0 0 \n", + "273 49.213800 -2.135800 0 0 0 0 0 \n", + "274 16.742498 -62.187366 0 0 0 0 0 \n", + "275 -24.376800 -128.324200 0 0 0 0 0 \n", + "276 -7.946700 -14.355900 0 0 0 0 0 \n", + "277 21.694000 -71.797900 0 0 0 0 0 \n", + "278 55.378100 -3.436000 0 0 0 0 0 \n", + "279 -32.522800 -55.765800 0 0 0 0 0 \n", + "280 41.377491 64.585262 0 0 0 0 0 \n", + "281 -15.376700 166.959200 0 0 0 0 0 \n", + "282 6.423800 -66.589700 0 0 0 0 0 \n", + "283 14.058324 108.277199 0 0 0 0 0 \n", + "284 31.952200 35.233200 0 0 0 0 0 \n", + "285 39.904200 116.407400 0 0 0 0 0 \n", + "286 15.552727 48.516388 0 0 0 0 0 \n", + "287 -13.133897 27.849332 0 0 0 0 0 \n", + "288 -19.015438 29.154857 0 0 0 0 0 \n", + "\n", + " 1/27/20 ... 2/28/23 3/1/23 3/2/23 3/3/23 3/4/23 \\\n", + "0 0 ... 7896.0 7896 7896 7896 7896.0 \n", + "1 0 ... 3598.0 3598 3598 3598 3598.0 \n", + "2 0 ... 6881.0 6881 6881 6881 6881.0 \n", + "3 0 ... 165.0 165 165 165 165.0 \n", + "4 0 ... 1933.0 1933 1933 1933 1933.0 \n", + "5 0 ... 0.0 0 0 0 0.0 \n", + "6 0 ... 146.0 146 146 146 146.0 \n", + "7 0 ... 130463.0 130463 130463 130463 130463.0 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et la Chine aue precedemment" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "0 42 56 82 ... 87589 87589 87589 87589 87589 87589 \n", + "\n", + " 3/6/23 3/7/23 3/8/23 3/9/23 \n", + "59 7 7 7 7 \n", + "60 20 20 20 20 \n", + "61 11 11 11 11 \n", + "62 2 2 2 2 \n", + "63 2 2 2 2 \n", + "64 10 10 10 10 \n", + "65 2 2 2 2 \n", + "66 2 2 2 2 \n", + "67 6 6 6 6 \n", + "68 7 7 7 7 \n", + "69 18 18 18 18 \n", + "70 23 23 23 23 \n", + "72 4515 4515 4515 4515 \n", + "73 4 4 4 4 \n", + "74 1 1 1 1 \n", + "75 0 0 0 0 \n", + "76 2 2 2 2 \n", + "77 5 5 5 5 \n", + "78 2 2 2 2 \n", + "79 121 121 121 121 \n", + "80 0 0 0 0 \n", + "81 0 0 0 0 \n", + "82 5 5 5 5 \n", + "83 10 10 10 10 \n", + "84 595 595 595 595 \n", + "85 1 1 1 1 \n", + "86 12 12 12 12 \n", + "87 3 3 3 3 \n", + "88 0 0 0 0 \n", + "89 82195 82195 82195 82195 \n", + "90 3 3 3 3 \n", + "91 4 4 4 4 \n", + "92 1 1 1 1 \n", + "0 87589 87589 87589 87589 \n", + "\n", + "[34 rows x 1147 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_data_death= clean_death_data.copy()\n", + "# Hong Kong \n", + "new_data_death.loc[(new_data_death['Province/State'] == \"Hong Kong\"),'Country/Region'] = \"Hong Kong\"\n", + "new_data_death.loc[(new_data_death['Province/State'] == \"Hong Kong\"),'Province/State'] = np.nan\n", + "new_data_death.loc[(new_data_death['Country/Region'] == \"Hong Kong\")]\n", + "# China\n", + "df_china_death = new_data_death.loc[(new_data['Country/Region'] == \"China\")]\n", + "df_China_death_combined = df_china_death.sum()\n", + "df_China_death_combined[\"Province/State\"] = np.nan\n", + "df_China_death_combined[\"Lat\"] = np.nan\n", + "df_China_death_combined[\"Long\"] = np.nan\n", + "df_China_death_combined[\"Country/Region\"] = \"China\"\n", + "df_China_death_combined = pd.DataFrame(df_China_death_combined)\n", + "df_China_death_combined = df_China_death_combined.transpose()\n", + "# compilation of data\n", + "newSet_death = pd.concat([new_data_death,df_China_death_combined])\n", + "newSet_death.loc[(newSet['Country/Region'] == \"China\")]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On selectionne alors uniquement les pays d'interet comme precedemment\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
24NaNBelgium50.8333004.469936000000...33717337173377533775337753377533775337753377533814
71NaNHong Kong22.300000114.200000000000...13459134621346213463134641346513466134661346613467
131NaNFrance46.2276002.213700000000...161340161365161386161407161407161407161450161474161501161512
135NaNGermany51.16569110.451526000000...168086168175168296168397168397168397168397168709168808168935
150NaNIran32.42790853.688046000000...144845144858144864144867144878144893144902144907144922144933
154NaNItaly41.87194012.567380000000...188094188094188094188322188322188322188322188322188322188322
156NaNJapan36.204824138.252924000000...72395724947258172648727297277972813728487291772997
162NaNKorea, South35.907757127.766922000000...33988340033401434020340203403434049340613408134093
200NaNNetherlands52.1326005.291300000000...22990229902299022990229902299022990229902299022990
218NaNPortugal39.399900-8.224500000000...26117261802618026180261802618026180261802626626266
241NaNSpain40.463667-3.749220000000...119380119380119380119479119479119479119479119479119479119479
260NaNUS40.000000-100.000000000000...1.11992e+06112089711216581122165NaNNaN1122181112251611232461123836
278NaNUnited Kingdom55.378100-3.436000000000...219948219948219948219948219948219948219948219948219948219948
0NaNChinaNaNNaN171826425682...87589875898758987589875898758987589875898758987589
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14 rows × 1147 columns

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144922 188322 72917 \n", + "2023-03-09 33814 13467 161512 168935 144933 188322 72997 \n", + "\n", + "Country/Region Korea, South Netherlands Portugal Spain US \\\n", + "2020-01-23 0 0 0 0 0 \n", + "2020-01-24 0 0 0 0 0 \n", + "2020-01-25 0 0 0 0 0 \n", + "2020-01-26 0 0 0 0 0 \n", + "2020-01-27 0 0 0 0 0 \n", + "2020-01-28 0 0 0 0 0 \n", + "2020-01-29 0 0 0 0 0 \n", + "2020-01-30 0 0 0 0 0 \n", + "2020-01-31 0 0 0 0 0 \n", + "2020-02-01 0 0 0 0 0 \n", + "2020-02-02 0 0 0 0 0 \n", + "2020-02-03 0 0 0 0 0 \n", + "2020-02-04 0 0 0 0 0 \n", + "2020-02-05 0 0 0 0 0 \n", + "2020-02-06 0 0 0 0 0 \n", + "2020-02-07 0 0 0 0 0 \n", + "2020-02-08 0 0 0 0 0 \n", + "2020-02-09 0 0 0 0 0 \n", + "2020-02-10 0 0 0 0 0 \n", + "2020-02-11 0 0 0 0 0 \n", + "2020-02-12 0 0 0 0 0 \n", + "2020-02-13 0 0 0 0 0 \n", + "2020-02-14 0 0 0 0 0 \n", + "2020-02-15 0 0 0 0 0 \n", + "2020-02-16 0 0 0 0 0 \n", + "2020-02-17 0 0 0 0 0 \n", + "2020-02-18 0 0 0 0 0 \n", + "2020-02-19 0 0 0 0 0 \n", + "2020-02-20 1 0 0 0 0 \n", + "2020-02-21 2 0 0 0 0 \n", + "... ... ... ... ... ... \n", + "2023-02-08 33680 22990 26052 118712 1.11343e+06 \n", + "2023-02-09 33697 22990 26052 118712 1.11438e+06 \n", + "2023-02-10 33697 22990 26059 118976 1.11449e+06 \n", + "2023-02-11 33736 22990 26059 118976 1114529 \n", + "2023-02-12 33747 22990 26059 118976 1.11454e+06 \n", + "2023-02-13 33758 22990 26059 118976 1.11471e+06 \n", + "2023-02-14 33782 22990 26103 118976 1.11516e+06 \n", + "2023-02-15 33804 22990 26103 118976 1.11574e+06 \n", + "2023-02-16 33832 22990 26117 118976 1.11685e+06 \n", + "2023-02-17 33844 22990 26117 119186 1.11757e+06 \n", + "2023-02-18 33856 22990 26117 119186 1117589 \n", + "2023-02-19 33865 22990 26117 119186 1117590 \n", + "2023-02-20 33873 22990 26117 119186 1117663 \n", + "2023-02-21 33887 22990 26117 119186 1.11802e+06 \n", + "2023-02-22 33909 22990 26117 119186 1.11889e+06 \n", + "2023-02-23 33929 22990 26117 119186 1119521 \n", + "2023-02-24 33940 22990 26117 119380 1119573 \n", + "2023-02-25 33940 22990 26117 119380 1119587 \n", + "2023-02-26 33961 22990 26117 119380 NaN \n", + "2023-02-27 33977 22990 26117 119380 NaN \n", + "2023-02-28 33988 22990 26117 119380 1.11992e+06 \n", + "2023-03-01 34003 22990 26180 119380 1120897 \n", + "2023-03-02 34014 22990 26180 119380 1121658 \n", + "2023-03-03 34020 22990 26180 119479 1122165 \n", + "2023-03-04 34020 22990 26180 119479 NaN \n", + "2023-03-05 34034 22990 26180 119479 NaN \n", + "2023-03-06 34049 22990 26180 119479 1122181 \n", + "2023-03-07 34061 22990 26180 119479 1122516 \n", + "2023-03-08 34081 22990 26266 119479 1123246 \n", + "2023-03-09 34093 22990 26266 119479 1123836 \n", + "\n", + "Country/Region United Kingdom China \n", + "2020-01-23 0 18 \n", + "2020-01-24 0 26 \n", + "2020-01-25 0 42 \n", + "2020-01-26 0 56 \n", + "2020-01-27 0 82 \n", + "2020-01-28 0 131 \n", + "2020-01-29 0 133 \n", + "2020-01-30 1 171 \n", + "2020-01-31 1 213 \n", + "2020-02-01 1 259 \n", + "2020-02-02 2 361 \n", + "2020-02-03 2 425 \n", + "2020-02-04 2 490 \n", + "2020-02-05 2 562 \n", + "2020-02-06 2 632 \n", + "2020-02-07 2 717 \n", + "2020-02-08 2 804 \n", + "2020-02-09 2 904 \n", + "2020-02-10 2 1011 \n", + "2020-02-11 2 1111 \n", + "2020-02-12 2 1116 \n", + "2020-02-13 2 1368 \n", + "2020-02-14 2 1520 \n", + "2020-02-15 2 1662 \n", + "2020-02-16 2 1765 \n", + "2020-02-17 2 1863 \n", + "2020-02-18 2 2002 \n", + "2020-02-19 2 2114 \n", + "2020-02-20 2 2236 \n", + "2020-02-21 2 2236 \n", + "... ... ... \n", + "2023-02-08 219819 87589 \n", + "2023-02-09 219880 87589 \n", + "2023-02-10 219948 87589 \n", + "2023-02-11 219948 87589 \n", + "2023-02-12 219948 87589 \n", + "2023-02-13 219948 87589 \n", + "2023-02-14 219948 87589 \n", + "2023-02-15 219948 87589 \n", + "2023-02-16 219948 87589 \n", + "2023-02-17 219948 87589 \n", + "2023-02-18 219948 87589 \n", + "2023-02-19 219948 87589 \n", + "2023-02-20 219948 87589 \n", + "2023-02-21 219948 87589 \n", + "2023-02-22 219948 87589 \n", + "2023-02-23 219948 87589 \n", + "2023-02-24 219948 87589 \n", + "2023-02-25 219948 87589 \n", + "2023-02-26 219948 87589 \n", + "2023-02-27 219948 87589 \n", + "2023-02-28 219948 87589 \n", + "2023-03-01 219948 87589 \n", + "2023-03-02 219948 87589 \n", + "2023-03-03 219948 87589 \n", + "2023-03-04 219948 87589 \n", + "2023-03-05 219948 87589 \n", + "2023-03-06 219948 87589 \n", + "2023-03-07 219948 87589 \n", + "2023-03-08 219948 87589 \n", + "2023-03-09 219948 87589 \n", + "\n", + "[1142 rows x 14 columns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_allCountries_death_final = df_allCountries_death.transpose()[5:]\n", + "df_allCountries_death_final.columns = df_allCountries_death[\"Country/Region\"]\n", + "\n", + "all_dates = pd.to_datetime(df_allCountries_death_final.index)\n", + "df_allCountries_death_final.index = all_dates\n", + "df_allCountries_death_final" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On regarde alors la distribution des deces dans les pays concernes. Attention ici on est en effectif cummule et ces donnees ne sont pas normalisees par le taux de deces classiquement observe hors epidemie de covid ni par la population de ces territoires. " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "df_allCountries_death_final.plot(ax=ax, color=color)\n", + "ax.legend(bbox_to_anchor=(1, 1), loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "plt.yscale(\"log\") \n", + "df_allCountries_death_final.plot(ax=ax, color=color)\n", + "ax.legend(bbox_to_anchor=(1, 1), loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On voit une fois encore aue ce sont les USA avec le plus grand effectif de deces, ce qui, compte tenu de sa population de environ 333 millions d'habitants n'est pas etonnant. \n", + "\n", + "Les epidemies se succedant (aujourd'hui 4 juillet 2024) malgre l'apparition des vaccins qui ont diminue le risque de mortalite, je vais m'arreter ici dans les analyses malgre une normalisation qui s'imposerait." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hideOutput": true + }, + "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 +}