{ "cells": [ { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "# Sujet 7 : Autour du SARS-CoV-2 (COVID-19)" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "Le but est ici de reproduire des graphes semblables à ceux du South China Morning Post (SCMP), sur la page The Coronavirus Pandemic et qui montrent pour différents pays le nombre cumulé (c'est-à-dire le nombre total de cas depuis le début de l'épidémie) de personnes atteintes de la maladie à coronavirus 2019." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données utilisées pour cet excercice proviennent du [Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE)](https://systems.jhu.edu/). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On commence par charger les données, qui ont été mises à disposition sur GitHub :" ] }, { "cell_type": "code", "execution_count": 1, "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\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si jamais l'url a été modifié, on preferera travailler sur une copie locale des fichiers." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_file = \"time_series_covid19_confirmed_global.csv.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le fichier présente des séries temporelles du nombre de décès dus au covid dans 288 régions du monde. Les données concernent une période temporelle s'étalant entre le 22 janvier 2020 et le 09 mars 2023. Il est au format csv et est organisé comme suit :\n", "\n", "| colonne 1 | colonne 2 | colonne 3 | colonne 4 | colonnes 5 à 1147 |\n", "|--- |:-: |:-: |:-: |--: |\n", "| Province/State | Country/Region | Latitude | Longitude | date format dd/mm/yy |" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour charger les données, on utilise la librairie pandas, que l'on doit importer.\n", "La première ligne du document renseigne les intitulés des différentes colonnes, on évite de la charger grâce à la commande 'skiprows=1'." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "data=pd.read_csv(data_url,skiprows=0)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On vérifie s'il existe des points manquant dans ce jeu de données. Pour cela, on vérifie d'abord que toutes les lignes sont associées à un pays, et que les nombres de décès ont bien été recensés pour toutes les dates : " ] }, { "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
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Province/State, Country/Region, Lat, Long, 1/22/20, 1/23/20, 1/24/20, 1/25/20, 1/26/20, 1/27/20, 1/28/20, 1/29/20, 1/30/20, 1/31/20, 2/1/20, 2/2/20, 2/3/20, 2/4/20, 2/5/20, 2/6/20, 2/7/20, 2/8/20, 2/9/20, 2/10/20, 2/11/20, 2/12/20, 2/13/20, 2/14/20, 2/15/20, 2/16/20, 2/17/20, 2/18/20, 2/19/20, 2/20/20, 2/21/20, 2/22/20, 2/23/20, 2/24/20, 2/25/20, 2/26/20, 2/27/20, 2/28/20, 2/29/20, 3/1/20, 3/2/20, 3/3/20, 3/4/20, 3/5/20, 3/6/20, 3/7/20, 3/8/20, 3/9/20, 3/10/20, 3/11/20, 3/12/20, 3/13/20, 3/14/20, 3/15/20, 3/16/20, 3/17/20, 3/18/20, 3/19/20, 3/20/20, 3/21/20, 3/22/20, 3/23/20, 3/24/20, 3/25/20, 3/26/20, 3/27/20, 3/28/20, 3/29/20, 3/30/20, 3/31/20, 4/1/20, 4/2/20, 4/3/20, 4/4/20, 4/5/20, 4/6/20, 4/7/20, 4/8/20, 4/9/20, 4/10/20, 4/11/20, 4/12/20, 4/13/20, 4/14/20, 4/15/20, 4/16/20, 4/17/20, 4/18/20, 4/19/20, 4/20/20, 4/21/20, 4/22/20, 4/23/20, 4/24/20, 4/25/20, 4/26/20, ...]\n", "Index: []\n", "\n", "[0 rows x 1147 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[data['Country/Region'].isna()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les lignes sont bien bien associées à des pays ou régions." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "liste_dates=list(data.columns[4:])\n", "for i in range(len(liste_dates)) :\n", " data[data[liste_dates[i]].isnull()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il semble qu'aucune donnée nécéssaire à notre étude ne soit manquante, on peut donc charger à nouveau le fichier en s'affranchissant des intitulés de colonnes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seules les données de certains pays nous intéresse. On stocke les noms de ces pays dans une liste." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "liste_pays=['Belgium','China','China, Hong-Kong','France','Germany','Iran','Italy','Japan','Korea, South','Netherlands','Portugal','Spain','United Kingdom','US']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parmi ces pays, certains sont divisés en plusieurs provinces dans le fichier. Il faut les rassembler pour calculer les nombres de cas cumulés. On prend également soin de séparer Hong-Kong des provinces de Chine.\n", "Pour la représentation graphique, on importe le module pyplot du package matplotlib." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On utilise la fonction groupby pour regrouper les données associées à un même pays, à l'exception de Hong-Kong, et des provinces des Pays-Bas et du Royaume-Uni." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour éviter que ces régions ne soient prises en compte dans le calcul des cas cumulés, on va modifier le nom du pays pour ces lignes. On affiche d'abord la liste des provinces associées à chaque pays concerné par l'étude." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'Belgium'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2524\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2526\u001b[0m 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\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2525\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2527\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2529\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'Belgium'" ] } ], "source": [ "dataBel=data['Belgium'].axis(0)\n", "dataBel" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "data2=data.groupby(['Country/Region']).sum()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Belgium\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "China\n", "France\n", "France\n", "France\n", "France\n", "France\n", "France\n", "France\n", "France\n", "France\n", "France\n", "France\n", "France\n", "Germany\n", "Iran\n", "Italy\n", "Japan\n", "Korea, South\n", "Netherlands\n", "Netherlands\n", "Netherlands\n", "Netherlands\n", "Netherlands\n", "Portugal\n", "Spain\n", "United Kingdom\n", "United Kingdom\n", "United Kingdom\n", "United Kingdom\n", "United Kingdom\n", "United Kingdom\n", "United Kingdom\n", "United Kingdom\n", "United Kingdom\n", "United Kingdom\n", "United Kingdom\n", "United Kingdom\n", "United Kingdom\n", "United Kingdom\n", "United Kingdom\n", "US\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "fig=plt.figure()\n", "ax=plt.subplot(111)\n", "\n", "for j in liste_pays :\n", " values=[]\n", " for i in range (len(data[:])):\n", " if data.iat[i,1]==j :\n", " print(j)\n", " for d in liste_dates : \n", " values.append(data.at[i,d])\n", " #plt.plot(liste_dates,values,'x')\n", " #plt.legend()\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1/23/20 0\n", "1/24/20 0\n", "Name: 0, dtype: object\n", "1/24/20 0\n", "Name: 0, dtype: object\n" ] } ], "source": [ "ligne=data.loc[0][4:1147]\n", "print(ligne[1:3])\n", "print(ligne[1:3][1:])\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "209451" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.iat[0,1146]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Province/State', 'Country/Region', 'Lat', 'Long', '1/22/20', '1/23/20',\n", " '1/24/20', '1/25/20', '1/26/20', '1/27/20',\n", " ...\n", " '2/28/23', '3/1/23', '3/2/23', '3/3/23', '3/4/23', '3/5/23', '3/6/23',\n", " '3/7/23', '3/8/23', '3/9/23'],\n", " dtype='object', length=1147)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "head=data.columns\n", "head" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "list(data.columns[5:8])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data2=data.groupby(by=['Country/Region']).sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data2 #apres on pourrait transposer et récupérer plus facilement les valeurs pour chaque pays dans une liste." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "DataFrame.pivot(*, columns, index=typing.Literal[], values=typing.Literal[])[source]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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0AfghanistanNaN00000000...209322209340209358209362209369209390209406209436209451209451
1AlbaniaNaN00000000...334391334408334408334427334427334427334427334427334443334457
2AlgeriaNaN00000000...271441271448271463271469271469271477271477271490271494271496
3AndorraNaN00000000...47866478754787547875478754787547875478754789047890
4AngolaNaN00000000...105255105277105277105277105277105277105277105277105288105288
5AntarcticaNaN00000000...11111111111111111111
6Antigua and BarbudaNaN00000000...9106910691069106910691069106910691069106
7ArgentinaNaN00000000...10044125100441251004412510044125100441251004412510044957100449571004495710044957
8ArmeniaNaN00000000...446819446819446819446819446819446819446819446819447308447308
9AustraliaAustralian Capital Territory00000000...232018232018232619232619232619232619232619232619232619232974
10AustraliaNew South Wales00003444...3900969390096939081293908129390812939081293908129390812939081293915992
11AustraliaNorthern Territory00000000...104931104931105021105021105021105021105021105021105021105111
12AustraliaQueensland00000001...1796633179663318002361800236180023618002361800236180023618002361800236
13AustraliaSouth Australia00000000...880207880207881911881911881911881911881911881911881911883620
14AustraliaTasmania00000000...286264286264286264286897286897286897286897286897286897287507
15AustraliaVictoria00001111...2874262287426228772602877260287726028772602877260287726028772602880559
16AustraliaWestern Australia00000000...1291077129107712934611293461129346112934611293461129346112934611293461
17AustriaNaN00000000...5911294591961659261485931247593666659409355943417594941859558605961143
18AzerbaijanNaN00000000...828548828588828628828648828682828721828730828783828819828825
19BahamasNaN00000000...37491374913749137491374913749137491374913749137491
20BahrainNaN00000000...707480707828708061708532708768709230709230709858710306710693
21BangladeshNaN00000000...2037773203782920378292037829203782920378292037829203782920378712037871
22BarbadosNaN00000000...106645106645106645106645106645106645106645106645106645106798
23BelarusNaN00000000...994037994037994037994037994037994037994037994037994037994037
24BelgiumNaN00000000...4717655471765547277954727795472779547277954727795472779547277954739365
25BelizeNaN00000000...70757707577075770757707577075770757707577075770757
26BeninNaN00000000...27990279902799027990279902799027990279992799927999
27BhutanNaN00000000...62615626206262062620626206262062620626206262762627
28BoliviaNaN00000000...1193009119325611934181193650119381511939081193970119406911941871194277
29Bosnia and HerzegovinaNaN00000000...401575401636401636401636401636401636401636401636401729401729
..................................................................
259TuvaluNaN00000000...2805280528052805280528052805280528052805
260USNaN11225556...103443455103533872103589757103648690103650837103646975103655539103690910103755771103802702
261UgandaNaN00000000...170504170504170504170504170504170504170504170504170544170544
262UkraineNaN00000000...5693846570124957013335701474570160257017435701855570195957118185711929
263United Arab EmiratesNaN00000004...1051998105212210522471052382105251910526641052664105292610530681053213
264United KingdomAnguilla00000000...3904390439043904390439043904390439043904
265United KingdomBermuda00000000...18799188141881418814188141881418814188141882818828
266United KingdomBritish Virgin Islands00000000...7305730573057305730573057305730573057305
267United KingdomCayman Islands00000000...31472314723147231472314723147231472314723147231472
268United KingdomChannel Islands00000000...0000000000
269United KingdomFalkland Islands (Malvinas)00000000...1930193019301930193019301930193019301930
270United KingdomGibraltar00000000...20423204232042320433204332043320433204332043320433
271United KingdomGuernsey00000000...34867349293492934929349293492934929349293499134991
272United KingdomIsle of Man00000000...38008380083800838008380083800838008380083800838008
273United KingdomJersey00000000...66391663916639166391663916639166391663916639166391
274United KingdomMontserrat00000000...1403140314031403140314031403140314031403
275United KingdomPitcairn Islands00000000...4444444444
276United KingdomSaint Helena, Ascension and Tristan da Cunha00000000...2166216621662166216621662166216621662166
277United KingdomTurks and Caicos Islands00000000...6551655165516551655165516551655765576561
278United KingdomNaN00000000...24370150243701502439653024396530243965302439653024396530243965302439653024425309
279UruguayNaN00000000...1034303103430310343031034303103430310343031034303103430310343031034303
280UzbekistanNaN00000000...250932251071251071251071251071251071251071251071251247251247
281VanuatuNaN00000000...12014120141201412014120141201412014120141201412014
282VenezuelaNaN00000000...551981551986551986552014552051552051552125552157552157552162
283VietnamNaN02222222...11526917115269261152693711526950115269621152696611526966115269861152699411526994
284West Bank and GazaNaN00000000...703228703228703228703228703228703228703228703228703228703228
285Winter Olympics 2022NaN00000000...535535535535535535535535535535
286YemenNaN00000000...11945119451194511945119451194511945119451194511945
287ZambiaNaN00000000...343012343012343079343079343079343135343135343135343135343135
288ZimbabweNaN00000000...263921264127264127264127264127264127264127264127264276264276
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289 rows × 1145 columns

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"metadata": {}, "output_type": "execute_result" } ], "source": [ "#data.set_index(['Province/State','Country/Region'])\n", "data=data.reindex(['Country/Region','Province/State']+liste_dates,axis=1)\n", "data" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.iloc[0,1] -> donne la province." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Int64Index([120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131], dtype='int64')" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.loc[data['Country/Region'] == 'France'].index" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Country/RegionProvince/State1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
120FranceFrench Guiana00000000...98041980419804198041980419804198041980419804198041
121FranceFrench Polynesia00000000...77957779577795777957779577795777957779577805578055
122FranceGuadeloupe00000000...201852201852201852201852201852201852201852201852201886201886
123FranceMartinique00000000...228875228875228875228875228875228875228875228875229020229020
124FranceMayotte00000000...42004420044200442004420044200442004420044200442004
125FranceNew Caledonia00000000...80007800078000780007800078000780007800078001780017
126FranceReunion00000000...494595494595494595494595494595494595494595494595494595494595
127FranceSaint Barthelemy00000000...5439543954395439543954395439543954415441
128FranceSaint Pierre and Miquelon00000000...3452345234523452345234523452345234523452
129FranceSt Martin00000000...12257122571225712257122571225712257122571227112271
130FranceWallis and Futuna00000000...3427342734273427342734273427342734273427
131FranceNaN00233345...38579269385837943858799038591184385911843859118438599330386063933861220138618509
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12 rows × 1145 columns

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" ], "text/plain": [ " Country/Region Province/State 1/22/20 1/23/20 1/24/20 \\\n", "120 France French Guiana 0 0 0 \n", "121 France French Polynesia 0 0 0 \n", "122 France Guadeloupe 0 0 0 \n", "123 France Martinique 0 0 0 \n", "124 France Mayotte 0 0 0 \n", "125 France New Caledonia 0 0 0 \n", "126 France Reunion 0 0 0 \n", "127 France Saint Barthelemy 0 0 0 \n", "128 France Saint Pierre and Miquelon 0 0 0 \n", "129 France St Martin 0 0 0 \n", "130 France Wallis and Futuna 0 0 0 \n", "131 France NaN 0 0 2 \n", "\n", " 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 ... 2/28/23 \\\n", "120 0 0 0 0 0 ... 98041 \n", "121 0 0 0 0 0 ... 77957 \n", "122 0 0 0 0 0 ... 201852 \n", "123 0 0 0 0 0 ... 228875 \n", "124 0 0 0 0 0 ... 42004 \n", "125 0 0 0 0 0 ... 80007 \n", "126 0 0 0 0 0 ... 494595 \n", "127 0 0 0 0 0 ... 5439 \n", "128 0 0 0 0 0 ... 3452 \n", "129 0 0 0 0 0 ... 12257 \n", "130 0 0 0 0 0 ... 3427 \n", "131 3 3 3 4 5 ... 38579269 \n", "\n", " 3/1/23 3/2/23 3/3/23 3/4/23 3/5/23 3/6/23 3/7/23 \\\n", "120 98041 98041 98041 98041 98041 98041 98041 \n", "121 77957 77957 77957 77957 77957 77957 77957 \n", "122 201852 201852 201852 201852 201852 201852 201852 \n", "123 228875 228875 228875 228875 228875 228875 228875 \n", "124 42004 42004 42004 42004 42004 42004 42004 \n", "125 80007 80007 80007 80007 80007 80007 80007 \n", "126 494595 494595 494595 494595 494595 494595 494595 \n", "127 5439 5439 5439 5439 5439 5439 5439 \n", "128 3452 3452 3452 3452 3452 3452 3452 \n", "129 12257 12257 12257 12257 12257 12257 12257 \n", "130 3427 3427 3427 3427 3427 3427 3427 \n", "131 38583794 38587990 38591184 38591184 38591184 38599330 38606393 \n", "\n", " 3/8/23 3/9/23 \n", "120 98041 98041 \n", "121 78055 78055 \n", "122 201886 201886 \n", "123 229020 229020 \n", "124 42004 42004 \n", "125 80017 80017 \n", "126 494595 494595 \n", "127 5441 5441 \n", "128 3452 3452 \n", "129 12271 12271 \n", "130 3427 3427 \n", "131 38612201 38618509 \n", "\n", "[12 rows x 1145 columns]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataFrance" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "Error in callback .post_execute at 0x7fa0aa5c9c80> (for post_execute):\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mpost_execute\u001b[0;34m()\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpost_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m \u001b[0mdraw_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[0;31m# IPython >= 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/_pylab_helpers.py\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mf_mgr\u001b[0m \u001b[0;32min\u001b[0m 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"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mflush_figures\u001b[0;34m()\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;31m# ignore the tracking, just draw and close all figures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 122\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;31m# safely show traceback if in IPython, else raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m 41\u001b[0m display(\n\u001b[1;32m 42\u001b[0m \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 44\u001b[0m )\n\u001b[1;32m 45\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 315\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m \u001b[0mcomponents\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 317\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 318\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "plt.plot(liste_dates,data.iloc[131,2:])" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "valeurs=data.iloc[131,2:5].values" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 0, 2], dtype=object)" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "valeurs" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FJCF++Ns1rCjZy69uuZA+Kr4WKh3xi0iLm7+shDnvfcQ/f+5MPn/eaWGHk/YaTPxmNsvMysyszqtnWcRjZlZsZivMLDembZSZrY+2TY1n4CKSGtZ/sp/vv7SSYX1P4d7PnxN2OEKwI/6ngFHHab8G6Be9TQSmA5hZBjAt2j4QGGdmA5sTrIiklv1HKpk0u4CO7dryxLihtFHxtaTQ4L+Cuy8Bdh2nyxjgaY94F+hiZj2IXFi92N03unsF8Ey0r4ikgWPF17bsOsS0m4fSTcXXkkY83n57Altjlkui6+pbLyJpYNbbm1m48hO+9/lzuOhMFV9LJvFI/HUV2PDjrK/7Qcwmmlm+meWXl5fHISwRCUv+5l38ZOFarh7YnYmfU/G1ZBOPxF8C9I5Z7gWUHmd9ndx9prvnuXtednZ2HMISkTDsOHCUyXML6dn1JB7+ioqvJaN4JP4FwFejZ/dcDOx1923AUqCfmfU1s0xgbLSviLRSkeJry9hzqJLp4y9U8bUk1eAPuMxsHjACyDKzEuAhoC2Au88AFgKjgWLgEHBrtK3KzKYArwMZwCx3X90Cr0FEksQvFm/g7eKd/OzLgxh4+slhhyP1aDDxu/u4BtodmFxP20Iibwwi0sr9ce12nvhTMTfl9ebGvN4NbyCh0Um1ItJsx4qvDexxMj8Yc17Y4UgDlPhFpFmOVFYzaU4BDsyYcKGKr6UAFWkTkWb5wW/XsOrjffz3V/PIObV92OFIADriF5Eme7GghHnvf8Ttl5/FVQO7hx2OBKTELyJNsu6Tfdz/8kouPvMUvnt1/7DDkUZQ4heRRtt3pJJJsws5+cS2PKbiaylHc/wi0ijuzveeX8FHuw4x75sX062Tiq+lGr1Ni0ij/OatTby2+hOmjjqXYX1PCTscaQIlfhEJbOnmXfzk1XWMOu80vvHZvmGHI02kxC8igZTvP8rkOYX07noSP/vKIBVfS2Ga4xeRBlVV13DnvGXsO1LJ/3x9GCefqOJrqUyJX0Qa9F+LN/DXjTv5+VcGM6CHiq+lOk31iMhxLV6znel//pBxw3rz5Qt7hR2OxIESv4jU66Odh/j2c0Wc3/NkHrpexddaCyV+EanTseJrBkwfr+JrrYnm+EWkTv+2YDWrS/fxm3/Ko/cpKr7WmgQ64jezUWa23syKzWxqHe33mllR9LbKzKrN7JRo22YzWxlty4/3CxCR+Hs+fyvPLN3KHSPO4soBKr7W2gS59GIGMA24isgF1Jea2QJ3X3Osj7s/DDwc7X89cI+774p5mJHuviOukYtIi1hTuo9/fXkVl5x5Kt++SsXXWqMgR/zDgGJ33+juFcAzwJjj9B8HzItHcCKSWPuOVHLHnAK6tFfxtdYsyL9qT2BrzHJJdN2nmFl7YBTwYsxqBxaZWYGZTWxqoCLSstyd7z63nJLdh5l2cy7ZndqFHZK0kCBf7tb1u2yvp+/1wNu1pnmGu3upmXUDFpvZOndf8qknibwpTATIyckJEJaIxNN/v7mRRWu286/XDiCvj4qvtWZBjvhLgN4xy72A0nr6jqXWNI+7l0b/lgHziUwdfYq7z3T3PHfPy87ODhCWiMTLext38tPX1jP6gtO47TIVX2vtgiT+pUA/M+trZplEkvuC2p3MrDNwOfBKzLoOZtbp2H3gamBVPAIXkfgo23+EKfOWccYp7fnpP6r4WjpocKrH3avMbArwOpABzHL31WZ2e7R9RrTrF4FF7n4wZvPuwPzoQGoDzHX31+L5AkSk6aqqa5gydxn7j1Tyv7cNo5OKr6WFQD/gcveFwMJa62bUWn4KeKrWuo3A4GZFKCIt5uFF63l/0y4euXEw556m4mvpQudqiaSpRas/4Vd/2cjNF+XwpVwVX0snSvwiaWjLzoN85/nlXNCzMw9eNzDscCTBlPhF0syRympun13ICWY8OT5XxdfSkIq0iaSZB19Zxdpt+5j1NRVfS1c64hdJI88t3cpz+SVMGXk2V5yr4mvpSolfJE2sLt3LA6+sYvjZp3KPiq+lNSV+kTSw93Alk2YX0rV9Jo+NHUrGCfqRVjrTHL9IK+fufPf55ZTuOcyz/3wJp3ZU8bV0pyN+kVbuV0s2snjNdu4bPYALz+gadjiSBJT4RVqxv364k5+9to5rB/Xg1uF9wg5HkoQSv0grVbbvCN+at4w+WR1UfE3+jub4RVqhymjxtYNHq5j7zYvo2E7/1eVvNBpEWqGHX1/P+5t38ehNQ+jfvVPY4UiS0VSPSCvz2qpPmLlkIxMuzuGGoXVeJVXSnBK/SCuyacdB7n1+OYN7deYBFV+Teijxi7QShyuqmTS7gIwMY9r4XNq1UfE1qVugxG9mo8xsvZkVm9nUOtpHmNleMyuK3h4Muq2INJ+788Arq1i/fT+/uGkIvbqq+JrUr8Evd80sA5gGXEXkwutLzWyBu6+p1fVNd7+uiduKSDM8u3QrLxSUcOeV/Rh5Treww5EkF+SIfxhQ7O4b3b0CeAYYE/Dxm7OtiASw6uO9PLhgNZ/tl8VdV/YLOxxJAUESf09ga8xySXRdbZeY2XIze9XMzmvktpjZRDPLN7P88vLyAGGJyN5DlUyaU8CpHTL5pYqvSUBBEn9dI8lrLRcCZ7j7YOBx4OVGbBtZ6T7T3fPcPS87OztAWCLprabG+c7zRXyy9wjTxudySofMsEOSFBEk8ZcAvWOWewGlsR3cfZ+7H4jeXwi0NbOsINuKSNPMWPIhf1hbxv2jB5Cbo+JrElyQxL8U6Gdmfc0sExgLLIjtYGanWbQQiJkNiz7uziDbikjjvfPhDn7++nquH3w6/3Rpn7DDkRTT4Fk97l5lZlOA14EMYJa7rzaz26PtM4AvA5PMrAo4DIx1dwfq3LaFXotIWvhk7xHunLeMvlkd+M8vXaDia9JoFsnPySUvL8/z8/PDDkMk6VRW1zBu5rus2baPVyYPp5/q8EiUmRW4e16QvirSJpJCfvrqOvK37OaXY4co6UuTqWSDSIp4deU2fv3WJr56yRmMGaLia9J0SvwiKWBj+QHufWEFg3t34f5rB4QdjqQ4JX6RJHe4opo75hTSNsN4UsXXJA40xy+SxNyd+19eyfrt+3nq1mH07HJS2CFJK6AjfpEkNu/9rbxU+DF3XdmPy/vrF+0SH0r8IklqZcle/m3Baj7XP5s7r1DxNYkfJX6RJLTnUAWT5hSQ1TGTR28awgkqviZxpDl+kSRTU+N8+7nlbN93hOdvv1TF1yTudMQvkmSe/HMxb6wr44HrBjKkd5eww5FWSIlfJIm8XbyDRxZv4AuDT+eWi88IOxxppZT4RZLEseJrZ2Z35CcqviYtSHP8IkmgsrqGyXMLOVxZzbMTcunQTv81peVodIkkgZ8sXEfBlt08Pm4oZ3dT8TVpWZrqEQnZ71dsY9bbm/japX24fvDpYYcjaUCJXyREH5Yf4HsvLGdoThfuG63ia5IYgRK/mY0ys/VmVmxmU+toH29mK6K3d8xscEzbZjNbaWZFZqarq4hEHaqoYtLsAtq1zWDazblkttFxmCRGg3P8ZpYBTAOuInLx9KVmtsDd18R02wRc7u67zewaYCZwUUz7SHffEce4RVKau3P//FV8UHaAp78+jNNVfE0SKMghxjCg2N03unsF8AwwJraDu7/j7ruji+8CveIbpkjrMue9j5i/7GPu+Yf+fLafiq9JYgVJ/D2BrTHLJdF19bkNeDVm2YFFZlZgZhPr28jMJppZvpnll5eXBwhLJDWtKNnDD3+7hhHnZDNl5NlhhyNpKMjpnHX9iqTOK7Sb2Ugiif+ymNXD3b3UzLoBi81snbsv+dQDus8kMkVEXl5e8l0BXiQOdh+sYNLsQrI7teMXN6r4moQjyBF/CdA7ZrkXUFq7k5kNAn4NjHH3ncfWu3tp9G8ZMJ/I1JFI2qmpce55rojy/Ud5cnwuXVV8TUISJPEvBfqZWV8zywTGAgtiO5hZDvAScIu7b4hZ38HMOh27D1wNrIpX8CKp5Ik/FfPn9eU8cP1ABqv4moSowaked68ysynA60AGMMvdV5vZ7dH2GcCDwKnAk9H6IlXungd0B+ZH17UB5rr7ay3ySkSS2JsflPOLP2zghiGnM+GinLDDkTRn7sk3nZ6Xl+f5+TrlX1qH0j2Hue7xt8jqmMnLk4fTPlOVUiT+zKwgesDdIP1iRKQFVVRFiq8draxm+oQLlfQlKWgUirSg/1i4lmUf7WHazbmcld0x7HBEAB3xi7SY3y4v5al3NnPr8D5cO6hH2OGI/H9K/CItoLjsAFNfXEFuThe+f42Kr0lyUeIXibODR2OKr41X8TVJPhqRInHk7tw3fyUflh/g8XFD6dFZxdck+Sjxi8TR7He38EpRKd++qj/Dz84KOxyROinxi8RJ0dY9/PB3a7ji3G7cMULF1yR5KfGLxMGugxXcMbuA7iefyCM3DlbxNUlqOo9fpJmqa5y7ny1ix4EKXph0CV3aq/iaJDcd8Ys00+NvfMCSDeU89IWBDOql4muS/JT4RZrhLxvK+eUfP+BLQ3ty8zAVX5PUoMQv0kQf7znM3c8so3+3Tvz4ixcQrUIrkvSU+EWaoKKqhslzCqmsdqZPyOWkzIywQxIJTF/uijTBj3+/hqKte3hyfC5nqviapJhAR/xmNsrM1ptZsZlNraPdzOyxaPsKM8sNuq1IqlmwvJT/+esWbrusL6MvUPE1ST0NJn4zywCmAdcAA4FxZjawVrdrgH7R20RgeiO2FUkZH2zfz9QXV5B3RlemXnNu2OGINEmQI/5hQLG7b3T3CuAZYEytPmOApz3iXaCLmfUIuK1ISjh4tIpJcwppn5nBEzfn0jZDX5FJagoyx98T2BqzXAJcFKBPz4Dbxs31j7/Fkcrqlnp4SXP7j1RRtv8Is79xEad1PjHscESaLEjir+sctdoX6q2vT5BtIw9gNpHINBE5OU07H/qs7A5UVNc0aVuRID5/3mlcepaKr0lqC5L4S4DeMcu9gNKAfTIDbAuAu88EZkLkYusB4vqUR8cObcpmIiJpJcgk5VKgn5n1NbNMYCywoFafBcBXo2f3XAzsdfdtAbcVEZEEavCI392rzGwK8DqQAcxy99Vmdnu0fQawEBgNFAOHgFuPt22LvBIREQnE3Js0q9Ki8vLyPD8/P+wwRERShpkVuHtekL46H01EJM0o8YuIpBklfhGRNKPELyKSZpT4RUTSTFKe1WNm5cCWJm6eBeyIYzjxorgaR3E1juJqnNYY1xnunh2kY1Im/uYws/ygpzQlkuJqHMXVOIqrcdI9Lk31iIikGSV+EZE00xoT/8ywA6iH4mocxdU4iqtx0jquVjfHLyIix9caj/hFROQ4Uibxm9ksMyszs1X1tIdywfcAcY2PxrPCzN4xs8ExbZvNbKWZFZlZXKvSBYhrhJntjT53kZk9GNMW5v66NyamVWZWbWanRNtacn/1NrM/mdlaM1ttZnfV0SfhYyxgXAkfYwHjSvgYCxhXwseYmZ1oZu+b2fJoXD+oo0/ixpe7p8QN+ByQC6yqp3008CqRq35dDLwXXZ8BfAicSeTCMMuBgQmM61Kga/T+Ncfiii5vBrJC2l8jgN/VsT7U/VWr7/XAGwnaXz2A3Oj9TsCG2q87jDEWMK6Ej7GAcSV8jAWJK4wxFh0zHaP32wLvAReHNb5S5ojf3ZcAu47TJZQLvjcUl7u/4+67o4vvErkKWYsLsL/qE+r+qmUcMC9ez3087r7N3Quj9/cDa4lcMzpWwsdYkLjCGGMB91d9Qt1ftSRkjEXHzIHoYtvorfYXrAkbXymT+ANozAXfgw7QeLuNyDv6MQ4sMrMCi1xzONEuiX70fNXMzouuS4r9ZWbtgVHAizGrE7K/zKwPMJTIUVmsUMfYceKKlfAx1kBcoY2xhvZXoseYmWWYWRFQBix299DGV5Br7qaKZl/wvSWZ2Ugi/ykvi1k93N1LzawbsNjM1kWPiBOhkMhPvA+Y2WjgZaAfSbK/iHwEf9vdYz8dtPj+MrOORBLB3e6+r3ZzHZskZIw1ENexPgkfYw3EFdoYC7K/SPAYc/dqYIiZdQHmm9n57h77XVfCxldrOuKv74LvQS4W36LMbBDwa2CMu+88tt7dS6O6uherAAABhElEQVR/y4D5RD7SJYS77zv20dPdFwJtzSyLJNhfUWOp9RG8pfeXmbUlkizmuPtLdXQJZYwFiCuUMdZQXGGNsSD7KyrhYyz62HuAPxP5tBErceOruV9aJPIG9KH+Lyuv5e+/GHk/ur4NsBHoy9++GDkvgXHlELkW8aW11ncAOsXcfwcYlcC4TuNvv+MYBnwU3Xeh7q9oe2ci3wN0SNT+ir72p4FHj9Mn4WMsYFwJH2MB40r4GAsSVxhjDMgGukTvnwS8CVwX1vhKmakeM5tH5CyBLDMrAR4i8gUJHuIF3wPE9SBwKvCkmQFUeaQIU3ciH/cg8g87191fS2BcXwYmmVkVcBgY65FRFvb+AvgisMjdD8Zs2qL7CxgO3AKsjM7DAtxHJKmGOcaCxBXGGAsSVxhjLEhckPgx1gP4HzPLIDLT8py7/87Mbo+JK2HjS7/cFRFJM61pjl9ERAJQ4hcRSTNK/CIiaUaJX0QkzSjxi4ikGSV+EZE0o8QvIpJmlPhFRNLM/wEMAQBpXiQYEQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot([1,2,3],valeurs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On commence par réorganiser la dataframe selon nos besoins." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data=data.reindex(['Country/Region','Province/State']+liste_dates,axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On récupère ensuite les données qui nous intéresse pour chaque pays. Pour les pays sans province/state on peut récupérer les données directement, sinon il faut sommer les données des régions au moyen de la fonction groupby. Pour les pays dont on ne considère par les territoires d'outre-mer, on prend uniquement la dernière ligne correspondante, soit le maximum des indices." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "dataBel=data.iloc[max(data.loc[data['Country/Region'] == 'Belgium'].index),2:]\n", "dataHK=data.iloc[max(data.loc[data['Province/State']=='Hong Kong'].index),2:]\n", "dataFra=data.iloc[max(data.loc[data['Country/Region'] == 'France'].index),2:]\n", "dataGer=data.iloc[max(data.loc[data['Country/Region'] == 'Germany'].index),2:]\n", "dataIra=data.iloc[max(data.loc[data['Country/Region'] == 'Iran'].index),2:]\n", "dataIta=data.iloc[max(data.loc[data['Country/Region'] == 'Italy'].index),2:]\n", "dataJap=data.iloc[max(data.loc[data['Country/Region'] == 'Japan'].index),2:]\n", "dataSK=data.iloc[max(data.loc[data['Country/Region'] == 'Korea, South'].index),2:]\n", "dataNeth=data.iloc[max(data.loc[data['Country/Region'] == 'Netherlands'].index),2:]\n", "dataPort=data.iloc[max(data.loc[data['Country/Region'] == 'Portugal'].index),2:]\n", "dataSpa=data.iloc[max(data.loc[data['Country/Region'] == 'Spain'].index),2:]\n", "dataUK=data.iloc[max(data.loc[data['Country/Region'] == 'United Kingdom'].index),2:]\n", "dataUS=data.iloc[max(data.loc[data['Country/Region'] == 'US'].index),2:]\n", "\n", "#on trouve les indices correpsondants à la Chine et à HK puis on retire la ligne correspondant à Hong Kong pour ne pas qu'elle soit prise en compte deux fois. \n", "indicesChi=data.loc[data['Country/Region'] == 'China'].index\n", "indiceHK=data.loc[data['Province/State']=='Hong Kong'].index\n", "listeindicesChi=list(indicesChi)\n", "listeindicesChi.remove(int(indiceHK.values[0]))\n", "\n", "dataChi=data.iloc[listeindicesChi,2:].sum() #on somme sur toutes les provinces de Chine\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 0, 0, ..., 4727795, 4727795, 4739365], dtype=object)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataBel.values" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [], "source": [ "dataUK=data.iloc[max(data.loc[data['Country/Region'] == 'Netherlands'].index),2:]" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Int64Index([59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,\n", " 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", " 92],\n", " dtype='int64')\n", "Int64Index([71], dtype='int64')\n", "[59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92]\n" ] } ], "source": [ "\n" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "71" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "indiceHK.values[0]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n" ] } ], "source": [ "print(indicesChi)" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [], "source": [ "indicesChi=data.loc[data['Country/Region'] == 'China'].index\n", "indiceHK=data.loc[data['Province/State']=='Hong Kong'].index" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Int64Index([71], dtype='int64')" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "indicesChi\n", "indiceHK" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [], "source": [ "list(indicesChi).remove(indiceHK)" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [], "source": [ "dataChi=data.iloc[indicesChi,2:]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import matplotlib.axes as ax\n", "import matplotlib.pyplot as plt\n", "def plotcumul(data,dates,legende,**argsupp):\n", " fig=plt.figure(figsize=(24,6))\n", " if 'ax' in argsupp.keys():\n", " ax=argsupp['ax']\n", " else : ax=plt.subplot(111) \n", " ax.plot(dates[:],data.values[:],label=legende)\n", " ax.legend()\n", " return(fig,ax)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,ax=plotcumul(dataFra,liste_dates,'France')\n", "fig,ax=plotcumul(dataBel,liste_dates,'Belgique',ax=ax)\n", "fig,ax=plotcumul(dataHK,liste_dates,'Hong-Kong',ax=ax)\n", "fig,ax=plotcumul(dataGer,liste_dates,'Germany',ax=ax)\n", "fig,ax=plotcumul(dataIra,liste_dates,'Iran',ax=ax)\n", "fig,ax=plotcumul(dataIta,liste_dates,'Italy',ax=ax)\n", "fig,ax=plotcumul(dataJap,liste_dates,'Japan',ax=ax)\n", "fig,ax=plotcumul(dataSK,liste_dates,'South Korea',ax=ax)\n", "fig,ax=plotcumul(dataNeth,liste_dates,'Netherlands',ax=ax)\n", "fig,ax=plotcumul(dataPort,liste_dates,'Portugal',ax=ax)\n", "fig,ax=plotcumul(dataSpa,liste_dates,'Spain',ax=ax)\n", "fig,ax=plotcumul(dataUK,liste_dates,'United-Kingdom',ax=ax)\n", "fig,ax=plotcumul(dataUS,liste_dates,'United-States',ax=ax)\n", "fig,ax=plotcumul(dataChi,liste_dates,'China',ax=ax)\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "x and y must have same first dimension, but have shapes (1143,) and (1, 1143)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mliste_dates\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdataPort\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 3361\u001b[0m mplDeprecation)\n\u001b[1;32m 3362\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 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callback (for post_execute):\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mflush_figures\u001b[0;34m()\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;31m# ignore the tracking, just draw and close all figures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 122\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m 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1891\u001b[0m \u001b[0mtlb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtlb2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick_bboxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticks_to_draw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1892\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtlb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 1026\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1027\u001b[0m \u001b[0minterval\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 153\u001b[0m r_mapping = {v: StrCategoryFormatter._text(k)\n\u001b[0;32m--> 154\u001b[0;31m for k, v in self._units.items()}\n\u001b[0m\u001b[1;32m 155\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mr_mapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/category.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 153\u001b[0m r_mapping = {v: StrCategoryFormatter._text(k)\n\u001b[0;32m--> 154\u001b[0;31m for k, v in self._units.items()}\n\u001b[0m\u001b[1;32m 155\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mr_mapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/category.py\u001b[0m in \u001b[0;36m_text\u001b[0;34m(value)\u001b[0m\n\u001b[1;32m 159\u001b[0m \"\"\"Converts text values into `utf-8` or `ascii` strings\n\u001b[1;32m 160\u001b[0m \"\"\"\n\u001b[0;32m--> 161\u001b[0;31m \u001b[0;32mif\u001b[0m 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