{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sujet 7 : Autour du SARS-CoV-2 (Covid-19)\n", "\n", "\n", "## Consignes :\n", "\n", "### Prérequis\n", "\n", "Techniques de présentation graphique. Cet exercice peut être réalisé indifféremment en R ou en Python.\n", "\n", "### Sujet\n", "\n", "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.\n", "\n", "Les données que nous utiliserons dans un premier temps sont compilées par le Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) et sont mises à disposition sur GitHub. C'est plus particulièrement sur les données time_series_covid19_confirmed_global.csv (des suites chronologiques au format csv) disponibles 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, que nous allons nous concentrer.\n", "\n", "Vous commencerez par télécharger les données pour créer un graphe montrant l’évolution du nombre de cas cumulé au cours du temps pour les pays suivants : la Belgique (Belgium), la Chine - toutes les provinces sauf Hong-Kong (China), Hong Kong (China, Hong-Kong), la France métropolitaine (France), l’Allemagne (Germany), l’Iran (Iran), l’Italie (Italy), le Japon (Japan), la Corée du Sud (Korea, South), la Hollande sans les colonies (Netherlands), le Portugal (Portugal), l’Espagne (Spain), le Royaume-Unis sans les colonies (United Kingdom), les États-Unis (US).\n", "\n", "Le nom entre parenthèses est le nom du « pays » tel qu’il apparaît dans le fichier time_series_covid19_confirmed_global.csv. Les données de la Chine apparaissent par province et nous avons séparé Hong-Kong, non pour prendre parti dans les différences entre cette province et l'état chinois, mais parce que c'est ainsi qu'apparaissent les données sur le site du SCMP. Les données pour la France, la Hollande et le Royaume-Uni excluent les territoires d'outre-mer et autres « résidus coloniaux ».\n", "\n", "Ensuite vous ferez un graphe avec la date en abscisse et le nombre cumulé de cas à cette date en ordonnée. Nous vous proposons de faire deux versions de ce graphe, une avec une échelle linéaire et une avec une échelle logarithmique.\n", "\n", "### Question subsidiaire à faire quand on sera sorti du « merdier »\n", "\n", "Vous pourrez également utiliser les données de décès (timeseriescovid19deathsglobal.csv) et refaire les courbes, mais là encore, faites attention lors de l'interprétation. Ces courbes, même si elles paraissent effrayantes, doivent être comparées à la mortalité « normale ». Pour la France des données sont disponibles sur le site de l'INSEE : https://www.insee.fr/fr/information/4470857, ainsi que dans les « Points hebdomadaires » de surveillance de la mortalité diffusés par Santé publique France, comme celui de la semaine 12 (le site étant très mal conçu pour quiconque souhaite une information spécifique, le plus simple est de passer par un moteur de recherche généraliste…).\n", "\n", "Pour atténuer les effets dus aux méthodes de comptage, etc., vous pourrez, une fois l'épidémie terminée, prendre les données du nombre total de décès et les normaliser pour 1000 habitants du pays concerné. Vous irez ensuite chercher les données sur le nombre de lits d'hôpital pour 1000 habitants sur le site de l'OCDE et vous pourrez corréler les deux (c'est-à-dire, faire un graphe avec le nombre de lits en abscisse et le nombre de décès en ordonnée)…\n", "\n", "---\n", "\n", "# Pour commencer\n", "\n", "Nous allons tout d'abord importer les outils necéssaires pour réaliser ce travail.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "# pour la vérification de la présence des données\n", "import os\n", "# pour télécharger les données\n", "import urllib.request\n", "\n", "# pour l'affichage des graphiques\n", "import matplotlib.pyplot as plt\n", "# pour le traitement des données\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons maintenant télécharger les [données](https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv) si elles ne sont pas déjà téléchargés." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/23/212/24/212/25/212/26/212/27/212/28/213/1/213/2/213/3/213/4/21
0NaNAfghanistan33.9391167.709953000000...55646556645568055696557075571455733557595577055775
1NaNAlbania41.1533020.168300000000...102306103327104313105229106215107167107931108823109674110521
2NaNAlgeria28.033901.659600000000...112279112461112622112805112960113092113255113430113593113761
3NaNAndorra42.506301.521800000000...10739107751079910822108491086610889109081094810976
4NaNAngola-11.2027017.873900000000...20584206402069520759207822080720854208822092320981
\n", "

5 rows × 412 columns

\n", "
" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", "0 NaN Afghanistan 33.93911 67.709953 0 0 \n", "1 NaN Albania 41.15330 20.168300 0 0 \n", "2 NaN Algeria 28.03390 1.659600 0 0 \n", "3 NaN Andorra 42.50630 1.521800 0 0 \n", "4 NaN Angola -11.20270 17.873900 0 0 \n", "\n", " 1/24/20 1/25/20 1/26/20 1/27/20 ... 2/23/21 2/24/21 2/25/21 \\\n", "0 0 0 0 0 ... 55646 55664 55680 \n", "1 0 0 0 0 ... 102306 103327 104313 \n", "2 0 0 0 0 ... 112279 112461 112622 \n", "3 0 0 0 0 ... 10739 10775 10799 \n", "4 0 0 0 0 ... 20584 20640 20695 \n", "\n", " 2/26/21 2/27/21 2/28/21 3/1/21 3/2/21 3/3/21 3/4/21 \n", "0 55696 55707 55714 55733 55759 55770 55775 \n", "1 105229 106215 107167 107931 108823 109674 110521 \n", "2 112805 112960 113092 113255 113430 113593 113761 \n", "3 10822 10849 10866 10889 10908 10948 10976 \n", "4 20759 20782 20807 20854 20882 20923 20981 \n", "\n", "[5 rows x 412 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# URL des données\n", "data_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n", "\n", "# Nom du fichier csv\n", "data_file = \"data_covid.csv\"\n", "\n", "# Téléchargement des données si elles ne sont pas déjà présentes dans le répertoire\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)\n", "\n", "# Affichage des données\n", "raw_data = pd.read_csv(data_file)\n", "raw_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ces données sont organisés sur 274 lignes pour chaque pays et (pour le moment) 412 colonnes présentant la province, le pays, les latitudes et longitudes suivi du nombre de cas par jour du 22 Janvier 2020 au 4 Mars 2021 au moment de l'écriture de ce rapport.\n", "\n", "Pour vérifier qu'aucune date pour aucun pays est manquante, filtrons les données en choisant les colonnes des dates avec la regex `\\d{1,2}\\/\\d{1,2}\\/\\d{2}` qui sélectionne les colonnes dont le nom commence par :\n", "un ou deux chiffres suivi d'un \"/\", deux fois, puis se terminent par deux chiffres\n", "c'est à dire les colonnes des dates (vous pouvez inverser la regex avec `[^\\d{1,2}\\/\\d{1,2}\\/\\d{2}]` pour constater que la commande fait bien l'inverse et nous rend des données avec des NaN)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/23/212/24/212/25/212/26/212/27/212/28/213/1/213/2/213/3/213/4/21
\n", "

0 rows × 412 columns

\n", "
" ], "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 412 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.filter(regex=\"\\d{1,2}\\/\\d{1,2}\\/\\d{2}\").isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le tableau en sortie est bien vide, il ne manque donc aucune donnée sur le nombre de cas par jour.\n", "\n", "Listons les pays que nous allons analyser, ils sont tous représentés dans les données par le nom du pays dans la colonne `Country/Region` et `Nan` dans la colonne `Province/State` sauf dans le cas de la Chine ou nous allons devoir faire une somme de toutes les provinces d'un coté et de récupérer Hong-Kong de l'autre." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "countries = [ \n", " \"Belgium\",\n", " \"France\",\n", " \"Germany\",\n", " \"Iran\",\n", " \"Italy\",\n", " \"Japan\",\n", " \"Korea, South\",\n", " \"Netherlands\",\n", " \"Portugal\",\n", " \"Spain\",\n", " \"United Kingdom\",\n", " \"US\",\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enregistrons les lignes consernées dans un nouveau tableau en ajoutant la Chine et Hong-Kong. Profitons en pour définir la localisation comme index et supprimons les colonnes inutiles et trions le tableau." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/201/30/201/31/20...2/24/212/25/212/26/212/27/212/28/213/1/213/2/213/3/213/4/21Sum
Localisation
Hong-Kong0225888101012...1091310926109501098311005110191103211046110551659701
Korea, South11223444411...88516889228932189676900319037290816912409163810964482
China5486419181401206728695501607781319790...89919899258993589941899608997189981899919000033057311
Japan222244771115...42773242881642987343109343209043277843370043494443609341743265
Portugal0000000000...80058680174680277380384480456280495680564780662680745670635994
Belgium0000000000...76080976388576665476941477151177229477434477760878025198653796
Netherlands0000000000...106896010739711079084108402110886901092452109643311014301105544111703914
Iran0000000000...159887516070811615184162315916311691639679164817416566991665103208249840
Germany0000014445...241603724270692436506244417724502952455569246206124729132484306255393641
Italy0000000002...284856428684352888923290782529252652938371295543429762742999119312517063
Spain0000000000...317064431802123188553318855331885533204531313018431363213142358355880364
France0023334555...363950136640503689034371247437324263736390375924737853263810605404492379
United Kingdom0000000002...414457741545624163085417051941765544182009418840041947854201358404528634
US1122555668...2830908528386492284631902852734428578548286373132869407128759980288271443343145027
\n", "

14 rows × 409 columns

\n", "
" ], "text/plain": [ " 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 \\\n", "Localisation \n", "Hong-Kong 0 2 2 5 8 8 8 \n", "Korea, South 1 1 2 2 3 4 4 \n", "China 548 641 918 1401 2067 2869 5501 \n", "Japan 2 2 2 2 4 4 7 \n", "Portugal 0 0 0 0 0 0 0 \n", "Belgium 0 0 0 0 0 0 0 \n", "Netherlands 0 0 0 0 0 0 0 \n", "Iran 0 0 0 0 0 0 0 \n", "Germany 0 0 0 0 0 1 4 \n", "Italy 0 0 0 0 0 0 0 \n", "Spain 0 0 0 0 0 0 0 \n", "France 0 0 2 3 3 3 4 \n", "United Kingdom 0 0 0 0 0 0 0 \n", "US 1 1 2 2 5 5 5 \n", "\n", " 1/29/20 1/30/20 1/31/20 ... 2/24/21 2/25/21 \\\n", "Localisation ... \n", "Hong-Kong 10 10 12 ... 10913 10926 \n", "Korea, South 4 4 11 ... 88516 88922 \n", "China 6077 8131 9790 ... 89919 89925 \n", "Japan 7 11 15 ... 427732 428816 \n", "Portugal 0 0 0 ... 800586 801746 \n", "Belgium 0 0 0 ... 760809 763885 \n", "Netherlands 0 0 0 ... 1068960 1073971 \n", "Iran 0 0 0 ... 1598875 1607081 \n", "Germany 4 4 5 ... 2416037 2427069 \n", "Italy 0 0 2 ... 2848564 2868435 \n", "Spain 0 0 0 ... 3170644 3180212 \n", "France 5 5 5 ... 3639501 3664050 \n", "United Kingdom 0 0 2 ... 4144577 4154562 \n", "US 6 6 8 ... 28309085 28386492 \n", "\n", " 2/26/21 2/27/21 2/28/21 3/1/21 3/2/21 3/3/21 \\\n", "Localisation \n", "Hong-Kong 10950 10983 11005 11019 11032 11046 \n", "Korea, South 89321 89676 90031 90372 90816 91240 \n", "China 89935 89941 89960 89971 89981 89991 \n", "Japan 429873 431093 432090 432778 433700 434944 \n", "Portugal 802773 803844 804562 804956 805647 806626 \n", "Belgium 766654 769414 771511 772294 774344 777608 \n", "Netherlands 1079084 1084021 1088690 1092452 1096433 1101430 \n", "Iran 1615184 1623159 1631169 1639679 1648174 1656699 \n", "Germany 2436506 2444177 2450295 2455569 2462061 2472913 \n", "Italy 2888923 2907825 2925265 2938371 2955434 2976274 \n", "Spain 3188553 3188553 3188553 3204531 3130184 3136321 \n", "France 3689034 3712474 3732426 3736390 3759247 3785326 \n", "United Kingdom 4163085 4170519 4176554 4182009 4188400 4194785 \n", "US 28463190 28527344 28578548 28637313 28694071 28759980 \n", "\n", " 3/4/21 Sum \n", "Localisation \n", "Hong-Kong 11055 1659701 \n", "Korea, South 91638 10964482 \n", "China 90000 33057311 \n", "Japan 436093 41743265 \n", "Portugal 807456 70635994 \n", "Belgium 780251 98653796 \n", "Netherlands 1105544 111703914 \n", "Iran 1665103 208249840 \n", "Germany 2484306 255393641 \n", "Italy 2999119 312517063 \n", "Spain 3142358 355880364 \n", "France 3810605 404492379 \n", "United Kingdom 4201358 404528634 \n", "US 28827144 3343145027 \n", "\n", "[14 rows x 409 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_list = []\n", "# récupération des données pour tous les pays sauf la Chine\n", "for country in countries:\n", " data_list.append(raw_data[(raw_data['Province/State'].isnull()) & (raw_data['Country/Region']==country)].values.tolist()[0])\n", "# récupération des données pour Hong-Kong\n", "data_hk = raw_data[(raw_data['Province/State'] == \"Hong Kong\") & (raw_data['Country/Region']== \"China\")].values.tolist()[0]\n", "data_hk[0] = pd.np.NaN\n", "data_hk[1] = \"Hong-Kong\"\n", "data_list.append(data_hk)\n", "# récupération des données pour le reste de la Chine en sommant les différentes colonnes\n", "data_china = raw_data[(raw_data['Province/State'] != \"Hong Kong\") & (raw_data['Country/Region']== \"China\")].sum().values.tolist()\n", "# mise à jour des premières colonnes des données de la Chine et ajout à la liste\n", "data_china[0] = pd.np.NaN\n", "data_china[1] = \"China\"\n", "data_list.append(data_china)\n", "# création du nouveau tableau avec toutes les données des pays recherchés\n", "data = pd.DataFrame(data_list, columns=raw_data.columns)\n", "# suppression des colonnes inutilisées\n", "data = data.drop(columns=['Province/State', 'Lat', 'Long'])\n", "# chaangement de l'index\n", "data = data.rename(columns={\"Country/Region\" : \"Localisation\"}).set_index(\"Localisation\")\n", "# création d'une colonne correspondant à la somme des cas et tri en fonction de cette colonne\n", "data[\"Sum\"] = data.sum(axis=1)\n", "data = data.sort_values(by ='Sum')\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous obtenons ainsi notre tableau avec 14 lignes contenant les informations que nous voulons afficher. Nous allons maintenant générer les graphiques." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# récupération des valeurs à afficher\n", "total = data[\"Sum\"].tolist()\n", "names = data.index.tolist()\n", "y_pos = pd.np.arange(len(names))\n", "\n", "# création du graphique\n", "fig, ax = plt.subplots()\n", "ax.barh(y_pos, total, align='center')\n", "ax.set_yticks(y_pos)\n", "ax.set_yticklabels(names)\n", "ax.set_xlabel('cases')\n", "ax.set_title(f'Cumulative confirmed cases ({data.columns[-2]})' , fontweight =\"bold\")\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbcAAAEaCAYAAACSFRnbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3XmcHFW5//HPlwBhCYYdIwqDrKJIgIDsBBCUy6IsV1BEkE3cuOBFLxe9EuCHgFxlFRUQQ1iUTbgQESJIZA0hgZCExbAFZCcCgbAESJ7fH+c0UzTds2Rmumeqv+/Xa17ddepU1anq5elTVXMeRQRmZmZlslCzG2BmZtbbHNzMzKx0HNzMzKx0HNzMzKx0HNzMzKx0HNzMzKx0HNysyySNlhSSRvVwPePzeg7onZY1nqTDJT2b92OSpJH5+cx+0La23JaQtHSz29NTko7P+7J9s9vSHZKWkfS6pPFdqLuWpHclXZSn338N+7yhfaC73xWS1pY0T9Lo3mqDg1s/JWkLSddJ+pektyU9JuksSYs2u21d1cEX/pXAGcCDjW9Vz0kaBpwGfBS4ALgUeJq0Txc0sWmlI2lZ4AfAtIi4OZdtI2mKpNckvSXpEUnHSlLVshvm998qhbI1JM3J5VNqbO8Hkh7Lz8+T9GCu/y9J10v6dKHu+pJuzgHsQ+/ziHgFGA1sI2mHTnb1OGBh0vuq5UTEP4Drgf0krd0b63Rw64ck7QP8HdgF+CdwEfA4cBiwRBOb1isi4uyIOCIiJja7LQtoddJn5+mIOCgifhkRj+Z9Or7eQpIWaVwTS+MbwJLAHwplHwNezmXXAasBo4ADqpbdmRQUnwKQNAi4GBjcwfZ2Bv6cnx8MvJa38xqwE3CjpMXy/FVIP3Du62B9lXZ/q14FSSsCewKPRMS9Hayr7P5A+lwd0itriwj/9aM/UvD6FxCkoLZQYd7qwKLAyDx/ZmHe+Fx2QJ4enafHAH8B3gLGAasCVwFvAHcBq+X63VnnqDy9A+mDPRt4F3gSOK5qfR/4q14vsG1+PrWw3W1y2fTCMTkZeDS3+17gy50cx/2AycDrpC/C3xbm7Q7ck+c9CfwKWDrPayu090DgKeAV4LQO9mt09fGrWs9hwLPALVXl3weeB17I7d0zb+8l4OhCexcGfgg8lPf/QeCQwvxFgV/ndj4KHFrYxtIdvM+OAx4mvTeerqwT+HrexuvAO8AM4DuFZTcEbiN94c8BpgPfLszfDZiY5z8J/AJYIs9bBrgCmAW8DTxRfG1qtPOmvB+bd1Dn2lzn+KryCcBJheljgTfzeymAKVX1l8r7+4U8vXlhXvF127BquS9T9dmpeu3ezMdp4Trt/3pe/txa26squwJ4Lr/WtwCfK8z/GOkz/gZwR359P7SfhfoCfkb6AT03vxdvBJbL85cFzgQey6/V48Aued5RwCN5W3OB+4G9CuseTeG7IpcdmOvNycseUzwmwMp5mYd75bu0N1biv977IwWMyodo7Tp1RlZ/mKgfiObnD8QzefpV4GZgWp6+eAHWOSpPHwD8Ffgt6XTcy3n+PsAapNOPQfqSOx04vXq9+QP2ZJ7+VJ7/qzz9ozz9hzw9mRSsX8r7NbLO8Tkk138v7/vFwN/yvJ3yvLnAhaQv5gBuyPPbCsf/KeASYF6e3r7Ofn2t+vhVredfwO+AE6vKHwP+Lz9/m/SldXHet/nAWnldJ+U6D+fX4Kk8vX+ef1xhOxeQAmlnwe2SPP/lvMy1wKl53o+BsaSAeRHpyzmAzfL82/P0lcB5pEB3fp73hTzvpbzs5Dz9+zz//+Xpu/L6ryf1rup9Hl7M9ZepKl8jH/sr8uv8NLBGYf4K+XXbIk9vTPoB9h3S+65WcNuT9MU7uEY71srLzAOGVc2rG9zy/KkU3t815v88zz+yUPb++yRPL0kKLkE6q3NVfv4GsHrV5+of+djPrbWfhW18vtJu4Bzg8vzeaSP1oG7N858Gzif90Di88Bn9U368PL8GbwNtdb4rvkX7Z2o06b0cwLFVbXotly/R4+/SRn1p+6+LLwjsS/sX02J16oys/jBRPxDdlKdH5enn8ht31zz9wAKss/KGXQj4N+AnpGsF91D4BVprnXXWe2JlvXmdz5O+RD5G+pKqfKmcRfpCqyz/xzrHpxKwil8Wi+TH64sfKmB50pdekL7A2grHf+Nc5+95+qgOjtUHyqrWs12hXrF8S9Iv+8r2v5PrVALCv5OC/+t5+oK8/5WeyoRc/9E8vV+e3rWwjQ8Ft7zPlfkb1DhGiwJ7kXo6p5G+LAM4Js+/m/ae7WeARYBBed6f87xxua3n0P4jawnglDx9OrAJMKSybJ3XsnJsFq4qrxzvyt9FwNDC/G+Qgv2gvN1/ANfneQdQO7hdAFxTow1DgDvzMj+vMb+z4Fb5MVCz90n6gRDAwbXeJ3n6K7T/IFIuuzqX/Qz4eOFYrJrnn1FrPwvbqPzQuykfzxXz+20hYESe9xaFYF54jyyZj/EJ+T3yXK7/tTrfFQ/Q/oPodNKPuACer2rT07n8Yz39Ll0Y629eLDxflfSh7IpBdcofyo+v5sdHI2K+pNfz9JILsM6KX5NOgVVboZPlql1IOkWxNymQrASMi4hnJW2c6ywEfK9quTXqrG+1/DihUhAR7+anbfnxoVw+S9Is0rWTVUmnSyoq11Iqx25IF/en2h11yh+KiPckvQEMpf21Lr42yxe2+82q5Sv7v3J+rCw/o5P2VI7POxHx/vWiwjG6DtixxnKV1/UHpKB1PunLcA7wU9KXXFuus0P+qxDwSdIX2/qkHtR/kH60XCZpv4iYX2Obr5KOwVKkU3GVto6XtBBp388lndp7m/brNTsDf4mIefk9tBbwsqSxpEAAsJqksRGxS74ZZSfgf4obl7Q86QfRxqQg9F812tiZjxT2pZZK+VIdrKMtP/6jEu1IvR9I79vKe+CtiHgyP+/shq1xpNdxP9IpTkg/UL9E+3vkqYh4rrJARLybb2qbQPphU63eZ7/S/j2ryleSNCQi5uTpzo5Vl/mGkv7nTto/xD/JH2AAJK2ab0p4IxctlcsXIX14a5nXyXRFd9ZZsXd+PIAUCH9daWrVtjp8n0XEDFJvYB3SKTZIpx8hnTKBdC1khYhQRIjUu9i9ziqfyI+fqxRIqvyQq6xvnVy+HOnLE9Lp0WK73qs87aj9nYmIuXVmdeW1mUX7a/PZwv5Xfl1DOuUMULnLrLPXrXJ8FpU0vFIoaeH8rwOVwLZt3s5fKlXy46SIWJ90/Wwkqed2cj7GM3Odwyttze1dPSKmAy9HxBdJ77P1Sb/ovwZsUaetU/PjpwrtXApSlyYiniadFn1/v3M7dqT9xpBKuzclBb318/RH8jTARqQfVdcXtrMq6YfJxsDJEXFoIbB0SW7LGqTX8JE61T60jzXMzI9rFe4KrbzeT9L+HlhcUiV4r9NJ8waRfjAunds4hrSvB9P+HllF0ker9mddUmCbB6xJeo9UAukH7lit0f7dqt4Xn6wENkkrk94XMyLizU7a3in33PqZiHhD0vdJb7SvA+tJmkg6RbcD6QM4g3QdZFlJY0i9jhV7uOkFWecLpB7H4aQvk+pg88/8+HFJ55PuBjulzrrGkILRVqSey9UAEfGSpMtJp2XulvRXYLlc7zekU5nVziD9mj9V0uakUyuV4/cr0i/0YyR9kvSltjDw14iYIamtk31uqIgISb8CfgT8VdJ1pJ7cpqRe7gGkf0X4H+B0SSNJp4o7WucsSZeSgsrNkq4hBapHSKeY5+RtjCL90Kr+/7Lr8p2Hj5Fe/8GkU4DzgLPz9n9eOPafJb1mqwFHS9qNdM33Hdp/0c+u09yxwHakm4zuzGXX5C/Zh3O7d8vlN+bHLUhfkjfk/R1P4UtX6f8rfw/cHxGV4L4zcF9EPFvY9p2k981TpKBxei6/NCImSloHOJp01yTA8vn/tGZFxFG5bBNgceDqQs+42jjS6deRdeZDCtQzSTeV3ZLPNuxOOr4XRMTTkv5OOk7jJE2i/cdnPZuTTh/eRbr2WvmB8Srppq3bSJ+zeyTdQOod/oX02ZxPCo6/JJ32XbOTbZ1N6iVeLOlq2n+cvVjY720K+9pzPT2v6b+++QO2zi/yy6QLw4/lN8iief5+pF9rL5J6THdR+/pY5SaOI/L0+Dw9kg9fN+rqOkfl6a1Ip/feIl0HOi3Pv6awzlNJX5BB+92P44vrzWXL0n4B/PdVx2II6aaKR0innp4lfcA27eD4Ve6WnMOH75b898K8p0hBcpk8r43CtY5cdk3Vftc6dh8oq7We6nLa79B8NU+PrHV8SD2jH5F+Hb9F+lFxA7BTnj+YdFPPq6Rf3N+v3kaN47MEcDzpVObbfPBuyT1IvYE3SEGgcvNJ5b10DO13bs4h3Rm5fWHdXyadtpqd2zQROCLP25XUS381b3cG8P0OXsfl8jaKd9P+NL8X3srbuJ90qrRyLernwK0drPMAqq5F5TZW320Zdf4qr8vIOvOL74uzctkOnXzeKzdNbdjB+/CTpGtWz+fjN558k0+evzIfvFuyclfo3XW2uSbphrAXST80niV97gcXPpNnkm5kmZsfd83zvpPbMZt0HXV83lbldR7NBz8zAg4CpuTXcxbphpWvF9pzHekH0jq98R1aeTOYmfVLko4n9Ux3iIibulD/AWBM1D9LUF1/RdIX9WYRcXePGvvB9S5D+vF0b0Rs00ndtUinaC+LiK8v4PaGRsTswvRvSdfEL46I/RZknY2S9/8hUlv375V1OriZWVnkmx3+Gxgd7TdWdLbMWqRTeCdG7ZtaBgRJR5BuBvk76RRwJUhuERET6i5YUg5uZmYlIGkn0jWwNtKpyenACZGHLWs1Dm5mZlY6/lcAMzMrHQc3MzMrHf+fW5Msv/zy0dbW1uxmmJkNKJMnT54VEZ2OguTg1iRtbW1MmjSp2c0wMxtQJHXpLlifljQzs9JxcDMzs9JxcDMzs9JxcDMzs9JxcDMzs9JxcDMzs9JxcDMzs9Lx/7k1ybRnZtN2dO/k5DMzG0hmnrxz55V6yD03MzMrHQc3MzMrHQe3bpDUJml6VdkoSUdJ2lTS3ZKmSHpI0qgmNdPMrOX5mlvvuRD4SkTcL2kQsHazG2Rm1qoc3HrPisBzABExD3iwuc0xM2tdPi3Ze04D/iHpaknfkrRYdQVJh0qaJGnSvDdnN6GJZmatwcGte6JeeUQcD4wAxgFfA26oUenciBgRESMGLTG0D5tpZtbaHNy651/AMlVlywKzACLisYj4NbA9sL6k5RrcPjMzw8GtWyJiDvCcpO0BJC0LfBG4XdLOkpSrrgnMA15tTkvNzFqbbyjpvm8Av5L0izx9XEQ8JulE4DRJbwLvAfvmG0vMzKzBHNy6KSIeBLatUb5PE5pjZmY1OLg1yXorD2VSA8ZXMzNrRb7mZmZmpePgZmZmpePTkk3ilDdmA0cjUrRY73LPzczMSqfD4NbRKPidLDdC0pn5+UhJm3e3YZJmSlq+o3JJG0l6QtIGknaTdHR3t1Nn2yMlje2NdZmZWeP1yWnJiJgETMqTI4E5wJ29uQ1JnwWuBPaOiPuA+4Bre3MbZmY2MPXotKSk8ZJOkTRR0gxJW+XykZLGSmoDDgOOzHnOtpK0gqSrJN2T/7bIyywnaZyk+yT9FlDdDcOngGuA/SJiYl7+AEln5+ejJZ0p6U5Jj0vaK5cvJOkcSQ/k9l1fmPdFSQ9Luh3Yo7CPy0q6RtJUSRNyUK30YC/MbZ4paQ9JP5c0TdINkhbpybE1M7MF1xvX3BaOiE2AI4BjizMiYibwG+C0iBgeEbcBZ+TpjYE9gfNz9WOB2yNiA1IPbJUOtvl/wPci4vYO6gwDtgR2AU7OZXsAbcB6wMHAZgB5BP/zgF2BrYCPFtZzHHBfRHwWOAYYU5i3OrAz8CXgYuCWiFgPeCuXf4CzApiZNUZnwa3uKPiF53/Kj5NJgaMznwfOljSFFMQ+ImkpYGtSgCAi/gy80sE6bgIOzklB67kmIubnEUVWymVbAlfk8ueBW3L5OsATEfFIRESlHYVlLsrt+huwnKTKkP5/iYh3gWnAINozAUyjxrFwVgAzs8boLLh1OAp+Njc/zqNr1/AWAjbLPbnhEbFyRLye59ULptW+lx/P6aDO3MJzVT3WUm/btZap1J0LEBHzgXdzYASYj//NwsysaToMbh2Ngt+NbbwOLFWYHkd7cELS8Pz0VmDfXLYTHw6qRfOBrwJrSzq+G225HdgzX3tbiXSzC8DDwGqSVs/TXy0sU2zXSGBWRLzWjW2amVmDdeWa2zeAn+TTiH8jj4LfjW1cB+xeuaEEOBwYkW/QeJB0wwmka1tbS7oX2BF4qqOVRsRc0rWu3SR9t4ttuQp4GpgO/Ba4G5gdEW8DhwJ/zjeUPFlYZlSlvaRrd/t3cVtmZtYkaj+T1hokDYmIOTmR6ERgi3z9raEGD1szhu1/eqM3a2YLwCOU9B+SJkfEiM7qteJ1obGSlgYWBU5oRmADZwUwM+tLLRfcImJks9tgZmZ9y2NLmplZ6bRcz62/cFYAs4HD19wGHvfczMysdErbc5M0jzRSSMWX83BgZmZWcqUNbsBbETG83kxJC0fEe41skJmZNUZLnZbMmQOukHQdME7SEEk3S7o3j+b/pVyvTdJDks7LGQTGSVo8z1tD0k2S7s/LrZ7Lf5izHEyVdFwTd9PMrOWVObgtnkdFmSLp6kL5ZsD+EbEd8Dawe0RsCGwL/EJSZSzJNYFfRcSngVdJGQwALsnl6wObk4Yn2zHX3wQYDmwkaeu+3kEzM6utFU9L/jUiXs7PBfwsB6L5wMq0ZxB4IiKm5OeTgbacvWDliLgaIA/bRQ5uO5ISpgIMIQW7W4sblnQoaZgvBn1khZ7voZmZ1VTm4FbPG4Xn+wIrABtFxLuSZgKL5XnFrALzgMWpn1VAwEkR8duONhwR5wLnQhp+q/tNNzOzrijzacmuGAq8mAPbtsCqHVXO2QCelvRlAEmDJS0B3AgcKGlILl9Z0op93HYzM6uj1YPbJaQR/yeRenEPd2GZ/YDDc5aAO4GPRsQ44FLgLknTgCv5YJofMzNroJbLCtBfOCuA2cDhEUr6j65mBWj1npuZmZVQK95Q0i845Y2ZWd9xz83MzErHPbcmcVYAs/7H19bKwz03MzMrHQc3MzMrnZYMbpJ+nAdEnprHnvzcAqxjN0lH90X7zMysZ1rumpukzYBdgA0jYq6k5YFFu7ueiLgWuLa322dmZj3Xij23YcCsiJgLEBGzIuJZSTMlnSJpYv5bA0DSrpLulnRfTnWzUi4/QNLZ+floSWdKulPS45L2atremZlZSwa3ccAnJM2QdI6kbQrzXouITYCzgcrwIbcDm0bEBsAfgR/VWe8wYEtSr/DkWhUkHSppkqRJ896c3Rv7YmZmNbTcacmImCNpI2ArUg63ywrXzv5QeDwtP/94rjOMdPryiTqrviYi5gMPVnp3NbbtrABmZg3Qij03ImJeRIyPiGOB79GeiLQYcCrPzwLOjoj1gG/RnhKnWjFFTr3UOGZm1gAtF9wkrS1pzULRcODJ/HzvwuNd+flQ4Jn8fP++b6GZmfVUy52WJGXJPkvS0sB7wKOk7Ni7AIMl3U0K+l/N9UcBV0h6BpgArNbwFpuZWbc45U2Ws3CPiIhZjdieU96Y9T8efqv/62rKm1bsufULzgpgZtZ3HNyyiGhrdhvMzKx3tNwNJWZmVn7uuTWJU96YNZevr5Wbe25mZlY6Dm4FkubkxzZJX+tC/TZJ0/u+ZWZm1h0ObrW1AZ0GNzMz658c3Go7Gdgq53o7MvfQbpN0b/7bvHqBPH94YfoOSZ9taKvNzAxwcKvnaOC2iBgeEacBLwI7RMSGpKG5zqyxzPnAAQCS1gIGR8TUYgVnBTAzawwHt65ZBDhP0jTgCmDdGnWuAHaRtAhwIDC6ukJEnBsRIyJixKAlhvZle83MWpr/FaBrjgReANYn/SB4u7pCRLwp6a/Al4CvAJ0OD2NmZn3Dwa2214GlCtNDgacjYr6k/YFBdZY7H7iOdErz5T5uo5mZ1eHTkrVNBd6TdL+kI4FzgP0lTQDWAt6otVBETAZeA37fsJaamdmHuOdWEBFD8uO7wPZVs4t3Pv53rjcT+EylUNLHSD8YxvVpQ83MrEMObr1E0jeAE4EfRMT8zuo7K4CZWd9xcOslETEGGNPsdpiZma+5mZlZCbnn1iTOCmDWtzzqf2tzz83MzEpnQAU3SStJulTS45ImS7pL0u7NbpeZmfUvAya4SRJwDXBrRHwyIjYC9gE+3sXl6/3jtZmZlcyACW7AdsA7EfGbSkFEPBkRZ0kaJOlUSfdImirpWwCSRkq6RdKlwLQ8uv/Dks6XNF3SJZI+n0fwf0TSJnm5TSTdKem+/Lh2Lj9A0p8k3ZDr/zyXHyTptEq7JB0i6ZeNPDhmZtZuIAW3TwP31pl3EDA7IjYGNgYOkbRanrcJ8OOIqAx2vAZwBumfstch5W3bEjgKOCbXeRjYOiI2AH4K/KywreGkzADrAXtL+gTwR2C3PGgywDfxKCVmZk0zYO+WlPQrUlB6B3gS+KykvfLsocCaed7EiHiisOgTETEtr+MB4OaIiDzif1th+QslrQkEKStAxc0RMTsv/yCwakT8U9LfSFkBHgIWqWyjqs2HAocCDPrICj0+BmZmVttA6rk9AGxYmYiI75KGyFoBEPD9nH9teESsFhGVIbCqx4GcW3g+vzA9n/ZgfwJwS0R8BtgVWKzO8vMKy1TyudXttTnljZlZYwyk4PY3YDFJ3y6ULZEfbwS+XTktKGktSUv2YFtDgWfy8wO6skBE3A18gnSa8w892LaZmfXQgAluERHAl4FtJD0haSJwIfBfpF7Tg8C9kqYDv6Vnp1x/Dpwk6Q7qp7ep5XLgjoh4pQfbNjOzHlKKGdYbJI0FTouImzurO3jYmjFs/9Mb0Cqz1uQRSspJ0uSI6DQZ9IDpufVnkpaWNAN4qyuBzczM+taAvVuyP4mIV0lJTLvMKW/MzPqOe25mZlY67rk1ibMCmPWMr6lZR9xzMzOz0nFwMzOz0nFwq0HSnGa3wczMFpyDWxc5ZY6Z2cDh4NaB6pQ5ueyanCj1gTwQcqXuHEknSrpf0gRJKzWt4WZmLc7BrXPVKXMOzIlSRwCHS1ouly8JTIiI9YFbgUOqVyTpUEmTJE2a9+bsRrTdzKwlObh1rjplzuGS7gcmkAZKXjOXvwOMzc8n054+533OCmBm1hj+P7fOvZ8yR9JI4PPAZhHxpqTxtKfDeTfaB+ospsIxM7MGc8+te4YCr+TAtg6wabMbZGZmH+bg1j03AAtLmkpKaDqhye0xM7MafOqshogYkh/HA+ML5XOBnTpaJj+/EriyTxtpZmZ1Obg1ibMCmJn1HZ+WNDOz0nFwMzOz0vFpySZxyhsrM6ejsWZzz83MzEqnocFNUkj6RWH6KEmjOllmpKTNC9OjJe3Vw3bMlLR8T9ZRWJczCJiZ9TON7rnNBfboZmAZCWzeWaWuUOLeqplZyTX6i/494FzgyOoZklaQdJWke/LfFpLagMOAIyVNkbRVrr61pDslPV7sxUn6YV52qqTjclmbpIcknQPcSxoPsrjdbo3yL2k1SXfl7ZxQqD9M0q25ndMLbTUzswZrRi/mV8C+kqpHDj4DOC0iNgb2BM6PiJnAb3L58Ii4LdcdBmwJ7AKcDCBpR9IgxpsAw4GNJG2d668NjImIDSLiyartdneU/zOAX+d2Pl9Yz9eAGyNiOLA+MKV6x50VwMysMRp+t2REvCZpDHA48FZh1ueBdSVVpj8iaak6q7kmIuYDDxbypu2Y/+7L00NIwe4p4MmIqDdU1uGSds/PK6P8/4sPj/K/Q36+BSn4AlwEnJKf3wNcIGmR3L4PBbeIOJfUc2XwsDWjer6ZmfWOZv0rwOmkU4S/L5QtRBptvxjwKAS7ornFKoXHkyLit1XLt1EY2b9q3kgWbJT/DwWmiLg19xR3Bi6SdGpEjKm1XTMz61tNubkiIl4GLgcOKhSPA75XmZA0PD99HajXgyu6EThQ0pC8/MqSVuxkmQUZ5f8OYJ/8fN9Ce1cFXoyI84DfARt2YV1mZtYHmnnn4C+A4l2ThwMj8s0gD5JuJAG4Dti96oaSD4mIccClwF2SppEGLu4sKC7IKP//AXxX0j2k4FgxEpgi6T7SacszurAuMzPrA2o/82aNNHjYmjFs/9Ob3QyzPuERSqyvSJocESM6q+fht5rEWQHMzPqO/6HZzMxKx8HNzMxKx6clm8RZAawsfH3N+iP33MzMrHQGdHCTNC//i8D9ku4tZg/oYJlOR/GXdL6kdXunlWZm1mgD/bTkW3ksRyR9ATgJ2KanK42Ig3u6DjMza54B3XOr8hHglcpErQwBRZIWknROzgYwVtL1lQwDksZLGpGfzykss5ek0fn5aEm/lnRLzk6wjaQLcgaC0X28r2Zm1oGB3nNbXNIU0niQw4Dt4EMZAgRcK2nriLi1sOweQBuwHrAi8BBwQTe3v0ze5m6kkVS2AA4G7pE0vNbgyWZm1vcGes/trZwKZx3gi8AYpZGWixkC7gXWIQW7oi2BKyJifkQ8D9yyANu/Lg+uPA14ISKm5WwFD5AC5wc45Y2ZWWMM9J7b+yLirpzhewXqZAioUjPdQK1VF54vVjWvkp1gPh/MVDCfGsfWKW/MzBpjoPfc3pdH9R9EysXWlQwBtwN75mtvK5EGPq7lBUmfkrQQsHudOmZm1o8M9J5b5ZobpJ7Y/hExDxgn6VOkDAEAc4CvAy8Wlr0K2B6YDswA7gZqnSs8mpS09J+57pA+2A8zM+tFLZ0VQNKQiJgjaTlgIrBFvv7W55wVwMrCI5RYIzkrQNeMlbQ0sChwQqMCGzgrgJlZX2rp4BYRI5vdBjMz632luaHEzMysoqV7bs3krADWH/n6mZWFe25mZlY6Dm5mZlY6Azq4FVLeTJd0haQlurn8MX3UrjZJ0/ti3WZm1rkBHdxoH1vyM8A7wGFdWUjJQkCfBDczM2uugR7cim4D1gCQ9IPcm5su6Yhc1pbT0ZxDGkz5d+QRTiRdUt0Z3DpNAAAOq0lEQVTbknSUpFH5+cY5dc5dkk6t1MvL3JYTpXYpWaqZmfW9UgQ3SQsDOwHTJG0EfBP4HLApcIikDXLVtYExEbFBRHyT9p7fvp1s4vfAYRGxGTCvUP4isENEbAjsDZzZSTudFcDMrAEGenCrjC05CXiK1BvbErg6It6IiDnAn4Ctcv0nI2JCdzaQRzBZKiLuzEWXFmYvApwnaRpwBbBuR+uKiHMjYkREjBi0xNDuNMPMzLphoP+f21sRMbxYkPO51fNGB/Pe44PBvpLepqP1HQm8AKyfl327g7pmZtYgA73nVsutwJclLSFpSVKamtvq1H1X0iL5+QvAipKWkzQY2AUgIl4BXpe0aa63T2H5ocBzOUHpfqSUO2Zm1mSlC24RcS8wmjTK/93A+RFxX53q5wJTJV0SEe8Cx+dlxgIPF+odBJwr6S5ST65ywewcYH9JE4C16LhnaGZmDdLSKW+6qpIaJz8/GhgWEf/Rk3U65Y31Rx5+y/o7p7zpXTtL+m/S8XoSOKCnK3TKGzOzvuPg1gURcRlwWbPbYWZmXVO6a25mZmbuuTWJU95YT/n6mFl97rmZmVnptGRwkzSn2W0wM7O+05LBzczMyq1lg5ukIZJuzqP5T5P0pVzeJulhSRfmTABXVvLESfqppHtytoFzK0N9SRov6RRJEyXNkLRVR9s2M7O+1bLBjTQO5O55RP9tgV8UxqVcGzg3Ij4LvAZ8J5efHREb5/xxi5OH6MoWjohNgCOAY2tt0FkBzMwao5WDm4CfSZoK3ASsDKyU5/0zIu7Izy8mZRoA2FbS3TkLwHbApwvr+1N+nAy01dqgswKYmTVGK/8rwL7ACsBGEfGupJm0ZwKoHpMsJC1GGktyRET8MycyXaxQZ25+nEdrH1czs6Zr5Z7bUODFHNi2BVYtzFtF0mb5+VeB22kPZLMkDQH2alxTzcysO1ouuOWs3XOBS4ARkiaRenHFLAAPkUb7nwosC/w6Il4FzgOmAdcA9zS04WZm1mWtePrs08BjETEL2Kx6pqQ2YH5EHFY9LyJ+AvykRvnIwvNZ1LnmZmZmjdFSwU3SYcDhpDsam8pZAczM+k5LBbeI+A3wm07qzAQ+05AGmZlZn2i5a25mZlZ+LdVz60+cFcDq8Wj/Zj3nnpuZmZVOywU3SR+V9EdJj0l6UNL1eVissXXqny9p3Ua308zMFlxLnZbMY0deDVwYEfvksuHArvWWiYiDG9Q8MzPrJa3Wc9sWeDffNQlAREwBbgOG5AwAD0u6pGrE/xH5+RxJJ0q6X9IESSvl8l3zmJP3SbqpUm5mZs3RasHtM6SBjWvZgPT/b+sCnwS2qFFnSWBCRKwP3AockstvBzaNiA2APwI/6s1Gm5lZ97TUaclOTIyIpwEkTSGNMnJ7VZ13gMq1ucnADvn5x4HLJA0DFgWeqLUBSYcChwIM+sgKvdl2MzMraLWe2wPARnXmzS08rzey/7sRETXqnEXK9bYe8C0+mC3gfU55Y2bWGK0W3P4GDJZUOZ2IpI2BbXq43qHAM/n5/j1cl5mZ9VBLBbfc69od2CH/K8ADwCjg2R6uehRwhaTbgFk9XJeZmfWQ2s+yWSMNHrZmDNv/9GY3w/ohj1BiVp+kyRExorN6vqGkSZwVwMys77TUaUkzM2sNDm5mZlY6Pi3ZJM4K0Di+hmXWetxzMzOz0nFwMzOz0unz4CZpTuH5v0l6RNIqfb3dOm05UNI0SVMlTZf0pQVcz3BJ/1aYHiXpqN5rqZmZ9UTDrrlJ2p40TNWOEfFUF5dZOCLe66Xtfxz4MbBhRMyWNARY0AEehwMjgOt7o21mZta7GnJaUtJWwHnAzhHxWC5bVdLNuRd1c6U3J2m0pF9KugU4RdKSki6QdE9OKfOlXK9N0m2S7s1/m3fSjBWB14E5ABExJyKeyOsanlPYTJV0taRlcnkx3c3ykmZKWhQ4Hthb0hRJe+f1r5vrPy7p8F48fGZm1k2NCG6Dgf8DvhwRDxfKzwbGRMRngUuAMwvz1gI+HxH/Sept/S0iNiblYztV0pLAi8AOEbEhsHfV8rXcD7wAPCHp95KKCUrHAP+V2zINOLbeSiLiHeCnwGURMTwiLsuz1gG+AGwCHCtpkeplc8bvSZImzXtzdifNNTOzBdWI4PYucCdwUFX5ZsCl+flFwJaFeVdExLz8fEfg6JyGZjxpxP1VgEWA8yRNA64g5WGrK6/vi8BewAzgtHytbCiwdET8PVe9ENi6uzsJ/Dki5kbELFLg/VDCUmcFMDNrjEYEt/nAV4CNJR3TQb3iIJdvFJ4L2DP3koZHxCoR8RBwJKkntj7p+teinTUkkokRcRKwD7BnJ4u8R/sxqpnGpqArKXPMzKwBGnLNLSLeBHYB9pVU6cHdSQowAPvy4cSgFTcC35ckAEkb5PKhwHMRMR/YDxhUWUDSw9UrkfQxSRsWioYDT0bEbOCVfF2QvK5KL24m7fnf9ios+zqwVN0dNjOzpmpY7yIiXpb0ReBWSbOAw4ELJP0QeAn4Zp1FTwBOB6bmADeTFCjPAa6S9O/ALeTenqTlSb29aosA/yvpY8DbeZuH5Xn7A7+RtATweKEt/wtcLmk/Ui64iltoP1V6UrcOhJmZ9bnSpbyRtAvwyYjo7AaTpnLKm8bx8Ftm5dGyKW8iYmyz29AVTnljZtZ3PPyWmZmVjoObmZmVTulOSw4UPU154+tIZmb1uedmZmal0y+CWzFzQJ4+QNLZfbzNNknTC9OH5DEql+nL7ZqZWd/zaUkg/x/b94HtIuKVZrfHzMx6pl/03DrSSfaAMyXdmUfi3yuXLyTpHEkPSBor6frKvDrr/wpwNCkVz6xc1lGWgFMkTZQ0ozKqiaQlJF2e618m6e5KNgEzM2u8/hLcFs/pY6bkUT+OL8zrKHvAMNKAy7sAJ+eyPYA2YD3gYNIAzfWsmte/Y0Q8XyjvKEvAwhGxCXBEofw7wCu5/gm0D9n1Ac4KYGbWGP0luL1VGBh5OCmlTEVH2QOuiYj5EfEg7aPwb0nKKjA/B6xbOtjuS8BTpIGdAehCloA/5cfJpCBa2eYfASJiOjC11sacFcDMrDH6S3DrjuJ4YcWR+FX1+AGSPlfoHe6Wi98EdgIOk7RvF7df2WZx5P+a2zQzs+YYCMGtq9kDKm4H9szX3lYCRgJExN2F3uG1lcoR8RIpz9vPJH2hkywBHW3zKwCS1iWdEjUzsyYZCHdLdjV7QMVVwPbAdFJS0ruBDi9wRcQTuTd3vaQ9qJ8loJ5zgAslTQXuI52W9EU1M7MmKV1WAABJQyJijqTlgInAFlU3jPT29gYBi0TE25JWB24G1oqId+ot09OsAB6hxMxaUctmBcjGSlqalJ37hL4MbNkSwC2SFiFdf/t2R4ENnBXAzKwvlTK4RcTIBm/vdcD/12Zm1k8MhBtKzMzMusXBzczMSsfBzczMSsfBzczMSsfBzczMSsfBzczMSsfBzczMSsfBzczMSqeUw28NBJJeAp7so9UPpWdjW3Z3+a7U76hOvXldLa9Vb3lgVidt6i0+3gPreHd3Hc0+3rXKWvl4rxoRK3S6lojwX8n+gHMbuXxX6ndUp968rpbXqgdM8vH28e6NdTT7eNd5DVr6eHflz6cly+m6Bi/flfod1ak3r6vlPd3fnvLxbqze2H531tHs493VNvSV/ni8O+XTklZKkiZFF0YOt97h491YPt6dc8/NyurcZjegxfh4N5aPdyfcczMzs9Jxz83MzErHwc3MzErHwc3MzEqnlJm4zYokbQXsS3q/rxsRmze5SaUmaRXgbNI/Gc+IiJOb3KRSk7QuMAr4F3BzRFzZ3Bb1D+652YAk6QJJL0qaXlX+RUn/kPSopKMBIuK2iDgMGAtc2Iz2DnTdOd7AWsCfI+JAYN2GN7YEunm8dwLOiohvA99oeGP7Kd8taQOSpK2BOcCYiPhMLhsEzAB2AJ4G7gG+GhEP5vmXAwdHxGvNafXA1Z3jDbwAXAkEcFFE/L4pjR7Aunm8ZwHHAm8Cm0fEFk1pdD/jnpsNSBFxK/ByVfEmwKMR8XhEvAP8EfgSvH+qbLYD24Lp5vH+JnBsRGwH7NzYlpZDd453RLwYEd8FjqZx4032ew5uViYrA/8sTD+dywAOAtyD6F31jvcNwOGSfgPMbEK7yqrm8ZbUJulcYAxwalNa1g/5hhIrE9UoC4CIOLbBbWkFNY93REwH9mp0Y1pAveM9Ezi0wW3p99xzszJ5GvhEYfrjwLNNaksr8PFuLB/vbnBwszK5B1hT0mqSFgX2Aa5tcpvKzMe7sXy8u8HBzQYkSX8A7gLWlvS0pIMi4j3ge8CNwEPA5RHxQDPbWRY+3o3l491z/lcAMzMrHffczMysdBzczMysdBzczMysdBzczMysdBzczMysdBzczMysdBzczMysdBzczMysdBzczEpO0jckTZV0v6SLJO0q6W5J90m6SdJKud42kqbkv/skLZXLfyjpnryO43LZkpL+nNc5XdLezdxHs2rOCmBWYpI+DfwY2CIiZklalpQpYdOICEkHAz8C/hM4CvhuRNwhaQjwtqQdgTVJucQEXJsTaa4APBsRO+ftDG34zpl1wD03s3LbDrgyImYBRMTLpNHkb5Q0Dfgh8Olc9w7gl5IOB5bOYxnumP/uA+4F1iEFu2nA5yWdImmriJjdyJ0y64yDm1m5iZzTruAs4OyIWA/4FrAYQEScDBwMLA5MkLROXv6kiBie/9aIiN9FxAxgI1KQO0nSTxu0P2Zd4uBmVm43A1+RtBxAPi05FHgmz9+/UlHS6hExLSJOASaRemk3Agfm05RIWlnSipI+BrwZERcD/wts2LA9MusCX3MzK7GIeEDSicDfJc0jnV4cBVwh6RlgArBarn6EpG2BecCDwF8iYq6kTwF3SQKYA3wdWAM4VdJ84F3g2w3cLbNOOeWNmZmVjk9LmplZ6Ti4mZlZ6Ti4mZlZ6Ti4mZlZ6Ti4mZlZ6Ti4mZlZ6Ti4mZlZ6Ti4mZlZ6fx/TE1guJYvnVEAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# création du graphique avec une échèle logarithmique\n", "fig, ax = plt.subplots()\n", "ax.barh(y_pos, total, align='center')\n", "ax.set_yticks(y_pos)\n", "ax.set_yticklabels(names)\n", "ax.set_xlabel('cases')\n", "ax.set_title(f'Cumulative confirmed cases ({data.columns[-2]}) (log scale)' , fontweight =\"bold\")\n", "plt.xscale(value=\"log\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comme vous pouvez le constater les États Unis comptent beaucoup plus de cas que tous les autres pays, ce qui rend le premier graphe difficile à analyser. Le second nous permet de mieux nuancer les différences sur les autres pays." ] } ], "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 }