diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..b05f997c389e1202be144c875847c569b322e538 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -1,5 +1,4513 @@
{
- "cells": [],
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Autour du SARS-CoV-2(Covid-19)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Sujet\n",
+ "\n",
+ "Le but est de reproduire des graphes semblables à ceux du (South China Morning Post)[https://www.scmp.com/] (SCMP), sur la page (The Coronavirus Pandemic)[https://www.scmp.com/coronavirus?src=homepage_covid_widget] 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)[https://fr.wikipedia.org/wiki/Maladie_%C3%A0_coronavirus_2019]."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "text/plain": [
+ " Province/State Country/Region Lat Long 1/22/20 \\\n",
+ "0 NaN Afghanistan 33.939110 67.709953 0 \n",
+ "1 NaN Albania 41.153300 20.168300 0 \n",
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+ "\n",
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+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\")\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Les données que nous utiliserons dans un premier temps sont compilées par le (Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE))[https://systems.jhu.edu/] et sont mises à disposition sur (GitHub)[https://github.com/CSSEGISandData/COVID-19]. C'est plus particulièrement sur les données time_series_covid19_confirmed_global.csv (des suites chronologiques au format (csv)[https://fr.wikipedia.org/wiki/Comma-separated_values]) 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."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Seuls les pays suivants devront figurer dans l'exercice montrant l’évolution du nombre de cas cumulé au cours du temps :\n",
+ "\n",
+ "* sélection des pays sans contraintes particulières.\n",
+ " 1) la Belgique (Belgium), \n",
+ " 2) l’Allemagne (Germany), \n",
+ " 3) l’Iran (Iran), \n",
+ " 4) l’Italie (Italy), \n",
+ " 5) le Japon (Japan), \n",
+ " 6) la Corée du Sud (Korea, South), \n",
+ " 7) le Portugal (Portugal), \n",
+ " 8) l’Espagne (Spain),\n",
+ " 9) les États-Unis (US)\n",
+ "\n",
+ "* traitement particulier pour les pays avec des territoires d'outre-mer et autres « résidus coloniaux » à soustraire.\n",
+ " 10) la France métropolitaine (France),\n",
+ " 11) la Hollande sans les colonies (Netherlands), \n",
+ " 12) le Royaume-Unis (United Kingdom)\n",
+ "\n",
+ "* traitement particulier pour les provinces chinoises (Pour le besoin de l'exercice, nous devons séparer *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).\n",
+ " 13) la Chine - toutes les provinces sauf Hong-Kong (China), \n",
+ " 14) Hong Kong (China, Hong-Kong)\n",
+ "\n",
+ "Nous devons donc isoler notre sélection sur ces 14 pays/régions. Le nom entre parenthèses est le nom du « pays » tel qu’il apparaît dans le fichier utilisé `time_series_covid19_confirmed_global.csv.`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## la sélection des 9 pays sans contraintes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 \\\n",
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+ "Belgium 50.833300 4.469936 0 0 0 0 \n",
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+ "Belgium 726483 728334 730951 733100 735220 737115 \n",
+ "Germany 2296323 2302051 2311297 2321225 2330422 2336906 \n",
+ "Iran 1473756 1481396 1488981 1496455 1503753 1510873 \n",
+ "Italy 2644707 2655319 2668266 2683403 2697296 2710819 \n",
+ "Japan 406992 408550 410434 412125 413441 414803 \n",
+ "Korea, South 81487 81930 82434 82837 83199 83525 \n",
+ "Portugal 767919 770502 774889 778369 781223 784079 \n",
+ "Spain 2989085 3005487 3023601 3041454 3056035 3056035 \n",
+ "US 27097095 27192455 27287159 27392512 27492023 27575344 \n",
+ "\n",
+ " 2/14/21 2/15/21 \n",
+ "Country/Region \n",
+ "Belgium 738631 739488 \n",
+ "Germany 2341744 2346876 \n",
+ "Iran 1518263 1526023 \n",
+ "Italy 2721879 2729223 \n",
+ "Japan 416154 417127 \n",
+ "Korea, South 83869 84325 \n",
+ "Portugal 785756 787059 \n",
+ "Spain 3056035 3086286 \n",
+ "US 27640282 27694165 \n",
+ "\n",
+ "[9 rows x 393 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_selection1 = data[data[\"Country/Region\"].isin([\"Belgium\", \"Germany\", \"Iran\", \"Italy\", \"Japan\", \"Korea, South\", \"Portugal\", \"Spain\", \"US\"])]\n",
+ "data_selection2 = data_selection1[data_selection1[\"Province/State\"].isna()]\n",
+ "data_selection3 = data_selection2.groupby('Country/Region').sum()\n",
+ "data_selection3.copy()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## les pays avec des territoires d'outre-mer et autres « résidus coloniaux »."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Province/State \n",
+ " Country/Region \n",
+ " Lat \n",
+ " Long \n",
+ " 1/22/20 \n",
+ " 1/23/20 \n",
+ " 1/24/20 \n",
+ " 1/25/20 \n",
+ " 1/26/20 \n",
+ " 1/27/20 \n",
+ " ... \n",
+ " 2/6/21 \n",
+ " 2/7/21 \n",
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+ " \n",
+ " \n",
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+ " France \n",
+ " -21.115100 \n",
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+ " 18.070800 \n",
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+ " France \n",
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+ " 260 \n",
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+ " United Kingdom \n",
+ " 54.236100 \n",
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+ " \n",
+ " 261 \n",
+ " Montserrat \n",
+ " United Kingdom \n",
+ " 16.742498 \n",
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+ " 263 \n",
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+ " United Kingdom \n",
+ " 55.378100 \n",
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+ " 0 \n",
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+ " 0 \n",
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+ " 3929835 \n",
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+ " 4038078 \n",
+ " 4047843 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
23 rows × 395 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Province/State Country/Region Lat Long \\\n",
+ "118 French Guiana France 3.933900 -53.125800 \n",
+ "119 French Polynesia France -17.679700 149.406800 \n",
+ "120 Guadeloupe France 16.265000 -61.551000 \n",
+ "121 Martinique France 14.641500 -61.024200 \n",
+ "122 Mayotte France -12.827500 45.166244 \n",
+ "123 New Caledonia France -20.904305 165.618042 \n",
+ "124 Reunion France -21.115100 55.536400 \n",
+ "125 Saint Barthelemy France 17.900000 -62.833300 \n",
+ "126 Saint Pierre and Miquelon France 46.885200 -56.315900 \n",
+ "127 St Martin France 18.070800 -63.050100 \n",
+ "128 Wallis and Futuna France -14.293800 -178.116500 \n",
+ "129 NaN France 46.227600 2.213700 \n",
+ "253 Anguilla United Kingdom 18.220600 -63.068600 \n",
+ "254 Bermuda United Kingdom 32.307800 -64.750500 \n",
+ "255 British Virgin Islands United Kingdom 18.420700 -64.640000 \n",
+ "256 Cayman Islands United Kingdom 19.313300 -81.254600 \n",
+ "257 Channel Islands United Kingdom 49.372300 -2.364400 \n",
+ "258 Falkland Islands (Malvinas) United Kingdom -51.796300 -59.523600 \n",
+ "259 Gibraltar United Kingdom 36.140800 -5.353600 \n",
+ "260 Isle of Man United Kingdom 54.236100 -4.548100 \n",
+ "261 Montserrat United Kingdom 16.742498 -62.187366 \n",
+ "262 Turks and Caicos Islands United Kingdom 21.694000 -71.797900 \n",
+ "263 NaN United Kingdom 55.378100 -3.436000 \n",
+ "\n",
+ " 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... 2/6/21 \\\n",
+ "118 0 0 0 0 0 0 ... 16296 \n",
+ "119 0 0 0 0 0 0 ... 18185 \n",
+ "120 0 0 0 0 0 0 ... 9156 \n",
+ "121 0 0 0 0 0 0 ... 6442 \n",
+ "122 0 0 0 0 0 0 ... 10755 \n",
+ "123 0 0 0 0 0 0 ... 49 \n",
+ "124 0 0 0 0 0 0 ... 10487 \n",
+ "125 0 0 0 0 0 0 ... 360 \n",
+ "126 0 0 0 0 0 0 ... 24 \n",
+ "127 0 0 0 0 0 0 ... 1234 \n",
+ "128 0 0 0 0 0 0 ... 5 \n",
+ "129 0 0 2 3 3 3 ... 3303273 \n",
+ "253 0 0 0 0 0 0 ... 17 \n",
+ "254 0 0 0 0 0 0 ... 692 \n",
+ "255 0 0 0 0 0 0 ... 114 \n",
+ "256 0 0 0 0 0 0 ... 405 \n",
+ "257 0 0 0 0 0 0 ... 3885 \n",
+ "258 0 0 0 0 0 0 ... 45 \n",
+ "259 0 0 0 0 0 0 ... 4177 \n",
+ "260 0 0 0 0 0 0 ... 434 \n",
+ "261 0 0 0 0 0 0 ... 15 \n",
+ "262 0 0 0 0 0 0 ... 1654 \n",
+ "263 0 0 0 0 0 0 ... 3929835 \n",
+ "\n",
+ " 2/7/21 2/8/21 2/9/21 2/10/21 2/11/21 2/12/21 2/13/21 2/14/21 \\\n",
+ "118 16296 16296 16296 16296 16296 16296 16456 16456 \n",
+ "119 18185 18206 18222 18244 18257 18263 18263 18263 \n",
+ "120 9156 9156 9302 9302 9302 9302 9302 9302 \n",
+ "121 6442 6442 6521 6521 6521 6521 6521 6521 \n",
+ "122 10755 10755 11147 11447 11783 12440 12702 13535 \n",
+ "123 49 50 50 50 50 52 52 52 \n",
+ "124 10487 10487 10487 10487 10907 10907 10907 10907 \n",
+ "125 360 360 360 455 455 455 455 455 \n",
+ "126 24 24 24 24 24 24 24 24 \n",
+ "127 1234 1234 1234 1408 1408 1408 1408 1408 \n",
+ "128 5 9 9 9 9 9 9 9 \n",
+ "129 3322988 3327305 3345558 3370645 3390952 3390952 3390952 3390952 \n",
+ "253 17 17 18 18 18 18 18 18 \n",
+ "254 692 694 694 694 694 694 694 694 \n",
+ "255 114 114 114 114 114 114 114 114 \n",
+ "256 405 408 408 408 411 416 416 416 \n",
+ "257 3896 3918 3939 3950 3961 3980 3989 3999 \n",
+ "258 45 45 53 53 53 53 53 53 \n",
+ "259 4182 4187 4190 4197 4203 4212 4215 4219 \n",
+ "260 434 435 436 436 436 436 436 436 \n",
+ "261 17 18 18 18 19 19 19 20 \n",
+ "262 1695 1695 1738 1784 1812 1833 1869 1873 \n",
+ "263 3945680 3959784 3972148 3985161 3998655 4013799 4027106 4038078 \n",
+ "\n",
+ " 2/15/21 \n",
+ "118 16456 \n",
+ "119 18293 \n",
+ "120 9302 \n",
+ "121 6521 \n",
+ "122 13535 \n",
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+ "262 1874 \n",
+ "263 4047843 \n",
+ "\n",
+ "[23 rows x 395 columns]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_selection4 = data[data[\"Country/Region\"].isin([\"France\", \"Netherlands »\", \"United Kingdom\"])]\n",
+ "data_selection4"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "L'opération de regroupement des données avec les territoires d'outre-mer et les « résidus coloniaux » modifie **la latitude et la longitude** des pays à étudier.\n",
+ "Afin de préserver une exploitation future sur les territoires, il sera modifié la coordonnées des trois pays avec \n",
+ "* pour la France : Lat(46.227600)-Long(2.213700) - **index 129**\n",
+ "* pour la Hollande (Netherlands) : Lat(52.132600)-Long(5.291300) - **index 194**\n",
+ "* pour le Royaume Unis (United Kingdom) : Lat(55.378100)-Long(-3.436000) - **index 262**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 10ème pays : la France"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Lat \n",
+ " Long \n",
+ " 1/22/20 \n",
+ " 1/23/20 \n",
+ " 1/24/20 \n",
+ " 1/25/20 \n",
+ " 1/26/20 \n",
+ " 1/27/20 \n",
+ " 1/28/20 \n",
+ " 1/29/20 \n",
+ " ... \n",
+ " 2/6/21 \n",
+ " 2/7/21 \n",
+ " 2/8/21 \n",
+ " 2/9/21 \n",
+ " 2/10/21 \n",
+ " 2/11/21 \n",
+ " 2/12/21 \n",
+ " 2/13/21 \n",
+ " 2/14/21 \n",
+ " 2/15/21 \n",
+ " \n",
+ " \n",
+ " Country/Region \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " France \n",
+ " 77.103595 \n",
+ " -118.075614 \n",
+ " 0 \n",
+ " 0 \n",
+ " 2 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 4 \n",
+ " 5 \n",
+ " ... \n",
+ " 3376266 \n",
+ " 3395981 \n",
+ " 3400324 \n",
+ " 3419210 \n",
+ " 3444888 \n",
+ " 3465964 \n",
+ " 3466629 \n",
+ " 3467051 \n",
+ " 3467884 \n",
+ " 3528856 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1 rows × 393 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 \\\n",
+ "Country/Region \n",
+ "France 77.103595 -118.075614 0 0 2 3 \n",
+ "\n",
+ " 1/26/20 1/27/20 1/28/20 1/29/20 ... 2/6/21 2/7/21 \\\n",
+ "Country/Region ... \n",
+ "France 3 3 4 5 ... 3376266 3395981 \n",
+ "\n",
+ " 2/8/21 2/9/21 2/10/21 2/11/21 2/12/21 2/13/21 2/14/21 \\\n",
+ "Country/Region \n",
+ "France 3400324 3419210 3444888 3465964 3466629 3467051 3467884 \n",
+ "\n",
+ " 2/15/21 \n",
+ "Country/Region \n",
+ "France 3528856 \n",
+ "\n",
+ "[1 rows x 393 columns]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_selection4 = data[data[\"Country/Region\"].isin([\"France\"])]\n",
+ "data_selection5 = data_selection4[data_selection4[\"Province/State\"].isna()]\n",
+ "data_selection5 = data_selection4.groupby('Country/Region').sum()\n",
+ "data_selection5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Lat \n",
+ " Long \n",
+ " 1/22/20 \n",
+ " 1/23/20 \n",
+ " 1/24/20 \n",
+ " 1/25/20 \n",
+ " 1/26/20 \n",
+ " 1/27/20 \n",
+ " 1/28/20 \n",
+ " 1/29/20 \n",
+ " ... \n",
+ " 2/6/21 \n",
+ " 2/7/21 \n",
+ " 2/8/21 \n",
+ " 2/9/21 \n",
+ " 2/10/21 \n",
+ " 2/11/21 \n",
+ " 2/12/21 \n",
+ " 2/13/21 \n",
+ " 2/14/21 \n",
+ " 2/15/21 \n",
+ " \n",
+ " \n",
+ " Country/Region \n",
+ " \n",
+ " \n",
+ " \n",
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+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " France \n",
+ " 46.2276 \n",
+ " 2.2137 \n",
+ " 0 \n",
+ " 0 \n",
+ " 2 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 4 \n",
+ " 5 \n",
+ " ... \n",
+ " 3376266 \n",
+ " 3395981 \n",
+ " 3400324 \n",
+ " 3419210 \n",
+ " 3444888 \n",
+ " 3465964 \n",
+ " 3466629 \n",
+ " 3467051 \n",
+ " 3467884 \n",
+ " 3528856 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1 rows × 393 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n",
+ "Country/Region \n",
+ "France 46.2276 2.2137 0 0 2 3 3 \n",
+ "\n",
+ " 1/27/20 1/28/20 1/29/20 ... 2/6/21 2/7/21 2/8/21 \\\n",
+ "Country/Region ... \n",
+ "France 3 4 5 ... 3376266 3395981 3400324 \n",
+ "\n",
+ " 2/9/21 2/10/21 2/11/21 2/12/21 2/13/21 2/14/21 2/15/21 \n",
+ "Country/Region \n",
+ "France 3419210 3444888 3465964 3466629 3467051 3467884 3528856 \n",
+ "\n",
+ "[1 rows x 393 columns]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_selection5.loc[:, 'Lat'] =46.227600\n",
+ "data_selection5.loc[:, 'Long'] =2.213700\n",
+ "data_selection5.copy()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 11ème pays : la Hollande"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Lat \n",
+ " Long \n",
+ " 1/22/20 \n",
+ " 1/23/20 \n",
+ " 1/24/20 \n",
+ " 1/25/20 \n",
+ " 1/26/20 \n",
+ " 1/27/20 \n",
+ " 1/28/20 \n",
+ " 1/29/20 \n",
+ " ... \n",
+ " 2/6/21 \n",
+ " 2/7/21 \n",
+ " 2/8/21 \n",
+ " 2/9/21 \n",
+ " 2/10/21 \n",
+ " 2/11/21 \n",
+ " 2/12/21 \n",
+ " 2/13/21 \n",
+ " 2/14/21 \n",
+ " 2/15/21 \n",
+ " \n",
+ " \n",
+ " Country/Region \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Netherlands \n",
+ " 107.0442 \n",
+ " -264.9603 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " ... \n",
+ " 1015757 \n",
+ " 1019720 \n",
+ " 1021966 \n",
+ " 1023779 \n",
+ " 1027023 \n",
+ " 1031454 \n",
+ " 1035841 \n",
+ " 1040070 \n",
+ " 1043541 \n",
+ " 1046381 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1 rows × 393 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 \\\n",
+ "Country/Region \n",
+ "Netherlands 107.0442 -264.9603 0 0 0 0 \n",
+ "\n",
+ " 1/26/20 1/27/20 1/28/20 1/29/20 ... 2/6/21 2/7/21 \\\n",
+ "Country/Region ... \n",
+ "Netherlands 0 0 0 0 ... 1015757 1019720 \n",
+ "\n",
+ " 2/8/21 2/9/21 2/10/21 2/11/21 2/12/21 2/13/21 2/14/21 \\\n",
+ "Country/Region \n",
+ "Netherlands 1021966 1023779 1027023 1031454 1035841 1040070 1043541 \n",
+ "\n",
+ " 2/15/21 \n",
+ "Country/Region \n",
+ "Netherlands 1046381 \n",
+ "\n",
+ "[1 rows x 393 columns]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_selection6 = data[data[\"Country/Region\"].isin([\"Netherlands\"])]\n",
+ "data_selection7 = data_selection6[data_selection6[\"Province/State\"].isna()]\n",
+ "data_selection7 = data_selection6.groupby('Country/Region').sum()\n",
+ "data_selection7"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Lat \n",
+ " Long \n",
+ " 1/22/20 \n",
+ " 1/23/20 \n",
+ " 1/24/20 \n",
+ " 1/25/20 \n",
+ " 1/26/20 \n",
+ " 1/27/20 \n",
+ " 1/28/20 \n",
+ " 1/29/20 \n",
+ " ... \n",
+ " 2/6/21 \n",
+ " 2/7/21 \n",
+ " 2/8/21 \n",
+ " 2/9/21 \n",
+ " 2/10/21 \n",
+ " 2/11/21 \n",
+ " 2/12/21 \n",
+ " 2/13/21 \n",
+ " 2/14/21 \n",
+ " 2/15/21 \n",
+ " \n",
+ " \n",
+ " Country/Region \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Netherlands \n",
+ " 55.3781 \n",
+ " 5.2913 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " ... \n",
+ " 1015757 \n",
+ " 1019720 \n",
+ " 1021966 \n",
+ " 1023779 \n",
+ " 1027023 \n",
+ " 1031454 \n",
+ " 1035841 \n",
+ " 1040070 \n",
+ " 1043541 \n",
+ " 1046381 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1 rows × 393 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n",
+ "Country/Region \n",
+ "Netherlands 55.3781 5.2913 0 0 0 0 0 \n",
+ "\n",
+ " 1/27/20 1/28/20 1/29/20 ... 2/6/21 2/7/21 2/8/21 \\\n",
+ "Country/Region ... \n",
+ "Netherlands 0 0 0 ... 1015757 1019720 1021966 \n",
+ "\n",
+ " 2/9/21 2/10/21 2/11/21 2/12/21 2/13/21 2/14/21 2/15/21 \n",
+ "Country/Region \n",
+ "Netherlands 1023779 1027023 1031454 1035841 1040070 1043541 1046381 \n",
+ "\n",
+ "[1 rows x 393 columns]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_selection7.loc[:, 'Lat'] =55.378100\n",
+ "data_selection7.loc[:, 'Long'] =5.291300\n",
+ "data_selection7.copy()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 12ème pays : le Royaume Unis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Lat \n",
+ " Long \n",
+ " 1/22/20 \n",
+ " 1/23/20 \n",
+ " 1/24/20 \n",
+ " 1/25/20 \n",
+ " 1/26/20 \n",
+ " 1/27/20 \n",
+ " 1/28/20 \n",
+ " 1/29/20 \n",
+ " ... \n",
+ " 2/6/21 \n",
+ " 2/7/21 \n",
+ " 2/8/21 \n",
+ " 2/9/21 \n",
+ " 2/10/21 \n",
+ " 2/11/21 \n",
+ " 2/12/21 \n",
+ " 2/13/21 \n",
+ " 2/14/21 \n",
+ " 2/15/21 \n",
+ " \n",
+ " \n",
+ " Country/Region \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " United Kingdom \n",
+ " 270.029898 \n",
+ " -482.924666 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " ... \n",
+ " 3941273 \n",
+ " 3957177 \n",
+ " 3971315 \n",
+ " 3983756 \n",
+ " 3996833 \n",
+ " 4010376 \n",
+ " 4025574 \n",
+ " 4038929 \n",
+ " 4049920 \n",
+ " 4059696 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1 rows × 393 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 \\\n",
+ "Country/Region \n",
+ "United Kingdom 270.029898 -482.924666 0 0 0 0 \n",
+ "\n",
+ " 1/26/20 1/27/20 1/28/20 1/29/20 ... 2/6/21 2/7/21 \\\n",
+ "Country/Region ... \n",
+ "United Kingdom 0 0 0 0 ... 3941273 3957177 \n",
+ "\n",
+ " 2/8/21 2/9/21 2/10/21 2/11/21 2/12/21 2/13/21 2/14/21 \\\n",
+ "Country/Region \n",
+ "United Kingdom 3971315 3983756 3996833 4010376 4025574 4038929 4049920 \n",
+ "\n",
+ " 2/15/21 \n",
+ "Country/Region \n",
+ "United Kingdom 4059696 \n",
+ "\n",
+ "[1 rows x 393 columns]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_selection8 = data[data[\"Country/Region\"].isin([\"United Kingdom\"])]\n",
+ "data_selection8\n",
+ "data_selection9 = data_selection8[data_selection8[\"Province/State\"].isna()]\n",
+ "data_selection9 = data_selection8.groupby('Country/Region').sum()\n",
+ "data_selection9.copy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Lat \n",
+ " Long \n",
+ " 1/22/20 \n",
+ " 1/23/20 \n",
+ " 1/24/20 \n",
+ " 1/25/20 \n",
+ " 1/26/20 \n",
+ " 1/27/20 \n",
+ " 1/28/20 \n",
+ " 1/29/20 \n",
+ " ... \n",
+ " 2/6/21 \n",
+ " 2/7/21 \n",
+ " 2/8/21 \n",
+ " 2/9/21 \n",
+ " 2/10/21 \n",
+ " 2/11/21 \n",
+ " 2/12/21 \n",
+ " 2/13/21 \n",
+ " 2/14/21 \n",
+ " 2/15/21 \n",
+ " \n",
+ " \n",
+ " Country/Region \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " United Kingdom \n",
+ " 52.1326 \n",
+ " 3.436 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " ... \n",
+ " 3941273 \n",
+ " 3957177 \n",
+ " 3971315 \n",
+ " 3983756 \n",
+ " 3996833 \n",
+ " 4010376 \n",
+ " 4025574 \n",
+ " 4038929 \n",
+ " 4049920 \n",
+ " 4059696 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1 rows × 393 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n",
+ "Country/Region \n",
+ "United Kingdom 52.1326 3.436 0 0 0 0 0 \n",
+ "\n",
+ " 1/27/20 1/28/20 1/29/20 ... 2/6/21 2/7/21 2/8/21 \\\n",
+ "Country/Region ... \n",
+ "United Kingdom 0 0 0 ... 3941273 3957177 3971315 \n",
+ "\n",
+ " 2/9/21 2/10/21 2/11/21 2/12/21 2/13/21 2/14/21 2/15/21 \n",
+ "Country/Region \n",
+ "United Kingdom 3983756 3996833 4010376 4025574 4038929 4049920 4059696 \n",
+ "\n",
+ "[1 rows x 393 columns]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_selection9.loc[:, 'Lat'] =52.132600\n",
+ "data_selection9.loc[:, 'Long'] =3.436000\n",
+ "data_selection9.copy()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## la chine, ses provinces dont Hong Kong\n",
+ "Pour répondre à l'exercice, nous devons cumuler les résultats des provinces de la Chine sans la région de Hong Kong qui doit être isolée."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 13ème \"pays\" : Hong Kong"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Lat \n",
+ " Long \n",
+ " 1/22/20 \n",
+ " 1/23/20 \n",
+ " 1/24/20 \n",
+ " 1/25/20 \n",
+ " 1/26/20 \n",
+ " 1/27/20 \n",
+ " 1/28/20 \n",
+ " 1/29/20 \n",
+ " ... \n",
+ " 2/6/21 \n",
+ " 2/7/21 \n",
+ " 2/8/21 \n",
+ " 2/9/21 \n",
+ " 2/10/21 \n",
+ " 2/11/21 \n",
+ " 2/12/21 \n",
+ " 2/13/21 \n",
+ " 2/14/21 \n",
+ " 2/15/21 \n",
+ " \n",
+ " \n",
+ " Country/Region \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " China \n",
+ " 22.3 \n",
+ " 114.2 \n",
+ " 0 \n",
+ " 2 \n",
+ " 2 \n",
+ " 5 \n",
+ " 8 \n",
+ " 8 \n",
+ " 8 \n",
+ " 10 \n",
+ " ... \n",
+ " 10608 \n",
+ " 10635 \n",
+ " 10667 \n",
+ " 10693 \n",
+ " 10710 \n",
+ " 10731 \n",
+ " 10755 \n",
+ " 10767 \n",
+ " 10779 \n",
+ " 10788 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1 rows × 393 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n",
+ "Country/Region \n",
+ "China 22.3 114.2 0 2 2 5 8 \n",
+ "\n",
+ " 1/27/20 1/28/20 1/29/20 ... 2/6/21 2/7/21 2/8/21 \\\n",
+ "Country/Region ... \n",
+ "China 8 8 10 ... 10608 10635 10667 \n",
+ "\n",
+ " 2/9/21 2/10/21 2/11/21 2/12/21 2/13/21 2/14/21 2/15/21 \n",
+ "Country/Region \n",
+ "China 10693 10710 10731 10755 10767 10779 10788 \n",
+ "\n",
+ "[1 rows x 393 columns]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_Hong_Kong = data[data[\"Province/State\"].isin([\"Hong Kong\"])]\n",
+ "data_Hong_Kong2 =data_Hong_Kong.groupby('Country/Region').sum()\n",
+ "data_Hong_Kong2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "modification de l'index China (Country/Region) en celui de Hong Kong (Province/State) correspondant au regroupement."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_Hong_Kong2.rename({\"China\":\"Hong Kong\"},inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Lat \n",
+ " Long \n",
+ " 1/22/20 \n",
+ " 1/23/20 \n",
+ " 1/24/20 \n",
+ " 1/25/20 \n",
+ " 1/26/20 \n",
+ " 1/27/20 \n",
+ " 1/28/20 \n",
+ " 1/29/20 \n",
+ " ... \n",
+ " 2/6/21 \n",
+ " 2/7/21 \n",
+ " 2/8/21 \n",
+ " 2/9/21 \n",
+ " 2/10/21 \n",
+ " 2/11/21 \n",
+ " 2/12/21 \n",
+ " 2/13/21 \n",
+ " 2/14/21 \n",
+ " 2/15/21 \n",
+ " \n",
+ " \n",
+ " Hong_Kong \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Hong Kong \n",
+ " 22.3 \n",
+ " 114.2 \n",
+ " 0 \n",
+ " 2 \n",
+ " 2 \n",
+ " 5 \n",
+ " 8 \n",
+ " 8 \n",
+ " 8 \n",
+ " 10 \n",
+ " ... \n",
+ " 10608 \n",
+ " 10635 \n",
+ " 10667 \n",
+ " 10693 \n",
+ " 10710 \n",
+ " 10731 \n",
+ " 10755 \n",
+ " 10767 \n",
+ " 10779 \n",
+ " 10788 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1 rows × 393 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 \\\n",
+ "Hong_Kong \n",
+ "Hong Kong 22.3 114.2 0 2 2 5 8 8 \n",
+ "\n",
+ " 1/28/20 1/29/20 ... 2/6/21 2/7/21 2/8/21 2/9/21 2/10/21 \\\n",
+ "Hong_Kong ... \n",
+ "Hong Kong 8 10 ... 10608 10635 10667 10693 10710 \n",
+ "\n",
+ " 2/11/21 2/12/21 2/13/21 2/14/21 2/15/21 \n",
+ "Hong_Kong \n",
+ "Hong Kong 10731 10755 10767 10779 10788 \n",
+ "\n",
+ "[1 rows x 393 columns]"
+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_Hong_Kong3 = data_Hong_Kong2.copy()\n",
+ "data_Hong_Kong3"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 14ème pays : la Chine\n",
+ "\n",
+ "33 provinces sont répertoriées pour la Chine dans cette extraction avec la province de Hong Kong"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Province/State \n",
+ " Country/Region \n",
+ " Lat \n",
+ " Long \n",
+ " 1/22/20 \n",
+ " 1/23/20 \n",
+ " 1/24/20 \n",
+ " 1/25/20 \n",
+ " 1/26/20 \n",
+ " 1/27/20 \n",
+ " ... \n",
+ " 2/6/21 \n",
+ " 2/7/21 \n",
+ " 2/8/21 \n",
+ " 2/9/21 \n",
+ " 2/10/21 \n",
+ " 2/11/21 \n",
+ " 2/12/21 \n",
+ " 2/13/21 \n",
+ " 2/14/21 \n",
+ " 2/15/21 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 58 \n",
+ " Anhui \n",
+ " China \n",
+ " 31.8257 \n",
+ " 117.2264 \n",
+ " 1 \n",
+ " 9 \n",
+ " 15 \n",
+ " 39 \n",
+ " 60 \n",
+ " 70 \n",
+ " ... \n",
+ " 994 \n",
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+ " 994 \n",
+ " 994 \n",
+ " 994 \n",
+ " 994 \n",
+ " 994 \n",
+ " 994 \n",
+ " 994 \n",
+ " 994 \n",
+ " \n",
+ " \n",
+ " 59 \n",
+ " Beijing \n",
+ " China \n",
+ " 40.1824 \n",
+ " 116.4142 \n",
+ " 14 \n",
+ " 22 \n",
+ " 36 \n",
+ " 41 \n",
+ " 68 \n",
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+ " 1046 \n",
+ " 1046 \n",
+ " 1046 \n",
+ " 1046 \n",
+ " 1046 \n",
+ " 1046 \n",
+ " 1046 \n",
+ " \n",
+ " \n",
+ " 60 \n",
+ " Chongqing \n",
+ " China \n",
+ " 30.0572 \n",
+ " 107.8740 \n",
+ " 6 \n",
+ " 9 \n",
+ " 27 \n",
+ " 57 \n",
+ " 75 \n",
+ " 110 \n",
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+ " 591 \n",
+ " 591 \n",
+ " 591 \n",
+ " \n",
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+ " 61 \n",
+ " Fujian \n",
+ " China \n",
+ " 26.0789 \n",
+ " 117.9874 \n",
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+ " 5 \n",
+ " 10 \n",
+ " 18 \n",
+ " 35 \n",
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+ " 548 \n",
+ " 548 \n",
+ " 548 \n",
+ " \n",
+ " \n",
+ " 62 \n",
+ " Gansu \n",
+ " China \n",
+ " 35.7518 \n",
+ " 104.2861 \n",
+ " 0 \n",
+ " 2 \n",
+ " 2 \n",
+ " 4 \n",
+ " 7 \n",
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+ " 187 \n",
+ " 187 \n",
+ " 187 \n",
+ " 187 \n",
+ " \n",
+ " \n",
+ " 63 \n",
+ " Guangdong \n",
+ " China \n",
+ " 23.3417 \n",
+ " 113.4244 \n",
+ " 26 \n",
+ " 32 \n",
+ " 53 \n",
+ " 78 \n",
+ " 111 \n",
+ " 151 \n",
+ " ... \n",
+ " 2137 \n",
+ " 2144 \n",
+ " 2151 \n",
+ " 2151 \n",
+ " 2152 \n",
+ " 2154 \n",
+ " 2157 \n",
+ " 2159 \n",
+ " 2163 \n",
+ " 2171 \n",
+ " \n",
+ " \n",
+ " 64 \n",
+ " Guangxi \n",
+ " China \n",
+ " 23.8298 \n",
+ " 108.7881 \n",
+ " 2 \n",
+ " 5 \n",
+ " 23 \n",
+ " 23 \n",
+ " 36 \n",
+ " 46 \n",
+ " ... \n",
+ " 267 \n",
+ " 267 \n",
+ " 267 \n",
+ " 267 \n",
+ " 267 \n",
+ " 267 \n",
+ " 267 \n",
+ " 267 \n",
+ " 267 \n",
+ " 267 \n",
+ " \n",
+ " \n",
+ " 65 \n",
+ " Guizhou \n",
+ " China \n",
+ " 26.8154 \n",
+ " 106.8748 \n",
+ " 1 \n",
+ " 3 \n",
+ " 3 \n",
+ " 4 \n",
+ " 5 \n",
+ " 7 \n",
+ " ... \n",
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+ " 147 \n",
+ " 147 \n",
+ " 147 \n",
+ " 147 \n",
+ " 147 \n",
+ " 147 \n",
+ " 147 \n",
+ " \n",
+ " \n",
+ " 66 \n",
+ " Hainan \n",
+ " China \n",
+ " 19.1959 \n",
+ " 109.7453 \n",
+ " 4 \n",
+ " 5 \n",
+ " 8 \n",
+ " 19 \n",
+ " 22 \n",
+ " 33 \n",
+ " ... \n",
+ " 171 \n",
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+ " 171 \n",
+ " 171 \n",
+ " 171 \n",
+ " 171 \n",
+ " 171 \n",
+ " 171 \n",
+ " 171 \n",
+ " 171 \n",
+ " \n",
+ " \n",
+ " 67 \n",
+ " Hebei \n",
+ " China \n",
+ " 39.5490 \n",
+ " 116.1306 \n",
+ " 1 \n",
+ " 1 \n",
+ " 2 \n",
+ " 8 \n",
+ " 13 \n",
+ " 18 \n",
+ " ... \n",
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+ " 1316 \n",
+ " 1316 \n",
+ " 1317 \n",
+ " 1317 \n",
+ " \n",
+ " \n",
+ " 68 \n",
+ " Heilongjiang \n",
+ " China \n",
+ " 47.8620 \n",
+ " 127.7615 \n",
+ " 0 \n",
+ " 2 \n",
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+ " 1609 \n",
+ " 1609 \n",
+ " 1609 \n",
+ " \n",
+ " \n",
+ " 69 \n",
+ " Henan \n",
+ " China \n",
+ " 37.8957 \n",
+ " 114.9042 \n",
+ " 5 \n",
+ " 5 \n",
+ " 9 \n",
+ " 32 \n",
+ " 83 \n",
+ " 128 \n",
+ " ... \n",
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+ " 1303 \n",
+ " 1303 \n",
+ " 1303 \n",
+ " 1303 \n",
+ " 1303 \n",
+ " 1303 \n",
+ " 1303 \n",
+ " 1304 \n",
+ " 1304 \n",
+ " \n",
+ " \n",
+ " 70 \n",
+ " Hong Kong \n",
+ " China \n",
+ " 22.3000 \n",
+ " 114.2000 \n",
+ " 0 \n",
+ " 2 \n",
+ " 2 \n",
+ " 5 \n",
+ " 8 \n",
+ " 8 \n",
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+ " 10788 \n",
+ " \n",
+ " \n",
+ " 71 \n",
+ " Hubei \n",
+ " China \n",
+ " 30.9756 \n",
+ " 112.2707 \n",
+ " 444 \n",
+ " 444 \n",
+ " 549 \n",
+ " 761 \n",
+ " 1058 \n",
+ " 1423 \n",
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+ " 68150 \n",
+ " 68150 \n",
+ " 68150 \n",
+ " 68150 \n",
+ " \n",
+ " \n",
+ " 72 \n",
+ " Hunan \n",
+ " China \n",
+ " 27.6104 \n",
+ " 111.7088 \n",
+ " 4 \n",
+ " 9 \n",
+ " 24 \n",
+ " 43 \n",
+ " 69 \n",
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+ " 1033 \n",
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+ " 1033 \n",
+ " 1033 \n",
+ " 1033 \n",
+ " 1033 \n",
+ " 1033 \n",
+ " \n",
+ " \n",
+ " 73 \n",
+ " Inner Mongolia \n",
+ " China \n",
+ " 44.0935 \n",
+ " 113.9448 \n",
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+ " 0 \n",
+ " 1 \n",
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+ " 367 \n",
+ " 367 \n",
+ " \n",
+ " \n",
+ " 74 \n",
+ " Jiangsu \n",
+ " China \n",
+ " 32.9711 \n",
+ " 119.4550 \n",
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+ " 703 \n",
+ " 703 \n",
+ " 703 \n",
+ " 703 \n",
+ " \n",
+ " \n",
+ " 75 \n",
+ " Jiangxi \n",
+ " China \n",
+ " 27.6140 \n",
+ " 115.7221 \n",
+ " 2 \n",
+ " 7 \n",
+ " 18 \n",
+ " 18 \n",
+ " 36 \n",
+ " 72 \n",
+ " ... \n",
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+ " 935 \n",
+ " 935 \n",
+ " 935 \n",
+ " 935 \n",
+ " 935 \n",
+ " \n",
+ " \n",
+ " 76 \n",
+ " Jilin \n",
+ " China \n",
+ " 43.6661 \n",
+ " 126.1923 \n",
+ " 0 \n",
+ " 1 \n",
+ " 3 \n",
+ " 4 \n",
+ " 4 \n",
+ " 6 \n",
+ " ... \n",
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+ " 573 \n",
+ " 573 \n",
+ " 573 \n",
+ " 573 \n",
+ " 573 \n",
+ " 573 \n",
+ " 573 \n",
+ " 573 \n",
+ " 573 \n",
+ " \n",
+ " \n",
+ " 77 \n",
+ " Liaoning \n",
+ " China \n",
+ " 41.2956 \n",
+ " 122.6085 \n",
+ " 2 \n",
+ " 3 \n",
+ " 4 \n",
+ " 17 \n",
+ " 21 \n",
+ " 27 \n",
+ " ... \n",
+ " 402 \n",
+ " 402 \n",
+ " 402 \n",
+ " 402 \n",
+ " 402 \n",
+ " 403 \n",
+ " 404 \n",
+ " 404 \n",
+ " 404 \n",
+ " 404 \n",
+ " \n",
+ " \n",
+ " 78 \n",
+ " Macau \n",
+ " China \n",
+ " 22.1667 \n",
+ " 113.5500 \n",
+ " 1 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 5 \n",
+ " 6 \n",
+ " ... \n",
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+ " 48 \n",
+ " 48 \n",
+ " 48 \n",
+ " 48 \n",
+ " 48 \n",
+ " 48 \n",
+ " 48 \n",
+ " 48 \n",
+ " 48 \n",
+ " \n",
+ " \n",
+ " 79 \n",
+ " Ningxia \n",
+ " China \n",
+ " 37.2692 \n",
+ " 106.1655 \n",
+ " 1 \n",
+ " 1 \n",
+ " 2 \n",
+ " 3 \n",
+ " 4 \n",
+ " 7 \n",
+ " ... \n",
+ " 75 \n",
+ " 75 \n",
+ " 75 \n",
+ " 75 \n",
+ " 75 \n",
+ " 75 \n",
+ " 75 \n",
+ " 75 \n",
+ " 75 \n",
+ " 75 \n",
+ " \n",
+ " \n",
+ " 80 \n",
+ " Qinghai \n",
+ " China \n",
+ " 35.7452 \n",
+ " 95.9956 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 1 \n",
+ " 1 \n",
+ " 6 \n",
+ " ... \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " \n",
+ " \n",
+ " 81 \n",
+ " Shaanxi \n",
+ " China \n",
+ " 35.1917 \n",
+ " 108.8701 \n",
+ " 0 \n",
+ " 3 \n",
+ " 5 \n",
+ " 15 \n",
+ " 22 \n",
+ " 35 \n",
+ " ... \n",
+ " 542 \n",
+ " 542 \n",
+ " 542 \n",
+ " 543 \n",
+ " 543 \n",
+ " 543 \n",
+ " 543 \n",
+ " 544 \n",
+ " 545 \n",
+ " 547 \n",
+ " \n",
+ " \n",
+ " 82 \n",
+ " Shandong \n",
+ " China \n",
+ " 36.3427 \n",
+ " 118.1498 \n",
+ " 2 \n",
+ " 6 \n",
+ " 15 \n",
+ " 27 \n",
+ " 46 \n",
+ " 75 \n",
+ " ... \n",
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+ " 867 \n",
+ " 867 \n",
+ " 867 \n",
+ " 867 \n",
+ " \n",
+ " \n",
+ " 83 \n",
+ " Shanghai \n",
+ " China \n",
+ " 31.2020 \n",
+ " 121.4491 \n",
+ " 9 \n",
+ " 16 \n",
+ " 20 \n",
+ " 33 \n",
+ " 40 \n",
+ " 53 \n",
+ " ... \n",
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+ " 1741 \n",
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+ " 1747 \n",
+ " 1754 \n",
+ " 1757 \n",
+ " 1759 \n",
+ " 1760 \n",
+ " 1765 \n",
+ " \n",
+ " \n",
+ " 84 \n",
+ " Shanxi \n",
+ " China \n",
+ " 37.5777 \n",
+ " 112.2922 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 6 \n",
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+ " 239 \n",
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+ " 239 \n",
+ " 239 \n",
+ " 239 \n",
+ " 239 \n",
+ " 239 \n",
+ " \n",
+ " \n",
+ " 85 \n",
+ " Sichuan \n",
+ " China \n",
+ " 30.6171 \n",
+ " 102.7103 \n",
+ " 5 \n",
+ " 8 \n",
+ " 15 \n",
+ " 28 \n",
+ " 44 \n",
+ " 69 \n",
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+ " 877 \n",
+ " 878 \n",
+ " 878 \n",
+ " 879 \n",
+ " \n",
+ " \n",
+ " 86 \n",
+ " Tianjin \n",
+ " China \n",
+ " 39.3054 \n",
+ " 117.3230 \n",
+ " 4 \n",
+ " 4 \n",
+ " 8 \n",
+ " 10 \n",
+ " 14 \n",
+ " 23 \n",
+ " ... \n",
+ " 346 \n",
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+ " 346 \n",
+ " 347 \n",
+ " 347 \n",
+ " 347 \n",
+ " 348 \n",
+ " 348 \n",
+ " 349 \n",
+ " 349 \n",
+ " \n",
+ " \n",
+ " 87 \n",
+ " Tibet \n",
+ " China \n",
+ " 31.6927 \n",
+ " 88.0924 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
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+ " \n",
+ " \n",
+ " 88 \n",
+ " Xinjiang \n",
+ " China \n",
+ " 41.1129 \n",
+ " 85.2401 \n",
+ " 0 \n",
+ " 2 \n",
+ " 2 \n",
+ " 3 \n",
+ " 4 \n",
+ " 5 \n",
+ " ... \n",
+ " 980 \n",
+ " 980 \n",
+ " 980 \n",
+ " 980 \n",
+ " 980 \n",
+ " 980 \n",
+ " 980 \n",
+ " 980 \n",
+ " 980 \n",
+ " 980 \n",
+ " \n",
+ " \n",
+ " 89 \n",
+ " Yunnan \n",
+ " China \n",
+ " 24.9740 \n",
+ " 101.4870 \n",
+ " 1 \n",
+ " 2 \n",
+ " 5 \n",
+ " 11 \n",
+ " 16 \n",
+ " 26 \n",
+ " ... \n",
+ " 231 \n",
+ " 231 \n",
+ " 231 \n",
+ " 231 \n",
+ " 231 \n",
+ " 231 \n",
+ " 231 \n",
+ " 231 \n",
+ " 231 \n",
+ " 231 \n",
+ " \n",
+ " \n",
+ " 90 \n",
+ " Zhejiang \n",
+ " China \n",
+ " 29.1832 \n",
+ " 120.0934 \n",
+ " 10 \n",
+ " 27 \n",
+ " 43 \n",
+ " 62 \n",
+ " 104 \n",
+ " 128 \n",
+ " ... \n",
+ " 1316 \n",
+ " 1316 \n",
+ " 1317 \n",
+ " 1320 \n",
+ " 1320 \n",
+ " 1320 \n",
+ " 1320 \n",
+ " 1320 \n",
+ " 1320 \n",
+ " 1320 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
33 rows × 395 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n",
+ "58 Anhui China 31.8257 117.2264 1 9 \n",
+ "59 Beijing China 40.1824 116.4142 14 22 \n",
+ "60 Chongqing China 30.0572 107.8740 6 9 \n",
+ "61 Fujian China 26.0789 117.9874 1 5 \n",
+ "62 Gansu China 35.7518 104.2861 0 2 \n",
+ "63 Guangdong China 23.3417 113.4244 26 32 \n",
+ "64 Guangxi China 23.8298 108.7881 2 5 \n",
+ "65 Guizhou China 26.8154 106.8748 1 3 \n",
+ "66 Hainan China 19.1959 109.7453 4 5 \n",
+ "67 Hebei China 39.5490 116.1306 1 1 \n",
+ "68 Heilongjiang China 47.8620 127.7615 0 2 \n",
+ "69 Henan China 37.8957 114.9042 5 5 \n",
+ "70 Hong Kong China 22.3000 114.2000 0 2 \n",
+ "71 Hubei China 30.9756 112.2707 444 444 \n",
+ "72 Hunan China 27.6104 111.7088 4 9 \n",
+ "73 Inner Mongolia China 44.0935 113.9448 0 0 \n",
+ "74 Jiangsu China 32.9711 119.4550 1 5 \n",
+ "75 Jiangxi China 27.6140 115.7221 2 7 \n",
+ "76 Jilin China 43.6661 126.1923 0 1 \n",
+ "77 Liaoning China 41.2956 122.6085 2 3 \n",
+ "78 Macau China 22.1667 113.5500 1 2 \n",
+ "79 Ningxia China 37.2692 106.1655 1 1 \n",
+ "80 Qinghai China 35.7452 95.9956 0 0 \n",
+ "81 Shaanxi China 35.1917 108.8701 0 3 \n",
+ "82 Shandong China 36.3427 118.1498 2 6 \n",
+ "83 Shanghai China 31.2020 121.4491 9 16 \n",
+ "84 Shanxi China 37.5777 112.2922 1 1 \n",
+ "85 Sichuan China 30.6171 102.7103 5 8 \n",
+ "86 Tianjin China 39.3054 117.3230 4 4 \n",
+ "87 Tibet China 31.6927 88.0924 0 0 \n",
+ "88 Xinjiang China 41.1129 85.2401 0 2 \n",
+ "89 Yunnan China 24.9740 101.4870 1 2 \n",
+ "90 Zhejiang China 29.1832 120.0934 10 27 \n",
+ "\n",
+ " 1/24/20 1/25/20 1/26/20 1/27/20 ... 2/6/21 2/7/21 2/8/21 2/9/21 \\\n",
+ "58 15 39 60 70 ... 994 994 994 994 \n",
+ "59 36 41 68 80 ... 1046 1046 1046 1046 \n",
+ "60 27 57 75 110 ... 591 591 591 591 \n",
+ "61 10 18 35 59 ... 545 545 546 547 \n",
+ "62 2 4 7 14 ... 187 187 187 187 \n",
+ "63 53 78 111 151 ... 2137 2144 2151 2151 \n",
+ "64 23 23 36 46 ... 267 267 267 267 \n",
+ "65 3 4 5 7 ... 147 147 147 147 \n",
+ "66 8 19 22 33 ... 171 171 171 171 \n",
+ "67 2 8 13 18 ... 1316 1316 1316 1316 \n",
+ "68 4 9 15 21 ... 1609 1609 1609 1609 \n",
+ "69 9 32 83 128 ... 1303 1303 1303 1303 \n",
+ "70 2 5 8 8 ... 10608 10635 10667 10693 \n",
+ "71 549 761 1058 1423 ... 68150 68150 68150 68150 \n",
+ "72 24 43 69 100 ... 1033 1033 1033 1033 \n",
+ "73 1 7 7 11 ... 366 366 366 366 \n",
+ "74 9 18 33 47 ... 701 701 702 703 \n",
+ "75 18 18 36 72 ... 935 935 935 935 \n",
+ "76 3 4 4 6 ... 573 573 573 573 \n",
+ "77 4 17 21 27 ... 402 402 402 402 \n",
+ "78 2 2 5 6 ... 48 48 48 48 \n",
+ "79 2 3 4 7 ... 75 75 75 75 \n",
+ "80 0 1 1 6 ... 18 18 18 18 \n",
+ "81 5 15 22 35 ... 542 542 542 543 \n",
+ "82 15 27 46 75 ... 866 866 867 867 \n",
+ "83 20 33 40 53 ... 1732 1739 1741 1747 \n",
+ "84 1 6 9 13 ... 239 239 239 239 \n",
+ "85 15 28 44 69 ... 873 873 874 875 \n",
+ "86 8 10 14 23 ... 346 346 346 347 \n",
+ "87 0 0 0 0 ... 1 1 1 1 \n",
+ "88 2 3 4 5 ... 980 980 980 980 \n",
+ "89 5 11 16 26 ... 231 231 231 231 \n",
+ "90 43 62 104 128 ... 1316 1316 1317 1320 \n",
+ "\n",
+ " 2/10/21 2/11/21 2/12/21 2/13/21 2/14/21 2/15/21 \n",
+ "58 994 994 994 994 994 994 \n",
+ "59 1046 1046 1046 1046 1046 1046 \n",
+ "60 591 591 591 591 591 591 \n",
+ "61 548 548 548 548 548 548 \n",
+ "62 187 187 187 187 187 187 \n",
+ "63 2152 2154 2157 2159 2163 2171 \n",
+ "64 267 267 267 267 267 267 \n",
+ "65 147 147 147 147 147 147 \n",
+ "66 171 171 171 171 171 171 \n",
+ "67 1316 1316 1316 1316 1317 1317 \n",
+ "68 1609 1609 1609 1609 1609 1609 \n",
+ "69 1303 1303 1303 1303 1304 1304 \n",
+ "70 10710 10731 10755 10767 10779 10788 \n",
+ "71 68150 68150 68150 68150 68150 68150 \n",
+ "72 1033 1033 1033 1033 1033 1033 \n",
+ "73 366 366 366 367 367 367 \n",
+ "74 703 703 703 703 703 703 \n",
+ "75 935 935 935 935 935 935 \n",
+ "76 573 573 573 573 573 573 \n",
+ "77 402 403 404 404 404 404 \n",
+ "78 48 48 48 48 48 48 \n",
+ "79 75 75 75 75 75 75 \n",
+ "80 18 18 18 18 18 18 \n",
+ "81 543 543 543 544 545 547 \n",
+ "82 867 867 867 867 867 867 \n",
+ "83 1747 1754 1757 1759 1760 1765 \n",
+ "84 239 239 239 239 239 239 \n",
+ "85 875 877 877 878 878 879 \n",
+ "86 347 347 348 348 349 349 \n",
+ "87 1 1 1 1 1 1 \n",
+ "88 980 980 980 980 980 980 \n",
+ "89 231 231 231 231 231 231 \n",
+ "90 1320 1320 1320 1320 1320 1320 \n",
+ "\n",
+ "[33 rows x 395 columns]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_selection10 = data[data[\"Country/Region\"].isin([\"China\"])]\n",
+ "data_selection10"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "La province de Hong Kong qui fait l'objet d'une sélection indépendante est soustraite de la somme des provinces en réutilisant **l'index (numéro 70)** sur la liste ci-dessus)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Lat \n",
+ " Long \n",
+ " 1/22/20 \n",
+ " 1/23/20 \n",
+ " 1/24/20 \n",
+ " 1/25/20 \n",
+ " 1/26/20 \n",
+ " 1/27/20 \n",
+ " 1/28/20 \n",
+ " 1/29/20 \n",
+ " ... \n",
+ " 2/6/21 \n",
+ " 2/7/21 \n",
+ " 2/8/21 \n",
+ " 2/9/21 \n",
+ " 2/10/21 \n",
+ " 2/11/21 \n",
+ " 2/12/21 \n",
+ " 2/13/21 \n",
+ " 2/14/21 \n",
+ " 2/15/21 \n",
+ " \n",
+ " \n",
+ " Country/Region \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " China \n",
+ " 1085.2923 \n",
+ " 3688.9377 \n",
+ " 548 \n",
+ " 643 \n",
+ " 920 \n",
+ " 1406 \n",
+ " 2075 \n",
+ " 2877 \n",
+ " 5509 \n",
+ " 6087 \n",
+ " ... \n",
+ " 100348 \n",
+ " 100389 \n",
+ " 100435 \n",
+ " 100475 \n",
+ " 100494 \n",
+ " 100527 \n",
+ " 100559 \n",
+ " 100578 \n",
+ " 100599 \n",
+ " 100624 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1 rows × 393 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 \\\n",
+ "Country/Region \n",
+ "China 1085.2923 3688.9377 548 643 920 1406 \n",
+ "\n",
+ " 1/26/20 1/27/20 1/28/20 1/29/20 ... 2/6/21 2/7/21 \\\n",
+ "Country/Region ... \n",
+ "China 2075 2877 5509 6087 ... 100348 100389 \n",
+ "\n",
+ " 2/8/21 2/9/21 2/10/21 2/11/21 2/12/21 2/13/21 2/14/21 \\\n",
+ "Country/Region \n",
+ "China 100435 100475 100494 100527 100559 100578 100599 \n",
+ "\n",
+ " 2/15/21 \n",
+ "Country/Region \n",
+ "China 100624 \n",
+ "\n",
+ "[1 rows x 393 columns]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_selection10[~data_selection10.index.isin([70])]\n",
+ "data_selection11 = data_selection10.groupby('Country/Region').sum()\n",
+ "data_selection11 "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Le résultat cumulé des colonnes sur l'infection journalière au SARS-COVID19 de l'ensemble des provinces présente des coordonnées non valides pour la latitude(Lat) et la longitude (Long) du pays. En prévison de graphiques sectoriels, je propose d'assigner la position du centre relatif de la Chine au niveau de la province d'**Hubei** Lat-30.9756 Long-1122707.\n",
+ "https://fr.wikipedia.org/wiki/P%C3%A9kin#/media/Fichier:RP_Chine_administrative2.jpg"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Lat \n",
+ " Long \n",
+ " 1/22/20 \n",
+ " 1/23/20 \n",
+ " 1/24/20 \n",
+ " 1/25/20 \n",
+ " 1/26/20 \n",
+ " 1/27/20 \n",
+ " 1/28/20 \n",
+ " 1/29/20 \n",
+ " ... \n",
+ " 2/6/21 \n",
+ " 2/7/21 \n",
+ " 2/8/21 \n",
+ " 2/9/21 \n",
+ " 2/10/21 \n",
+ " 2/11/21 \n",
+ " 2/12/21 \n",
+ " 2/13/21 \n",
+ " 2/14/21 \n",
+ " 2/15/21 \n",
+ " \n",
+ " \n",
+ " Country/Region \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " China \n",
+ " 30.9756 \n",
+ " 112.2707 \n",
+ " 548 \n",
+ " 643 \n",
+ " 920 \n",
+ " 1406 \n",
+ " 2075 \n",
+ " 2877 \n",
+ " 5509 \n",
+ " 6087 \n",
+ " ... \n",
+ " 100348 \n",
+ " 100389 \n",
+ " 100435 \n",
+ " 100475 \n",
+ " 100494 \n",
+ " 100527 \n",
+ " 100559 \n",
+ " 100578 \n",
+ " 100599 \n",
+ " 100624 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1 rows × 393 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 \\\n",
+ "Country/Region \n",
+ "China 30.9756 112.2707 548 643 920 1406 \n",
+ "\n",
+ " 1/26/20 1/27/20 1/28/20 1/29/20 ... 2/6/21 2/7/21 \\\n",
+ "Country/Region ... \n",
+ "China 2075 2877 5509 6087 ... 100348 100389 \n",
+ "\n",
+ " 2/8/21 2/9/21 2/10/21 2/11/21 2/12/21 2/13/21 2/14/21 \\\n",
+ "Country/Region \n",
+ "China 100435 100475 100494 100527 100559 100578 100599 \n",
+ "\n",
+ " 2/15/21 \n",
+ "Country/Region \n",
+ "China 100624 \n",
+ "\n",
+ "[1 rows x 393 columns]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_selection11.loc[:, 'Lat'] =30.9756\n",
+ "data_selection11.loc[:, 'Long'] =112.2707\n",
+ "data_selection11.copy()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## les 14 pays ou territoires à représenter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Lat \n",
+ " Long \n",
+ " 1/22/20 \n",
+ " 1/23/20 \n",
+ " 1/24/20 \n",
+ " 1/25/20 \n",
+ " 1/26/20 \n",
+ " 1/27/20 \n",
+ " 1/28/20 \n",
+ " 1/29/20 \n",
+ " ... \n",
+ " 2/6/21 \n",
+ " 2/7/21 \n",
+ " 2/8/21 \n",
+ " 2/9/21 \n",
+ " 2/10/21 \n",
+ " 2/11/21 \n",
+ " 2/12/21 \n",
+ " 2/13/21 \n",
+ " 2/14/21 \n",
+ " 2/15/21 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Belgium \n",
+ " 50.833300 \n",
+ " 4.469936 \n",
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+ " 0 \n",
+ " 0 \n",
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+ " 0 \n",
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+ " 735220 \n",
+ " 737115 \n",
+ " 738631 \n",
+ " 739488 \n",
+ " \n",
+ " \n",
+ " Germany \n",
+ " 51.165691 \n",
+ " 10.451526 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 1 \n",
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+ " 4 \n",
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+ " 2330422 \n",
+ " 2336906 \n",
+ " 2341744 \n",
+ " 2346876 \n",
+ " \n",
+ " \n",
+ " Iran \n",
+ " 32.427908 \n",
+ " 53.688046 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " ... \n",
+ " 1459370 \n",
+ " 1466435 \n",
+ " 1473756 \n",
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+ " 1503753 \n",
+ " 1510873 \n",
+ " 1518263 \n",
+ " 1526023 \n",
+ " \n",
+ " \n",
+ " Italy \n",
+ " 41.871940 \n",
+ " 12.567380 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " ... \n",
+ " 2625098 \n",
+ " 2636738 \n",
+ " 2644707 \n",
+ " 2655319 \n",
+ " 2668266 \n",
+ " 2683403 \n",
+ " 2697296 \n",
+ " 2710819 \n",
+ " 2721879 \n",
+ " 2729223 \n",
+ " \n",
+ " \n",
+ " Japan \n",
+ " 36.204824 \n",
+ " 138.252924 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 4 \n",
+ " 4 \n",
+ " 7 \n",
+ " 7 \n",
+ " ... \n",
+ " 404128 \n",
+ " 405765 \n",
+ " 406992 \n",
+ " 408550 \n",
+ " 410434 \n",
+ " 412125 \n",
+ " 413441 \n",
+ " 414803 \n",
+ " 416154 \n",
+ " 417127 \n",
+ " \n",
+ " \n",
+ " Korea, South \n",
+ " 35.907757 \n",
+ " 127.766922 \n",
+ " 1 \n",
+ " 1 \n",
+ " 2 \n",
+ " 2 \n",
+ " 3 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " ... \n",
+ " 80896 \n",
+ " 81185 \n",
+ " 81487 \n",
+ " 81930 \n",
+ " 82434 \n",
+ " 82837 \n",
+ " 83199 \n",
+ " 83525 \n",
+ " 83869 \n",
+ " 84325 \n",
+ " \n",
+ " \n",
+ " Portugal \n",
+ " 39.399900 \n",
+ " -8.224500 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " ... \n",
+ " 761906 \n",
+ " 765414 \n",
+ " 767919 \n",
+ " 770502 \n",
+ " 774889 \n",
+ " 778369 \n",
+ " 781223 \n",
+ " 784079 \n",
+ " 785756 \n",
+ " 787059 \n",
+ " \n",
+ " \n",
+ " Spain \n",
+ " 40.463667 \n",
+ " -3.749220 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " ... \n",
+ " 2941990 \n",
+ " 2941990 \n",
+ " 2989085 \n",
+ " 3005487 \n",
+ " 3023601 \n",
+ " 3041454 \n",
+ " 3056035 \n",
+ " 3056035 \n",
+ " 3056035 \n",
+ " 3086286 \n",
+ " \n",
+ " \n",
+ " US \n",
+ " 40.000000 \n",
+ " -100.000000 \n",
+ " 1 \n",
+ " 1 \n",
+ " 2 \n",
+ " 2 \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 6 \n",
+ " ... \n",
+ " 26917787 \n",
+ " 27007368 \n",
+ " 27097095 \n",
+ " 27192455 \n",
+ " 27287159 \n",
+ " 27392512 \n",
+ " 27492023 \n",
+ " 27575344 \n",
+ " 27640282 \n",
+ " 27694165 \n",
+ " \n",
+ " \n",
+ " Hong Kong \n",
+ " 22.300000 \n",
+ " 114.200000 \n",
+ " 0 \n",
+ " 2 \n",
+ " 2 \n",
+ " 5 \n",
+ " 8 \n",
+ " 8 \n",
+ " 8 \n",
+ " 10 \n",
+ " ... \n",
+ " 10608 \n",
+ " 10635 \n",
+ " 10667 \n",
+ " 10693 \n",
+ " 10710 \n",
+ " 10731 \n",
+ " 10755 \n",
+ " 10767 \n",
+ " 10779 \n",
+ " 10788 \n",
+ " \n",
+ " \n",
+ " France \n",
+ " 46.227600 \n",
+ " 2.213700 \n",
+ " 0 \n",
+ " 0 \n",
+ " 2 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 4 \n",
+ " 5 \n",
+ " ... \n",
+ " 3376266 \n",
+ " 3395981 \n",
+ " 3400324 \n",
+ " 3419210 \n",
+ " 3444888 \n",
+ " 3465964 \n",
+ " 3466629 \n",
+ " 3467051 \n",
+ " 3467884 \n",
+ " 3528856 \n",
+ " \n",
+ " \n",
+ " Netherlands \n",
+ " 55.378100 \n",
+ " 5.291300 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " ... \n",
+ " 1015757 \n",
+ " 1019720 \n",
+ " 1021966 \n",
+ " 1023779 \n",
+ " 1027023 \n",
+ " 1031454 \n",
+ " 1035841 \n",
+ " 1040070 \n",
+ " 1043541 \n",
+ " 1046381 \n",
+ " \n",
+ " \n",
+ " United Kingdom \n",
+ " 52.132600 \n",
+ " 3.436000 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " ... \n",
+ " 3941273 \n",
+ " 3957177 \n",
+ " 3971315 \n",
+ " 3983756 \n",
+ " 3996833 \n",
+ " 4010376 \n",
+ " 4025574 \n",
+ " 4038929 \n",
+ " 4049920 \n",
+ " 4059696 \n",
+ " \n",
+ " \n",
+ " China \n",
+ " 30.975600 \n",
+ " 112.270700 \n",
+ " 548 \n",
+ " 643 \n",
+ " 920 \n",
+ " 1406 \n",
+ " 2075 \n",
+ " 2877 \n",
+ " 5509 \n",
+ " 6087 \n",
+ " ... \n",
+ " 100348 \n",
+ " 100389 \n",
+ " 100435 \n",
+ " 100475 \n",
+ " 100494 \n",
+ " 100527 \n",
+ " 100559 \n",
+ " 100578 \n",
+ " 100599 \n",
+ " 100624 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
14 rows × 393 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 \\\n",
+ "Belgium 50.833300 4.469936 0 0 0 0 \n",
+ "Germany 51.165691 10.451526 0 0 0 0 \n",
+ "Iran 32.427908 53.688046 0 0 0 0 \n",
+ "Italy 41.871940 12.567380 0 0 0 0 \n",
+ "Japan 36.204824 138.252924 2 2 2 2 \n",
+ "Korea, South 35.907757 127.766922 1 1 2 2 \n",
+ "Portugal 39.399900 -8.224500 0 0 0 0 \n",
+ "Spain 40.463667 -3.749220 0 0 0 0 \n",
+ "US 40.000000 -100.000000 1 1 2 2 \n",
+ "Hong Kong 22.300000 114.200000 0 2 2 5 \n",
+ "France 46.227600 2.213700 0 0 2 3 \n",
+ "Netherlands 55.378100 5.291300 0 0 0 0 \n",
+ "United Kingdom 52.132600 3.436000 0 0 0 0 \n",
+ "China 30.975600 112.270700 548 643 920 1406 \n",
+ "\n",
+ " 1/26/20 1/27/20 1/28/20 1/29/20 ... 2/6/21 2/7/21 \\\n",
+ "Belgium 0 0 0 0 ... 723870 725610 \n",
+ "Germany 0 1 4 4 ... 2285003 2291673 \n",
+ "Iran 0 0 0 0 ... 1459370 1466435 \n",
+ "Italy 0 0 0 0 ... 2625098 2636738 \n",
+ "Japan 4 4 7 7 ... 404128 405765 \n",
+ "Korea, South 3 4 4 4 ... 80896 81185 \n",
+ "Portugal 0 0 0 0 ... 761906 765414 \n",
+ "Spain 0 0 0 0 ... 2941990 2941990 \n",
+ "US 5 5 5 6 ... 26917787 27007368 \n",
+ "Hong Kong 8 8 8 10 ... 10608 10635 \n",
+ "France 3 3 4 5 ... 3376266 3395981 \n",
+ "Netherlands 0 0 0 0 ... 1015757 1019720 \n",
+ "United Kingdom 0 0 0 0 ... 3941273 3957177 \n",
+ "China 2075 2877 5509 6087 ... 100348 100389 \n",
+ "\n",
+ " 2/8/21 2/9/21 2/10/21 2/11/21 2/12/21 2/13/21 \\\n",
+ "Belgium 726483 728334 730951 733100 735220 737115 \n",
+ "Germany 2296323 2302051 2311297 2321225 2330422 2336906 \n",
+ "Iran 1473756 1481396 1488981 1496455 1503753 1510873 \n",
+ "Italy 2644707 2655319 2668266 2683403 2697296 2710819 \n",
+ "Japan 406992 408550 410434 412125 413441 414803 \n",
+ "Korea, South 81487 81930 82434 82837 83199 83525 \n",
+ "Portugal 767919 770502 774889 778369 781223 784079 \n",
+ "Spain 2989085 3005487 3023601 3041454 3056035 3056035 \n",
+ "US 27097095 27192455 27287159 27392512 27492023 27575344 \n",
+ "Hong Kong 10667 10693 10710 10731 10755 10767 \n",
+ "France 3400324 3419210 3444888 3465964 3466629 3467051 \n",
+ "Netherlands 1021966 1023779 1027023 1031454 1035841 1040070 \n",
+ "United Kingdom 3971315 3983756 3996833 4010376 4025574 4038929 \n",
+ "China 100435 100475 100494 100527 100559 100578 \n",
+ "\n",
+ " 2/14/21 2/15/21 \n",
+ "Belgium 738631 739488 \n",
+ "Germany 2341744 2346876 \n",
+ "Iran 1518263 1526023 \n",
+ "Italy 2721879 2729223 \n",
+ "Japan 416154 417127 \n",
+ "Korea, South 83869 84325 \n",
+ "Portugal 785756 787059 \n",
+ "Spain 3056035 3086286 \n",
+ "US 27640282 27694165 \n",
+ "Hong Kong 10779 10788 \n",
+ "France 3467884 3528856 \n",
+ "Netherlands 1043541 1046381 \n",
+ "United Kingdom 4049920 4059696 \n",
+ "China 100599 100624 \n",
+ "\n",
+ "[14 rows x 393 columns]"
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_selection12=data_selection3.append(data_Hong_Kong2)\n",
+ "data_selection13=data_selection12.append(data_selection5)\n",
+ "data_selection14=data_selection13.append(data_selection7)\n",
+ "data_selection15=data_selection14.append(data_selection9)\n",
+ "data_selection16=data_selection15.append(data_selection11)\n",
+ "data_selection16"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# data_selection16 = pd.DataFrame(data_selection16,\n",
+ " index = ['Belgium','Germany','Iran','Italy','Japan','Korea, South','Portugal','Spain','US','Hong Kong','France','Netherlands','United Kingdom','China'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## représentation graphique de la situation du Covid19"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### choisir sa date d'observation entre le 26 janvier 2020 et la dernière mise à jour (mois/jour/année xx/xx/xx)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "entrée la date d'observation : 2/15/21\n"
+ ]
+ }
+ ],
+ "source": [
+ "valeur = input(\"entrée la date d'observation : \")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.DataFrame(data=data_selection16)\n",
+ "data2 = {'Belgium': 677209,\n",
+ " 'Germany': 2038645,\n",
+ " 'Iran': 1324395,\n",
+ " 'Italy': 2368733,\n",
+ " 'Japan': 324942,\n",
+ " 'Korea, South': 72340,\n",
+ " 'Portugal': 539416,\n",
+ " 'Spain': 2252164,\n",
+ " 'US': 23758855,\n",
+ " 'Hong Kong': 9502,\n",
+ " 'France': 2931686,\n",
+ " 'Netherlands': 919712,\n",
+ " 'United Kingdom': 3367070,\n",
+ " 'China': 97775}\n",
+ "\n",
+ "group_data = list(df[valeur])\n",
+ "group_names = list(data2.keys())\n",
+ "group_mean = np.mean(group_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[(0.0, 30000000.0),\n",
+ " Text(0.5, 0, 'nbre de personnes'),\n",
+ " Text(0, 0.5, 'pays/territoires'),\n",
+ " Text(0.5, 1.0, 'Cumul des contaminations au Covid19')]"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.style.use('seaborn-white')\n",
+ "fig, ax = plt.subplots(figsize=(10.6,8))\n",
+ "ax.barh(group_names, group_data)\n",
+ "labels = ax.get_xticklabels()\n",
+ "plt.setp(labels, horizontalalignment='right')\n",
+ "ax.set(xlim=[0, 30000000], xlabel='nbre de personnes', ylabel='pays/territoires',\n",
+ " title='Total des contaminations au Covid19')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For publications that use the data, please cite the following publication: \"Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1\"\n",
+ "\n",
+ "https://github.com/CSSEGISandData/COVID-19/blob/master/README.md"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
@@ -16,10 +4524,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.3"
+ "version": "3.8.5"
}
},
"nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
}
-