{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Autour du SARS-CoV-2 (Covid-19)\n", "\n", "Le but est ici 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).\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)](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) disponibles [ici](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": [ "## Téléchargement et traitement des données\n", "\n", "Les données relevées sont stockées dans un fichier. Celles-ci sont à la date du 22 juin 2021." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'time_series_covid19_confirmed_global.csv'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_file = \"time_series_covid19_confirmed_global.csv\"\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", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)\n", "data_file" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "raw_data = pd.read_csv(data_file, sep=',')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On s'intéressera ici spécifiquement aux données de la **Belgique**, la **Chine** (en traitant **Hong-Kong** à part), la **France métroploitaine**, l'**Allemagne**, l'**Iran**, l'**Italie**, le **Japon**, la **Corée du Sud**, les **Pays-Bas** (*hors colonies*), le **Portugal**, l'**Espagne**, le **Royaume-Uni** (*hors colonies*) et les **États-Unis**.\n", "\n", "[//]: # \"Initialement, il est demandé de tenir compte également de la **Chine** (en traitant **Hong-Kong** à part). Cependant, et comme on peut le voir juste au dessus, le format utilisé pour le fichier `.csv` traite chacune des 34 provinces chinoises à part, avec aucune donnée générale sur la Chine. Plusieurs choix s'offrent à nous : reconstituer une ligne *globale* pour ce pays en mélangeant **toutes** ses provinces, faire la même chose en gardant de côté **Hong-Kong** pour coller à la consigne ou se simplifier la vie en mettant de côté les données chinoises.\"\n", "\n", "[//]: # \"Je choisis cette dernière options pour plusieurs raisons. La première, et plus évidente, est la facilité : je ne pense pas parvenir à mélanger toutes les provinces de la Chine efficacement/élégamment, et suis presque certain d'effectuer une erreur en m'y frottant. Par ailleurs, on remarque en lisant l'énoncé de cet exercice :\"\n", "\n", "[//]: # \"> 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.\"\n", "\n", "[//]: # \"Ce qui laisse penser que cette difficulté n'est pas initialement prévue, et que la consigne initiale est tournée de manière à ne pas devoir réaliser de fusion de lignes. Pour toutes ces raisons, laisser de côté les données pour la **Chine** me semble à la fois bien plus judicieux en terme de temps, mais aussi plus proche de l'intention initiale de la consigne.\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "selectedCountries = ['Belgium', 'France', 'China', 'Germany', 'Iran', 'Italy',\n", " 'Japan', 'Korea,South', 'Netherlands', 'Portugal', 'Spain',\n", " 'United Kingdom', 'US']\n", "\n", "selectedData = raw_data[raw_data['Country/Region'].isin(selectedCountries)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour tous les pays - sauf la Chine - les données hors provinces/colonies présentent `NaN` dans leur colonne `Province/State`. On peut donc récupérer d'une part toutes les données chinoises, et d'autre part les données des autres pays." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "dataChina = selectedData[selectedData['Country/Region'] == 'China']" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "dataOther = selectedData[selectedData['Province/State'] != selectedData['Province/State']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut finalement concaténer ces deux jeux de données." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\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", "6/12/21 | \n", "6/13/21 | \n", "6/14/21 | \n", "6/15/21 | \n", "6/16/21 | \n", "6/17/21 | \n", "6/18/21 | \n", "6/19/21 | \n", "6/20/21 | \n", "6/21/21 | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
23 | \n", "NaN | \n", "Belgium | \n", "50.833300 | \n", "4.469936 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "... | \n", "1075765 | \n", "1076338 | \n", "1076579 | \n", "1077087 | \n", "1077758 | \n", "1078251 | \n", "1078251 | \n", "1079084 | \n", "1079415 | \n", "1079640 | \n", "
58 | \n", "Anhui | \n", "China | \n", "31.825700 | \n", "117.226400 | \n", "1 | \n", "9 | \n", "15 | \n", "39 | \n", "60 | \n", "70 | \n", "... | \n", "1004 | \n", "1004 | \n", "1004 | \n", "1004 | \n", "1004 | \n", "1004 | \n", "1004 | \n", "1004 | \n", "1004 | \n", "1004 | \n", "
59 | \n", "Beijing | \n", "China | \n", "40.182400 | \n", "116.414200 | \n", "14 | \n", "22 | \n", "36 | \n", "41 | \n", "68 | \n", "80 | \n", "... | \n", "1069 | \n", "1070 | \n", "1071 | \n", "1071 | \n", "1072 | \n", "1072 | \n", "1073 | \n", "1073 | \n", "1075 | \n", "1075 | \n", "
60 | \n", "Chongqing | \n", "China | \n", "30.057200 | \n", "107.874000 | \n", "6 | \n", "9 | \n", "27 | \n", "57 | \n", "75 | \n", "110 | \n", "... | \n", "598 | \n", "598 | \n", "598 | \n", "598 | \n", "598 | \n", "598 | \n", "598 | \n", "598 | \n", "598 | \n", "598 | \n", "
61 | \n", "Fujian | \n", "China | \n", "26.078900 | \n", "117.987400 | \n", "1 | \n", "5 | \n", "10 | \n", "18 | \n", "35 | \n", "59 | \n", "... | \n", "637 | \n", "637 | \n", "638 | \n", "638 | \n", "641 | \n", "646 | \n", "650 | \n", "651 | \n", "652 | \n", "659 | \n", "
62 | \n", "Gansu | \n", "China | \n", "35.751800 | \n", "104.286100 | \n", "0 | \n", "2 | \n", "2 | \n", "4 | \n", "7 | \n", "14 | \n", "... | \n", "194 | \n", "194 | \n", "194 | \n", "194 | \n", "194 | \n", "194 | \n", "194 | \n", "194 | \n", "194 | \n", "194 | \n", "
63 | \n", "Guangdong | \n", "China | \n", "23.341700 | \n", "113.424400 | \n", "26 | \n", "32 | \n", "53 | \n", "78 | \n", "111 | \n", "151 | \n", "... | \n", "2618 | \n", "2625 | \n", "2635 | \n", "2650 | \n", "2657 | \n", "2666 | \n", "2680 | \n", "2692 | \n", "2699 | \n", "2706 | \n", "
64 | \n", "Guangxi | \n", "China | \n", "23.829800 | \n", "108.788100 | \n", "2 | \n", "5 | \n", "23 | \n", "23 | \n", "36 | \n", "46 | \n", "... | \n", "275 | \n", "275 | \n", "275 | \n", "275 | \n", "275 | \n", "275 | \n", "275 | \n", "275 | \n", "275 | \n", "275 | \n", "
65 | \n", "Guizhou | \n", "China | \n", "26.815400 | \n", "106.874800 | \n", "1 | \n", "3 | \n", "3 | \n", "4 | \n", "5 | \n", "7 | \n", "... | \n", "147 | \n", "147 | \n", "147 | \n", "147 | \n", "147 | \n", "147 | \n", "147 | \n", "147 | \n", "147 | \n", "147 | \n", "
66 | \n", "Hainan | \n", "China | \n", "19.195900 | \n", "109.745300 | \n", "4 | \n", "5 | \n", "8 | \n", "19 | \n", "22 | \n", "33 | \n", "... | \n", "188 | \n", "188 | \n", "188 | \n", "188 | \n", "188 | \n", "188 | \n", "188 | \n", "188 | \n", "188 | \n", "188 | \n", "
67 | \n", "Hebei | \n", "China | \n", "39.549000 | \n", "116.130600 | \n", "1 | \n", "1 | \n", "2 | \n", "8 | \n", "13 | \n", "18 | \n", "... | \n", "1317 | \n", "1317 | \n", "1317 | \n", "1317 | \n", "1317 | \n", "1317 | \n", "1317 | \n", "1317 | \n", "1317 | \n", "1317 | \n", "
68 | \n", "Heilongjiang | \n", "China | \n", "47.862000 | \n", "127.761500 | \n", "0 | \n", "2 | \n", "4 | \n", "9 | \n", "15 | \n", "21 | \n", "... | \n", "1612 | \n", "1612 | \n", "1612 | \n", "1612 | \n", "1612 | \n", "1612 | \n", "1612 | \n", "1612 | \n", "1612 | \n", "1612 | \n", "
69 | \n", "Henan | \n", "China | \n", "37.895700 | \n", "114.904200 | \n", "5 | \n", "5 | \n", "9 | \n", "32 | \n", "83 | \n", "128 | \n", "... | \n", "1316 | \n", "1316 | \n", "1316 | \n", "1316 | \n", "1316 | \n", "1316 | \n", "1316 | \n", "1316 | \n", "1317 | \n", "1317 | \n", "
70 | \n", "Hong Kong | \n", "China | \n", "22.300000 | \n", "114.200000 | \n", "0 | \n", "2 | \n", "2 | \n", "5 | \n", "8 | \n", "8 | \n", "... | \n", "11877 | \n", "11877 | \n", "11878 | \n", "11880 | \n", "11881 | \n", "11881 | \n", "11884 | \n", "11885 | \n", "11886 | \n", "11889 | \n", "
71 | \n", "Hubei | \n", "China | \n", "30.975600 | \n", "112.270700 | \n", "444 | \n", "444 | \n", "549 | \n", "761 | \n", "1058 | \n", "1423 | \n", "... | \n", "68159 | \n", "68159 | \n", "68159 | \n", "68159 | \n", "68160 | \n", "68160 | \n", "68160 | \n", "68160 | \n", "68160 | \n", "68160 | \n", "
72 | \n", "Hunan | \n", "China | \n", "27.610400 | \n", "111.708800 | \n", "4 | \n", "9 | \n", "24 | \n", "43 | \n", "69 | \n", "100 | \n", "... | \n", "1051 | \n", "1051 | \n", "1051 | \n", "1051 | \n", "1051 | \n", "1051 | \n", "1051 | \n", "1051 | \n", "1051 | \n", "1051 | \n", "
73 | \n", "Inner Mongolia | \n", "China | \n", "44.093500 | \n", "113.944800 | \n", "0 | \n", "0 | \n", "1 | \n", "7 | \n", "7 | \n", "11 | \n", "... | \n", "390 | \n", "393 | \n", "393 | \n", "393 | \n", "393 | \n", "393 | \n", "393 | \n", "393 | \n", "393 | \n", "394 | \n", "
74 | \n", "Jiangsu | \n", "China | \n", "32.971100 | \n", "119.455000 | \n", "1 | \n", "5 | \n", "9 | \n", "18 | \n", "33 | \n", "47 | \n", "... | \n", "735 | \n", "736 | \n", "736 | \n", "738 | \n", "739 | \n", "739 | \n", "739 | \n", "739 | \n", "740 | \n", "740 | \n", "
75 | \n", "Jiangxi | \n", "China | \n", "27.614000 | \n", "115.722100 | \n", "2 | \n", "7 | \n", "18 | \n", "18 | \n", "36 | \n", "72 | \n", "... | \n", "937 | \n", "937 | \n", "937 | \n", "937 | \n", "937 | \n", "937 | \n", "937 | \n", "937 | \n", "937 | \n", "937 | \n", "
76 | \n", "Jilin | \n", "China | \n", "43.666100 | \n", "126.192300 | \n", "0 | \n", "1 | \n", "3 | \n", "4 | \n", "4 | \n", "6 | \n", "... | \n", "573 | \n", "573 | \n", "573 | \n", "573 | \n", "573 | \n", "573 | \n", "573 | \n", "573 | \n", "573 | \n", "573 | \n", "
77 | \n", "Liaoning | \n", "China | \n", "41.295600 | \n", "122.608500 | \n", "2 | \n", "3 | \n", "4 | \n", "17 | \n", "21 | \n", "27 | \n", "... | \n", "426 | \n", "426 | \n", "426 | \n", "426 | \n", "426 | \n", "426 | \n", "426 | \n", "426 | \n", "426 | \n", "426 | \n", "
78 | \n", "Macau | \n", "China | \n", "22.166700 | \n", "113.550000 | \n", "1 | \n", "2 | \n", "2 | \n", "2 | \n", "5 | \n", "6 | \n", "... | \n", "52 | \n", "52 | \n", "52 | \n", "52 | \n", "52 | \n", "52 | \n", "53 | \n", "53 | \n", "53 | \n", "53 | \n", "
79 | \n", "Ningxia | \n", "China | \n", "37.269200 | \n", "106.165500 | \n", "1 | \n", "1 | \n", "2 | \n", "3 | \n", "4 | \n", "7 | \n", "... | \n", "76 | \n", "76 | \n", "76 | \n", "76 | \n", "76 | \n", "76 | \n", "76 | \n", "76 | \n", "76 | \n", "76 | \n", "
80 | \n", "Qinghai | \n", "China | \n", "35.745200 | \n", "95.995600 | \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", "
81 | \n", "Shaanxi | \n", "China | \n", "35.191700 | \n", "108.870100 | \n", "0 | \n", "3 | \n", "5 | \n", "15 | \n", "22 | \n", "35 | \n", "... | \n", "622 | \n", "622 | \n", "622 | \n", "622 | \n", "622 | \n", "622 | \n", "624 | \n", "624 | \n", "624 | \n", "624 | \n", "
82 | \n", "Shandong | \n", "China | \n", "36.342700 | \n", "118.149800 | \n", "2 | \n", "6 | \n", "15 | \n", "27 | \n", "46 | \n", "75 | \n", "... | \n", "883 | \n", "883 | \n", "883 | \n", "883 | \n", "883 | \n", "883 | \n", "883 | \n", "883 | \n", "883 | \n", "883 | \n", "
83 | \n", "Shanghai | \n", "China | \n", "31.202000 | \n", "121.449100 | \n", "9 | \n", "16 | \n", "20 | \n", "33 | \n", "40 | \n", "53 | \n", "... | \n", "2155 | \n", "2160 | \n", "2165 | \n", "2168 | \n", "2170 | \n", "2173 | \n", "2179 | \n", "2182 | \n", "2183 | \n", "2184 | \n", "
84 | \n", "Shanxi | \n", "China | \n", "37.577700 | \n", "112.292200 | \n", "1 | \n", "1 | \n", "1 | \n", "6 | \n", "9 | \n", "13 | \n", "... | \n", "253 | \n", "253 | \n", "253 | \n", "253 | \n", "253 | \n", "253 | \n", "253 | \n", "253 | \n", "253 | \n", "253 | \n", "
85 | \n", "Sichuan | \n", "China | \n", "30.617100 | \n", "102.710300 | \n", "5 | \n", "8 | \n", "15 | \n", "28 | \n", "44 | \n", "69 | \n", "... | \n", "1050 | \n", "1054 | \n", "1055 | \n", "1056 | \n", "1057 | \n", "1057 | \n", "1057 | \n", "1058 | \n", "1059 | \n", "1064 | \n", "
86 | \n", "Tianjin | \n", "China | \n", "39.305400 | \n", "117.323000 | \n", "4 | \n", "4 | \n", "8 | \n", "10 | \n", "14 | \n", "23 | \n", "... | \n", "398 | \n", "398 | \n", "398 | \n", "398 | \n", "398 | \n", "399 | \n", "399 | \n", "399 | \n", "399 | \n", "399 | \n", "
87 | \n", "Tibet | \n", "China | \n", "31.692700 | \n", "88.092400 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "... | \n", "1 | \n", "1 | \n", "1 | \n", "1 | \n", "1 | \n", "1 | \n", "1 | \n", "1 | \n", "1 | \n", "1 | \n", "
88 | \n", "Unknown | \n", "China | \n", "NaN | \n", "NaN | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "... | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "
89 | \n", "Xinjiang | \n", "China | \n", "41.112900 | \n", "85.240100 | \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", "
90 | \n", "Yunnan | \n", "China | \n", "24.974000 | \n", "101.487000 | \n", "1 | \n", "2 | \n", "5 | \n", "11 | \n", "16 | \n", "26 | \n", "... | \n", "374 | \n", "376 | \n", "377 | \n", "377 | \n", "380 | \n", "382 | \n", "384 | \n", "388 | \n", "391 | \n", "391 | \n", "
91 | \n", "Zhejiang | \n", "China | \n", "29.183200 | \n", "120.093400 | \n", "10 | \n", "27 | \n", "43 | \n", "62 | \n", "104 | \n", "128 | \n", "... | \n", "1372 | \n", "1372 | \n", "1373 | \n", "1373 | \n", "1373 | \n", "1376 | \n", "1377 | \n", "1379 | \n", "1379 | \n", "1383 | \n", "
130 | \n", "NaN | \n", "France | \n", "46.227600 | \n", "2.213700 | \n", "0 | \n", "0 | \n", "2 | \n", "3 | \n", "3 | \n", "3 | \n", "... | \n", "5675604 | \n", "5678209 | \n", "5678893 | \n", "5681846 | \n", "5683536 | \n", "5685387 | \n", "5688557 | \n", "5691181 | \n", "5692996 | \n", "5692968 | \n", "
134 | \n", "NaN | \n", "Germany | \n", "51.165691 | \n", "10.451526 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "1 | \n", "... | \n", "3722295 | \n", "3723295 | \n", "3724168 | \n", "3725328 | \n", "3726767 | \n", "3727668 | \n", "3728601 | \n", "3729597 | \n", "3730126 | \n", "3730619 | \n", "
149 | \n", "NaN | \n", "Iran | \n", "32.427908 | \n", "53.688046 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "... | \n", "3020522 | \n", "3028717 | \n", "3039432 | \n", "3049648 | \n", "3060135 | \n", "3070426 | \n", "3080526 | \n", "3086974 | \n", "3095135 | \n", "3105620 | \n", "
153 | \n", "NaN | \n", "Italy | \n", "41.871940 | \n", "12.567380 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "... | \n", "4243482 | \n", "4244872 | \n", "4245779 | \n", "4247032 | \n", "4248432 | \n", "4249755 | \n", "4250902 | \n", "4252095 | \n", "4252976 | \n", "4253460 | \n", "
155 | \n", "NaN | \n", "Japan | \n", "36.204824 | \n", "138.252924 | \n", "2 | \n", "2 | \n", "2 | \n", "2 | \n", "4 | \n", "4 | \n", "... | \n", "774240 | \n", "775624 | \n", "776565 | \n", "777979 | \n", "779696 | \n", "781241 | \n", "782877 | \n", "784384 | \n", "785702 | \n", "786566 | \n", "
197 | \n", "NaN | \n", "Netherlands | \n", "52.132600 | \n", "5.291300 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "... | \n", "1671703 | \n", "1672744 | \n", "1673596 | \n", "1674628 | \n", "1675644 | \n", "1676708 | \n", "1677596 | \n", "1678282 | \n", "1678983 | \n", "1679542 | \n", "
214 | \n", "NaN | \n", "Portugal | \n", "39.399900 | \n", "-8.224500 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "... | \n", "856740 | \n", "857447 | \n", "858072 | \n", "859045 | \n", "860395 | \n", "861628 | \n", "862926 | \n", "864109 | \n", "865050 | \n", "865806 | \n", "
237 | \n", "NaN | \n", "Spain | \n", "40.463667 | \n", "-3.749220 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "... | \n", "3733600 | \n", "3733600 | \n", "3741767 | \n", "3745199 | \n", "3749031 | \n", "3753228 | \n", "3757442 | \n", "3757442 | \n", "3757442 | \n", "3764651 | \n", "
253 | \n", "NaN | \n", "US | \n", "40.000000 | \n", "-100.000000 | \n", "1 | \n", "1 | \n", "2 | \n", "2 | \n", "5 | \n", "5 | \n", "... | \n", "33457228 | \n", "33462003 | \n", "33474734 | \n", "33486038 | \n", "33498468 | \n", "33508867 | \n", "33529475 | \n", "33537995 | \n", "33541887 | \n", "33554275 | \n", "
268 | \n", "NaN | \n", "United Kingdom | \n", "55.378100 | \n", "-3.436000 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "... | \n", "4558494 | \n", "4565813 | \n", "4573419 | \n", "4581006 | \n", "4589814 | \n", "4600623 | \n", "4610893 | \n", "4620968 | \n", "4630040 | \n", "4640507 | \n", "
45 rows × 521 columns
\n", "