{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Document Computationnel : Sujet 7 - Autour du SARS-CoV-2 (Covid-19)\n", "- Dernière modification : *01/06/2020*\n", "- Langage utilisé : *Python*\n", "\n", "## Table des matières \n", "\n", "1. [Résumé / *abstract*](#résumé)\n", "2. [Importation des données](#importation-des-données)\n", "3. [Formatage des données](#formatage-des-données)\n", "4. [Traitement des données](#traitement-des-données)\n", "5. [Elément complémentaire](#etude-complémentaire)\n", "6. [Conclusion](#conclusion)\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Résumé\n", "\n", "Vous trouverez dans ce notebook le cheminement nécessaire pour représenter une figure semblable à celle présente sur le site du [South China morning post](https://www.scmp.com/coronavirus?src=homepage_covid_widget). Toutes les étapes sont décrites et commentées. \n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Importation des données\n", "\n", "## Sources :\n", "\n", "* Graphique exemple de [South Chine Morning Post](https://www.scmp.com/coronavirus?src=homepage_covid_widget). Datant du 20 Mai 2020.\n", "* Données brutes utilisées dans ce document : [time_series_covid19_confirmed_global.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", "\n", "\n", "On procède à un test afin de savoir si les données sont disponibles en local ou si l'on doit utiliser l'URL d'origine." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", "#import isoweek not needed here\n", "\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\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Local data \n", "localData = \"time_series_covid19_confirmed_global.csv\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Local File Selected\n" ] }, { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...5/19/205/20/205/21/205/22/205/23/205/24/205/25/205/26/205/27/205/28/20
0NaNAfghanistan33.00000065.000000000000...765381458676921699981058211173118311245613036
1NaNAlbania41.15330020.168300000000...9499649699819899981004102910501076
2NaNAlgeria28.0339001.659600000000...7377754277287918811383068503869788578997
3NaNAndorra42.5063001.521800000000...761762762762762762763763763763
4NaNAngola-11.20270017.873900000000...52525860616970707174
5NaNAntigua and Barbuda17.060800-61.796400000000...25252525252525252525
6NaNArgentina-38.416100-63.616700000000...88099283993110649113531207612628132281393314702
7NaNArmenia40.06910045.038200000000...5041527156065928630266617113740277748216
8Australian Capital TerritoryAustralia-35.473500149.012400000000...107107107107107107107107107107
9New South WalesAustralia-33.868800151.209300000034...3081308230843086308730903092308930903092
10Northern TerritoryAustralia-12.463400130.845600000000...29292929292929292929
11QueenslandAustralia-28.016700153.400000000000...1058105810581060106110561057105810581058
12South AustraliaAustralia-34.928500138.600700000000...439439439439439439439440440440
13TasmaniaAustralia-41.454500145.970700000000...228228228228228228228228228228
14VictoriaAustralia-37.813600144.963100000011...1573158115931593160316051610161816281634
15Western AustraliaAustralia-31.950500115.860500000000...557557557557560560564570570577
16NaNAustria47.51620014.550100000000...16321163531640416436164861650316539165571659116628
17NaNAzerbaijan40.14310047.576900000000...3518363137493855398241224271440345684759
18NaNBahamas25.034300-77.396300000000...96979797100100100100100101
19NaNBahrain26.02750050.550000000000...75327888817484148802913891719366969210052
20NaNBangladesh23.68500090.356300000000...25121267382851130205320783361035585367513829240321
21NaNBarbados13.193900-59.543200000000...90909090929292929292
22NaNBelarus53.70980027.953400000000...31508324263337134303352443619837144380593895639858
23NaNBelgium50.8333004.000000000000...55791559835623556511568105709257342574555759257849
24NaNBenin9.3077002.315800000000...130130135135135191191208210210
25NaNBhutan27.51420090.433600000000...21212121242427272831
26NaNBolivia-16.290200-63.588700000000...4481491951875579591562636660713677688387
27NaNBosnia and Herzegovina43.91590017.679100000000...2321233823502372239124012406241624352462
28NaNBrazil-14.235000-51.925300000000...271885291579310087330890347398363211374898391222411821438238
29NaNBrunei4.535300114.727700000000...141141141141141141141141141141
..................................................................
236NaNTimor-Leste-8.874217125.727539000000...24242424242424242424
237NaNBelize13.193900-59.543200000000...18181818181818181818
238NaNLaos19.856270102.495496000000...19191919191919191919
239NaNLibya26.33510017.228331000000...686971727575757799105
240NaNWest Bank and Gaza31.95220035.233200000000...391398423423423423423429434446
241NaNGuinea-Bissau11.803700-15.180400000000...1038108911091114111411141178117811951195
242NaNMali17.570692-3.996166000000...901931947969101510301059107711161194
243NaNSaint Kitts and Nevis17.357822-62.782998000000...15151515151515151515
244Northwest TerritoriesCanada64.825500-124.845700000000...5555555555
245YukonCanada64.282300-135.000000000000...11111111111111111111
246NaNKosovo42.60263620.902977000000...98998910031004102510321038103810471048
247NaNBurma21.91620095.956000000000...193199199199201201203206206206
248AnguillaUnited Kingdom18.220600-63.068600000000...3333333333
249British Virgin IslandsUnited Kingdom18.420700-64.640000000000...8888888888
250Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...12121212121212121212
251NaNMS Zaandam0.0000000.000000000000...9999999999
252NaNBotswana-22.32850024.684900000000...25252930303535353535
253NaNBurundi-3.37310029.918900000000...42424242424242424242
254NaNSierra Leone8.460555-11.779889000000...534570585606621707735754782812
255Bonaire, Sint Eustatius and SabaNetherlands12.178400-68.238500000000...6666666666
256NaNMalawi-13.25430834.301525000000...707172828283101101101203
257Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...13131313131313131313
258Saint Pierre and MiquelonFrance46.885200-56.315900000000...1111111111
259NaNSouth Sudan6.87700031.307000000000...290290481563655655806806994994
260NaNWestern Sahara24.215500-12.885800000000...6666699999
261NaNSao Tome and Principe0.1863606.613081000000...251251251251251251299441443458
262NaNYemen15.55272748.516388000000...167184197209212222233249256278
263NaNComoros-11.64550043.333300000000...11343478788787878787
264NaNTajikistan38.86103471.276093000000...1936214023502551273829293100326634243563
265NaNLesotho-29.60998828.233608000000...1112222222
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266 rows × 132 columns

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" ], "text/plain": [ " Province/State Country/Region Lat \\\n", "0 NaN Afghanistan 33.000000 \n", "1 NaN Albania 41.153300 \n", "2 NaN Algeria 28.033900 \n", "3 NaN Andorra 42.506300 \n", "4 NaN Angola -11.202700 \n", "5 NaN Antigua and Barbuda 17.060800 \n", "6 NaN Argentina -38.416100 \n", "7 NaN Armenia 40.069100 \n", "8 Australian Capital Territory Australia -35.473500 \n", "9 New South Wales Australia -33.868800 \n", "10 Northern Territory Australia -12.463400 \n", "11 Queensland Australia -28.016700 \n", "12 South Australia Australia -34.928500 \n", "13 Tasmania Australia -41.454500 \n", "14 Victoria Australia -37.813600 \n", "15 Western Australia Australia -31.950500 \n", "16 NaN Austria 47.516200 \n", "17 NaN Azerbaijan 40.143100 \n", "18 NaN Bahamas 25.034300 \n", "19 NaN Bahrain 26.027500 \n", "20 NaN Bangladesh 23.685000 \n", "21 NaN Barbados 13.193900 \n", "22 NaN Belarus 53.709800 \n", "23 NaN Belgium 50.833300 \n", "24 NaN Benin 9.307700 \n", "25 NaN Bhutan 27.514200 \n", "26 NaN Bolivia -16.290200 \n", "27 NaN Bosnia and Herzegovina 43.915900 \n", "28 NaN Brazil -14.235000 \n", "29 NaN Brunei 4.535300 \n", ".. ... ... ... \n", "236 NaN Timor-Leste -8.874217 \n", "237 NaN Belize 13.193900 \n", "238 NaN Laos 19.856270 \n", "239 NaN Libya 26.335100 \n", "240 NaN West Bank and Gaza 31.952200 \n", "241 NaN Guinea-Bissau 11.803700 \n", "242 NaN Mali 17.570692 \n", "243 NaN Saint Kitts and Nevis 17.357822 \n", "244 Northwest Territories Canada 64.825500 \n", "245 Yukon Canada 64.282300 \n", "246 NaN Kosovo 42.602636 \n", "247 NaN Burma 21.916200 \n", "248 Anguilla United Kingdom 18.220600 \n", "249 British Virgin Islands United Kingdom 18.420700 \n", "250 Turks and Caicos Islands United Kingdom 21.694000 \n", "251 NaN MS Zaandam 0.000000 \n", "252 NaN Botswana -22.328500 \n", "253 NaN Burundi -3.373100 \n", "254 NaN Sierra Leone 8.460555 \n", "255 Bonaire, Sint Eustatius and Saba Netherlands 12.178400 \n", "256 NaN Malawi -13.254308 \n", "257 Falkland Islands (Malvinas) United Kingdom -51.796300 \n", "258 Saint Pierre and Miquelon France 46.885200 \n", "259 NaN South Sudan 6.877000 \n", "260 NaN Western Sahara 24.215500 \n", "261 NaN Sao Tome and Principe 0.186360 \n", "262 NaN Yemen 15.552727 \n", "263 NaN Comoros -11.645500 \n", "264 NaN Tajikistan 38.861034 \n", "265 NaN Lesotho -29.609988 \n", "\n", " Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 \\\n", "0 65.000000 0 0 0 0 0 0 \n", "1 20.168300 0 0 0 0 0 0 \n", "2 1.659600 0 0 0 0 0 0 \n", "3 1.521800 0 0 0 0 0 0 \n", "4 17.873900 0 0 0 0 0 0 \n", "5 -61.796400 0 0 0 0 0 0 \n", "6 -63.616700 0 0 0 0 0 0 \n", "7 45.038200 0 0 0 0 0 0 \n", "8 149.012400 0 0 0 0 0 0 \n", "9 151.209300 0 0 0 0 3 4 \n", "10 130.845600 0 0 0 0 0 0 \n", "11 153.400000 0 0 0 0 0 0 \n", "12 138.600700 0 0 0 0 0 0 \n", "13 145.970700 0 0 0 0 0 0 \n", "14 144.963100 0 0 0 0 1 1 \n", "15 115.860500 0 0 0 0 0 0 \n", "16 14.550100 0 0 0 0 0 0 \n", "17 47.576900 0 0 0 0 0 0 \n", "18 -77.396300 0 0 0 0 0 0 \n", 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228 228 228 228 \n", "14 ... 1573 1581 1593 1593 1603 1605 1610 \n", "15 ... 557 557 557 557 560 560 564 \n", "16 ... 16321 16353 16404 16436 16486 16503 16539 \n", "17 ... 3518 3631 3749 3855 3982 4122 4271 \n", "18 ... 96 97 97 97 100 100 100 \n", "19 ... 7532 7888 8174 8414 8802 9138 9171 \n", "20 ... 25121 26738 28511 30205 32078 33610 35585 \n", "21 ... 90 90 90 90 92 92 92 \n", "22 ... 31508 32426 33371 34303 35244 36198 37144 \n", "23 ... 55791 55983 56235 56511 56810 57092 57342 \n", "24 ... 130 130 135 135 135 191 191 \n", "25 ... 21 21 21 21 24 24 27 \n", "26 ... 4481 4919 5187 5579 5915 6263 6660 \n", "27 ... 2321 2338 2350 2372 2391 2401 2406 \n", "28 ... 271885 291579 310087 330890 347398 363211 374898 \n", "29 ... 141 141 141 141 141 141 141 \n", ".. ... ... ... ... ... ... ... ... \n", "236 ... 24 24 24 24 24 24 24 \n", "237 ... 18 18 18 18 18 18 18 \n", "238 ... 19 19 19 19 19 19 19 \n", "239 ... 68 69 71 72 75 75 75 \n", "240 ... 391 398 423 423 423 423 423 \n", "241 ... 1038 1089 1109 1114 1114 1114 1178 \n", "242 ... 901 931 947 969 1015 1030 1059 \n", "243 ... 15 15 15 15 15 15 15 \n", "244 ... 5 5 5 5 5 5 5 \n", "245 ... 11 11 11 11 11 11 11 \n", "246 ... 989 989 1003 1004 1025 1032 1038 \n", "247 ... 193 199 199 199 201 201 203 \n", "248 ... 3 3 3 3 3 3 3 \n", "249 ... 8 8 8 8 8 8 8 \n", "250 ... 12 12 12 12 12 12 12 \n", "251 ... 9 9 9 9 9 9 9 \n", "252 ... 25 25 29 30 30 35 35 \n", "253 ... 42 42 42 42 42 42 42 \n", "254 ... 534 570 585 606 621 707 735 \n", "255 ... 6 6 6 6 6 6 6 \n", "256 ... 70 71 72 82 82 83 101 \n", "257 ... 13 13 13 13 13 13 13 \n", "258 ... 1 1 1 1 1 1 1 \n", "259 ... 290 290 481 563 655 655 806 \n", "260 ... 6 6 6 6 6 9 9 \n", "261 ... 251 251 251 251 251 251 299 \n", "262 ... 167 184 197 209 212 222 233 \n", "263 ... 11 34 34 78 78 87 87 \n", "264 ... 1936 2140 2350 2551 2738 2929 3100 \n", "265 ... 1 1 1 2 2 2 2 \n", "\n", " 5/26/20 5/27/20 5/28/20 \n", "0 11831 12456 13036 \n", "1 1029 1050 1076 \n", "2 8697 8857 8997 \n", "3 763 763 763 \n", "4 70 71 74 \n", "5 25 25 25 \n", "6 13228 13933 14702 \n", "7 7402 7774 8216 \n", "8 107 107 107 \n", "9 3089 3090 3092 \n", "10 29 29 29 \n", "11 1058 1058 1058 \n", "12 440 440 440 \n", "13 228 228 228 \n", "14 1618 1628 1634 \n", "15 570 570 577 \n", "16 16557 16591 16628 \n", "17 4403 4568 4759 \n", "18 100 100 101 \n", "19 9366 9692 10052 \n", "20 36751 38292 40321 \n", "21 92 92 92 \n", "22 38059 38956 39858 \n", "23 57455 57592 57849 \n", "24 208 210 210 \n", "25 27 28 31 \n", "26 7136 7768 8387 \n", "27 2416 2435 2462 \n", "28 391222 411821 438238 \n", "29 141 141 141 \n", ".. ... ... ... \n", "236 24 24 24 \n", "237 18 18 18 \n", "238 19 19 19 \n", "239 77 99 105 \n", "240 429 434 446 \n", "241 1178 1195 1195 \n", "242 1077 1116 1194 \n", "243 15 15 15 \n", "244 5 5 5 \n", "245 11 11 11 \n", "246 1038 1047 1048 \n", "247 206 206 206 \n", "248 3 3 3 \n", "249 8 8 8 \n", "250 12 12 12 \n", "251 9 9 9 \n", "252 35 35 35 \n", "253 42 42 42 \n", "254 754 782 812 \n", "255 6 6 6 \n", "256 101 101 203 \n", "257 13 13 13 \n", "258 1 1 1 \n", "259 806 994 994 \n", "260 9 9 9 \n", "261 441 443 458 \n", "262 249 256 278 \n", "263 87 87 87 \n", "264 3266 3424 3563 \n", "265 2 2 2 \n", "\n", "[266 rows x 132 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "import urllib.request\n", "\n", "if os.path.exists(localData):\n", " raw_data = pd.read_csv(localData)\n", " print(\"Local File Selected\")\n", "else :\n", " urllib.request.urlretrieve(data_url, data_data)\n", " raw_data = pd.read_csv(data_url)\n", " print(\"Online File Selected\")\n", " \n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données ci-dessus sont les données brutes provenant du fichier CSV, de gauche à droite elles correspondent à :\n", "\n", "| Column's Name | Meaning |\n", "| ---------------|:------------------------------------------------------------------------------:|\n", "| ID | unique identity for the row |\n", "| Province/State | gives data for a specific regions |\n", "| Country/Region | the country or the region to which the data are corresponding |\n", "| Lat | latitude |\n", "| Long | longitude |\n", "| 1/22/20 | from here it gives the number citizens having the covid19 |" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données manquantes correspondent aux pays qui ne sont pas représentés à travers différentes provinces et états les composants.\n", "Cependant, nous ne sommes pas dépendant de ces données, seul les données relatives aux pays suivants nous intéressent. \n", "\n", "* Belgique \n", "* Chine - toutes les provinces sauf Hong-Kong (China),\n", "* Hong Kong \n", "* France métropolitaine\n", "* Allemagne\n", "* Iran\n", "* Italie\n", "* Japon\n", "* Corée du Sud\n", "* Hollande\n", "* Portugal \n", "* Espagne\n", "* Royaume-Unis\n", "* États-Unis\n", "\n", "---\n", "\n", "# Formatage des données\n", "\n", "## Regroupement des données à inclure dans l'étude\n", "\n", "Ici nous utilisons la méthode [*loc*](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html) de pandas pour extraire des données brutes, les lignes correspondantes aux pays cités ci-dessus.\n", "\n", "Afin de ne pas rendre le *code* illisible le processus est divisé en de multiples étapes. (toutes ces étapes peuvent être regroupées)\n", "\n", "1. Exemple d'ajout de données liées à un pays;\n", "2. Ajout de tous les autres pays excepté la Chine;\n", "3. Ajout de la Chine en cumulant chacunes des ses provinces, sans Hong-Kong." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...5/19/205/20/205/21/205/22/205/23/205/24/205/25/205/26/205/27/205/28/20
23NaNBelgium50.83334.0000000...55791559835623556511568105709257342574555759257849
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1 rows × 132 columns

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" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n", "23 NaN Belgium 50.8333 4.0 0 0 0 \n", "\n", " 1/25/20 1/26/20 1/27/20 ... 5/19/20 5/20/20 5/21/20 5/22/20 \\\n", "23 0 0 0 ... 55791 55983 56235 56511 \n", "\n", " 5/23/20 5/24/20 5/25/20 5/26/20 5/27/20 5/28/20 \n", "23 56810 57092 57342 57455 57592 57849 \n", "\n", "[1 rows x 132 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# let's create a new variable to store our new data frame\n", "# starting with Belgium\n", "dataCountries = raw_data.loc[(raw_data['Country/Region'] == 'Belgium')]\n", "\n", "dataCountries" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...5/19/205/20/205/21/205/22/205/23/205/24/205/25/205/26/205/27/205/28/20
23NaNBelgium50.83334.0000000000...55791559835623556511568105709257342574555759257849
116NaNFrance46.22762.2137002333...178428179069179306179645179964179859180166179887180044183309
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2 rows × 132 columns

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" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n", "23 NaN Belgium 50.8333 4.0000 0 0 0 \n", "116 NaN France 46.2276 2.2137 0 0 2 \n", "\n", " 1/25/20 1/26/20 1/27/20 ... 5/19/20 5/20/20 5/21/20 5/22/20 \\\n", "23 0 0 0 ... 55791 55983 56235 56511 \n", "116 3 3 3 ... 178428 179069 179306 179645 \n", "\n", " 5/23/20 5/24/20 5/25/20 5/26/20 5/27/20 5/28/20 \n", "23 56810 57092 57342 57455 57592 57849 \n", "116 179964 179859 180166 179887 180044 183309 \n", "\n", "[2 rows x 132 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# now let's add to dataCountries the rest of the countries needed \n", "# Here with & Province/State.isnull we are only including metropolitan France's row and not the specific regions from France detailed in the data.\n", "\n", "dataCountries = dataCountries.append(raw_data.loc[(raw_data['Country/Region'] == 'France') & (raw_data['Province/State'].isnull())])\n", "\n", "dataCountries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les mêmes étapes sont utilisées pour le reste des pays manquants, sauf pour la Chine qui nécessite une opération spécial. (Voir ci-dessous)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [], "source": [ "countries_list= list(['Germany', 'Iran', 'Italy', 'Japan', 'Korea, South', 'Netherlands', 'Portugal', 'Spain', 'United Kingdom', 'US'])\n", "#print(countries_list)\n", "\n", "for country in countries_list : \n", " dataCountries = dataCountries.append(raw_data.loc[(raw_data['Country/Region'] == country) & (raw_data['Province/State'].isnull())])\n", "\n", "# Manualy adding Hong-Kong \n", "dataCountries = dataCountries.append(raw_data.loc[(raw_data['Country/Region'] == 'China') & (raw_data['Province/State'] == 'Hong Kong')]) \n", "\n", "#Uncomment to see the dataframe\n", "#dataCountries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour éviter que deux lignes correspondent au même pays, la Chine. On renome *Hong Kong, China* en *Hong Kong, Hong Kong*. Ainsi, nous pourrons ajouter toutes les provinces de Chine dans une même ligne nommée *China*. Nous utilisons donc la méthode [replace](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.replace.html) pour remplacer le nom du pays." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...5/19/205/20/205/21/205/22/205/23/205/24/205/25/205/26/205/27/205/28/20
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116NaNFrance46.22762.2137002333...178428179069179306179645179964179859180166179887180044183309
120NaNGermany51.00009.0000000001...177778178473179021179710179986180328180600181200181524182196
133NaNIran32.000053.0000000000...124603126949129341131652133521135701137724139511141591143849
137NaNItaly43.000012.0000000000...226699227364228006228658229327229858230158230555231139231732
139NaNJapan36.0000138.0000222244...16367163671642416513165361655016581166231665116598
143NaNKorea, South36.0000128.0000112234...11110111221114211165111901120611225112651134411402
169NaNNetherlands52.13265.2913000000...44249444474470044888450644523645445455784576845950
184NaNPortugal39.3999-8.2245000000...29432296602991230200304713062330788310073129231596
201NaNSpain40.0000-4.0000000000...232037232555233037234824235290235772235400236259236259237906
223NaNUnited Kingdom55.3781-3.4360000000...248818248293250908254195257154259559261184265227267240269127
225NaNUS37.0902-95.7129112255...1528568155185315771471600937162261216432461662302168091316991761721753
61Hong KongHong Kong22.3000114.2000022588...1055105510551065106510651065106510661066
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13 rows × 132 columns

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" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", "23 NaN Belgium 50.8333 4.0000 0 0 \n", "116 NaN France 46.2276 2.2137 0 0 \n", "120 NaN Germany 51.0000 9.0000 0 0 \n", "133 NaN Iran 32.0000 53.0000 0 0 \n", "137 NaN Italy 43.0000 12.0000 0 0 \n", "139 NaN Japan 36.0000 138.0000 2 2 \n", "143 NaN Korea, South 36.0000 128.0000 1 1 \n", "169 NaN Netherlands 52.1326 5.2913 0 0 \n", "184 NaN Portugal 39.3999 -8.2245 0 0 \n", "201 NaN Spain 40.0000 -4.0000 0 0 \n", "223 NaN United Kingdom 55.3781 -3.4360 0 0 \n", "225 NaN US 37.0902 -95.7129 1 1 \n", "61 Hong Kong Hong Kong 22.3000 114.2000 0 2 \n", "\n", " 1/24/20 1/25/20 1/26/20 1/27/20 ... 5/19/20 5/20/20 5/21/20 \\\n", "23 0 0 0 0 ... 55791 55983 56235 \n", "116 2 3 3 3 ... 178428 179069 179306 \n", "120 0 0 0 1 ... 177778 178473 179021 \n", "133 0 0 0 0 ... 124603 126949 129341 \n", "137 0 0 0 0 ... 226699 227364 228006 \n", "139 2 2 4 4 ... 16367 16367 16424 \n", "143 2 2 3 4 ... 11110 11122 11142 \n", "169 0 0 0 0 ... 44249 44447 44700 \n", "184 0 0 0 0 ... 29432 29660 29912 \n", "201 0 0 0 0 ... 232037 232555 233037 \n", "223 0 0 0 0 ... 248818 248293 250908 \n", "225 2 2 5 5 ... 1528568 1551853 1577147 \n", "61 2 5 8 8 ... 1055 1055 1055 \n", "\n", " 5/22/20 5/23/20 5/24/20 5/25/20 5/26/20 5/27/20 5/28/20 \n", "23 56511 56810 57092 57342 57455 57592 57849 \n", "116 179645 179964 179859 180166 179887 180044 183309 \n", "120 179710 179986 180328 180600 181200 181524 182196 \n", "133 131652 133521 135701 137724 139511 141591 143849 \n", "137 228658 229327 229858 230158 230555 231139 231732 \n", "139 16513 16536 16550 16581 16623 16651 16598 \n", "143 11165 11190 11206 11225 11265 11344 11402 \n", "169 44888 45064 45236 45445 45578 45768 45950 \n", "184 30200 30471 30623 30788 31007 31292 31596 \n", "201 234824 235290 235772 235400 236259 236259 237906 \n", "223 254195 257154 259559 261184 265227 267240 269127 \n", "225 1600937 1622612 1643246 1662302 1680913 1699176 1721753 \n", "61 1065 1065 1065 1065 1065 1066 1066 \n", "\n", "[13 rows x 132 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "dataCountries[\"Country/Region\"].replace({\"China\": \"Hong Kong\"}, inplace=True)\n", "\n", "dataCountries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La chine est composée de plusieurs provinces. Pour étudier l'ensemble de la Chine moins Hong-kong (voir consigne) nous additionnons le nombre de cas par jour dans une nouvelle ligne nommée China avec l'index 1 car non utilisé (orignellement utilisé par l'Afghanistan). Pour se faire nous utilisons les méthodes [at](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.at.html) et [sum](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sum.html).\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py:2035: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " self.loc[index, col] = value\n" ] }, { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...5/19/205/20/205/21/205/22/205/23/205/24/205/25/205/26/205/27/205/28/20
23NaNBelgium50.83334.00000.00.00.00.00.00.0...55791.055983.056235.056511.056810.057092.057342.057455.057592.057849.0
116NaNFrance46.22762.21370.00.02.03.03.03.0...178428.0179069.0179306.0179645.0179964.0179859.0180166.0179887.0180044.0183309.0
120NaNGermany51.00009.00000.00.00.00.00.01.0...177778.0178473.0179021.0179710.0179986.0180328.0180600.0181200.0181524.0182196.0
133NaNIran32.000053.00000.00.00.00.00.00.0...124603.0126949.0129341.0131652.0133521.0135701.0137724.0139511.0141591.0143849.0
137NaNItaly43.000012.00000.00.00.00.00.00.0...226699.0227364.0228006.0228658.0229327.0229858.0230158.0230555.0231139.0231732.0
139NaNJapan36.0000138.00002.02.02.02.04.04.0...16367.016367.016424.016513.016536.016550.016581.016623.016651.016598.0
143NaNKorea, South36.0000128.00001.01.02.02.03.04.0...11110.011122.011142.011165.011190.011206.011225.011265.011344.011402.0
169NaNNetherlands52.13265.29130.00.00.00.00.00.0...44249.044447.044700.044888.045064.045236.045445.045578.045768.045950.0
184NaNPortugal39.3999-8.22450.00.00.00.00.00.0...29432.029660.029912.030200.030471.030623.030788.031007.031292.031596.0
201NaNSpain40.0000-4.00000.00.00.00.00.00.0...232037.0232555.0233037.0234824.0235290.0235772.0235400.0236259.0236259.0237906.0
223NaNUnited Kingdom55.3781-3.43600.00.00.00.00.00.0...248818.0248293.0250908.0254195.0257154.0259559.0261184.0265227.0267240.0269127.0
225NaNUS37.0902-95.71291.01.02.02.05.05.0...1528568.01551853.01577147.01600937.01622612.01643246.01662302.01680913.01699176.01721753.0
61Hong KongHong Kong22.3000114.20000.02.02.05.08.08.0...1055.01055.01055.01065.01065.01065.01065.01065.01066.01066.0
1NaNChinaNaNNaN548.0641.0918.01401.02067.02869.0...83008.083008.083008.083016.083019.083030.083037.083038.083040.083040.0
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14 rows × 132 columns

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" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", "23 NaN Belgium 50.8333 4.0000 0.0 0.0 \n", "116 NaN France 46.2276 2.2137 0.0 0.0 \n", "120 NaN Germany 51.0000 9.0000 0.0 0.0 \n", "133 NaN Iran 32.0000 53.0000 0.0 0.0 \n", "137 NaN Italy 43.0000 12.0000 0.0 0.0 \n", "139 NaN Japan 36.0000 138.0000 2.0 2.0 \n", "143 NaN Korea, South 36.0000 128.0000 1.0 1.0 \n", "169 NaN Netherlands 52.1326 5.2913 0.0 0.0 \n", "184 NaN Portugal 39.3999 -8.2245 0.0 0.0 \n", "201 NaN Spain 40.0000 -4.0000 0.0 0.0 \n", "223 NaN United Kingdom 55.3781 -3.4360 0.0 0.0 \n", "225 NaN US 37.0902 -95.7129 1.0 1.0 \n", "61 Hong Kong Hong Kong 22.3000 114.2000 0.0 2.0 \n", "1 NaN China NaN NaN 548.0 641.0 \n", "\n", " 1/24/20 1/25/20 1/26/20 1/27/20 ... 5/19/20 5/20/20 \\\n", "23 0.0 0.0 0.0 0.0 ... 55791.0 55983.0 \n", "116 2.0 3.0 3.0 3.0 ... 178428.0 179069.0 \n", "120 0.0 0.0 0.0 1.0 ... 177778.0 178473.0 \n", "133 0.0 0.0 0.0 0.0 ... 124603.0 126949.0 \n", "137 0.0 0.0 0.0 0.0 ... 226699.0 227364.0 \n", "139 2.0 2.0 4.0 4.0 ... 16367.0 16367.0 \n", "143 2.0 2.0 3.0 4.0 ... 11110.0 11122.0 \n", "169 0.0 0.0 0.0 0.0 ... 44249.0 44447.0 \n", "184 0.0 0.0 0.0 0.0 ... 29432.0 29660.0 \n", "201 0.0 0.0 0.0 0.0 ... 232037.0 232555.0 \n", "223 0.0 0.0 0.0 0.0 ... 248818.0 248293.0 \n", "225 2.0 2.0 5.0 5.0 ... 1528568.0 1551853.0 \n", "61 2.0 5.0 8.0 8.0 ... 1055.0 1055.0 \n", "1 918.0 1401.0 2067.0 2869.0 ... 83008.0 83008.0 \n", "\n", " 5/21/20 5/22/20 5/23/20 5/24/20 5/25/20 5/26/20 \\\n", "23 56235.0 56511.0 56810.0 57092.0 57342.0 57455.0 \n", "116 179306.0 179645.0 179964.0 179859.0 180166.0 179887.0 \n", "120 179021.0 179710.0 179986.0 180328.0 180600.0 181200.0 \n", "133 129341.0 131652.0 133521.0 135701.0 137724.0 139511.0 \n", "137 228006.0 228658.0 229327.0 229858.0 230158.0 230555.0 \n", "139 16424.0 16513.0 16536.0 16550.0 16581.0 16623.0 \n", "143 11142.0 11165.0 11190.0 11206.0 11225.0 11265.0 \n", "169 44700.0 44888.0 45064.0 45236.0 45445.0 45578.0 \n", "184 29912.0 30200.0 30471.0 30623.0 30788.0 31007.0 \n", "201 233037.0 234824.0 235290.0 235772.0 235400.0 236259.0 \n", "223 250908.0 254195.0 257154.0 259559.0 261184.0 265227.0 \n", "225 1577147.0 1600937.0 1622612.0 1643246.0 1662302.0 1680913.0 \n", "61 1055.0 1065.0 1065.0 1065.0 1065.0 1065.0 \n", "1 83008.0 83016.0 83019.0 83030.0 83037.0 83038.0 \n", "\n", " 5/27/20 5/28/20 \n", "23 57592.0 57849.0 \n", "116 180044.0 183309.0 \n", "120 181524.0 182196.0 \n", "133 141591.0 143849.0 \n", "137 231139.0 231732.0 \n", "139 16651.0 16598.0 \n", "143 11344.0 11402.0 \n", "169 45768.0 45950.0 \n", "184 31292.0 31596.0 \n", "201 236259.0 237906.0 \n", "223 267240.0 269127.0 \n", "225 1699176.0 1721753.0 \n", "61 1066.0 1066.0 \n", "1 83040.0 83040.0 \n", "\n", "[14 rows x 132 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# For china the data have to be summed between region in order to get the results for the whole country.\n", "dataChina = raw_data.loc[((raw_data['Country/Region'] == 'China') & (raw_data['Province/State'] != 'Hong Kong' ))]\n", "\n", "#print(dataChina)\n", "\n", "#We want to sum per date and not the regions or the latitude so we remove them from our temporary list of column.\n", "col_list= list(dataChina)\n", "col_list.remove(\"Province/State\")\n", "col_list.remove(\"Country/Region\")\n", "col_list.remove(\"Lat\")\n", "col_list.remove(\"Long\")\n", "\n", "\n", "#let's use df.sum() to sum rows \n", "for col in col_list: \n", " dataChina.at['1', col] = dataChina[col].sum()\n", "\n", "#Rename the Country in the column we have just created above.\n", "dataChina.at['1', \"Country/Region\"] = \"China\"\n", "\n", "dataChina\n", "#Now add the data to Data Countries\n", "dataCountries= dataCountries.append(dataChina.loc[(dataChina['Country/Region'] == 'China') & (dataChina['Province/State'].isnull())])\n", "\n", "dataCountries\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous avons donc un dataFrame regroupant l'ensemble des données necéssaire nous pouvons encore supprimer les données que nous n'utiliserons pas telles que les provinces et régions ou la latitude et la longitude. Nous utilisons la méthode [drop](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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Country/Region1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/201/30/20...5/19/205/20/205/21/205/22/205/23/205/24/205/25/205/26/205/27/205/28/20
23Belgium0.00.00.00.00.00.00.00.00.0...55791.055983.056235.056511.056810.057092.057342.057455.057592.057849.0
116France0.00.02.03.03.03.04.05.05.0...178428.0179069.0179306.0179645.0179964.0179859.0180166.0179887.0180044.0183309.0
120Germany0.00.00.00.00.01.04.04.04.0...177778.0178473.0179021.0179710.0179986.0180328.0180600.0181200.0181524.0182196.0
133Iran0.00.00.00.00.00.00.00.00.0...124603.0126949.0129341.0131652.0133521.0135701.0137724.0139511.0141591.0143849.0
137Italy0.00.00.00.00.00.00.00.00.0...226699.0227364.0228006.0228658.0229327.0229858.0230158.0230555.0231139.0231732.0
139Japan2.02.02.02.04.04.07.07.011.0...16367.016367.016424.016513.016536.016550.016581.016623.016651.016598.0
143Korea, South1.01.02.02.03.04.04.04.04.0...11110.011122.011142.011165.011190.011206.011225.011265.011344.011402.0
169Netherlands0.00.00.00.00.00.00.00.00.0...44249.044447.044700.044888.045064.045236.045445.045578.045768.045950.0
184Portugal0.00.00.00.00.00.00.00.00.0...29432.029660.029912.030200.030471.030623.030788.031007.031292.031596.0
201Spain0.00.00.00.00.00.00.00.00.0...232037.0232555.0233037.0234824.0235290.0235772.0235400.0236259.0236259.0237906.0
223United Kingdom0.00.00.00.00.00.00.00.00.0...248818.0248293.0250908.0254195.0257154.0259559.0261184.0265227.0267240.0269127.0
225US1.01.02.02.05.05.05.05.05.0...1528568.01551853.01577147.01600937.01622612.01643246.01662302.01680913.01699176.01721753.0
61Hong Kong0.02.02.05.08.08.08.010.010.0...1055.01055.01055.01065.01065.01065.01065.01065.01066.01066.0
1China548.0641.0918.01401.02067.02869.05501.06077.08131.0...83008.083008.083008.083016.083019.083030.083037.083038.083040.083040.0
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14 rows × 129 columns

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" ], "text/plain": [ " Country/Region 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 \\\n", "23 Belgium 0.0 0.0 0.0 0.0 0.0 0.0 \n", "116 France 0.0 0.0 2.0 3.0 3.0 3.0 \n", "120 Germany 0.0 0.0 0.0 0.0 0.0 1.0 \n", "133 Iran 0.0 0.0 0.0 0.0 0.0 0.0 \n", "137 Italy 0.0 0.0 0.0 0.0 0.0 0.0 \n", "139 Japan 2.0 2.0 2.0 2.0 4.0 4.0 \n", "143 Korea, South 1.0 1.0 2.0 2.0 3.0 4.0 \n", "169 Netherlands 0.0 0.0 0.0 0.0 0.0 0.0 \n", "184 Portugal 0.0 0.0 0.0 0.0 0.0 0.0 \n", "201 Spain 0.0 0.0 0.0 0.0 0.0 0.0 \n", "223 United Kingdom 0.0 0.0 0.0 0.0 0.0 0.0 \n", "225 US 1.0 1.0 2.0 2.0 5.0 5.0 \n", "61 Hong Kong 0.0 2.0 2.0 5.0 8.0 8.0 \n", "1 China 548.0 641.0 918.0 1401.0 2067.0 2869.0 \n", "\n", " 1/28/20 1/29/20 1/30/20 ... 5/19/20 5/20/20 5/21/20 \\\n", "23 0.0 0.0 0.0 ... 55791.0 55983.0 56235.0 \n", "116 4.0 5.0 5.0 ... 178428.0 179069.0 179306.0 \n", "120 4.0 4.0 4.0 ... 177778.0 178473.0 179021.0 \n", "133 0.0 0.0 0.0 ... 124603.0 126949.0 129341.0 \n", "137 0.0 0.0 0.0 ... 226699.0 227364.0 228006.0 \n", "139 7.0 7.0 11.0 ... 16367.0 16367.0 16424.0 \n", "143 4.0 4.0 4.0 ... 11110.0 11122.0 11142.0 \n", "169 0.0 0.0 0.0 ... 44249.0 44447.0 44700.0 \n", "184 0.0 0.0 0.0 ... 29432.0 29660.0 29912.0 \n", "201 0.0 0.0 0.0 ... 232037.0 232555.0 233037.0 \n", "223 0.0 0.0 0.0 ... 248818.0 248293.0 250908.0 \n", "225 5.0 5.0 5.0 ... 1528568.0 1551853.0 1577147.0 \n", "61 8.0 10.0 10.0 ... 1055.0 1055.0 1055.0 \n", "1 5501.0 6077.0 8131.0 ... 83008.0 83008.0 83008.0 \n", "\n", " 5/22/20 5/23/20 5/24/20 5/25/20 5/26/20 5/27/20 \\\n", "23 56511.0 56810.0 57092.0 57342.0 57455.0 57592.0 \n", "116 179645.0 179964.0 179859.0 180166.0 179887.0 180044.0 \n", "120 179710.0 179986.0 180328.0 180600.0 181200.0 181524.0 \n", "133 131652.0 133521.0 135701.0 137724.0 139511.0 141591.0 \n", "137 228658.0 229327.0 229858.0 230158.0 230555.0 231139.0 \n", "139 16513.0 16536.0 16550.0 16581.0 16623.0 16651.0 \n", "143 11165.0 11190.0 11206.0 11225.0 11265.0 11344.0 \n", "169 44888.0 45064.0 45236.0 45445.0 45578.0 45768.0 \n", "184 30200.0 30471.0 30623.0 30788.0 31007.0 31292.0 \n", "201 234824.0 235290.0 235772.0 235400.0 236259.0 236259.0 \n", "223 254195.0 257154.0 259559.0 261184.0 265227.0 267240.0 \n", "225 1600937.0 1622612.0 1643246.0 1662302.0 1680913.0 1699176.0 \n", "61 1065.0 1065.0 1065.0 1065.0 1065.0 1066.0 \n", "1 83016.0 83019.0 83030.0 83037.0 83038.0 83040.0 \n", "\n", " 5/28/20 \n", "23 57849.0 \n", "116 183309.0 \n", "120 182196.0 \n", "133 143849.0 \n", "137 231732.0 \n", "139 16598.0 \n", "143 11402.0 \n", "169 45950.0 \n", "184 31596.0 \n", "201 237906.0 \n", "223 269127.0 \n", "225 1721753.0 \n", "61 1066.0 \n", "1 83040.0 \n", "\n", "[14 rows x 129 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataCountries = dataCountries.drop(['Province/State','Lat', 'Long'], axis=1)\n", "dataCountries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Traitement des données \n", "\n", "Ici le traitement des données consiste uniquement à représenter des données temporelles dans un graphique. \n", "Dans un premier temps les données sont transposées d'un format horizontal en format vertical pour être correctement représenté dans un graphique (voir méthode [transpose](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.transpose.html)). Enfin les columns sont renommées afin de correspondre à leur pays.\n", "\n", "Tout ce qui suit permet de dessiner le graphique suivant. Un détail est important à noter, l'ensemble des dates a été remplacé par le nombre de jour depuis la première donnée datant du 22 janvier 2020 (0 à 128). Pour éviter qu'ils ne se chevauchent 1 jour sur 4 est affiché sur l'absice. \n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dataTransposed = dataCountries.transpose()\n", "\n", "header_row = 0\n", "dataTransposed.columns = dataTransposed.iloc[header_row]\n", "dataTransposed = dataTransposed.drop(['Country/Region'], axis=0)\n", "\n", "days = []\n", "for day in range(dataTransposed.index.size):\n", " days.append(day)\n", "\n", "############################################## Figure ######################################################################\n", "fig, ax = plt.subplots()\n", "\n", "\n", "for col in dataTransposed.columns :\n", " dataTransposed.plot(kind='line',x=dataTransposed.index,y=col,ax=ax)\n", "\n", "\n", "ax.set_xlabel(\"Days\")\n", "ax.set_ylabel(\"Coronavirus Cases\")\n", "fig.set_size_inches(20, 20)\n", "\n", "# To specify the number of ticks on both or any single axes\n", "plt.locator_params(axis='y', nbins=20)\n", "\n", "plt.text(0.80, 0.95, ('US May 28 '+'\\n'+str(dataTransposed.iloc[dataTransposed.index.size-1]['US'])+' cases'),\n", " horizontalalignment='center',\n", " verticalalignment='center',\n", " transform=ax.transAxes,\n", " fontsize=20)\n", "\n", "\n", "plt.text(0.85, 0.20, ('China May 28 '+'\\n'+str(dataTransposed.iloc[dataTransposed.index.size-1]['China'])+' cases'),\n", " horizontalalignment='center',\n", " verticalalignment='center',\n", " transform=ax.transAxes,\n", " fontsize=20)\n", "\n", "\n", "# only print one over four of the days since the beginning\n", "ax.set_xticks(days[::4])\n", "ax.set_xticklabels(days[::4], rotation=45)\n", "\n", "\n", "\n", "ax.set_title('Cumulative coronavirus cases')\n", "ax.legend(loc = 'upper left',fontsize='x-large')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "# Elément complémentaire\n", "\n", "Si nous ajoutons plus de données relatives aux pays étudiés nous pourrions réaliser un grand nombre de comparaisons entre pays.\n", "\n", "Un exemple : On peut comparer l'efficacité du confinement dans les pays qui ont appliqués un confinement strict, prenons la France et l'Italie. On peut facilement obtenir les dates de début de confinement ainsi que ne nombre de décès liés aux cas de coronavirus, ainsi qu'une multitude de facteurs démographiques. Avec un tel corpus de données, nous pourrions mener des tests statistiques suivants le modèle linéaire général. Probablement une régression linéaire multiple dans notre cas et ainsi voir si le nombre de jour passé depuis le début du confinement explique bien la stagnation du nombre de cas puis sa diminution.\n", "\n", "Étant donné la complexité d'une telle étude et le risque d'erreur d'interprétation et de calcul, ce serait prétentieux que de prétendre pouvoir conduire un tel test. Cependant, il est probable que de tels tests aient été menés par l'OMS ou les agences nationales de santé.\n", "\n", "# Conclusion \n", "\n", "Ce notebook présente la méthode employée afin de réaliser la figure ci-dessus **Cumulative coronavirus cases**. Les principales difficultés consistent à correctement regrouper les données dans un même jeu de données puis de les afficher de façon lisible dans un graphique. Ce notebook ne présente aucun algorithme complexe, mais nécessite une bonne connaissance des librairies python tel que *pyplot* et *pandas*.\n", "\n", "# Référence \n", "\n", "* [Matplotlib](https://matplotlib.org/3.1.0/index.html)\n", "* [Pandas](https://pandas.pydata.org/docs/)\n" ] } ], "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 }