From b9b4eb87366d45af9cc5969066ca6e89f1f3cecd Mon Sep 17 00:00:00 2001 From: 5212fa3d0a7441c34b57f854081c7450 <5212fa3d0a7441c34b57f854081c7450@app-learninglab.inria.fr> Date: Sun, 2 Feb 2025 09:24:19 +0000 Subject: [PATCH] Upload New File for test --- module3/exo1/analyse-syndrome-grippal2.ipynb | 2543 ++++++++++++++++++ 1 file changed, 2543 insertions(+) create mode 100644 module3/exo1/analyse-syndrome-grippal2.ipynb diff --git a/module3/exo1/analyse-syndrome-grippal2.ipynb b/module3/exo1/analyse-syndrome-grippal2.ipynb new file mode 100644 index 0000000..4556eb3 --- /dev/null +++ b/module3/exo1/analyse-syndrome-grippal2.ipynb @@ -0,0 +1,2543 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence du syndrome grippal" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", + "\n", + "| Nom de colonne | Libellé de colonne |\n", + "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", + "| week | Semaine calendaire (ISO 8601) |\n", + "| indicator | Code de l'indicateur de surveillance |\n", + "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", + "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", + "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", + "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", + "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", + "\n", + "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02025043375118356288.0393948.0560532.0588.0FRFrance
12025033253215239337.0267093.0378357.0399.0FRFrance
22025023257247242991.0271503.0384363.0405.0FRFrance
32025013231549214627.0248471.0345320.0370.0FRFrance
42024523201726185870.0217582.0302278.0326.0FRFrance
52024513201697187843.0215551.0302281.0323.0FRFrance
62024503136694126369.0147019.0205190.0220.0FRFrance
7202449310848799037.0117937.0163149.0177.0FRFrance
820244838738178687.096075.0131118.0144.0FRFrance
920244737628667626.084946.0114101.0127.0FRFrance
1020244635639949006.063792.08574.096.0FRFrance
1120244534734740843.053851.07161.081.0FRFrance
1220244433603930122.041956.05445.063.0FRFrance
1320244334657239928.053216.07060.080.0FRFrance
1420244236778560009.075561.010290.0114.0FRFrance
1520244137943571386.087484.0119107.0131.0FRFrance
1620244038496576555.093375.0127114.0140.0FRFrance
1720243939166082937.0100383.0137124.0150.0FRFrance
1820243839178682903.0100669.0138125.0151.0FRFrance
1920243735646049319.063601.08574.096.0FRFrance
2020243633365727906.039408.05041.059.0FRFrance
2120243532740422036.032772.04133.049.0FRFrance
2220243432671721003.032431.04031.049.0FRFrance
2320243332062315349.025897.03123.039.0FRFrance
2420243232318717532.028842.03527.043.0FRFrance
2520243132603520267.031803.03930.048.0FRFrance
2620243033639328593.044193.05543.067.0FRFrance
2720242933956032592.046528.05949.069.0FRFrance
2820242835434245781.062903.08168.094.0FRFrance
2920242734736440234.054494.07160.082.0FRFrance
.................................
207019852132609619621.032571.04735.059.0FRFrance
207119852032789620885.034907.05138.064.0FRFrance
207219851934315432821.053487.07859.097.0FRFrance
207319851834055529935.051175.07455.093.0FRFrance
207419851733405324366.043740.06244.080.0FRFrance
207519851635036236451.064273.09166.0116.0FRFrance
207619851536388145538.082224.011683.0149.0FRFrance
20771985143134545114400.0154690.0244207.0281.0FRFrance
20781985133197206176080.0218332.0357319.0395.0FRFrance
20791985123245240223304.0267176.0445405.0485.0FRFrance
20801985113276205252399.0300011.0501458.0544.0FRFrance
20811985103353231326279.0380183.0640591.0689.0FRFrance
20821985093369895341109.0398681.0670618.0722.0FRFrance
20831985083389886359529.0420243.0707652.0762.0FRFrance
20841985073471852432599.0511105.0855784.0926.0FRFrance
20851985063565825518011.0613639.01026939.01113.0FRFrance
20861985053637302592795.0681809.011551074.01236.0FRFrance
20871985043424937390794.0459080.0770708.0832.0FRFrance
20881985033213901174689.0253113.0388317.0459.0FRFrance
208919850239758680949.0114223.0177147.0207.0FRFrance
209019850138548965918.0105060.0155120.0190.0FRFrance
209119845238483060602.0109058.0154110.0198.0FRFrance
2092198451310172680242.0123210.0185146.0224.0FRFrance
20931984503123680101401.0145959.0225184.0266.0FRFrance
2094198449310107381684.0120462.0184149.0219.0FRFrance
209519844837862060634.096606.0143110.0176.0FRFrance
209619844737202954274.089784.013199.0163.0FRFrance
209719844638733067686.0106974.0159123.0195.0FRFrance
20981984453135223101414.0169032.0246184.0308.0FRFrance
209919844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2100 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202504 3 375118 356288.0 393948.0 560 532.0 \n", + "1 202503 3 253215 239337.0 267093.0 378 357.0 \n", + "2 202502 3 257247 242991.0 271503.0 384 363.0 \n", + "3 202501 3 231549 214627.0 248471.0 345 320.0 \n", + "4 202452 3 201726 185870.0 217582.0 302 278.0 \n", + "5 202451 3 201697 187843.0 215551.0 302 281.0 \n", + "6 202450 3 136694 126369.0 147019.0 205 190.0 \n", + "7 202449 3 108487 99037.0 117937.0 163 149.0 \n", + "8 202448 3 87381 78687.0 96075.0 131 118.0 \n", + "9 202447 3 76286 67626.0 84946.0 114 101.0 \n", + "10 202446 3 56399 49006.0 63792.0 85 74.0 \n", + "11 202445 3 47347 40843.0 53851.0 71 61.0 \n", + "12 202444 3 36039 30122.0 41956.0 54 45.0 \n", + "13 202443 3 46572 39928.0 53216.0 70 60.0 \n", + "14 202442 3 67785 60009.0 75561.0 102 90.0 \n", + "15 202441 3 79435 71386.0 87484.0 119 107.0 \n", + "16 202440 3 84965 76555.0 93375.0 127 114.0 \n", + "17 202439 3 91660 82937.0 100383.0 137 124.0 \n", + "18 202438 3 91786 82903.0 100669.0 138 125.0 \n", + "19 202437 3 56460 49319.0 63601.0 85 74.0 \n", + "20 202436 3 33657 27906.0 39408.0 50 41.0 \n", + "21 202435 3 27404 22036.0 32772.0 41 33.0 \n", + "22 202434 3 26717 21003.0 32431.0 40 31.0 \n", + "23 202433 3 20623 15349.0 25897.0 31 23.0 \n", + "24 202432 3 23187 17532.0 28842.0 35 27.0 \n", + "25 202431 3 26035 20267.0 31803.0 39 30.0 \n", + "26 202430 3 36393 28593.0 44193.0 55 43.0 \n", + "27 202429 3 39560 32592.0 46528.0 59 49.0 \n", + "28 202428 3 54342 45781.0 62903.0 81 68.0 \n", + "29 202427 3 47364 40234.0 54494.0 71 60.0 \n", + "... ... ... ... ... ... ... ... \n", + "2070 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2071 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2072 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2073 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2074 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2075 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2076 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2077 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2078 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2079 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2080 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2081 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2082 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2083 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2084 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2085 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2086 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2087 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2088 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2089 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2090 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2091 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2092 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2093 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2094 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2095 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2096 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2097 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2098 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2099 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 588.0 FR France \n", + "1 399.0 FR France \n", + "2 405.0 FR France \n", + "3 370.0 FR France \n", + "4 326.0 FR France \n", + "5 323.0 FR France \n", + "6 220.0 FR France \n", + "7 177.0 FR France \n", + "8 144.0 FR France \n", + "9 127.0 FR France \n", + "10 96.0 FR France \n", + "11 81.0 FR France \n", + "12 63.0 FR France \n", + "13 80.0 FR France \n", + "14 114.0 FR France \n", + "15 131.0 FR France \n", + "16 140.0 FR France \n", + "17 150.0 FR France \n", + "18 151.0 FR France \n", + "19 96.0 FR France \n", + "20 59.0 FR France \n", + "21 49.0 FR France \n", + "22 49.0 FR France \n", + "23 39.0 FR France \n", + "24 43.0 FR France \n", + "25 48.0 FR France \n", + "26 67.0 FR France \n", + "27 69.0 FR France \n", + "28 94.0 FR France \n", + "29 82.0 FR France \n", + "... ... ... ... \n", + "2070 59.0 FR France \n", + "2071 64.0 FR France \n", + "2072 97.0 FR France \n", + "2073 93.0 FR France \n", + "2074 80.0 FR France \n", + "2075 116.0 FR France \n", + "2076 149.0 FR France \n", + "2077 281.0 FR France \n", + "2078 395.0 FR France \n", + "2079 485.0 FR France \n", + "2080 544.0 FR France \n", + "2081 689.0 FR France \n", + "2082 722.0 FR France \n", + "2083 762.0 FR France \n", + "2084 926.0 FR France \n", + "2085 1113.0 FR France \n", + "2086 1236.0 FR France \n", + "2087 832.0 FR France \n", + "2088 459.0 FR France \n", + "2089 207.0 FR France \n", + "2090 190.0 FR France \n", + "2091 198.0 FR France \n", + "2092 224.0 FR France \n", + "2093 266.0 FR France \n", + "2094 219.0 FR France \n", + "2095 176.0 FR France \n", + "2096 163.0 FR France \n", + "2097 195.0 FR France \n", + "2098 308.0 FR France \n", + "2099 213.0 FR France \n", + "\n", + "[2100 rows x 10 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Modification du code pour utiliser le fichier local contenant les données :" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "ename": "ParserError", + "evalue": "Error tokenizing data. C error: Expected 1 fields in line 30, saw 21\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mParserError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mraw_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'https://app-learninglab.inria.fr/moocrr/gitlab/5212fa3d0a7441c34b57f854081c7450/mooc-rr/blob/master/module3/exo1/inc-25-PAY.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'iso-8859-1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskiprows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 707\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 709\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 710\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 711\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 455\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 456\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 457\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m 1067\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'skipfooter not supported for iteration'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1069\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1070\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1071\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'as_recarray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m 1837\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1838\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1839\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1840\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1841\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_first_chunk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.read\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_low_memory\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mParserError\u001b[0m: Error tokenizing data. C error: Expected 1 fields in line 30, saw 21\n" + ] + } + ], + "source": [ + "raw_data = pd.read_csv('https://app-learninglab.inria.fr/moocrr/gitlab/5212fa3d0a7441c34b57f854081c7450/mooc-rr/blob/master/module3/exo1/inc-25-PAY.csv', encoding = 'iso-8859-1', skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18631989193-NaNNaN-NaNNaNFRFrance
\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1863 198919 3 - NaN NaN - NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1863 FR France " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02025043375118356288.0393948.0560532.0588.0FRFrance
12025033253215239337.0267093.0378357.0399.0FRFrance
22025023257247242991.0271503.0384363.0405.0FRFrance
32025013231549214627.0248471.0345320.0370.0FRFrance
42024523201726185870.0217582.0302278.0326.0FRFrance
52024513201697187843.0215551.0302281.0323.0FRFrance
62024503136694126369.0147019.0205190.0220.0FRFrance
7202449310848799037.0117937.0163149.0177.0FRFrance
820244838738178687.096075.0131118.0144.0FRFrance
920244737628667626.084946.0114101.0127.0FRFrance
1020244635639949006.063792.08574.096.0FRFrance
1120244534734740843.053851.07161.081.0FRFrance
1220244433603930122.041956.05445.063.0FRFrance
1320244334657239928.053216.07060.080.0FRFrance
1420244236778560009.075561.010290.0114.0FRFrance
1520244137943571386.087484.0119107.0131.0FRFrance
1620244038496576555.093375.0127114.0140.0FRFrance
1720243939166082937.0100383.0137124.0150.0FRFrance
1820243839178682903.0100669.0138125.0151.0FRFrance
1920243735646049319.063601.08574.096.0FRFrance
2020243633365727906.039408.05041.059.0FRFrance
2120243532740422036.032772.04133.049.0FRFrance
2220243432671721003.032431.04031.049.0FRFrance
2320243332062315349.025897.03123.039.0FRFrance
2420243232318717532.028842.03527.043.0FRFrance
2520243132603520267.031803.03930.048.0FRFrance
2620243033639328593.044193.05543.067.0FRFrance
2720242933956032592.046528.05949.069.0FRFrance
2820242835434245781.062903.08168.094.0FRFrance
2920242734736440234.054494.07160.082.0FRFrance
.................................
207019852132609619621.032571.04735.059.0FRFrance
207119852032789620885.034907.05138.064.0FRFrance
207219851934315432821.053487.07859.097.0FRFrance
207319851834055529935.051175.07455.093.0FRFrance
207419851733405324366.043740.06244.080.0FRFrance
207519851635036236451.064273.09166.0116.0FRFrance
207619851536388145538.082224.011683.0149.0FRFrance
20771985143134545114400.0154690.0244207.0281.0FRFrance
20781985133197206176080.0218332.0357319.0395.0FRFrance
20791985123245240223304.0267176.0445405.0485.0FRFrance
20801985113276205252399.0300011.0501458.0544.0FRFrance
20811985103353231326279.0380183.0640591.0689.0FRFrance
20821985093369895341109.0398681.0670618.0722.0FRFrance
20831985083389886359529.0420243.0707652.0762.0FRFrance
20841985073471852432599.0511105.0855784.0926.0FRFrance
20851985063565825518011.0613639.01026939.01113.0FRFrance
20861985053637302592795.0681809.011551074.01236.0FRFrance
20871985043424937390794.0459080.0770708.0832.0FRFrance
20881985033213901174689.0253113.0388317.0459.0FRFrance
208919850239758680949.0114223.0177147.0207.0FRFrance
209019850138548965918.0105060.0155120.0190.0FRFrance
209119845238483060602.0109058.0154110.0198.0FRFrance
2092198451310172680242.0123210.0185146.0224.0FRFrance
20931984503123680101401.0145959.0225184.0266.0FRFrance
2094198449310107381684.0120462.0184149.0219.0FRFrance
209519844837862060634.096606.0143110.0176.0FRFrance
209619844737202954274.089784.013199.0163.0FRFrance
209719844638733067686.0106974.0159123.0195.0FRFrance
20981984453135223101414.0169032.0246184.0308.0FRFrance
209919844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2099 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202504 3 375118 356288.0 393948.0 560 532.0 \n", + "1 202503 3 253215 239337.0 267093.0 378 357.0 \n", + "2 202502 3 257247 242991.0 271503.0 384 363.0 \n", + "3 202501 3 231549 214627.0 248471.0 345 320.0 \n", + "4 202452 3 201726 185870.0 217582.0 302 278.0 \n", + "5 202451 3 201697 187843.0 215551.0 302 281.0 \n", + "6 202450 3 136694 126369.0 147019.0 205 190.0 \n", + "7 202449 3 108487 99037.0 117937.0 163 149.0 \n", + "8 202448 3 87381 78687.0 96075.0 131 118.0 \n", + "9 202447 3 76286 67626.0 84946.0 114 101.0 \n", + "10 202446 3 56399 49006.0 63792.0 85 74.0 \n", + "11 202445 3 47347 40843.0 53851.0 71 61.0 \n", + "12 202444 3 36039 30122.0 41956.0 54 45.0 \n", + "13 202443 3 46572 39928.0 53216.0 70 60.0 \n", + "14 202442 3 67785 60009.0 75561.0 102 90.0 \n", + "15 202441 3 79435 71386.0 87484.0 119 107.0 \n", + "16 202440 3 84965 76555.0 93375.0 127 114.0 \n", + "17 202439 3 91660 82937.0 100383.0 137 124.0 \n", + "18 202438 3 91786 82903.0 100669.0 138 125.0 \n", + "19 202437 3 56460 49319.0 63601.0 85 74.0 \n", + "20 202436 3 33657 27906.0 39408.0 50 41.0 \n", + "21 202435 3 27404 22036.0 32772.0 41 33.0 \n", + "22 202434 3 26717 21003.0 32431.0 40 31.0 \n", + "23 202433 3 20623 15349.0 25897.0 31 23.0 \n", + "24 202432 3 23187 17532.0 28842.0 35 27.0 \n", + "25 202431 3 26035 20267.0 31803.0 39 30.0 \n", + "26 202430 3 36393 28593.0 44193.0 55 43.0 \n", + "27 202429 3 39560 32592.0 46528.0 59 49.0 \n", + "28 202428 3 54342 45781.0 62903.0 81 68.0 \n", + "29 202427 3 47364 40234.0 54494.0 71 60.0 \n", + "... ... ... ... ... ... ... ... \n", + "2070 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2071 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2072 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2073 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2074 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2075 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2076 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2077 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2078 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2079 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2080 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2081 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2082 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2083 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2084 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2085 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2086 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2087 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2088 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2089 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2090 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2091 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2092 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2093 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2094 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2095 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2096 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2097 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2098 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2099 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 588.0 FR France \n", + "1 399.0 FR France \n", + "2 405.0 FR France \n", + "3 370.0 FR France \n", + "4 326.0 FR France \n", + "5 323.0 FR France \n", + "6 220.0 FR France \n", + "7 177.0 FR France \n", + "8 144.0 FR France \n", + "9 127.0 FR France \n", + "10 96.0 FR France \n", + "11 81.0 FR France \n", + "12 63.0 FR France \n", + "13 80.0 FR France \n", + "14 114.0 FR France \n", + "15 131.0 FR France \n", + "16 140.0 FR France \n", + "17 150.0 FR France \n", + "18 151.0 FR France \n", + "19 96.0 FR France \n", + "20 59.0 FR France \n", + "21 49.0 FR France \n", + "22 49.0 FR France \n", + "23 39.0 FR France \n", + "24 43.0 FR France \n", + "25 48.0 FR France \n", + "26 67.0 FR France \n", + "27 69.0 FR France \n", + "28 94.0 FR France \n", + "29 82.0 FR France \n", + "... ... ... ... \n", + "2070 59.0 FR France \n", + "2071 64.0 FR France \n", + "2072 97.0 FR France \n", + "2073 93.0 FR France \n", + "2074 80.0 FR France \n", + "2075 116.0 FR France \n", + "2076 149.0 FR France \n", + "2077 281.0 FR France \n", + "2078 395.0 FR France \n", + "2079 485.0 FR France \n", + "2080 544.0 FR France \n", + "2081 689.0 FR France \n", + "2082 722.0 FR France \n", + "2083 762.0 FR France \n", + "2084 926.0 FR France \n", + "2085 1113.0 FR France \n", + "2086 1236.0 FR France \n", + "2087 832.0 FR France \n", + "2088 459.0 FR France \n", + "2089 207.0 FR France \n", + "2090 190.0 FR France \n", + "2091 198.0 FR France \n", + "2092 224.0 FR France \n", + "2093 266.0 FR France \n", + "2094 219.0 FR France \n", + "2095 176.0 FR France \n", + "2096 163.0 FR France \n", + "2097 195.0 FR France \n", + "2098 308.0 FR France \n", + "2099 213.0 FR France \n", + "\n", + "[2099 rows x 10 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nos données utilisent une convention inhabituelle: le numéro de\n", + "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", + "de nombre entier. C'est comme ça que Pandas les interprète.\n", + " \n", + "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", + "semaine. Il faut lui fournir les dates de début et de fin de\n", + "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", + "\n", + "Comme la conversion des semaines est devenu assez complexe, nous\n", + "écrivons une petite fonction Python pour cela. Ensuite, nous\n", + "l'appliquons à tous les points de nos donnés. Les résultats vont\n", + "dans une nouvelle colonne 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int)\n", + " year = int(year_and_week_str[:4])\n", + " week = int(year_and_week_str[4:])\n", + " w = isoweek.Week(year, week)\n", + " return pd.Period(w.day(0), 'W')\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il restent deux petites modifications à faire.\n", + "\n", + "Premièrement, nous définissons les périodes d'observation\n", + "comme nouvel index de notre jeux de données. Ceci en fait\n", + "une suite chronologique, ce qui sera pratique par la suite.\n", + "\n", + "Deuxièmement, nous trions les points par période, dans\n", + "le sens chronologique." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", + "le début de la période qui suit, la différence temporelle doit être\n", + "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", + "d'une seconde.\n", + "\n", + "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n", + "entre lesquelles il manque une semaine.\n", + "\n", + "Nous reconnaissons ces dates: c'est la semaine sans observations\n", + "que nous avions supprimées !" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" + ] + } + ], + "source": [ + "periods = sorted_data.index\n", + "for p1, p2 in zip(periods[:-1], periods[1:]):\n", + " delta = p2.to_timestamp() - p1.end_time\n", + " if delta > pd.Timedelta('1s'):\n", + " print(p1, p2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un premier regard sur les données !" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc_up'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc_up'][-200:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", + "entre deux années civiles, nous définissons la période de référence\n", + "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", + "1er août de l'année $N+1$.\n", + "\n", + "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", + "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", + "de référence: à la place du 1er août de chaque année, nous utilisons le\n", + "premier jour de la semaine qui contient le 1er août.\n", + "\n", + "Comme l'incidence de syndrome grippal est très faible en été, cette\n", + "modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1985." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + " first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1985,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", + "\n", + "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + " year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_august_week[:-1],\n", + " first_august_week[1:]):\n", + " one_year = sorted_data['inc_up'][week1:week2-1]\n", + " assert abs(len(one_year)-52) < 2\n", + " yearly_incidence.append(one_year.sum())\n", + " year.append(week2.year)\n", + "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici les incidences annuelles." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAD8CAYAAABU4IIeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHnhJREFUeJzt3X+Q1PWd5/HnCwfBJKBAwPBDHFLhuKC51TAFbmVvL+oFyGUrkDo1RKPchSqyxuya261TWLniKpK7cHUV9zgvrlY0Yogi58aSXSWEoKl19wgIITmDhB024UZWdDCDgqkCHXjfH/2Ztacz09PT/e3pb8+8HlVd/e1Pfz6f/sxH+b778/18vp9WRGBmZpaVUY1ugJmZDS8OLGZmlikHFjMzy5QDi5mZZcqBxczMMuXAYmZmmXJgMTOzTDmwmJlZphxYzMwsUy2VZJJ0EfAt4HIggC8Ah4DHgVbgCHBDRJxI+VcDK4CzwB9HxPaUPg94GLgAeAa4PSJC0hjgEWAe8GvgsxFxJJVZDqxJTVkXERtT+ixgMzAR+Alwc0S8Xe7veP/73x+tra2V/MlmZpbs27fv9YiYXGl+VbKli6SNwPMR8S1J5wPvAf4M6IqIr0taBUyIiDslzQUeA+YD04AfAv8sIs5K2gPcDvyYQmDZEBHbJH0J+BcR8YeSlgGfiYjPSpoI7AXaKAS0fcC8iDghaQvwvYjYLOkvgJ9FxH3l/o62trbYu3dvpX1jZmaApH0R0VZp/gEvhUkaD/w+8CBARLwdEW8AS4CNKdtGYGk6XgJsjogzEfEr4DAwX9JUYHxE7IpCNHukpExPXU8A10oSsAjYERFdaTS0A1ic3rsm5S39fDMza6BK5lg+CBwHvi1pv6RvSXovcHFEHANIz1NS/unAy0Xlj6a06em4NL1XmYjoBt4EJpWpaxLwRspbWpeZmTVQJYGlBfgocF9EXAn8BlhVJr/6SIsy6dWUKVdX78ZIKyXtlbT3+PHjfWUxM7MMVRJYjgJHI2J3ev0EhUDzWrq8RXruLMp/SVH5GcArKX1GH+m9ykhqAS4EusrU9TpwUcpbWlcvEfFARLRFRNvkyRXPPZmZWZUGDCwR8SrwsqQ5Kela4CVgK7A8pS0HnkrHW4FlksaklVuzgT3pctkpSVelOZJbSsr01HUd8Gyah9kOLJQ0QdIEYCGwPb33XMpb+vlmZtZAFS03Bv4I+G5aEfZL4N9TCEpbJK0AOoDrASLiQFqx9RLQDdwWEWdTPbfy7nLjbekBhYUB35F0mMJIZVmqq0vS3cALKd9XI6IrHd8JbJa0Dtif6mhKnSdP8+XH9nPvjVcyZdzYRjfHzKwmFS03Hi7yutx4zZMv8t09Hdw0fybrPvORRjfHzKyXwS43rnTEYnUwZ802znSf+6fXm3Z3sGl3B2NaRnFo3Scb2DIzs+p5S5cGev6Oq/n0FdMYO7rwn2Hs6FEsuWIaz995dYNbZmZWPQeWCnSePM0N9++i89TpTOudMn4s48a0cKb7HGNaRnGm+xzjxrR4nsXMmpoDSwU27GznhSNdbPhhe+Z1v/7WGW5acClPfulj3LTgUo6/dSbzzzAzG0qevC+jdA6kh+dAzGwkyXyvsJEsL3Mg9boUZ2ZWDw4sZeRlDqSel+LMzLLm5cYD6JkDuXH+TB7d08HxIRw1eDmymTUjz7HkWOfJ06x75iA/OPAqp985x9jRo1h02Qe461Mf9soxMxsynmMZRvJyKc7MbDB8KSznGnkpzsysGr4UZmZmZflSmJmZNZQDi5mZZcqBxczMMuXAYmZmmXJgMTOzTDmwmJlZphxYzMwsUw4sZmaWKQcWMzPLlAOLmZllyoHFzMwy5cBiZmaZcmAxM7NMObCYmVmmHFjMzCxTDixmZpYpBxYzM8uUA4uZmWXKgcXMzDJVUWCRdETSi5J+KmlvSpsoaYek9vQ8oSj/akmHJR2StKgofV6q57CkDZKU0sdIejyl75bUWlRmefqMdknLi9Jnpbztqez5tXeHmZnVajAjlqsj4oqIaEuvVwE7I2I2sDO9RtJcYBlwGbAY+Kak81KZ+4CVwOz0WJzSVwAnIuJDwD3A+lTXRGAtsACYD6wtCmDrgXvS559IdZiZWYPVcilsCbAxHW8Elhalb46IMxHxK+AwMF/SVGB8ROyKiAAeKSnTU9cTwLVpNLMI2BERXRFxAtgBLE7vXZPyln6+mZk1UKWBJYAfSNonaWVKuzgijgGk5ykpfTrwclHZoyltejouTe9VJiK6gTeBSWXqmgS8kfKW1mVmZg3UUmG+j0XEK5KmADsk/aJMXvWRFmXSqylTrq7ejSkEwpUAM2fO7CuLmZllqKIRS0S8kp47gScpzHe8li5vkZ47U/ajwCVFxWcAr6T0GX2k9yojqQW4EOgqU9frwEUpb2ldpW1/ICLaIqJt8uTJlfy5ZmZWgwEDi6T3ShrXcwwsBH4ObAV6VmktB55Kx1uBZWml1ywKk/R70uWyU5KuSnMkt5SU6anrOuDZNA+zHVgoaUKatF8IbE/vPZfyln6+mZk1UCWXwi4Gnkwrg1uARyPi+5JeALZIWgF0ANcDRMQBSVuAl4Bu4LaIOJvquhV4GLgA2JYeAA8C35F0mMJIZVmqq0vS3cALKd9XI6IrHd8JbJa0Dtif6jAzswZT4cv/yNDW1hZ79+5tdDPMzJqKpH1Ft5oMyHfem5lZphxYzMwsUw4sZmaWKQcWMzPLlAOLmZllyoHFzMwy5cBiZg3VefI0N9y/i85TpxvdFMuIA4uZNdSGne28cKSLDT9sb3RTLCOVbkJpZiNU58nTfPmx/dx745VMGTc2s3rnrNnGme5z//R60+4ONu3uYEzLKA6t+2Rmn2NDzyMWMyurXiOK5++4mk9fMY2xowunobGjR7Hkimk8f+fVmX6ODT2PWMysT/UeUUwZP5ZxY1o4032OMS2jONN9jnFjWjIdFVljeMRiZn0aihHF62+d4aYFl/Lklz7GTQsu5fhbZzKr2xrHIxYz69NQjCjuv/ndfQ3XLb08s3qtsRxYzKxfPSOKG+fP5NE9HRz3kmCrgLfNNzOzsrxtvpmZNZQDi5mZZcqBxczMMuXAYk3Pe02Z5YsDizW9Zt9ryoHRhhsHFmtac9Zso3XV02za3UFE4c7w1lVPM2fNtl758n7ibvbAaFbKgcWaVqV3huf1xF1pYDRrNr5B0prWQHeG53333OfvuJp1zxzkBwde5fQ75xg7ehSLLvsAd33qw41umllNPGKxplZur6m8757rTRhtuPKIxZpaub2mmuHE7S1TbDhyYLFhLe8nbm/CaMOR9wozM7OyvFeYWc7kfbmzWdYcWMzqLK/Lnc3qxXMsZnWS9+XONjJ0njzNlx/bz703XjlkC1c8YjGrk7wvd7aRoREj5ooDi6TzJO2X9Nfp9URJOyS1p+cJRXlXSzos6ZCkRUXp8yS9mN7bIEkpfYykx1P6bkmtRWWWp89ol7S8KH1Wytueyp5fW1eYZasZljvb8NXInR0GM2K5HThY9HoVsDMiZgM702skzQWWAZcBi4FvSjovlbkPWAnMTo/FKX0FcCIiPgTcA6xPdU0E1gILgPnA2qIAth64J33+iVSHWa6Uu4HTsuHFEX1r5Ii5osAiaQbwKeBbRclLgI3peCOwtCh9c0SciYhfAYeB+ZKmAuMjYlcU1jg/UlKmp64ngGvTaGYRsCMiuiLiBLADWJzeuyblLf18s9y4/+Y21i29nLnTxrNu6eW97lvJi2Y/MXtxRN8aOWKudPL+z4E7gHFFaRdHxDGAiDgmaUpKnw78uCjf0ZT2TjouTe8p83Kqq1vSm8Ck4vSSMpOANyKiu4+6zGwQik/M6z7zkUY3p2JeHDGwRt0gPGBgkfQHQGdE7JP08QrqVB9pUSa9mjLl6urdGGklhctvzJw5s68s1mCNWLVizX9i9iaeA2vUzg6VXAr7GPBpSUeAzcA1kjYBr6XLW6TnzpT/KHBJUfkZwCspfUYf6b3KSGoBLgS6ytT1OnBRyltaVy8R8UBEtEVE2+TJkyv4c22o+VJGYzT7qjUvjsivAQNLRKyOiBkR0UphUv7ZiPg8sBXoWaW1HHgqHW8FlqWVXrMoTNLvSZfNTkm6Ks2R3FJSpqeu69JnBLAdWChpQpq0XwhsT+89l/KWfr41Cf8eSWMNhxOzF0fkUy03SH4d2CJpBdABXA8QEQckbQFeArqB2yLibCpzK/AwcAGwLT0AHgS+I+kwhZHKslRXl6S7gRdSvq9GRFc6vhPYLGkdsD/VYU3ElzIaL++bdA7Em3jmkzehtLorN4dy15Mv8uieDs4/bxRvnz3HTfNnNtUEstlI4E0oLXfKzaH4UobZ8OMRiw2o2lVbpauOejTLqiMzK/CIxTJX7aqtZl91ZGbV8e7G1q9a73MYDquOzGzwPGKxfmUx4vAcitnI4xGL9SuLEcdwWA7qnQHMBscjFivLIw7vDGA2WF4VZtYPr2ozK/CqMLOMeFWbWXUcWMz64VVtZtXx5L1ZGc2+l5ZZI3iOxczMyvIci5mZNZQDi5mZZcqBxczMMuXAYmZmmXJgsdzrPHmaG+7fRadXZFXF/WdDzYHFcs9bqtTG/WdDzcuNLbe8pUpt3H+WFS83tmHDW6rUxv1njeLAYrnlLVVq4/6zRvGWLpZr3lKlNu4/awTPsWTAPwRlZsOZ51gawKtuzKxR8ric3IGlBnPWbKN11dNs2t1BBGza3UHrqqeZs2Zbo5tmNmTyeGIbSfL4xdaBpQZedWN50OgTex5PbMUa3T/1kucvtg4sNfCqG8uDRp3Y83xiK5b3wFetPH+x9aqwGlWy6saT+1YPpTdAbtrdwabdHUN2A+Tzd1zNumcO8oMDr3L6nXOMHT2KRZd9gLs+9eG6f3YlGt0/9ZbnL7YesdTo/pvbWLf0cuZOG8+6pZdz/82/vXBiuH5jssZq9DfWPJ/YoPH9MxR6vtg++aWPcdOCSzn+1plGNwnwiKWuhvs3JmusPJzY83yfTB76p96Kv8iuW3p5A1vSmwNLHeX9UoE1v0af2PN6YuvR6P4ZqQYMLJLGAn8DjEn5n4iItZImAo8DrcAR4IaIOJHKrAZWAGeBP46I7Sl9HvAwcAHwDHB7RISkMcAjwDzg18BnI+JIKrMcWJOasy4iNqb0WcBmYCLwE+DmiHi7hr7I3Ej4xjQcNPMcWN5P7I3m/mmMSuZYzgDXRMTvAFcAiyVdBawCdkbEbGBneo2kucAy4DJgMfBNSeeluu4DVgKz02NxSl8BnIiIDwH3AOtTXROBtcACYD6wVtKEVGY9cE/6/BOpjtzJ6zVQe5fnwKyc4bpcuZ4GtaWLpPcAfwvcSmGE8fGIOCZpKvCjiJiTRitExH9NZbYD/5nCqOa5iPjnKf1zqfwXe/JExC5JLcCrwGQKAerjEfHFVOZ+4EcURirHgQ9ERLek303lF5Vrv7fN71szf2OvhbeVt0qsefJFvrung5vmz2TdZz7S6OY0RF22dJF0nqSfAp3AjojYDVwcEccA0vOUlH068HJR8aMpbXo6Lk3vVSYiuoE3gUll6poEvJHyltZlgzRSv7GPhFVDVr1muU8njyoKLBFxNiKuAGYA8yWVu1ipvqook15NmXJ19W6MtFLSXkl7jx8/3leWYa+/ofxI/4fjOTArx188qjeo+1gi4g0Kl6IWA6+lS2Ck586U7ShwSVGxGcArKX1GH+m9yqRLYRcCXWXqeh24KOUtrau0zQ9ERFtEtE2ePHkwf+6w0d+IxP9wPAdm/fMXj+pVsipsMvBORLwh6QLgX1OYON8KLAe+np6fSkW2Ao9K+gYwjcIk/Z6IOCvpVJr43w3cAvzPojLLgV3AdcCzabXYduC/FE3YLwRWp/eeS3k3l3y+JQPdR+N/OF41ZOV5uXJ1KrmPZSqwMa3sGgVsiYi/lrQL2CJpBdABXA8QEQckbQFeArqB2yLibKrrVt5dbrwtPQAeBL4j6TCFkcqyVFeXpLuBF1K+r0ZEVzq+E9gsaR2wP9VhRSq5j8b/cMz65y8e1fEPfQ1zdz35Io/u6eD880bx9tlzI3pli5lVZ7Crwnzn/TDnEYmZDTWPWMzMrCz/NLGZmTWUA4tZjeq95Ye3FLFm48BiVqN671wwUndGsOblORazKtV7rzHvZWZ54TkWsyFS750LvDOCNSsHFrMq1XvnAu+MYM3KgcWsBvXea8x7mdWfF0dkz3MsZjai+fdWBuY7783MKjDQJq1WPV8KM7O6yuulJi+OqB8HFjOrq7zeh+PFEfXjS2FmVhfNcKkpi01aO0+e5suP7efeG690UEo8eW9mddF58nS/vwc0nE7AI2Hy35P3ZpYLw/1SUzOMyBrFcyxmVjfD+T4cT/73zyMWM6ub4fzTvsN9RFYLBxYzsyr5F1r75sl7M7Mcy8OqM+9ubGY2jOT1PqByfCnMzCyHmnnVmUcsZmY51MyrzhxYzMzqqNq90pp51ZkDi5lZHdUyR9Ks9wF5VZiZWR2UzpH0aIY5klJeFWZmlgPNPEdSKwcWM7M6aOY5klp5ubGZWZ2M1DvzPcdiNcvDncFmVj+eY7Eh14x3BptZ/fhSmFWtme8MNrP6GXDEIukSSc9JOijpgKTbU/pESTsktafnCUVlVks6LOmQpEVF6fMkvZje2yBJKX2MpMdT+m5JrUVllqfPaJe0vCh9Vsrbnsqen02XWKVG8qoXM+tfJZfCuoE/jYgPA1cBt0maC6wCdkbEbGBnek16bxlwGbAY+Kak81Jd9wErgdnpsTilrwBORMSHgHuA9amuicBaYAEwH1hbFMDWA/ekzz+R6hiRqr2zt1YjedWLmfVvwMASEcci4ifp+BRwEJgOLAE2pmwbgaXpeAmwOSLORMSvgMPAfElTgfERsSsKKwYeKSnTU9cTwLVpNLMI2BERXRFxAtgBLE7vXZPyln7+iNPIOY5mvTPYzOpnUHMs6RLVlcBu4OKIOAaF4CNpSso2HfhxUbGjKe2ddFya3lPm5VRXt6Q3gUnF6SVlJgFvRER3H3WVtnklhVESM2fOHMyfm3t5mOMYzr8QaGbVqXhVmKT3AX8JfCUiTpbL2kdalEmvpky5unonRjwQEW0R0TZ58uS+sjQtz3HYSNCoS71WvYoCi6TRFILKdyPieyn5tXR5i/TcmdKPApcUFZ8BvJLSZ/SR3quMpBbgQqCrTF2vAxelvKV1jRie47CRwMvZm08lq8IEPAgcjIhvFL21FehZpbUceKoofVla6TWLwiT9nnTZ7JSkq1Kdt5SU6anrOuDZNA+zHVgoaUKatF8IbE/vPZfyln7+iOI5Dhuu5qzZRuuqp9m0u4OIwqXe1lVPM2fNtkY3zQYw4J33kn4PeB54Eei5oP9nFOZZtgAzgQ7g+ojoSmXuAr5AYUXZVyJiW0pvAx4GLgC2AX8UESFpLPAdCvM3XcCyiPhlKvOF9HkAX4uIb6f0DwKbgYnAfuDzEVH2rOo7782aR+fJ06x75iA/OPAqp985x9jRo1h02Qe461Mf9qh8iA32zvsBJ+8j4m/pe04D4Np+ynwN+Fof6XuB35rhjYjTwPX91PUQ8FAf6b+ksATZzIYhX+ptXr7z3sxya6Ru4tjsvAmlmZmV5U0ozcysoRxYzMwsUw4sZmaWKQcWMzPLlAOLmZllyoHFzMwy5cBiZmaZcmAxM7NMObCYmVmmHFjMzCxTDixmZpYpBxYzM8uUA4uZmWXKgcXMzDLlwGJmZplyYDEzs0w5sJiZWaYcWMzMLFMOLGZmlikHFjMzy5QDi5mZZcqBxczMMuXAYmZmmXJgMTOzTDmwmJlZphxYzMwsUw4sZmaWKQcWMzPLlAOLmZllasDAIukhSZ2Sfl6UNlHSDknt6XlC0XurJR2WdEjSoqL0eZJeTO9tkKSUPkbS4yl9t6TWojLL02e0S1pelD4r5W1PZc+vvSvMzCwLlYxYHgYWl6StAnZGxGxgZ3qNpLnAMuCyVOabks5LZe4DVgKz06OnzhXAiYj4EHAPsD7VNRFYCywA5gNriwLYeuCe9PknUh1mZpYDAwaWiPgboKskeQmwMR1vBJYWpW+OiDMR8SvgMDBf0lRgfETsiogAHikp01PXE8C1aTSzCNgREV0RcQLYASxO712T8pZ+vpmZNVi1cywXR8QxgPQ8JaVPB14uync0pU1Px6XpvcpERDfwJjCpTF2TgDdS3tK6fouklZL2Stp7/PjxQf6ZZmY2WFlP3quPtCiTXk2ZcnX99hsRD0REW0S0TZ48ub9sZmYN0XnyNDfcv4vOU6cb3ZTMVBtYXkuXt0jPnSn9KHBJUb4ZwCspfUYf6b3KSGoBLqRw6a2/ul4HLkp5S+syM2sqG3a288KRLjb8sL3RTclMtYFlK9CzSms58FRR+rK00msWhUn6Pely2SlJV6U5kltKyvTUdR3wbJqH2Q4slDQhTdovBLan955LeUs/38ysKcxZs43WVU+zaXcHEbBpdwetq55mzpptjW5azSpZbvwYsAuYI+mopBXA14FPSGoHPpFeExEHgC3AS8D3gdsi4myq6lbgWxQm9P8B6Om9B4FJkg4Df0JaYRYRXcDdwAvp8dWUBnAn8CepzKRUh5lZ03j+jqv59BXTGDu6cBoeO3oUS66YxvN3Xt3gltWuZaAMEfG5ft66tp/8XwO+1kf6XuDyPtJPA9f3U9dDwEN9pP+SwhJkM7OmNGX8WMaNaeFM9znGtIziTPc5xo1pYcq4sY1uWs0GDCxmZlYfr791hpsWXMqN82fy6J4Ojg+TCXwVpixGhra2tti7d2+jm2Fm1lQk7YuItkrze68wMzPLlAOLmZllyoHFzMwy5cBiZmaZcmAxM7NMObCYmVmmRtRyY0nHgf/Xz9vvp7APWV65fbVx+2rj9tWm2dt3aURUvIvviAos5UjaO5h12kPN7auN21cbt682I619vhRmZmaZcmAxM7NMObC864FGN2AAbl9t3L7auH21GVHt8xyLmZllyiMWMzPL1LANLJIektQp6edFab8jaZekFyX9laTxKX20pI0p/aCk1UVlfiTpkKSfpseUBrTvfEnfTuk/k/TxojLzUvphSRvSL3RmIsM2Zt6Hki6R9Fz673VA0u0pfaKkHZLa0/OEojKrUz8dkrSoKD3zPsy4fQ3vP0mTUv63JN1bUlfD+2+A9uWh/z4haV/qp32SrimqKw/9V659g++/iBiWD+D3gY8CPy9KewH4V+n4C8Dd6fhGYHM6fg9wBGhNr38EtDW4fbcB307HU4B9wKj0eg/wu4Ao/CrnJ3PYxsz7EJgKfDQdjwP+HpgL/DdgVUpfBaxPx3OBnwFjgFkUfsX0vHr1Ycbty0P/vRf4PeAPgXtL6spD/5VrXx7670pgWjq+HPjHnPVfufYNuv8y6+g8PoBWep8UT/LuvNIlwEvp+HPAX1H44bNJ6T/CxHr9T1lF+/4X8PmifDsp/ILmVOAXRemfA+7PUxvr3YdFn/cUhZ/JPgRMTWlTgUPpeDWwuij/9vSPue59WEv78tJ/Rfn+HUUn7rz0X3/ty1v/pXQBv6bwJSJX/Vfavmr7b9heCuvHz4FPp+PrKZwYAZ4AfgMcAzqA/x4RXUXlvp2GgP8pi2FqFe37GbBEUoukWcC89N504GhR+aMprZ4G28YedetDSa0UvnHtBi6OiGMA6bln2D4deLmoWE9f1b0Pa2xfj0b3X3/y0n8DyVP//Vtgf0ScIZ/9V9y+HoPqv5EWWL4A3CZpH4Xh4dspfT5wFphG4TLEn0r6YHrvpoj4CPAv0+PmBrTvIQr/w+0F/hz4P0A3hW8Wpeq9zG+wbYQ69qGk9wF/CXwlIk6Wy9pHWpRJz0QG7YN89F+/VfSR1oj+Kyc3/SfpMmA98MWepD6yNaz/+mgfVNF/IyqwRMQvImJhRMwDHqNwHRsKcyzfj4h3IqIT+DugLZX5x/R8CniUQhAa0vZFRHdE/IeIuCIilgAXAe0UTuQziqqYAbxSr/ZV2ca69aGk0RT+0Xw3Ir6Xkl+TNDW9PxXoTOlH6T2C6umruvVhRu3LS//1Jy/916+89J+kGcCTwC0R0XPuyU3/9dO+qvpvRAWWntUMkkYBa4C/SG91ANeo4L3AVcAv0mWd96cyo4E/oHApaEjbJ+k9qV1I+gTQHREvpaHsKUlXpeHpLRSupdbNYNtYrz5Mf++DwMGI+EbRW1uB5el4Oe/2x1ZgmaQx6VLdbGBPvfowq/blqP/6lKP+66+eXPSfpIuApynMo/1dT+a89F9/7au6/7KeJMrLg8K36WPAOxS+FawAbqcwMf/3wNd5dxL6fcD/Bg4ALwH/MaW/l8Lqpv+b3vsfpJU6Q9y+VgqTbgeBH1LYabSnnrb0H/ofgHt7yuSljfXqQworgCLV+9P0+DcUFl/spDBa2klahJHK3JX66RBFK2/q0YdZtS9n/XcE6ALeSv8/zM1Z//1W+/LSfxS+hP2mKO9PgSl56b/+2ldt//nOezMzy9SIuhRmZmb158BiZmaZcmAxM7NMObCYmVmmHFjMzCxTDixmZpYpBxYzM8uUA4uZmWXq/wNH+xFUiPPs/AAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.plot(style='*')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2021 910589.0\n", + "2014 1911251.0\n", + "1991 1980417.0\n", + "1995 2090418.0\n", + "2020 2226761.0\n", + "2022 2338967.0\n", + "2019 2481665.0\n", + "2012 2536425.0\n", + "2017 2593378.0\n", + "2006 2719258.0\n", + "2003 2734405.0\n", + "1992 2921510.0\n", + "1993 2986279.0\n", + "2018 2991551.0\n", + "2001 3040167.0\n", + "1988 3131459.0\n", + "2016 3177327.0\n", + "2007 3181219.0\n", + "2011 3205326.0\n", + "2023 3217613.0\n", + "1987 3253239.0\n", + "2008 3403787.0\n", + "1998 3410332.0\n", + "2002 3688034.0\n", + "1994 3765327.0\n", + "1996 3837601.0\n", + "1997 3923810.0\n", + "2009 3965230.0\n", + "2015 4002562.0\n", + "2024 4075202.0\n", + "2004 4133721.0\n", + "2000 4288499.0\n", + "2005 4326419.0\n", + "1999 4350757.0\n", + "2010 4601495.0\n", + "2013 4657613.0\n", + "1990 5675038.0\n", + "1986 5758913.0\n", + "1989 5840764.0\n", + "dtype: float64" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", + " française, sont assez rares: il y en eu trois au cours des 35 dernières années." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAFWtJREFUeJzt3Xu0XGV5x/HvY4IQOIBo9KABjReqIhGQgxapeAJeEFCrUlFRwarxLtW4upClUrVUvGCrLVajIixUjop2ecF6KfbgHUlAjYCi1SgEARFFAyhEnv7x7tOcRTNJPLPncub9ftZiMbP3nr3f58xMfvO++xaZiSSpXncadAMkSYNlEEhS5QwCSaqcQSBJlTMIJKlyBoEkVc4gkKTKGQSSVDmDQJIqt7CfG1u8eHEuXbq01XXedNNN7LTTTq2uc9jVWDNYd21qrLtTzWvWrLk+M+/eq+32NQiWLl3K6tWrW13n9PQ0k5OTra5z2NVYM1h3bWqsu1PNEfHzXm7XoSFJqpxBIEmVMwgkqXIGgSRVziCQpMptNQgi4oyIuC4ifjBr2l0j4ssR8ePm/7v1tpmSpF7Zlh7BmcDhd5h2InB+Zu4FnN88lyTNQ1sNgsz8KnDDHSY/GTireXwW8Nctt0uS1CexLfcsjoilwOcyc5/m+W8z8y6z5v8mMzc7PBQRK4AVAOPj4wdMTU210OxNNmzYwNjYWKvrHHajXPPa9Td2nDe+CK69pTfbXbZk196suAWj/H5vSY11d6p5+fLlazJzolfb7fmZxZm5ClgFMDExkW2fKejZh6Pl+BPP6zhv5bKNnLa2Nx/ZdcdO9mS9bRjl93tLaqx7UDXP9aihayPingDN/69rr0mSpH6aaxB8BjiueXwc8Ol2miNJ6rdtOXz0HOBbwAMj4qqIeD5wKvDYiPgx8NjmuSRpHtrqgGtmPrPDrMNaboskaQA8s1iSKmcQSFLlDAJJqpxBIEmVMwgkqXIGgSRVziCQpMoZBJJUOYNAkipnEEhS5QwCSaqcQSBJlTMIJKlyBoEkVc4gkKTKGQSSVDmDQJIqZxBIUuUMAkmqnEEgSZUzCCSpcgaBJFXOIJCkyhkEklQ5g0CSKmcQSFLlDAJJqpxBIEmVMwgkqXIGgSRVziCQpMoZBJJUua6CICJeFRGXRsQPIuKciNihrYZJkvpjzkEQEUuAVwITmbkPsAB4RlsNkyT1R7dDQwuBRRGxENgRuLr7JkmS+ikyc+4vjjgBOAW4BfhSZh67mWVWACsAxsfHD5iamprz9jZnw4YNjI2NtbrOYTfKNa9df2PHeeOL4NpberPdZUt27c2Kt8GWaobe1T3ImrfFKH/OO+lU8/Lly9dk5kSvtjvnIIiI3YBPAscAvwU+AZybmR/u9JqJiYlcvXr1nLbXyfT0NJOTk62uc9iNcs1LTzyv47yVyzZy2tqFPdnuulOP7Ml6t8WWaobe1T3ImrfFKH/OO+lUc0T0NAi6GRp6DPCzzPxVZt4GfAp4ZDvNkiT1SzdB8AvgLyNix4gI4DDg8naaJUnqlzkHQWZeCJwLXAysbda1qqV2SZL6pKuBx8w8GTi5pbZIkgbAM4slqXIGgSRVziCQpMoZBJJUOYNAkipnEEhS5QwCSaqcQSBJlTMIJKlyBoEkVc4gkKTKGQSSVDmDQJIqZxBIUuUMAkmqnEEgSZUzCCSpcgaBJFXOIJCkyhkEklQ5g0CSKmcQSFLlDAJJqpxBIEmVMwgkqXIGgSRVziCQpMoZBJJUOYNAkipnEEhS5QwCSaqcQSBJlTMIJKlyXQVBRNwlIs6NiB9GxOURcVBbDZMk9cfCLl//LuALmXl0RNwZ2LGFNkmS+mjOQRARuwCHAMcDZOatwK3tNEuS1C+RmXN7YcR+wCrgMmBfYA1wQmbedIflVgArAMbHxw+YmprqqsF3tGHDBsbGxlpd57Ab5ZrXrr+x47zxRXDtLb3Z7rIlu/ZmxdtgSzVD7+oeZM3bYpQ/5510qnn58uVrMnOiV9vtJggmgG8DB2fmhRHxLuB3mfn6Tq+ZmJjI1atXz62lHUxPTzM5OdnqOofdKNe89MTzOs5buWwjp63tdjRz89ademRP1rsttlQz9K7uQda8LUb5c95Jp5ojoqdB0M3O4quAqzLzwub5ucDDum+SJKmf5hwEmXkNcGVEPLCZdBhlmEiSNI902998BfCR5oihnwLP675JkqR+6ioIMvO7QM/GrSRJveeZxZJUOYNAkipnEEhS5QwCSaqcQSBJlTMIJKlyBoEkVc4gkKTKGQSSVDmDQJIqZxBIUuUMAkmqnEEgSZUzCCSpcgaBJFWuNzeAVSs63ct25bKNHL+V+9zqz7O1+wZLo8wegSRVziCQpMoZBJJUOYNAkipnEEhS5QwCSaqcQSBJlTMIJKlyBoEkVc4gkKTKGQSSVDmDQJIqZxBIUuUMAkmqnEEgSZUzCCSpcl0HQUQsiIhLIuJzbTRIktRfbfQITgAub2E9kqQB6CoIImIP4EjgA+00R5LUb5GZc39xxLnAW4Cdgddk5lGbWWYFsAJgfHz8gKmpqTlvb3M2bNjA2NhYq+scFmvX37jZ6eOL4Npb+tyYIWDd7Vq2ZNf2V9qiUf5ud9Kp5uXLl6/JzIlebXfON6+PiKOA6zJzTURMdlouM1cBqwAmJiZycrLjonMyPT1N2+scFp1uUL9y2UZOWzvnt27esu52rTt2svV1tmmUv9udDKrmboaGDgaeFBHrgCng0Ij4cCutkiT1zZyDIDNfm5l7ZOZS4BnAVzLz2a21TJLUF55HIEmVa2XgMTOngek21iVJ6i97BJJUOYNAkipnEEhS5QwCSaqcQSBJlTMIJKlyBoEkVc4gkKTKGQSSVDmDQJIqZxBIUuUMAkmqnEEgSZUzCCSpcgaBJFWuvhvASqrW0g73Ae+HdaceObBtb409AkmqnEEgSZUzCCSpcgaBJFXOIJCkyhkEklQ5g0CSKmcQSFLlDAJJqpxBIEmVMwgkqXIGgSRVziCQpMoZBJJUOYNAkipnEEhS5QwCSarcnIMgIvaMiP+OiMsj4tKIOKHNhkmS+qObW1VuBFZm5sURsTOwJiK+nJmXtdQ2SVIfzLlHkJm/zMyLm8e/By4HlrTVMElSf0Rmdr+SiKXAV4F9MvN3d5i3AlgBMD4+fsDU1FTX25ttw4YNjI2NtbrOYbF2/Y2bnT6+CK69pc+NGQLWPTqWLdl1q8v04rvd6TvVD93UvHz58jWZOdGLdkELQRARY8AFwCmZ+aktLTsxMZGrV6/uant3ND09zeTkZKvrHBZLTzxvs9NXLtvIaWu7GdWbn6x7dKw79citLtOL73an71Q/dFNzRPQ0CLo6aigitgM+CXxkayEgSRpO3Rw1FMAHgcsz853tNUmS1E/d9AgOBp4DHBoR323+O6KldkmS+mTOA4+Z+XUgWmyLJGkAPLNYkipnEEhS5QwCSaqcQSBJlTMIJKlyBoEkVc4gkKTKGQSSVDmDQJIqZxBIUuUMAkmqnEEgSZUzCCSpcgaBJFVu3tz/bku3bTy+x7ef25ZbzEnSfGWPQJIqZxBIUuUMAkmqnEEgSZUzCCSpcgaBJFXOIJCkyhkEklQ5g0CSKmcQSFLlDAJJqpxBIEmVMwgkqXIGgSRVziCQpMoZBJJUOYNAkirXVRBExOER8aOI+ElEnNhWoyRJ/TPnIIiIBcDpwBOAvYFnRsTebTVMktQf3fQIHg78JDN/mpm3AlPAk9tpliSpXyIz5/bCiKOBwzPzBc3z5wCPyMyX32G5FcCK5ukDgR/NvbmbtRi4vuV1Drsaawbrrk2NdXeq+T6ZefdebXRhF6+NzUz7f6mSmauAVV1sZ8uNiFidmRO9Wv8wqrFmsO5Bt6Pfaqx7UDV3MzR0FbDnrOd7AFd31xxJUr91EwQXAXtFxH0j4s7AM4DPtNMsSVK/zHloKDM3RsTLgS8CC4AzMvPS1lq27Xo27DTEaqwZrLs2NdY9kJrnvLNYkjQaPLNYkipnEEhS5QwCSapc1UEQEUsiYsmg29FPEXG/iHhVRBw66Lb0U41111gzWPdc6q4yCCJiaURcAHwBeHtEPGrQbeqHiPgr4MuUa0O9OCJeMuAm9UWNdddYM1g3c6y7miCIiB1mPX0YcFFmLqMc/vrKiFjWLLe5M6bnpYg4NCLu2zwO4FDg5Mx8IXAacERETM6aPxJqrLvGmsG6m8dd1z3SQRARu0TEeyPiCuAdEXGfZtZTgF80j6eAnwAvmHlZn5vZuojYOyK+D/wD8KGIODTLccJ7A7sDZOaFwDeB5828bBBtbVONdddYM1g3Ldc90kEAHA7sQPkj3Qq8ISIWUbpQTwTIzD8C5wKPap7fPpimzl1E7BERu8yadAzwycw8hBJ0z4qIvYCP0tTd+A9gn4jY3rrnhxprBuueNakndc/7IIhiYUQ8PyK+FhEnRMT9m9kPAG7NzI3APwO/AZ4NfAm4Z0TctVnuCuDKiDio7wV0ISIeHBGfB74OvCkiZi4D/gdgx+bxx4FrgCMpvxLuNqtndAPlarD79q/V3aux7hprBuumT3XP+yBoukWPBp4LvA3YHnh/M/sa4LomFa+k/GHuT/ljXsamy2NvB/y6mT7UImKnWU/3A67KzKXAV4B3NNNvAP4YETtn5g3Aj4F7Uer7JvDqZrk7A38C1vW+5d2pse4aawbrbvS17nkXBBFxUES8NSKOb54H8GDgC5n52cx8G3CfiHgksJ6Sng9uXn45MNZM+zfKDpUnUkJkHPheX4vZRhGxW0ScGREXAadGxN2bupcB34iIyMzPAL+NiCMpPZydm/k0z+8B3E7pGd0jIt4PnANszMzr+l3Ttqix7hprBusedN3zKggi4iHAvwO/B54eEa+m1LAE+H3zBwQ4E3gW5R/2jcAjm+kXU/au35yZXwVOBI4HDgbenJm3z1rHMDmEUscRlB0/JwG7UC72t3tuumDUWZS6v0P5Gz0BIDO/1axjYWZeDrwIuBT4p8x8HsOrxrprrBmse7B1Z+ZQ/kf55f4CShdpYTPtncAJzeMJ4N3A0cBjgC/Oeu2elG4VlH/4L6HcHW1/4NPAPWctG4OudVZbFjRv5AWUYavFzfSPA69sHt8XOLWZfyBlDHHBrL/Zr5r1LKH0gF4OfAh4D7DToGu07nprtu7hrXsoewQRsS9lh+6TgZOB1zWz1lPulQwl9b4BPA04H9g9Ih4aEdtl2R+wPiIelZlfoVza9a3Ap4BzMvOXM9vK5q88JI4CngS8ETiIss8DylFOM72aK4GvAU/IzIsovyKWA2TmBuBC4MDMXA88hzLkdQ3wusy8qU91/LlqrLvGmsG6h7Lubm5V2ZqI2B+4KTOvaCY9HLgiM4+PiIcBp0TEBDANPD4idszMmyPie8DTKcfPfhR4IfDuiLgFWAv8rFnfe4GPZuaN/atq85oxv4yIAyldva8B52U5jPUvgJ9m5lci4meUs54fB6wBnhIRizPz+oj4MXBTRNwb+Ffg2RFxD8pd4n5N6T6SmauB1X0vcjNqrLvGmqHeumfMx7oH2iOIiAdExNcpY/pviIjnNrNuB9Y1v+4vpnSDDgJuZtPhUgC3UbpLu1N+9f+gWdcFwPWZeRWUX/1DFgKHAGdQ9vQ/BnhLs8jtwBURsSgzf0ap+6GUMcGrKccQQzkaYAHl/fskpfZjgQOAVTlkx0tHxIKm7kdTurIjX3dTS0Y5u7Om93qXSuu+W/P/R1L+DZpfdfd5nGwn4KBZz48C3tU8fgQl4e4DHEfpQi1p5h1N2R8wM++CZvoOlGGhxbPWuT9w537WtZWadwRezKYey3bA3wEva+bvBny/afcxlDHCpbP+PquAxc3jtcCulP0en59dJ3CnQde6mff6BZQP80rKDrAa6t4ZOI9yxz6AV1VQ847N9/J8yslOI183m27q9TeUsfxpyn7IefkZ71uPICJOAn4KfD4ixpvJj6cc20+W06K/A7yCctzsnpRj/qHsC9iPcrTPWcBvIuJsyk7gHwH/Nz6WmZdk5q29r2jrImJ34HPAJHA2ZSfQUym9m40Amfkbyg7sV1LGC+/BpsNdv0o5R+LWzPwc8EHKWdCnU44iuG1mWzlEv5CiHA99PuVD/X7gcZR9OQdSfhmNZN2NRZRzWe4fEYspn+EFMJo1R8R2lP11RwNvz8ynNbP2n1lmFOvOzIyIXSlD0/+SmZOZ+SPKL/eZZeZP3X1M0ElKV+gDwMpm2qspY2gzy+wNXNk8PgV406x5FwH7N4+3pxxudeCgfxlspeZFwCNmPT+espPoOOA7s6bfC7i6efwyyqnjuzWv/yxw71nLLu5H21uo/S6zHv895ctwbAV1Hwe8HXg98HzKaf8XjXjNnwKOvcO0Y4ALR7zul1IOO4dNPYSnzse6+7mP4GuZ+X1KMj6pmXYGcHA0VwbNzMuAa5qdx28HHhwRqyLiPylnAl/WLPfHzPx8lj3rw+wPwHdmnZvwfUownEX5xThzkairgUsj4hGZeTrlIngfA64Cvp6ZMxfIIzOv72sFc5SZv41y0b8zKUNDiykf/PvP9AhHqe5Z7/GdgP+h/Eo+NDM/C9x3FGue5Qzg5Ig4LSKmI+INwLeBvZodnaNa9/XAoyLiWGBNRJxFGeN/UNMbnD91DyBF70Y5sWvf5vl/0Zwb0Dx/H3BI83h34CWU42+3H2T6d1nzzK+Fs9h0HsTZwFubx3el9JTu3TzfDtgH2GHQbW+h9pdSjo9eRdln8E3gDZRD40aubuATlKGgXSkHLbyOchDD60f5vaZczv0kypDux4ATgG+N+Hu9V1Pjuym/8p9FOdfpV5Re8J3mS90z/0D1VUS8hzLe/5rmCJqXUnYU70Y5UeyIzPxT3xvWQxGxB2Uc8BWZeUWUC+OtAB5COUHkuzncZ0B2JSL2owT6tyjjpPtQDokbmbojYozSk92eUuODKDsST6JcEngvRqzmGTOHdDeP96V8tr9BuRTCyL3XABGxPeV8p0sz86VRrhL6t5QLvf2c8m/ZvPhuDyoI9qOk6GGUL8zNlMs93AK8NzMv7XujeizKNY0eDbyWMo58FWWY7Bjgh1kOkx1ZEbEnJQifmZm/johnU75Alwy4aa1phjjfR9nRdw5lmOCkzHxcM3/kat6cKFfA/ABwTGbeMMp1R8QTgH/MzAMi4i6U9//UzLxkPtU9qCB4BuVwypuBN1OONhiaIwJ6ISK+AdyPcjXAq4E3ZtlnMrKaoyoOo3SZ96YMD52embdt8YUjojkp6KnAVGZeM+j29FLz6/hwyhmvM9cEe0+WS8CPtIg4hfLd3p/SQzg5yxFD80bfgyAiHkq53MO5wEcyc+gv/dyt5hC7kylnOn84y5mGIy8iFlKGg/5IqXvk32soJ9ABt+cgfmUNUES8iHJ48Nm1vNczIuKBwM/na90D6RFIkobHUF50TpLUPwaBJFXOIJCkyhkEklQ5g0CSKmcQSFLlDAJJqtz/AmfGWFlQ4xIRAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.hist(xrot=20)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} -- 2.18.1