{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 2, "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": 3, "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": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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2056 rows × 11 columns

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202345 3 72003 63178.0 80828.0 108 \n", "20 20 202344 3 49952 42813.0 57091.0 75 \n", "21 21 202343 3 44982 38170.0 51794.0 68 \n", "22 22 202342 3 56842 49277.0 64407.0 86 \n", "23 23 202341 3 58357 51032.0 65682.0 88 \n", "24 24 202340 3 68894 60069.0 77719.0 104 \n", "25 25 202339 3 72003 63452.0 80554.0 108 \n", "26 26 202338 3 63218 55227.0 71209.0 95 \n", "27 27 202337 3 49085 42079.0 56091.0 74 \n", "28 28 202336 3 38247 32237.0 44257.0 58 \n", "29 29 202335 3 31695 26013.0 37377.0 48 \n", "... ... ... ... ... ... ... ... \n", "2026 2026 198521 3 26096 19621.0 32571.0 47 \n", "2027 2027 198520 3 27896 20885.0 34907.0 51 \n", "2028 2028 198519 3 43154 32821.0 53487.0 78 \n", "2029 2029 198518 3 40555 29935.0 51175.0 74 \n", "2030 2030 198517 3 34053 24366.0 43740.0 62 \n", "2031 2031 198516 3 50362 36451.0 64273.0 91 \n", "2032 2032 198515 3 63881 45538.0 82224.0 116 \n", "2033 2033 198514 3 134545 114400.0 154690.0 244 \n", "2034 2034 198513 3 197206 176080.0 218332.0 357 \n", "2035 2035 198512 3 245240 223304.0 267176.0 445 \n", "2036 2036 198511 3 276205 252399.0 300011.0 501 \n", "2037 2037 198510 3 353231 326279.0 380183.0 640 \n", "2038 2038 198509 3 369895 341109.0 398681.0 670 \n", "2039 2039 198508 3 389886 359529.0 420243.0 707 \n", "2040 2040 198507 3 471852 432599.0 511105.0 855 \n", "2041 2041 198506 3 565825 518011.0 613639.0 1026 \n", "2042 2042 198505 3 637302 592795.0 681809.0 1155 \n", "2043 2043 198504 3 424937 390794.0 459080.0 770 \n", "2044 2044 198503 3 213901 174689.0 253113.0 388 \n", "2045 2045 198502 3 97586 80949.0 114223.0 177 \n", "2046 2046 198501 3 85489 65918.0 105060.0 155 \n", "2047 2047 198452 3 84830 60602.0 109058.0 154 \n", "2048 2048 198451 3 101726 80242.0 123210.0 185 \n", "2049 2049 198450 3 123680 101401.0 145959.0 225 \n", "2050 2050 198449 3 101073 81684.0 120462.0 184 \n", "2051 2051 198448 3 78620 60634.0 96606.0 143 \n", "2052 2052 198447 3 72029 54274.0 89784.0 131 \n", "2053 2053 198446 3 87330 67686.0 106974.0 159 \n", "2054 2054 198445 3 135223 101414.0 169032.0 246 \n", "2055 2055 198444 3 68422 20056.0 116788.0 125 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "0 58.0 80.0 FR France \n", "1 66.0 86.0 FR France \n", "2 79.0 101.0 FR France \n", "3 95.0 119.0 FR France \n", "4 142.0 172.0 FR France \n", "5 190.0 224.0 FR France \n", "6 267.0 303.0 FR France \n", "7 305.0 343.0 FR France \n", "8 301.0 339.0 FR France \n", "9 228.0 262.0 FR France \n", "10 179.0 209.0 FR France \n", "11 164.0 198.0 FR France \n", "12 156.0 192.0 FR France \n", "13 206.0 242.0 FR France \n", "14 206.0 240.0 FR France \n", "15 205.0 239.0 FR France \n", "16 171.0 203.0 FR France \n", "17 152.0 182.0 FR France \n", "18 113.0 139.0 FR France \n", "19 95.0 121.0 FR France \n", "20 64.0 86.0 FR France \n", "21 58.0 78.0 FR France \n", "22 75.0 97.0 FR France \n", "23 77.0 99.0 FR France \n", "24 91.0 117.0 FR France \n", "25 95.0 121.0 FR France \n", "26 83.0 107.0 FR France \n", "27 63.0 85.0 FR France \n", "28 49.0 67.0 FR France \n", "29 39.0 57.0 FR France \n", "... ... ... ... ... \n", "2026 35.0 59.0 FR France \n", "2027 38.0 64.0 FR France \n", "2028 59.0 97.0 FR France \n", "2029 55.0 93.0 FR France \n", "2030 44.0 80.0 FR France \n", "2031 66.0 116.0 FR France \n", "2032 83.0 149.0 FR France \n", "2033 207.0 281.0 FR France \n", "2034 319.0 395.0 FR France \n", "2035 405.0 485.0 FR France \n", "2036 458.0 544.0 FR France \n", "2037 591.0 689.0 FR France \n", "2038 618.0 722.0 FR France \n", "2039 652.0 762.0 FR France \n", "2040 784.0 926.0 FR France \n", "2041 939.0 1113.0 FR France \n", "2042 1074.0 1236.0 FR France \n", "2043 708.0 832.0 FR France \n", "2044 317.0 459.0 FR France \n", "2045 147.0 207.0 FR France \n", "2046 120.0 190.0 FR France \n", "2047 110.0 198.0 FR France \n", "2048 146.0 224.0 FR France \n", "2049 184.0 266.0 FR France \n", "2050 149.0 219.0 FR France \n", "2051 110.0 176.0 FR France \n", "2052 99.0 163.0 FR France \n", "2053 123.0 195.0 FR France \n", "2054 184.0 308.0 FR France \n", "2055 37.0 213.0 FR France \n", "\n", "[2056 rows x 11 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "if \"raw_data.csv\" not in os.listdir():\n", " raw_data = pd.read_csv(data_url, skiprows=1)\n", " raw_data.to_csv(\"raw_data.csv\")\n", "else:\n", " raw_data = pd.read_csv(\"raw_data.csv\")\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": 8, "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", "
Unnamed: 0weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
181918191989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up inc100 inc100_low \\\n", "1819 1819 198919 3 - NaN NaN - NaN \n", "\n", " inc100_up geo_insee geo_name \n", "1819 NaN FR France " ] }, "execution_count": 8, "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": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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2055 rows × 11 columns

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" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up inc100 \\\n", "0 0 202412 3 46315 38805.0 53825.0 69 \n", "1 1 202411 3 50466 43470.0 57462.0 76 \n", "2 2 202410 3 60107 52623.0 67591.0 90 \n", "3 3 202409 3 71121 62920.0 79322.0 107 \n", "4 4 202408 3 104566 94520.0 114612.0 157 \n", "5 5 202407 3 138078 127050.0 149106.0 207 \n", "6 6 202406 3 190062 177955.0 202169.0 285 \n", "7 7 202405 3 216237 203595.0 228879.0 324 \n", "8 8 202404 3 213196 200547.0 225845.0 320 \n", "9 9 202403 3 163457 152276.0 174638.0 245 \n", "10 10 202402 3 129436 119453.0 139419.0 194 \n", "11 11 202401 3 120769 109452.0 132086.0 181 \n", "12 12 202352 3 115446 103738.0 127154.0 174 \n", "13 13 202351 3 148755 136546.0 160964.0 224 \n", "14 14 202350 3 147971 136787.0 159155.0 223 \n", "15 15 202349 3 147552 136422.0 158682.0 222 \n", "16 16 202348 3 124204 113479.0 134929.0 187 \n", "17 17 202347 3 110948 100694.0 121202.0 167 \n", "18 18 202346 3 83894 75134.0 92654.0 126 \n", "19 19 202345 3 72003 63178.0 80828.0 108 \n", "20 20 202344 3 49952 42813.0 57091.0 75 \n", "21 21 202343 3 44982 38170.0 51794.0 68 \n", "22 22 202342 3 56842 49277.0 64407.0 86 \n", "23 23 202341 3 58357 51032.0 65682.0 88 \n", "24 24 202340 3 68894 60069.0 77719.0 104 \n", "25 25 202339 3 72003 63452.0 80554.0 108 \n", "26 26 202338 3 63218 55227.0 71209.0 95 \n", "27 27 202337 3 49085 42079.0 56091.0 74 \n", "28 28 202336 3 38247 32237.0 44257.0 58 \n", "29 29 202335 3 31695 26013.0 37377.0 48 \n", "... ... ... ... ... ... ... ... \n", "2026 2026 198521 3 26096 19621.0 32571.0 47 \n", "2027 2027 198520 3 27896 20885.0 34907.0 51 \n", "2028 2028 198519 3 43154 32821.0 53487.0 78 \n", "2029 2029 198518 3 40555 29935.0 51175.0 74 \n", "2030 2030 198517 3 34053 24366.0 43740.0 62 \n", "2031 2031 198516 3 50362 36451.0 64273.0 91 \n", "2032 2032 198515 3 63881 45538.0 82224.0 116 \n", "2033 2033 198514 3 134545 114400.0 154690.0 244 \n", "2034 2034 198513 3 197206 176080.0 218332.0 357 \n", "2035 2035 198512 3 245240 223304.0 267176.0 445 \n", "2036 2036 198511 3 276205 252399.0 300011.0 501 \n", "2037 2037 198510 3 353231 326279.0 380183.0 640 \n", "2038 2038 198509 3 369895 341109.0 398681.0 670 \n", "2039 2039 198508 3 389886 359529.0 420243.0 707 \n", "2040 2040 198507 3 471852 432599.0 511105.0 855 \n", "2041 2041 198506 3 565825 518011.0 613639.0 1026 \n", "2042 2042 198505 3 637302 592795.0 681809.0 1155 \n", "2043 2043 198504 3 424937 390794.0 459080.0 770 \n", "2044 2044 198503 3 213901 174689.0 253113.0 388 \n", "2045 2045 198502 3 97586 80949.0 114223.0 177 \n", "2046 2046 198501 3 85489 65918.0 105060.0 155 \n", "2047 2047 198452 3 84830 60602.0 109058.0 154 \n", "2048 2048 198451 3 101726 80242.0 123210.0 185 \n", "2049 2049 198450 3 123680 101401.0 145959.0 225 \n", "2050 2050 198449 3 101073 81684.0 120462.0 184 \n", "2051 2051 198448 3 78620 60634.0 96606.0 143 \n", "2052 2052 198447 3 72029 54274.0 89784.0 131 \n", "2053 2053 198446 3 87330 67686.0 106974.0 159 \n", "2054 2054 198445 3 135223 101414.0 169032.0 246 \n", "2055 2055 198444 3 68422 20056.0 116788.0 125 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "0 58.0 80.0 FR France \n", "1 66.0 86.0 FR France \n", "2 79.0 101.0 FR France \n", "3 95.0 119.0 FR France \n", "4 142.0 172.0 FR France \n", "5 190.0 224.0 FR France \n", "6 267.0 303.0 FR France \n", "7 305.0 343.0 FR France \n", "8 301.0 339.0 FR France \n", "9 228.0 262.0 FR France \n", "10 179.0 209.0 FR France \n", "11 164.0 198.0 FR France \n", "12 156.0 192.0 FR France \n", "13 206.0 242.0 FR France \n", "14 206.0 240.0 FR France \n", "15 205.0 239.0 FR France \n", "16 171.0 203.0 FR France \n", "17 152.0 182.0 FR France \n", "18 113.0 139.0 FR France \n", "19 95.0 121.0 FR France \n", "20 64.0 86.0 FR France \n", "21 58.0 78.0 FR France \n", "22 75.0 97.0 FR France \n", "23 77.0 99.0 FR France \n", "24 91.0 117.0 FR France \n", "25 95.0 121.0 FR France \n", "26 83.0 107.0 FR France \n", "27 63.0 85.0 FR France \n", "28 49.0 67.0 FR France \n", "29 39.0 57.0 FR France \n", "... ... ... ... ... \n", "2026 35.0 59.0 FR France \n", "2027 38.0 64.0 FR France \n", "2028 59.0 97.0 FR France \n", "2029 55.0 93.0 FR France \n", "2030 44.0 80.0 FR France \n", "2031 66.0 116.0 FR France \n", "2032 83.0 149.0 FR France \n", "2033 207.0 281.0 FR France \n", "2034 319.0 395.0 FR France \n", "2035 405.0 485.0 FR France \n", "2036 458.0 544.0 FR France \n", "2037 591.0 689.0 FR France \n", "2038 618.0 722.0 FR France \n", "2039 652.0 762.0 FR France \n", "2040 784.0 926.0 FR France \n", "2041 939.0 1113.0 FR France \n", "2042 1074.0 1236.0 FR France \n", "2043 708.0 832.0 FR France \n", "2044 317.0 459.0 FR France \n", "2045 147.0 207.0 FR France \n", "2046 120.0 190.0 FR France \n", "2047 110.0 198.0 FR France \n", "2048 146.0 224.0 FR France \n", "2049 184.0 266.0 FR France \n", "2050 149.0 219.0 FR France \n", "2051 110.0 176.0 FR France \n", "2052 99.0 163.0 FR France \n", "2053 123.0 195.0 FR France \n", "2054 184.0 308.0 FR France \n", "2055 37.0 213.0 FR France \n", "\n", "[2055 rows x 11 columns]" ] }, "execution_count": 9, "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": 10, "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": 11, "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": 12, "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": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'] = sorted_data['inc'].astype(int)\n", "sorted_data['inc'].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": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-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": 17, "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": 18, "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'][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": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "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": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2021 743449\n", "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2010315\n", "2022 2060304\n", "2012 2175217\n", "2003 2234584\n", "2019 2254386\n", "2006 2307352\n", "2017 2321583\n", "2001 2529279\n", "1992 2574578\n", "1993 2703886\n", "2018 2705325\n", "1988 2765617\n", "2007 2780164\n", "1987 2855570\n", "2016 2856393\n", "2011 2857040\n", "2023 2873501\n", "2008 2973918\n", "1998 3034904\n", "2002 3125418\n", "2009 3444020\n", "1994 3514763\n", "1996 3539413\n", "2004 3567744\n", "1997 3620066\n", "2015 3654892\n", "2000 3826372\n", "2005 3835025\n", "1999 3908112\n", "2010 4111392\n", "2013 4182691\n", "1986 5115251\n", "1990 5235827\n", "1989 5466192\n", "dtype: int64" ] }, "execution_count": 20, "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": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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