{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os\n", "import urllib.request" ] }, { "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": 2, "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": "markdown", "metadata": {}, "source": [ "Nom du fichier local des données sentinelles" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "datalocal= \"donnsentinelles.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On télécharge les données si elles n'existent pas avant. Ici il faut éviter de télécharger un fichier brisé qui ne contiendrait plus de data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "os.path.exists vérifie si le fichier local existe, renvoie True si oui , False sinon\n", "os contient des fonctions de gestions de fichier et du système d'exploitation'\n", "urllib.request contient des fonctions pour ouvrir des URL, authentification, redirection, cookies , etc.. selon\n", "https://docs.python.org/fr/3.11/library/urllib.request.html\n", "urllib.request.urlretrieve télécharge la page url et l'enregistre vers le nom de fichier en 2 ème position\n", "Des messages consoles affichent l'état, déjà chargé ou non." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "déjà présent sur le serveur\n" ] } ], "source": [ "if not os.path.exists(datalocal):\n", " urllib.request.urlretrieve(data_url, datalocal)\n", " print(\"données téléchargées depuis le réseau Sentinelles\")\n", "else:\n", " print(\"déjà présent sur le serveur\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020241234631538805.053825.06958.080.0FRFrance
120241135046643470.057462.07666.086.0FRFrance
220241036010752623.067591.09079.0101.0FRFrance
320240937112162920.079322.010795.0119.0FRFrance
4202408310456694520.0114612.0157142.0172.0FRFrance
52024073138078127050.0149106.0207190.0224.0FRFrance
62024063190062177955.0202169.0285267.0303.0FRFrance
72024053216237203595.0228879.0324305.0343.0FRFrance
82024043213196200547.0225845.0320301.0339.0FRFrance
92024033163457152276.0174638.0245228.0262.0FRFrance
102024023129436119453.0139419.0194179.0209.0FRFrance
112024013120769109452.0132086.0181164.0198.0FRFrance
122023523115446103738.0127154.0174156.0192.0FRFrance
132023513148755136546.0160964.0224206.0242.0FRFrance
142023503147971136787.0159155.0223206.0240.0FRFrance
152023493147552136422.0158682.0222205.0239.0FRFrance
162023483124204113479.0134929.0187171.0203.0FRFrance
172023473110948100694.0121202.0167152.0182.0FRFrance
1820234638389475134.092654.0126113.0139.0FRFrance
1920234537200363178.080828.010895.0121.0FRFrance
2020234434995242813.057091.07564.086.0FRFrance
2120234334498238170.051794.06858.078.0FRFrance
2220234235684249277.064407.08675.097.0FRFrance
2320234135835751032.065682.08877.099.0FRFrance
2420234036889460069.077719.010491.0117.0FRFrance
2520233937200363452.080554.010895.0121.0FRFrance
2620233836321855227.071209.09583.0107.0FRFrance
2720233734908542079.056091.07463.085.0FRFrance
2820233633824732237.044257.05849.067.0FRFrance
2920233533169526013.037377.04839.057.0FRFrance
.................................
202619852132609619621.032571.04735.059.0FRFrance
202719852032789620885.034907.05138.064.0FRFrance
202819851934315432821.053487.07859.097.0FRFrance
202919851834055529935.051175.07455.093.0FRFrance
203019851733405324366.043740.06244.080.0FRFrance
203119851635036236451.064273.09166.0116.0FRFrance
203219851536388145538.082224.011683.0149.0FRFrance
20331985143134545114400.0154690.0244207.0281.0FRFrance
20341985133197206176080.0218332.0357319.0395.0FRFrance
20351985123245240223304.0267176.0445405.0485.0FRFrance
20361985113276205252399.0300011.0501458.0544.0FRFrance
20371985103353231326279.0380183.0640591.0689.0FRFrance
20381985093369895341109.0398681.0670618.0722.0FRFrance
20391985083389886359529.0420243.0707652.0762.0FRFrance
20401985073471852432599.0511105.0855784.0926.0FRFrance
20411985063565825518011.0613639.01026939.01113.0FRFrance
20421985053637302592795.0681809.011551074.01236.0FRFrance
20431985043424937390794.0459080.0770708.0832.0FRFrance
20441985033213901174689.0253113.0388317.0459.0FRFrance
204519850239758680949.0114223.0177147.0207.0FRFrance
204619850138548965918.0105060.0155120.0190.0FRFrance
204719845238483060602.0109058.0154110.0198.0FRFrance
2048198451310172680242.0123210.0185146.0224.0FRFrance
20491984503123680101401.0145959.0225184.0266.0FRFrance
2050198449310107381684.0120462.0184149.0219.0FRFrance
205119844837862060634.096606.0143110.0176.0FRFrance
205219844737202954274.089784.013199.0163.0FRFrance
205319844638733067686.0106974.0159123.0195.0FRFrance
20541984453135223101414.0169032.0246184.0308.0FRFrance
205519844436842220056.0116788.012537.0213.0FRFrance
\n", "

2056 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202412 3 46315 38805.0 53825.0 69 58.0 \n", "1 202411 3 50466 43470.0 57462.0 76 66.0 \n", "2 202410 3 60107 52623.0 67591.0 90 79.0 \n", "3 202409 3 71121 62920.0 79322.0 107 95.0 \n", "4 202408 3 104566 94520.0 114612.0 157 142.0 \n", "5 202407 3 138078 127050.0 149106.0 207 190.0 \n", "6 202406 3 190062 177955.0 202169.0 285 267.0 \n", "7 202405 3 216237 203595.0 228879.0 324 305.0 \n", "8 202404 3 213196 200547.0 225845.0 320 301.0 \n", "9 202403 3 163457 152276.0 174638.0 245 228.0 \n", "10 202402 3 129436 119453.0 139419.0 194 179.0 \n", "11 202401 3 120769 109452.0 132086.0 181 164.0 \n", "12 202352 3 115446 103738.0 127154.0 174 156.0 \n", "13 202351 3 148755 136546.0 160964.0 224 206.0 \n", "14 202350 3 147971 136787.0 159155.0 223 206.0 \n", "15 202349 3 147552 136422.0 158682.0 222 205.0 \n", "16 202348 3 124204 113479.0 134929.0 187 171.0 \n", "17 202347 3 110948 100694.0 121202.0 167 152.0 \n", "18 202346 3 83894 75134.0 92654.0 126 113.0 \n", "19 202345 3 72003 63178.0 80828.0 108 95.0 \n", "20 202344 3 49952 42813.0 57091.0 75 64.0 \n", "21 202343 3 44982 38170.0 51794.0 68 58.0 \n", "22 202342 3 56842 49277.0 64407.0 86 75.0 \n", "23 202341 3 58357 51032.0 65682.0 88 77.0 \n", "24 202340 3 68894 60069.0 77719.0 104 91.0 \n", "25 202339 3 72003 63452.0 80554.0 108 95.0 \n", "26 202338 3 63218 55227.0 71209.0 95 83.0 \n", "27 202337 3 49085 42079.0 56091.0 74 63.0 \n", "28 202336 3 38247 32237.0 44257.0 58 49.0 \n", "29 202335 3 31695 26013.0 37377.0 48 39.0 \n", "... ... ... ... ... ... ... ... \n", "2026 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2027 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2028 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2029 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2030 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2031 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2032 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2033 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2034 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2035 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2036 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2037 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2038 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2039 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2040 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2041 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2042 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2043 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2044 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2045 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2046 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2047 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2048 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2049 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2050 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2051 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2052 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2053 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2054 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2055 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 80.0 FR France \n", "1 86.0 FR France \n", "2 101.0 FR France \n", "3 119.0 FR France \n", "4 172.0 FR France \n", "5 224.0 FR France \n", "6 303.0 FR France \n", "7 343.0 FR France \n", "8 339.0 FR France \n", "9 262.0 FR France \n", "10 209.0 FR France \n", "11 198.0 FR France \n", "12 192.0 FR France \n", "13 242.0 FR France \n", "14 240.0 FR France \n", "15 239.0 FR France \n", "16 203.0 FR France \n", "17 182.0 FR France \n", "18 139.0 FR France \n", "19 121.0 FR France \n", "20 86.0 FR France \n", "21 78.0 FR France \n", "22 97.0 FR France \n", "23 99.0 FR France \n", "24 117.0 FR France \n", "25 121.0 FR France \n", "26 107.0 FR France \n", "27 85.0 FR France \n", "28 67.0 FR France \n", "29 57.0 FR France \n", "... ... ... ... \n", "2026 59.0 FR France \n", "2027 64.0 FR France \n", "2028 97.0 FR France \n", "2029 93.0 FR France \n", "2030 80.0 FR France \n", "2031 116.0 FR France \n", "2032 149.0 FR France \n", "2033 281.0 FR France \n", "2034 395.0 FR France \n", "2035 485.0 FR France \n", "2036 544.0 FR France \n", "2037 689.0 FR France \n", "2038 722.0 FR France \n", "2039 762.0 FR France \n", "2040 926.0 FR France \n", "2041 1113.0 FR France \n", "2042 1236.0 FR France \n", "2043 832.0 FR France \n", "2044 459.0 FR France \n", "2045 207.0 FR France \n", "2046 190.0 FR France \n", "2047 198.0 FR France \n", "2048 224.0 FR France \n", "2049 266.0 FR France \n", "2050 219.0 FR France \n", "2051 176.0 FR France \n", "2052 163.0 FR France \n", "2053 195.0 FR France \n", "2054 308.0 FR France \n", "2055 213.0 FR France \n", "\n", "[2056 rows x 10 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(datalocal, 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": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 18, "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
18191989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1819 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1819 FR France " ] }, "execution_count": 18, "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": 19, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020241234631538805.053825.06958.080.0FRFrance
120241135046643470.057462.07666.086.0FRFrance
220241036010752623.067591.09079.0101.0FRFrance
320240937112162920.079322.010795.0119.0FRFrance
4202408310456694520.0114612.0157142.0172.0FRFrance
52024073138078127050.0149106.0207190.0224.0FRFrance
62024063190062177955.0202169.0285267.0303.0FRFrance
72024053216237203595.0228879.0324305.0343.0FRFrance
82024043213196200547.0225845.0320301.0339.0FRFrance
92024033163457152276.0174638.0245228.0262.0FRFrance
102024023129436119453.0139419.0194179.0209.0FRFrance
112024013120769109452.0132086.0181164.0198.0FRFrance
122023523115446103738.0127154.0174156.0192.0FRFrance
132023513148755136546.0160964.0224206.0242.0FRFrance
142023503147971136787.0159155.0223206.0240.0FRFrance
152023493147552136422.0158682.0222205.0239.0FRFrance
162023483124204113479.0134929.0187171.0203.0FRFrance
172023473110948100694.0121202.0167152.0182.0FRFrance
1820234638389475134.092654.0126113.0139.0FRFrance
1920234537200363178.080828.010895.0121.0FRFrance
2020234434995242813.057091.07564.086.0FRFrance
2120234334498238170.051794.06858.078.0FRFrance
2220234235684249277.064407.08675.097.0FRFrance
2320234135835751032.065682.08877.099.0FRFrance
2420234036889460069.077719.010491.0117.0FRFrance
2520233937200363452.080554.010895.0121.0FRFrance
2620233836321855227.071209.09583.0107.0FRFrance
2720233734908542079.056091.07463.085.0FRFrance
2820233633824732237.044257.05849.067.0FRFrance
2920233533169526013.037377.04839.057.0FRFrance
.................................
202619852132609619621.032571.04735.059.0FRFrance
202719852032789620885.034907.05138.064.0FRFrance
202819851934315432821.053487.07859.097.0FRFrance
202919851834055529935.051175.07455.093.0FRFrance
203019851733405324366.043740.06244.080.0FRFrance
203119851635036236451.064273.09166.0116.0FRFrance
203219851536388145538.082224.011683.0149.0FRFrance
20331985143134545114400.0154690.0244207.0281.0FRFrance
20341985133197206176080.0218332.0357319.0395.0FRFrance
20351985123245240223304.0267176.0445405.0485.0FRFrance
20361985113276205252399.0300011.0501458.0544.0FRFrance
20371985103353231326279.0380183.0640591.0689.0FRFrance
20381985093369895341109.0398681.0670618.0722.0FRFrance
20391985083389886359529.0420243.0707652.0762.0FRFrance
20401985073471852432599.0511105.0855784.0926.0FRFrance
20411985063565825518011.0613639.01026939.01113.0FRFrance
20421985053637302592795.0681809.011551074.01236.0FRFrance
20431985043424937390794.0459080.0770708.0832.0FRFrance
20441985033213901174689.0253113.0388317.0459.0FRFrance
204519850239758680949.0114223.0177147.0207.0FRFrance
204619850138548965918.0105060.0155120.0190.0FRFrance
204719845238483060602.0109058.0154110.0198.0FRFrance
2048198451310172680242.0123210.0185146.0224.0FRFrance
20491984503123680101401.0145959.0225184.0266.0FRFrance
2050198449310107381684.0120462.0184149.0219.0FRFrance
205119844837862060634.096606.0143110.0176.0FRFrance
205219844737202954274.089784.013199.0163.0FRFrance
205319844638733067686.0106974.0159123.0195.0FRFrance
20541984453135223101414.0169032.0246184.0308.0FRFrance
205519844436842220056.0116788.012537.0213.0FRFrance
\n", "

2055 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202412 3 46315 38805.0 53825.0 69 58.0 \n", "1 202411 3 50466 43470.0 57462.0 76 66.0 \n", "2 202410 3 60107 52623.0 67591.0 90 79.0 \n", "3 202409 3 71121 62920.0 79322.0 107 95.0 \n", "4 202408 3 104566 94520.0 114612.0 157 142.0 \n", "5 202407 3 138078 127050.0 149106.0 207 190.0 \n", "6 202406 3 190062 177955.0 202169.0 285 267.0 \n", "7 202405 3 216237 203595.0 228879.0 324 305.0 \n", "8 202404 3 213196 200547.0 225845.0 320 301.0 \n", "9 202403 3 163457 152276.0 174638.0 245 228.0 \n", "10 202402 3 129436 119453.0 139419.0 194 179.0 \n", "11 202401 3 120769 109452.0 132086.0 181 164.0 \n", "12 202352 3 115446 103738.0 127154.0 174 156.0 \n", "13 202351 3 148755 136546.0 160964.0 224 206.0 \n", "14 202350 3 147971 136787.0 159155.0 223 206.0 \n", "15 202349 3 147552 136422.0 158682.0 222 205.0 \n", "16 202348 3 124204 113479.0 134929.0 187 171.0 \n", "17 202347 3 110948 100694.0 121202.0 167 152.0 \n", "18 202346 3 83894 75134.0 92654.0 126 113.0 \n", "19 202345 3 72003 63178.0 80828.0 108 95.0 \n", "20 202344 3 49952 42813.0 57091.0 75 64.0 \n", "21 202343 3 44982 38170.0 51794.0 68 58.0 \n", "22 202342 3 56842 49277.0 64407.0 86 75.0 \n", "23 202341 3 58357 51032.0 65682.0 88 77.0 \n", "24 202340 3 68894 60069.0 77719.0 104 91.0 \n", "25 202339 3 72003 63452.0 80554.0 108 95.0 \n", "26 202338 3 63218 55227.0 71209.0 95 83.0 \n", "27 202337 3 49085 42079.0 56091.0 74 63.0 \n", "28 202336 3 38247 32237.0 44257.0 58 49.0 \n", "29 202335 3 31695 26013.0 37377.0 48 39.0 \n", "... ... ... ... ... ... ... ... \n", "2026 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2027 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2028 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2029 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2030 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2031 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2032 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2033 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2034 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2035 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2036 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2037 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2038 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2039 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2040 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2041 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2042 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2043 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2044 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2045 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2046 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2047 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2048 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2049 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2050 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2051 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2052 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2053 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2054 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2055 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 80.0 FR France \n", "1 86.0 FR France \n", "2 101.0 FR France \n", "3 119.0 FR France \n", "4 172.0 FR France \n", "5 224.0 FR France \n", "6 303.0 FR France \n", "7 343.0 FR France \n", "8 339.0 FR France \n", "9 262.0 FR France \n", "10 209.0 FR France \n", "11 198.0 FR France \n", "12 192.0 FR France \n", "13 242.0 FR France \n", "14 240.0 FR France \n", "15 239.0 FR France \n", "16 203.0 FR France \n", "17 182.0 FR France \n", "18 139.0 FR France \n", "19 121.0 FR France \n", "20 86.0 FR France \n", "21 78.0 FR France \n", "22 97.0 FR France \n", "23 99.0 FR France \n", "24 117.0 FR France \n", "25 121.0 FR France \n", "26 107.0 FR France \n", "27 85.0 FR France \n", "28 67.0 FR France \n", "29 57.0 FR France \n", "... ... ... ... \n", "2026 59.0 FR France \n", "2027 64.0 FR France \n", "2028 97.0 FR France \n", "2029 93.0 FR France \n", "2030 80.0 FR France \n", "2031 116.0 FR France \n", "2032 149.0 FR France \n", "2033 281.0 FR France \n", "2034 395.0 FR France \n", "2035 485.0 FR France \n", "2036 544.0 FR France \n", "2037 689.0 FR France \n", "2038 722.0 FR France \n", "2039 762.0 FR France \n", "2040 926.0 FR France \n", "2041 1113.0 FR France \n", "2042 1236.0 FR France \n", "2043 832.0 FR France \n", "2044 459.0 FR France \n", "2045 207.0 FR France \n", "2046 190.0 FR France \n", "2047 198.0 FR France \n", "2048 224.0 FR France \n", "2049 266.0 FR France \n", "2050 219.0 FR France \n", "2051 176.0 FR France \n", "2052 163.0 FR France \n", "2053 195.0 FR France \n", "2054 308.0 FR France \n", "2055 213.0 FR France \n", "\n", "[2055 rows x 10 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "à cause des données manquantes, les types des valeurs sont en str et non int" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "str" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(data.loc[1,'inc100'])\n" ] }, { "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": 21, "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": 22, "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": 23, "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": 24, "metadata": {}, "outputs": [], "source": [ "sorted_data['inc']= sorted_data['inc'].astype(int)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On change le type des données pour la colonne inc en int. Comme dans la vidéo" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "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'].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": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "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": 27, "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": 28, "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": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "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": 30, "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": 30, "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": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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