{ "cells": [ { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "# Incidence de la varicelle" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Les données de l'incidence de la varicelle 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 1991 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\"" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/json-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": { "hideCode": false, "hidePrompt": false }, "source": [ "Téléchargement du fichier en local si il n'est pas déjà présent en local. Cela est là pour limiter le temps d'exécution et continuer à travailler s'il y a un problème sur le serveur." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Le fichier existe déjà : fichiervaricelle.txt\n" ] } ], "source": [ "import os\n", "import requests\n", "\n", "def télécharger_fichier_si_nécessaire(url, chemin_local):\n", " # Vérifier si le fichier existe déjà en local\n", " if os.path.exists(chemin_local):\n", " print(f\"Le fichier existe déjà : {chemin_local}\")\n", " else:\n", " # Télécharger le fichier depuis l'URL\n", " print(f\"Téléchargement du fichier depuis : {url}\")\n", " réponse = requests.get(url)\n", "\n", " # Vérifier si la requête a réussi\n", " if réponse.status_code == 200:\n", " # Écrire le contenu dans le fichier local\n", " with open(chemin_local, 'wb') as fichier:\n", " fichier.write(réponse.content)\n", " print(f\"Fichier téléchargé et enregistré : {chemin_local}\")\n", " else:\n", " print(f\"Échec du téléchargement. Code d'état : {réponse.status_code}\")\n", "\n", "chemin_local = 'fichiervaricelle.txt'\n", "télécharger_fichier_si_nécessaire(data_url, chemin_local)\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02025107315313884918528FRFrance
12025097338614705302528FRFrance
22025087283512864384426FRFrance
320250774502238266227410FRFrance
42025067345519584952537FRFrance
52025057208710563118315FRFrance
6202504768954466932410614FRFrance
72025037246211613763426FRFrance
820250275966275791759414FRFrance
920250176059245196679414FRFrance
1020245274356177669367311FRFrance
1120245174670223971017311FRFrance
122024507736344381028811715FRFrance
1320244976077363185239513FRFrance
1420244874189145469246210FRFrance
15202447719317263136315FRFrance
16202446722608633657315FRFrance
172024457271312164210426FRFrance
18202444721356763594315FRFrance
19202443721246413607315FRFrance
202024427262112463996426FRFrance
21202441720353813689315FRFrance
22202440721257253525315FRFrance
232024397289813334463426FRFrance
24202438775101513102FRFrance
252024377916281804102FRFrance
26202436722358703600315FRFrance
27202435716232842962204FRFrance
28202434725606224498417FRFrance
29202433719715363406315FRFrance
.................................
17581991267176081130423912312042FRFrance
17591991257161691070021638281838FRFrance
17601991247161711007122271281739FRFrance
1761199123711947767116223211329FRFrance
1762199122715452995320951271737FRFrance
1763199121714903897520831261636FRFrance
17641991207190531274225364342345FRFrance
17651991197167391124622232291939FRFrance
17661991187213851388228888382551FRFrance
1767199117713462887718047241632FRFrance
17681991167148571006819646261834FRFrance
1769199115713975978118169251832FRFrance
1770199114712265768416846221430FRFrance
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17841990527193751329525455342345FRFrance
17851990517190801380724353342543FRFrance
1786199050711079666015498201228FRFrance
17871990497114302610205FRFrance
\n", "

1788 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202510 7 3153 1388 4918 5 2 \n", "1 202509 7 3386 1470 5302 5 2 \n", "2 202508 7 2835 1286 4384 4 2 \n", "3 202507 7 4502 2382 6622 7 4 \n", "4 202506 7 3455 1958 4952 5 3 \n", "5 202505 7 2087 1056 3118 3 1 \n", "6 202504 7 6895 4466 9324 10 6 \n", "7 202503 7 2462 1161 3763 4 2 \n", "8 202502 7 5966 2757 9175 9 4 \n", "9 202501 7 6059 2451 9667 9 4 \n", "10 202452 7 4356 1776 6936 7 3 \n", "11 202451 7 4670 2239 7101 7 3 \n", "12 202450 7 7363 4438 10288 11 7 \n", "13 202449 7 6077 3631 8523 9 5 \n", "14 202448 7 4189 1454 6924 6 2 \n", "15 202447 7 1931 726 3136 3 1 \n", "16 202446 7 2260 863 3657 3 1 \n", "17 202445 7 2713 1216 4210 4 2 \n", "18 202444 7 2135 676 3594 3 1 \n", "19 202443 7 2124 641 3607 3 1 \n", "20 202442 7 2621 1246 3996 4 2 \n", "21 202441 7 2035 381 3689 3 1 \n", "22 202440 7 2125 725 3525 3 1 \n", "23 202439 7 2898 1333 4463 4 2 \n", "24 202438 7 751 0 1513 1 0 \n", "25 202437 7 916 28 1804 1 0 \n", "26 202436 7 2235 870 3600 3 1 \n", "27 202435 7 1623 284 2962 2 0 \n", "28 202434 7 2560 622 4498 4 1 \n", "29 202433 7 1971 536 3406 3 1 \n", "... ... ... ... ... ... ... ... \n", "1758 199126 7 17608 11304 23912 31 20 \n", "1759 199125 7 16169 10700 21638 28 18 \n", "1760 199124 7 16171 10071 22271 28 17 \n", "1761 199123 7 11947 7671 16223 21 13 \n", "1762 199122 7 15452 9953 20951 27 17 \n", "1763 199121 7 14903 8975 20831 26 16 \n", "1764 199120 7 19053 12742 25364 34 23 \n", "1765 199119 7 16739 11246 22232 29 19 \n", "1766 199118 7 21385 13882 28888 38 25 \n", "1767 199117 7 13462 8877 18047 24 16 \n", "1768 199116 7 14857 10068 19646 26 18 \n", "1769 199115 7 13975 9781 18169 25 18 \n", "1770 199114 7 12265 7684 16846 22 14 \n", "1771 199113 7 9567 6041 13093 17 11 \n", "1772 199112 7 10864 7331 14397 19 13 \n", "1773 199111 7 15574 11184 19964 27 19 \n", "1774 199110 7 16643 11372 21914 29 20 \n", "1775 199109 7 13741 8780 18702 24 15 \n", "1776 199108 7 13289 8813 17765 23 15 \n", "1777 199107 7 12337 8077 16597 22 15 \n", "1778 199106 7 10877 7013 14741 19 12 \n", "1779 199105 7 10442 6544 14340 18 11 \n", "1780 199104 7 7913 4563 11263 14 8 \n", "1781 199103 7 15387 10484 20290 27 18 \n", "1782 199102 7 16277 11046 21508 29 20 \n", "1783 199101 7 15565 10271 20859 27 18 \n", "1784 199052 7 19375 13295 25455 34 23 \n", "1785 199051 7 19080 13807 24353 34 25 \n", "1786 199050 7 11079 6660 15498 20 12 \n", "1787 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 8 FR France \n", "1 8 FR France \n", "2 6 FR France \n", "3 10 FR France \n", "4 7 FR France \n", "5 5 FR France \n", "6 14 FR France \n", "7 6 FR France \n", "8 14 FR France \n", "9 14 FR France \n", "10 11 FR France \n", "11 11 FR France \n", "12 15 FR France \n", "13 13 FR France \n", "14 10 FR France \n", "15 5 FR France \n", "16 5 FR France \n", "17 6 FR France \n", "18 5 FR France \n", "19 5 FR France \n", "20 6 FR France \n", "21 5 FR France \n", "22 5 FR France \n", "23 6 FR France \n", "24 2 FR France \n", "25 2 FR France \n", "26 5 FR France \n", "27 4 FR France \n", "28 7 FR France \n", "29 5 FR France \n", "... ... ... ... \n", "1758 42 FR France \n", "1759 38 FR France \n", "1760 39 FR France \n", "1761 29 FR France \n", "1762 37 FR France \n", "1763 36 FR France \n", "1764 45 FR France \n", "1765 39 FR France \n", "1766 51 FR France \n", "1767 32 FR France \n", "1768 34 FR France \n", "1769 32 FR France \n", "1770 30 FR France \n", "1771 23 FR France \n", "1772 25 FR France \n", "1773 35 FR France \n", "1774 38 FR France \n", "1775 33 FR France \n", "1776 31 FR France \n", "1777 29 FR France \n", "1778 26 FR France \n", "1779 25 FR France \n", "1780 20 FR France \n", "1781 36 FR France \n", "1782 38 FR France \n", "1783 36 FR France \n", "1784 45 FR France \n", "1785 43 FR France \n", "1786 28 FR France \n", "1787 5 FR France \n", "\n", "[1788 rows x 10 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(chemin_local, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Non." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
\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
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", "Index: []" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Pas de point à éliminer mais on garde la routine, si certains points arrivent." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02025107315313884918528FRFrance
12025097338614705302528FRFrance
22025087283512864384426FRFrance
320250774502238266227410FRFrance
42025067345519584952537FRFrance
52025057208710563118315FRFrance
6202504768954466932410614FRFrance
72025037246211613763426FRFrance
820250275966275791759414FRFrance
920250176059245196679414FRFrance
1020245274356177669367311FRFrance
1120245174670223971017311FRFrance
122024507736344381028811715FRFrance
1320244976077363185239513FRFrance
1420244874189145469246210FRFrance
15202447719317263136315FRFrance
16202446722608633657315FRFrance
172024457271312164210426FRFrance
18202444721356763594315FRFrance
19202443721246413607315FRFrance
202024427262112463996426FRFrance
21202441720353813689315FRFrance
22202440721257253525315FRFrance
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24202438775101513102FRFrance
252024377916281804102FRFrance
26202436722358703600315FRFrance
27202435716232842962204FRFrance
28202434725606224498417FRFrance
29202433719715363406315FRFrance
.................................
17581991267176081130423912312042FRFrance
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17601991247161711007122271281739FRFrance
1761199123711947767116223211329FRFrance
1762199122715452995320951271737FRFrance
1763199121714903897520831261636FRFrance
17641991207190531274225364342345FRFrance
17651991197167391124622232291939FRFrance
17661991187213851388228888382551FRFrance
1767199117713462887718047241632FRFrance
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17841990527193751329525455342345FRFrance
17851990517190801380724353342543FRFrance
1786199050711079666015498201228FRFrance
17871990497114302610205FRFrance
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1788 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202510 7 3153 1388 4918 5 2 \n", "1 202509 7 3386 1470 5302 5 2 \n", "2 202508 7 2835 1286 4384 4 2 \n", "3 202507 7 4502 2382 6622 7 4 \n", "4 202506 7 3455 1958 4952 5 3 \n", "5 202505 7 2087 1056 3118 3 1 \n", "6 202504 7 6895 4466 9324 10 6 \n", "7 202503 7 2462 1161 3763 4 2 \n", "8 202502 7 5966 2757 9175 9 4 \n", "9 202501 7 6059 2451 9667 9 4 \n", "10 202452 7 4356 1776 6936 7 3 \n", "11 202451 7 4670 2239 7101 7 3 \n", "12 202450 7 7363 4438 10288 11 7 \n", "13 202449 7 6077 3631 8523 9 5 \n", "14 202448 7 4189 1454 6924 6 2 \n", "15 202447 7 1931 726 3136 3 1 \n", "16 202446 7 2260 863 3657 3 1 \n", "17 202445 7 2713 1216 4210 4 2 \n", "18 202444 7 2135 676 3594 3 1 \n", "19 202443 7 2124 641 3607 3 1 \n", "20 202442 7 2621 1246 3996 4 2 \n", "21 202441 7 2035 381 3689 3 1 \n", "22 202440 7 2125 725 3525 3 1 \n", "23 202439 7 2898 1333 4463 4 2 \n", "24 202438 7 751 0 1513 1 0 \n", "25 202437 7 916 28 1804 1 0 \n", "26 202436 7 2235 870 3600 3 1 \n", "27 202435 7 1623 284 2962 2 0 \n", "28 202434 7 2560 622 4498 4 1 \n", "29 202433 7 1971 536 3406 3 1 \n", "... ... ... ... ... ... ... ... \n", "1758 199126 7 17608 11304 23912 31 20 \n", "1759 199125 7 16169 10700 21638 28 18 \n", "1760 199124 7 16171 10071 22271 28 17 \n", "1761 199123 7 11947 7671 16223 21 13 \n", "1762 199122 7 15452 9953 20951 27 17 \n", "1763 199121 7 14903 8975 20831 26 16 \n", "1764 199120 7 19053 12742 25364 34 23 \n", "1765 199119 7 16739 11246 22232 29 19 \n", "1766 199118 7 21385 13882 28888 38 25 \n", "1767 199117 7 13462 8877 18047 24 16 \n", "1768 199116 7 14857 10068 19646 26 18 \n", "1769 199115 7 13975 9781 18169 25 18 \n", "1770 199114 7 12265 7684 16846 22 14 \n", "1771 199113 7 9567 6041 13093 17 11 \n", "1772 199112 7 10864 7331 14397 19 13 \n", "1773 199111 7 15574 11184 19964 27 19 \n", "1774 199110 7 16643 11372 21914 29 20 \n", "1775 199109 7 13741 8780 18702 24 15 \n", "1776 199108 7 13289 8813 17765 23 15 \n", "1777 199107 7 12337 8077 16597 22 15 \n", "1778 199106 7 10877 7013 14741 19 12 \n", "1779 199105 7 10442 6544 14340 18 11 \n", "1780 199104 7 7913 4563 11263 14 8 \n", "1781 199103 7 15387 10484 20290 27 18 \n", "1782 199102 7 16277 11046 21508 29 20 \n", "1783 199101 7 15565 10271 20859 27 18 \n", "1784 199052 7 19375 13295 25455 34 23 \n", "1785 199051 7 19080 13807 24353 34 25 \n", "1786 199050 7 11079 6660 15498 20 12 \n", "1787 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 8 FR France \n", "1 8 FR France \n", "2 6 FR France \n", "3 10 FR France \n", "4 7 FR France \n", "5 5 FR France \n", "6 14 FR France \n", "7 6 FR France \n", "8 14 FR France \n", "9 14 FR France \n", "10 11 FR France \n", "11 11 FR France \n", "12 15 FR France \n", "13 13 FR France \n", "14 10 FR France \n", "15 5 FR France \n", "16 5 FR France \n", "17 6 FR France \n", "18 5 FR France \n", "19 5 FR France \n", "20 6 FR France \n", "21 5 FR France \n", "22 5 FR France \n", "23 6 FR France \n", "24 2 FR France \n", "25 2 FR France \n", "26 5 FR France \n", "27 4 FR France \n", "28 7 FR France \n", "29 5 FR France \n", "... ... ... ... \n", "1758 42 FR France \n", "1759 38 FR France \n", "1760 39 FR France \n", "1761 29 FR France \n", "1762 37 FR France \n", "1763 36 FR France \n", "1764 45 FR France \n", "1765 39 FR France \n", "1766 51 FR France \n", "1767 32 FR France \n", "1768 34 FR France \n", "1769 32 FR France \n", "1770 30 FR France \n", "1771 23 FR France \n", "1772 25 FR France \n", "1773 35 FR France \n", "1774 38 FR France \n", "1775 33 FR France \n", "1776 31 FR France \n", "1777 29 FR France \n", "1778 26 FR France \n", "1779 25 FR France \n", "1780 20 FR France \n", "1781 36 FR France \n", "1782 38 FR France \n", "1783 36 FR France \n", "1784 45 FR France \n", "1785 43 FR France \n", "1786 28 FR France \n", "1787 5 FR France \n", "\n", "[1788 rows x 10 columns]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "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": 31, "metadata": { "hideCode": false, "hidePrompt": false }, "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": { "hideCode": false, "hidePrompt": false }, "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": 32, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "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": 33, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "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": { "hideCode": false, "hidePrompt": false }, "source": [ "Un premier regard sur les données !" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "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": 35, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "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'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "## Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "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 septembre de l'année $N$ au\n", "1er septembre 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 spetembre de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er septembre.\n", "\n", "Comme l'incidence de la varicelle 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 1990, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1991. Comme l'année en cours n'est pas terminée, on arrête l'année précédente." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "En partant de cette liste des semaines qui contiennent un 1er septembre, 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": 37, "metadata": { "hideCode": false, "hidePrompt": false }, "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": { "hideCode": false, "hidePrompt": false }, "source": [ "Voici les incidences annuelles." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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i50fEQYD0PCPFZwPvFJU9kGKz0/HI+LAyETEIfACcW6KuYSStltQlqauvr6/Ml2RmZmNV7hLfL0bEu5JmAJ2SflLiXJ0kFiXiWcucCERsADZAYTirRNvMzKyKyuqJRMS76bkXeJbCfMd7aYiK9NybTj8AXFBUfA7wborPOUl8WBlJTcA5QH+JuszMrA6MmkQknS1pytAxsBR4HdgCDK2W6gCeS8dbgPa04upiYB6wKw15HZG0OM133DiizFBd1wIvp3mTF4Clkqal1V9LU8zMzOpAOcNZ5wPPptW4TcCTEfE/Je0GNklaBewHrgOIiL2SNgFvAIPArRFxLNV1M/AocCawNT0AHgYel9RDoQfSnurql3QvsDudd09E9I/h9ZqZNaTew0f5s6de4YHrr5jQ+6y8FbyZWQO469nX+P6u/dyw8ELWXfO5istnXeLrvbPMzHKs1vvPedsTM7Mcq/X+c04iZmY5Vuv95zycZWaWc7X8HHlPrJuZmT9j3czMJp6TiJmZZeYkYmZmmTmJmJlZZk4iZmaWmZOImZll5iRiZmaZOYmYmVlmTiJmZpaZk4iZmWXmJGJmZpk5iZiZWWZOImZmlpmTiJmZZeYkYmZmmTmJmJlZZk4iZmaWWdlJRNIZkl6R9A/p6+mSOiV1p+dpReeuldQjaZ+kZUXxBZJeS99bL0kp3izpmRTfKWluUZmO9DO6JXVU40WbmVl1VNITuQ14s+jrNcC2iJgHbEtfI2k+0A5cCiwHvivpjFTmQWA1MC89lqf4KuBQRHwa+A5wf6prOnA3sAhYCNxdnKzMzKy2ykoikuYAXwG+VxReAWxMxxuBlUXxpyNiICLeAnqAhZJmAlMjYkcUPtj9sRFlhuraDCxJvZRlQGdE9EfEIaCTE4nHzMxqrNyeyF8DtwPHi2LnR8RBgPQ8I8VnA+8UnXcgxWan45HxYWUiYhD4ADi3RF1mZlYHRk0ikv4I6I2IPWXWqZPEokQ8a5niNq6W1CWpq6+vr8xmmpnZWJXTE/kicLWkt4GngS9LegJ4Lw1RkZ570/kHgAuKys8B3k3xOSeJDysjqQk4B+gvUdcwEbEhItoioq21tbWMl2RmZtUwahKJiLURMSci5lKYMH85Iv4E2AIMrZbqAJ5Lx1uA9rTi6mIKE+i70pDXEUmL03zHjSPKDNV1bfoZAbwALJU0LU2oL00xMzOrA01jKHsfsEnSKmA/cB1AROyVtAl4AxgEbo2IY6nMzcCjwJnA1vQAeBh4XFIPhR5Ie6qrX9K9wO503j0R0T+GNpuZWRWp8Ia/cbS1tUVXV1etm2FmliuS9kREW6XlfMe6mVmV9R4+ylcf2kHvkaO1bsq4cxIxM6uy9du62f12P+tf6q51U8bdWOZEzMysyCV3bWVg8MTtdE/s3M8TO/fT3DSJfeuuqmHLxo97ImZmVbL99iu5+vJZtEwu/GltmTyJFZfPYvsdV46p3noeHnMSMTOrkhlTW5jS3MTA4HGamyYxMHicKc1NzJjSMqZ663l4zMNZZmZV9P6HA9yw6CKuX3ghT+7aT98Yeg95GB7zEl8zszrVe/go655/kxf3/pKjHx2nZfIkll36Ke78ymfH3LsZyUt8zcwazHgNj1WTh7PMzOpYNYfHxoOHs8zMzMNZZmY28ZxEzMwsMycRMzPLzEnEzMwycxIxM7PMnETMzCwzJxEzM8vMScTMzDJzEjEzs8ycRMzMLDMnETMzy8xJxMzMMnMSMTOzzEZNIpJaJO2S9GNJeyX9pxSfLqlTUnd6nlZUZq2kHkn7JC0rii+Q9Fr63npJSvFmSc+k+E5Jc4vKdKSf0S2po5ov3szMxqacnsgA8OWI+B3gcmC5pMXAGmBbRMwDtqWvkTQfaAcuBZYD35V0RqrrQWA1MC89lqf4KuBQRHwa+A5wf6prOnA3sAhYCNxdnKzMzKy2Rk0iUfBh+nJyegSwAtiY4huBlel4BfB0RAxExFtAD7BQ0kxgakTsiMKHmDw2osxQXZuBJamXsgzojIj+iDgEdHIi8ZiZWY2VNSci6QxJrwK9FP6o7wTOj4iDAOl5Rjp9NvBOUfEDKTY7HY+MDysTEYPAB8C5JeoyM7M6UFYSiYhjEXE5MIdCr+KyEqfrZFWUiGctc+IHSqsldUnq6uvrK9E0MzOrpopWZ0XEr4AfUBhSei8NUZGee9NpB4ALiorNAd5N8TkniQ8rI6kJOAfoL1HXyHZtiIi2iGhrbW2t5CWZmdkYlLM6q1XSJ9PxmcAfAD8BtgBDq6U6gOfS8RagPa24upjCBPquNOR1RNLiNN9x44gyQ3VdC7yc5k1eAJZKmpYm1JemmJmZ1YGmMs6ZCWxMK6wmAZsi4h8k7QA2SVoF7AeuA4iIvZI2AW8Ag8CtEXEs1XUz8ChwJrA1PQAeBh6X1EOhB9Ke6uqXdC+wO513T0T0j+UFm5lZ9ajwhr9xtLW1RVdXV62bYWaWK5L2RERbpeV8x7qZmWXmJGJmZpk5iZiZWWZOImZmlpmTiJmZZeYkYmZmmTmJmJlZZk4iZmaWmZOImZll5iRiZmaZOYmYmVlmTiJmZpaZk4iZmWXmJGJmZpk5iZiZWWZOImZmlpmTiJmZZeYkYmZWgd7DR/nqQzvoPXK01k2pC04iZmYVWL+tm91v97P+pe5aN6UuNNW6AWZmeXDJXVsZGDz+8ddP7NzPEzv309w0iX3rrqphy2rLPREzszJsv/1Krr58Fi2TC382WyZPYsXls9h+x5U1blltOYmYmZVhxtQWpjQ3MTB4nOamSQwMHmdKcxMzprTUumk15eEsM7Myvf/hADcsuojrF17Ik7v20+fJdRQRpU+QLgAeAz4FHAc2RMTfSJoOPAPMBd4GvhoRh1KZtcAq4Bjw5xHxQoovAB4FzgSeB26LiJDUnH7GAuD/AV+LiLdTmQ7grtScdRGxsVR729raoqurq/wrYGZmSNoTEW2VlitnOGsQ+A8R8VlgMXCrpPnAGmBbRMwDtqWvSd9rBy4FlgPflXRGqutBYDUwLz2Wp/gq4FBEfBr4DnB/qms6cDewCFgI3C1pWqUv0szMxseoSSQiDkbEj9LxEeBNYDawAhjqFWwEVqbjFcDTETEQEW8BPcBCSTOBqRGxIwrdn8dGlBmqazOwRJKAZUBnRPSnXk4nJxKPmZnVWEUT65LmAlcAO4HzI+IgFBINMCOdNht4p6jYgRSbnY5HxoeViYhB4APg3BJ1mZlZHSg7iUj6LeC/Ad+KiMOlTj1JLErEs5YpbttqSV2Suvr6+ko0zczMqqmsJCJpMoUE8v2I+PsUfi8NUZGee1P8AHBBUfE5wLspPuck8WFlJDUB5wD9JeoaJiI2RERbRLS1traW85LMzKwKRk0iaW7iYeDNiPgvRd/aAnSk4w7guaJ4u6RmSRdTmEDflYa8jkhanOq8cUSZobquBV5O8yYvAEslTUsT6ktTzMzM6kA5S3y/BGwHXqOwxBfg2xTmRTYBFwL7gesioj+VuRO4icLKrm9FxNYUb+PEEt+twDfTEt8W4HEK8y39QHtE/CyVuSn9PIC/jIhHRmlvH/DzMl9/PTgPeL/WjRiDPLc/z22HfLc/z22Hxmz/RRFR8VDOqEnExpekrixrs+tFntuf57ZDvtuf57aD21/M256YmVlmTiJmZpaZk0jtbah1A8Yoz+3Pc9sh3+3Pc9vB7f+Y50TMzCwz90TMzCwzJ5FxIOnvJPVKer0o9juSdkh6TdJ/lzQ1xT8h6ZEU/7Gk3y8q8wNJ+yS9mh4zTvLjqt32CyT9L0lvStor6bYUny6pU1J3ep5WVGatpJ7U1mVF8QXpdfVIWp/uD8pL2+v+2ks6N53/oaQHRtRV19d+lLbn4dr/oaQ96RrvkfTlorom9NqPQ/sru/4R4UeVH8DvAV8AXi+K7Qb+TTq+Cbg3Hd8KPJKOZwB7gEnp6x8AbRPc9pnAF9LxFOCfgfnAXwFrUnwNcH86ng/8GGgGLgZ+CpyRvrcL+F0K29dsBa7KUdvzcO3PBr4EfAN4YERd9X7tS7U9D9f+CmBWOr4M+EWtrv04tL+i6++eyDiIiH+kcNNksUuAf0zHncAfp+P5FLbSJyJ6gV8BNVt/HhOza3Ndt30821hKpe2PiF9HxP8Ghn0yUh6u/anaXisZ2v9KRAxtwbQXaFFhl44Jv/bVbH+Wn+0kMnFeB65Ox9dxYk+wHwMrJDWpsE3MAobvF/ZI6lL+x4noFhfT+O3aPO7G2PYh9X7tTyUP1340ebr2fwy8EhED1Pjaw5jbP6Ts6+8kMnFuovCBXnsodDf/JcX/jsIvWhfw18D/pbBdDMANEfE54F+nx9cnqrEa312bx1UV2g75uPanrOIksXq79qXk5tpLupTCh+j96VDoJKdN2BLYKrQfKrz+TiITJCJ+EhFLI2IB8BSF8XciYjAi/iIiLo+IFcAnge70vV+k5yPAk0zQUIvGf9fmcVOltufl2p9KHq79KeXl2kuaAzwL3BgRP03hmlz71J5qtL/i6+8kMkGGVjhImkThM+P/Nn19lqSz0/EfAoMR8UYa3jovxScDf0RhSGy82zkRuzbXddtzdO1PKifX/lT15OLaS/ok8D+AtRHxf4ZOrsW1r2b7M13/SlcB+FHWSomngIPARxTemawCbqOwYuKfgfs4caPnXGAfhYmwlyjspAmF1St7gH+iMPH1N6SVQ+Pc9i9R6H7/E/BqevxbCp80uY1CL2kbML2ozJ0Uelb7KFqJQmGBwOvpew8MveZ6b3vOrv3bFBZxfJh+1+bn6Nr/Rtvzcu0pvBH8ddG5rwIzanHtq9n+LNffd6ybmVlmHs4yM7PMnETMzCwzJxEzM8vMScTMzDJzEjEzs8ycRMzMLDMnETMzy8xJxMzMMvv/r4lGV8I8mfkAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin) et les plus faibles (début)." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/plain": [ "2020 221186\n", "2023 366227\n", "2021 376290\n", "2024 479258\n", "2002 516689\n", "2018 542312\n", "2017 551041\n", "1996 564901\n", "2019 584066\n", "2015 604382\n", "2000 617597\n", "2001 619041\n", "2012 624573\n", "2005 628464\n", "2006 632833\n", "2022 641397\n", "2011 642368\n", "1993 643387\n", "1995 652478\n", "1994 661409\n", "1998 677775\n", "1997 683434\n", "2014 685769\n", "2013 698332\n", "2007 717352\n", "2008 749478\n", "1999 756456\n", "2003 758363\n", "2004 777388\n", "2016 782114\n", "2010 829911\n", "1992 832939\n", "2009 842373\n", "dtype: int64" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence_sort= yearly_incidence.sort_values()\n", "yearly_incidence_sort" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici les années où l'incidence est la plus faible et la plus forte. " ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'numpy.int64' object has no attribute 'index'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\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[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"L'année avec la plus faible incidence\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myearly_incidence_sort\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"L'année avec la plus forte incidence\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myearly_incidence_sort\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'numpy.int64' object has no attribute 'index'" ] } ], "source": [ "print(\"L'année avec la plus faible incidence\",yearly_incidence_sort.index[-1].index[0])\n", "print(\"L'année avec la plus forte incidence\",yearly_incidence_sort.index[0].year)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "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": 40, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.hist(xrot=20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [] } ], "metadata": { "hide_code_all_hidden": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }