diff --git a/module3/exo2/analyse-varicelle.ipynb b/module3/exo2/analyse-varicelle.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..62085b5edf61410d12f3fdb2f4e2a00c4a2ea13d --- /dev/null +++ b/module3/exo2/analyse-varicelle.ipynb @@ -0,0 +1,2371 @@ +{ + "cells": [ + { + "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 pathlib" + ] + }, + { + "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 = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.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": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020212176232354189239513FRFrance
12021207751946241041411715FRFrance
2202119766544370893810713FRFrance
32021187391221105714639FRFrance
420211774686287864947410FRFrance
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "0 202121 7 6232 3541 8923 9 5 13 \n", + "1 202120 7 7519 4624 10414 11 7 15 \n", + "2 202119 7 6654 4370 8938 10 7 13 \n", + "3 202118 7 3912 2110 5714 6 3 9 \n", + "4 202117 7 4686 2878 6494 7 4 10 \n", + "\n", + " geo_insee geo_name \n", + "0 FR France \n", + "1 FR France \n", + "2 FR France \n", + "3 FR France \n", + "4 FR France " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "if pathlib.Path(\"incidence-PAY-7.csv\").exists():\n", + " raw_data = pd.read_csv(\"incidence-PAY-7.csv\")\n", + "else:\n", + " raw_data = pd.read_csv(data_url, skiprows=1)\n", + " raw_data.to_csv(\"incidence-PAY-7.csv\", index=False)\n", + "raw_data.head()" + ] + }, + { + "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": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" + ], + "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": 4, + "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": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020212176232354189239513FRFrance
12021207751946241041411715FRFrance
2202119766544370893810713FRFrance
32021187391221105714639FRFrance
420211774686287864947410FRFrance
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "0 202121 7 6232 3541 8923 9 5 13 \n", + "1 202120 7 7519 4624 10414 11 7 15 \n", + "2 202119 7 6654 4370 8938 10 7 13 \n", + "3 202118 7 3912 2110 5714 6 3 9 \n", + "4 202117 7 4686 2878 6494 7 4 10 \n", + "\n", + " geo_insee geo_name \n", + "0 FR France \n", + "1 FR France \n", + "2 FR France \n", + "3 FR France \n", + "4 FR France " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data.head()" + ] + }, + { + "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": 6, + "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": 7, + "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": 8, + "metadata": {}, + "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": {}, + "source": [ + "Un premier regard sur les données !" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "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": {}, + "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 septembre.\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": 11, + "metadata": {}, + "outputs": [], + "source": [ + "first_september_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": {}, + "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": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1991-08-26/1991-09-01 1992-08-31/1992-09-06 period\n", + "1991-08-26/1991-09-01 805\n", + "1991-09-02/1991-09-08 3053\n", + "1991-09-09/1991-09-15 1457\n", + "1991-09-16/1991-09-22 4446\n", + "1991-09-23/1991-09-29 3105\n", + "1991-09-30/1991-10-06 3615\n", + "1991-10-07/1991-10-13 6168\n", + "1991-10-14/1991-10-20 6419\n", + "1991-10-21/1991-10-27 14307\n", + "1991-10-28/1991-11-03 9882\n", + "1991-11-04/1991-11-10 8048\n", + "1991-11-11/1991-11-17 6453\n", + "1991-11-18/1991-11-24 12813\n", + "1991-11-25/1991-12-01 15370\n", + "1991-12-02/1991-12-08 13304\n", + "1991-12-09/1991-12-15 14115\n", + "1991-12-16/1991-12-22 15043\n", + "1991-12-23/1991-12-29 15184\n", + "1991-12-30/1992-01-05 23607\n", + "1992-01-06/1992-01-12 16588\n", + "1992-01-13/1992-01-19 18586\n", + "1992-01-20/1992-01-26 20690\n", + "1992-01-27/1992-02-02 15997\n", + "1992-02-03/1992-02-09 17009\n", + "1992-02-10/1992-02-16 22633\n", + "1992-02-17/1992-02-23 15243\n", + "1992-02-24/1992-03-01 21824\n", + "1992-03-02/1992-03-08 22906\n", + "1992-03-09/1992-03-15 20003\n", + "1992-03-16/1992-03-22 15838\n", + "1992-03-23/1992-03-29 17146\n", + "1992-03-30/1992-04-05 14455\n", + "1992-04-06/1992-04-12 15473\n", + "1992-04-13/1992-04-19 15583\n", + "1992-04-20/1992-04-26 19162\n", + "1992-04-27/1992-05-03 30273\n", + "1992-05-04/1992-05-10 19210\n", + "1992-05-11/1992-05-17 21092\n", + "1992-05-18/1992-05-24 22595\n", + "1992-05-25/1992-05-31 20438\n", + "1992-06-01/1992-06-07 23628\n", + "1992-06-08/1992-06-14 19614\n", + "1992-06-15/1992-06-21 26446\n", + "1992-06-22/1992-06-28 27933\n", + "1992-06-29/1992-07-05 29388\n", + "1992-07-06/1992-07-12 28144\n", + "1992-07-13/1992-07-19 18926\n", + "1992-07-20/1992-07-26 21780\n", + "1992-07-27/1992-08-02 17156\n", + "1992-08-03/1992-08-09 16090\n", + "1992-08-10/1992-08-16 9951\n", + "1992-08-17/1992-08-23 7517\n", + "1992-08-24/1992-08-30 6428\n", + "Freq: W-SUN, Name: inc, dtype: int64\n", + "1992-08-31/1992-09-06 1993-08-30/1993-09-05 period\n", + "1992-08-31/1992-09-06 2801\n", + "1992-09-07/1992-09-13 4103\n", + "1992-09-14/1992-09-20 2842\n", + "1992-09-21/1992-09-27 4747\n", + "1992-09-28/1992-10-04 5654\n", + "1992-10-05/1992-10-11 3587\n", + "1992-10-12/1992-10-18 5748\n", + "1992-10-19/1992-10-25 7196\n", + "1992-10-26/1992-11-01 10205\n", + "1992-11-02/1992-11-08 10791\n", + "1992-11-09/1992-11-15 6900\n", + "1992-11-16/1992-11-22 13069\n", + "1992-11-23/1992-11-29 10048\n", + "1992-11-30/1992-12-06 14411\n", + "1992-12-07/1992-12-13 15252\n", + "1992-12-14/1992-12-20 14635\n", + "1992-12-21/1992-12-27 14330\n", + "1992-12-28/1993-01-03 17829\n", + "1993-01-04/1993-01-10 18678\n", + "1993-01-11/1993-01-17 13366\n", + "1993-01-18/1993-01-24 15283\n", + "1993-01-25/1993-01-31 12374\n", + "1993-02-01/1993-02-07 15954\n", + "1993-02-08/1993-02-14 15887\n", + "1993-02-15/1993-02-21 16057\n", + "1993-02-22/1993-02-28 16967\n", + "1993-03-01/1993-03-07 15084\n", + "1993-03-08/1993-03-14 15847\n", + "1993-03-15/1993-03-21 11021\n", + "1993-03-22/1993-03-28 10821\n", + "1993-03-29/1993-04-04 12266\n", + "1993-04-05/1993-04-11 12425\n", + "1993-04-12/1993-04-18 15058\n", + "1993-04-19/1993-04-25 15102\n", + "1993-04-26/1993-05-02 14969\n", + "1993-05-03/1993-05-09 18898\n", + "1993-05-10/1993-05-16 12086\n", + "1993-05-17/1993-05-23 11343\n", + "1993-05-24/1993-05-30 15218\n", + "1993-05-31/1993-06-06 12652\n", + "1993-06-07/1993-06-13 16003\n", + "1993-06-14/1993-06-20 18285\n", + "1993-06-21/1993-06-27 19581\n", + "1993-06-28/1993-07-04 17937\n", + "1993-07-05/1993-07-11 14134\n", + "1993-07-12/1993-07-18 15293\n", + "1993-07-19/1993-07-25 13179\n", + "1993-07-26/1993-08-01 12584\n", + "1993-08-02/1993-08-08 12481\n", + "1993-08-09/1993-08-15 12711\n", + "1993-08-16/1993-08-22 5925\n", + "1993-08-23/1993-08-29 3770\n", + "Freq: W-SUN, Name: inc, dtype: int64\n", + "1993-08-30/1993-09-05 1994-08-29/1994-09-04 period\n", + "1993-08-30/1993-09-05 2335\n", + "1993-09-06/1993-09-12 3214\n", + "1993-09-13/1993-09-19 1762\n", + "1993-09-20/1993-09-26 2179\n", + "1993-09-27/1993-10-03 5520\n", + "1993-10-04/1993-10-10 5725\n", + "1993-10-11/1993-10-17 9657\n", + "1993-10-18/1993-10-24 7669\n", + "1993-10-25/1993-10-31 5976\n", + "1993-11-01/1993-11-07 6964\n", + "1993-11-08/1993-11-14 5648\n", + "1993-11-15/1993-11-21 7862\n", + "1993-11-22/1993-11-28 7543\n", + "1993-11-29/1993-12-05 6413\n", + "1993-12-06/1993-12-12 10095\n", + "1993-12-13/1993-12-19 6195\n", + "1993-12-20/1993-12-26 9565\n", + "1993-12-27/1994-01-02 9874\n", + "1994-01-03/1994-01-09 11104\n", + 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"1994-07-25/1994-07-31 17674\n", + "1994-08-01/1994-08-07 7346\n", + "1994-08-08/1994-08-14 4851\n", + "1994-08-15/1994-08-21 6933\n", + "1994-08-22/1994-08-28 6830\n", + "Freq: W-SUN, Name: inc, dtype: int64\n", + "1994-08-29/1994-09-04 1995-08-28/1995-09-03 period\n", + "1994-08-29/1994-09-04 3676\n", + "1994-09-05/1994-09-11 1991\n", + "1994-09-12/1994-09-18 4364\n", + "1994-09-19/1994-09-25 2734\n", + "1994-09-26/1994-10-02 4090\n", + "1994-10-03/1994-10-09 5936\n", + "1994-10-10/1994-10-16 4921\n", + "1994-10-17/1994-10-23 5848\n", + "1994-10-24/1994-10-30 5622\n", + "1994-10-31/1994-11-06 9276\n", + "1994-11-07/1994-11-13 6759\n", + "1994-11-14/1994-11-20 5021\n", + "1994-11-21/1994-11-27 7140\n", + "1994-11-28/1994-12-04 6481\n", + "1994-12-05/1994-12-11 8212\n", + "1994-12-12/1994-12-18 8048\n", + "1994-12-19/1994-12-25 10390\n", + "1994-12-26/1995-01-01 12696\n", + "1995-01-02/1995-01-08 13733\n", + "1995-01-09/1995-01-15 11495\n", + "1995-01-16/1995-01-22 13763\n", + "1995-01-23/1995-01-29 15743\n", + "1995-01-30/1995-02-05 13414\n", + "1995-02-06/1995-02-12 18089\n", + "1995-02-13/1995-02-19 14315\n", + "1995-02-20/1995-02-26 14521\n", + "1995-02-27/1995-03-05 14754\n", + "1995-03-06/1995-03-12 15346\n", + "1995-03-13/1995-03-19 12844\n", + "1995-03-20/1995-03-26 15875\n", + "1995-03-27/1995-04-02 16864\n", + "1995-04-03/1995-04-09 15247\n", + "1995-04-10/1995-04-16 16126\n", + "1995-04-17/1995-04-23 15939\n", + "1995-04-24/1995-04-30 15551\n", + "1995-05-01/1995-05-07 15333\n", + "1995-05-08/1995-05-14 15806\n", + "1995-05-15/1995-05-21 12831\n", + "1995-05-22/1995-05-28 14986\n", + "1995-05-29/1995-06-04 16158\n", + "1995-06-05/1995-06-11 19371\n", + "1995-06-12/1995-06-18 24813\n", + "1995-06-19/1995-06-25 27941\n", + "1995-06-26/1995-07-02 23680\n", + "1995-07-03/1995-07-09 31707\n", + "1995-07-10/1995-07-16 21385\n", + "1995-07-17/1995-07-23 15520\n", + "1995-07-24/1995-07-30 16283\n", + "1995-07-31/1995-08-06 12034\n", + 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6280\n", + "Freq: W-SUN, Name: inc, dtype: int64\n", + "1996-08-26/1996-09-01 1997-09-01/1997-09-07 period\n", + "1996-08-26/1996-09-01 2624\n", + "1996-09-02/1996-09-08 4577\n", + "1996-09-09/1996-09-15 2746\n", + "1996-09-16/1996-09-22 3835\n", + "1996-09-23/1996-09-29 6376\n", + "1996-09-30/1996-10-06 9514\n", + "1996-10-07/1996-10-13 10302\n", + "1996-10-14/1996-10-20 14106\n", + "1996-10-21/1996-10-27 14958\n", + "1996-10-28/1996-11-03 12095\n", + "1996-11-04/1996-11-10 17313\n", + "1996-11-11/1996-11-17 10269\n", + "1996-11-18/1996-11-24 11826\n", + "1996-11-25/1996-12-01 7489\n", + "1996-12-02/1996-12-08 10776\n", + "1996-12-09/1996-12-15 14236\n", + "1996-12-16/1996-12-22 14038\n", + "1996-12-23/1996-12-29 15556\n", + "1996-12-30/1997-01-05 14432\n", + "1997-01-06/1997-01-12 15541\n", + "1997-01-13/1997-01-19 12135\n", + "1997-01-20/1997-01-26 14475\n", + "1997-01-27/1997-02-02 12543\n", + "1997-02-03/1997-02-09 17151\n", + "1997-02-10/1997-02-16 16751\n", + 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inc, dtype: int64\n", + "1997-09-01/1997-09-07 1998-08-31/1998-09-06 period\n", + "1997-09-01/1997-09-07 2767\n", + "1997-09-08/1997-09-14 4944\n", + "1997-09-15/1997-09-21 2916\n", + "1997-09-22/1997-09-28 3242\n", + "1997-09-29/1997-10-05 2851\n", + "1997-10-06/1997-10-12 6622\n", + "1997-10-13/1997-10-19 4781\n", + "1997-10-20/1997-10-26 4829\n", + "1997-10-27/1997-11-02 5492\n", + "1997-11-03/1997-11-09 7499\n", + "1997-11-10/1997-11-16 8391\n", + "1997-11-17/1997-11-23 10482\n", + "1997-11-24/1997-11-30 7368\n", + "1997-12-01/1997-12-07 12183\n", + "1997-12-08/1997-12-14 9622\n", + "1997-12-15/1997-12-21 10933\n", + "1997-12-22/1997-12-28 18582\n", + "1997-12-29/1998-01-04 19222\n", + "1998-01-05/1998-01-11 10764\n", + "1998-01-12/1998-01-18 13085\n", + "1998-01-19/1998-01-25 15746\n", + "1998-01-26/1998-02-01 11805\n", + "1998-02-02/1998-02-08 15316\n", + "1998-02-09/1998-02-15 14618\n", + "1998-02-16/1998-02-22 15378\n", + "1998-02-23/1998-03-01 12550\n", + 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3930\n", + "2012-09-10/2012-09-16 2505\n", + "2012-09-17/2012-09-23 2684\n", + "2012-09-24/2012-09-30 4058\n", + "2012-10-01/2012-10-07 9175\n", + "2012-10-08/2012-10-14 8700\n", + "2012-10-15/2012-10-21 11450\n", + "2012-10-22/2012-10-28 11375\n", + "2012-10-29/2012-11-04 8472\n", + "2012-11-05/2012-11-11 20339\n", + "2012-11-12/2012-11-18 10741\n", + "2012-11-19/2012-11-25 11934\n", + "2012-11-26/2012-12-02 12897\n", + "2012-12-03/2012-12-09 11967\n", + "2012-12-10/2012-12-16 19840\n", + "2012-12-17/2012-12-23 15811\n", + "2012-12-24/2012-12-30 14682\n", + "2012-12-31/2013-01-06 22537\n", + "2013-01-07/2013-01-13 14746\n", + "2013-01-14/2013-01-20 10434\n", + "2013-01-21/2013-01-27 16505\n", + "2013-01-28/2013-02-03 11184\n", + "2013-02-04/2013-02-10 13305\n", + "2013-02-11/2013-02-17 12245\n", + "2013-02-18/2013-02-24 17915\n", + "2013-02-25/2013-03-03 16338\n", + "2013-03-04/2013-03-10 16515\n", + "2013-03-11/2013-03-17 16229\n", + "2013-03-18/2013-03-24 17413\n", + 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4449\n", + "2013-09-23/2013-09-29 5037\n", + "2013-09-30/2013-10-06 4114\n", + "2013-10-07/2013-10-13 9487\n", + "2013-10-14/2013-10-20 12324\n", + "2013-10-21/2013-10-27 8192\n", + "2013-10-28/2013-11-03 10655\n", + "2013-11-04/2013-11-10 7669\n", + "2013-11-11/2013-11-17 5110\n", + "2013-11-18/2013-11-24 9940\n", + "2013-11-25/2013-12-01 6708\n", + "2013-12-02/2013-12-08 10146\n", + "2013-12-09/2013-12-15 11421\n", + "2013-12-16/2013-12-22 12787\n", + "2013-12-23/2013-12-29 13843\n", + "2013-12-30/2014-01-05 22504\n", + "2014-01-06/2014-01-12 12465\n", + "2014-01-13/2014-01-19 12852\n", + "2014-01-20/2014-01-26 15767\n", + "2014-01-27/2014-02-02 12071\n", + "2014-02-03/2014-02-09 16534\n", + "2014-02-10/2014-02-16 15327\n", + "2014-02-17/2014-02-23 19870\n", + "2014-02-24/2014-03-02 15621\n", + "2014-03-03/2014-03-09 16941\n", + "2014-03-10/2014-03-16 15073\n", + "2014-03-17/2014-03-23 14097\n", + "2014-03-24/2014-03-30 17723\n", + "2014-03-31/2014-04-06 17565\n", + 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16554\n", + "2018-06-18/2018-06-24 11282\n", + "2018-06-25/2018-07-01 15317\n", + "2018-07-02/2018-07-08 13975\n", + "2018-07-09/2018-07-15 12115\n", + "2018-07-16/2018-07-22 8513\n", + "2018-07-23/2018-07-29 9338\n", + "2018-07-30/2018-08-05 6610\n", + "2018-08-06/2018-08-12 3225\n", + "2018-08-13/2018-08-19 3803\n", + "2018-08-20/2018-08-26 3736\n", + "Freq: W-SUN, Name: inc, dtype: int64\n", + "2018-08-27/2018-09-02 2019-08-26/2019-09-01 period\n", + "2018-08-27/2018-09-02 872\n", + "2018-09-03/2018-09-09 559\n", + "2018-09-10/2018-09-16 1891\n", + "2018-09-17/2018-09-23 2401\n", + "2018-09-24/2018-09-30 2584\n", + "2018-10-01/2018-10-07 3858\n", + "2018-10-08/2018-10-14 2956\n", + "2018-10-15/2018-10-21 4640\n", + "2018-10-22/2018-10-28 8705\n", + "2018-10-29/2018-11-04 7749\n", + "2018-11-05/2018-11-11 7479\n", + "2018-11-12/2018-11-18 7039\n", + "2018-11-19/2018-11-25 8670\n", + "2018-11-26/2018-12-02 8093\n", + "2018-12-03/2018-12-09 12632\n", + "2018-12-10/2018-12-16 11554\n", 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"2019-07-01/2019-07-07 16602\n", + "2019-07-08/2019-07-14 11251\n", + "2019-07-15/2019-07-21 10658\n", + "2019-07-22/2019-07-28 8875\n", + "2019-07-29/2019-08-04 7611\n", + "2019-08-05/2019-08-11 3627\n", + "2019-08-12/2019-08-18 3279\n", + "2019-08-19/2019-08-25 1997\n", + "Freq: W-SUN, Name: inc, dtype: int64\n", + "2019-08-26/2019-09-01 2020-08-31/2020-09-06 period\n", + "2019-08-26/2019-09-01 922\n", + "2019-09-02/2019-09-08 1277\n", + "2019-09-09/2019-09-15 970\n", + "2019-09-16/2019-09-22 3078\n", + "2019-09-23/2019-09-29 3137\n", + "2019-09-30/2019-10-06 4211\n", + "2019-10-07/2019-10-13 4130\n", + "2019-10-14/2019-10-20 6279\n", + "2019-10-21/2019-10-27 4834\n", + "2019-10-28/2019-11-03 5728\n", + "2019-11-04/2019-11-10 4492\n", + "2019-11-11/2019-11-17 2638\n", + "2019-11-18/2019-11-24 7536\n", + "2019-11-25/2019-12-01 5542\n", + "2019-12-02/2019-12-08 6621\n", + "2019-12-09/2019-12-15 6424\n", + "2019-12-16/2019-12-22 5823\n", + "2019-12-23/2019-12-29 7941\n", + "2019-12-30/2020-01-05 9835\n", + "2020-01-06/2020-01-12 6534\n", + "2020-01-13/2020-01-19 5968\n", + "2020-01-20/2020-01-26 7991\n", + "2020-01-27/2020-02-02 8505\n", + "2020-02-03/2020-02-09 9264\n", + "2020-02-10/2020-02-16 8959\n", + "2020-02-17/2020-02-23 10424\n", + "2020-02-24/2020-03-01 13631\n", + "2020-03-02/2020-03-08 9011\n", + "2020-03-09/2020-03-15 10198\n", + "2020-03-16/2020-03-22 8123\n", + "2020-03-23/2020-03-29 7326\n", + "2020-03-30/2020-04-05 3879\n", + "2020-04-06/2020-04-12 1918\n", + "2020-04-13/2020-04-19 758\n", + "2020-04-20/2020-04-26 272\n", + "2020-04-27/2020-05-03 849\n", + "2020-05-04/2020-05-10 310\n", + "2020-05-11/2020-05-17 824\n", + "2020-05-18/2020-05-24 602\n", + "2020-05-25/2020-05-31 277\n", + "2020-06-01/2020-06-07 558\n", + "2020-06-08/2020-06-14 388\n", + "2020-06-15/2020-06-21 228\n", + "2020-06-22/2020-06-28 694\n", + "2020-06-29/2020-07-05 986\n", + "2020-07-06/2020-07-12 728\n", + "2020-07-13/2020-07-19 841\n", + "2020-07-20/2020-07-26 1385\n", + "2020-07-27/2020-08-02 1303\n", + "2020-08-03/2020-08-09 2650\n", + "2020-08-10/2020-08-16 1284\n", + "2020-08-17/2020-08-23 2272\n", + "2020-08-24/2020-08-30 828\n", + "Freq: W-SUN, Name: inc, dtype: int64\n" + ] + } + ], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_september_week[:-1],\n", + " first_september_week[1:]):\n", + " one_year = sorted_data['inc'][week1:week2-1]\n", + " print(week1, week2, one_year)\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": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "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": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 221186\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", + "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": 14, + "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": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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