diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..6767764dec385acc54eb98de97d7bd30b9ebba4e 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2472 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence de la varicelle" + ] + }, + { + "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.path" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"\n", + "saving_file = \"varicelle.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", + "df.to_csv('file_name.csv')La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On vérifie que les donées ont bien été téléchargées. Si c'est le cas, on les charge. Si ce n'est pas le cas, on les telecharge et puis on les enregistre." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from url\n" + ] + }, + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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282019477753650581001411715FRFrance
292019467263813163960426FRFrance
.................................
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1540 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202023 7 842 0 1690 1 0 \n", + "1 202022 7 280 0 638 0 0 \n", + "2 202021 7 602 36 1168 1 0 \n", + "3 202020 7 824 20 1628 1 0 \n", + "4 202019 7 310 0 753 0 0 \n", + "5 202018 7 849 98 1600 1 0 \n", + "6 202017 7 272 0 658 0 0 \n", + "7 202016 7 758 78 1438 1 0 \n", + "8 202015 7 1918 675 3161 3 1 \n", + "9 202014 7 3879 2227 5531 6 3 \n", + "10 202013 7 7326 5236 9416 11 8 \n", + "11 202012 7 8123 5790 10456 12 8 \n", + "12 202011 7 10198 7568 12828 15 11 \n", + "13 202010 7 9011 6691 11331 14 10 \n", + "14 202009 7 13631 10544 16718 21 16 \n", + "15 202008 7 10424 7708 13140 16 12 \n", + "16 202007 7 8959 6574 11344 14 10 \n", + "17 202006 7 9264 6925 11603 14 10 \n", + "18 202005 7 8505 6314 10696 13 10 \n", + "19 202004 7 7991 5831 10151 12 9 \n", + "20 202003 7 5968 4100 7836 9 6 \n", + "21 202002 7 6534 4530 8538 10 7 \n", + "22 202001 7 9835 7019 12651 15 11 \n", + "23 201952 7 7941 5246 10636 12 8 \n", + "24 201951 7 5823 3675 7971 9 6 \n", + "25 201950 7 6424 4276 8572 10 7 \n", + "26 201949 7 6621 4540 8702 10 7 \n", + "27 201948 7 5542 3383 7701 8 5 \n", + "28 201947 7 7536 5058 10014 11 7 \n", + "29 201946 7 2638 1316 3960 4 2 \n", + "... ... ... ... ... ... ... ... \n", + "1510 199126 7 17608 11304 23912 31 20 \n", + "1511 199125 7 16169 10700 21638 28 18 \n", + "1512 199124 7 16171 10071 22271 28 17 \n", + "1513 199123 7 11947 7671 16223 21 13 \n", + "1514 199122 7 15452 9953 20951 27 17 \n", + "1515 199121 7 14903 8975 20831 26 16 \n", + "1516 199120 7 19053 12742 25364 34 23 \n", + "1517 199119 7 16739 11246 22232 29 19 \n", + "1518 199118 7 21385 13882 28888 38 25 \n", + "1519 199117 7 13462 8877 18047 24 16 \n", + "1520 199116 7 14857 10068 19646 26 18 \n", + "1521 199115 7 13975 9781 18169 25 18 \n", + "1522 199114 7 12265 7684 16846 22 14 \n", + "1523 199113 7 9567 6041 13093 17 11 \n", + "1524 199112 7 10864 7331 14397 19 13 \n", + "1525 199111 7 15574 11184 19964 27 19 \n", + "1526 199110 7 16643 11372 21914 29 20 \n", + "1527 199109 7 13741 8780 18702 24 15 \n", + "1528 199108 7 13289 8813 17765 23 15 \n", + "1529 199107 7 12337 8077 16597 22 15 \n", + "1530 199106 7 10877 7013 14741 19 12 \n", + "1531 199105 7 10442 6544 14340 18 11 \n", + "1532 199104 7 7913 4563 11263 14 8 \n", + "1533 199103 7 15387 10484 20290 27 18 \n", + "1534 199102 7 16277 11046 21508 29 20 \n", + "1535 199101 7 15565 10271 20859 27 18 \n", + "1536 199052 7 19375 13295 25455 34 23 \n", + "1537 199051 7 19080 13807 24353 34 25 \n", + "1538 199050 7 11079 6660 15498 20 12 \n", + "1539 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 2 FR France \n", + "1 1 FR France \n", + "2 2 FR France \n", + "3 2 FR France \n", + "4 1 FR France \n", + "5 2 FR France \n", + "6 1 FR France \n", + "7 2 FR France \n", + "8 5 FR France \n", + "9 9 FR France \n", + "10 14 FR France \n", + "11 16 FR France \n", + "12 19 FR France \n", + "13 18 FR France \n", + "14 26 FR France \n", + "15 20 FR France \n", + "16 18 FR France \n", + "17 18 FR France \n", + "18 16 FR France \n", + "19 15 FR France \n", + "20 12 FR France \n", + "21 13 FR France \n", + "22 19 FR France \n", + "23 16 FR France \n", + "24 12 FR France \n", + "25 13 FR France \n", + "26 13 FR France \n", + "27 11 FR France \n", + "28 15 FR France \n", + "29 6 FR France \n", + "... ... ... ... \n", + "1510 42 FR France \n", + "1511 38 FR France \n", + "1512 39 FR France \n", + "1513 29 FR France \n", + "1514 37 FR France \n", + "1515 36 FR France \n", + "1516 45 FR France \n", + "1517 39 FR France \n", + "1518 51 FR France \n", + "1519 32 FR France \n", + "1520 34 FR France \n", + "1521 32 FR France \n", + "1522 30 FR France \n", + "1523 23 FR France \n", + "1524 25 FR France \n", + "1525 35 FR France \n", + "1526 38 FR France \n", + "1527 33 FR France \n", + "1528 31 FR France \n", + "1529 29 FR France \n", + "1530 26 FR France \n", + "1531 25 FR France \n", + "1532 20 FR France \n", + "1533 36 FR France \n", + "1534 38 FR France \n", + "1535 36 FR France \n", + "1536 45 FR France \n", + "1537 43 FR France \n", + "1538 28 FR France \n", + "1539 5 FR France \n", + "\n", + "[1540 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "if os.path.exists(saving_file):\n", + " print(\"Loaging existing data\")\n", + " raw_data = pd.read_csv(saving_file, skiprows=1)\n", + "else:\n", + " print(\"Downloading data from url\")\n", + " raw_data = pd.read_csv(data_url, skiprows=1)\n", + " raw_data.to_csv(saving_file)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Y a-t-il des points manquants dans ce jeux de données ? En principe non." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous éliminons ce(s) point(s), s'il y en a (bien que ce ne soit pas notre cas) ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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1540 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202023 7 842 0 1690 1 0 \n", + "1 202022 7 280 0 638 0 0 \n", + "2 202021 7 602 36 1168 1 0 \n", + "3 202020 7 824 20 1628 1 0 \n", + "4 202019 7 310 0 753 0 0 \n", + "5 202018 7 849 98 1600 1 0 \n", + "6 202017 7 272 0 658 0 0 \n", + "7 202016 7 758 78 1438 1 0 \n", + "8 202015 7 1918 675 3161 3 1 \n", + "9 202014 7 3879 2227 5531 6 3 \n", + "10 202013 7 7326 5236 9416 11 8 \n", + "11 202012 7 8123 5790 10456 12 8 \n", + "12 202011 7 10198 7568 12828 15 11 \n", + "13 202010 7 9011 6691 11331 14 10 \n", + "14 202009 7 13631 10544 16718 21 16 \n", + "15 202008 7 10424 7708 13140 16 12 \n", + "16 202007 7 8959 6574 11344 14 10 \n", + "17 202006 7 9264 6925 11603 14 10 \n", + "18 202005 7 8505 6314 10696 13 10 \n", + "19 202004 7 7991 5831 10151 12 9 \n", + "20 202003 7 5968 4100 7836 9 6 \n", + "21 202002 7 6534 4530 8538 10 7 \n", + "22 202001 7 9835 7019 12651 15 11 \n", + "23 201952 7 7941 5246 10636 12 8 \n", + "24 201951 7 5823 3675 7971 9 6 \n", + "25 201950 7 6424 4276 8572 10 7 \n", + "26 201949 7 6621 4540 8702 10 7 \n", + "27 201948 7 5542 3383 7701 8 5 \n", + "28 201947 7 7536 5058 10014 11 7 \n", + "29 201946 7 2638 1316 3960 4 2 \n", + "... ... ... ... ... ... ... ... \n", + "1510 199126 7 17608 11304 23912 31 20 \n", + "1511 199125 7 16169 10700 21638 28 18 \n", + "1512 199124 7 16171 10071 22271 28 17 \n", + "1513 199123 7 11947 7671 16223 21 13 \n", + "1514 199122 7 15452 9953 20951 27 17 \n", + "1515 199121 7 14903 8975 20831 26 16 \n", + "1516 199120 7 19053 12742 25364 34 23 \n", + "1517 199119 7 16739 11246 22232 29 19 \n", + "1518 199118 7 21385 13882 28888 38 25 \n", + "1519 199117 7 13462 8877 18047 24 16 \n", + "1520 199116 7 14857 10068 19646 26 18 \n", + "1521 199115 7 13975 9781 18169 25 18 \n", + "1522 199114 7 12265 7684 16846 22 14 \n", + "1523 199113 7 9567 6041 13093 17 11 \n", + "1524 199112 7 10864 7331 14397 19 13 \n", + "1525 199111 7 15574 11184 19964 27 19 \n", + "1526 199110 7 16643 11372 21914 29 20 \n", + "1527 199109 7 13741 8780 18702 24 15 \n", + "1528 199108 7 13289 8813 17765 23 15 \n", + "1529 199107 7 12337 8077 16597 22 15 \n", + "1530 199106 7 10877 7013 14741 19 12 \n", + "1531 199105 7 10442 6544 14340 18 11 \n", + "1532 199104 7 7913 4563 11263 14 8 \n", + "1533 199103 7 15387 10484 20290 27 18 \n", + "1534 199102 7 16277 11046 21508 29 20 \n", + "1535 199101 7 15565 10271 20859 27 18 \n", + "1536 199052 7 19375 13295 25455 34 23 \n", + "1537 199051 7 19080 13807 24353 34 25 \n", + "1538 199050 7 11079 6660 15498 20 12 \n", + "1539 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 2 FR France \n", + "1 1 FR France \n", + "2 2 FR France \n", + "3 2 FR France \n", + "4 1 FR France \n", + "5 2 FR France \n", + "6 1 FR France \n", + "7 2 FR France \n", + "8 5 FR France \n", + "9 9 FR France \n", + "10 14 FR France \n", + "11 16 FR France \n", + "12 19 FR France \n", + "13 18 FR France \n", + "14 26 FR France \n", + "15 20 FR France \n", + "16 18 FR France \n", + "17 18 FR France \n", + "18 16 FR France \n", + "19 15 FR France \n", + "20 12 FR France \n", + "21 13 FR France \n", + "22 19 FR France \n", + "23 16 FR France \n", + "24 12 FR France \n", + "25 13 FR France \n", + "26 13 FR France \n", + "27 11 FR France \n", + "28 15 FR France \n", + "29 6 FR France \n", + "... ... ... ... \n", + "1510 42 FR France \n", + "1511 38 FR France \n", + "1512 39 FR France \n", + "1513 29 FR France \n", + "1514 37 FR France \n", + "1515 36 FR France \n", + "1516 45 FR France \n", + "1517 39 FR France \n", + "1518 51 FR France \n", + "1519 32 FR France \n", + "1520 34 FR France \n", + "1521 32 FR France \n", + "1522 30 FR France \n", + "1523 23 FR France \n", + "1524 25 FR France \n", + "1525 35 FR France \n", + "1526 38 FR France \n", + "1527 33 FR France \n", + "1528 31 FR France \n", + "1529 29 FR France \n", + "1530 26 FR France \n", + "1531 25 FR France \n", + "1532 20 FR France \n", + "1533 36 FR France \n", + "1534 38 FR France \n", + "1535 36 FR France \n", + "1536 45 FR France \n", + "1537 43 FR France \n", + "1538 28 FR France \n", + "1539 5 FR France \n", + "\n", + "[1540 rows x 10 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nos données utilisent une convention inhabituelle: le numéro de\n", + "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", + "de nombre entier. C'est comme ça que Pandas les interprète.\n", + " \n", + "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", + "semaine. Il faut lui fournir les dates de début et de fin de\n", + "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", + "\n", + "Comme la conversion des semaines est devenu assez complexe, nous\n", + "écrivons une petite fonction Python pour cela. Ensuite, nous\n", + "l'appliquons à tous les points de nos donnés. Les résultats vont\n", + "dans une nouvelle colonne 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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 comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique, ce qui sera pratique par la suite.\n", + "\n", + "Deuxièmement, nous trions les points par période, dans le sens chronologique.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "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 le début de la période qui suit, la différence temporelle doit être zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\" d'une seconde.\n", + "\n", + "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives entre lesquelles il manque une semaine.\n", + "\n", + "Nous reconnaissons ces dates: c'est la semaine sans observations que nous avions supprimées !\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "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": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "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.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "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 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 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 en décembre 1990, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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 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.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "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", + " 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.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "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": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "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": 18, + "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 française, sont assez rares: il y en eu trois au cours des 35 dernières années.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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