{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome 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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de l'incidence du syndrome 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 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": "markdown", "metadata": {}, "source": [ "Edit : on souhaite travailler avec un fichier local. On tente d'abord d'ouvrir ce fichier (datant du 06/05/2020) avant de retélécharger les données. Si le téléchargement échoue, l'exception est affichée." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_2 = 'syndromesVaricelle.csv'" ] }, { "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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1120200679264692511603141018FRFrance
1220200578505631410696131016FRFrance
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27201942762793989856910713FRFrance
282019417413020306230639FRFrance
292019407421122186204639FRFrance
.................................
15041991267176081130423912312042FRFrance
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15121991187213851388228888382551FRFrance
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\n", "

1534 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202017 7 272 0 658 0 0 \n", "1 202016 7 758 78 1438 1 0 \n", "2 202015 7 1918 675 3161 3 1 \n", "3 202014 7 3879 2227 5531 6 3 \n", "4 202013 7 7326 5236 9416 11 8 \n", "5 202012 7 8123 5790 10456 12 8 \n", "6 202011 7 10198 7568 12828 15 11 \n", "7 202010 7 9011 6691 11331 14 10 \n", "8 202009 7 13631 10544 16718 21 16 \n", "9 202008 7 10424 7708 13140 16 12 \n", "10 202007 7 8959 6574 11344 14 10 \n", "11 202006 7 9264 6925 11603 14 10 \n", "12 202005 7 8505 6314 10696 13 10 \n", "13 202004 7 7991 5831 10151 12 9 \n", "14 202003 7 5968 4100 7836 9 6 \n", "15 202002 7 6534 4530 8538 10 7 \n", "16 202001 7 9835 7019 12651 15 11 \n", "17 201952 7 7941 5246 10636 12 8 \n", "18 201951 7 5823 3675 7971 9 6 \n", "19 201950 7 6424 4276 8572 10 7 \n", "20 201949 7 6621 4540 8702 10 7 \n", "21 201948 7 5542 3383 7701 8 5 \n", "22 201947 7 7536 5058 10014 11 7 \n", "23 201946 7 2638 1316 3960 4 2 \n", "24 201945 7 4492 2615 6369 7 4 \n", "25 201944 7 5728 3627 7829 9 6 \n", "26 201943 7 4834 2751 6917 7 4 \n", "27 201942 7 6279 3989 8569 10 7 \n", "28 201941 7 4130 2030 6230 6 3 \n", "29 201940 7 4211 2218 6204 6 3 \n", "... ... ... ... ... ... ... ... \n", "1504 199126 7 17608 11304 23912 31 20 \n", "1505 199125 7 16169 10700 21638 28 18 \n", "1506 199124 7 16171 10071 22271 28 17 \n", "1507 199123 7 11947 7671 16223 21 13 \n", "1508 199122 7 15452 9953 20951 27 17 \n", "1509 199121 7 14903 8975 20831 26 16 \n", "1510 199120 7 19053 12742 25364 34 23 \n", "1511 199119 7 16739 11246 22232 29 19 \n", "1512 199118 7 21385 13882 28888 38 25 \n", "1513 199117 7 13462 8877 18047 24 16 \n", "1514 199116 7 14857 10068 19646 26 18 \n", "1515 199115 7 13975 9781 18169 25 18 \n", "1516 199114 7 12265 7684 16846 22 14 \n", "1517 199113 7 9567 6041 13093 17 11 \n", "1518 199112 7 10864 7331 14397 19 13 \n", "1519 199111 7 15574 11184 19964 27 19 \n", "1520 199110 7 16643 11372 21914 29 20 \n", "1521 199109 7 13741 8780 18702 24 15 \n", "1522 199108 7 13289 8813 17765 23 15 \n", "1523 199107 7 12337 8077 16597 22 15 \n", "1524 199106 7 10877 7013 14741 19 12 \n", "1525 199105 7 10442 6544 14340 18 11 \n", "1526 199104 7 7913 4563 11263 14 8 \n", "1527 199103 7 15387 10484 20290 27 18 \n", "1528 199102 7 16277 11046 21508 29 20 \n", "1529 199101 7 15565 10271 20859 27 18 \n", "1530 199052 7 19375 13295 25455 34 23 \n", "1531 199051 7 19080 13807 24353 34 25 \n", "1532 199050 7 11079 6660 15498 20 12 \n", "1533 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 1 FR France \n", "1 2 FR France \n", "2 5 FR France \n", "3 9 FR France \n", "4 14 FR France \n", "5 16 FR France \n", "6 19 FR France \n", "7 18 FR France \n", "8 26 FR France \n", "9 20 FR France \n", "10 18 FR France \n", "11 18 FR France \n", "12 16 FR France \n", "13 15 FR France \n", "14 12 FR France \n", "15 13 FR France \n", "16 19 FR France \n", "17 16 FR France \n", "18 12 FR France \n", "19 13 FR France \n", "20 13 FR France \n", "21 11 FR France \n", "22 15 FR France \n", "23 6 FR France \n", "24 10 FR France \n", "25 12 FR France \n", "26 10 FR France \n", "27 13 FR France \n", "28 9 FR France \n", "29 9 FR France \n", "... ... ... ... \n", "1504 42 FR France \n", "1505 38 FR France \n", "1506 39 FR France \n", "1507 29 FR France \n", "1508 37 FR France \n", "1509 36 FR France \n", "1510 45 FR France \n", "1511 39 FR France \n", "1512 51 FR France \n", "1513 32 FR France \n", "1514 34 FR France \n", "1515 32 FR France \n", "1516 30 FR France \n", "1517 23 FR France \n", "1518 25 FR France \n", "1519 35 FR France \n", "1520 38 FR France \n", "1521 33 FR France \n", "1522 31 FR France \n", "1523 29 FR France \n", "1524 26 FR France \n", "1525 25 FR France \n", "1526 20 FR France \n", "1527 36 FR France \n", "1528 38 FR France \n", "1529 36 FR France \n", "1530 45 FR France \n", "1531 43 FR France \n", "1532 28 FR France \n", "1533 5 FR France \n", "\n", "[1534 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "try:\n", " raw_data = pd.read_csv(data_2, skiprows=1)\n", "except:\n", " try:\n", " raw_data = pd.read_csv(data_url, skiprows=1)\n", " except:\n", " print(\"Error : the file doesn't exist\")\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Non" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "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": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On garde cette ligne pour le changement de variable mais ce n'est pas indispensable car rien n'est supprimé" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020201772720658001FRFrance
12020167758781438102FRFrance
2202015719186753161315FRFrance
32020147387922275531639FRFrance
4202013773265236941611814FRFrance
52020127812357901045612816FRFrance
6202011710198756812828151119FRFrance
720201079011669111331141018FRFrance
82020097136311054416718211626FRFrance
9202008710424770813140161220FRFrance
1020200778959657411344141018FRFrance
1120200679264692511603141018FRFrance
1220200578505631410696131016FRFrance
132020047799158311015112915FRFrance
1420200375968410078369612FRFrance
15202002765344530853810713FRFrance
1620200179835701912651151119FRFrance
172019527794152461063612816FRFrance
1820195175823367579719612FRFrance
19201950764244276857210713FRFrance
20201949766214540870210713FRFrance
2120194875542338377018511FRFrance
222019477753650581001411715FRFrance
232019467263813163960426FRFrance
2420194574492261563697410FRFrance
2520194475728362778299612FRFrance
2620194374834275169177410FRFrance
27201942762793989856910713FRFrance
282019417413020306230639FRFrance
292019407421122186204639FRFrance
.................................
15041991267176081130423912312042FRFrance
15051991257161691070021638281838FRFrance
15061991247161711007122271281739FRFrance
1507199123711947767116223211329FRFrance
1508199122715452995320951271737FRFrance
1509199121714903897520831261636FRFrance
15101991207190531274225364342345FRFrance
15111991197167391124622232291939FRFrance
15121991187213851388228888382551FRFrance
1513199117713462887718047241632FRFrance
15141991167148571006819646261834FRFrance
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15271991037153871048420290271836FRFrance
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15301990527193751329525455342345FRFrance
15311990517190801380724353342543FRFrance
1532199050711079666015498201228FRFrance
15331990497114302610205FRFrance
\n", "

1534 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202017 7 272 0 658 0 0 \n", "1 202016 7 758 78 1438 1 0 \n", "2 202015 7 1918 675 3161 3 1 \n", "3 202014 7 3879 2227 5531 6 3 \n", "4 202013 7 7326 5236 9416 11 8 \n", "5 202012 7 8123 5790 10456 12 8 \n", "6 202011 7 10198 7568 12828 15 11 \n", "7 202010 7 9011 6691 11331 14 10 \n", "8 202009 7 13631 10544 16718 21 16 \n", "9 202008 7 10424 7708 13140 16 12 \n", "10 202007 7 8959 6574 11344 14 10 \n", "11 202006 7 9264 6925 11603 14 10 \n", "12 202005 7 8505 6314 10696 13 10 \n", "13 202004 7 7991 5831 10151 12 9 \n", "14 202003 7 5968 4100 7836 9 6 \n", "15 202002 7 6534 4530 8538 10 7 \n", "16 202001 7 9835 7019 12651 15 11 \n", "17 201952 7 7941 5246 10636 12 8 \n", "18 201951 7 5823 3675 7971 9 6 \n", "19 201950 7 6424 4276 8572 10 7 \n", "20 201949 7 6621 4540 8702 10 7 \n", "21 201948 7 5542 3383 7701 8 5 \n", "22 201947 7 7536 5058 10014 11 7 \n", "23 201946 7 2638 1316 3960 4 2 \n", "24 201945 7 4492 2615 6369 7 4 \n", "25 201944 7 5728 3627 7829 9 6 \n", "26 201943 7 4834 2751 6917 7 4 \n", "27 201942 7 6279 3989 8569 10 7 \n", "28 201941 7 4130 2030 6230 6 3 \n", "29 201940 7 4211 2218 6204 6 3 \n", "... ... ... ... ... ... ... ... \n", "1504 199126 7 17608 11304 23912 31 20 \n", "1505 199125 7 16169 10700 21638 28 18 \n", "1506 199124 7 16171 10071 22271 28 17 \n", "1507 199123 7 11947 7671 16223 21 13 \n", "1508 199122 7 15452 9953 20951 27 17 \n", "1509 199121 7 14903 8975 20831 26 16 \n", "1510 199120 7 19053 12742 25364 34 23 \n", "1511 199119 7 16739 11246 22232 29 19 \n", "1512 199118 7 21385 13882 28888 38 25 \n", "1513 199117 7 13462 8877 18047 24 16 \n", "1514 199116 7 14857 10068 19646 26 18 \n", "1515 199115 7 13975 9781 18169 25 18 \n", "1516 199114 7 12265 7684 16846 22 14 \n", "1517 199113 7 9567 6041 13093 17 11 \n", "1518 199112 7 10864 7331 14397 19 13 \n", "1519 199111 7 15574 11184 19964 27 19 \n", "1520 199110 7 16643 11372 21914 29 20 \n", "1521 199109 7 13741 8780 18702 24 15 \n", "1522 199108 7 13289 8813 17765 23 15 \n", "1523 199107 7 12337 8077 16597 22 15 \n", "1524 199106 7 10877 7013 14741 19 12 \n", "1525 199105 7 10442 6544 14340 18 11 \n", "1526 199104 7 7913 4563 11263 14 8 \n", "1527 199103 7 15387 10484 20290 27 18 \n", "1528 199102 7 16277 11046 21508 29 20 \n", "1529 199101 7 15565 10271 20859 27 18 \n", "1530 199052 7 19375 13295 25455 34 23 \n", "1531 199051 7 19080 13807 24353 34 25 \n", "1532 199050 7 11079 6660 15498 20 12 \n", "1533 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 1 FR France \n", "1 2 FR France \n", "2 5 FR France \n", "3 9 FR France \n", "4 14 FR France \n", "5 16 FR France \n", "6 19 FR France \n", "7 18 FR France \n", "8 26 FR France \n", "9 20 FR France \n", "10 18 FR France \n", "11 18 FR France \n", "12 16 FR France \n", "13 15 FR France \n", "14 12 FR France \n", "15 13 FR France \n", "16 19 FR France \n", "17 16 FR France \n", "18 12 FR France \n", "19 13 FR France \n", "20 13 FR France \n", "21 11 FR France \n", "22 15 FR France \n", "23 6 FR France \n", "24 10 FR France \n", "25 12 FR France \n", "26 10 FR France \n", "27 13 FR France \n", "28 9 FR France \n", "29 9 FR France \n", "... ... ... ... \n", "1504 42 FR France \n", "1505 38 FR France \n", "1506 39 FR France \n", "1507 29 FR France \n", "1508 37 FR France \n", "1509 36 FR France \n", "1510 45 FR France \n", "1511 39 FR France \n", "1512 51 FR France \n", "1513 32 FR France \n", "1514 34 FR France \n", "1515 32 FR France \n", "1516 30 FR France \n", "1517 23 FR France \n", "1518 25 FR France \n", "1519 35 FR France \n", "1520 38 FR France \n", "1521 33 FR France \n", "1522 31 FR France \n", "1523 29 FR France \n", "1524 26 FR France \n", "1525 25 FR France \n", "1526 20 FR France \n", "1527 36 FR France \n", "1528 38 FR France \n", "1529 36 FR France \n", "1530 45 FR France \n", "1531 43 FR France \n", "1532 28 FR France \n", "1533 5 FR France \n", "\n", "[1534 rows x 10 columns]" ] }, "execution_count": 6, "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": 7, "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": 8, "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." ] }, { "cell_type": "code", "execution_count": 9, "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": [ "Rien n'est print car les données sont complètes. Un premier regard sur les données !" ] }, { "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'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un zoom sur les dernières années montre mieux la situation des pics en hiver et printemps. Le creux des incidences se trouve en fin d'été." ] }, { "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'][-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 septempbre 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 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 fin 90, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 91." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "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": {}, "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": 13, "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": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "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": 15, "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": 15, "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 presque 15 % de la population\n", " française, sont assez rares: il y en eu trois au cours des 39 dernières années." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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