{ "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" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true }, "source": [ "Les données de l'incidence de la varicelle sont disponibles sur le 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. Le jeu de données complet 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\"" ] }, { "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": [ "Pour éviter une perte de données en cas de changements sur le serveur du Réseau Sentinelles, les données sont stockées en local dans le fichier syndrome-varicelle.csv. Si ce fichier n'est pas présent, les données seront automatiquement téléchargées et le fichier créé." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data_file = \"syndrome-varicelle.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file) :\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "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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120204774999296370358511FRFrance
22020467375219635541639FRFrance
32020457369620165376639FRFrance
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62020427400019796021639FRFrance
72020417396120995823639FRFrance
8202040720786753481315FRFrance
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10202038722537823724315FRFrance
11202037715844052763204FRFrance
1220203679191001738102FRFrance
13202035782801694102FRFrance
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15202033712841772391204FRFrance
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20202028772801515102FRFrance
2120202779861491823102FRFrance
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2320202572280597001FRFrance
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25202023755811115102FRFrance
2620202272770633001FRFrance
272020217602361168102FRFrance
282020207824201628102FRFrance
2920201973100753001FRFrance
.................................
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\n", "

1565 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202048 7 6747 4331 9163 10 6 \n", "1 202047 7 4999 2963 7035 8 5 \n", "2 202046 7 3752 1963 5541 6 3 \n", "3 202045 7 3696 2016 5376 6 3 \n", "4 202044 7 4391 2375 6407 7 4 \n", "5 202043 7 4376 2505 6247 7 4 \n", "6 202042 7 4000 1979 6021 6 3 \n", "7 202041 7 3961 2099 5823 6 3 \n", "8 202040 7 2078 675 3481 3 1 \n", "9 202039 7 1049 237 1861 2 1 \n", "10 202038 7 2253 782 3724 3 1 \n", "11 202037 7 1584 405 2763 2 0 \n", "12 202036 7 919 100 1738 1 0 \n", "13 202035 7 828 0 1694 1 0 \n", "14 202034 7 2272 371 4173 3 0 \n", "15 202033 7 1284 177 2391 2 0 \n", "16 202032 7 2650 689 4611 4 1 \n", "17 202031 7 1303 100 2506 2 0 \n", "18 202030 7 1385 75 2695 2 0 \n", "19 202029 7 841 10 1672 1 0 \n", "20 202028 7 728 0 1515 1 0 \n", "21 202027 7 986 149 1823 1 0 \n", "22 202026 7 694 0 1454 1 0 \n", "23 202025 7 228 0 597 0 0 \n", "24 202024 7 388 0 959 1 0 \n", "25 202023 7 558 1 1115 1 0 \n", "26 202022 7 277 0 633 0 0 \n", "27 202021 7 602 36 1168 1 0 \n", "28 202020 7 824 20 1628 1 0 \n", "29 202019 7 310 0 753 0 0 \n", "... ... ... ... ... ... ... ... \n", "1535 199126 7 17608 11304 23912 31 20 \n", "1536 199125 7 16169 10700 21638 28 18 \n", "1537 199124 7 16171 10071 22271 28 17 \n", "1538 199123 7 11947 7671 16223 21 13 \n", "1539 199122 7 15452 9953 20951 27 17 \n", "1540 199121 7 14903 8975 20831 26 16 \n", "1541 199120 7 19053 12742 25364 34 23 \n", "1542 199119 7 16739 11246 22232 29 19 \n", "1543 199118 7 21385 13882 28888 38 25 \n", "1544 199117 7 13462 8877 18047 24 16 \n", "1545 199116 7 14857 10068 19646 26 18 \n", "1546 199115 7 13975 9781 18169 25 18 \n", "1547 199114 7 12265 7684 16846 22 14 \n", "1548 199113 7 9567 6041 13093 17 11 \n", "1549 199112 7 10864 7331 14397 19 13 \n", "1550 199111 7 15574 11184 19964 27 19 \n", "1551 199110 7 16643 11372 21914 29 20 \n", "1552 199109 7 13741 8780 18702 24 15 \n", "1553 199108 7 13289 8813 17765 23 15 \n", "1554 199107 7 12337 8077 16597 22 15 \n", "1555 199106 7 10877 7013 14741 19 12 \n", "1556 199105 7 10442 6544 14340 18 11 \n", "1557 199104 7 7913 4563 11263 14 8 \n", "1558 199103 7 15387 10484 20290 27 18 \n", "1559 199102 7 16277 11046 21508 29 20 \n", "1560 199101 7 15565 10271 20859 27 18 \n", "1561 199052 7 19375 13295 25455 34 23 \n", "1562 199051 7 19080 13807 24353 34 25 \n", "1563 199050 7 11079 6660 15498 20 12 \n", "1564 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 14 FR France \n", "1 11 FR France \n", "2 9 FR France \n", "3 9 FR France \n", "4 10 FR France \n", "5 10 FR France \n", "6 9 FR France \n", "7 9 FR France \n", "8 5 FR France \n", "9 3 FR France \n", "10 5 FR France \n", "11 4 FR France \n", "12 2 FR France \n", "13 2 FR France \n", "14 6 FR France \n", "15 4 FR France \n", "16 7 FR France \n", "17 4 FR France \n", "18 4 FR France \n", "19 2 FR France \n", "20 2 FR France \n", "21 2 FR France \n", "22 2 FR France \n", "23 1 FR France \n", "24 2 FR France \n", "25 2 FR France \n", "26 1 FR France \n", "27 2 FR France \n", "28 2 FR France \n", "29 1 FR France \n", "... ... ... ... \n", "1535 42 FR France \n", "1536 38 FR France \n", "1537 39 FR France \n", "1538 29 FR France \n", "1539 37 FR France \n", "1540 36 FR France \n", "1541 45 FR France \n", "1542 39 FR France \n", "1543 51 FR France \n", "1544 32 FR France \n", "1545 34 FR France \n", "1546 32 FR France \n", "1547 30 FR France \n", "1548 23 FR France \n", "1549 25 FR France \n", "1550 35 FR France \n", "1551 38 FR France \n", "1552 33 FR France \n", "1553 31 FR France \n", "1554 29 FR France \n", "1555 26 FR France \n", "1556 25 FR France \n", "1557 20 FR France \n", "1558 36 FR France \n", "1559 38 FR France \n", "1560 36 FR France \n", "1561 45 FR France \n", "1562 43 FR France \n", "1563 28 FR France \n", "1564 5 FR France \n", "\n", "[1565 rows x 10 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous vérifions que jeu de données ne contient pas de points manquants." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le jeu données est complet, nous le copions donc directement dans une nouvelle variable 'data' sur laquelle nous pourrons travailler sans modifier les données brutes." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "data = raw_data.copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mais nos données utilisent une convention inhabituelle : le numéro de semaine est collé à l'année, donnant l'impression qu'il s'agit 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 semaine. Il faut lui fournir les dates de début et de fin de semaine. Nous utilisons pour cela la bibliothèque 'isoweek'.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous écrivons une petite fonction Python pour cela. Ensuite, nous l'appliquons à tous les points de nos donnés. Les résultats vont dans une nouvelle colonne 'period'." ] }, { "cell_type": "code", "execution_count": 9, "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 reste deux modifications à faire.\n", "\n", "Premièrement, nous définissons les périodes d'observation comme nouvel index de notre jeu de données pour ne faire une suite chonologique, plus facile à utiliser.\n", "\n", "Deuxièmement , nous tirons les points par période dans le sens chonologique." ] }, { "cell_type": "code", "execution_count": 10, "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 laisson une \"marge d'erreur\" d'une seconde." ] }, { "cell_type": "code", "execution_count": 13, "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": [ "Il n'y a visiblement pas de problème de cohérence des données.\n", "Regardons ce que ça donne :" ] }, { "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", "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 un creux des incidences en septembre de chaque année." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "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": [ "Pour simplifier le décompte des incidences par années, nous définissons la période de référence entre deux minima de l'incidence, du 1er septembre de l'année **N** au 1er septembre de l'année **N + 1**.\n", "\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence : à la place du 1er spetembre de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er septembre.\n", "\n", "Comme l'incidence de la varicelle est très faible en septembre, cette modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail : les données commencent en décembre 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range (1991, sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er septemebre, 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 protéfer contre des éventuelles erreurs dans notre code." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "\n", "for week1, week2 in zip(first_sept_week[:-1], first_sept_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": true }, "source": [ "Voici les incidences annuelles." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { 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pcjOrd04mw8ALLJpZvfPU4CHkp8nNbKRwy2QIeelrMxspnEyGkJ8mN7ORwslkiA106WsP1ptZLSoqmUj6qKSNkn4laY+kz0i6R9Lbkl5L2xdyx98pqVXSXkmLcvG5kl5P362RpBQfI+mZFG+RND1XZpmkfWlbVr6qD49Hbp7H6qWXM2vyOFYvvbzbUti98WC9mdWiopZTkbQOeDkivi/pI8A5wNeB9yLi2z2OnQWsBxqBycCLwJ9ExElJ24GvAf8EPAesiYgtkr4C/OuI+FtJTcB1EfGXkiYAO4F5QACvAHMj4uiZrrVWl1PpOVhf4MF6MxsOQ76ciqRxwGeBRwEi4g8R8bs+iiwBno6Izoh4E2gFGiVdBIyLiG2RZbDHgaW5MuvS/kZgQWq1LAKaI6IjJZBmYPGAa1kDPFhvZrWsmG6uPwbagR9KelXS9yWdm767XdIvJf1A0vgUmwK8lSt/MMWmpP2e8W5lIqILeBe4oI9z1R0P1ptZLSsmmTQAnwYejogrgN8DdwAPA58A5gCHge+k49XLOaKP+GDLfEDSCkk7Je1sb2/voyrVze+pNrNaVcxDiweBgxHRkn7eCNwREUcKB0j6HvA/c8dfnCs/FTiU4lN7iefLHJTUAJwPdKT453qU+VnPC4yItcBayMZMiqhTydqOneD29a/y4I1XlK314PdUm1mt6rdlEhHvAG9JuiyFFgBvpDGQguuAXWl/M9CUZmjNAGYC2yPiMHBc0pVpPOQW4NlcmcJMreuBl9K4yvPAQknjUzfawhSrOM+6MiuOp7uPDMUup/JV4EdpJte/AF8G1kiaQ9bttB+4FSAidkvaALwBdAErI+JkOs9twGPA2cCWtEE2uP+EpFayFklTOleHpPuAHem4eyOiY3BVLQ8vkWI2MPk/vFZf96lKX44NEb9pcYDajp1g9XN7eGH3O5x4/xRjR49i0eyPc9cXP+nBcrMcT3evLX7T4jDzrCuz4ni6+8jiVYMHoTDr6sbGaTy1/QDt7gs2+xD/4TWyOJkMgmddWbkMxazAauI/vEYOj5mYVdDdm17nR9sPcFPjNA9OW0WVOmbilolZBXhWoNUbD8CblVkxz1V4cNrqjZOJWZkV80CrB6et3riby6xMBtp15cHpD6v3CQn1zAPwZmXiB1pL5wkJleMBeLMq4a6rwfOEhNrnMROzMvJrBAbHExJqn1smVtVqrQ/dD7QOjlt1tc8tExt2A1mS3Ev9jxxu1dU2D8DbsCtmkNUrzpoNr1IH4J1MbNgMJEF4ZpTZ8PIS9FYzBjLI6j50s9riAXgbNgNNEH6oz6x2OJnYsBpIgvDMKLPa4TETMzPzmImZmVWek4mZWQ8DeRbKMk4mZmY9+GHZgSsqmUj6qKSNkn4laY+kz0iaIKlZ0r70OT53/J2SWiXtlbQoF58r6fX03RpJSvExkp5J8RZJ03NllqV/Y5+kZeWruplZd5fdvYXpd/yEJ1sOEJEtODn9jp9w2d1bKn1pVa/Ylsl3gZ9GxL8C/hTYA9wBbI2ImcDW9DOSZgFNwGxgMfCQpLPSeR4GVgAz07Y4xZcDRyPiUuAB4P50rgnAKmA+0AisyictM7Ny8oKTg9dvMpE0Dvgs8ChARPwhIn4HLAHWpcPWAUvT/hLg6YjojIg3gVagUdJFwLiI2BbZFLLHe5QpnGsjsCC1WhYBzRHRERFHgWZOJyAzs7Lyw7KDV0zL5I+BduCHkl6V9H1J5wIXRsRhgPQ5KR0/BXgrV/5gik1J+z3j3cpERBfwLnBBH+fqRtIKSTsl7Wxvby+iSmZmvfOCk4NTzEOLDcCnga9GRIuk75K6tM5AvcSij/hgy5wORKwF1kL2nEkf12Zm1ic/LDs4xbRMDgIHI6Il/byRLLkcSV1XpM+23PEX58pPBQ6l+NRe4t3KSGoAzgc6+jiXmZlVkX6TSUS8A7wl6bIUWgC8AWwGCrOrlgHPpv3NQFOaoTWDbKB9e+oKOy7pyjQeckuPMoVzXQ+8lMZVngcWShqfBt4XppiZmVWRYtfm+irwI0kfAf4F+DJZItogaTlwALgBICJ2S9pAlnC6gJURcTKd5zbgMeBsYEvaIBvcf0JSK1mLpCmdq0PSfcCOdNy9EdExyLqamdkQ8dpcZmbmtbnMzKzynEzMzOpApdcTczIxM6sDlV5PzC/HMjOrYZfdvYXOrlMf/PxkywGebDnAmIZR7F19zbBdh1smZmY1rFrWE3MyMTOrYdWynpi7uczMalxhPbEbG6fx1PYDtFdgEN7PmZiZmZ8zMTOzynMyMTOzkjmZmJlZyZxMzMysZE4mZmZWMieTGlfp9XjMzMDJpOZVej0eMzPwQ4s1q1rW4zEzA7dMala1rMdjZgZOJjWrWtbjMTMDd3PVtGpYj8fMDLw2l5mZ4bW5zMysChSVTCTtl/S6pNck7UyxeyS9nWKvSfpC7vg7JbVK2itpUS4+N52nVdIaSUrxMZKeSfEWSdNzZZZJ2pe2ZeWquJnZcKvn58IG0jK5OiLm9GgGPZBicyLiOQBJs4AmYDawGHhI0lnp+IeBFcDMtC1O8eXA0Yi4FHgAuD+dawKwCpgPNAKrJI0fRD3NzCqunp8LG4oB+CXA0xHRCbwpqRVolLQfGBcR2wAkPQ4sBbakMvek8huBB1OrZRHQHBEdqUwzWQJaPwTXbWY2JEbCc2HFtkwCeEHSK5JW5OK3S/qlpB/kWgxTgLdyxxxMsSlpv2e8W5mI6ALeBS7o41xmZjVjJDwXVmwyuSoiPg1cA6yU9FmyLqtPAHOAw8B30rHqpXz0ER9smQ9IWiFpp6Sd7e3tfVbEzGy4jYTnwopKJhFxKH22AZuAxog4EhEnI+IU8D2yMQ3IWg8X54pPBQ6l+NRe4t3KSGoAzgc6+jhXz+tbGxHzImLexIkTi6mSmdmwKjwXtukrV3HT/Etof6+z3zK1NGDfbzKRdK6k8wr7wEJgl6SLcoddB+xK+5uBpjRDawbZQPv2iDgMHJd0ZRoPuQV4NlemMFPreuClyB6AeR5YKGl86kZbmGJmZjXlkZvnsXrp5cyaPI7VSy/nkZv7f6SjlgbsixmAvxDYlGbxNgBPRcRPJT0haQ5Zt9N+4FaAiNgtaQPwBtAFrIyIk+lctwGPAWeTDbxvSfFHgSfSYH0H2WwwIqJD0n3AjnTcvYXBeDOzelWLA/Z+At7MrMq0HTvB6uf28MLudzjx/inGjh7Fotkf564vfnLIxln8BLyZWZ2pxQF7L/RoZlaFam0hV3dzmZmZu7nMzKzynEzMzKxkTiZmZlYyJxMzMyuZk4mZmZXMycTMzErmZGJmZiVzMjEzs5I5mZiZWcmcTKxu1NK7H8zqjZOJ1Y1aeveDWb3xQo9W82rx3Q9m9cYtE6t5L3/zaq6dM5mxo7P/nceOHsWSOZN5+VtXV/jKzEYOJxOrebX47gezeuNuLqsLtfbuB7N64/eZmJmZ32diZmaV52RiZmYlKyqZSNov6XVJr0namWITJDVL2pc+x+eOv1NSq6S9khbl4nPTeVolrZGkFB8j6ZkUb5E0PVdmWfo39klaVq6Km5lZ+QykZXJ1RMzJ9andAWyNiJnA1vQzkmYBTcBsYDHwkKSzUpmHgRXAzLQtTvHlwNGIuBR4ALg/nWsCsAqYDzQCq/JJy8zMqkMp3VxLgHVpfx2wNBd/OiI6I+JNoBVolHQRMC4itkU26v94jzKFc20EFqRWyyKgOSI6IuIo0MzpBGRmZlWi2GQSwAuSXpG0IsUujIjDAOlzUopPAd7KlT2YYlPSfs94tzIR0QW8C1zQx7nMzKyKFPucyVURcUjSJKBZ0q/6OFa9xKKP+GDLnP4HswRXSHLvSdrbx/XVgo8Bv630RQyxeq9jvdcP6r+OI61+l5RysqKSSUQcSp9tkjaRjV8ckXRRRBxOXVht6fCDwMW54lOBQyk+tZd4vsxBSQ3A+UBHin+uR5mf9XJ9a4G1xdSlFkjaWcp871pQ73Ws9/pB/dfR9RuYfru5JJ0r6bzCPrAQ2AVsBgqzq5YBz6b9zUBTmqE1g2ygfXvqCjsu6co0HnJLjzKFc10PvJTGVZ4HFkoanwbeF6aYmZlVkWJaJhcCm9Is3gbgqYj4qaQdwAZJy4EDwA0AEbFb0gbgDaALWBkRJ9O5bgMeA84GtqQN4FHgCUmtZC2SpnSuDkn3ATvScfdGREcJ9TUzsyFQd8up1ANJK1LXXd2q9zrWe/2g/uvo+g3wfE4mZmZWKi+nYmZmJXMyGSaSfiCpTdKuXOxPJW1LS8z8D0njUvwjkn6Y4r+Q9LlcmZ+lZWpeS9ukXv65YSfpYkn/S9IeSbslfS3Fy7bsTiWVuX51cQ8lXZCOf0/Sgz3OVfP3sJ/6Vd09HET9Pq/s2cHX0+e/z51r4PcvIrwNwwZ8Fvg0sCsX2wH8u7T/N8B9aX8l8MO0Pwl4BRiVfv4ZMK/S9emlfhcBn0775wH/DMwC/gG4I8XvAO5P+7OAXwBjgBnAr4Gz0nfbgc+QPWe0BbimzupXL/fwXODfAH8LPNjjXPVwD/uqX9Xdw0HU7wpgctq/HHi7lPvnlskwiYh/JJuplncZ8I9pvxn4i7Q/i2y9MyKiDfgdUNXz3SPicET8PO0fB/aQrVZQzmV3KqZc9Rveqx6YgdYxIn4fEf8b6PYmsnq5h2eqX7UaRP1ejfQMIbAbGKvskY5B3T8nk8raBVyb9m/g9MOevwCWSGpQ9qzOXLo/CPrD1LT+T9XQfdCTslWfrwBaKO+yO1WhxPoV1MM9PJN6uYf9qdp7OIj6/QXwakR0Msj752RSWX8DrJT0Clmz9A8p/gOyG7gT+C/A/yV7Zgfgpoj4FPBv03bzsF5xPyT9EfDfga9HxLG+Du0lVvQSOpVShvpB/dzDM56il1gt3sO+VO09HGj9JM0mW6n91kKol8P6vX9OJhUUEb+KiIURMRdYT9avTkR0RcQ3IlvyfwnwUWBf+u7t9HkceIoq6jqRNJrsf+IfRcSPU/hIajYXuj9KWXanospUv3q6h2dSL/fwjKr1Hg60fpKmApuAWyLi1yk8qPvnZFJBhRkgkkYBdwP/Nf18jrKla5D0eaArIt5I3V4fS/HRwH8g6yqruNTMfxTYExH/OfdVOZfdqZhy1a/O7mGv6ugenuk8VXkPB1o/SR8FfgLcGRH/p3DwoO9fpWYejLSNrOVxGHifLPMvB75GNuPin4G/5/RDpNOBvWQDaC8Cl6T4uWQzu35JNmD2XdIMoUpvZLNeIl3ba2n7AtmrBLaStay2AhNyZe4ia43tJTdbhGyywa703YOF/y71UL86vIf7ySaWvJf+v55VZ/fwQ/Wr1ns40PqR/QH7+9yxrwGTBnv//AS8mZmVzN1cZmZWMicTMzMrmZOJmZmVzMnEzMxK5mRiZmYlczIxM7OSOZmYmVnJnEzMzKxk/x+qftJYVEr3+gAAAABJRU5ErkJggg==\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 repérer les valeurs extrêmes." ] }, { "cell_type": "code", "execution_count": 28, "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": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'épidémie de varicelle fut donc la plus forte depuis 1991 en 2009 et la plus faible en 2002." ] } ], "metadata": { "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": 2 }