{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence de la varicelle" ] }, { "cell_type": "code", "execution_count": 23, "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 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": 35, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv?v=bj5wh\"" ] }, { "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`.\n", "ESTA CELDA NO REPRESENTA LOS DATOS DE LA VARICELA SI DE LA GRIPE" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02024027802151321091012816FRFrance
1202401713467928517649201426FRFrance
2202352711636735415918181224FRFrance
3202351769124227959710614FRFrance
42023507879962151138313917FRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "0 202402 7 8021 5132 10910 12 8 16 \n", "1 202401 7 13467 9285 17649 20 14 26 \n", "2 202352 7 11636 7354 15918 18 12 24 \n", "3 202351 7 6912 4227 9597 10 6 14 \n", "4 202350 7 8799 6215 11383 13 9 17 \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": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n", "raw_data\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": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": 39, "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": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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1202401713467928517649201426FRFrance
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3202351769124227959710614FRFrance
42023507879962151138313917FRFrance
52023497781753621027212816FRFrance
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2820232679192622312161141018FRFrance
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.................................
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1728 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202402 7 8021 5132 10910 12 8 \n", "1 202401 7 13467 9285 17649 20 14 \n", "2 202352 7 11636 7354 15918 18 12 \n", "3 202351 7 6912 4227 9597 10 6 \n", "4 202350 7 8799 6215 11383 13 9 \n", "5 202349 7 7817 5362 10272 12 8 \n", "6 202348 7 7351 4749 9953 11 7 \n", "7 202347 7 6537 4277 8797 10 7 \n", "8 202346 7 5223 2968 7478 8 5 \n", "9 202345 7 5007 2675 7339 8 4 \n", "10 202344 7 3688 1664 5712 6 3 \n", "11 202343 7 3891 1675 6107 6 3 \n", "12 202342 7 3968 1212 6724 6 2 \n", "13 202341 7 3356 1764 4948 5 3 \n", "14 202340 7 2845 1410 4280 4 2 \n", "15 202339 7 1739 629 2849 3 1 \n", "16 202338 7 1663 274 3052 3 1 \n", "17 202337 7 1122 223 2021 2 1 \n", "18 202336 7 726 10 1442 1 0 \n", "19 202335 7 961 96 1826 1 0 \n", "20 202334 7 1168 9 2327 2 0 \n", "21 202333 7 3308 1184 5432 5 2 \n", "22 202332 7 7996 1120 14872 12 2 \n", "23 202331 7 3318 1398 5238 5 2 \n", "24 202330 7 5821 3269 8373 9 5 \n", "25 202329 7 13558 8297 18819 20 12 \n", "26 202328 7 6700 4043 9357 10 6 \n", "27 202327 7 7253 4599 9907 11 7 \n", "28 202326 7 9192 6223 12161 14 10 \n", "29 202325 7 11498 8257 14739 17 12 \n", "... ... ... ... ... ... ... ... \n", "1698 199126 7 17608 11304 23912 31 20 \n", "1699 199125 7 16169 10700 21638 28 18 \n", "1700 199124 7 16171 10071 22271 28 17 \n", "1701 199123 7 11947 7671 16223 21 13 \n", "1702 199122 7 15452 9953 20951 27 17 \n", "1703 199121 7 14903 8975 20831 26 16 \n", "1704 199120 7 19053 12742 25364 34 23 \n", "1705 199119 7 16739 11246 22232 29 19 \n", "1706 199118 7 21385 13882 28888 38 25 \n", "1707 199117 7 13462 8877 18047 24 16 \n", "1708 199116 7 14857 10068 19646 26 18 \n", "1709 199115 7 13975 9781 18169 25 18 \n", "1710 199114 7 12265 7684 16846 22 14 \n", "1711 199113 7 9567 6041 13093 17 11 \n", "1712 199112 7 10864 7331 14397 19 13 \n", "1713 199111 7 15574 11184 19964 27 19 \n", "1714 199110 7 16643 11372 21914 29 20 \n", "1715 199109 7 13741 8780 18702 24 15 \n", "1716 199108 7 13289 8813 17765 23 15 \n", "1717 199107 7 12337 8077 16597 22 15 \n", "1718 199106 7 10877 7013 14741 19 12 \n", "1719 199105 7 10442 6544 14340 18 11 \n", "1720 199104 7 7913 4563 11263 14 8 \n", "1721 199103 7 15387 10484 20290 27 18 \n", "1722 199102 7 16277 11046 21508 29 20 \n", "1723 199101 7 15565 10271 20859 27 18 \n", "1724 199052 7 19375 13295 25455 34 23 \n", "1725 199051 7 19080 13807 24353 34 25 \n", "1726 199050 7 11079 6660 15498 20 12 \n", "1727 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 16 FR France \n", "1 26 FR France \n", "2 24 FR France \n", "3 14 FR France \n", "4 17 FR France \n", "5 16 FR France \n", "6 15 FR France \n", "7 13 FR France \n", "8 11 FR France \n", "9 12 FR France \n", "10 9 FR France \n", "11 9 FR France \n", "12 10 FR France \n", "13 7 FR France \n", "14 6 FR France \n", "15 5 FR France \n", "16 5 FR France \n", "17 3 FR France \n", "18 2 FR France \n", "19 2 FR France \n", "20 4 FR France \n", "21 8 FR France \n", "22 22 FR France \n", "23 8 FR France \n", "24 13 FR France \n", "25 28 FR France \n", "26 14 FR France \n", "27 15 FR France \n", "28 18 FR France \n", "29 22 FR France \n", "... ... ... ... \n", "1698 42 FR France \n", "1699 38 FR France \n", "1700 39 FR France \n", "1701 29 FR France \n", "1702 37 FR France \n", "1703 36 FR France \n", "1704 45 FR France \n", "1705 39 FR France \n", "1706 51 FR France \n", "1707 32 FR France \n", "1708 34 FR France \n", "1709 32 FR France \n", "1710 30 FR France \n", "1711 23 FR France \n", "1712 25 FR France \n", "1713 35 FR France \n", "1714 38 FR France \n", "1715 33 FR France \n", "1716 31 FR France \n", "1717 29 FR France \n", "1718 26 FR France \n", "1719 25 FR France \n", "1720 20 FR France \n", "1721 36 FR France \n", "1722 38 FR France \n", "1723 36 FR France \n", "1724 45 FR France \n", "1725 43 FR France \n", "1726 28 FR France \n", "1727 5 FR France \n", "\n", "[1728 rows x 10 columns]" ] }, "execution_count": 40, "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": 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": { "collapsed": true }, "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": [ { "name": "stdout", "output_type": "stream", "text": [ "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" ] } ], "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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SMIz2YNww9z0cOFtV7xGR7+BcU/WXc5GX95AeY7dw4fkAnH8+dHZ20tnZWVyJCiB0gGyH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\nnUXRaOD2lkZHR/H3afiYQFG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F/dUP3n/1V/CmN7nvl15aq2v94P3Vr9YmHP/4j+7zgAPc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\nYRiGYQRjomEYhmEE8/8B7bUFBeO7tZIAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "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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"text/plain": [ "" ] }, "metadata": {}, "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 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 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": { "collapsed": true }, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1985,\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": 12, "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": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FzoqamfVLTU1NNDU1dVku1zoNSauBf4mIezJ5iylOXi+W9FVgcETUponwJyhO\nXA8HfgqMjoiQtBWYD2wD/gpYFhGbJM0Ffjci5kqqAW6IiJo0Ef4SMIHiqOgl4MqIaJG0FtgQEWsl\nPQy8GhGPdHDuXqdhZtZNna3T6DJoSLoaeA54neItqADuBV4EnqI4QvglcEuarEZSHcWnmY5QvJ3V\nmPKvBB4DLgA2RsTdKf98YA1wBXAAqEmT6Ei6Hfha+rn3RcTqlH8Z0AAMBrYDt0XEkQ7O30HDzKyb\nehw0Kp2DhplZ93lFuJmZnTEHDTMzy81Bw3pVf/w6TLNq5qBhvao/fh2mWTVz0LBe0Z+/DtOsmnln\nWOsVs2ffypAhQ1mw4DlAHD7cxv33z2PGjKld1jWzvssjDesV/fnrMM2qmUca1mv669dhmlUzL+4z\nM7NTeHGfmZmdMQcNMzPLzUHDzMxyc9CoQF5lbWbl4qBRgbzK2szKxUGjgniVtZmVm4NGBZk9+1bq\n6+/i8OE22ldZL1o0j9mzby33qZlZifT1288OGhXEq6zNql9fv/3soFFh2ldZv/HGElaunO5V1mZV\nolJuP3tFuJlZHxARrFu3iQULnmP37gcYObKOhx76DDNmTC3L3YQerwiX9Kik/ZJey+QtlLRH0svp\nNS1zrE5Ss6QdkqZk8idIek3SW5KWZvLPk9SQ6jwv6dLMsVmp/E5JMzP5oyRtTceelOQ9tMysolXK\n7ec8t6dWAh3tZ/1QRExIr00AksYCtwBjgenAch3v8cPAnRExBhgjqb3NO4GDETEaWAo8mNoaDHwD\nmAhcBSyUNCjVWQwsSW21pDYqXlNTU7lPoVe4X5XF/Sqfntx+Ptv96jJoRMTfAIc6ONRR+LseaIiI\noxGxC2gGJkkaBgyMiG2p3GrghkydVSm9Dpic0lOBxohojYgWoBFoH9FMBtan9Crgxq76UQkq4T91\nT7hflcX9Kp+6ui8dux01Y8ZUamu/2GWdPhc0TmOepFckfT8zAhgOZEPj3pQ3HNiTyd+T8k6oExHv\nA62ShnTWlqShwKGIaMu0dckZ9MPMzHLqadBYDnwsIi4H9gFLSndKHY5gelLGzMxKLSK6fAEfBV7r\n6hhQC3w1c2wTxfmIYcCOTH4N8HC2TEqfA7ybKfNIps4jwOdT+l1gQEr/PvCT05x7+OWXX3751f1X\nR++peZ86EplP95KGRcS+9NebgDdS+hngCUnfoXh76ePAixERklolTQK2ATOBZZk6s4AXgJuBZ1P+\nZuDP062aupIdAAAEBUlEQVSvAcA1FIMSwJZUdm2q+3RnJ97RI2NmZtYzXa7TkPSXQAEYCuwHFgJ/\nCFwOtAG7gDkRsT+Vr6P4NNMR4O6IaEz5VwKPARcAGyPi7pR/PrAGuAI4ANSkSXQk3Q58jWLUuy8i\nVqf8y4AGYDCwHbgtIo6c2T+FmZl1peoX95mZWel4G5Fe1MnCyN+T9LeSXpX0tKQPp/xzJf0gLYDc\nLukzmTodLowslxL2a4ukv0v5L0v6SDn6kzmfEZKelfQLSa9Lmp/yB0tqTItMN2eeFuz2YtZyKHG/\n+sw1626/JA1J5d+TtOyktir2enXRr9JfrzwT4X717AV8muJtvNcyeS8Cn07p24FvpvRc4NGU/i3g\npUydF4CJKb0RmFol/doCXFHu65Q5n2HA5Sn9YWAn8AmKi0n/W8r/KvDtlB5H8fboB4BRwN9zfPTe\nZ65ZifvVZ65ZD/r1IeA/AbOBZSe1VcnX63T9Kvn18kijF0XHCyNHp3yAn1F8kACKv6jPpnr/DLRI\n+o9dLIwsi1L0K1Ovz/wfjIh9EfFKSv8K2AGM4MQFqKs4/u9/Hd1fzHrWlapfmSb7xDXrbr8i4tcR\n8bfAv2fbqfTr1Vm/Mkp6vfrExe9nfiHpupS+BRiZ0q8C10k6J030X5mOnW5hZF/S3X61eywNm79+\nFs+1S5JGURxNbQUujvSgRxSfGrwoFevJYtayOsN+tetz1yxnvzpT6derKyW9Xg4aZ98dwF2StgG/\nAfy/lP8Dir+c24CHgP8NvF+WM+yZnvTrjyPik8B/Bv6zpNvO7il3LM3HrKP49N+vKD69l1WRT4+U\nqF997pr5ep1Wya+Xg8ZZFhFvRcTUiJhI8bHhf0j570fEPVHcAPJGio8Tv0XxDTf7yXxEyutTetAv\nIuKd9Of/Bf6SE2+BlIWKOyavA9ZERPv6n/2SLk7Hh1FcXAqdX5s+d81K1K8+d8262a/OVPr16lRv\nXC8Hjd538sLI30p/DgC+TnGlO5I+KOlDKX0NcCQi/i4NQ1slTZIkigsjO13MeBadUb/S7aqhKf9c\n4FqOLxItpx8Ab0bEdzN5z1Cc3IcTF5M+A9SouL3/ZRxfzNoXr9kZ96uPXrPu9Cvr2P/dKrheWdnf\nyd65XmfriYD++KIY2d+mOEH1T8B/AeZTfBri74D7M2U/mvJ+QXFH35GZY1cCr1OckPxuNfSL4hMf\nLwGvpL59h/SEThn7dTXFW2evUHx66GWKOysPoTi5vzP14TczdeooPl20A5jSF69ZqfrV165ZD/v1\nj8C/AP+a/u9+okqu1yn96q3r5cV9ZmaWm29PmZlZbg4aZmaWm4OGmZnl5qBhZma5OWiYmVluDhpm\nZpabg4aZmeXmoGFmZrn9f1gAJxPxoq2fAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "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": [ "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2012 2175217\n", "2003 2234584\n", "2006 2307352\n", "2017 2321583\n", "2001 2529279\n", "1992 2574578\n", "1993 2703886\n", "1988 2765617\n", "2007 2780164\n", "1987 2855570\n", "2016 2856393\n", "2011 2857040\n", "2008 2973918\n", "1998 3034904\n", "2002 3125418\n", "2009 3444020\n", "1994 3514763\n", "1996 3539413\n", "2004 3567744\n", "1997 3620066\n", "2015 3654892\n", "2000 3826372\n", "2005 3835025\n", "1999 3908112\n", "2010 4111392\n", "2013 4182691\n", "1986 5115251\n", "1990 5235827\n", "1989 5466192\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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