diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..0e8a98c673ba6242400a60c140a787e9e6e965c7 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,6 +1,2550 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "# Incidence de la varicelle" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "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": 7, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "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": 10, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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.................................
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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": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Il n'y a aucune semaine manquante ce qui nous permet d'ignorer l'étape de suppression de données manquante et la transformation de la colonne 'inc' en string-> int" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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272024177153031121919387231729FRFrance
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1770 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202444 7 2354 489 4219 4 1 \n", + "1 202443 7 2130 625 3635 3 1 \n", + "2 202442 7 2621 1246 3996 4 2 \n", + "3 202441 7 2035 381 3689 3 1 \n", + "4 202440 7 2125 725 3525 3 1 \n", + "5 202439 7 2898 1333 4463 4 2 \n", + "6 202438 7 751 0 1513 1 0 \n", + "7 202437 7 916 28 1804 1 0 \n", + "8 202436 7 2235 870 3600 3 1 \n", + "9 202435 7 1620 285 2955 2 0 \n", + "10 202434 7 2560 622 4498 4 1 \n", + "11 202433 7 1971 536 3406 3 1 \n", + "12 202432 7 4399 1944 6854 7 3 \n", + "13 202431 7 4500 2213 6787 7 4 \n", + "14 202430 7 7004 4278 9730 11 7 \n", + "15 202429 7 9270 6303 12237 14 10 \n", + "16 202428 7 9364 6498 12230 14 10 \n", + "17 202427 7 10247 7090 13404 15 10 \n", + "18 202426 7 14368 10399 18337 22 16 \n", + "19 202425 7 11174 8039 14309 17 12 \n", + "20 202424 7 12621 9357 15885 19 14 \n", + "21 202423 7 14657 11339 17975 22 17 \n", + "22 202422 7 11628 8361 14895 17 12 \n", + "23 202421 7 9701 6851 12551 15 11 \n", + "24 202420 7 13661 10209 17113 20 15 \n", + "25 202419 7 10083 6413 13753 15 9 \n", + "26 202418 7 13438 9514 17362 20 14 \n", + "27 202417 7 15303 11219 19387 23 17 \n", + "28 202416 7 18138 13540 22736 27 20 \n", + "29 202415 7 24929 17315 32543 37 26 \n", + "... ... ... ... ... ... ... ... \n", + "1740 199126 7 17608 11304 23912 31 20 \n", + "1741 199125 7 16169 10700 21638 28 18 \n", + "1742 199124 7 16171 10071 22271 28 17 \n", + "1743 199123 7 11947 7671 16223 21 13 \n", + "1744 199122 7 15452 9953 20951 27 17 \n", + "1745 199121 7 14903 8975 20831 26 16 \n", + "1746 199120 7 19053 12742 25364 34 23 \n", + "1747 199119 7 16739 11246 22232 29 19 \n", + "1748 199118 7 21385 13882 28888 38 25 \n", + "1749 199117 7 13462 8877 18047 24 16 \n", + "1750 199116 7 14857 10068 19646 26 18 \n", + "1751 199115 7 13975 9781 18169 25 18 \n", + "1752 199114 7 12265 7684 16846 22 14 \n", + "1753 199113 7 9567 6041 13093 17 11 \n", + "1754 199112 7 10864 7331 14397 19 13 \n", + "1755 199111 7 15574 11184 19964 27 19 \n", + "1756 199110 7 16643 11372 21914 29 20 \n", + "1757 199109 7 13741 8780 18702 24 15 \n", + "1758 199108 7 13289 8813 17765 23 15 \n", + "1759 199107 7 12337 8077 16597 22 15 \n", + "1760 199106 7 10877 7013 14741 19 12 \n", + "1761 199105 7 10442 6544 14340 18 11 \n", + "1762 199104 7 7913 4563 11263 14 8 \n", + "1763 199103 7 15387 10484 20290 27 18 \n", + "1764 199102 7 16277 11046 21508 29 20 \n", + "1765 199101 7 15565 10271 20859 27 18 \n", + "1766 199052 7 19375 13295 25455 34 23 \n", + "1767 199051 7 19080 13807 24353 34 25 \n", + "1768 199050 7 11079 6660 15498 20 12 \n", + "1769 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 7 FR France \n", + "1 5 FR France \n", + "2 6 FR France \n", + "3 5 FR France \n", + "4 5 FR France \n", + "5 6 FR France \n", + "6 2 FR France \n", + "7 2 FR France \n", + "8 5 FR France \n", + "9 4 FR France \n", + "10 7 FR France \n", + "11 5 FR France \n", + "12 11 FR France \n", + "13 10 FR France \n", + "14 15 FR France \n", + "15 18 FR France \n", + "16 18 FR France \n", + "17 20 FR France \n", + "18 28 FR France \n", + "19 22 FR France \n", + "20 24 FR France \n", + "21 27 FR France \n", + "22 22 FR France \n", + "23 19 FR France \n", + "24 25 FR France \n", + "25 21 FR France \n", + "26 26 FR France \n", + "27 29 FR France \n", + "28 34 FR France \n", + "29 48 FR France \n", + "... ... ... ... \n", + "1740 42 FR France \n", + "1741 38 FR France \n", + "1742 39 FR France \n", + "1743 29 FR France \n", + "1744 37 FR France \n", + "1745 36 FR France \n", + "1746 45 FR France \n", + "1747 39 FR France \n", + "1748 51 FR France \n", + "1749 32 FR France \n", + "1750 34 FR France \n", + "1751 32 FR France \n", + "1752 30 FR France \n", + "1753 23 FR France \n", + "1754 25 FR France \n", + "1755 35 FR France \n", + "1756 38 FR France \n", + "1757 33 FR France \n", + "1758 31 FR France \n", + "1759 29 FR France \n", + "1760 26 FR France \n", + "1761 25 FR France \n", + "1762 20 FR France \n", + "1763 36 FR France \n", + "1764 38 FR France \n", + "1765 36 FR France \n", + "1766 45 FR France \n", + "1767 43 FR France \n", + "1768 28 FR France \n", + "1769 5 FR France \n", + "\n", + "[1770 rows x 10 columns]" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "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": 15, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "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": { + "hideCode": false, + "hidePrompt": false + }, + "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": 16, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "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", + "Sauf que dans notre cas à nous il ne manque pour l'instant aucune semaine mais nous devons toujours rester prudent quand aux prochaines semaines futur." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "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": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Un premier regard sur les données !" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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Le creux des incidences se trouve en automne." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "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": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Etant donné que le pic de l'épidémie se situe en printemps, à cheval\n", + "entre deux années civiles, nous définissons la période de référence\n", + "entre deux minima de l'incidence, du 1er octobre de l'année $N$ au\n", + "1er octobre 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 octobre de chaque année, nous utilisons le\n", + "premier jour de la semaine qui contient le 1er octobre.\n", + "\n", + "Comme l'incidence de la varicelle est très faible en automne, cette\n", + "modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail: les données commencent an octobre 1990, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "first_october_week = [pd.Period(pd.Timestamp(y, 10, 1), 'W')\n", + " for y in range(1991,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "En partant de cette liste des semaines qui contiennent un 1er octobre, 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": 68, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_october_week[:-1],\n", + " first_october_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": false, + "hidePrompt": false + }, + "source": [ + "Voici les incidences annuelles." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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IYwDpeU6KzwfeLyp7NMXmp+3B8QFlIqIP+BiYPcyxzMwsB1mn3F4TER9ImgO0SvrhMPtqiFgMEx9tmTN/sJDI1gNccsklw5yamZmNRaaWRkR8kJ67gOcpjC98mLqcSM9dafejwMVFxRcAH6T4giHiA8pIagAuAHqGOdbg89saEU0R0dTY2JilSmZmNgojJg1Jn5U0vX8bWAm8CewC+mcztQAvpO1dQHOaEXUZhQHv/akL64Skq9N4xe2DyvQf6ybglTTu8RKwUtLMNAC+MsXMzCwHWbqnLgKeT7NjG4CnI+K7kg4AOyStAzqBmwEi4rCkHcBbQB9wZ0ScSse6A3gCOA/YnR4AjwNPSeqg0MJoTsfqkXQ/cCDtd19E9IyhvmZmNga+I9zMzHxHuJmZlZ+ThpmZZeakYWZmmTlpmJlZZk4aZmaWmZOGmZll5qRhZmaZOWmYmVlmThpmZpaZk4aZmWXmpGFmZpk5aZiZWWZOGmZmlpmThpmZZeakYWZmmTlpmJlZZk4aZmaWmZOGmZll5qRhZmaZOWmYmVlmThpmZpaZk4aZmWXmpGFmZpllThqSJkt6XdLfpdezJLVKak/PM4v2vVdSh6QjklYVxZdJOpTe2yJJKT5V0nMpvk/SwqIyLelvtEtqKUelzcxsdEppadwFvF30egOwJyIWAXvSayQtAZqBK4DVwCOSJqcyjwLrgUXpsTrF1wHHI+Jy4CHgwXSsWcAmYAWwHNhUnJzMzKyyMiUNSQuArwB/VRReA2xL29uAtUXxZyOiNyLeBTqA5ZLmAjMiYm9EBPDkoDL9x9oJXJdaIauA1ojoiYjjQCtnEo2ZmVVY1pbGXwB3A6eLYhdFxDGA9DwnxecD7xftdzTF5qftwfEBZSKiD/gYmD3MsczMLAcjJg1JvwN0RcTBjMfUELEYJj7aMsXnuF5Sm6S27u7ujKdpZmalytLSuAa4UdJ7wLPAlyVtBz5MXU6k5660/1Hg4qLyC4APUnzBEPEBZSQ1ABcAPcMca4CI2BoRTRHR1NjYmKFKZmY2GiMmjYi4NyIWRMRCCgPcr0TE7wO7gP7ZTC3AC2l7F9CcZkRdRmHAe3/qwjoh6eo0XnH7oDL9x7op/Y0AXgJWSpqZBsBXppiZWU3r+uQkX3tsL10nTuZ9KiUZy30aDwDXS2oHrk+viYjDwA7gLeC7wJ0RcSqVuYPCYHoH8A6wO8UfB2ZL6gD+jDQTKyJ6gPuBA+lxX4qZmdW0LXvaOfBeD1tebs/7VEqiwgV9/Whqaoq2tra8T8PMbEiLN+6mt+/0WfGpDZM4svmGHM6oQNLBiGgaaT/fEW5mVkGv3X0tNy6dx7QphX9+p02ZxJql83jtnmtzPrNsnDTMzCpozoxpTJ/aQG/faaY2TKK37zTTpzYwZ/q0vE8tk4a8T8DMbKL56NNebl1xKbcsv4Sn93fSXUOD4R7TMDOrA12fnOSPn3mdh2+5alStFo9pmJlNIJWajeXuKTOzGjZ4Ntb2fZ1s39c5brOx3NIwM6thlZ6N5aRhZlbDKj0by91TZmY1rpKzsTx7yszMPHvKzMzKz0nDzMwyc9IwM7PMnDTMzCwzJw0zM8vMScPMzDJz0jAzs8ycNMzMLDMnDTMzy8xJw8zMMnPSMDOzzJw0zMwsMycNMzPLbMSkIWmapP2SfiDpsKT/nOKzJLVKak/PM4vK3CupQ9IRSauK4sskHUrvbZGkFJ8q6bkU3ydpYVGZlvQ32iW1lLPyZmZWmiwtjV7gyxHxBWApsFrS1cAGYE9ELAL2pNdIWgI0A1cAq4FHJE1Ox3oUWA8sSo/VKb4OOB4RlwMPAQ+mY80CNgErgOXApuLkZGZmlTVi0oiCT9PLKekRwBpgW4pvA9am7TXAsxHRGxHvAh3AcklzgRkRsTcKP+Lx5KAy/cfaCVyXWiGrgNaI6ImI40ArZxKNmZlVWKYxDUmTJb0BdFH4R3wfcFFEHANIz3PS7vOB94uKH02x+Wl7cHxAmYjoAz4GZg9zLDMzy0GmpBERpyJiKbCAQqvhymF211CHGCY+2jJn/qC0XlKbpLbu7u5hTs3MzMaipNlTEfFT4FUKXUQfpi4n0nNX2u0ocHFRsQXABym+YIj4gDKSGoALgJ5hjjX4vLZGRFNENDU2NpZSJTMzK0GW2VONkj6Xts8Dfhv4IbAL6J/N1AK8kLZ3Ac1pRtRlFAa896curBOSrk7jFbcPKtN/rJuAV9K4x0vASkkz0wD4yhQzM7McNGTYZy6wLc2AmgTsiIi/k7QX2CFpHdAJ3AwQEYcl7QDeAvqAOyPiVDrWHcATwHnA7vQAeBx4SlIHhRZGczpWj6T7gQNpv/siomcsFTYzs9FT4YK+fjQ1NUVbW1vep2FmVlMkHYyIppH28x3hZmaWmZOGmZll5qRhZmaZOWmYmVlmThpmZpaZk4aZmWXmpGFmZpk5aZiZWWZOGmZmlpmThpmZZeakYWZmmTlpmJlZZk4aZmaWmZOGmZll5qRhZmaZOWmYmVlmThpmZpaZk4aZmWXmpGFmVmZdn5zka4/tpevEybxPpeycNMzMymzLnnYOvNfDlpfb8z6VsmvI+wTMzOrF4o276e07/cvX2/d1sn1fJ1MbJnFk8w05nln5uKVhZlYmr919LTcunce0KYV/WqdNmcSapfN47Z5rcz6z8nHSMDMrkzkzpjF9agO9faeZ2jCJ3r7TTJ/awJzp0/I+tbIZMWlIuljS/5T0tqTDku5K8VmSWiW1p+eZRWXuldQh6YikVUXxZZIOpfe2SFKKT5X0XIrvk7SwqExL+hvtklrKWXkzs3L76NNebl1xKc9//RpuXXEp3Z/25n1KZaWIGH4HaS4wNyK+L2k6cBBYC/xboCciHpC0AZgZEfdIWgI8AywH5gEvA78WEack7QfuAv4B+A6wJSJ2S/o68M8j4o8kNQNfjYjfkzQLaAOagEh/e1lEHD/X+TY1NUVbW9vo/4uYmU1Akg5GRNNI+43Y0oiIYxHx/bR9AngbmA+sAbal3bZRSCSk+LMR0RsR7wIdwPKUfGZExN4oZKonB5XpP9ZO4LrUClkFtEZET0oUrcDqkc7ZzMzGR0ljGqnb6CpgH3BRRByDQmIB5qTd5gPvFxU7mmLz0/bg+IAyEdEHfAzMHuZYZmaWg8xJQ9KvAH8D/ElEfDLcrkPEYpj4aMsUn9t6SW2S2rq7u4c5NTMzG4tMSUPSFAoJ41sR8e0U/jB1OfWPe3Sl+FHg4qLiC4APUnzBEPEBZSQ1ABcAPcMca4CI2BoRTRHR1NjYmKVKZmY2CllmTwl4HHg7Iv5b0Vu7gP7ZTC3AC0Xx5jQj6jJgEbA/dWGdkHR1Oubtg8r0H+sm4JU07vESsFLSzDQ7a2WKmZlZDrLcEX4NcBtwSNIbKfbnwAPADknrgE7gZoCIOCxpB/AW0AfcGRGnUrk7gCeA84Dd6QGFpPSUpA4KLYzmdKweSfcDB9J+90VEzyjramZmYzTilNtaI6kb+FHe51GiC4GP8j6JMXIdqoPrUD1qrR6XRsSI/ft1lzRqkaS2LPOjq5nrUB1ch+pRL/UYzMuImJlZZk4aZmaWmZNGddia9wmUgetQHVyH6lEv9RjAYxpmZpaZWxpmZpaZk8Y4kPTXkrokvVkU+4KkvWlp+L+VNCPFPyPpmyn+A0lfKirzalpe/o30mDPEnxuvOoz7kvg1VodcPotS6yBpdtr/U0kPDzpWTXwOI9ShZr4Tkq6XdDD9Nz8o6ctFx8rlsyiLiPCjzA/gt4AvAm8WxQ4A/ypt/wFwf9q+E/hm2p5DYfn3Sen1q0BTTnWYC3wxbU8H/glYAvwXYEOKbwAeTNtLgB8AU4HLgHeAyem9/cBvUFhLbDdwQw3WIZfPYhR1+Czwm8AfAQ8POlatfA7D1aGWvhNXAfPS9pXAj/P+LMrxcEtjHETE31O4s73YYuDv03Yr8LtpewmwJ5XrAn5K4fdDchWVWRK/JupQiXM9l1LrEBE/i4j/BZwsPk4tfQ7nqkPeRlGP1yOif628w8A0FZZXyu2zKAcnjcp5E7gxbd/MmYUYfwCskdSgwlpdyxi4SOM3UzP8P+bVhNX4LYlfMWOsQ79cP4uMdTiXWvocRlIr34livwu8HhG9VMlnMVpOGpXzB8Cdkg5SaNr+PMX/msL/NG3AXwD/h8KaXQC3RsTngX+ZHrdV9IwZ9yXxK6IMdYCcP4sS6nDOQwwRq9bPYTi19J3o3/8K4EHgD/tDQ+xWM9NYnTQqJCJ+GBErI2IZhZ/DfSfF+yLiTyNiaUSsAT4HtKf3fpyeTwBPU+GuEo3/kvjjrkx1yPWzKLEO51JLn8M51dh3AkkLgOeB2yPinRTO9bMYKyeNCumf5SFpErAR+O/p9fmSPpu2rwf6IuKt1F11YYpPAX6HQhdXpc63Ekvi10Qd8vwsRlGHIdXY53Cu49TUd0LS54AXgXsj4n/375znZ1EWeY/E1+ODQkviGPALClcV64C7KMy2+CcKy8r331i5EDhCYVDtZQorTUJhBslB4B8pDKL9JWkmT4Xq8JsUmsz/CLyRHv+aws/w7qHQGtoDzCoq8w0KLagjFM0GoTCw/2Z67+H+utdKHfL8LEZZh/coTMT4NP3/t6QGP4ez6lBr3wkKF4c/K9r3DWBOnp9FOR6+I9zMzDJz95SZmWXmpGFmZpk5aZiZWWZOGmZmlpmThpmZZeakYWZmmTlpmJlZZk4aZmaW2f8Hl3GEw5IYRsQAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.plot(style='*')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 217605\n", + "2023 364553\n", + "2021 382779\n", + "2002 526035\n", + "2018 540799\n", + "2017 552105\n", + "1996 574093\n", + "2019 585143\n", + "2001 606520\n", + "2015 611634\n", + "2005 620796\n", + "2006 626180\n", + "2012 627384\n", + "2000 627405\n", + "2022 635251\n", + "1993 638384\n", + "2011 644660\n", + "1995 650679\n", + "1994 664684\n", + "2014 672401\n", + "1997 677145\n", + "1998 682638\n", + "2013 703305\n", + "2007 729321\n", + "1999 746617\n", + "2008 750410\n", + "2003 752007\n", + "2016 775321\n", + "2004 786328\n", + "2010 830938\n", + "1992 834566\n", + "2009 836245\n", + "dtype: int64" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Enfin, un histogramme montre bien que la varicelle, 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": 71, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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