{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 27, "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": 30, "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": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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272024157249291731532543372648FRFrance
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292024137183221420622438272133FRFrance
.................................
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\n", "

1768 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202442 7 3104 1332 4876 5 2 \n", "1 202441 7 2010 361 3659 3 1 \n", "2 202440 7 2125 725 3525 3 1 \n", "3 202439 7 2898 1333 4463 4 2 \n", "4 202438 7 751 0 1513 1 0 \n", "5 202437 7 916 28 1804 1 0 \n", "6 202436 7 2235 870 3600 3 1 \n", "7 202435 7 1620 285 2955 2 0 \n", "8 202434 7 2560 622 4498 4 1 \n", "9 202433 7 1971 536 3406 3 1 \n", "10 202432 7 4399 1944 6854 7 3 \n", "11 202431 7 4500 2213 6787 7 4 \n", "12 202430 7 7004 4278 9730 11 7 \n", "13 202429 7 9270 6303 12237 14 10 \n", "14 202428 7 9364 6498 12230 14 10 \n", "15 202427 7 10247 7090 13404 15 10 \n", "16 202426 7 14368 10399 18337 22 16 \n", "17 202425 7 11174 8039 14309 17 12 \n", "18 202424 7 12621 9357 15885 19 14 \n", "19 202423 7 14657 11339 17975 22 17 \n", "20 202422 7 11628 8361 14895 17 12 \n", "21 202421 7 9701 6851 12551 15 11 \n", "22 202420 7 13661 10209 17113 20 15 \n", "23 202419 7 10083 6413 13753 15 9 \n", "24 202418 7 13438 9514 17362 20 14 \n", "25 202417 7 15303 11219 19387 23 17 \n", "26 202416 7 18138 13540 22736 27 20 \n", "27 202415 7 24929 17315 32543 37 26 \n", "28 202414 7 16181 12544 19818 24 19 \n", "29 202413 7 18322 14206 22438 27 21 \n", "... ... ... ... ... ... ... ... \n", "1738 199126 7 17608 11304 23912 31 20 \n", "1739 199125 7 16169 10700 21638 28 18 \n", "1740 199124 7 16171 10071 22271 28 17 \n", "1741 199123 7 11947 7671 16223 21 13 \n", "1742 199122 7 15452 9953 20951 27 17 \n", "1743 199121 7 14903 8975 20831 26 16 \n", "1744 199120 7 19053 12742 25364 34 23 \n", "1745 199119 7 16739 11246 22232 29 19 \n", "1746 199118 7 21385 13882 28888 38 25 \n", "1747 199117 7 13462 8877 18047 24 16 \n", "1748 199116 7 14857 10068 19646 26 18 \n", "1749 199115 7 13975 9781 18169 25 18 \n", "1750 199114 7 12265 7684 16846 22 14 \n", "1751 199113 7 9567 6041 13093 17 11 \n", "1752 199112 7 10864 7331 14397 19 13 \n", "1753 199111 7 15574 11184 19964 27 19 \n", "1754 199110 7 16643 11372 21914 29 20 \n", "1755 199109 7 13741 8780 18702 24 15 \n", "1756 199108 7 13289 8813 17765 23 15 \n", "1757 199107 7 12337 8077 16597 22 15 \n", "1758 199106 7 10877 7013 14741 19 12 \n", "1759 199105 7 10442 6544 14340 18 11 \n", "1760 199104 7 7913 4563 11263 14 8 \n", "1761 199103 7 15387 10484 20290 27 18 \n", "1762 199102 7 16277 11046 21508 29 20 \n", "1763 199101 7 15565 10271 20859 27 18 \n", "1764 199052 7 19375 13295 25455 34 23 \n", "1765 199051 7 19080 13807 24353 34 25 \n", "1766 199050 7 11079 6660 15498 20 12 \n", "1767 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 8 FR France \n", "1 5 FR France \n", "2 5 FR France \n", "3 6 FR France \n", "4 2 FR France \n", "5 2 FR France \n", "6 5 FR France \n", "7 4 FR France \n", "8 7 FR France \n", "9 5 FR France \n", "10 11 FR France \n", "11 10 FR France \n", "12 15 FR France \n", "13 18 FR France \n", "14 18 FR France \n", "15 20 FR France \n", "16 28 FR France \n", "17 22 FR France \n", "18 24 FR France \n", "19 27 FR France \n", "20 22 FR France \n", "21 19 FR France \n", "22 25 FR France \n", "23 21 FR France \n", "24 26 FR France \n", "25 29 FR France \n", "26 34 FR France \n", "27 48 FR France \n", "28 29 FR France \n", "29 33 FR France \n", "... ... ... ... \n", "1738 42 FR France \n", "1739 38 FR France \n", "1740 39 FR France \n", "1741 29 FR France \n", "1742 37 FR France \n", "1743 36 FR France \n", "1744 45 FR France \n", "1745 39 FR France \n", "1746 51 FR France \n", "1747 32 FR France \n", "1748 34 FR France \n", "1749 32 FR France \n", "1750 30 FR France \n", "1751 23 FR France \n", "1752 25 FR France \n", "1753 35 FR France \n", "1754 38 FR France \n", "1755 33 FR France \n", "1756 31 FR France \n", "1757 29 FR France \n", "1758 26 FR France \n", "1759 25 FR France \n", "1760 20 FR France \n", "1761 36 FR France \n", "1762 38 FR France \n", "1763 36 FR France \n", "1764 45 FR France \n", "1765 43 FR France \n", "1766 28 FR France \n", "1767 5 FR France \n", "\n", "[1768 rows x 10 columns]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n", "raw_data" ] }, { "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": 32, "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": 32, "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": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02024427310413324876528FRFrance
1202441720103613659315FRFrance
2202440721257253525315FRFrance
32024397289813334463426FRFrance
4202438775101513102FRFrance
52024377916281804102FRFrance
6202436722358703600315FRFrance
7202435716202852955204FRFrance
8202434725606224498417FRFrance
9202433719715363406315FRFrance
1020243274399194468547311FRFrance
1120243174500221367877410FRFrance
12202430770044278973011715FRFrance
1320242979270630312237141018FRFrance
1420242879364649812230141018FRFrance
15202427710247709013404151020FRFrance
162024267143681039918337221628FRFrance
17202425711174803914309171222FRFrance
18202424712621935715885191424FRFrance
192024237146571133917975221727FRFrance
20202422711628836114895171222FRFrance
2120242179701685112551151119FRFrance
222024207136611020917113201525FRFrance
2320241971008364131375315921FRFrance
24202418713438951417362201426FRFrance
252024177153031121919387231729FRFrance
262024167181381354022736272034FRFrance
272024157249291731532543372648FRFrance
282024147161811254419818241929FRFrance
292024137183221420622438272133FRFrance
.................................
17381991267176081130423912312042FRFrance
17391991257161691070021638281838FRFrance
17401991247161711007122271281739FRFrance
1741199123711947767116223211329FRFrance
1742199122715452995320951271737FRFrance
1743199121714903897520831261636FRFrance
17441991207190531274225364342345FRFrance
17451991197167391124622232291939FRFrance
17461991187213851388228888382551FRFrance
1747199117713462887718047241632FRFrance
17481991167148571006819646261834FRFrance
1749199115713975978118169251832FRFrance
1750199114712265768416846221430FRFrance
175119911379567604113093171123FRFrance
1752199112710864733114397191325FRFrance
17531991117155741118419964271935FRFrance
17541991107166431137221914292038FRFrance
1755199109713741878018702241533FRFrance
1756199108713289881317765231531FRFrance
1757199107712337807716597221529FRFrance
1758199106710877701314741191226FRFrance
1759199105710442654414340181125FRFrance
17601991047791345631126314820FRFrance
17611991037153871048420290271836FRFrance
17621991027162771104621508292038FRFrance
17631991017155651027120859271836FRFrance
17641990527193751329525455342345FRFrance
17651990517190801380724353342543FRFrance
1766199050711079666015498201228FRFrance
17671990497114302610205FRFrance
\n", "

1768 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202442 7 3104 1332 4876 5 2 \n", "1 202441 7 2010 361 3659 3 1 \n", "2 202440 7 2125 725 3525 3 1 \n", "3 202439 7 2898 1333 4463 4 2 \n", "4 202438 7 751 0 1513 1 0 \n", "5 202437 7 916 28 1804 1 0 \n", "6 202436 7 2235 870 3600 3 1 \n", "7 202435 7 1620 285 2955 2 0 \n", "8 202434 7 2560 622 4498 4 1 \n", "9 202433 7 1971 536 3406 3 1 \n", "10 202432 7 4399 1944 6854 7 3 \n", "11 202431 7 4500 2213 6787 7 4 \n", "12 202430 7 7004 4278 9730 11 7 \n", "13 202429 7 9270 6303 12237 14 10 \n", "14 202428 7 9364 6498 12230 14 10 \n", "15 202427 7 10247 7090 13404 15 10 \n", "16 202426 7 14368 10399 18337 22 16 \n", "17 202425 7 11174 8039 14309 17 12 \n", "18 202424 7 12621 9357 15885 19 14 \n", "19 202423 7 14657 11339 17975 22 17 \n", "20 202422 7 11628 8361 14895 17 12 \n", "21 202421 7 9701 6851 12551 15 11 \n", "22 202420 7 13661 10209 17113 20 15 \n", "23 202419 7 10083 6413 13753 15 9 \n", "24 202418 7 13438 9514 17362 20 14 \n", "25 202417 7 15303 11219 19387 23 17 \n", "26 202416 7 18138 13540 22736 27 20 \n", "27 202415 7 24929 17315 32543 37 26 \n", "28 202414 7 16181 12544 19818 24 19 \n", "29 202413 7 18322 14206 22438 27 21 \n", "... ... ... ... ... ... ... ... \n", "1738 199126 7 17608 11304 23912 31 20 \n", "1739 199125 7 16169 10700 21638 28 18 \n", "1740 199124 7 16171 10071 22271 28 17 \n", "1741 199123 7 11947 7671 16223 21 13 \n", "1742 199122 7 15452 9953 20951 27 17 \n", "1743 199121 7 14903 8975 20831 26 16 \n", "1744 199120 7 19053 12742 25364 34 23 \n", "1745 199119 7 16739 11246 22232 29 19 \n", "1746 199118 7 21385 13882 28888 38 25 \n", "1747 199117 7 13462 8877 18047 24 16 \n", "1748 199116 7 14857 10068 19646 26 18 \n", "1749 199115 7 13975 9781 18169 25 18 \n", "1750 199114 7 12265 7684 16846 22 14 \n", "1751 199113 7 9567 6041 13093 17 11 \n", "1752 199112 7 10864 7331 14397 19 13 \n", "1753 199111 7 15574 11184 19964 27 19 \n", "1754 199110 7 16643 11372 21914 29 20 \n", "1755 199109 7 13741 8780 18702 24 15 \n", "1756 199108 7 13289 8813 17765 23 15 \n", "1757 199107 7 12337 8077 16597 22 15 \n", "1758 199106 7 10877 7013 14741 19 12 \n", "1759 199105 7 10442 6544 14340 18 11 \n", "1760 199104 7 7913 4563 11263 14 8 \n", "1761 199103 7 15387 10484 20290 27 18 \n", "1762 199102 7 16277 11046 21508 29 20 \n", "1763 199101 7 15565 10271 20859 27 18 \n", "1764 199052 7 19375 13295 25455 34 23 \n", "1765 199051 7 19080 13807 24353 34 25 \n", "1766 199050 7 11079 6660 15498 20 12 \n", "1767 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 8 FR France \n", "1 5 FR France \n", "2 5 FR France \n", "3 6 FR France \n", "4 2 FR France \n", "5 2 FR France \n", "6 5 FR France \n", "7 4 FR France \n", "8 7 FR France \n", "9 5 FR France \n", "10 11 FR France \n", "11 10 FR France \n", "12 15 FR France \n", "13 18 FR France \n", "14 18 FR France \n", "15 20 FR France \n", "16 28 FR France \n", "17 22 FR France \n", "18 24 FR France \n", "19 27 FR France \n", "20 22 FR France \n", "21 19 FR France \n", "22 25 FR France \n", "23 21 FR France \n", "24 26 FR France \n", "25 29 FR France \n", "26 34 FR France \n", "27 48 FR France \n", "28 29 FR France \n", "29 33 FR France \n", "... ... ... ... \n", "1738 42 FR France \n", "1739 38 FR France \n", "1740 39 FR France \n", "1741 29 FR France \n", "1742 37 FR France \n", "1743 36 FR France \n", "1744 45 FR France \n", "1745 39 FR France \n", "1746 51 FR France \n", "1747 32 FR France \n", "1748 34 FR France \n", "1749 32 FR France \n", "1750 30 FR France \n", "1751 23 FR France \n", "1752 25 FR France \n", "1753 35 FR France \n", "1754 38 FR France \n", "1755 33 FR France \n", "1756 31 FR France \n", "1757 29 FR France \n", "1758 26 FR France \n", "1759 25 FR France \n", "1760 20 FR France \n", "1761 36 FR France \n", "1762 38 FR France \n", "1763 36 FR France \n", "1764 45 FR France \n", "1765 43 FR France \n", "1766 28 FR France \n", "1767 5 FR France \n", "\n", "[1768 rows x 10 columns]" ] }, "execution_count": 33, "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": 34, "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": 35, "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.\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": 36, "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": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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BLI0U4pZG1iyy6Jv7irA0io5peEI6jbcwkkSjbEsjr3slxNIoKqaRx9Io647w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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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\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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