diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..3505d89b3857dea95e5adabeb061b862d297ecc2 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2410 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Incidence de la varicelle en France métropolitaine" + ] + }, + { + "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 grippal 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. Nous téléchargeons toujours le jeu de données complet, qui commence en 1991 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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292021307719041911018911616FRFrance
.................................
15991991267176081130423912312042FRFrance
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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": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "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.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`.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": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_nameperiod
0202207713531977617286201426FRFrance2022-02-14/2022-02-20
120220679935710412766151119FRFrance2022-02-07/2022-02-13
2202205710851779713905161121FRFrance2022-01-31/2022-02-06
320220479547672112373141018FRFrance2022-01-24/2022-01-30
42022037139721068017264211626FRFrance2022-01-17/2022-01-23
52022027849560261096413917FRFrance2022-01-10/2022-01-16
62022017137931059716989211626FRFrance2022-01-03/2022-01-09
7202152713239961116867201525FRFrance2021-12-27/2022-01-02
8202151713326962917023201426FRFrance2021-12-20/2021-12-26
92021507141281031217944211527FRFrance2021-12-13/2021-12-19
102021497136741036916979211626FRFrance2021-12-06/2021-12-12
11202148711549850314595171222FRFrance2021-11-29/2021-12-05
12202147711419837614462171222FRFrance2021-11-22/2021-11-28
132021467821657241070812816FRFrance2021-11-15/2021-11-21
1420214578965646811462141018FRFrance2021-11-08/2021-11-14
152021447873656361183613818FRFrance2021-11-01/2021-11-07
162021437814551641112612717FRFrance2021-10-25/2021-10-31
172021427944360371284914919FRFrance2021-10-18/2021-10-24
182021417402122395803639FRFrance2021-10-11/2021-10-17
1920214074441245464287410FRFrance2021-10-04/2021-10-10
202021397229110563526315FRFrance2021-09-27/2021-10-03
2120213874325226763837410FRFrance2021-09-20/2021-09-26
22202137719647543174315FRFrance2021-09-13/2021-09-19
232021367344117305152528FRFrance2021-09-06/2021-09-12
242021357256211074017426FRFrance2021-08-30/2021-09-05
25202134714293782480204FRFrance2021-08-23/2021-08-29
262021337382918305828639FRFrance2021-08-16/2021-08-22
272021327410818956321639FRFrance2021-08-09/2021-08-15
2820213174793230172857311FRFrance2021-08-02/2021-08-08
292021307719041911018911616FRFrance2021-07-26/2021-08-01
....................................
15991991267176081130423912312042FRFrance1991-06-24/1991-06-30
16001991257161691070021638281838FRFrance1991-06-17/1991-06-23
16011991247161711007122271281739FRFrance1991-06-10/1991-06-16
1602199123711947767116223211329FRFrance1991-06-03/1991-06-09
1603199122715452995320951271737FRFrance1991-05-27/1991-06-02
1604199121714903897520831261636FRFrance1991-05-20/1991-05-26
16051991207190531274225364342345FRFrance1991-05-13/1991-05-19
16061991197167391124622232291939FRFrance1991-05-06/1991-05-12
16071991187213851388228888382551FRFrance1991-04-29/1991-05-05
1608199117713462887718047241632FRFrance1991-04-22/1991-04-28
16091991167148571006819646261834FRFrance1991-04-15/1991-04-21
1610199115713975978118169251832FRFrance1991-04-08/1991-04-14
1611199114712265768416846221430FRFrance1991-04-01/1991-04-07
161219911379567604113093171123FRFrance1991-03-25/1991-03-31
1613199112710864733114397191325FRFrance1991-03-18/1991-03-24
16141991117155741118419964271935FRFrance1991-03-11/1991-03-17
16151991107166431137221914292038FRFrance1991-03-04/1991-03-10
1616199109713741878018702241533FRFrance1991-02-25/1991-03-03
1617199108713289881317765231531FRFrance1991-02-18/1991-02-24
1618199107712337807716597221529FRFrance1991-02-11/1991-02-17
1619199106710877701314741191226FRFrance1991-02-04/1991-02-10
1620199105710442654414340181125FRFrance1991-01-28/1991-02-03
16211991047791345631126314820FRFrance1991-01-21/1991-01-27
16221991037153871048420290271836FRFrance1991-01-14/1991-01-20
16231991027162771104621508292038FRFrance1991-01-07/1991-01-13
16241991017155651027120859271836FRFrance1990-12-31/1991-01-06
16251990527193751329525455342345FRFrance1990-12-24/1990-12-30
16261990517190801380724353342543FRFrance1990-12-17/1990-12-23
1627199050711079666015498201228FRFrance1990-12-10/1990-12-16
16281990497114302610205FRFrance1990-12-03/1990-12-09
\n", + "

1629 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202207 7 13531 9776 17286 20 14 \n", + "1 202206 7 9935 7104 12766 15 11 \n", + "2 202205 7 10851 7797 13905 16 11 \n", + "3 202204 7 9547 6721 12373 14 10 \n", + "4 202203 7 13972 10680 17264 21 16 \n", + "5 202202 7 8495 6026 10964 13 9 \n", + "6 202201 7 13793 10597 16989 21 16 \n", + "7 202152 7 13239 9611 16867 20 15 \n", + "8 202151 7 13326 9629 17023 20 14 \n", + "9 202150 7 14128 10312 17944 21 15 \n", + "10 202149 7 13674 10369 16979 21 16 \n", + "11 202148 7 11549 8503 14595 17 12 \n", + "12 202147 7 11419 8376 14462 17 12 \n", + "13 202146 7 8216 5724 10708 12 8 \n", + "14 202145 7 8965 6468 11462 14 10 \n", + "15 202144 7 8736 5636 11836 13 8 \n", + "16 202143 7 8145 5164 11126 12 7 \n", + "17 202142 7 9443 6037 12849 14 9 \n", + "18 202141 7 4021 2239 5803 6 3 \n", + "19 202140 7 4441 2454 6428 7 4 \n", + "20 202139 7 2291 1056 3526 3 1 \n", + "21 202138 7 4325 2267 6383 7 4 \n", + "22 202137 7 1964 754 3174 3 1 \n", + "23 202136 7 3441 1730 5152 5 2 \n", + "24 202135 7 2562 1107 4017 4 2 \n", + "25 202134 7 1429 378 2480 2 0 \n", + "26 202133 7 3829 1830 5828 6 3 \n", + "27 202132 7 4108 1895 6321 6 3 \n", + "28 202131 7 4793 2301 7285 7 3 \n", + "29 202130 7 7190 4191 10189 11 6 \n", + "... ... ... ... ... ... ... ... \n", + "1599 199126 7 17608 11304 23912 31 20 \n", + "1600 199125 7 16169 10700 21638 28 18 \n", + "1601 199124 7 16171 10071 22271 28 17 \n", + "1602 199123 7 11947 7671 16223 21 13 \n", + "1603 199122 7 15452 9953 20951 27 17 \n", + "1604 199121 7 14903 8975 20831 26 16 \n", + "1605 199120 7 19053 12742 25364 34 23 \n", + "1606 199119 7 16739 11246 22232 29 19 \n", + "1607 199118 7 21385 13882 28888 38 25 \n", + "1608 199117 7 13462 8877 18047 24 16 \n", + "1609 199116 7 14857 10068 19646 26 18 \n", + "1610 199115 7 13975 9781 18169 25 18 \n", + "1611 199114 7 12265 7684 16846 22 14 \n", + "1612 199113 7 9567 6041 13093 17 11 \n", + "1613 199112 7 10864 7331 14397 19 13 \n", + "1614 199111 7 15574 11184 19964 27 19 \n", + "1615 199110 7 16643 11372 21914 29 20 \n", + "1616 199109 7 13741 8780 18702 24 15 \n", + "1617 199108 7 13289 8813 17765 23 15 \n", + "1618 199107 7 12337 8077 16597 22 15 \n", + "1619 199106 7 10877 7013 14741 19 12 \n", + "1620 199105 7 10442 6544 14340 18 11 \n", + "1621 199104 7 7913 4563 11263 14 8 \n", + "1622 199103 7 15387 10484 20290 27 18 \n", + "1623 199102 7 16277 11046 21508 29 20 \n", + "1624 199101 7 15565 10271 20859 27 18 \n", + "1625 199052 7 19375 13295 25455 34 23 \n", + "1626 199051 7 19080 13807 24353 34 25 \n", + "1627 199050 7 11079 6660 15498 20 12 \n", + "1628 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name period \n", + "0 26 FR France 2022-02-14/2022-02-20 \n", + "1 19 FR France 2022-02-07/2022-02-13 \n", + "2 21 FR France 2022-01-31/2022-02-06 \n", + "3 18 FR France 2022-01-24/2022-01-30 \n", + "4 26 FR France 2022-01-17/2022-01-23 \n", + "5 17 FR France 2022-01-10/2022-01-16 \n", + "6 26 FR France 2022-01-03/2022-01-09 \n", + "7 25 FR France 2021-12-27/2022-01-02 \n", + "8 26 FR France 2021-12-20/2021-12-26 \n", + "9 27 FR France 2021-12-13/2021-12-19 \n", + "10 26 FR France 2021-12-06/2021-12-12 \n", + "11 22 FR France 2021-11-29/2021-12-05 \n", + "12 22 FR France 2021-11-22/2021-11-28 \n", + "13 16 FR France 2021-11-15/2021-11-21 \n", + "14 18 FR France 2021-11-08/2021-11-14 \n", + "15 18 FR France 2021-11-01/2021-11-07 \n", + "16 17 FR France 2021-10-25/2021-10-31 \n", + "17 19 FR France 2021-10-18/2021-10-24 \n", + "18 9 FR France 2021-10-11/2021-10-17 \n", + "19 10 FR France 2021-10-04/2021-10-10 \n", + "20 5 FR France 2021-09-27/2021-10-03 \n", + "21 10 FR France 2021-09-20/2021-09-26 \n", + "22 5 FR France 2021-09-13/2021-09-19 \n", + "23 8 FR France 2021-09-06/2021-09-12 \n", + "24 6 FR France 2021-08-30/2021-09-05 \n", + "25 4 FR France 2021-08-23/2021-08-29 \n", + "26 9 FR France 2021-08-16/2021-08-22 \n", + "27 9 FR France 2021-08-09/2021-08-15 \n", + "28 11 FR France 2021-08-02/2021-08-08 \n", + "29 16 FR France 2021-07-26/2021-08-01 \n", + "... ... ... ... ... \n", + "1599 42 FR France 1991-06-24/1991-06-30 \n", + "1600 38 FR France 1991-06-17/1991-06-23 \n", + "1601 39 FR France 1991-06-10/1991-06-16 \n", + "1602 29 FR France 1991-06-03/1991-06-09 \n", + "1603 37 FR France 1991-05-27/1991-06-02 \n", + "1604 36 FR France 1991-05-20/1991-05-26 \n", + "1605 45 FR France 1991-05-13/1991-05-19 \n", + "1606 39 FR France 1991-05-06/1991-05-12 \n", + "1607 51 FR France 1991-04-29/1991-05-05 \n", + "1608 32 FR France 1991-04-22/1991-04-28 \n", + "1609 34 FR France 1991-04-15/1991-04-21 \n", + "1610 32 FR France 1991-04-08/1991-04-14 \n", + "1611 30 FR France 1991-04-01/1991-04-07 \n", + "1612 23 FR France 1991-03-25/1991-03-31 \n", + "1613 25 FR France 1991-03-18/1991-03-24 \n", + "1614 35 FR France 1991-03-11/1991-03-17 \n", + "1615 38 FR France 1991-03-04/1991-03-10 \n", + "1616 33 FR France 1991-02-25/1991-03-03 \n", + "1617 31 FR France 1991-02-18/1991-02-24 \n", + "1618 29 FR France 1991-02-11/1991-02-17 \n", + "1619 26 FR France 1991-02-04/1991-02-10 \n", + "1620 25 FR France 1991-01-28/1991-02-03 \n", + "1621 20 FR France 1991-01-21/1991-01-27 \n", + "1622 36 FR France 1991-01-14/1991-01-20 \n", + "1623 38 FR France 1991-01-07/1991-01-13 \n", + "1624 36 FR France 1990-12-31/1991-01-06 \n", + "1625 45 FR France 1990-12-24/1990-12-30 \n", + "1626 43 FR France 1990-12-17/1990-12-23 \n", + "1627 28 FR France 1990-12-10/1990-12-16 \n", + "1628 5 FR France 1990-12-03/1990-12-09 \n", + "\n", + "[1629 rows x 11 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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", + "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il restent deux petites modifications à faire.Premièrement, nous définissons les périodes d'observation comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique, ce qui sera pratique par la suite.Deuxièmement, nous trions les points par période, dans le sens chronologique." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = raw_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 laissons une \"marge d'erreur\" d'une seconde. Ceci s'avère tout à fait juste." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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": [ + "Nous pouvons maintenant commencer à regarder les données" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "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": [ + "Zoomons sur les dernières années afin de mieux voir des éventuels effets de saisonnalités. On observe bien une forme de saisonnalité avec des creux entre le printemps jusqu'au début de l'automne, et des pics en hiver (janvier-mars environ, selon les années). Notons que ces pics durent, contrairement au pic de la grippe par exemple (plus larges)." + ] + }, + { + "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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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'][-300:].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 entre deux années civiles, 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.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\n", + "de référence: à la place du 1er septembre de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er septembre.Comme l'incidence de la varicelle est très faible en fin d'été, cette modification ne risque pas de fausser nos conclusions. Encore un petit détail: les données commencent fin 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "first_sept_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 septembre, 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. 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": 11, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_sept_week[:-1],\n", + " 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": {}, + "source": [ + "Voici les incidences annuelles." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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). On observe que l'épidémie a été la plus forte en 2009, et la plus faible en 2020." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 221186\n", + "2021 376290\n", + "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": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +2421,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -