diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..ee0f0eaf0c1a1425afa464264d9e1b9c57559a94 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,1386 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyse de l'incidence de la varicelle" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "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 de la varicelle sont disponibles à partir 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 1990 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "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": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \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
02022337742701749811026FRFrance
12022327780140861151612618FRFrance
2202231768964170962210614FRFrance
32022307903957701230814919FRFrance
42022297148511006019642221529FRFrance
52022287154711102819914231630FRFrance
62022277211911619826184322440FRFrance
72022267168541280620902251931FRFrance
82022257222461801126481342840FRFrance
92022247224581810526811342741FRFrance
102022237187721487522669282234FRFrance
112022227189161494122891292335FRFrance
122022217203101630724313312537FRFrance
132022207235851900428166362943FRFrance
142022197185931418123005282135FRFrance
152022187178511396321739272133FRFrance
162022177203141600124627312438FRFrance
172022167196601486024460302337FRFrance
182022157177991371521883272133FRFrance
192022147170051316220848262032FRFrance
202022137154481165919237231729FRFrance
212022127147021079418610221628FRFrance
22202211711729834715111181323FRFrance
232022107133141003616592201525FRFrance
24202209710485760013370161220FRFrance
25202208712088874115435181323FRFrance
262022077140031078917217211626FRFrance
2720220679798704812548151119FRFrance
28202205710851779713905161121FRFrance
2920220479547672112373141018FRFrance
.................................
16251991267176081130423912312042FRFrance
16261991257161691070021638281838FRFrance
16271991247161711007122271281739FRFrance
1628199123711947767116223211329FRFrance
1629199122715452995320951271737FRFrance
1630199121714903897520831261636FRFrance
16311991207190531274225364342345FRFrance
16321991197167391124622232291939FRFrance
16331991187213851388228888382551FRFrance
1634199117713462887718047241632FRFrance
16351991167148571006819646261834FRFrance
1636199115713975978118169251832FRFrance
1637199114712265768416846221430FRFrance
163819911379567604113093171123FRFrance
1639199112710864733114397191325FRFrance
16401991117155741118419964271935FRFrance
16411991107166431137221914292038FRFrance
1642199109713741878018702241533FRFrance
1643199108713289881317765231531FRFrance
1644199107712337807716597221529FRFrance
1645199106710877701314741191226FRFrance
1646199105710442654414340181125FRFrance
16471991047791345631126314820FRFrance
16481991037153871048420290271836FRFrance
16491991027162771104621508292038FRFrance
16501991017155651027120859271836FRFrance
16511990527193751329525455342345FRFrance
16521990517190801380724353342543FRFrance
1653199050711079666015498201228FRFrance
16541990497114302610205FRFrance
\n", + "

1655 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202233 7 7427 0 17498 11 0 \n", + "1 202232 7 7801 4086 11516 12 6 \n", + "2 202231 7 6896 4170 9622 10 6 \n", + "3 202230 7 9039 5770 12308 14 9 \n", + "4 202229 7 14851 10060 19642 22 15 \n", + "5 202228 7 15471 11028 19914 23 16 \n", + "6 202227 7 21191 16198 26184 32 24 \n", + "7 202226 7 16854 12806 20902 25 19 \n", + "8 202225 7 22246 18011 26481 34 28 \n", + "9 202224 7 22458 18105 26811 34 27 \n", + "10 202223 7 18772 14875 22669 28 22 \n", + "11 202222 7 18916 14941 22891 29 23 \n", + "12 202221 7 20310 16307 24313 31 25 \n", + "13 202220 7 23585 19004 28166 36 29 \n", + "14 202219 7 18593 14181 23005 28 21 \n", + "15 202218 7 17851 13963 21739 27 21 \n", + "16 202217 7 20314 16001 24627 31 24 \n", + "17 202216 7 19660 14860 24460 30 23 \n", + "18 202215 7 17799 13715 21883 27 21 \n", + "19 202214 7 17005 13162 20848 26 20 \n", + "20 202213 7 15448 11659 19237 23 17 \n", + "21 202212 7 14702 10794 18610 22 16 \n", + "22 202211 7 11729 8347 15111 18 13 \n", + "23 202210 7 13314 10036 16592 20 15 \n", + "24 202209 7 10485 7600 13370 16 12 \n", + "25 202208 7 12088 8741 15435 18 13 \n", + "26 202207 7 14003 10789 17217 21 16 \n", + "27 202206 7 9798 7048 12548 15 11 \n", + "28 202205 7 10851 7797 13905 16 11 \n", + "29 202204 7 9547 6721 12373 14 10 \n", + "... ... ... ... ... ... ... ... \n", + "1625 199126 7 17608 11304 23912 31 20 \n", + "1626 199125 7 16169 10700 21638 28 18 \n", + "1627 199124 7 16171 10071 22271 28 17 \n", + "1628 199123 7 11947 7671 16223 21 13 \n", + "1629 199122 7 15452 9953 20951 27 17 \n", + "1630 199121 7 14903 8975 20831 26 16 \n", + "1631 199120 7 19053 12742 25364 34 23 \n", + "1632 199119 7 16739 11246 22232 29 19 \n", + "1633 199118 7 21385 13882 28888 38 25 \n", + "1634 199117 7 13462 8877 18047 24 16 \n", + "1635 199116 7 14857 10068 19646 26 18 \n", + "1636 199115 7 13975 9781 18169 25 18 \n", + "1637 199114 7 12265 7684 16846 22 14 \n", + "1638 199113 7 9567 6041 13093 17 11 \n", + "1639 199112 7 10864 7331 14397 19 13 \n", + "1640 199111 7 15574 11184 19964 27 19 \n", + "1641 199110 7 16643 11372 21914 29 20 \n", + "1642 199109 7 13741 8780 18702 24 15 \n", + "1643 199108 7 13289 8813 17765 23 15 \n", + "1644 199107 7 12337 8077 16597 22 15 \n", + "1645 199106 7 10877 7013 14741 19 12 \n", + "1646 199105 7 10442 6544 14340 18 11 \n", + "1647 199104 7 7913 4563 11263 14 8 \n", + "1648 199103 7 15387 10484 20290 27 18 \n", + "1649 199102 7 16277 11046 21508 29 20 \n", + "1650 199101 7 15565 10271 20859 27 18 \n", + "1651 199052 7 19375 13295 25455 34 23 \n", + "1652 199051 7 19080 13807 24353 34 25 \n", + "1653 199050 7 11079 6660 15498 20 12 \n", + "1654 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 26 FR France \n", + "1 18 FR France \n", + "2 14 FR France \n", + "3 19 FR France \n", + "4 29 FR France \n", + "5 30 FR France \n", + "6 40 FR France \n", + "7 31 FR France \n", + "8 40 FR France \n", + "9 41 FR France \n", + "10 34 FR France \n", + "11 35 FR France \n", + "12 37 FR France \n", + "13 43 FR France \n", + "14 35 FR France \n", + "15 33 FR France \n", + "16 38 FR France \n", + "17 37 FR France \n", + "18 33 FR France \n", + "19 32 FR France \n", + "20 29 FR France \n", + "21 28 FR France \n", + "22 23 FR France \n", + "23 25 FR France \n", + "24 20 FR France \n", + "25 23 FR France \n", + "26 26 FR France \n", + "27 19 FR France \n", + "28 21 FR France \n", + "29 18 FR France \n", + "... ... ... ... \n", + "1625 42 FR France \n", + "1626 38 FR France \n", + "1627 39 FR France \n", + "1628 29 FR France \n", + "1629 37 FR France \n", + "1630 36 FR France \n", + "1631 45 FR France \n", + "1632 39 FR France \n", + "1633 51 FR France \n", + "1634 32 FR France \n", + "1635 34 FR France \n", + "1636 32 FR France \n", + "1637 30 FR France \n", + "1638 23 FR France \n", + "1639 25 FR France \n", + "1640 35 FR France \n", + "1641 38 FR France \n", + "1642 33 FR France \n", + "1643 31 FR France \n", + "1644 29 FR France \n", + "1645 26 FR France \n", + "1646 25 FR France \n", + "1647 20 FR France \n", + "1648 36 FR France \n", + "1649 38 FR France \n", + "1650 36 FR France \n", + "1651 45 FR France \n", + "1652 43 FR France \n", + "1653 28 FR France \n", + "1654 5 FR France \n", + "\n", + "[1655 rows x 10 columns]" + ] + }, + "execution_count": 3, + "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 ? Non." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "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": 5, + "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 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 semaine.\n", + "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\n", + "l'appliquons à tous les points de nos donnés. Les résultats vont dans une nouvelle colonne 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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", + "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il restent deux petites modifications à faire.\n", + "\n", + "Premièrement, nous définissons les périodes d'observation 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 le sens chronologique." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "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.\n", + "\n", + "Comme il n'y a aucune ligne vide, il n'y a rien à afficher." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "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": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "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": "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": [ + "sorted_data['inc'][-200:].plot() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On décide, dans le cadre de notre exercice, de choisir le 1er septembre comme début de chaque période annuelle." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "first_september_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 septembre, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. \n", + "\n", + "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": 14, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_september_week[:-1], first_september_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": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "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)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "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": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +1397,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -