diff --git a/module3/exo2/analyse_varicelle.ipynb b/module3/exo2/analyse_varicelle.ipynb index c9f4f20bbe3507fd85880ac11985afed3afed2bc..b5422e1b1209f2a4f97a208b3973b763ad0e91af 100644 --- a/module3/exo2/analyse_varicelle.ipynb +++ b/module3/exo2/analyse_varicelle.ipynb @@ -25,12 +25,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](https://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." + " Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](https://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 décembre 1990 et se termine avec une semaine récente." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -46,13 +46,2407 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "if not os.path.exists(\"incidence-PAY-7.csv\"):\n", " urllib.request.urlretrieve(data_url, \"incidence-PAY-7.csv\")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Voici l'explication des colonnes données sur le site d'origine:\n", + "\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", + "\n", + "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202234723065934019306FRFrance
12022337735301741411026FRFrance
22022327780140861151612618FRFrance
3202231768964170962210614FRFrance
42022307903957701230814919FRFrance
52022297148511006019642221529FRFrance
62022287154711102819914231630FRFrance
72022277211911619826184322440FRFrance
82022267168541280620902251931FRFrance
92022257222461801126481342840FRFrance
102022247224581810526811342741FRFrance
112022237187721487522669282234FRFrance
122022227189161494122891292335FRFrance
132022217203101630724313312537FRFrance
142022207235851900428166362943FRFrance
152022197185931418123005282135FRFrance
162022187178511396321739272133FRFrance
172022177203141600124627312438FRFrance
182022167196601486024460302337FRFrance
192022157177991371521883272133FRFrance
202022147170051316220848262032FRFrance
212022137154481165919237231729FRFrance
222022127147021079418610221628FRFrance
23202211711729834715111181323FRFrance
242022107133141003616592201525FRFrance
25202209710485760013370161220FRFrance
26202208712088874115435181323FRFrance
272022077140031078917217211626FRFrance
2820220679798704812548151119FRFrance
29202205710851779713905161121FRFrance
.................................
16261991267176081130423912312042FRFrance
16271991257161691070021638281838FRFrance
16281991247161711007122271281739FRFrance
1629199123711947767116223211329FRFrance
1630199122715452995320951271737FRFrance
1631199121714903897520831261636FRFrance
16321991207190531274225364342345FRFrance
16331991197167391124622232291939FRFrance
16341991187213851388228888382551FRFrance
1635199117713462887718047241632FRFrance
16361991167148571006819646261834FRFrance
1637199115713975978118169251832FRFrance
1638199114712265768416846221430FRFrance
163919911379567604113093171123FRFrance
1640199112710864733114397191325FRFrance
16411991117155741118419964271935FRFrance
16421991107166431137221914292038FRFrance
1643199109713741878018702241533FRFrance
1644199108713289881317765231531FRFrance
1645199107712337807716597221529FRFrance
1646199106710877701314741191226FRFrance
1647199105710442654414340181125FRFrance
16481991047791345631126314820FRFrance
16491991037153871048420290271836FRFrance
16501991027162771104621508292038FRFrance
16511991017155651027120859271836FRFrance
16521990527193751329525455342345FRFrance
16531990517190801380724353342543FRFrance
1654199050711079666015498201228FRFrance
16551990497114302610205FRFrance
\n", + "

1656 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202234 7 2306 593 4019 3 0 \n", + "1 202233 7 7353 0 17414 11 0 \n", + "2 202232 7 7801 4086 11516 12 6 \n", + "3 202231 7 6896 4170 9622 10 6 \n", + "4 202230 7 9039 5770 12308 14 9 \n", + "5 202229 7 14851 10060 19642 22 15 \n", + "6 202228 7 15471 11028 19914 23 16 \n", + "7 202227 7 21191 16198 26184 32 24 \n", + "8 202226 7 16854 12806 20902 25 19 \n", + "9 202225 7 22246 18011 26481 34 28 \n", + "10 202224 7 22458 18105 26811 34 27 \n", + "11 202223 7 18772 14875 22669 28 22 \n", + "12 202222 7 18916 14941 22891 29 23 \n", + "13 202221 7 20310 16307 24313 31 25 \n", + "14 202220 7 23585 19004 28166 36 29 \n", + "15 202219 7 18593 14181 23005 28 21 \n", + "16 202218 7 17851 13963 21739 27 21 \n", + "17 202217 7 20314 16001 24627 31 24 \n", + "18 202216 7 19660 14860 24460 30 23 \n", + "19 202215 7 17799 13715 21883 27 21 \n", + "20 202214 7 17005 13162 20848 26 20 \n", + "21 202213 7 15448 11659 19237 23 17 \n", + "22 202212 7 14702 10794 18610 22 16 \n", + "23 202211 7 11729 8347 15111 18 13 \n", + "24 202210 7 13314 10036 16592 20 15 \n", + "25 202209 7 10485 7600 13370 16 12 \n", + "26 202208 7 12088 8741 15435 18 13 \n", + "27 202207 7 14003 10789 17217 21 16 \n", + "28 202206 7 9798 7048 12548 15 11 \n", + "29 202205 7 10851 7797 13905 16 11 \n", + "... ... ... ... ... ... ... ... \n", + "1626 199126 7 17608 11304 23912 31 20 \n", + "1627 199125 7 16169 10700 21638 28 18 \n", + "1628 199124 7 16171 10071 22271 28 17 \n", + "1629 199123 7 11947 7671 16223 21 13 \n", + "1630 199122 7 15452 9953 20951 27 17 \n", + "1631 199121 7 14903 8975 20831 26 16 \n", + "1632 199120 7 19053 12742 25364 34 23 \n", + "1633 199119 7 16739 11246 22232 29 19 \n", + "1634 199118 7 21385 13882 28888 38 25 \n", + "1635 199117 7 13462 8877 18047 24 16 \n", + "1636 199116 7 14857 10068 19646 26 18 \n", + "1637 199115 7 13975 9781 18169 25 18 \n", + "1638 199114 7 12265 7684 16846 22 14 \n", + "1639 199113 7 9567 6041 13093 17 11 \n", + "1640 199112 7 10864 7331 14397 19 13 \n", + "1641 199111 7 15574 11184 19964 27 19 \n", + "1642 199110 7 16643 11372 21914 29 20 \n", + "1643 199109 7 13741 8780 18702 24 15 \n", + "1644 199108 7 13289 8813 17765 23 15 \n", + "1645 199107 7 12337 8077 16597 22 15 \n", + "1646 199106 7 10877 7013 14741 19 12 \n", + "1647 199105 7 10442 6544 14340 18 11 \n", + "1648 199104 7 7913 4563 11263 14 8 \n", + "1649 199103 7 15387 10484 20290 27 18 \n", + "1650 199102 7 16277 11046 21508 29 20 \n", + "1651 199101 7 15565 10271 20859 27 18 \n", + "1652 199052 7 19375 13295 25455 34 23 \n", + "1653 199051 7 19080 13807 24353 34 25 \n", + "1654 199050 7 11079 6660 15498 20 12 \n", + "1655 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 6 FR France \n", + "1 26 FR France \n", + "2 18 FR France \n", + "3 14 FR France \n", + "4 19 FR France \n", + "5 29 FR France \n", + "6 30 FR France \n", + "7 40 FR France \n", + "8 31 FR France \n", + "9 40 FR France \n", + "10 41 FR France \n", + "11 34 FR France \n", + "12 35 FR France \n", + "13 37 FR France \n", + "14 43 FR France \n", + "15 35 FR France \n", + "16 33 FR France \n", + "17 38 FR France \n", + "18 37 FR France \n", + "19 33 FR France \n", + "20 32 FR France \n", + "21 29 FR France \n", + "22 28 FR France \n", + "23 23 FR France \n", + "24 25 FR France \n", + "25 20 FR France \n", + "26 23 FR France \n", + "27 26 FR France \n", + "28 19 FR France \n", + "29 21 FR France \n", + "... ... ... ... \n", + "1626 42 FR France \n", + "1627 38 FR France \n", + "1628 39 FR France \n", + "1629 29 FR France \n", + "1630 37 FR France \n", + "1631 36 FR France \n", + "1632 45 FR France \n", + "1633 39 FR France \n", + "1634 51 FR France \n", + "1635 32 FR France \n", + "1636 34 FR France \n", + "1637 32 FR France \n", + "1638 30 FR France \n", + "1639 23 FR France \n", + "1640 25 FR France \n", + "1641 35 FR France \n", + "1642 38 FR France \n", + "1643 33 FR France \n", + "1644 31 FR France \n", + "1645 29 FR France \n", + "1646 26 FR France \n", + "1647 25 FR France \n", + "1648 20 FR France \n", + "1649 36 FR France \n", + "1650 38 FR France \n", + "1651 36 FR France \n", + "1652 45 FR France \n", + "1653 43 FR France \n", + "1654 28 FR France \n", + "1655 5 FR France \n", + "\n", + "[1656 rows x 10 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(\"incidence-PAY-7.csv\", skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous vérifions s'il existe des données manquantes dans ce fichier. En l'occurence, aucune donnée ne manque de jusqu'à septembre 2022." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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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": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour rester robuste à de futures analyses des incidences, où certaines données pourraient manquer, nous prenons la précaution d'ôter les semaines où les données sont manquantes." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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102022247224581810526811342741FRFrance
112022237187721487522669282234FRFrance
122022227189161494122891292335FRFrance
132022217203101630724313312537FRFrance
142022207235851900428166362943FRFrance
152022197185931418123005282135FRFrance
162022187178511396321739272133FRFrance
172022177203141600124627312438FRFrance
182022167196601486024460302337FRFrance
192022157177991371521883272133FRFrance
202022147170051316220848262032FRFrance
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222022127147021079418610221628FRFrance
23202211711729834715111181323FRFrance
242022107133141003616592201525FRFrance
25202209710485760013370161220FRFrance
26202208712088874115435181323FRFrance
272022077140031078917217211626FRFrance
2820220679798704812548151119FRFrance
29202205710851779713905161121FRFrance
.................................
16261991267176081130423912312042FRFrance
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1656 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202234 7 2306 593 4019 3 0 \n", + "1 202233 7 7353 0 17414 11 0 \n", + "2 202232 7 7801 4086 11516 12 6 \n", + "3 202231 7 6896 4170 9622 10 6 \n", + "4 202230 7 9039 5770 12308 14 9 \n", + "5 202229 7 14851 10060 19642 22 15 \n", + "6 202228 7 15471 11028 19914 23 16 \n", + "7 202227 7 21191 16198 26184 32 24 \n", + "8 202226 7 16854 12806 20902 25 19 \n", + "9 202225 7 22246 18011 26481 34 28 \n", + "10 202224 7 22458 18105 26811 34 27 \n", + "11 202223 7 18772 14875 22669 28 22 \n", + "12 202222 7 18916 14941 22891 29 23 \n", + "13 202221 7 20310 16307 24313 31 25 \n", + "14 202220 7 23585 19004 28166 36 29 \n", + "15 202219 7 18593 14181 23005 28 21 \n", + "16 202218 7 17851 13963 21739 27 21 \n", + "17 202217 7 20314 16001 24627 31 24 \n", + "18 202216 7 19660 14860 24460 30 23 \n", + "19 202215 7 17799 13715 21883 27 21 \n", + "20 202214 7 17005 13162 20848 26 20 \n", + "21 202213 7 15448 11659 19237 23 17 \n", + "22 202212 7 14702 10794 18610 22 16 \n", + "23 202211 7 11729 8347 15111 18 13 \n", + "24 202210 7 13314 10036 16592 20 15 \n", + "25 202209 7 10485 7600 13370 16 12 \n", + "26 202208 7 12088 8741 15435 18 13 \n", + "27 202207 7 14003 10789 17217 21 16 \n", + "28 202206 7 9798 7048 12548 15 11 \n", + "29 202205 7 10851 7797 13905 16 11 \n", + "... ... ... ... ... ... ... ... \n", + "1626 199126 7 17608 11304 23912 31 20 \n", + "1627 199125 7 16169 10700 21638 28 18 \n", + "1628 199124 7 16171 10071 22271 28 17 \n", + "1629 199123 7 11947 7671 16223 21 13 \n", + "1630 199122 7 15452 9953 20951 27 17 \n", + "1631 199121 7 14903 8975 20831 26 16 \n", + "1632 199120 7 19053 12742 25364 34 23 \n", + "1633 199119 7 16739 11246 22232 29 19 \n", + "1634 199118 7 21385 13882 28888 38 25 \n", + "1635 199117 7 13462 8877 18047 24 16 \n", + "1636 199116 7 14857 10068 19646 26 18 \n", + "1637 199115 7 13975 9781 18169 25 18 \n", + "1638 199114 7 12265 7684 16846 22 14 \n", + "1639 199113 7 9567 6041 13093 17 11 \n", + "1640 199112 7 10864 7331 14397 19 13 \n", + "1641 199111 7 15574 11184 19964 27 19 \n", + "1642 199110 7 16643 11372 21914 29 20 \n", + "1643 199109 7 13741 8780 18702 24 15 \n", + "1644 199108 7 13289 8813 17765 23 15 \n", + "1645 199107 7 12337 8077 16597 22 15 \n", + "1646 199106 7 10877 7013 14741 19 12 \n", + "1647 199105 7 10442 6544 14340 18 11 \n", + "1648 199104 7 7913 4563 11263 14 8 \n", + "1649 199103 7 15387 10484 20290 27 18 \n", + "1650 199102 7 16277 11046 21508 29 20 \n", + "1651 199101 7 15565 10271 20859 27 18 \n", + "1652 199052 7 19375 13295 25455 34 23 \n", + "1653 199051 7 19080 13807 24353 34 25 \n", + "1654 199050 7 11079 6660 15498 20 12 \n", + "1655 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 6 FR France \n", + "1 26 FR France \n", + "2 18 FR France \n", + "3 14 FR France \n", + "4 19 FR France \n", + "5 29 FR France \n", + "6 30 FR France \n", + "7 40 FR France \n", + "8 31 FR France \n", + "9 40 FR France \n", + "10 41 FR France \n", + "11 34 FR France \n", + "12 35 FR France \n", + "13 37 FR France \n", + "14 43 FR France \n", + "15 35 FR France \n", + "16 33 FR France \n", + "17 38 FR France \n", + "18 37 FR France \n", + "19 33 FR France \n", + "20 32 FR France \n", + "21 29 FR France \n", + "22 28 FR France \n", + "23 23 FR France \n", + "24 25 FR France \n", + "25 20 FR France \n", + "26 23 FR France \n", + "27 26 FR France \n", + "28 19 FR France \n", + "29 21 FR France \n", + "... ... ... ... \n", + "1626 42 FR France \n", + "1627 38 FR France \n", + "1628 39 FR France \n", + "1629 29 FR France \n", + "1630 37 FR France \n", + "1631 36 FR France \n", + "1632 45 FR France \n", + "1633 39 FR France \n", + "1634 51 FR France \n", + "1635 32 FR France \n", + "1636 34 FR France \n", + "1637 32 FR France \n", + "1638 30 FR France \n", + "1639 23 FR France \n", + "1640 25 FR France \n", + "1641 35 FR France \n", + "1642 38 FR France \n", + "1643 33 FR France \n", + "1644 31 FR France \n", + "1645 29 FR France \n", + "1646 26 FR France \n", + "1647 25 FR France \n", + "1648 20 FR France \n", + "1649 36 FR France \n", + "1650 38 FR France \n", + "1651 36 FR France \n", + "1652 45 FR France \n", + "1653 43 FR France \n", + "1654 28 FR France \n", + "1655 5 FR France \n", + "\n", + "[1656 rows x 10 columns]" + ] + }, + "execution_count": 22, + "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 semaine est collé à l'année, donnant l'impression qu'il s'agit 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. 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 l'appliquons à tous les points de nos donnés. Les résultats vont dans une nouvelle colonne intitulée 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "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 reste 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 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": 26, + "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 le début de la période qui suit, la différence temporelle doit être zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", + "d'une seconde.\n", + "\n", + "Nos données couvrent donc bien tout l'intervalle des relevés d'incidence, confortant le fait que nous n'avons pas trouvé de données manquantes." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "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": [ + "Effectuons un premier affichage graphique des données d'incidence de varicelle de 1991 à aujourd'hui." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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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": [ + "Etant donné que le creux de l'épidémie se situe vers le mois de septembre, 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\n", + "1er septembre de l'année $N+1$.\n", + "\n", + "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 de référence : à la place du 1er septembre de chaque année, nous utilisons le\n", + "premier jour de la semaine qui contient le 1er septembre. Comme l'incidence de la varicelle est beaucoup plus faible à cette période de l'année, cette modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail: les données commencent en décembre 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "first_september_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": 42, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_september_week[:-1],\n", + " 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": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "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": 47, + "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": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Enfin, un histogramme montre que l'incidence reste stable, restant vers 0,1% de la population annuellement." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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