{ "cells": [ { "cell_type": "code", "execution_count": 11, "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 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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maintenant, je vais tester si un fichier local existe déjà avant de telecharger les donnés pour eviter d'avoir des donnés répétées. Si le fichier n'existe pas, je le telecharge." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The file incidence-PAY-7.csv exists\n" ] }, { "data": { "text/plain": [ "72398" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pathlib import Path\n", "\n", "path_to_file = 'incidence-PAY-7.csv'\n", "path = Path(path_to_file)\n", "\n", "if path.is_file():\n", " print(f'The file {path_to_file} exists')\n", "else:\n", " print(f'The file {path_to_file} does not exist')\n", " import requests\n", "\n", "\n", "url = 'http://www.sentiweb.fr/datasets/incidence-PAY-7.csv'\n", "r = requests.get(url, allow_redirects=True)\n", "\n", "open('incidence-PAY-7.csv', 'wb').write(r.content)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data = \"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": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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12022197186741422023128282135FRFrance
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42022167196601486024460302337FRFrance
52022157177991371521883272133FRFrance
62022147170051316220848262032FRFrance
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9202211711729834715111181323FRFrance
102022107133141003616592201525FRFrance
11202209710485760013370161220FRFrance
12202208712088874115435181323FRFrance
132022077140031078917217211626FRFrance
1420220679798704812548151119FRFrance
15202205710851779713905161121FRFrance
1620220479547672112373141018FRFrance
172022037139721068017264211626FRFrance
182022027849560261096413917FRFrance
192022017137931059716989211626FRFrance
20202152713239961116867201525FRFrance
21202151713326962917023201426FRFrance
222021507141281031217944211527FRFrance
232021497136741036916979211626FRFrance
24202148711549850314595171222FRFrance
25202147711419837614462171222FRFrance
262021467821657241070812816FRFrance
2720214578965646811462141018FRFrance
282021447873656361183613818FRFrance
292021437814551641112612717FRFrance
.................................
16121991267176081130423912312042FRFrance
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1640199050711079666015498201228FRFrance
16411990497114302610205FRFrance
\n", "

1642 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202220 7 25273 19093 31453 38 29 \n", "1 202219 7 18674 14220 23128 28 21 \n", "2 202218 7 17851 13963 21739 27 21 \n", "3 202217 7 20314 16001 24627 31 24 \n", "4 202216 7 19660 14860 24460 30 23 \n", "5 202215 7 17799 13715 21883 27 21 \n", "6 202214 7 17005 13162 20848 26 20 \n", "7 202213 7 15448 11659 19237 23 17 \n", "8 202212 7 14702 10794 18610 22 16 \n", "9 202211 7 11729 8347 15111 18 13 \n", "10 202210 7 13314 10036 16592 20 15 \n", "11 202209 7 10485 7600 13370 16 12 \n", "12 202208 7 12088 8741 15435 18 13 \n", "13 202207 7 14003 10789 17217 21 16 \n", "14 202206 7 9798 7048 12548 15 11 \n", "15 202205 7 10851 7797 13905 16 11 \n", "16 202204 7 9547 6721 12373 14 10 \n", "17 202203 7 13972 10680 17264 21 16 \n", "18 202202 7 8495 6026 10964 13 9 \n", "19 202201 7 13793 10597 16989 21 16 \n", "20 202152 7 13239 9611 16867 20 15 \n", "21 202151 7 13326 9629 17023 20 14 \n", "22 202150 7 14128 10312 17944 21 15 \n", "23 202149 7 13674 10369 16979 21 16 \n", "24 202148 7 11549 8503 14595 17 12 \n", "25 202147 7 11419 8376 14462 17 12 \n", "26 202146 7 8216 5724 10708 12 8 \n", "27 202145 7 8965 6468 11462 14 10 \n", "28 202144 7 8736 5636 11836 13 8 \n", "29 202143 7 8145 5164 11126 12 7 \n", "... ... ... ... ... ... ... ... \n", "1612 199126 7 17608 11304 23912 31 20 \n", "1613 199125 7 16169 10700 21638 28 18 \n", "1614 199124 7 16171 10071 22271 28 17 \n", "1615 199123 7 11947 7671 16223 21 13 \n", "1616 199122 7 15452 9953 20951 27 17 \n", "1617 199121 7 14903 8975 20831 26 16 \n", "1618 199120 7 19053 12742 25364 34 23 \n", "1619 199119 7 16739 11246 22232 29 19 \n", "1620 199118 7 21385 13882 28888 38 25 \n", "1621 199117 7 13462 8877 18047 24 16 \n", "1622 199116 7 14857 10068 19646 26 18 \n", "1623 199115 7 13975 9781 18169 25 18 \n", "1624 199114 7 12265 7684 16846 22 14 \n", "1625 199113 7 9567 6041 13093 17 11 \n", "1626 199112 7 10864 7331 14397 19 13 \n", "1627 199111 7 15574 11184 19964 27 19 \n", "1628 199110 7 16643 11372 21914 29 20 \n", "1629 199109 7 13741 8780 18702 24 15 \n", "1630 199108 7 13289 8813 17765 23 15 \n", "1631 199107 7 12337 8077 16597 22 15 \n", "1632 199106 7 10877 7013 14741 19 12 \n", "1633 199105 7 10442 6544 14340 18 11 \n", "1634 199104 7 7913 4563 11263 14 8 \n", "1635 199103 7 15387 10484 20290 27 18 \n", "1636 199102 7 16277 11046 21508 29 20 \n", "1637 199101 7 15565 10271 20859 27 18 \n", "1638 199052 7 19375 13295 25455 34 23 \n", "1639 199051 7 19080 13807 24353 34 25 \n", "1640 199050 7 11079 6660 15498 20 12 \n", "1641 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 47 FR France \n", "1 35 FR France \n", "2 33 FR France \n", "3 38 FR France \n", "4 37 FR France \n", "5 33 FR France \n", "6 32 FR France \n", "7 29 FR France \n", "8 28 FR France \n", "9 23 FR France \n", "10 25 FR France \n", "11 20 FR France \n", "12 23 FR France \n", "13 26 FR France \n", "14 19 FR France \n", "15 21 FR France \n", "16 18 FR France \n", "17 26 FR France \n", "18 17 FR France \n", "19 26 FR France \n", "20 25 FR France \n", "21 26 FR France \n", "22 27 FR France \n", "23 26 FR France \n", "24 22 FR France \n", "25 22 FR France \n", "26 16 FR France \n", "27 18 FR France \n", "28 18 FR France \n", "29 17 FR France \n", "... ... ... ... \n", "1612 42 FR France \n", "1613 38 FR France \n", "1614 39 FR France \n", "1615 29 FR France \n", "1616 37 FR France \n", "1617 36 FR France \n", "1618 45 FR France \n", "1619 39 FR France \n", "1620 51 FR France \n", "1621 32 FR France \n", "1622 34 FR France \n", "1623 32 FR France \n", "1624 30 FR France \n", "1625 23 FR France \n", "1626 25 FR France \n", "1627 35 FR France \n", "1628 38 FR France \n", "1629 33 FR France \n", "1630 31 FR France \n", "1631 29 FR France \n", "1632 26 FR France \n", "1633 25 FR France \n", "1634 20 FR France \n", "1635 36 FR France \n", "1636 38 FR France \n", "1637 36 FR France \n", "1638 45 FR France \n", "1639 43 FR France \n", "1640 28 FR France \n", "1641 5 FR France \n", "\n", "[1642 rows x 10 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? " ] }, { "cell_type": "code", "execution_count": 14, "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": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il n'a pas de point manquant dans ce jeux de données." ] }, { "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": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02022207252731909331453382947FRFrance
12022197186741422023128282135FRFrance
22022187178511396321739272133FRFrance
32022177203141600124627312438FRFrance
42022167196601486024460302337FRFrance
52022157177991371521883272133FRFrance
62022147170051316220848262032FRFrance
72022137154481165919237231729FRFrance
82022127147021079418610221628FRFrance
9202211711729834715111181323FRFrance
102022107133141003616592201525FRFrance
11202209710485760013370161220FRFrance
12202208712088874115435181323FRFrance
132022077140031078917217211626FRFrance
1420220679798704812548151119FRFrance
15202205710851779713905161121FRFrance
1620220479547672112373141018FRFrance
172022037139721068017264211626FRFrance
182022027849560261096413917FRFrance
192022017137931059716989211626FRFrance
20202152713239961116867201525FRFrance
21202151713326962917023201426FRFrance
222021507141281031217944211527FRFrance
232021497136741036916979211626FRFrance
24202148711549850314595171222FRFrance
25202147711419837614462171222FRFrance
262021467821657241070812816FRFrance
2720214578965646811462141018FRFrance
282021447873656361183613818FRFrance
292021437814551641112612717FRFrance
.................................
16121991267176081130423912312042FRFrance
16131991257161691070021638281838FRFrance
16141991247161711007122271281739FRFrance
1615199123711947767116223211329FRFrance
1616199122715452995320951271737FRFrance
1617199121714903897520831261636FRFrance
16181991207190531274225364342345FRFrance
16191991197167391124622232291939FRFrance
16201991187213851388228888382551FRFrance
1621199117713462887718047241632FRFrance
16221991167148571006819646261834FRFrance
1623199115713975978118169251832FRFrance
1624199114712265768416846221430FRFrance
162519911379567604113093171123FRFrance
1626199112710864733114397191325FRFrance
16271991117155741118419964271935FRFrance
16281991107166431137221914292038FRFrance
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1630199108713289881317765231531FRFrance
1631199107712337807716597221529FRFrance
1632199106710877701314741191226FRFrance
1633199105710442654414340181125FRFrance
16341991047791345631126314820FRFrance
16351991037153871048420290271836FRFrance
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16391990517190801380724353342543FRFrance
1640199050711079666015498201228FRFrance
16411990497114302610205FRFrance
\n", "

1642 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202220 7 25273 19093 31453 38 29 \n", "1 202219 7 18674 14220 23128 28 21 \n", "2 202218 7 17851 13963 21739 27 21 \n", "3 202217 7 20314 16001 24627 31 24 \n", "4 202216 7 19660 14860 24460 30 23 \n", "5 202215 7 17799 13715 21883 27 21 \n", "6 202214 7 17005 13162 20848 26 20 \n", "7 202213 7 15448 11659 19237 23 17 \n", "8 202212 7 14702 10794 18610 22 16 \n", "9 202211 7 11729 8347 15111 18 13 \n", "10 202210 7 13314 10036 16592 20 15 \n", "11 202209 7 10485 7600 13370 16 12 \n", "12 202208 7 12088 8741 15435 18 13 \n", "13 202207 7 14003 10789 17217 21 16 \n", "14 202206 7 9798 7048 12548 15 11 \n", "15 202205 7 10851 7797 13905 16 11 \n", "16 202204 7 9547 6721 12373 14 10 \n", "17 202203 7 13972 10680 17264 21 16 \n", "18 202202 7 8495 6026 10964 13 9 \n", "19 202201 7 13793 10597 16989 21 16 \n", "20 202152 7 13239 9611 16867 20 15 \n", "21 202151 7 13326 9629 17023 20 14 \n", "22 202150 7 14128 10312 17944 21 15 \n", "23 202149 7 13674 10369 16979 21 16 \n", "24 202148 7 11549 8503 14595 17 12 \n", "25 202147 7 11419 8376 14462 17 12 \n", "26 202146 7 8216 5724 10708 12 8 \n", "27 202145 7 8965 6468 11462 14 10 \n", "28 202144 7 8736 5636 11836 13 8 \n", "29 202143 7 8145 5164 11126 12 7 \n", "... ... ... ... ... ... ... ... \n", "1612 199126 7 17608 11304 23912 31 20 \n", "1613 199125 7 16169 10700 21638 28 18 \n", "1614 199124 7 16171 10071 22271 28 17 \n", "1615 199123 7 11947 7671 16223 21 13 \n", "1616 199122 7 15452 9953 20951 27 17 \n", "1617 199121 7 14903 8975 20831 26 16 \n", "1618 199120 7 19053 12742 25364 34 23 \n", "1619 199119 7 16739 11246 22232 29 19 \n", "1620 199118 7 21385 13882 28888 38 25 \n", "1621 199117 7 13462 8877 18047 24 16 \n", "1622 199116 7 14857 10068 19646 26 18 \n", "1623 199115 7 13975 9781 18169 25 18 \n", "1624 199114 7 12265 7684 16846 22 14 \n", "1625 199113 7 9567 6041 13093 17 11 \n", "1626 199112 7 10864 7331 14397 19 13 \n", "1627 199111 7 15574 11184 19964 27 19 \n", "1628 199110 7 16643 11372 21914 29 20 \n", "1629 199109 7 13741 8780 18702 24 15 \n", "1630 199108 7 13289 8813 17765 23 15 \n", "1631 199107 7 12337 8077 16597 22 15 \n", "1632 199106 7 10877 7013 14741 19 12 \n", "1633 199105 7 10442 6544 14340 18 11 \n", "1634 199104 7 7913 4563 11263 14 8 \n", "1635 199103 7 15387 10484 20290 27 18 \n", "1636 199102 7 16277 11046 21508 29 20 \n", "1637 199101 7 15565 10271 20859 27 18 \n", "1638 199052 7 19375 13295 25455 34 23 \n", "1639 199051 7 19080 13807 24353 34 25 \n", "1640 199050 7 11079 6660 15498 20 12 \n", "1641 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 47 FR France \n", "1 35 FR France \n", "2 33 FR France \n", "3 38 FR France \n", "4 37 FR France \n", "5 33 FR France \n", "6 32 FR France \n", "7 29 FR France \n", "8 28 FR France \n", "9 23 FR France \n", "10 25 FR France \n", "11 20 FR France \n", "12 23 FR France \n", "13 26 FR France \n", "14 19 FR France \n", "15 21 FR France \n", "16 18 FR France \n", "17 26 FR France \n", "18 17 FR France \n", "19 26 FR France \n", "20 25 FR France \n", "21 26 FR France \n", "22 27 FR France \n", "23 26 FR France \n", "24 22 FR France \n", "25 22 FR France \n", "26 16 FR France \n", "27 18 FR France \n", "28 18 FR France \n", "29 17 FR France \n", "... ... ... ... \n", "1612 42 FR France \n", "1613 38 FR France \n", "1614 39 FR France \n", "1615 29 FR France \n", "1616 37 FR France \n", "1617 36 FR France \n", "1618 45 FR France \n", "1619 39 FR France \n", "1620 51 FR France \n", "1621 32 FR France \n", "1622 34 FR France \n", "1623 32 FR France \n", "1624 30 FR France \n", "1625 23 FR France \n", "1626 25 FR France \n", "1627 35 FR France \n", "1628 38 FR France \n", "1629 33 FR France \n", "1630 31 FR France \n", "1631 29 FR France \n", "1632 26 FR France \n", "1633 25 FR France \n", "1634 20 FR France \n", "1635 36 FR France \n", "1636 38 FR France \n", "1637 36 FR France \n", "1638 45 FR France \n", "1639 43 FR France \n", "1640 28 FR France \n", "1641 5 FR France \n", "\n", "[1642 rows x 10 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "code", "execution_count": 18, "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": 21, "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\" d'une seconde.\n", "\n", "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives entre lesquelles il manque une semaine.\n", "\n", "Nous reconnaissons ces dates: c'est la semaine sans observations que nous avions supprimées !" ] }, { "cell_type": "code", "execution_count": 23, "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": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "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": [ "Un zoom sur les dernières années montre mieux la situation des pics en hiver." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "nous définissons la période de référence du 1er septembre de l'année 𝑁 au 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 premier jour de la semaine qui contient le 1er septembre.\n", "\n", "\n", "Encore un petit détail: les données commencent a la fin de 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 33, "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.\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": 35, "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": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "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": 39, "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": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, un histogramme " ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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