{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence de la Varicelle" ] }, { "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 du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour éviter une modification ou une perte des données issues du site Réseau Sentinelles, ces dernières sont copiées localement. l'analyse de l'incidence de la varicelle est faite en priorité à partir des données locales. Si les données n'existent pas elle sont téléchargées depuis l'url." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file = \"Incidence_Varicelle.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "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):" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02020367898761720102FRFrance
1202035782801694102FRFrance
2202034722723714173306FRFrance
3202033712841772391204FRFrance
4202032726506894611417FRFrance
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620203071385752695204FRFrance
72020297841101672102FRFrance
8202028772801515102FRFrance
920202779861491823102FRFrance
10202026769401454102FRFrance
1120202572280597001FRFrance
1220202473880959102FRFrance
13202023755811115102FRFrance
1420202272770633001FRFrance
152020217602361168102FRFrance
162020207824201628102FRFrance
1720201973100753001FRFrance
182020187849981600102FRFrance
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202020167758781438102FRFrance
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25202011710198756812828151119FRFrance
2620201079011669111331141018FRFrance
272020097136311054416718211626FRFrance
28202008710424770813140161220FRFrance
2920200778959657411344141018FRFrance
.................................
15231991267176081130423912312042FRFrance
15241991257161691070021638281838FRFrance
15251991247161711007122271281739FRFrance
1526199123711947767116223211329FRFrance
1527199122715452995320951271737FRFrance
1528199121714903897520831261636FRFrance
15291991207190531274225364342345FRFrance
15301991197167391124622232291939FRFrance
15311991187213851388228888382551FRFrance
1532199117713462887718047241632FRFrance
15331991167148571006819646261834FRFrance
1534199115713975978118169251832FRFrance
1535199114712265768416846221430FRFrance
153619911379567604113093171123FRFrance
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15381991117155741118419964271935FRFrance
15391991107166431137221914292038FRFrance
1540199109713741878018702241533FRFrance
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15501990517190801380724353342543FRFrance
1551199050711079666015498201228FRFrance
15521990497114302610205FRFrance
\n", "

1553 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202036 7 898 76 1720 1 0 \n", "1 202035 7 828 0 1694 1 0 \n", "2 202034 7 2272 371 4173 3 0 \n", "3 202033 7 1284 177 2391 2 0 \n", "4 202032 7 2650 689 4611 4 1 \n", "5 202031 7 1303 100 2506 2 0 \n", "6 202030 7 1385 75 2695 2 0 \n", "7 202029 7 841 10 1672 1 0 \n", "8 202028 7 728 0 1515 1 0 \n", "9 202027 7 986 149 1823 1 0 \n", "10 202026 7 694 0 1454 1 0 \n", "11 202025 7 228 0 597 0 0 \n", "12 202024 7 388 0 959 1 0 \n", "13 202023 7 558 1 1115 1 0 \n", "14 202022 7 277 0 633 0 0 \n", "15 202021 7 602 36 1168 1 0 \n", "16 202020 7 824 20 1628 1 0 \n", "17 202019 7 310 0 753 0 0 \n", "18 202018 7 849 98 1600 1 0 \n", "19 202017 7 272 0 658 0 0 \n", "20 202016 7 758 78 1438 1 0 \n", "21 202015 7 1918 675 3161 3 1 \n", "22 202014 7 3879 2227 5531 6 3 \n", "23 202013 7 7326 5236 9416 11 8 \n", "24 202012 7 8123 5790 10456 12 8 \n", "25 202011 7 10198 7568 12828 15 11 \n", "26 202010 7 9011 6691 11331 14 10 \n", "27 202009 7 13631 10544 16718 21 16 \n", "28 202008 7 10424 7708 13140 16 12 \n", "29 202007 7 8959 6574 11344 14 10 \n", "... ... ... ... ... ... ... ... \n", "1523 199126 7 17608 11304 23912 31 20 \n", "1524 199125 7 16169 10700 21638 28 18 \n", "1525 199124 7 16171 10071 22271 28 17 \n", "1526 199123 7 11947 7671 16223 21 13 \n", "1527 199122 7 15452 9953 20951 27 17 \n", "1528 199121 7 14903 8975 20831 26 16 \n", "1529 199120 7 19053 12742 25364 34 23 \n", "1530 199119 7 16739 11246 22232 29 19 \n", "1531 199118 7 21385 13882 28888 38 25 \n", "1532 199117 7 13462 8877 18047 24 16 \n", "1533 199116 7 14857 10068 19646 26 18 \n", "1534 199115 7 13975 9781 18169 25 18 \n", "1535 199114 7 12265 7684 16846 22 14 \n", "1536 199113 7 9567 6041 13093 17 11 \n", "1537 199112 7 10864 7331 14397 19 13 \n", "1538 199111 7 15574 11184 19964 27 19 \n", "1539 199110 7 16643 11372 21914 29 20 \n", "1540 199109 7 13741 8780 18702 24 15 \n", "1541 199108 7 13289 8813 17765 23 15 \n", "1542 199107 7 12337 8077 16597 22 15 \n", "1543 199106 7 10877 7013 14741 19 12 \n", "1544 199105 7 10442 6544 14340 18 11 \n", "1545 199104 7 7913 4563 11263 14 8 \n", "1546 199103 7 15387 10484 20290 27 18 \n", "1547 199102 7 16277 11046 21508 29 20 \n", "1548 199101 7 15565 10271 20859 27 18 \n", "1549 199052 7 19375 13295 25455 34 23 \n", "1550 199051 7 19080 13807 24353 34 25 \n", "1551 199050 7 11079 6660 15498 20 12 \n", "1552 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 2 FR France \n", "1 2 FR France \n", "2 6 FR France \n", "3 4 FR France \n", "4 7 FR France \n", "5 4 FR France \n", "6 4 FR France \n", "7 2 FR France \n", "8 2 FR France \n", "9 2 FR France \n", "10 2 FR France \n", "11 1 FR France \n", "12 2 FR France \n", "13 2 FR France \n", "14 1 FR France \n", "15 2 FR France \n", "16 2 FR France \n", "17 1 FR France \n", "18 2 FR France \n", "19 1 FR France \n", "20 2 FR France \n", "21 5 FR France \n", "22 9 FR France \n", "23 14 FR France \n", "24 16 FR France \n", "25 19 FR France \n", "26 18 FR France \n", "27 26 FR France \n", "28 20 FR France \n", "29 18 FR France \n", "... ... ... ... \n", "1523 42 FR France \n", "1524 38 FR France \n", "1525 39 FR France \n", "1526 29 FR France \n", "1527 37 FR France \n", "1528 36 FR France \n", "1529 45 FR France \n", "1530 39 FR France \n", "1531 51 FR France \n", "1532 32 FR France \n", "1533 34 FR France \n", "1534 32 FR France \n", "1535 30 FR France \n", "1536 23 FR France \n", "1537 25 FR France \n", "1538 35 FR France \n", "1539 38 FR France \n", "1540 33 FR France \n", "1541 31 FR France \n", "1542 29 FR France \n", "1543 26 FR France \n", "1544 25 FR France \n", "1545 20 FR France \n", "1546 36 FR France \n", "1547 38 FR France \n", "1548 36 FR France \n", "1549 45 FR France \n", "1550 43 FR France \n", "1551 28 FR France \n", "1552 5 FR France \n", "\n", "[1553 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, 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": 7, "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": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si Oui ce/ces points sont supprimés du jeu de données" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02020367898761720102FRFrance
1202035782801694102FRFrance
2202034722723714173306FRFrance
3202033712841772391204FRFrance
4202032726506894611417FRFrance
5202031713031002506204FRFrance
620203071385752695204FRFrance
72020297841101672102FRFrance
8202028772801515102FRFrance
920202779861491823102FRFrance
10202026769401454102FRFrance
1120202572280597001FRFrance
1220202473880959102FRFrance
13202023755811115102FRFrance
1420202272770633001FRFrance
152020217602361168102FRFrance
162020207824201628102FRFrance
1720201973100753001FRFrance
182020187849981600102FRFrance
1920201772720658001FRFrance
202020167758781438102FRFrance
21202015719186753161315FRFrance
222020147387922275531639FRFrance
23202013773265236941611814FRFrance
242020127812357901045612816FRFrance
25202011710198756812828151119FRFrance
2620201079011669111331141018FRFrance
272020097136311054416718211626FRFrance
28202008710424770813140161220FRFrance
2920200778959657411344141018FRFrance
.................................
15231991267176081130423912312042FRFrance
15241991257161691070021638281838FRFrance
15251991247161711007122271281739FRFrance
1526199123711947767116223211329FRFrance
1527199122715452995320951271737FRFrance
1528199121714903897520831261636FRFrance
15291991207190531274225364342345FRFrance
15301991197167391124622232291939FRFrance
15311991187213851388228888382551FRFrance
1532199117713462887718047241632FRFrance
15331991167148571006819646261834FRFrance
1534199115713975978118169251832FRFrance
1535199114712265768416846221430FRFrance
153619911379567604113093171123FRFrance
1537199112710864733114397191325FRFrance
15381991117155741118419964271935FRFrance
15391991107166431137221914292038FRFrance
1540199109713741878018702241533FRFrance
1541199108713289881317765231531FRFrance
1542199107712337807716597221529FRFrance
1543199106710877701314741191226FRFrance
1544199105710442654414340181125FRFrance
15451991047791345631126314820FRFrance
15461991037153871048420290271836FRFrance
15471991027162771104621508292038FRFrance
15481991017155651027120859271836FRFrance
15491990527193751329525455342345FRFrance
15501990517190801380724353342543FRFrance
1551199050711079666015498201228FRFrance
15521990497114302610205FRFrance
\n", "

1553 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202036 7 898 76 1720 1 0 \n", "1 202035 7 828 0 1694 1 0 \n", "2 202034 7 2272 371 4173 3 0 \n", "3 202033 7 1284 177 2391 2 0 \n", "4 202032 7 2650 689 4611 4 1 \n", "5 202031 7 1303 100 2506 2 0 \n", "6 202030 7 1385 75 2695 2 0 \n", "7 202029 7 841 10 1672 1 0 \n", "8 202028 7 728 0 1515 1 0 \n", "9 202027 7 986 149 1823 1 0 \n", "10 202026 7 694 0 1454 1 0 \n", "11 202025 7 228 0 597 0 0 \n", "12 202024 7 388 0 959 1 0 \n", "13 202023 7 558 1 1115 1 0 \n", "14 202022 7 277 0 633 0 0 \n", "15 202021 7 602 36 1168 1 0 \n", "16 202020 7 824 20 1628 1 0 \n", "17 202019 7 310 0 753 0 0 \n", "18 202018 7 849 98 1600 1 0 \n", "19 202017 7 272 0 658 0 0 \n", "20 202016 7 758 78 1438 1 0 \n", "21 202015 7 1918 675 3161 3 1 \n", "22 202014 7 3879 2227 5531 6 3 \n", "23 202013 7 7326 5236 9416 11 8 \n", "24 202012 7 8123 5790 10456 12 8 \n", "25 202011 7 10198 7568 12828 15 11 \n", "26 202010 7 9011 6691 11331 14 10 \n", "27 202009 7 13631 10544 16718 21 16 \n", "28 202008 7 10424 7708 13140 16 12 \n", "29 202007 7 8959 6574 11344 14 10 \n", "... ... ... ... ... ... ... ... \n", "1523 199126 7 17608 11304 23912 31 20 \n", "1524 199125 7 16169 10700 21638 28 18 \n", "1525 199124 7 16171 10071 22271 28 17 \n", "1526 199123 7 11947 7671 16223 21 13 \n", "1527 199122 7 15452 9953 20951 27 17 \n", "1528 199121 7 14903 8975 20831 26 16 \n", "1529 199120 7 19053 12742 25364 34 23 \n", "1530 199119 7 16739 11246 22232 29 19 \n", "1531 199118 7 21385 13882 28888 38 25 \n", "1532 199117 7 13462 8877 18047 24 16 \n", "1533 199116 7 14857 10068 19646 26 18 \n", "1534 199115 7 13975 9781 18169 25 18 \n", "1535 199114 7 12265 7684 16846 22 14 \n", "1536 199113 7 9567 6041 13093 17 11 \n", "1537 199112 7 10864 7331 14397 19 13 \n", "1538 199111 7 15574 11184 19964 27 19 \n", "1539 199110 7 16643 11372 21914 29 20 \n", "1540 199109 7 13741 8780 18702 24 15 \n", "1541 199108 7 13289 8813 17765 23 15 \n", "1542 199107 7 12337 8077 16597 22 15 \n", "1543 199106 7 10877 7013 14741 19 12 \n", "1544 199105 7 10442 6544 14340 18 11 \n", "1545 199104 7 7913 4563 11263 14 8 \n", "1546 199103 7 15387 10484 20290 27 18 \n", "1547 199102 7 16277 11046 21508 29 20 \n", "1548 199101 7 15565 10271 20859 27 18 \n", "1549 199052 7 19375 13295 25455 34 23 \n", "1550 199051 7 19080 13807 24353 34 25 \n", "1551 199050 7 11079 6660 15498 20 12 \n", "1552 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 2 FR France \n", "1 2 FR France \n", "2 6 FR France \n", "3 4 FR France \n", "4 7 FR France \n", "5 4 FR France \n", "6 4 FR France \n", "7 2 FR France \n", "8 2 FR France \n", "9 2 FR France \n", "10 2 FR France \n", "11 1 FR France \n", "12 2 FR France \n", "13 2 FR France \n", "14 1 FR France \n", "15 2 FR France \n", "16 2 FR France \n", "17 1 FR France \n", "18 2 FR France \n", "19 1 FR France \n", "20 2 FR France \n", "21 5 FR France \n", "22 9 FR France \n", "23 14 FR France \n", "24 16 FR France \n", "25 19 FR France \n", "26 18 FR France \n", "27 26 FR France \n", "28 20 FR France \n", "29 18 FR France \n", "... ... ... ... \n", "1523 42 FR France \n", "1524 38 FR France \n", "1525 39 FR France \n", "1526 29 FR France \n", "1527 37 FR France \n", "1528 36 FR France \n", "1529 45 FR France \n", "1530 39 FR France \n", "1531 51 FR France \n", "1532 32 FR France \n", "1533 34 FR France \n", "1534 32 FR France \n", "1535 30 FR France \n", "1536 23 FR France \n", "1537 25 FR France \n", "1538 35 FR France \n", "1539 38 FR France \n", "1540 33 FR France \n", "1541 31 FR France \n", "1542 29 FR France \n", "1543 26 FR France \n", "1544 25 FR France \n", "1545 20 FR France \n", "1546 36 FR France \n", "1547 38 FR France \n", "1548 36 FR France \n", "1549 45 FR France \n", "1550 43 FR France \n", "1551 28 FR France \n", "1552 5 FR France \n", "\n", "[1553 rows x 10 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nos données utilisent une convention inhabituelle: le numéro de\n", "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", "de nombre entier. C'est comme ça que Pandas les interprète.\n", " \n", "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", "semaine. Il faut lui fournir les dates de début et de fin de\n", "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous\n", "écrivons une petite fonction Python pour cela. Ensuite, nous\n", "l'appliquons à tous les points de nos donnés. Les résultats vont\n", "dans une nouvelle colonne 'period'." ] }, { "cell_type": "code", "execution_count": 10, "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": 11, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", "le début de la période qui suit, la différence temporelle doit être\n", "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", "d'une seconde.\n", "\n", "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n", "entre lesquelles il manque une semaine.\n", "\n", "Nous reconnaissons ces dates: c'est la semaine sans observations\n", "que nous avions supprimées !" ] }, { "cell_type": "code", "execution_count": 12, "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": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "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": [ "zoom sur les dernières années" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "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'][-150:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Etude incidence annuelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Etant donné que le pic de l'épidémie se situe en , à cheval\n", "entre deux années civiles, nous définissons la période de référence\n", "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\n", "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\n", "premier jour de la semaine qui contient le 1er septembre.\n" ] }, { "cell_type": "code", "execution_count": 29, "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.\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": 30, "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": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "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": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "les valeurs d'incidence annuelle sont triées : " ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "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": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2009 est l'année avec le plus de cas !\n", "\n", "On trace l'histogramme :" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 36, "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": [ "yearly_incidence.hist(xrot=20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }