{ "cells": [ { "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 de la varicelle sont disponibles du site Web du Réseau Sentinelles. 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.(Donées téléchargées le 29/11/2021)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"incidence-PAY-7.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant skiprows=1" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02021467929661081248414919FRFrance
120214579178662511731141018FRFrance
22021447876256531187113818FRFrance
32021437814551641112612717FRFrance
42021427944360371284914919FRFrance
52021417402122395803639FRFrance
620214074441245464287410FRFrance
72021397229110563526315FRFrance
820213874325226763837410FRFrance
9202137719647543174315FRFrance
102021367344117305152528FRFrance
112021357256211074017426FRFrance
12202134714293782480204FRFrance
132021337382918305828639FRFrance
142021327410818956321639FRFrance
1520213174793230172857311FRFrance
162021307719041911018911616FRFrance
17202129768004109949110614FRFrance
182021287973402173115033FRFrance
192021277902643161373614721FRFrance
202021267728441081046011616FRFrance
2120212579351654012162141018FRFrance
22202124712034893715131181323FRFrance
2320212379116642011812141018FRFrance
2420212274817275268827410FRFrance
2520212176092345887269513FRFrance
262021207748546011036911715FRFrance
27202119766544370893810713FRFrance
282021187391221105714639FRFrance
2920211774686287864947410FRFrance
.................................
15861991267176081130423912312042FRFrance
15871991257161691070021638281838FRFrance
15881991247161711007122271281739FRFrance
1589199123711947767116223211329FRFrance
1590199122715452995320951271737FRFrance
1591199121714903897520831261636FRFrance
15921991207190531274225364342345FRFrance
15931991197167391124622232291939FRFrance
15941991187213851388228888382551FRFrance
1595199117713462887718047241632FRFrance
15961991167148571006819646261834FRFrance
1597199115713975978118169251832FRFrance
1598199114712265768416846221430FRFrance
159919911379567604113093171123FRFrance
1600199112710864733114397191325FRFrance
16011991117155741118419964271935FRFrance
16021991107166431137221914292038FRFrance
1603199109713741878018702241533FRFrance
1604199108713289881317765231531FRFrance
1605199107712337807716597221529FRFrance
1606199106710877701314741191226FRFrance
1607199105710442654414340181125FRFrance
16081991047791345631126314820FRFrance
16091991037153871048420290271836FRFrance
16101991027162771104621508292038FRFrance
16111991017155651027120859271836FRFrance
16121990527193751329525455342345FRFrance
16131990517190801380724353342543FRFrance
1614199050711079666015498201228FRFrance
16151990497114302610205FRFrance
\n", "

1616 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202146 7 9296 6108 12484 14 9 \n", "1 202145 7 9178 6625 11731 14 10 \n", "2 202144 7 8762 5653 11871 13 8 \n", "3 202143 7 8145 5164 11126 12 7 \n", "4 202142 7 9443 6037 12849 14 9 \n", "5 202141 7 4021 2239 5803 6 3 \n", "6 202140 7 4441 2454 6428 7 4 \n", "7 202139 7 2291 1056 3526 3 1 \n", "8 202138 7 4325 2267 6383 7 4 \n", "9 202137 7 1964 754 3174 3 1 \n", "10 202136 7 3441 1730 5152 5 2 \n", "11 202135 7 2562 1107 4017 4 2 \n", "12 202134 7 1429 378 2480 2 0 \n", "13 202133 7 3829 1830 5828 6 3 \n", "14 202132 7 4108 1895 6321 6 3 \n", "15 202131 7 4793 2301 7285 7 3 \n", "16 202130 7 7190 4191 10189 11 6 \n", "17 202129 7 6800 4109 9491 10 6 \n", "18 202128 7 9734 0 21731 15 0 \n", "19 202127 7 9026 4316 13736 14 7 \n", "20 202126 7 7284 4108 10460 11 6 \n", "21 202125 7 9351 6540 12162 14 10 \n", "22 202124 7 12034 8937 15131 18 13 \n", "23 202123 7 9116 6420 11812 14 10 \n", "24 202122 7 4817 2752 6882 7 4 \n", "25 202121 7 6092 3458 8726 9 5 \n", "26 202120 7 7485 4601 10369 11 7 \n", "27 202119 7 6654 4370 8938 10 7 \n", "28 202118 7 3912 2110 5714 6 3 \n", "29 202117 7 4686 2878 6494 7 4 \n", "... ... ... ... ... ... ... ... \n", "1586 199126 7 17608 11304 23912 31 20 \n", "1587 199125 7 16169 10700 21638 28 18 \n", "1588 199124 7 16171 10071 22271 28 17 \n", "1589 199123 7 11947 7671 16223 21 13 \n", "1590 199122 7 15452 9953 20951 27 17 \n", "1591 199121 7 14903 8975 20831 26 16 \n", "1592 199120 7 19053 12742 25364 34 23 \n", "1593 199119 7 16739 11246 22232 29 19 \n", "1594 199118 7 21385 13882 28888 38 25 \n", "1595 199117 7 13462 8877 18047 24 16 \n", "1596 199116 7 14857 10068 19646 26 18 \n", "1597 199115 7 13975 9781 18169 25 18 \n", "1598 199114 7 12265 7684 16846 22 14 \n", "1599 199113 7 9567 6041 13093 17 11 \n", "1600 199112 7 10864 7331 14397 19 13 \n", "1601 199111 7 15574 11184 19964 27 19 \n", "1602 199110 7 16643 11372 21914 29 20 \n", "1603 199109 7 13741 8780 18702 24 15 \n", "1604 199108 7 13289 8813 17765 23 15 \n", "1605 199107 7 12337 8077 16597 22 15 \n", "1606 199106 7 10877 7013 14741 19 12 \n", "1607 199105 7 10442 6544 14340 18 11 \n", "1608 199104 7 7913 4563 11263 14 8 \n", "1609 199103 7 15387 10484 20290 27 18 \n", "1610 199102 7 16277 11046 21508 29 20 \n", "1611 199101 7 15565 10271 20859 27 18 \n", "1612 199052 7 19375 13295 25455 34 23 \n", "1613 199051 7 19080 13807 24353 34 25 \n", "1614 199050 7 11079 6660 15498 20 12 \n", "1615 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 19 FR France \n", "1 18 FR France \n", "2 18 FR France \n", "3 17 FR France \n", "4 19 FR France \n", "5 9 FR France \n", "6 10 FR France \n", "7 5 FR France \n", "8 10 FR France \n", "9 5 FR France \n", "10 8 FR France \n", "11 6 FR France \n", "12 4 FR France \n", "13 9 FR France \n", "14 9 FR France \n", "15 11 FR France \n", "16 16 FR France \n", "17 14 FR France \n", "18 33 FR France \n", "19 21 FR France \n", "20 16 FR France \n", "21 18 FR France \n", "22 23 FR France \n", "23 18 FR France \n", "24 10 FR France \n", "25 13 FR France \n", "26 15 FR France \n", "27 13 FR France \n", "28 9 FR France \n", "29 10 FR France \n", "... ... ... ... \n", "1586 42 FR France \n", "1587 38 FR France \n", "1588 39 FR France \n", "1589 29 FR France \n", "1590 37 FR France \n", "1591 36 FR France \n", "1592 45 FR France \n", "1593 39 FR France \n", "1594 51 FR France \n", "1595 32 FR France \n", "1596 34 FR France \n", "1597 32 FR France \n", "1598 30 FR France \n", "1599 23 FR France \n", "1600 25 FR France \n", "1601 35 FR France \n", "1602 38 FR France \n", "1603 33 FR France \n", "1604 31 FR France \n", "1605 29 FR France \n", "1606 26 FR France \n", "1607 25 FR France \n", "1608 20 FR France \n", "1609 36 FR France \n", "1610 38 FR France \n", "1611 36 FR France \n", "1612 45 FR France \n", "1613 43 FR France \n", "1614 28 FR France \n", "1615 5 FR France \n", "\n", "[1616 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On verifie si il n'y a pas de semaines manquantes." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il n'y a pas de semaines manquantes, nous n'avons donc pas besoins de supprimer celles-ci." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02021467929661081248414919FRFrance
120214579178662511731141018FRFrance
22021447876256531187113818FRFrance
32021437814551641112612717FRFrance
42021427944360371284914919FRFrance
52021417402122395803639FRFrance
620214074441245464287410FRFrance
72021397229110563526315FRFrance
820213874325226763837410FRFrance
9202137719647543174315FRFrance
102021367344117305152528FRFrance
112021357256211074017426FRFrance
12202134714293782480204FRFrance
132021337382918305828639FRFrance
142021327410818956321639FRFrance
1520213174793230172857311FRFrance
162021307719041911018911616FRFrance
17202129768004109949110614FRFrance
182021287973402173115033FRFrance
192021277902643161373614721FRFrance
202021267728441081046011616FRFrance
2120212579351654012162141018FRFrance
22202124712034893715131181323FRFrance
2320212379116642011812141018FRFrance
2420212274817275268827410FRFrance
2520212176092345887269513FRFrance
262021207748546011036911715FRFrance
27202119766544370893810713FRFrance
282021187391221105714639FRFrance
2920211774686287864947410FRFrance
.................................
15861991267176081130423912312042FRFrance
15871991257161691070021638281838FRFrance
15881991247161711007122271281739FRFrance
1589199123711947767116223211329FRFrance
1590199122715452995320951271737FRFrance
1591199121714903897520831261636FRFrance
15921991207190531274225364342345FRFrance
15931991197167391124622232291939FRFrance
15941991187213851388228888382551FRFrance
1595199117713462887718047241632FRFrance
15961991167148571006819646261834FRFrance
1597199115713975978118169251832FRFrance
1598199114712265768416846221430FRFrance
159919911379567604113093171123FRFrance
1600199112710864733114397191325FRFrance
16011991117155741118419964271935FRFrance
16021991107166431137221914292038FRFrance
1603199109713741878018702241533FRFrance
1604199108713289881317765231531FRFrance
1605199107712337807716597221529FRFrance
1606199106710877701314741191226FRFrance
1607199105710442654414340181125FRFrance
16081991047791345631126314820FRFrance
16091991037153871048420290271836FRFrance
16101991027162771104621508292038FRFrance
16111991017155651027120859271836FRFrance
16121990527193751329525455342345FRFrance
16131990517190801380724353342543FRFrance
1614199050711079666015498201228FRFrance
16151990497114302610205FRFrance
\n", "

1616 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202146 7 9296 6108 12484 14 9 \n", "1 202145 7 9178 6625 11731 14 10 \n", "2 202144 7 8762 5653 11871 13 8 \n", "3 202143 7 8145 5164 11126 12 7 \n", "4 202142 7 9443 6037 12849 14 9 \n", "5 202141 7 4021 2239 5803 6 3 \n", "6 202140 7 4441 2454 6428 7 4 \n", "7 202139 7 2291 1056 3526 3 1 \n", "8 202138 7 4325 2267 6383 7 4 \n", "9 202137 7 1964 754 3174 3 1 \n", "10 202136 7 3441 1730 5152 5 2 \n", "11 202135 7 2562 1107 4017 4 2 \n", "12 202134 7 1429 378 2480 2 0 \n", "13 202133 7 3829 1830 5828 6 3 \n", "14 202132 7 4108 1895 6321 6 3 \n", "15 202131 7 4793 2301 7285 7 3 \n", "16 202130 7 7190 4191 10189 11 6 \n", "17 202129 7 6800 4109 9491 10 6 \n", "18 202128 7 9734 0 21731 15 0 \n", "19 202127 7 9026 4316 13736 14 7 \n", "20 202126 7 7284 4108 10460 11 6 \n", "21 202125 7 9351 6540 12162 14 10 \n", "22 202124 7 12034 8937 15131 18 13 \n", "23 202123 7 9116 6420 11812 14 10 \n", "24 202122 7 4817 2752 6882 7 4 \n", "25 202121 7 6092 3458 8726 9 5 \n", "26 202120 7 7485 4601 10369 11 7 \n", "27 202119 7 6654 4370 8938 10 7 \n", "28 202118 7 3912 2110 5714 6 3 \n", "29 202117 7 4686 2878 6494 7 4 \n", "... ... ... ... ... ... ... ... \n", "1586 199126 7 17608 11304 23912 31 20 \n", "1587 199125 7 16169 10700 21638 28 18 \n", "1588 199124 7 16171 10071 22271 28 17 \n", "1589 199123 7 11947 7671 16223 21 13 \n", "1590 199122 7 15452 9953 20951 27 17 \n", "1591 199121 7 14903 8975 20831 26 16 \n", "1592 199120 7 19053 12742 25364 34 23 \n", "1593 199119 7 16739 11246 22232 29 19 \n", "1594 199118 7 21385 13882 28888 38 25 \n", "1595 199117 7 13462 8877 18047 24 16 \n", "1596 199116 7 14857 10068 19646 26 18 \n", "1597 199115 7 13975 9781 18169 25 18 \n", "1598 199114 7 12265 7684 16846 22 14 \n", "1599 199113 7 9567 6041 13093 17 11 \n", "1600 199112 7 10864 7331 14397 19 13 \n", "1601 199111 7 15574 11184 19964 27 19 \n", "1602 199110 7 16643 11372 21914 29 20 \n", "1603 199109 7 13741 8780 18702 24 15 \n", "1604 199108 7 13289 8813 17765 23 15 \n", "1605 199107 7 12337 8077 16597 22 15 \n", "1606 199106 7 10877 7013 14741 19 12 \n", "1607 199105 7 10442 6544 14340 18 11 \n", "1608 199104 7 7913 4563 11263 14 8 \n", "1609 199103 7 15387 10484 20290 27 18 \n", "1610 199102 7 16277 11046 21508 29 20 \n", "1611 199101 7 15565 10271 20859 27 18 \n", "1612 199052 7 19375 13295 25455 34 23 \n", "1613 199051 7 19080 13807 24353 34 25 \n", "1614 199050 7 11079 6660 15498 20 12 \n", "1615 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 19 FR France \n", "1 18 FR France \n", "2 18 FR France \n", "3 17 FR France \n", "4 19 FR France \n", "5 9 FR France \n", "6 10 FR France \n", "7 5 FR France \n", "8 10 FR France \n", "9 5 FR France \n", "10 8 FR France \n", "11 6 FR France \n", "12 4 FR France \n", "13 9 FR France \n", "14 9 FR France \n", "15 11 FR France \n", "16 16 FR France \n", "17 14 FR France \n", "18 33 FR France \n", "19 21 FR France \n", "20 16 FR France \n", "21 18 FR France \n", "22 23 FR France \n", "23 18 FR France \n", "24 10 FR France \n", "25 13 FR France \n", "26 15 FR France \n", "27 13 FR France \n", "28 9 FR France \n", "29 10 FR France \n", "... ... ... ... \n", "1586 42 FR France \n", "1587 38 FR France \n", "1588 39 FR France \n", "1589 29 FR France \n", "1590 37 FR France \n", "1591 36 FR France \n", "1592 45 FR France \n", "1593 39 FR France \n", "1594 51 FR France \n", "1595 32 FR France \n", "1596 34 FR France \n", "1597 32 FR France \n", "1598 30 FR France \n", "1599 23 FR France \n", "1600 25 FR France \n", "1601 35 FR France \n", "1602 38 FR France \n", "1603 33 FR France \n", "1604 31 FR France \n", "1605 29 FR France \n", "1606 26 FR France \n", "1607 25 FR France \n", "1608 20 FR France \n", "1609 36 FR France \n", "1610 38 FR France \n", "1611 36 FR France \n", "1612 45 FR France \n", "1613 43 FR France \n", "1614 28 FR France \n", "1615 5 FR France \n", "\n", "[1616 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data\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 'period'." ] }, { "cell_type": "code", "execution_count": 6, "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.Premièrement, nous définissons les périodes d'observation comme nouvel index de notre jeux de données.\n", "\n", "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": 7, "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.\n", "\n", "Nous laissons une \"marge d'erreur\" d'une seconde.Ceci s'avère tout à fait juste sauf pour deux périodes consécutives entre lesquelles il manque une semaine." ] }, { "cell_type": "code", "execution_count": 8, "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": [ "Il n'y a pas d'erreur/ de valeurs manquantes de nos données." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un zoom sur les dernières années montre mieux la situation des pics." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "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 choisissons le 1er septembre comme debut de chaque période annuelle. On a pu voir que les relevées de données commencer en fin 1990, on choisit donc de commencer l'analyse en 1991 pour ne pas avoir d'années incomplètes." ] }, { "cell_type": "code", "execution_count": 11, "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": 12, "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": 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": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\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": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On fait un histogramme." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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j8DLg2cA1Vepvl/tR4JSdznshcHWVPmj29v66nZ7ZEz+5Sg80Af2AWacvotmgPrdKD+0yLwf+sJ1+GfCa9rFU5jm9lL8uY96/AL7WHrgOzcuQYzPzYpo9wEMAMvNW4NqIODYzzwe+D3yI5seLr8rMm2ZuMDNv77DcZZWZd0TEARHxPpphk3U0K+/IiBjtcw+z7vu9gP+g2Xs9PjMvB47oe/07uQg4KyLOi4ipiHgT8FWa30g9uK2t733cDvxeRJwCbImIi2n29n67fUXU6x4y81fZvIk3M2YfNF+0dDnNeijxnAb+FTgtIj4CvBl4LPBtmh4Oauvqew+Lt4Ct3MwexsXAGe30+4G/bacfDFxIuyWjGY96NPDAYW+hdurjlTQvDzfTjIF/GXgTzQO41z0AH6HZa1pLc7zqG2gerG8stg4+RfNhiMNpnkhnAF8ptB4e0db7DzR7cS8B3k5zHPDraTayve5hp36+AZzcTl9S6TndPocvotkZezNwFnB9+/gqtR4W+tf5aJPMzIg4DDiEZotHe2cREVfQjCmvynZLlpn3Zua3M/MXXZexEjLzgmzebb6AZpzyn2heQn2cHvcQESM0e3zvBj5J8075scCLgQMj4nJ6XP9OnpeZb83MHwFvpTmM610UWA+tm2g+Dr06m3HVK9rzrqB5Q7ZCD0TEzPP/Kpo3+wDObi4q83g6CpjKZq/5vTQ7Nx+kzmNp8Ra4lXs2zRe07E2zxXsGzZ31EuDxw94SLbCXw4FPAw9pT78UOHrYde2m3gfSvOq5kOaNpgng07Mu73X9u+nrYTRvJj24Uh80H4/e0k4/iOYVxNGVemhr3Zdmw3nyTuef0vce2uz5M+DC9vQ6mh3LI6qth8X8LejHGCLiS8BvAjfSHBv5lsz8VucbGLL2KIETaDY2R9EMnZyfmffu9oo91H6I4GRgMjN/POx6FiIiHkCz4T8V+B2aw7wuyMztQy1sgSLibJrnw9E0OwJnZbMnXkpEXA+8KTM/NPM5jmHX1FVEHEnzPL6HZl38C/A32Xwico/WObzbw7zOAm4ALsn2DY9KImI1zfcb/JKmh3IvnSJiFXBfpSfYrkTEK2gO03x/xfUwIyIeCfywYg+zPnD3OJo3wbdXfFy1OzKPAL6cmXcPu56V0tufQZMkzW3ov6QjSVo4w1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jamg/wNI6CKPSu0/QAAAAABJRU5ErkJggg==\n", 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