{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Importer les bibliothèques nécessaires\n", "import matplotlib.pyplot as plt # Pour la création de graphiques\n", "import pandas as pd # Pour la manipulation des données\n", "import isoweek # Pour gérer les semaines ISO" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# URL où les données d'incidence du syndrome grippal sont téléchargées\n", "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv?v=64ay2\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Nom du fichier local où les données seront stockées\n", "data_file = \"syndrome-varicelle.csv\"\n", "\n", "# Vérifier si le fichier local existe, et s'il n'existe pas, le télécharger depuis l'URL\n", "import os\n", "import urllib.request\n", "\n", "# Vérifier si le fichier local n'existe pas\n", "if not os.path.exists(data_file):\n", " # Télécharger les données depuis l'URL et les enregistrer dans le fichier local\n", "urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202339713901182662204FRFrance
1202338716702783062315FRFrance
2202337711222232021213FRFrance
32023367726101442102FRFrance
42023357961961826102FRFrance
52023347116892327204FRFrance
62023337330811845432528FRFrance
72023327799611201487212222FRFrance
82023317331813985238528FRFrance
920233075821326983739513FRFrance
10202329713558829718819201228FRFrance
11202328767004043935710614FRFrance
12202327772534599990711715FRFrance
1320232679192622312161141018FRFrance
14202325711498825714739171222FRFrance
15202324711115796814262171222FRFrance
1620232371256361341899219929FRFrance
17202322712184812516243181224FRFrance
18202321711349759815100171123FRFrance
192023207900046151338514721FRFrance
202023197934460911259714919FRFrance
21202318710671729114051161121FRFrance
222023177918461621220614919FRFrance
23202316711387801414760171222FRFrance
24202315714040761320467211131FRFrance
252023147152471103219462231729FRFrance
26202313713322970016944201525FRFrance
27202312710374721813530161121FRFrance
2820231174919288069587410FRFrance
2920231074854273169777410FRFrance
.................................
16831991267176081130423912312042FRFrance
16841991257161691070021638281838FRFrance
16851991247161711007122271281739FRFrance
1686199123711947767116223211329FRFrance
1687199122715452995320951271737FRFrance
1688199121714903897520831261636FRFrance
16891991207190531274225364342345FRFrance
16901991197167391124622232291939FRFrance
16911991187213851388228888382551FRFrance
1692199117713462887718047241632FRFrance
16931991167148571006819646261834FRFrance
1694199115713975978118169251832FRFrance
1695199114712265768416846221430FRFrance
169619911379567604113093171123FRFrance
1697199112710864733114397191325FRFrance
16981991117155741118419964271935FRFrance
16991991107166431137221914292038FRFrance
1700199109713741878018702241533FRFrance
1701199108713289881317765231531FRFrance
1702199107712337807716597221529FRFrance
1703199106710877701314741191226FRFrance
1704199105710442654414340181125FRFrance
17051991047791345631126314820FRFrance
17061991037153871048420290271836FRFrance
17071991027162771104621508292038FRFrance
17081991017155651027120859271836FRFrance
17091990527193751329525455342345FRFrance
17101990517190801380724353342543FRFrance
1711199050711079666015498201228FRFrance
17121990497114302610205FRFrance
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1713 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202339 7 1390 118 2662 2 0 \n", "1 202338 7 1670 278 3062 3 1 \n", "2 202337 7 1122 223 2021 2 1 \n", "3 202336 7 726 10 1442 1 0 \n", "4 202335 7 961 96 1826 1 0 \n", "5 202334 7 1168 9 2327 2 0 \n", "6 202333 7 3308 1184 5432 5 2 \n", "7 202332 7 7996 1120 14872 12 2 \n", "8 202331 7 3318 1398 5238 5 2 \n", "9 202330 7 5821 3269 8373 9 5 \n", "10 202329 7 13558 8297 18819 20 12 \n", "11 202328 7 6700 4043 9357 10 6 \n", "12 202327 7 7253 4599 9907 11 7 \n", "13 202326 7 9192 6223 12161 14 10 \n", "14 202325 7 11498 8257 14739 17 12 \n", "15 202324 7 11115 7968 14262 17 12 \n", "16 202323 7 12563 6134 18992 19 9 \n", "17 202322 7 12184 8125 16243 18 12 \n", "18 202321 7 11349 7598 15100 17 11 \n", "19 202320 7 9000 4615 13385 14 7 \n", "20 202319 7 9344 6091 12597 14 9 \n", "21 202318 7 10671 7291 14051 16 11 \n", "22 202317 7 9184 6162 12206 14 9 \n", "23 202316 7 11387 8014 14760 17 12 \n", "24 202315 7 14040 7613 20467 21 11 \n", "25 202314 7 15247 11032 19462 23 17 \n", "26 202313 7 13322 9700 16944 20 15 \n", "27 202312 7 10374 7218 13530 16 11 \n", "28 202311 7 4919 2880 6958 7 4 \n", "29 202310 7 4854 2731 6977 7 4 \n", "... ... ... ... ... ... ... ... \n", "1683 199126 7 17608 11304 23912 31 20 \n", "1684 199125 7 16169 10700 21638 28 18 \n", "1685 199124 7 16171 10071 22271 28 17 \n", "1686 199123 7 11947 7671 16223 21 13 \n", "1687 199122 7 15452 9953 20951 27 17 \n", "1688 199121 7 14903 8975 20831 26 16 \n", "1689 199120 7 19053 12742 25364 34 23 \n", "1690 199119 7 16739 11246 22232 29 19 \n", "1691 199118 7 21385 13882 28888 38 25 \n", "1692 199117 7 13462 8877 18047 24 16 \n", "1693 199116 7 14857 10068 19646 26 18 \n", "1694 199115 7 13975 9781 18169 25 18 \n", "1695 199114 7 12265 7684 16846 22 14 \n", "1696 199113 7 9567 6041 13093 17 11 \n", "1697 199112 7 10864 7331 14397 19 13 \n", "1698 199111 7 15574 11184 19964 27 19 \n", "1699 199110 7 16643 11372 21914 29 20 \n", "1700 199109 7 13741 8780 18702 24 15 \n", "1701 199108 7 13289 8813 17765 23 15 \n", "1702 199107 7 12337 8077 16597 22 15 \n", "1703 199106 7 10877 7013 14741 19 12 \n", "1704 199105 7 10442 6544 14340 18 11 \n", "1705 199104 7 7913 4563 11263 14 8 \n", "1706 199103 7 15387 10484 20290 27 18 \n", "1707 199102 7 16277 11046 21508 29 20 \n", "1708 199101 7 15565 10271 20859 27 18 \n", "1709 199052 7 19375 13295 25455 34 23 \n", "1710 199051 7 19080 13807 24353 34 25 \n", "1711 199050 7 11079 6660 15498 20 12 \n", "1712 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 4 FR France \n", "1 5 FR France \n", "2 3 FR France \n", "3 2 FR France \n", "4 2 FR France \n", "5 4 FR France \n", "6 8 FR France \n", "7 22 FR France \n", "8 8 FR France \n", "9 13 FR France \n", "10 28 FR France \n", "11 14 FR France \n", "12 15 FR France \n", "13 18 FR France \n", "14 22 FR France \n", "15 22 FR France \n", "16 29 FR France \n", "17 24 FR France \n", "18 23 FR France \n", "19 21 FR France \n", "20 19 FR France \n", "21 21 FR France \n", "22 19 FR France \n", "23 22 FR France \n", "24 31 FR France \n", "25 29 FR France \n", "26 25 FR France \n", "27 21 FR France \n", "28 10 FR France \n", "29 10 FR France \n", "... ... ... ... \n", "1683 42 FR France \n", "1684 38 FR France \n", "1685 39 FR France \n", "1686 29 FR France \n", "1687 37 FR France \n", "1688 36 FR France \n", "1689 45 FR France \n", "1690 39 FR France \n", "1691 51 FR France \n", "1692 32 FR France \n", "1693 34 FR France \n", "1694 32 FR France \n", "1695 30 FR France \n", "1696 23 FR France \n", "1697 25 FR France \n", "1698 35 FR France \n", "1699 38 FR France \n", "1700 33 FR France \n", "1701 31 FR France \n", "1702 29 FR France \n", "1703 26 FR France \n", "1704 25 FR France \n", "1705 20 FR France \n", "1706 36 FR France \n", "1707 38 FR France \n", "1708 36 FR France \n", "1709 45 FR France \n", "1710 43 FR France \n", "1711 28 FR France \n", "1712 5 FR France \n", "\n", "[1713 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Lire les données depuis le fichier local CSV en sautant la première ligne (commentaire)\n", "raw_data = pd.read_csv(data_file, skiprows=1)\n", "\n", "# Afficher les données brutes\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "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": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Sélectionner les lignes contenant au moins une valeur manquante (NaN)\n", "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202339713901182662204FRFrance
1202338716702783062315FRFrance
2202337711222232021213FRFrance
32023367726101442102FRFrance
42023357961961826102FRFrance
52023347116892327204FRFrance
62023337330811845432528FRFrance
72023327799611201487212222FRFrance
82023317331813985238528FRFrance
920233075821326983739513FRFrance
10202329713558829718819201228FRFrance
11202328767004043935710614FRFrance
12202327772534599990711715FRFrance
1320232679192622312161141018FRFrance
14202325711498825714739171222FRFrance
15202324711115796814262171222FRFrance
1620232371256361341899219929FRFrance
17202322712184812516243181224FRFrance
18202321711349759815100171123FRFrance
192023207900046151338514721FRFrance
202023197934460911259714919FRFrance
21202318710671729114051161121FRFrance
222023177918461621220614919FRFrance
23202316711387801414760171222FRFrance
24202315714040761320467211131FRFrance
252023147152471103219462231729FRFrance
26202313713322970016944201525FRFrance
27202312710374721813530161121FRFrance
2820231174919288069587410FRFrance
2920231074854273169777410FRFrance
.................................
16831991267176081130423912312042FRFrance
16841991257161691070021638281838FRFrance
16851991247161711007122271281739FRFrance
1686199123711947767116223211329FRFrance
1687199122715452995320951271737FRFrance
1688199121714903897520831261636FRFrance
16891991207190531274225364342345FRFrance
16901991197167391124622232291939FRFrance
16911991187213851388228888382551FRFrance
1692199117713462887718047241632FRFrance
16931991167148571006819646261834FRFrance
1694199115713975978118169251832FRFrance
1695199114712265768416846221430FRFrance
169619911379567604113093171123FRFrance
1697199112710864733114397191325FRFrance
16981991117155741118419964271935FRFrance
16991991107166431137221914292038FRFrance
1700199109713741878018702241533FRFrance
1701199108713289881317765231531FRFrance
1702199107712337807716597221529FRFrance
1703199106710877701314741191226FRFrance
1704199105710442654414340181125FRFrance
17051991047791345631126314820FRFrance
17061991037153871048420290271836FRFrance
17071991027162771104621508292038FRFrance
17081991017155651027120859271836FRFrance
17091990527193751329525455342345FRFrance
17101990517190801380724353342543FRFrance
1711199050711079666015498201228FRFrance
17121990497114302610205FRFrance
\n", "

1713 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202339 7 1390 118 2662 2 0 \n", "1 202338 7 1670 278 3062 3 1 \n", "2 202337 7 1122 223 2021 2 1 \n", "3 202336 7 726 10 1442 1 0 \n", "4 202335 7 961 96 1826 1 0 \n", "5 202334 7 1168 9 2327 2 0 \n", "6 202333 7 3308 1184 5432 5 2 \n", "7 202332 7 7996 1120 14872 12 2 \n", "8 202331 7 3318 1398 5238 5 2 \n", "9 202330 7 5821 3269 8373 9 5 \n", "10 202329 7 13558 8297 18819 20 12 \n", "11 202328 7 6700 4043 9357 10 6 \n", "12 202327 7 7253 4599 9907 11 7 \n", "13 202326 7 9192 6223 12161 14 10 \n", "14 202325 7 11498 8257 14739 17 12 \n", "15 202324 7 11115 7968 14262 17 12 \n", "16 202323 7 12563 6134 18992 19 9 \n", "17 202322 7 12184 8125 16243 18 12 \n", "18 202321 7 11349 7598 15100 17 11 \n", "19 202320 7 9000 4615 13385 14 7 \n", "20 202319 7 9344 6091 12597 14 9 \n", "21 202318 7 10671 7291 14051 16 11 \n", "22 202317 7 9184 6162 12206 14 9 \n", "23 202316 7 11387 8014 14760 17 12 \n", "24 202315 7 14040 7613 20467 21 11 \n", "25 202314 7 15247 11032 19462 23 17 \n", "26 202313 7 13322 9700 16944 20 15 \n", "27 202312 7 10374 7218 13530 16 11 \n", "28 202311 7 4919 2880 6958 7 4 \n", "29 202310 7 4854 2731 6977 7 4 \n", "... ... ... ... ... ... ... ... \n", "1683 199126 7 17608 11304 23912 31 20 \n", "1684 199125 7 16169 10700 21638 28 18 \n", "1685 199124 7 16171 10071 22271 28 17 \n", "1686 199123 7 11947 7671 16223 21 13 \n", "1687 199122 7 15452 9953 20951 27 17 \n", "1688 199121 7 14903 8975 20831 26 16 \n", "1689 199120 7 19053 12742 25364 34 23 \n", "1690 199119 7 16739 11246 22232 29 19 \n", "1691 199118 7 21385 13882 28888 38 25 \n", "1692 199117 7 13462 8877 18047 24 16 \n", "1693 199116 7 14857 10068 19646 26 18 \n", "1694 199115 7 13975 9781 18169 25 18 \n", "1695 199114 7 12265 7684 16846 22 14 \n", "1696 199113 7 9567 6041 13093 17 11 \n", "1697 199112 7 10864 7331 14397 19 13 \n", "1698 199111 7 15574 11184 19964 27 19 \n", "1699 199110 7 16643 11372 21914 29 20 \n", "1700 199109 7 13741 8780 18702 24 15 \n", "1701 199108 7 13289 8813 17765 23 15 \n", "1702 199107 7 12337 8077 16597 22 15 \n", "1703 199106 7 10877 7013 14741 19 12 \n", "1704 199105 7 10442 6544 14340 18 11 \n", "1705 199104 7 7913 4563 11263 14 8 \n", "1706 199103 7 15387 10484 20290 27 18 \n", "1707 199102 7 16277 11046 21508 29 20 \n", "1708 199101 7 15565 10271 20859 27 18 \n", "1709 199052 7 19375 13295 25455 34 23 \n", "1710 199051 7 19080 13807 24353 34 25 \n", "1711 199050 7 11079 6660 15498 20 12 \n", "1712 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 4 FR France \n", "1 5 FR France \n", "2 3 FR France \n", "3 2 FR France \n", "4 2 FR France \n", "5 4 FR France \n", "6 8 FR France \n", "7 22 FR France \n", "8 8 FR France \n", "9 13 FR France \n", "10 28 FR France \n", "11 14 FR France \n", "12 15 FR France \n", "13 18 FR France \n", "14 22 FR France \n", "15 22 FR France \n", "16 29 FR France \n", "17 24 FR France \n", "18 23 FR France \n", "19 21 FR France \n", "20 19 FR France \n", "21 21 FR France \n", "22 19 FR France \n", "23 22 FR France \n", "24 31 FR France \n", "25 29 FR France \n", "26 25 FR France \n", "27 21 FR France \n", "28 10 FR France \n", "29 10 FR France \n", "... ... ... ... \n", "1683 42 FR France \n", "1684 38 FR France \n", "1685 39 FR France \n", "1686 29 FR France \n", "1687 37 FR France \n", "1688 36 FR France \n", "1689 45 FR France \n", "1690 39 FR France \n", "1691 51 FR France \n", "1692 32 FR France \n", "1693 34 FR France \n", "1694 32 FR France \n", "1695 30 FR France \n", "1696 23 FR France \n", "1697 25 FR France \n", "1698 35 FR France \n", "1699 38 FR France \n", "1700 33 FR France \n", "1701 31 FR France \n", "1702 29 FR France \n", "1703 26 FR France \n", "1704 25 FR France \n", "1705 20 FR France \n", "1706 36 FR France \n", "1707 38 FR France \n", "1708 36 FR France \n", "1709 45 FR France \n", "1710 43 FR France \n", "1711 28 FR France \n", "1712 5 FR France \n", "\n", "[1713 rows x 10 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Supprimer les lignes contenant des valeurs manquantes (NaN) à partir des données brutes\n", "data = raw_data.dropna().copy()\n", "\n", "# Afficher les données nettoyées (sans valeurs manquantes) et en créer une copie\n", "data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Définition d'une fonction pour convertir l'année et la semaine en période\n", "def convert_week(year_and_week_int):\n", " # Convertir l'entier en une chaîne de caractères\n", " year_and_week_str = str(year_and_week_int)\n", " \n", " # Extraire l'année (les 4 premiers caractères de la chaîne)\n", " year = int(year_and_week_str[:4])\n", " \n", " # Extraire le numéro de semaine (le reste de la chaîne)\n", " week = int(year_and_week_str[4:])\n", " \n", " # Créer un objet isoweek.Week avec l'année et la semaine\n", " w = isoweek.Week(year, week)\n", " \n", " # Convertir l'objet isoweek.Week en une période pandas\n", " return pd.Period(w.day(0), 'W')\n", "\n", "# Appliquer la fonction convert_week à la colonne 'week' et créer une nouvelle colonne 'period'\n", "data['period'] = [convert_week(yw) for yw in data['week']]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Définir la colonne 'period' comme index du DataFrame et trier le DataFrame par cet index\n", "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Obtenir l'index (colonne 'period') du DataFrame trié\n", "periods = sorted_data.index\n", "\n", "# Parcourir les périodes consécutives et vérifier la différence temporelle entre elles\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", " # Calculer la différence temporelle entre la fin de la période p1 et le début de la période p2\n", " delta = p2.to_timestamp() - p1.end_time\n", " \n", " # Vérifier si la différence temporelle est supérieure à 1 seconde\n", " if delta > pd.Timedelta('1s'):\n", " # Afficher les paires de périodes consécutives qui ont une différence temporelle inattendue\n", " print(p1, p2)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "int64\n" ] } ], "source": [ "# Convertir la colonne 'inc' en type numérique (float)\n", "sorted_data['inc'] = pd.to_numeric(sorted_data['inc'], errors='coerce')\n", "print(sorted_data['inc'].dtypes)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Tracer un graphique de la colonne 'inc' du DataFrame trié\n", "sorted_data['inc'].plot()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Tracer un graphique des 200 dernières entrées de la colonne 'inc' du DataFrame trié\n", "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Créer une liste des premières semaines d'août pour chaque année entre 1985 et la dernière année de l'index de sorted_data\n", "first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", "for y in range(1985,sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Initialisation des listes pour stocker les données annuelles\n", "year = [] # Liste des années\n", "yearly_incidence = [] # Liste des incidences annuelles\n", "\n", "# Parcourir les paires d'intervalles annuels définies par first_august_week\n", "for week1, week2 in zip(first_august_week[:-1],\n", " first_august_week[1:]):\n", " # Extraire les données d'incidence pour une année donnée\n", " one_year = sorted_data['inc'][week1:week2-1]\n", " \n", " # Vérifier que chaque année a environ 52 semaines d'incidence\n", " #assert abs(len(one_year) - 52) < 2\n", " \n", " # Ajouter la somme des incidences de l'année à la liste yearly_incidence\n", " yearly_incidence.append(one_year.sum())\n", " \n", " # Ajouter l'année correspondante à la liste year\n", " year.append(week2.year)\n", "\n", "# Créer une série pandas avec les données annuelles et les années comme index\n", "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" ] }, { "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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Tracer un graphique de dispersion des données d'incidence annuelle avec un style en étoile\n", "yearly_incidence.plot(style='*')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1986 0\n", "1987 0\n", "1988 0\n", "1989 0\n", "1990 0\n", "2020 221186\n", "2021 376290\n", "2002 516689\n", "2018 542312\n", "2017 551041\n", "1991 553090\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", "2022 641397\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": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Trier les données d'incidence annuelle par ordre croissant\n", "yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Minimum d'incidence annuelle: 0\n", "Maximum d'incidence annuelle: 842373\n" ] } ], "source": [ "# Supposons que yearly_incidence soit une série de données pandas\n", "# Triez les données d'incidence annuelle par ordre croissant\n", "yearly_incidence = yearly_incidence.sort_values()\n", "\n", "# Trouver le minimum et le maximum\n", "minimum = yearly_incidence.min()\n", "maximum = yearly_incidence.max()\n", "\n", "print(\"Minimum d'incidence annuelle:\", minimum)\n", "print(\"Maximum d'incidence annuelle:\", maximum)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Tracer un histogramme des données d'incidence annuelle avec une rotation de l'axe des x de 20 degrés\n", "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 }