{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Importation des données\n", "Les fichiers a récupérer sont au format csv et téléchargeable sur le site du réseau Sentinelles. Pour anticiper les changements de la structure des données, nous faisons une copie de ce fichier csv en local. Nous récupérerons les donnés locales." ] }, { "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\n", "import os\n", "data_url = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\"\n", "data_local = \"./inc-7-PAY.csv\"\n", "import urllib.request\n", "if not os.path.exists(data_local):\n", " urllib.request.urlretrieve(data_url, data_local)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lecture des données" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202444723544894219417FRFrance
1202443721306253635315FRFrance
22024427262112463996426FRFrance
3202441720353813689315FRFrance
4202440721257253525315FRFrance
52024397289813334463426FRFrance
6202438775101513102FRFrance
72024377916281804102FRFrance
8202436722358703600315FRFrance
9202435716202852955204FRFrance
10202434725606224498417FRFrance
11202433719715363406315FRFrance
1220243274399194468547311FRFrance
1320243174500221367877410FRFrance
14202430770044278973011715FRFrance
1520242979270630312237141018FRFrance
1620242879364649812230141018FRFrance
17202427710247709013404151020FRFrance
182024267143681039918337221628FRFrance
19202425711174803914309171222FRFrance
20202424712621935715885191424FRFrance
212024237146571133917975221727FRFrance
22202422711628836114895171222FRFrance
2320242179701685112551151119FRFrance
242024207136611020917113201525FRFrance
2520241971008364131375315921FRFrance
26202418713438951417362201426FRFrance
272024177153031121919387231729FRFrance
282024167181381354022736272034FRFrance
292024157249291731532543372648FRFrance
.................................
17401991267176081130423912312042FRFrance
17411991257161691070021638281838FRFrance
17421991247161711007122271281739FRFrance
1743199123711947767116223211329FRFrance
1744199122715452995320951271737FRFrance
1745199121714903897520831261636FRFrance
17461991207190531274225364342345FRFrance
17471991197167391124622232291939FRFrance
17481991187213851388228888382551FRFrance
1749199117713462887718047241632FRFrance
17501991167148571006819646261834FRFrance
1751199115713975978118169251832FRFrance
1752199114712265768416846221430FRFrance
175319911379567604113093171123FRFrance
1754199112710864733114397191325FRFrance
17551991117155741118419964271935FRFrance
17561991107166431137221914292038FRFrance
1757199109713741878018702241533FRFrance
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1760199106710877701314741191226FRFrance
1761199105710442654414340181125FRFrance
17621991047791345631126314820FRFrance
17631991037153871048420290271836FRFrance
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17661990527193751329525455342345FRFrance
17671990517190801380724353342543FRFrance
1768199050711079666015498201228FRFrance
17691990497114302610205FRFrance
\n", "

1770 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202444 7 2354 489 4219 4 1 \n", "1 202443 7 2130 625 3635 3 1 \n", "2 202442 7 2621 1246 3996 4 2 \n", "3 202441 7 2035 381 3689 3 1 \n", "4 202440 7 2125 725 3525 3 1 \n", "5 202439 7 2898 1333 4463 4 2 \n", "6 202438 7 751 0 1513 1 0 \n", "7 202437 7 916 28 1804 1 0 \n", "8 202436 7 2235 870 3600 3 1 \n", "9 202435 7 1620 285 2955 2 0 \n", "10 202434 7 2560 622 4498 4 1 \n", "11 202433 7 1971 536 3406 3 1 \n", "12 202432 7 4399 1944 6854 7 3 \n", "13 202431 7 4500 2213 6787 7 4 \n", "14 202430 7 7004 4278 9730 11 7 \n", "15 202429 7 9270 6303 12237 14 10 \n", "16 202428 7 9364 6498 12230 14 10 \n", "17 202427 7 10247 7090 13404 15 10 \n", "18 202426 7 14368 10399 18337 22 16 \n", "19 202425 7 11174 8039 14309 17 12 \n", "20 202424 7 12621 9357 15885 19 14 \n", "21 202423 7 14657 11339 17975 22 17 \n", "22 202422 7 11628 8361 14895 17 12 \n", "23 202421 7 9701 6851 12551 15 11 \n", "24 202420 7 13661 10209 17113 20 15 \n", "25 202419 7 10083 6413 13753 15 9 \n", "26 202418 7 13438 9514 17362 20 14 \n", "27 202417 7 15303 11219 19387 23 17 \n", "28 202416 7 18138 13540 22736 27 20 \n", "29 202415 7 24929 17315 32543 37 26 \n", "... ... ... ... ... ... ... ... \n", "1740 199126 7 17608 11304 23912 31 20 \n", "1741 199125 7 16169 10700 21638 28 18 \n", "1742 199124 7 16171 10071 22271 28 17 \n", "1743 199123 7 11947 7671 16223 21 13 \n", "1744 199122 7 15452 9953 20951 27 17 \n", "1745 199121 7 14903 8975 20831 26 16 \n", "1746 199120 7 19053 12742 25364 34 23 \n", "1747 199119 7 16739 11246 22232 29 19 \n", "1748 199118 7 21385 13882 28888 38 25 \n", "1749 199117 7 13462 8877 18047 24 16 \n", "1750 199116 7 14857 10068 19646 26 18 \n", "1751 199115 7 13975 9781 18169 25 18 \n", "1752 199114 7 12265 7684 16846 22 14 \n", "1753 199113 7 9567 6041 13093 17 11 \n", "1754 199112 7 10864 7331 14397 19 13 \n", "1755 199111 7 15574 11184 19964 27 19 \n", "1756 199110 7 16643 11372 21914 29 20 \n", "1757 199109 7 13741 8780 18702 24 15 \n", "1758 199108 7 13289 8813 17765 23 15 \n", "1759 199107 7 12337 8077 16597 22 15 \n", "1760 199106 7 10877 7013 14741 19 12 \n", "1761 199105 7 10442 6544 14340 18 11 \n", "1762 199104 7 7913 4563 11263 14 8 \n", "1763 199103 7 15387 10484 20290 27 18 \n", "1764 199102 7 16277 11046 21508 29 20 \n", "1765 199101 7 15565 10271 20859 27 18 \n", "1766 199052 7 19375 13295 25455 34 23 \n", "1767 199051 7 19080 13807 24353 34 25 \n", "1768 199050 7 11079 6660 15498 20 12 \n", "1769 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 7 FR France \n", "1 5 FR France \n", "2 6 FR France \n", "3 5 FR France \n", "4 5 FR France \n", "5 6 FR France \n", "6 2 FR France \n", "7 2 FR France \n", "8 5 FR France \n", "9 4 FR France \n", "10 7 FR France \n", "11 5 FR France \n", "12 11 FR France \n", "13 10 FR France \n", "14 15 FR France \n", "15 18 FR France \n", "16 18 FR France \n", "17 20 FR France \n", "18 28 FR France \n", "19 22 FR France \n", "20 24 FR France \n", "21 27 FR France \n", "22 22 FR France \n", "23 19 FR France \n", "24 25 FR France \n", "25 21 FR France \n", "26 26 FR France \n", "27 29 FR France \n", "28 34 FR France \n", "29 48 FR France \n", "... ... ... ... \n", "1740 42 FR France \n", "1741 38 FR France \n", "1742 39 FR France \n", "1743 29 FR France \n", "1744 37 FR France \n", "1745 36 FR France \n", "1746 45 FR France \n", "1747 39 FR France \n", "1748 51 FR France \n", "1749 32 FR France \n", "1750 34 FR France \n", "1751 32 FR France \n", "1752 30 FR France \n", "1753 23 FR France \n", "1754 25 FR France \n", "1755 35 FR France \n", "1756 38 FR France \n", "1757 33 FR France \n", "1758 31 FR France \n", "1759 29 FR France \n", "1760 26 FR France \n", "1761 25 FR France \n", "1762 20 FR France \n", "1763 36 FR France \n", "1764 38 FR France \n", "1765 36 FR France \n", "1766 45 FR France \n", "1767 43 FR France \n", "1768 28 FR France \n", "1769 5 FR France \n", "\n", "[1770 rows x 10 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_local, encoding=\"utf-8\", skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Vérification de données manquantes" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "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": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Aucune donnée manquante détecté." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conversion des dates" ] }, { "cell_type": "code", "execution_count": 4, "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", "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "sorted_data = raw_data.set_index('period').sort_index()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "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": [ "# Etude de l'incidence" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données commencent en " ] } ], "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 }