{ "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": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici l'explication des colonnes donnée [sur le site d'origine](http://www.sentiweb.fr/france/fr/?page=json&file=csv-schema-v1&type=csv)\n", "\n", "\n", "|Name\t|Type\t|Description\n", "|--- | --- | --:\n", "|week PK\t|integer\t\t|ISO8601 Yearweek number as numeric (year*100 + week nubmer)\n", "|geo_insee PK\t|string\t\t|Identifier of the geographic area, from INSEE https://www.insee.fr\n", "|geo_name\t|string\t\t|Geographic label of the area, corresponding to INSEE code. This label is not an id and is only provided for human reading\n", "|indicator PK\t|integer\t|\tUnique identifier of the indicator, see metadata document https://www.sentiweb.fr/meta.json\n", "|inc\t|integer\t\t|Estimated incidence value for the time step, in the geographic level\n", "|inc_low\t|integer\t\t|Lower bound of the estimated incidence 95% Confidence Interval\n", "|inc_up\t|integer\t\t|Upper bound of the estimated incidence 95% Confidence Interval\n", "|inc100\t|integer\t\t|Estimated rate incidence per 100,000 inhabitants\n", "|inc100_low\t|integer\t|\tLower bound of the estimated incidence 95% Confidence Interval\n", "|inc100_up\t|integer\t|\tUpper bound of the estimated rate incidence 95% Confidence Interval\n", "\n", "Missing value : -" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le code ci-dessous est exécuté **une seule fois** afin de créer *localement* le fichier de données." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data\n", "raw_data.to_csv('incidence-PAY-7.csv', header = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les lignes ci-dessus sont maintenant mises en commentaires et remplacées par le code ci-dessous, où on va récupérer en priorité les données localement, si elles sont disponibles, sinon sur le site du *réseau Sentinelle*." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "existe\n" ] }, { "data": { "text/html": [ "
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Unnamed: 0weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
002020507801850011103512717FRFrance
1120204975100319170098511FRFrance
22202048766834312905410614FRFrance
3320204774999296370358511FRFrance
442020467375219635541639FRFrance
552020457369620165376639FRFrance
6620204474391237564077410FRFrance
7720204374376250562477410FRFrance
882020427400019796021639FRFrance
992020417396120995823639FRFrance
\n", "
" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 0 202050 7 8018 5001 11035 12 7 \n", "1 1 202049 7 5100 3191 7009 8 5 \n", "2 2 202048 7 6683 4312 9054 10 6 \n", "3 3 202047 7 4999 2963 7035 8 5 \n", "4 4 202046 7 3752 1963 5541 6 3 \n", "5 5 202045 7 3696 2016 5376 6 3 \n", "6 6 202044 7 4391 2375 6407 7 4 \n", "7 7 202043 7 4376 2505 6247 7 4 \n", "8 8 202042 7 4000 1979 6021 6 3 \n", "9 9 202041 7 3961 2099 5823 6 3 \n", "\n", " inc100_up geo_insee geo_name \n", "0 17 FR France \n", "1 11 FR France \n", "2 14 FR France \n", "3 11 FR France \n", "4 9 FR France \n", "5 9 FR France \n", "6 10 FR France \n", "7 10 FR France \n", "8 9 FR France \n", "9 9 FR France " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "if os.path.isfile('incidence-PAY-7.csv'):\n", " raw_data = pd.read_csv('incidence-PAY-7.csv')\n", " print('existe')\n", "else:\n", " print(\"n'existe pas\")\n", " raw_data = pd.read_csv(data_url, skiprows = 1)\n", "len(raw_data)\n", "raw_data[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recherche des points manquants dans le jeu de données." ] }, { "cell_type": "code", "execution_count": 6, "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", " \n", "
Unnamed: 0weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Unnamed: 0, week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", "Index: []" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il semble qu'il n'y ait aucun point manquant dans les données. Je garde la même convention que pour la grippe, en copiant les données dans la variable _data_" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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1120204975100319170098511FRFrance
22202048766834312905410614FRFrance
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6620204474391237564077410FRFrance
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29292020217602361168102FRFrance
....................................
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\n", "

1567 rows × 11 columns

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" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up inc100 \\\n", "0 0 202050 7 8018 5001 11035 12 \n", "1 1 202049 7 5100 3191 7009 8 \n", "2 2 202048 7 6683 4312 9054 10 \n", "3 3 202047 7 4999 2963 7035 8 \n", "4 4 202046 7 3752 1963 5541 6 \n", "5 5 202045 7 3696 2016 5376 6 \n", "6 6 202044 7 4391 2375 6407 7 \n", "7 7 202043 7 4376 2505 6247 7 \n", "8 8 202042 7 4000 1979 6021 6 \n", "9 9 202041 7 3961 2099 5823 6 \n", "10 10 202040 7 2078 675 3481 3 \n", "11 11 202039 7 1049 237 1861 2 \n", "12 12 202038 7 2253 782 3724 3 \n", "13 13 202037 7 1584 405 2763 2 \n", "14 14 202036 7 919 100 1738 1 \n", "15 15 202035 7 828 0 1694 1 \n", "16 16 202034 7 2272 371 4173 3 \n", "17 17 202033 7 1284 177 2391 2 \n", "18 18 202032 7 2650 689 4611 4 \n", "19 19 202031 7 1303 100 2506 2 \n", "20 20 202030 7 1385 75 2695 2 \n", "21 21 202029 7 841 10 1672 1 \n", "22 22 202028 7 728 0 1515 1 \n", "23 23 202027 7 986 149 1823 1 \n", "24 24 202026 7 694 0 1454 1 \n", "25 25 202025 7 228 0 597 0 \n", "26 26 202024 7 388 0 959 1 \n", "27 27 202023 7 558 1 1115 1 \n", "28 28 202022 7 277 0 633 0 \n", "29 29 202021 7 602 36 1168 1 \n", "... ... ... ... ... ... ... ... \n", "1537 1537 199126 7 17608 11304 23912 31 \n", "1538 1538 199125 7 16169 10700 21638 28 \n", "1539 1539 199124 7 16171 10071 22271 28 \n", "1540 1540 199123 7 11947 7671 16223 21 \n", "1541 1541 199122 7 15452 9953 20951 27 \n", "1542 1542 199121 7 14903 8975 20831 26 \n", "1543 1543 199120 7 19053 12742 25364 34 \n", "1544 1544 199119 7 16739 11246 22232 29 \n", "1545 1545 199118 7 21385 13882 28888 38 \n", "1546 1546 199117 7 13462 8877 18047 24 \n", "1547 1547 199116 7 14857 10068 19646 26 \n", "1548 1548 199115 7 13975 9781 18169 25 \n", "1549 1549 199114 7 12265 7684 16846 22 \n", "1550 1550 199113 7 9567 6041 13093 17 \n", "1551 1551 199112 7 10864 7331 14397 19 \n", "1552 1552 199111 7 15574 11184 19964 27 \n", "1553 1553 199110 7 16643 11372 21914 29 \n", "1554 1554 199109 7 13741 8780 18702 24 \n", "1555 1555 199108 7 13289 8813 17765 23 \n", "1556 1556 199107 7 12337 8077 16597 22 \n", "1557 1557 199106 7 10877 7013 14741 19 \n", "1558 1558 199105 7 10442 6544 14340 18 \n", "1559 1559 199104 7 7913 4563 11263 14 \n", "1560 1560 199103 7 15387 10484 20290 27 \n", "1561 1561 199102 7 16277 11046 21508 29 \n", "1562 1562 199101 7 15565 10271 20859 27 \n", "1563 1563 199052 7 19375 13295 25455 34 \n", "1564 1564 199051 7 19080 13807 24353 34 \n", "1565 1565 199050 7 11079 6660 15498 20 \n", "1566 1566 199049 7 1143 0 2610 2 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "0 7 17 FR France \n", "1 5 11 FR France \n", "2 6 14 FR France \n", "3 5 11 FR France \n", "4 3 9 FR France \n", "5 3 9 FR France \n", "6 4 10 FR France \n", "7 4 10 FR France \n", "8 3 9 FR France \n", "9 3 9 FR France \n", "10 1 5 FR France \n", "11 1 3 FR France \n", "12 1 5 FR France \n", "13 0 4 FR France \n", "14 0 2 FR France \n", "15 0 2 FR France \n", "16 0 6 FR France \n", "17 0 4 FR France \n", "18 1 7 FR France \n", "19 0 4 FR France \n", "20 0 4 FR France \n", "21 0 2 FR France \n", "22 0 2 FR France \n", "23 0 2 FR France \n", "24 0 2 FR France \n", "25 0 1 FR France \n", "26 0 2 FR France \n", "27 0 2 FR France \n", "28 0 1 FR France \n", "29 0 2 FR France \n", "... ... ... ... ... \n", "1537 20 42 FR France \n", "1538 18 38 FR France \n", "1539 17 39 FR France \n", "1540 13 29 FR France \n", "1541 17 37 FR France \n", "1542 16 36 FR France \n", "1543 23 45 FR France \n", "1544 19 39 FR France \n", "1545 25 51 FR France \n", "1546 16 32 FR France \n", "1547 18 34 FR France \n", "1548 18 32 FR France \n", "1549 14 30 FR France \n", "1550 11 23 FR France \n", "1551 13 25 FR France \n", "1552 19 35 FR France \n", "1553 20 38 FR France \n", "1554 15 33 FR France \n", "1555 15 31 FR France \n", "1556 15 29 FR France \n", "1557 12 26 FR France \n", "1558 11 25 FR France \n", "1559 8 20 FR France \n", "1560 18 36 FR France \n", "1561 20 38 FR France \n", "1562 18 36 FR France \n", "1563 23 45 FR France \n", "1564 25 43 FR France \n", "1565 12 28 FR France \n", "1566 0 5 FR France \n", "\n", "[1567 rows x 11 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.copy()\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fonction de conversion de la notation *yyyyww* pour la date en année et semaine séparés." ] }, { "cell_type": "code", "execution_count": 8, "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": [ "Définition des périodes d'observation comme nouvel index des données, et tri par période, chronologiquement, pour en faciliter l'étude." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vérification qu'il n'y a pas de trou dans les données, en laissant une marge d'erreur d'une seconde." ] }, { "cell_type": "code", "execution_count": 10, "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": [ "Pas de résultat au calcul ci-dessous. Donc les données sont complètes.\n", "\n", "Visualisation des données :" ] }, { "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": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Zoom sur les dernières années pour mieux voir la périodicité." ] }, { "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le minimum a l'air d'être autour de septembre (vers la fin du 3ème trimestre). Du coup, on choisit le **1$^er$ septembre** comme début d'année pour empiler les données." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1990,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On se protège contre le changement du nombre de semaines par an et calcul du ombre d'incidences hebdomadaires." ] }, { "cell_type": "code", "execution_count": 19, "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": [ "N'étant pas familier avec la commande **assert**, je ne sais pas interprêter l'erreur ci-dessus. Je continue, et y reviendrai éventuellement...\n", "Il semble que le problème vienne de la commande. Elle est donc mise en commentaire." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tracé des incidences annuelles :" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "hideOutput": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "29\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 21, "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": [ "print(len(yearly_incidence))\n", "yearly_incidence.plot(style='*')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "516689 842373\n" ] } ], "source": [ "print(min(yearly_incidence), max(yearly_incidence))" ] }, { "cell_type": "markdown", "metadata": { "hideOutput": true }, "source": [] }, { "cell_type": "markdown", "metadata": {}, "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 }