diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..b34117c3251d15658946d5636b7c72d6377759ba 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,1471 @@ { - "cells": [], + "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 = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1769 rows × 10 columns

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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": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data = raw_data.dropna().copy()" + ] + }, + { + "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": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "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": "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'] = pd.to_numeric(sorted_data['inc'], errors='coerce')\n", + "sorted_data['inc'].plot()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "period\n", + "1990-12-03/1990-12-09 1143\n", + "1990-12-10/1990-12-16 11079\n", + "1990-12-17/1990-12-23 19080\n", + "1990-12-24/1990-12-30 19375\n", + "1990-12-31/1991-01-06 15565\n", + "1991-01-07/1991-01-13 16277\n", + "1991-01-14/1991-01-20 15387\n", + "1991-01-21/1991-01-27 7913\n", + "1991-01-28/1991-02-03 10442\n", + "1991-02-04/1991-02-10 10877\n", + "1991-02-11/1991-02-17 12337\n", + "1991-02-18/1991-02-24 13289\n", + "1991-02-25/1991-03-03 13741\n", + "1991-03-04/1991-03-10 16643\n", + "1991-03-11/1991-03-17 15574\n", + "1991-03-18/1991-03-24 10864\n", + "1991-03-25/1991-03-31 9567\n", + "1991-04-01/1991-04-07 12265\n", + "1991-04-08/1991-04-14 13975\n", + "1991-04-15/1991-04-21 14857\n", + "1991-04-22/1991-04-28 13462\n", + "1991-04-29/1991-05-05 21385\n", + "1991-05-06/1991-05-12 16739\n", + "1991-05-13/1991-05-19 19053\n", + "1991-05-20/1991-05-26 14903\n", + "1991-05-27/1991-06-02 15452\n", + "1991-06-03/1991-06-09 11947\n", + "1991-06-10/1991-06-16 16171\n", + "1991-06-17/1991-06-23 16169\n", + "1991-06-24/1991-06-30 17608\n", + " ... \n", + "2024-04-01/2024-04-07 16181\n", + "2024-04-08/2024-04-14 24929\n", + "2024-04-15/2024-04-21 18138\n", + "2024-04-22/2024-04-28 15303\n", + "2024-04-29/2024-05-05 13438\n", + "2024-05-06/2024-05-12 10083\n", + "2024-05-13/2024-05-19 13661\n", + "2024-05-20/2024-05-26 9701\n", + "2024-05-27/2024-06-02 11628\n", + "2024-06-03/2024-06-09 14657\n", + "2024-06-10/2024-06-16 12621\n", + "2024-06-17/2024-06-23 11174\n", + "2024-06-24/2024-06-30 14368\n", + "2024-07-01/2024-07-07 10247\n", + "2024-07-08/2024-07-14 9364\n", + "2024-07-15/2024-07-21 9270\n", + "2024-07-22/2024-07-28 7004\n", + "2024-07-29/2024-08-04 4500\n", + "2024-08-05/2024-08-11 4399\n", + "2024-08-12/2024-08-18 1971\n", + "2024-08-19/2024-08-25 2560\n", + "2024-08-26/2024-09-01 1620\n", + "2024-09-02/2024-09-08 2235\n", + "2024-09-09/2024-09-15 916\n", + "2024-09-16/2024-09-22 751\n", + "2024-09-23/2024-09-29 2898\n", + "2024-09-30/2024-10-06 2125\n", + "2024-10-07/2024-10-13 2035\n", + "2024-10-14/2024-10-20 2659\n", + "2024-10-21/2024-10-27 2701\n", + "Freq: W-SUN, Name: inc, Length: 1769, dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_data['inc']" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Period('1990-10-01/1990-10-07', 'W-SUN'),\n", + " Period('1991-09-30/1991-10-06', 'W-SUN'),\n", + " Period('1992-09-28/1992-10-04', 'W-SUN'),\n", + " Period('1993-09-27/1993-10-03', 'W-SUN'),\n", + " Period('1994-09-26/1994-10-02', 'W-SUN'),\n", + " Period('1995-09-25/1995-10-01', 'W-SUN'),\n", + " Period('1996-09-30/1996-10-06', 'W-SUN'),\n", + " Period('1997-09-29/1997-10-05', 'W-SUN'),\n", + " Period('1998-09-28/1998-10-04', 'W-SUN'),\n", + " Period('1999-09-27/1999-10-03', 'W-SUN'),\n", + " Period('2000-09-25/2000-10-01', 'W-SUN'),\n", + " Period('2001-10-01/2001-10-07', 'W-SUN'),\n", + " Period('2002-09-30/2002-10-06', 'W-SUN'),\n", + " Period('2003-09-29/2003-10-05', 'W-SUN'),\n", + " Period('2004-09-27/2004-10-03', 'W-SUN'),\n", + " Period('2005-09-26/2005-10-02', 'W-SUN'),\n", + " Period('2006-09-25/2006-10-01', 'W-SUN'),\n", + " Period('2007-10-01/2007-10-07', 'W-SUN'),\n", + " Period('2008-09-29/2008-10-05', 'W-SUN'),\n", + " Period('2009-09-28/2009-10-04', 'W-SUN'),\n", + " Period('2010-09-27/2010-10-03', 'W-SUN'),\n", + " Period('2011-09-26/2011-10-02', 'W-SUN'),\n", + " Period('2012-10-01/2012-10-07', 'W-SUN'),\n", + " Period('2013-09-30/2013-10-06', 'W-SUN'),\n", + " Period('2014-09-29/2014-10-05', 'W-SUN'),\n", + " Period('2015-09-28/2015-10-04', 'W-SUN'),\n", + " Period('2016-09-26/2016-10-02', 'W-SUN'),\n", + " Period('2017-09-25/2017-10-01', 'W-SUN'),\n", + " Period('2018-10-01/2018-10-07', 'W-SUN'),\n", + " Period('2019-09-30/2019-10-06', 'W-SUN'),\n", + " Period('2020-09-28/2020-10-04', 'W-SUN'),\n", + " Period('2021-09-27/2021-10-03', 'W-SUN'),\n", + " Period('2022-09-26/2022-10-02', 'W-SUN'),\n", + " Period('2023-09-25/2023-10-01', 'W-SUN')]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first_september_week = [pd.Period(pd.Timestamp(y, 10, 1), 'W')\n", + " for y in range(1990,\n", + " sorted_data.index[-1].year)]\n", + "first_september_week" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "43\n", + "52\n", + "52\n", + "52\n", + "52\n", + "53\n", + "52\n", + "52\n", + "52\n", + "52\n", + "53\n", + "52\n", + "52\n", + "52\n", + "52\n", + "52\n", + "53\n", + "52\n", + "52\n", + "52\n", + "52\n", + "53\n", + "52\n", + "52\n", + "52\n", + "52\n", + "52\n", + "53\n", + "52\n", + "52\n", + "52\n", + "52\n", + "52\n" + ] + } + ], + "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", + " print(len(one_year))\n", + " if len(one_year) != 43:\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": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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