{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2021     772545\n",
       "2014    1601698\n",
       "1991    1663610\n",
       "1995    1828304\n",
       "2020    2017296\n",
       "2022    2057596\n",
       "2012    2183912\n",
       "2003    2234514\n",
       "2019    2254363\n",
       "2006    2297262\n",
       "2017    2322818\n",
       "2001    2540826\n",
       "1992    2590314\n",
       "1993    2699482\n",
       "2018    2701716\n",
       "1988    2759663\n",
       "2007    2786458\n",
       "2011    2852504\n",
       "2016    2859019\n",
       "1987    2867464\n",
       "2008    2984311\n",
       "1998    3047298\n",
       "2002    3115484\n",
       "1994    3514133\n",
       "1996    3540251\n",
       "2009    3558474\n",
       "2004    3572810\n",
       "1997    3624129\n",
       "2015    3647492\n",
       "2000    3808190\n",
       "2005    3831409\n",
       "1999    3914003\n",
       "2010    3992174\n",
       "2013    4176872\n",
       "1986    5050543\n",
       "1990    5214494\n",
       "1989    5461328\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import isoweek\n",
    "\n",
    "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",
    "\n",
    "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n",
    "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n",
    "raw_data[raw_data.isnull().any(axis=1)]\n",
    "data = raw_data.dropna().copy()\n",
    "\n",
    "\n",
    "data['period'] = [convert_week(yw) for yw in data['week']]\n",
    "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)\n",
    "\n",
    "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
    "                     for y in range(1991,sorted_data.index[-1].year)]\n",
    "\n",
    "year = []\n",
    "yearly_incidence = []\n",
    "for week1, week2 in zip(first_sept_week[:-1],\n",
    "                        first_sept_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)\n",
    "\n",
    "yearly_incidence.plot(style='*')\n",
    "yearly_incidence.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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