diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..21a9825e47b9ed85aab2d572a468541541e22551 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,1217 @@ { - "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/incidence-PAY-7.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1673 rows × 10 columns

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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": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "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", + "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = raw_data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "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": 18, + "metadata": {}, + "outputs": [], + "source": [ + " first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", + " for y in range(1990,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_august_week[:-1],\n", + " first_august_week[1:]):\n", + " one_year = sorted_data['inc'][week1:week2-1]\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": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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