From 38a7f6599da9f34bcd15596424c208e3e76517ec Mon Sep 17 00:00:00 2001
From: c3999b8e2c915bb9cd7c17fe013dc1a2
 <c3999b8e2c915bb9cd7c17fe013dc1a2@app-learninglab.inria.fr>
Date: Sat, 13 Nov 2021 15:49:16 +0000
Subject: [PATCH] module3-exo2

---
 module3/exo2/exercice.ipynb | 632 +++++++++++++++++++++++++++++++++++-
 1 file changed, 629 insertions(+), 3 deletions(-)

diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 0bbbe37..2310851 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -1,5 +1,632 @@
 {
- "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 = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>week</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>inc</th>\n",
+       "      <th>inc_low</th>\n",
+       "      <th>inc_up</th>\n",
+       "      <th>inc100</th>\n",
+       "      <th>inc100_low</th>\n",
+       "      <th>inc100_up</th>\n",
+       "      <th>geo_insee</th>\n",
+       "      <th>geo_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>202144</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26136</td>\n",
+       "      <td>20345.0</td>\n",
+       "      <td>31927.0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202143</td>\n",
+       "      <td>3</td>\n",
+       "      <td>27302</td>\n",
+       "      <td>22077.0</td>\n",
+       "      <td>32527.0</td>\n",
+       "      <td>41</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202142</td>\n",
+       "      <td>3</td>\n",
+       "      <td>28343</td>\n",
+       "      <td>23382.0</td>\n",
+       "      <td>33304.0</td>\n",
+       "      <td>43</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202141</td>\n",
+       "      <td>3</td>\n",
+       "      <td>25043</td>\n",
+       "      <td>20586.0</td>\n",
+       "      <td>29500.0</td>\n",
+       "      <td>38</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202140</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26286</td>\n",
+       "      <td>21842.0</td>\n",
+       "      <td>30730.0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     week  indicator    inc  inc_low   inc_up  inc100  inc100_low  inc100_up  \\\n",
+       "0  202144          3  26136  20345.0  31927.0      40        31.0       49.0   \n",
+       "1  202143          3  27302  22077.0  32527.0      41        33.0       49.0   \n",
+       "2  202142          3  28343  23382.0  33304.0      43        35.0       51.0   \n",
+       "3  202141          3  25043  20586.0  29500.0      38        31.0       45.0   \n",
+       "4  202140          3  26286  21842.0  30730.0      40        33.0       47.0   \n",
+       "\n",
+       "  geo_insee geo_name  \n",
+       "0        FR   France  \n",
+       "1        FR   France  \n",
+       "2        FR   France  \n",
+       "3        FR   France  \n",
+       "4        FR   France  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(data_url, skiprows=1)\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>week</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>inc</th>\n",
+       "      <th>inc_low</th>\n",
+       "      <th>inc_up</th>\n",
+       "      <th>inc100</th>\n",
+       "      <th>inc100_low</th>\n",
+       "      <th>inc100_up</th>\n",
+       "      <th>geo_insee</th>\n",
+       "      <th>geo_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1695</th>\n",
+       "      <td>198919</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator  inc  inc_low  inc_up  inc100  inc100_low  inc100_up  \\\n",
+       "1695  198919          3    0      NaN     NaN       0         NaN        NaN   \n",
+       "\n",
+       "     geo_insee geo_name  \n",
+       "1695        FR   France  "
+      ]
+     },
+     "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": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>week</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>inc</th>\n",
+       "      <th>inc_low</th>\n",
+       "      <th>inc_up</th>\n",
+       "      <th>inc100</th>\n",
+       "      <th>inc100_low</th>\n",
+       "      <th>inc100_up</th>\n",
+       "      <th>geo_insee</th>\n",
+       "      <th>geo_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>202144</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26136</td>\n",
+       "      <td>20345.0</td>\n",
+       "      <td>31927.0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202143</td>\n",
+       "      <td>3</td>\n",
+       "      <td>27302</td>\n",
+       "      <td>22077.0</td>\n",
+       "      <td>32527.0</td>\n",
+       "      <td>41</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202142</td>\n",
+       "      <td>3</td>\n",
+       "      <td>28343</td>\n",
+       "      <td>23382.0</td>\n",
+       "      <td>33304.0</td>\n",
+       "      <td>43</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202141</td>\n",
+       "      <td>3</td>\n",
+       "      <td>25043</td>\n",
+       "      <td>20586.0</td>\n",
+       "      <td>29500.0</td>\n",
+       "      <td>38</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202140</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26286</td>\n",
+       "      <td>21842.0</td>\n",
+       "      <td>30730.0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     week  indicator    inc  inc_low   inc_up  inc100  inc100_low  inc100_up  \\\n",
+       "0  202144          3  26136  20345.0  31927.0      40        31.0       49.0   \n",
+       "1  202143          3  27302  22077.0  32527.0      41        33.0       49.0   \n",
+       "2  202142          3  28343  23382.0  33304.0      43        35.0       51.0   \n",
+       "3  202141          3  25043  20586.0  29500.0      38        31.0       45.0   \n",
+       "4  202140          3  26286  21842.0  30730.0      40        33.0       47.0   \n",
+       "\n",
+       "  geo_insee geo_name  \n",
+       "0        FR   France  \n",
+       "1        FR   France  \n",
+       "2        FR   France  \n",
+       "3        FR   France  \n",
+       "4        FR   France  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.dropna().copy()\n",
+    "data.head()"
+   ]
+  },
+  {
+   "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": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
+     ]
+    }
+   ],
+   "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": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd36c474fd0>"
+      ]
+     },
+     "execution_count": 9,
+     "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": [
+    "sorted_data['inc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd36a2d4780>"
+      ]
+     },
+     "execution_count": 10,
+     "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": [
+    "sorted_data['inc'][-200:].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(1985,\n",
+    "                                    sorted_data.index[-1].year)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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",
+    "    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": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3a10f4c50>"
+      ]
+     },
+     "execution_count": 19,
+     "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": [
+    "yearly_incidence.plot(style='*')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2014    1601698\n",
+       "1991    1663610\n",
+       "1995    1828304\n",
+       "2020    2078964\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": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd36a1ad320>"
+      ]
+     },
+     "execution_count": 21,
+     "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": [
+    "yearly_incidence.hist(xrot=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +643,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
-- 
2.18.1