From 4b3b72ca697eb41cefd90b77ed6fef56ddae15a6 Mon Sep 17 00:00:00 2001
From: 1f2b5799c7e6329c0238867cf93010b4
 <1f2b5799c7e6329c0238867cf93010b4@app-learninglab.inria.fr>
Date: Wed, 27 Sep 2023 23:06:20 +0000
Subject: [PATCH] done

---
 module3/exo2/exercice.ipynb     | 463 +++++++++++++++++++++++++++++++-
 module3/exo2/small_pox_data.csv | 186 +++++++++++++
 2 files changed, 646 insertions(+), 3 deletions(-)
 create mode 100644 module3/exo2/small_pox_data.csv

diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 0bbbe37..1944516 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -1,5 +1,463 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Incidence of small pox illness in France"
+   ]
+  },
+  {
+   "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\"\n",
+    "\n",
+    "data_file = \"small_pox_data.csv\"\n",
+    "\n",
+    "import os\n",
+    "import urllib.request\n",
+    "if not os.path.exists(data_file):\n",
+    "    urllib.request.urlretrieve(data_url, data_file)"
+   ]
+  },
+  {
+   "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>202338</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2161</td>\n",
+       "      <td>220</td>\n",
+       "      <td>4102</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202337</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1088</td>\n",
+       "      <td>190</td>\n",
+       "      <td>1986</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202336</td>\n",
+       "      <td>7</td>\n",
+       "      <td>726</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1442</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202335</td>\n",
+       "      <td>7</td>\n",
+       "      <td>961</td>\n",
+       "      <td>96</td>\n",
+       "      <td>1826</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202334</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1168</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2327</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</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  202338          7  2161      220    4102       3           0          6   \n",
+       "1  202337          7  1088      190    1986       2           1          3   \n",
+       "2  202336          7   726       10    1442       1           0          2   \n",
+       "3  202335          7   961       96    1826       1           0          2   \n",
+       "4  202334          7  1168        9    2327       2           0          4   \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",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f28d5597780>"
+      ]
+     },
+     "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 0x7f28d34b64e0>"
+      ]
+     },
+     "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": 11,
+   "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": "code",
+   "execution_count": 12,
+   "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": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f28d33f8748>"
+      ]
+     },
+     "execution_count": 13,
+     "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": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2020    221186\n",
+       "2021    376290\n",
+       "2002    516689\n",
+       "2018    542312\n",
+       "2017    551041\n",
+       "1991    553090\n",
+       "1996    564901\n",
+       "2019    584066\n",
+       "2015    604382\n",
+       "2000    617597\n",
+       "2001    619041\n",
+       "2012    624573\n",
+       "2005    628464\n",
+       "2006    632833\n",
+       "2022    641397\n",
+       "2011    642368\n",
+       "1993    643387\n",
+       "1995    652478\n",
+       "1994    661409\n",
+       "1998    677775\n",
+       "1997    683434\n",
+       "2014    685769\n",
+       "2013    698332\n",
+       "2007    717352\n",
+       "2008    749478\n",
+       "1999    756456\n",
+       "2003    758363\n",
+       "2004    777388\n",
+       "2016    782114\n",
+       "2010    829911\n",
+       "1992    832939\n",
+       "2009    842373\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +474,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module3/exo2/small_pox_data.csv b/module3/exo2/small_pox_data.csv
new file mode 100644
index 0000000..cef09ad
--- /dev/null
+++ b/module3/exo2/small_pox_data.csv
@@ -0,0 +1,186 @@
+# @source="r&#233;seau Sentinelles, INSERM, Sorbonne Universit&#233;, https://www.sentiweb.fr", @meta={"period":[202012,202338],"geo":["PAY",1],"geo_ref":"insee","indicator":"25","type":"all","conf_int":true,"compact":false,"age_group":false}, @date=2023-09-27T12:27:18+02:00, bundle=1695810415
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202338,25,160809,146033,175585,242,220,264,FR,France
+202337,25,110408,99739,121077,166,150,182,FR,France
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+202335,25,75353,66521,84185,113,100,126,FR,France
+202334,25,62298,53692,70904,94,81,107,FR,France
+202333,25,51315,41862,60768,77,63,91,FR,France
+202332,25,44411,36803,52019,67,56,78,FR,France
+202331,25,45046,37089,53003,68,56,80,FR,France
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+202325,25,33506,27383,39629,50,41,59,FR,France
+202324,25,36369,29696,43042,55,45,65,FR,France
+202323,25,42752,36381,49123,64,54,74,FR,France
+202322,25,50539,43068,58010,76,65,87,FR,France
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+202320,25,44849,37807,51891,67,56,78,FR,France
+202319,25,48729,41382,56076,73,62,84,FR,France
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+202246,25,147262,135893,158631,222,205,239,FR,France
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+202219,25,42681,36818,48544,64,55,73,FR,France
+202218,25,61938,54472,69404,93,82,104,FR,France
+202217,25,75024,66766,83282,113,101,125,FR,France
+202216,25,103633,93419,113847,156,141,171,FR,France
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+202206,25,94774,85822,103726,143,130,156,FR,France
+202205,25,135656,125440,145872,205,190,220,FR,France
+202204,25,144768,134227,155309,218,202,234,FR,France
+202203,25,152651,141883,163419,230,214,246,FR,France
+202202,25,131009,121146,140872,198,183,213,FR,France
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+202148,25,102691,94009,111373,155,142,168,FR,France
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+202146,25,85173,77239,93107,129,117,141,FR,France
+202145,25,60241,53646,66836,91,81,101,FR,France
+202144,25,57800,50959,64641,87,77,97,FR,France
+202143,25,66246,58313,74179,100,88,112,FR,France
+202142,25,85033,76428,93638,129,116,142,FR,France
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+202135,25,24219,19591,28847,37,30,44,FR,France
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+202129,25,33514,27312,39716,51,42,60,FR,France
+202128,25,24111,18384,29838,36,27,45,FR,France
+202127,25,25266,19878,30654,38,30,46,FR,France
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+202112,25,75578,68194,82962,114,103,125,FR,France
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+202110,25,50943,45071,56815,77,68,86,FR,France
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+202101,25,56078,49481,62675,85,75,95,FR,France
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+202020,25,28416,23875,32957,43,36,50,FR,France
+202019,25,27347,22880,31814,42,35,49,FR,France
+202018,25,34618,29499,39737,53,45,61,FR,France
+202017,25,45903,40032,51774,70,61,79,FR,France
+202016,25,60969,54118,67820,93,83,103,FR,France
+202015,25,95364,86665,104063,145,132,158,FR,France
+202014,25,213772,201334,226210,325,306,344,FR,France
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+202012,25,273062,259400,286724,415,394,436,FR,France
-- 
2.18.1