change

module3/exo2/exercice.ipynb

 {
- "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": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "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>202109</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11766</td>\n",
+       "      <td>8111</td>\n",
+       "      <td>15421</td>\n",
+       "      <td>18</td>\n",
+       "      <td>12</td>\n",
+       "      <td>24</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202108</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11382</td>\n",
+       "      <td>8422</td>\n",
+       "      <td>14342</td>\n",
+       "      <td>17</td>\n",
+       "      <td>13</td>\n",
+       "      <td>21</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202107</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13561</td>\n",
+       "      <td>10315</td>\n",
+       "      <td>16807</td>\n",
+       "      <td>21</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202106</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13401</td>\n",
+       "      <td>9810</td>\n",
+       "      <td>16992</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15</td>\n",
+       "      <td>25</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202105</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12210</td>\n",
+       "      <td>8988</td>\n",
+       "      <td>15432</td>\n",
+       "      <td>18</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</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  202109          7  11766     8111   15421      18          12         24   \n",
+       "1  202108          7  11382     8422   14342      17          13         21   \n",
+       "2  202107          7  13561    10315   16807      21          16         26   \n",
+       "3  202106          7  13401     9810   16992      20          15         25   \n",
+       "4  202105          7  12210     8988   15432      18          13         23   \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": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(data_url, skiprows=1)\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data.dropna().copy()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "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": 38,
+   "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",
+       "      <th>period</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>202109</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11766</td>\n",
+       "      <td>8111</td>\n",
+       "      <td>15421</td>\n",
+       "      <td>18</td>\n",
+       "      <td>12</td>\n",
+       "      <td>24</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>2021-03-01/2021-03-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202108</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11382</td>\n",
+       "      <td>8422</td>\n",
+       "      <td>14342</td>\n",
+       "      <td>17</td>\n",
+       "      <td>13</td>\n",
+       "      <td>21</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>2021-02-22/2021-02-28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202107</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13561</td>\n",
+       "      <td>10315</td>\n",
+       "      <td>16807</td>\n",
+       "      <td>21</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>2021-02-15/2021-02-21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202106</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13401</td>\n",
+       "      <td>9810</td>\n",
+       "      <td>16992</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15</td>\n",
+       "      <td>25</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>2021-02-08/2021-02-14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202105</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12210</td>\n",
+       "      <td>8988</td>\n",
+       "      <td>15432</td>\n",
+       "      <td>18</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>2021-02-01/2021-02-07</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  202109          7  11766     8111   15421      18          12         24   \n",
+       "1  202108          7  11382     8422   14342      17          13         21   \n",
+       "2  202107          7  13561    10315   16807      21          16         26   \n",
+       "3  202106          7  13401     9810   16992      20          15         25   \n",
+       "4  202105          7  12210     8988   15432      18          13         23   \n",
+       "\n",
+       "  geo_insee geo_name                period  \n",
+       "0        FR   France 2021-03-01/2021-03-07  \n",
+       "1        FR   France 2021-02-22/2021-02-28  \n",
+       "2        FR   France 2021-02-15/2021-02-21  \n",
+       "3        FR   France 2021-02-08/2021-02-14  \n",
+       "4        FR   France 2021-02-01/2021-02-07  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_data = data.set_index('period').sort_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(1991,\n",
+    "                                    sorted_data.index[-1].year)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f097c6a2b00>"
+      ]
+     },
+     "execution_count": 48,
+     "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": 49,
+   "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",
+       "    <tr>\n",
+       "      <th>period</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1990-12-03/1990-12-09</th>\n",
+       "      <td>199049</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1143</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2610</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1990-12-10/1990-12-16</th>\n",
+       "      <td>199050</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11079</td>\n",
+       "      <td>6660</td>\n",
+       "      <td>15498</td>\n",
+       "      <td>20</td>\n",
+       "      <td>12</td>\n",
+       "      <td>28</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1990-12-17/1990-12-23</th>\n",
+       "      <td>199051</td>\n",
+       "      <td>7</td>\n",
+       "      <td>19080</td>\n",
+       "      <td>13807</td>\n",
+       "      <td>24353</td>\n",
+       "      <td>34</td>\n",
+       "      <td>25</td>\n",
+       "      <td>43</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1990-12-24/1990-12-30</th>\n",
+       "      <td>199052</td>\n",
+       "      <td>7</td>\n",
+       "      <td>19375</td>\n",
+       "      <td>13295</td>\n",
+       "      <td>25455</td>\n",
+       "      <td>34</td>\n",
+       "      <td>23</td>\n",
+       "      <td>45</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1990-12-31/1991-01-06</th>\n",
+       "      <td>199101</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15565</td>\n",
+       "      <td>10271</td>\n",
+       "      <td>20859</td>\n",
+       "      <td>27</td>\n",
+       "      <td>18</td>\n",
+       "      <td>36</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  \\\n",
+       "period                                                                     \n",
+       "1990-12-03/1990-12-09  199049          7   1143        0    2610       2   \n",
+       "1990-12-10/1990-12-16  199050          7  11079     6660   15498      20   \n",
+       "1990-12-17/1990-12-23  199051          7  19080    13807   24353      34   \n",
+       "1990-12-24/1990-12-30  199052          7  19375    13295   25455      34   \n",
+       "1990-12-31/1991-01-06  199101          7  15565    10271   20859      27   \n",
+       "\n",
+       "                       inc100_low  inc100_up geo_insee geo_name  \n",
+       "period                                                           \n",
+       "1990-12-03/1990-12-09           0          5        FR   France  \n",
+       "1990-12-10/1990-12-16          12         28        FR   France  \n",
+       "1990-12-17/1990-12-23          25         43        FR   France  \n",
+       "1990-12-24/1990-12-30          23         45        FR   France  \n",
+       "1990-12-31/1991-01-06          18         36        FR   France  "
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "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": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2020    221186\n",
+       "2002    516689\n",
+       "2018    542312\n",
+       "2017    551041\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",
+       "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": 51,
+     "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 +609,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-