{
 "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-7.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On extrait les données de varicelles depuis le site du [Réseau sentinelles](http://www.sentiweb.fr/france/fr/?page=table&maladie=7).\n",
    "\n"
   ]
  },
  {
   "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",
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       "    }\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>202136</td>\n",
       "      <td>7</td>\n",
       "      <td>4910</td>\n",
       "      <td>2188</td>\n",
       "      <td>7632</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202135</td>\n",
       "      <td>7</td>\n",
       "      <td>2592</td>\n",
       "      <td>1116</td>\n",
       "      <td>4068</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202134</td>\n",
       "      <td>7</td>\n",
       "      <td>1429</td>\n",
       "      <td>378</td>\n",
       "      <td>2480</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202133</td>\n",
       "      <td>7</td>\n",
       "      <td>3829</td>\n",
       "      <td>1830</td>\n",
       "      <td>5828</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202132</td>\n",
       "      <td>7</td>\n",
       "      <td>4108</td>\n",
       "      <td>1895</td>\n",
       "      <td>6321</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>9</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  202136          7  4910     2188    7632       7           3         11   \n",
       "1  202135          7  2592     1116    4068       4           2          6   \n",
       "2  202134          7  1429      378    2480       2           0          4   \n",
       "3  202133          7  3829     1830    5828       6           3          9   \n",
       "4  202132          7  4108     1895    6321       6           3          9   \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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[raw_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il n'y a pas de valeurs manquantes a priori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>202136</td>\n",
       "      <td>7</td>\n",
       "      <td>4910</td>\n",
       "      <td>2188</td>\n",
       "      <td>7632</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "      <td>2021-09-06/2021-09-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202135</td>\n",
       "      <td>7</td>\n",
       "      <td>2592</td>\n",
       "      <td>1116</td>\n",
       "      <td>4068</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "      <td>2021-08-30/2021-09-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202134</td>\n",
       "      <td>7</td>\n",
       "      <td>1429</td>\n",
       "      <td>378</td>\n",
       "      <td>2480</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "      <td>2021-08-23/2021-08-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202133</td>\n",
       "      <td>7</td>\n",
       "      <td>3829</td>\n",
       "      <td>1830</td>\n",
       "      <td>5828</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "      <td>2021-08-16/2021-08-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202132</td>\n",
       "      <td>7</td>\n",
       "      <td>4108</td>\n",
       "      <td>1895</td>\n",
       "      <td>6321</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "      <td>2021-08-09/2021-08-15</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  202136          7  4910     2188    7632       7           3         11   \n",
       "1  202135          7  2592     1116    4068       4           2          6   \n",
       "2  202134          7  1429      378    2480       2           0          4   \n",
       "3  202133          7  3829     1830    5828       6           3          9   \n",
       "4  202132          7  4108     1895    6321       6           3          9   \n",
       "\n",
       "  geo_insee geo_name                period  \n",
       "0        FR   France 2021-09-06/2021-09-12  \n",
       "1        FR   France 2021-08-30/2021-09-05  \n",
       "2        FR   France 2021-08-23/2021-08-29  \n",
       "3        FR   France 2021-08-16/2021-08-22  \n",
       "4        FR   France 2021-08-09/2021-08-15  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data = raw_data.set_index('period').sort_index()\n",
    "sorted_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1fbb54518>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,7])\n",
    "sorted_data['inc'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1fb855668>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,7])\n",
    "sorted_data['inc'][-200:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1990-08-27/1990-09-02 38\n"
     ]
    }
   ],
   "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",
    "    \n",
    "    if ( abs(len(one_year)-52) > 2):\n",
    "        print(week1,len(one_year)) \n",
    "    else:\n",
    "        yearly_incidence.append(one_year.sum())\n",
    "        year.append(week2.year)\n",
    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'année 90 n'est pas complète puisque les données ne correspondent qu'à 4 semaines en décembre. L'incidence de cette année n'est pas prise en compte "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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",
       "  </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",
       "\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  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data[sorted_data.week<199100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1f2db22b0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [12,5])\n",
    "yearly_incidence.plot(style='*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1f3421080>"
      ]
     },
     "execution_count": 47,
     "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",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}