{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse Incidence de la Varicelle\n",
    "\n",
    "Première chose à faire c'est importer et inspecter les données avant de faire les questions et réponses. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os \n",
    "import isoweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Analyse Incidence de la Varicelle.ipynb',\n",
       " '.DS_Store',\n",
       " 'exercice_en.ipynb',\n",
       " 'incidence-PAY-7.csv',\n",
       " 'exercice.ipynb',\n",
       " 'exercice_python_en.org',\n",
       " 'exercice_python_fr.org',\n",
       " 'exercice_R_en.org',\n",
       " 'exercice_R_fr.org',\n",
       " '.ipynb_checkpoints',\n",
       " 'exercice_fr.Rmd',\n",
       " 'exercice_en.Rmd',\n",
       " 'exercice_fr.ipynb']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()\n",
    "os.listdir()"
   ]
  },
  {
   "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>202036</td>\n",
       "      <td>7</td>\n",
       "      <td>904</td>\n",
       "      <td>77</td>\n",
       "      <td>1731</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>1</th>\n",
       "      <td>202035</td>\n",
       "      <td>7</td>\n",
       "      <td>828</td>\n",
       "      <td>0</td>\n",
       "      <td>1694</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>2</th>\n",
       "      <td>202034</td>\n",
       "      <td>7</td>\n",
       "      <td>2272</td>\n",
       "      <td>371</td>\n",
       "      <td>4173</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>3</th>\n",
       "      <td>202033</td>\n",
       "      <td>7</td>\n",
       "      <td>1284</td>\n",
       "      <td>177</td>\n",
       "      <td>2391</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>4</th>\n",
       "      <td>202032</td>\n",
       "      <td>7</td>\n",
       "      <td>2650</td>\n",
       "      <td>689</td>\n",
       "      <td>4611</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1548</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",
       "    <tr>\n",
       "      <th>1549</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>1550</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>1551</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>1552</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>1553 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
       "0     202036          7    904       77    1731       1           0   \n",
       "1     202035          7    828        0    1694       1           0   \n",
       "2     202034          7   2272      371    4173       3           0   \n",
       "3     202033          7   1284      177    2391       2           0   \n",
       "4     202032          7   2650      689    4611       4           1   \n",
       "...      ...        ...    ...      ...     ...     ...         ...   \n",
       "1548  199101          7  15565    10271   20859      27          18   \n",
       "1549  199052          7  19375    13295   25455      34          23   \n",
       "1550  199051          7  19080    13807   24353      34          25   \n",
       "1551  199050          7  11079     6660   15498      20          12   \n",
       "1552  199049          7   1143        0    2610       2           0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "0             2        FR   France  \n",
       "1             2        FR   France  \n",
       "2             6        FR   France  \n",
       "3             4        FR   France  \n",
       "4             7        FR   France  \n",
       "...         ...       ...      ...  \n",
       "1548         36        FR   France  \n",
       "1549         45        FR   France  \n",
       "1550         43        FR   France  \n",
       "1551         28        FR   France  \n",
       "1552          5        FR   France  \n",
       "\n",
       "[1553 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Since there is no missing line in the data we shall proceed\n",
    "data = pd.read_csv(\"incidence-PAY-7.csv\", skiprows=1)\n",
    "data"
   ]
  },
  {
   "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": [
    "#Checkin everything is good\n",
    "data[data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling to adapt to isoweek format\n",
    "\n",
    "Since everything is good no data missing we can proceed to the wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_data = data.set_index('period').sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='period'>"
      ]
     },
     "execution_count": 8,
     "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": [
    "##Visual\n",
    "sorted_data['inc'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='period'>"
      ]
     },
     "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'][-200:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('1991-01-07/1991-01-13', 'W-SUN')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data.index[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Determine the annual frequency\n",
    "\n",
    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
    "                     for y in range(sorted_data.index[0].year,\n",
    "                                    sorted_data.index[-1].year)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Period('1990-08-27/1990-09-02', 'W-SUN'),\n",
       " Period('1991-08-26/1991-09-01', 'W-SUN'),\n",
       " Period('1992-08-31/1992-09-06', 'W-SUN'),\n",
       " Period('1993-08-30/1993-09-05', 'W-SUN'),\n",
       " Period('1994-08-29/1994-09-04', 'W-SUN'),\n",
       " Period('1995-08-28/1995-09-03', 'W-SUN'),\n",
       " Period('1996-08-26/1996-09-01', 'W-SUN'),\n",
       " Period('1997-09-01/1997-09-07', 'W-SUN'),\n",
       " Period('1998-08-31/1998-09-06', 'W-SUN'),\n",
       " Period('1999-08-30/1999-09-05', 'W-SUN'),\n",
       " Period('2000-08-28/2000-09-03', 'W-SUN'),\n",
       " Period('2001-08-27/2001-09-02', 'W-SUN'),\n",
       " Period('2002-08-26/2002-09-01', 'W-SUN'),\n",
       " Period('2003-09-01/2003-09-07', 'W-SUN'),\n",
       " Period('2004-08-30/2004-09-05', 'W-SUN'),\n",
       " Period('2005-08-29/2005-09-04', 'W-SUN'),\n",
       " Period('2006-08-28/2006-09-03', 'W-SUN'),\n",
       " Period('2007-08-27/2007-09-02', 'W-SUN'),\n",
       " Period('2008-09-01/2008-09-07', 'W-SUN'),\n",
       " Period('2009-08-31/2009-09-06', 'W-SUN'),\n",
       " Period('2010-08-30/2010-09-05', 'W-SUN'),\n",
       " Period('2011-08-29/2011-09-04', 'W-SUN'),\n",
       " Period('2012-08-27/2012-09-02', 'W-SUN'),\n",
       " Period('2013-08-26/2013-09-01', 'W-SUN'),\n",
       " Period('2014-09-01/2014-09-07', 'W-SUN'),\n",
       " Period('2015-08-31/2015-09-06', 'W-SUN'),\n",
       " Period('2016-08-29/2016-09-04', 'W-SUN'),\n",
       " Period('2017-08-28/2017-09-03', 'W-SUN'),\n",
       " Period('2018-08-27/2018-09-02', 'W-SUN'),\n",
       " Period('2019-08-26/2019-09-01', 'W-SUN')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_september_week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38\n",
      "53\n",
      "52\n",
      "52\n",
      "52\n",
      "52\n",
      "53\n",
      "52\n",
      "52\n",
      "52\n",
      "52\n",
      "52\n",
      "53\n",
      "52\n",
      "52\n",
      "52\n",
      "52\n",
      "53\n",
      "52\n",
      "52\n",
      "52\n",
      "52\n",
      "52\n",
      "53\n",
      "52\n",
      "52\n",
      "52\n",
      "52\n",
      "52\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",
    "    #print (one_year)\n",
    "    print (len(one_year))\n",
    "    #assert abs(len(one_year)-52) < 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": "markdown",
   "metadata": {},
   "source": [
    "We can see that for the first year we have a biased estimate since we only have 38 weeks approximately...\n",
    "\n",
    "So we shall not interpret the first annual incidence as \"fiable\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAD4CAYAAADGmmByAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdhElEQVR4nO3df5CV1Z3n8fcHm4A6YsBAIiA2ExkmYDYYuhozmc2OywQw2RKs0UyPjlKJVThGM0lqqxIo2cJSsjVOJeOEtXQl0YgaUZeNJbtKlOim4u4yjRBMFAlDJ5q2Be1ONRHMVnds+O4f91z76U7Tffve7r4/+vOqutVPf+9zjs/hSfp7z4/nXEUEZmZmxZpQ7gswM7Pq5kRiZmYlcSIxM7OSOJGYmVlJnEjMzKwkdeW+gJH2gQ98IOrr68t9GWZmVWXv3r2/iYjpxZStuURSX1/Pnj17yn0ZZmZVRdKviy1b0NCWpK9K2i/pZUlbJU2WdIukNyS9mF6fyZy/TlKLpIOSlmfiiyW9lN7bJEkpPknSoyneLKk+U2a1pEPptbrYhpqZ2egYMpFImgX8PdAQERcCpwFN6e07ImJRej2Vzl+Q3l8IrADuknRaOv9uYA0wL71WpPh1wNGIuAC4A7g91TUN2AAsARqBDZKmltZkMzMbSYVOttcBp0uqA84ADg9y7krgkYjojohXgRagUdK5wJSI2BW5x+kfAFZlymxJx9uApam3shzYGRGdEXEU2Elv8jEzswowZCKJiDeAbwKtwBHg7Yh4Jr19k6SfS7ov01OYBbyeqaItxWal4/7xPmUiogd4GzhnkLrMzKxCFDK0NZVcj2EuMBM4U9Lfkhum+jCwiFyC+Va+yADVxCDxYstkr3GNpD2S9nR0dJy6MWZmNuIKGdr6S+DViOiIiHeBHwB/FhFvRcSJiDgJfIfcHAbkeg3nZcrPJjcU1paO+8f7lEnDZ2cDnYPU1UdEbI6IhohomD69qNVrZpbRfqyLz92zi/bjXeW+FKsChSSSVuBiSWekeYulwIE055F3OfByOt4ONKWVWHPJTarvjogjwHFJF6d6rgWeyJTJr8i6AnguzaM8DSyTNDX1jJalmJmNok3PHuKF1zrZ9KND5b4UqwJDPkcSEc2StgE/BXqAfcBm4LuSFpEbanoNuD6dv1/SY8Ar6fwbI+JEqu4G4H7gdGBHegHcCzwoqYVcT6Qp1dUp6TbghXTerRHRWUJ7zWwQ89fvoLvn5Hu/P9TcykPNrUyqm8DBjZeW8cqskqnWvo+koaEh/ECiWXHaj3Wx8akDPLP/TbrePcnkiRNYvvBD3PzZjzDjrMnlvjwbRZL2RkRDMWW915aZvWfGlMmcNamO7p6TTKqbQHfPSc6aVOckYoOquS1SzKw0v3mnm6uXnM9VjXN4eHcrHZ5wtyF4aKtI7ce6uGnrPu686iJ/WjOzquehrTLwqhYzsxwPbQ2TV7WY9eXeublHMkzPf+0SLls0k8kTc/90kydOYOWimTz/9UvKfGVm5eHeublHMkxe1WKW49655blHUoT8qpbHv/hJrl5yPh3vdJf7kszGnHvnluceSRHuuaZ3YcPGVReW8UrMyse9c8tzIjGzovmZEwM/R2JmZvg5EjMzKyMnEjMzK4kTiZmZlcSJxMzMSuJEYmZmJXEiMTOzkjiRmJlZSZxIzMysJE4kZmZWEicSMzMriROJmZmVpKBEIumrkvZLelnSVkmTJU2TtFPSofRzaub8dZJaJB2UtDwTXyzppfTeJklK8UmSHk3xZkn1mTKr03/jkKTVI9h2MzMbAUMmEkmzgL8HGiLiQuA0oAlYCzwbEfOAZ9PvSFqQ3l8IrADuknRaqu5uYA0wL71WpPh1wNGIuAC4A7g91TUN2AAsARqBDdmEZWZm5Vfo0FYdcLqkOuAM4DCwEtiS3t8CrErHK4FHIqI7Il4FWoBGSecCUyJiV+S2HH6gX5l8XduApam3shzYGRGdEXEU2Elv8jEzswowZCKJiDeAbwKtwBHg7Yh4BvhgRBxJ5xwBZqQis4DXM1W0pdisdNw/3qdMRPQAbwPnDFJXH5LWSNojaU9HR8dQTTIzsxFUyNDWVHI9hrnATOBMSX87WJEBYjFIvNgyvYGIzRHREBEN06dPH+TSzMxspBUytPWXwKsR0RER7wI/AP4MeCsNV5F+tqfz24DzMuVnkxsKa0vH/eN9yqThs7OBzkHqMjOzClFIImkFLpZ0Rpq3WAocALYD+VVUq4En0vF2oCmtxJpLblJ9dxr+Oi7p4lTPtf3K5Ou6AnguzaM8DSyTNDX1jJalmJmZVYghv7M9IpolbQN+CvQA+4DNwB8Bj0m6jlyyuTKdv1/SY8Ar6fwbI+JEqu4G4H7gdGBHegHcCzwoqYVcT6Qp1dUp6TbghXTerRHRWVKLzcxsRPk7283MzN/ZbmZm5eNEYmZmJXEiMTMrUfuxLj53zy7aj3eV+1LKwonEzKxEm549xAuvdbLpR4fKfSllMeSqLTMzG9j89Tvo7jn53u8PNbfyUHMrk+omcHDjpWW8srHlHolZCcb7kMZ49/zXLuGyRTOZPDH3p3TyxAmsXDST579+SZmvbGw5kZiVYLwPaYx3M6ZM5qxJdXT3nGRS3QS6e05y1qQ6Zpw1udyXNqY8tGVWBA9pWN5v3unm6iXnc1XjHB7e3UrHOOyd+oFEsyK0H+ti41MHeGb/m3S9e5LJEyewfOGHuPmzHxl3n0atNviBRLMx5iENs14e2jIrkoc0zHI8tGVmZh7aMjOz8nEiMTOzkjiRmJlZSZxIzMysJE4kZv142xOz4XEisTFRTX+cve2J2fD4ORIbE9k/zhsv/2i5L2dA3vbErDh+jsRGVf8/znmV+MfZ257YeObnSKxiVdM22972xKw4QyYSSfMlvZh5HZP0FUm3SHojE/9Mpsw6SS2SDkpanokvlvRSem+TJKX4JEmPpnizpPpMmdWSDqXX6hFuv42yavvjnN/25PEvfpKrl5xPxzvd5b4ks4o35BxJRBwEFgFIOg14A3gc+DxwR0R8M3u+pAVAE7AQmAn8SNKfRMQJ4G5gDfAvwFPACmAHcB1wNCIukNQE3A78taRpwAagAQhgr6TtEXG01Ibb2KmmPanuuaa3Z79x1YVlvJKR136si5u27uPOqy6q2ERu1Wm4k+1LgV9GxK9TZ2IgK4FHIqIbeFVSC9Ao6TVgSkTsApD0ALCKXCJZCdySym8D7ky9leXAzojoTGV2kks+W4d53VZGtfzHuZpUw4IHq07DTSRN9P0jfpOka4E9wH9MPYVZ5HoceW0p9m467h8n/XwdICJ6JL0NnJOND1DmPZLWkOvpMGfOnGE2yay2eTWajbaCJ9slvQ+4DPhvKXQ38GFyw15HgG/lTx2geAwSL7ZMbyBic0Q0RETD9OnTT9UEs3GpmhY8WHUazqqtS4GfRsRbABHxVkSciIiTwHeAxnReG3Beptxs4HCKzx4g3qeMpDrgbKBzkLrMrEDVtuDBqs9wEsnfkBnWknRu5r3LgZfT8XagKa3EmgvMA3ZHxBHguKSL0/zHtcATmTL5FVlXAM9F7gGXp4FlkqZKmgosSzEzGwavRrPRVNAciaQzgE8D12fC/yhpEbmhptfy70XEfkmPAa8APcCNacUWwA3A/cDp5CbZd6T4vcCDaWK+k9xcDBHRKek24IV03q35iXczK5wXPNho8pPtZmbmJ9vNzKx8nEjMzKwkTiRmZlYSJxIzMyuJE4mZmZXEicTMzEriRGJmZiVxIjEzs5I4kZiZWUmcSMzMrCROJGZmVhInErMq136si8/ds4v2Cv4KY6ttTiRmVS77Fbpm5TDcr9o1swrhr9C1SuEeiVmV8lfoWqVwIjGrUv4KXasUHtoyq2L5r9C9qnEOD+9upcMT7lYG/oZEMzPzNySamVn5OJGYmVlJnEjMzKwkQyYSSfMlvZh5HZP0FUnTJO2UdCj9nJops05Si6SDkpZn4oslvZTe2yRJKT5J0qMp3iypPlNmdfpvHJK0eoTbb2ZmJRoykUTEwYhYFBGLgMXA/wMeB9YCz0bEPODZ9DuSFgBNwEJgBXCXpNNSdXcDa4B56bUixa8DjkbEBcAdwO2prmnABmAJ0AhsyCYsMzMrv+EObS0FfhkRvwZWAltSfAuwKh2vBB6JiO6IeBVoARolnQtMiYhdkVsq9kC/Mvm6tgFLU29lObAzIjoj4iiwk97kY2YGeL+xchtuImkCtqbjD0bEEYD0c0aKzwJez5RpS7FZ6bh/vE+ZiOgB3gbOGaQuM7P3eL+x8ir4gURJ7wMuA9YNdeoAsRgkXmyZ7LWtITdkxpw5c4a4PDOrFd5vrDIMp0dyKfDTiHgr/f5WGq4i/WxP8TbgvEy52cDhFJ89QLxPGUl1wNlA5yB19RERmyOiISIapk+fPowmmVk1835jlWE4ieRv6B3WAtgO5FdRrQaeyMSb0kqsueQm1Xen4a/jki5O8x/X9iuTr+sK4Lk0j/I0sEzS1DTJvizFzMy831iFKGhoS9IZwKeB6zPhfwAek3Qd0ApcCRAR+yU9BrwC9AA3RsSJVOYG4H7gdGBHegHcCzwoqYVcT6Qp1dUp6TbghXTerRHRWUQ7zaxGeb+x8vNeW2Zm5r22zMwK5aXCI8+JxMzGFS8VHnn+PhLro/1YFzdt3cedV13kCUurKV4qPHrcI7E+/GnNapWXCo8e90gM8Kc1q31eKjx63CMxoLhPa560tGqTXyr8+Bc/ydVLzqfjne5yX1JNcI/EgOI+rWWHwTZe/tExvFqz4txzTe/q1o2rLizjldQWJxJ7T6EPdnkYzMyy/ECiDVv7sS42PnWAZ/a/Sde7J5k8cQLLF36Imz/7EY83m1UpP5BoY8qTlmaW5aEtK4r3NzKzPA9tmZmZh7bMzKx8nEjMzKwkTiRmZlYSJ5IK4ifFzawaOZFUEG+YaGbVyMt/K4CfFDezauYeSQXw9tZmVs2cSCqAnxQ3s2rmRFIhhru9tSfmzaxS+Mn2KrX+8Zf4/u5Wrm6c4y3czaxko/5ku6T3S9om6ReSDkj6hKRbJL0h6cX0+kzm/HWSWiQdlLQ8E18s6aX03iZJSvFJkh5N8WZJ9ZkyqyUdSq/VxTSylsxfv4P6tU/yUHMrEbmJ+fq1TzJ//Y5yX5qZjVOFDm19G/hhRPwp8DHgQIrfERGL0uspAEkLgCZgIbACuEvSaen8u4E1wLz0WpHi1wFHI+IC4A7g9lTXNGADsARoBDZImlpsY2uBJ+bNrNIMmUgkTQE+BdwLEBG/j4jfDlJkJfBIRHRHxKtAC9Ao6VxgSkTsitx42gPAqkyZLel4G7A09VaWAzsjojMijgI76U0+45In5s2s0hTSI/ljoAP4nqR9kr4r6cz03k2Sfi7pvkxPYRbweqZ8W4rNSsf9433KREQP8DZwziB19SFpjaQ9kvZ0dHQU0KTq5u+dNrNKUkgiqQM+DtwdERcBvwPWkhum+jCwCDgCfCudrwHqiEHixZbpDURsjoiGiGiYPn36qVtSBqOxuuqeaxrYuOpCFsycwsZVF/b5Hmozs7FWSCJpA9oiojn9vg34eES8FREnIuIk8B1ycxj588/LlJ8NHE7x2QPE+5SRVAecDXQOUlfV8LYnZr28bL02DZlIIuJN4HVJ81NoKfBKmvPIuxx4OR1vB5rSSqy55CbVd0fEEeC4pIvT/Me1wBOZMvkVWVcAz6V5lKeBZZKmpqGzZSlW8by6yuwP+YNVbSp0r60vAd+X9D7gV8DngU2SFpEbanoNuB4gIvZLegx4BegBboyIE6meG4D7gdOBHekFuYn8ByW1kOuJNKW6OiXdBryQzrs1IjqLaukYe/5rl7DxqQM8s/9Nut49yeSJE1i+8EPc/NmPlPvSzMac95OrbQUlkoh4Eeg/EH/NIOd/A/jGAPE9wIUDxLuAK09R133AfYVcZyXx6iqzXv5gVdu8++8oyq+uuqpxDg/vbqXD48I2TvmDVW1zIhlF2dVUG1f9QUfM7JTaj3Vx09Z93HnVRTXzx9YfrGqX99oyq0DeS83GWil7bblHYlZBPClt1cjbyJuNkUKeofBealaNnEjMxkghz1B4UtqqkYe2zEbZcIerPCmdU4sLDmqVJ9vNRln7sa5TPkPhP5Cn5gUHY8uT7WYVzMNVw+MFB9XHcyRmY8Bb/xfOCw6qj3skVrWqaQzdD6cWzj246uMeiVWU4Wwz7p1ka5d7cNXFk+1WUQqZYO0/hp7nMXSz4pUy2e5EYhVhOMnBq6DMRl4picRDW1YRhjPB6jF0s8riyXarCMNNDn5oz6xyOJFYxRhOcvAqKLPK4TkSMzPzHImZmZWPE4mZ2SkM57mm8cyJxMzsFPzQa2EKSiSS3i9pm6RfSDog6ROSpknaKelQ+jk1c/46SS2SDkpanokvlvRSem+TJKX4JEmPpnizpPpMmdXpv3FI0uoRbLuZ2YDmr99B/doneai5lYjcxpH1a59k/vod5b60ilRoj+TbwA8j4k+BjwEHgLXAsxExD3g2/Y6kBUATsBBYAdwl6bRUz93AGmBeeq1I8euAoxFxAXAHcHuqaxqwAVgCNAIbsgnLzGw0eOPI4RkykUiaAnwKuBcgIn4fEb8FVgJb0mlbgFXpeCXwSER0R8SrQAvQKOlcYEpE7IrcUrEH+pXJ17UNWJp6K8uBnRHRGRFHgZ30Jh8zs1Hhh16Hp5AeyR8DHcD3JO2T9F1JZwIfjIgjAOnnjHT+LOD1TPm2FJuVjvvH+5SJiB7gbeCcQerqQ9IaSXsk7eno6CigSWZmg/PGkYUr5IHEOuDjwJciolnSt0nDWKegAWIxSLzYMr2BiM3AZsg9RzLItZmZFcQPvRaukB5JG9AWEc3p923kEstbabiK9LM9c/55mfKzgcMpPnuAeJ8ykuqAs4HOQeoyM7MKMWQiiYg3gdclzU+hpcArwHYgv4pqNfBEOt4ONKWVWHPJTarvTsNfxyVdnOY/ru1XJl/XFcBzaR7laWCZpKlpkn1ZipmZWYUodK+tLwHfl/Q+4FfA58klocckXQe0AlcCRMR+SY+RSzY9wI0RcSLVcwNwP3A6sCO9IDeR/6CkFnI9kaZUV6ek24AX0nm3RkRnkW01M7NR4L22zMzMe22ZmVn5OJGYmdWAcu4L5kRiZlYDyrkvmL/Yysysis1fv4PunpPv/f5QcysPNbcyqW4CBzdeOibX4B6JmVkVq4R9wZxIzMyqWCXsC+ahLTOzKpffF+yqxjk8vLuVjjGecPdzJGZm5udIzMysfJxIzMysJE4kZmZWEicSMzMriROJmZmVxIlkHCjnHjxmVvucSMaBcu7BY2a1zw8k1rBK2IPHzGqfeyQ1rBL24DGz2udEUsMqYQ8eM6t9HtqqceXeg8fMap/32jIzM++1NVK8TNbMbPicSDK8TNbMRlstfmAtKJFIek3SS5JelLQnxW6R9EaKvSjpM5nz10lqkXRQ0vJMfHGqp0XSJklK8UmSHk3xZkn1mTKrJR1Kr9Uj1vKM+et3UL/2SR5qbiUit0y2fu2TzF+/YzT+c2Y2jtXiB9aC5kgkvQY0RMRvMrFbgHci4pv9zl0AbAUagZnAj4A/iYgTknYDXwb+BXgK2BQROyR9Efg3EfF3kpqAyyPiryVNA/YADUAAe4HFEXH0VNdazBxJ+7EuNj51gGf2v0nXuyeZPHECyxd+iJs/+xGvcDKzEdH/ua68Snmuq9LmSFYCj0REd0S8CrQAjZLOBaZExK7IZa8HgFWZMlvS8TZgaeqtLAd2RkRnSh47gRUjfcFeJmtmo62Wn+sqNJEE8IykvZLWZOI3Sfq5pPskTU2xWcDrmXPaUmxWOu4f71MmInqAt4FzBqmrD0lrJO2RtKejo6PAJvWVXyb7+Bc/ydVLzqfjne6i6jEzG0gtf2At9DmST0bEYUkzgJ2SfgHcDdxGLsncBnwL+AKgAcrHIHGKLNMbiNgMbIbc0NbgTRnYPdf09ug2rrqwmCrMzAY13Oe62o91cdPWfdx51UUVnXAKSiQRcTj9bJf0ONAYET/Jvy/pO8D/TL+2Aedlis8GDqf47AHi2TJtkuqAs4HOFP+LfmV+XMg1m5lVmuF+YM1OzG+8/KOjeWklGTKRSDoTmBARx9PxMuBWSedGxJF02uXAy+l4O/CwpH8iN9k+D9idJtuPS7oYaAauBf5LpsxqYBdwBfBcRISkp4H/nBk2WwasK7HNZmYVrdo2XC2kR/JB4PG0UrcOeDgifijpQUmLyA01vQZcDxAR+yU9BrwC9AA3RsSJVNcNwP3A6cCO9AK4F3hQUgu5nkhTqqtT0m3AC+m8WyOis+jWmplVgee/dskpV5JWoiETSUT8CvjYAPFrBinzDeAbA8T3AH/Qn4uILuDKU9R1H3DfUNdpZlYrqm1i3ps2mplVoGracNWbNpqZWcU9kGhmZuOIE4mZmZXEicTMzEriRGJmZiVxIjEzs5I4kZiZWUmcSMzMrCROJGZmVhInEjMzK4kTiY0L7ce6+Nw9u2iv4G0mzKqVE4mNC9nvdTCzkeVNG62mVdv3OphVI/dIrKY9/7VLuGzRTCZPzP1PffLECaxcNJPnv35Jma/MrHY4kVhNq7bvdTCrRh7asppXTd/rYFaN/H0kZmbm7yMxM7PycSIxM7OSOJGYmVlJnEjMzKwkTiRmZlYSJxIzMytJzS3/ldQB/Lpf+APAb8pwOaPN7ao+tdq2Wm0X1G7b+rfr/IiYXkxFNZdIBiJpT7HroyuZ21V9arVttdouqN22jWS7PLRlZmYlcSIxM7OSjJdEsrncFzBK3K7qU6ttq9V2Qe22bcTaNS7mSMzMbPSMlx6JmZmNEicSMzMrSVUmEkn3SWqX9HIm9jFJuyS9JOl/SJqS4u+T9L0U/5mkv8iUWZziLZI2SdLYt6avEWzbjyUdlPRies0Y+9a8dy3nSfpfkg5I2i/pyyk+TdJOSYfSz6mZMuvSfTkoaXkmXlH3bITbVrX3TNI56fx3JN3Zr66qvmdDtK2a79mnJe1N92avpH+fqWt49ywiqu4FfAr4OPByJvYC8O/S8ReA29LxjcD30vEMYC8wIf2+G/gEIGAHcGkNte3HQEO525Ou5Vzg4+n4LOBfgQXAPwJrU3wtcHs6XgD8DJgEzAV+CZxWifdshNtWzffsTODPgb8D7uxXV7Xfs8HaVs337CJgZjq+EHij2HtWlT2SiPgJ0NkvPB/4STreCfxVOl4APJvKtQO/BRoknQtMiYhdkfuXewBYNbpXPrSRaNvoX+XwRMSRiPhpOj4OHABmASuBLem0LfT++68EHomI7oh4FWgBGivxno1U28b0ogsw3HZFxO8i4n8Dfb5+shbu2anaVmmKaNe+iDic4vuByZImFXPPqjKRnMLLwGXp+ErgvHT8M2ClpDpJc4HF6b1ZQFumfFuKVaLhti3ve6m7/Z/KPZyQJ6me3CehZuCDEXEEcv8nINergtx9eD1TLH9vKvqeldi2vGq9Z6dSC/dsKLVwz/4K2BcR3RRxz2opkXwBuFHSXnLdut+n+H3k/iH2AP8M/F+gh1yXrb9KXQs93LYBXB0RHwX+bXpdM5YXPBBJfwT8d+ArEXFssFMHiMUg8bIbgbZBdd+zU1YxQKza7tlgqv6eSVoI3A5cnw8NcNqg96xmEklE/CIilkXEYmArubFnIqInIr4aEYsiYiXwfuAQuT/AszNVzAYOU4GKaBsR8Ub6eRx4mDIPn0iaSO5/3N+PiB+k8FupG50fAmlP8Tb69qzy96Yi79kIta3a79mp1MI9O6Vqv2eSZgOPA9dGxC9TeNj3rGYSSX61hKQJwHrgv6bfz5B0Zjr+NNATEa+kLt5xSRen7ui1wBPlufrBDbdtaajrAyk+EfgP5IbHyiL9+94LHIiIf8q8tR1YnY5X0/vvvx1oSuO1c4F5wO5KvGcj1bYauGcDqpF7dqp6qvqeSXo/8CSwLiL+T/7kou5ZKasEyvUi96n8CPAuuex5HfBlcqsU/hX4B3qf2q8HDpKbePoRua2S8/U0kLvxvwTuzJep9raRW2WyF/g5uUm0b5NWBpWpTX9Ormv8c+DF9PoMcA65xQKH0s9pmTI3p/tykMyKkUq7ZyPVthq5Z6+RWyjyTvrf7oIaumd/0LZqv2fkPpT+LnPui8CMYu6Zt0gxM7OS1MzQlpmZlYcTiZmZlcSJxMzMSuJEYmZmJXEiMTOzkjiRmJlZSZxIzMysJP8f+b91pAR88HUAAAAASUVORK5CYII=\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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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