05.09_Pandas_Pivot_Tables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pivot Tables\n",
    "\n",
    "The pivot tables takes simple column-wise data as input. and groups the entries into a two-dimensional table that provides a multidimensional smmarizetion of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can think of pivot tables as essentially a **multidimensional** version of `GroupBy` aggregation.  \n",
    "That is , we split-apply-combine, but both the split and the combine happen across not a one-dimensional index, but across a two-dimensional grid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motivating Pivot Tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:24:16.453192Z",
     "start_time": "2019-10-29T08:24:14.269239Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "titanic = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:24:16.548473Z",
     "start_time": "2019-10-29T08:24:16.457624Z"
    }
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.45</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch   fare embarked   class  \\\n",
       "886         0       2    male  27.0      0      0  13.00        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.00        S   First   \n",
       "888         0       3  female   NaN      1      2  23.45        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.00        C   First   \n",
       "890         0       3    male  32.0      0      0   7.75        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivot Tables by Hand\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:24:16.579974Z",
     "start_time": "2019-10-29T08:24:16.553542Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: survived, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby('sex')['survived'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:24:16.627666Z",
     "start_time": "2019-10-29T08:24:16.584963Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
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      ],
      "text/plain": [
       "            mean                    \n",
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(['sex', 'class'])['survived'].aggregate(['mean']).unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivot Table Syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:24:16.693002Z",
     "start_time": "2019-10-29T08:24:16.631735Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
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       "    </tr>\n",
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       "      <th>sex</th>\n",
       "      <th></th>\n",
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       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
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      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "titanic.pivot_table('survived', index='sex', columns='class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-level pivot tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:24:16.741012Z",
     "start_time": "2019-10-29T08:24:16.696261Z"
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       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:24:16.837374Z",
     "start_time": "2019-10-29T08:24:16.744693Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
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       "      <th>Third</th>\n",
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       "      <th>sex</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.423729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.133663</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "class               First    Second     Third\n",
       "sex    age                                   \n",
       "female (0, 18]   0.909091  1.000000  0.511628\n",
       "       (18, 80]  0.972973  0.900000  0.423729\n",
       "male   (0, 18]   0.800000  0.600000  0.215686\n",
       "       (18, 80]  0.375000  0.071429  0.133663"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = pd.cut(titanic['age'], [0, 18, 80])\n",
    "titanic.pivot_table('survived', index=['sex', age], columns='class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:24:17.044164Z",
     "start_time": "2019-10-29T08:24:16.840863Z"
    }
   },
   "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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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th colspan=\"2\" halign=\"left\">First</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Second</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th>(-0.001, 14.454]</th>\n",
       "      <th>(14.454, 512.329]</th>\n",
       "      <th>(-0.001, 14.454]</th>\n",
       "      <th>(14.454, 512.329]</th>\n",
       "      <th>(-0.001, 14.454]</th>\n",
       "      <th>(14.454, 512.329]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</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 rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.318182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>0.914286</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.391304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.178571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.098039</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.192308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class                      First                             Second  \\\n",
       "fare            (-0.001, 14.454] (14.454, 512.329] (-0.001, 14.454]   \n",
       "sex    age                                                            \n",
       "female (0, 18]               NaN          0.909091         1.000000   \n",
       "       (18, 80]              NaN          0.972973         0.880000   \n",
       "male   (0, 18]               NaN          0.800000         0.000000   \n",
       "       (18, 80]              0.0          0.391304         0.098039   \n",
       "\n",
       "class                                        Third                    \n",
       "fare            (14.454, 512.329] (-0.001, 14.454] (14.454, 512.329]  \n",
       "sex    age                                                            \n",
       "female (0, 18]           1.000000         0.714286          0.318182  \n",
       "       (18, 80]          0.914286         0.444444          0.391304  \n",
       "male   (0, 18]           0.818182         0.260870          0.178571  \n",
       "       (18, 80]          0.030303         0.125000          0.192308  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can apply the same strategy when working with the columns\n",
    "fare = pd.qcut(titanic['fare'], 2)\n",
    "titanic.pivot_table('survived', index=['sex', age], columns=['class', fare])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional pivot table options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The full `pivot_table()` signature is as follow:  \n",
    "```python\n",
    "titanic.pivot_table(  \n",
    "    values=None,  \n",
    "    index=None,  \n",
    "    columns=None,  \n",
    "    aggfunc='mean',  \n",
    "    fill_value=None,  \n",
    "    margins=False,  \n",
    "    dropna=True,  \n",
    "    margins_name='All',  \n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `aggfunc` keyword controls what type of aggregation is applied, which is `mean` by default.As in the GroupBy, the aggregation specification can be a string representing one of serveral common choices.(e.g., `sum`, `mean`, `count`...) or a function that implements an aggregation(e.g., `np.sum()`...)  \n",
    "Additionally, it can be specified as a dictionary mapping a column to any of the above desired options\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:24:17.138205Z",
     "start_time": "2019-10-29T08:24:17.051303Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">fare</th>\n",
       "      <th colspan=\"3\" halign=\"left\">survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</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>female</th>\n",
       "      <td>106.125798</td>\n",
       "      <td>21.970121</td>\n",
       "      <td>16.118810</td>\n",
       "      <td>92</td>\n",
       "      <td>71</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>67.226127</td>\n",
       "      <td>19.741782</td>\n",
       "      <td>12.661633</td>\n",
       "      <td>46</td>\n",
       "      <td>18</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              fare                       survived             \n",
       "class        First     Second      Third    First Second Third\n",
       "sex                                                           \n",
       "female  106.125798  21.970121  16.118810       92     71    73\n",
       "male     67.226127  19.741782  12.661633       46     18    48"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def sum_func(x):\n",
    "    return np.sum(x)+1\n",
    "\n",
    "# Notice here that we've omitted the `value` keyword,\n",
    "# when specifying a mapping for aggfunc, this is determined automatically\n",
    "titanic.pivot_table(index='sex', columns='class',\n",
    "                    aggfunc={'survived':sum_func, 'fare':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:24:17.203094Z",
     "start_time": "2019-10-29T08:24:17.141700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">fare</th>\n",
       "      <th colspan=\"3\" halign=\"left\">survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</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>female</th>\n",
       "      <td>106.125798</td>\n",
       "      <td>21.970121</td>\n",
       "      <td>16.118810</td>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>67.226127</td>\n",
       "      <td>19.741782</td>\n",
       "      <td>12.661633</td>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              fare                       survived             \n",
       "class        First     Second      Third    First Second Third\n",
       "sex                                                           \n",
       "female  106.125798  21.970121  16.118810       91     70    72\n",
       "male     67.226127  19.741782  12.661633       45     17    47"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(index='sex', columns='class',\n",
    "                    aggfunc={'survived':sum, 'fare':'mean'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to compute totals along each grouping, it can be done via `margins` keyword.(in probiblity theory, it is named **edge distribution rate**)  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:24:17.315175Z",
     "start_time": "2019-10-29T08:24:17.205859Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third       All\n",
       "sex                                           \n",
       "female  0.968085  0.921053  0.500000  0.742038\n",
       "male    0.368852  0.157407  0.135447  0.188908\n",
       "All     0.629630  0.472826  0.242363  0.383838"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(values='survived',\n",
    "                    index='sex', columns='class', margins=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the margin label is `all`, we can specify the `margins_name` keyword to change the margin label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:24:17.448350Z",
     "start_time": "2019-10-29T08:24:17.319091Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>margins distribution rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>margins distribution rate</th>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class                         First    Second     Third  \\\n",
       "sex                                                       \n",
       "female                     0.968085  0.921053  0.500000   \n",
       "male                       0.368852  0.157407  0.135447   \n",
       "margins distribution rate  0.629630  0.472826  0.242363   \n",
       "\n",
       "class                      margins distribution rate  \n",
       "sex                                                   \n",
       "female                                      0.742038  \n",
       "male                                        0.188908  \n",
       "margins distribution rate                   0.383838  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', index='sex', columns='class', \n",
    "                    margins=True, margins_name='margins distribution rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Birthrate Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:24:17.481625Z",
     "start_time": "2019-10-29T08:24:17.455699Z"
    }
   },
   "outputs": [],
   "source": [
    "births = pd.read_csv('data/births.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:24:17.515367Z",
     "start_time": "2019-10-29T08:24:17.487919Z"
    }
   },
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>gender</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day gender  births\n",
       "0  1969      1  1.0      F    4046\n",
       "1  1969      1  1.0      M    4440\n",
       "2  1969      1  2.0      F    4454\n",
       "3  1969      1  2.0      M    4548\n",
       "4  1969      1  3.0      F    4548"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's add a decade column, and take a look at male and female births as a function of decade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:35:30.702280Z",
     "start_time": "2019-10-29T08:35:30.299194Z"
    }
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births['decade'] = 10* (births['year'] // 10)\n",
    "births.pivot_table(values='births', index=decade, columns='gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We immediately see the male births outnumber female births in every decade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:35:31.823002Z",
     "start_time": "2019-10-29T08:35:31.455868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set()\n",
    "births.pivot_table('births', index='year', columns='gender', aggfunc=sum).plot()\n",
    "plt.ylabel('total birhs per year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further data exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning the data via robust **$\\sigma$-clipping opertion**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:35:42.183322Z",
     "start_time": "2019-10-29T08:35:42.158652Z"
    }
   },
   "outputs": [],
   "source": [
    "quartiles = np.percentile(births['births'], [25, 50, 75])\n",
    "mu = quartiles[1]  # the sample's mean\n",
    "sig = 0.74 * (quartiles[2] - [quartiles[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:35:43.048676Z",
     "start_time": "2019-10-29T08:35:43.011178Z"
    }
   },
   "outputs": [],
   "source": [
    "births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:20:47.981121Z",
     "start_time": "2019-10-29T08:20:47.922158Z"
    }
   },
   "source": [
    "Next, set `day` column to integers, it had been a float previously"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:35:43.817173Z",
     "start_time": "2019-10-29T08:35:43.802812Z"
    }
   },
   "outputs": [],
   "source": [
    "births['day'] = births['day'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:35:44.618512Z",
     "start_time": "2019-10-29T08:35:44.587250Z"
    }
   },
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>gender</th>\n",
       "      <th>births</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>decade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1969-01-01</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>4046</td>\n",
       "      <td>2</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-01</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>4440</td>\n",
       "      <td>2</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-02</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>4454</td>\n",
       "      <td>3</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-02</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>4548</td>\n",
       "      <td>3</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-03</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>4548</td>\n",
       "      <td>4</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            year  month  day gender  births  dayofweek  decade\n",
       "1969-01-01  1969      1    1      F    4046          2    1960\n",
       "1969-01-01  1969      1    1      M    4440          2    1960\n",
       "1969-01-02  1969      1    2      F    4454          3    1960\n",
       "1969-01-02  1969      1    2      M    4548          3    1960\n",
       "1969-01-03  1969      1    3      F    4548          4    1960"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we can combine the day, month and year to create a Data index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:35:47.281312Z",
     "start_time": "2019-10-29T08:35:47.239214Z"
    }
   },
   "outputs": [],
   "source": [
    "# 19690101\n",
    "births.index = pd.to_datetime(10000 * births.year + \n",
    "                              100 * births.month + \n",
    "                              births.day, format='%Y%m%d')\n",
    "births['dayofweek'] = births.index.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:35:48.791560Z",
     "start_time": "2019-10-29T08:35:48.756327Z"
    }
   },
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>gender</th>\n",
       "      <th>births</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>decade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1969-01-01</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>4046</td>\n",
       "      <td>2</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-01</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>4440</td>\n",
       "      <td>2</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-02</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>4454</td>\n",
       "      <td>3</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-02</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>4548</td>\n",
       "      <td>3</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-03</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>4548</td>\n",
       "      <td>4</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            year  month  day gender  births  dayofweek  decade\n",
       "1969-01-01  1969      1    1      F    4046          2    1960\n",
       "1969-01-01  1969      1    1      M    4440          2    1960\n",
       "1969-01-02  1969      1    2      F    4454          3    1960\n",
       "1969-01-02  1969      1    2      M    4548          3    1960\n",
       "1969-01-03  1969      1    3      F    4548          4    1960"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "births.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:38:45.315330Z",
     "start_time": "2019-10-29T08:38:44.869243Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "births.pivot_table('births', index='dayofweek', columns='decade'\n",
    "                  , aggfunc='mean').plot()\n",
    "plt.gca().set_xticklabels(['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun'])\n",
    "plt.ylabel('mean births by day');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the mean number of births by the day of the year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:41:03.411916Z",
     "start_time": "2019-10-29T08:41:03.385136Z"
    }
   },
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>gender</th>\n",
       "      <th>births</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>decade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1969-01-01</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>4046</td>\n",
       "      <td>2</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-01</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>4440</td>\n",
       "      <td>2</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-02</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>4454</td>\n",
       "      <td>3</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-02</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>4548</td>\n",
       "      <td>3</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-03</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>4548</td>\n",
       "      <td>4</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            year  month  day gender  births  dayofweek  decade\n",
       "1969-01-01  1969      1    1      F    4046          2    1960\n",
       "1969-01-01  1969      1    1      M    4440          2    1960\n",
       "1969-01-02  1969      1    2      F    4454          3    1960\n",
       "1969-01-02  1969      1    2      M    4548          3    1960\n",
       "1969-01-03  1969      1    3      F    4548          4    1960"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:44:57.126284Z",
     "start_time": "2019-10-29T08:44:57.071310Z"
    }
   },
   "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></th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>4009.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4247.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4500.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4571.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4603.625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       births\n",
       "1 1  4009.225\n",
       "  2  4247.400\n",
       "  3  4500.900\n",
       "  4  4571.350\n",
       "  5  4603.625"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births_by_day = births.pivot_table('births',\n",
    "                                   index=[births.index.month, births.index.day])\n",
    "births_by_day.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:49:58.462389Z",
     "start_time": "2019-10-29T08:49:58.371287Z"
    }
   },
   "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>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>4009.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>4247.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>4500.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>4571.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>4603.625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              births\n",
       "2012-01-01  4009.225\n",
       "2012-01-02  4247.400\n",
       "2012-01-03  4500.900\n",
       "2012-01-04  4571.350\n",
       "2012-01-05  4603.625"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births_by_day.index = [pd.datetime(2012, month, day)\n",
    "                       for (month, day) in births_by_day.index]\n",
    "births_by_day.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-29T08:50:58.233570Z",
     "start_time": "2019-10-29T08:50:57.056587Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d168700ac8>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l5YUmAOqGtbCLddnInIb66Zi7b1xAKCzz0Skr84tNaNVKAMoKjCgVEifre6btdwvCRPU6fFTXTf41mTSgdjqdPPnkk/j9flwuF9u3b+e6667j7/7u73jqqae45ZbBaQUrVqygoaGBpqYmwuEwO3fuZMOGDRQVFaHVajl48CAAO3bsYMOGDZM+aUEQBEGYSmdb+gmFZdq63dP6e9451Mor79VN2+PHOnzEmI1aTAYN9e2JddR9AwH1dExIjMnN0rNlYLXEyrLBTiIatZKyAiMnREAtXES276nn2e3HJn180pKPa6+9lqNHj3LbbbcRiUS488472bVrF36/nyeeeCK+3+c//3m+8IUv8MQTT/CVr3wFv9/Pxo0buemmmwB46qmneOSRR3C5XFRWVrJt27ZJn7QgCIIwdbr7vfzhoxYkSeILN8y/0KdzQbRYo7euY5P6psufD7ZitXu5eV0p+jT1lD9+j8NHRZEp/rMkSZQXmqhp7efnu07TZffyjc+vxD6Qic+c5pUMb7y8BLVKwfrK/ITtFcUm/lTVGu8CIggXkizLnGy0EwhFCE+yc0lK3azvv/9+7r///oRtjzzyyKj7rl+/ntdee23E9kWLFvHyyy9P4hQFQRCE6VLT2sf3f3mYiCwjAZ/ZOA/NwK35i4E/GOaXfzjLtZcVjbuEdipeea8Oty/Etk0LE7bLskxzlwuIrjKYjCzLHDpr470j7fS7A/ztbUvJN+uTHufyBuno8QBwtLYnvqLgVHKOUgdeXmjk0Fkbu4+0A2B3+LE7/SgkCeM014yrlAo+uaZkxPZ5BUZC4QgtVhfzCs/t7yoI56qz1xMvgwoEJxdQi8tCQRCES9jZlj4isswd15YjE/1iSZU/GMYfCE/fyQG/+uNZ9h7rYPfhtnN+rI9PWTk2So1kr8OPxx9dbMyaQob64BkbP9p+nPYeN3ann8dfPEhDhyPpcbWt0bILhSRx8Kwtyd4TFwxF/x4GfWLme92SPNZX5vH56yoA6Ohx0+f0YzJoJry8+FSJBdHDS1EE4UI41WSP//9ASATUgiAIwgQ5PUG0aiXL5mUD0N6Teg3xMy9X88wr1RP6fR+e7Eop+AQ4cKKTPdUdaNVKTjb2IsuT7/Tk8QWx9nmj3S2GPU7zQLmHQadOqeSjprUfjUrBE/eu5+G7V6NVK/nfN04lPa62rR+lQuKKZfkcr++Z8osRpye6xHjGsIDabEzjS5sr+cRA2UVHjwe7yz+t9dPJmI1pmI1p1Kf4WhCE6XSqcTCg9gcn974UAbUgCMIlzOkJkqFXk5elRyFJdHSnlqFus7k41WTndJMdpyeQ0jG+QIjn3jjJS+/UJt1XlmVeeqeWiiITt19TTo/DP6Hs+XCxko5wRMbpTjzfli4XErCyIgdbnzdp4N5idVKca0ClVJBn1nPNqkLaut1Jn4fa1j5K8zJYX5lPIBTheMPUTsqLB9S60cs4MvRq0tNUdAzc3s6a5vrpZBbOyaK+XQTUwoUVicicbrbHL0SDouRDEARBmCinN0CGXo1apcCSpUs5Qx2rx5WBYyl2azjd1EcoLHO2tQ9HkuCzu99HvzvA+qX5LCuPZs9PDskiTVRz12C/3F5nYmu8ZquLXLOe4lwDXn8Ylzc45uPE6q1Lcwf71M4vzgSiGeixhMIRGjqdzC82saDEhEGnZt+xzskOZ1Sx8x6eoY6RJImC7HQ6ut30ufzT2jIvFQtKs7DavSlfkAnCdGjqcuL2hVhRngNAICQy1IIgCMIEOT1BDAMZzcJsPe0ptI3zB8PsP97J2sW5GNM1KfduPVbfgySBLMPR2u5x960bqK0tLzSSm6nDkpnGiYbelH5PjN3pp9UazUw3DQmo7cMC6hark9JcA7mZ0XZz49VR9zh8ePwhSvIGV4grK8hApZSoaR07oG7qdBIMRagoMqFUKLhhTTFHars5O4VLcMcC07ECaoCCbD3NVidef/iClnwALCyNLnueagmQIEyH2IXw8oELdzEpURAEQZgwlycQD8AKc9Kx2r2EwuN/oVSdtuL1h7hmZRHLy7M5Xt+b9BhZljlW38OK8hyyjVoOnx0/oK5vd6BRKygaWEmvsiyb0832pL8HopPuHvzJAb7xo308+r8fUdfWT3OXi7KCaBA8NKD2+ELY+nyU5hmwZKYBg63zAsEwr+1r4ETjYCDfMlA6MjRDrVYpmZtvjE86HE3sS7uiONrSbtPlpZgMGn73bu051YYPFSv5MOjGC6jT8fqjGbgLXfJRUZKJJCHKPoQLqtXqipa9DXTqCYgaakEQBGGiYjXUAIXZ6YQjMtYkreNqWvsx6NQsLM1kRXk2Hn+IunHKHSDaPaS738ey8mxWzbdwvKEXXyA05v717Q7m5htRKqJfU5Vzs/AFwkmDr3Akwv/sPIXbG+Tz18/HoFPz+/frae9xU1mWjVIhJQTU+451ANE2bjkDGWqb3UurzcV3Xqji1T0NvL6vMb5/U5cTCSi2JC5NPL/YREOHY8wv46ZOJ2ajNt73WatR8umr51HX7uDQFHX8cHqDSBKkjxNQ52cPtve70CUfOq2KEouBk02TL+URhHPVanNRbDGg1UTbhYqSD0EQBGFC/MEwgVAk3re4MCeaDe5IUkft8gYxpWuQJIklc80oFRJHk5R9HKuPZnmXlZm5bIGFUDjC8frRSziCoQjNXc6E/sSx8oAzzeMHX28eaKKhw8HdmxZy4+UlfPLyEk412ZFlmJufQVaGFrvTB4DV7uGV9+tYXp7NojlZaNVKTAYNzV0ufvi7o7i8QRbPyaKhw0FwoJVWi9VFnlkf//KNqSg2EY7INHY6R5wTRAPx0tyMhG1XLSsgx5QWr0c/Vy5PAINOjUIauxVe4ZCA+kKXfABcvjiX2tZ+us5hwqkgTFYkItNmc1OSa0A70H/fL0o+BEEQhImI19wOZDRji5Mkq6N2DikT0WlVLCzNTFpHfeiMlYJsPTmZOuaXmEjTKMfMTDZbnYTCMuVDAmqDTk2xJX3cmuO2bjev7Wtk7eJc1i7OA+D6y4rRaaNrmM3JiwXUfmRZ5vm3TqNUSGzbtBBpIAjNzdRx8KwNu9PPVz6zjOsuKyIYisRrsJu7XJTmGUb87tjqhDWtI8/PHwzT2esZcZxCIfGJynxONvbS5/KPOG6inN7guOUeADkmHSpl9Kv/Qpd8AFyxtABJgr0DdwoE4XzqsnsIhCLRDLU6+r4QGWpBEARhQuI1twPBsVajJMeURnvP+NlC17DAbXl5Du3d7jF7ODd0ODjb2s/VywsBUCoUzC/OHDPbHCvrmFdoSti+sCSL2jbHqHXUsizzyz+cIU2j5M5PLohv16ep2HzFXErzDJiNWrIytPQ6/VjtXk4397H5ijLMxrT4/rGJiTetK6W80ERFrINHaz9uX5Aeh4+S3JEBdYZeQ2mugb3HOkecX6vNhSxDaV7GiOOuWJqPLMMHJ7pGfS4mIlq+M/7KhwqFRL5Zh06rGpFlvxCyMrQsLctm//FOIpGpqSUXhFS12qLJg5JcQ3yFWFFDLQiCIEzI4EIgg0HYnPwMTjWNP/nP6QliGHLMioro7PixstRvfdiMTqti48rC+LYFJSY6ejw43CNbptW3O8jK0I4oSVhYmok/GKap00lHj5s9Q1ZP/PBUF6eb+9i6sXzEcto3rSvlW/9nLZIkYc5Iw+70c3ogmI+de8yqBRZWVuRw21VlAJjSNeRl6ahp7aPqtBUYzEYPt3XjPLp6PfypqjVh+2gTGWPyzXrKCowcOHHuLfSG3jkYz7xCI0UD5T0Xg6uWF2B3+jnZOLEuLoJwrlqsLhSSRGFOtA+/Rq0QXT4EQRCEiRmtzdpVywpwuAMcqRm9C0ckIuP2JWao87L05Jn1HK0beUyX3cPBM1auXVUUL72AwZro0Uo4GjudzM0fmc1dUBLNFp9ssvPs9uM89csqvANLhr/6fgNz8jPYuKJwxHFDZWVoCYYiHDxrw5iuiZe5xFy2wMJ9ty9HrRrM3s4vzqSmtZ+d+5uYV2iMn8dwy8tzWF6ezY59DQklHM1WF3qtimxT2qjHra/Mo8XqomWgxd9kubzBePnOeO765AK+/rkV5/S7ptLKihzSNEoOjfGaE4Tp0mp1kZ+tj7/ftWqlWHpcEARBmJj4QiBDVtZbNi+bbKOW3UfaRj3G7QsiyyN7Ha8oz+Z0U9+I5bT3HO1AIUncsKY4Yfvc/Aw0KsWIgNoXCGHt9TBnlPIIY7qGgmw9bxxopK3bTUSG+g4H3f1erH1erlpWgEIx9oQ8GJyId7LBzsKSzHjt9HjmF5tweaPlHluuKhv3mC/cMJ9gMMI7hwaz1M1dTkrzDGMet25JHiqlxJ6jk5+cGJHlaClOkpIPiLb5S9Ooku53vqhVCsoLjdQn6RQjCFOtxeqi2DJ4t0arVoqSD0EQBGFinJ4gSoWETjuYjVUoJK5eUcjJRjtd9pG11INB+MiAOhSOcLIp8bZ9q81FUU56vF1cjEqpoLzINCKgbrG6kBm93hhgYUkmgWCERaXRHsa1rf3xxxgrczxUljF6HhFZZmFp8v0B5g88bnmRkaVl5nH3zcvSU1aYEV/VMRKRabW6KMkdfTwQLbm5bIGF/cc7CQTDvHu4jSd+cXBC/and3tEvdGaKeYUmWmyuhFaKR2q7eebl6hEXaYIwFTy+0Ig5EVq1Uiw9LgiCIEyM0xPAoFePyJxevbwQhSSxt3pk54XhExlj5pdkkqZRjqijbu92J/Q+HmphSSYtVhdu3+BS382xeuNROmkArF6YS1aGli/evIg5+UZqW/s429KHXquKLwIzHnPGYNnFwhQCcIC8LB2fumIOd9+4MKWM9pI5Zho6HHh8QTp7o10ExhpPzDUri/D4Q7y+v5Ff/6mGs6398QVYUjHWhc5MUV5kRJahsSPaTcXjC/H8W6c5UtvNro+aL/DZCbNR50CrxsIh8wk0aiV+0eVDEARBmAinJ5hQ7hGTlaFlXpGR06O0tRutTASiGefKMjPVdT3xzGogGKan30dB9uiBbmWZGRn41xeq+GBgUl5TpxODTj1mj+TKMjP/9++uJDdLz+IyM7XtDk439bGgJHPc/ssxpnQNCknCoFMnfJGOR5Iktm4oHzNrPtySuVnIMpxp7ot3MhmthGWohaWZ5Jv1vHGgKT4htN+deiu90SaYziSxji6xJed37G3A6Q4wr9DIWx800evwXcjTE2ah2GfZ0EnMaRolflFDLQiCIEC0Dnn3oVY+Pm2lbZye0k7v2F0h5heZaOx0jqgnHG0iY8yK8hzsTn98cl2X3YsMFIyRoS4vMvGVzywjTaPkp6+fpKa1j+YuJ3PGqTceaslcM/5AGGufN6VyD4iWtOSY0lg8Jyul3zEZ5UUmNGoFxxp62fVRM3PzM5JmzyVJ4pqBLihXLS8AoM81sgPKWMb7u8wEBl106ee6NgctVhd/PtjKxpWFfPnWSiIyvPJe3YU+RWGWid0ZG7qyqFatJDjJEqOUAuq7776bW265hS1btrBlyxaOHj3K66+/zl/8xV9w44038stf/jK+7/79+9m8eTM33ngjTz/9dHz7qVOn2Lp1K5s2beLhhx8mFBp7yVlBEARh8nZ92Mz//eVBfvzqcZ76zeEx93MNWXZ8uPnFmYQjMg0diUt9x7I6oy1vvaw82oLuaG20W0NsxcXhnTSGWjXfwj/ftZr0NBVvHGiirdtN6SgdPkazuGyw5V2qATXAV+9Yzl1DelVPNZVSwYKSTPYcbcfW52PzFXNTCt6vW13Mg3ddxs3rSgHoH2Oxl57+kdla58DfJdnCLhez8kIjde39/PfrJzHo1WzdWE5Opo7rVxfx4UlrwpLxgnCu4p9laYMTdDVqxfQt7CLLMo2NjezYsSP+X35+Pk8//TS/+tWvePXVV/ntb39LbW0tPp+Phx56iGeffZY333yT48eP89577wHwwAMP8Oijj/L2228jyzIvvfTSpE5YEARBGJssy3x4ysqSMjM3rC6m3xUYs6f0WCUfEF1KG6B2WOcFpyeIRq2IL9M7lCldQ1mBMV5H3dnjQWL8gBqiC8pcs6qI6roewhE5aXlETG6WjkyDBq1ambRGeaiC7HSM6dNbGrFkjplwRKbYYmAlPmEqAAAgAElEQVTF/JyUjokF4qb0aLnL8Ay1yxvkJ6+d4IEf7+dwjS3h32Z6yQdEM/tOT5BWm4sv3rwofnFwzaoiIrLMnuqpWaJdECA6kReiiz/FTGvbvPr6egDuuecebr31Vn7xi1+wf/9+PvGJT5CZmYler2fTpk3s2rWL6upq5syZQ0lJCSqVis2bN7Nr1y7a2trw+XysXLkSgK1bt7Jr165JnbAgCIIwthari65eD9esLonXCMeCraFC4Qgef2jMDLVBp6YgW09Na2JAHe11PHbQtqI8m/p2Bw5PgI5eD9mmtPgKZOO5fnUxKmU0i5tqrbIkSWxYUcjVywviy2lfLFZUZKNUSNx2dVlKtd1D6bRKNCpFQg21xxfkO89/TNVpK0qFxMmGxPp2pydAmkaJWnVxPQ8TMX9gwZwNKwpYWTF4EZKXpWfJ3Cz2HG0XqykKU8btC6HTqlAqBt8zWs3k2+YlbUTpcDhYv3493/zmNwkGg2zbto2bb74Zi8US3yc3N5fq6mqsVuuI7V1dXSO2WywWuromtsxqdnbq2YeZymJJ7Utkppmt44qZzeObzWOLmW1jfOvjFhQKiSuWFXCyIdrCTqFRjRhnbJJXQV7GmM/BsgoL+6rbyc42xPs7+0MRsozaMY+5bt0cXt3bwLFGO7Z+H3MKjCk9xxZLBtetKeWD4x1Uzs9N2k865ktbL54FSoayWDL4zWN/Mel+z2ZTGr6gHH+sZ18+Sq/Dx+P//1X8+g+naeh0JjyvgbCMyTD23+ViNfR8LZYMvnvvFSwuM4+4CLt1QwVP/PxjWnq9rFmcd75P85zMtL/JRMzksYXlaG/7oWPIMukINExuxc6k7/RVq1axatWq+M+333473/ve9/jbv/3b+DZZlpEkiUgkklAnlmz7RPT0uGb1lanFkoHN5rzQpzHlZuu4Ymbz+Gbz2GJm2xhlWWb3wRYWz8nCZNBCOJppaWrtw6RNDFBiEwelcGTM56AkR4/bG+To6U6KLdGkRm+/l/Q09ZjHGNTRsoXtu2txeYNUFBpTfo4/c/VcNq0ppqcntRUDZ8Lfb7JnZ9Cp6Rp4Hg4caeWtA43ceHkJuRka5uQaeH1/I82tdnRaFZGIzNGzNiqKTBf98zHUaH+/wqw0+vtG9j+fl5eOUa9m5/t1zMkZv4ToYjITXqOTNdPH1tPnJU2jTBhDOBiedKyZ9N5QVVUVBw4ciP8syzJFRUXYbIP1WzabjdzcXPLz81Pa3t3dTW5u7qROWBAEQRhdi9WFrc/H2kXRz9dYnbDDPbJbhHucyYUx8wfqqIcuvuL0BEf0oB7uprWl9Dr8BIKRMXtQj0atUo7ZLu9Sk5muiddQv/ROLWajltuuLgOiE0ZlebDF3NmWPvrdAS5fPHu/V1VKBasX5XKsoSd+S77X4SMygcVvBGEoty+IIS0xrzza3JBUJQ2onU4nTz75JH6/H5fLxfbt2/nBD37AgQMH6O3txev18oc//IENGzawYsUKGhoaaGpqIhwOs3PnTjZs2EBRURFarZaDBw8CsGPHDjZs2DDpkxYEQRBGarVFM5qxCYWmgQlqDs/IgNo30BoqTTP2F4glU4clMy1hsZZkNdQAyyuy4xMRC5JMSBRGZzJo6Xf7CYYiNHQ4WF+ZHy8fmVdoRJKgpiUaUH902opGrWBFeWqTH2eqVfNzCAQjnGy009Tp5IEf7+fQGVvyA4UZz+uf+s5wbm9wREJBO87nYTJJSz6uvfZajh49ym233UYkEuHOO+9k9erVfO1rX2Pbtm0Eg0Fuv/12li9fDsATTzzBV77yFfx+Pxs3buSmm24C4KmnnuKRRx7B5XJRWVnJtm3bJn3SgiAIwkhWuxcJyDHpgOiXg1atHDVD7QtGv6DGC6glSWJFRQ67D7fjD4RRKCR8gXDSDLVCkth8xVxe/MMZinNn//yX6ZBp0OD1h6lt6SMckROWR9ZpVZTmZlDT2kc4EuHgGSsrK3LOKRiYCRaVZqHTKjlcYyMYiiDLg60ZhdmrqdPJd39exWc2lnPTQEvJ2PZQOEL5wGTWiXL7QiMD6nPIUKc0W+L+++/n/vvvT9i2efNmNm/ePGLf9evX89prr43YvmjRIl5++eVJnqYgCIKQjK3Pi9moTej0YExX0z9aQB3PUI//NbCyIoc/VbVysrGXuQVGILXlrdcvzefyxbkXXfeNmSLWOu/g6egE/uGdTyqKTew52h5dUdAT5PJFs7fcI0alVLBsXjaHztriy7KL3tSzX9UZK+GIzEvv1pJjSmPNolwCwTD//vJR0tPU/Otfr5vwY0ZkGbcvSHra1AXU4pNOEARhlrD2ebFk6hK2mdK1o2ao/QMBdbIvkAUlmei0So7UdsdX40t18RARTE9epiFaVnPwTLScI3fY33VpmZlAKMLO/U0Y9WqWzcse7WFmnZXzc3D7ondXMg0aekVAPesdq+thXqGRiiIT/73zJHVt/bx7uI0+V4DOXg/hyMT7Rvv8IWSZUWqoJ/+ZNbl+PoIgCMJFx2r3smrYIiLGdA1dvSO7JvhTqKGGaFC8tCybo3U9rB1oVzZTl7eeSUyGaIa6tqWP8kLjiDaCKypy+Le/vzIaFOhUqFWzu9wjZvm8bFRKicsWWAgEI3T3ey/0KQnTyO7002x1cfs15Vy9vIDHfn6Qf3+5GhhY1TAYwWr3UpCdPqHHHWvFV805lE2J9IEgCMIs4PWHcHqCIzLUxnTN6CUfwTAalSKlfs8rK3JwuAP88o9nATDM4NX4ZgqTYfA5LhljoZtMg5asDO0lE0wD6NPUPHT3au7etBCzUUuvY/QM9Uenuqg6bT3PZydMtWP10QnRy+dlk6HXcP9nVyDLMi5vkDuuqQCgo2dkwiCZ2F0OUfIhCIIgJLD1RTN1uVmJXTWMejVub3DEbVFfIJzyJLbLF+dy87pSQuEIaRolZtHabtoZdGqUAxc7JWJiZ4K5+UbS09SYjWl4/CF8gZEdIF56t5Yfv3qcD09ObBE54eJSXdeD2ailyBLNQOeb9TzwhVX81S2LuWJpPjC5iamDbUMTCzXSpntSoiAIgnBxs9oHAuoRNdQaZKL9ozMNg4GwPxBKORujUiq449oKbr+mnHBEFrXR54FCkjCma7A7/ZSKgHpUsZ7lvQ4/hTmD4YzDHaDX4UerVvI/O09iTNeweE7WhTpNYZLCkQgnGntZvyQvYTHA0ryM+CTdrAztpDLULt9AQJ0mSj4EQRCEIWIZ6tFKPmDk4i6+QHjCy2JLkiSC6fMo06BBkoivUikkit0p6XX6ErY3dkZXvrt3SyVpGiUHTnSe93MTzp3d6ccfCMe7C42mIFs/yQz1QMnHFLbNE5+MgiAIs4C1z4tBp0Y/bNb6+AH1pVN7OxNZMnWU5mXM+v7Sk2U2pgGMqKNu7HQgAQtLMimyGOgcZVKucPGLtUQcb/XUAnM6HT0e5AmumOmOZ6gTPy81KgXJZ5WMTpR8CIIgXMQcngC/+XMNKytyWLMwd8xJhFa7l9ws3YjtsYB6+MREfzCMTiu+Ai5md96wgAyjDsLhC30qF6WsDC0SA0uQR6IT1YzpGpo6neSZ9ei0KvLNOg7XdF/oUxUmIR5QG8YJqHP0+AJh7E5//AIrFW5viDSNcsQdN0mSUE8ySy0y1IIgCBexD0508cGJLv5rxwm+88LHBIKjB1dWu3dE/TSAcYzlx/0iQ33RM6ZryBVLt49JpVRgTI/2on7742b+8cf76e730tjpZG5BtMY235yO0xOMZySFmaNvIKDOHC9DPdAub6J11KMt6hKjEQG1IAjC7HOsrpt8s567b1xAc5eLQzW2EfsEQ2F6nb4R9dMQ7TOtUStGKfkIndOMdkG4GJiNWuwOH3urOwiEIvz6TzXYnX7m5sUC6ugFiSj7mHl6nX40KsWIsoyhCrOjf9+J1lG7vcERHT5iNKrJFX2IgFo4L7z+ENY+0YBfECbCFwhxpqWPFRXZbFxVRLYxjf3HRk6wenVPA7IcXdVwOEmSMOpH9qKeSNs8QbhYmTPSqGnrp6PHQ1aGNl7eEZvIlmeOXmR2TqIThHBh9bn8ZGZoEzp8DGdM16DXqmjvnlhA7RIZamGm2rm/ke/87GOCoYkvESoIs4Usy9F6zxQn0JxstBMKyywvz0EhSaxfms+Jxt54bSFEFz5468NmrllZSGWZedTHMaVrcI5SQy0CamGmyzJqCQQjKBUSX719OUqFhASU5kU7o1gydSgkKaUMdSQysYltF7M+l5/a1v4LfRrnxO70j1s/DdGEwZz8DOo7HBN6bLc3NKLDR4x2kgsliYD6EvDmB01862cfTdnjhcIRGjsn9uLt7PXg8Yeoa5vZb3BBmKyq01b+5X8/5h+e3c/e6o6Ujqmu60GnVTK/2ATAlUvzkWX4YKAN2JHabv5rxwmKLOl8/vr5Yz6OVqPEHxy8mA2FI4TC8oTb5gnCxcacEZ2ItmxeNqV5GWxaW8rSednx17ZKqcCSmUZXkoA6EAzz4E8O8MaBxmk+4+kXicj88KWjPPHLQzQNtBCciexOP1nG5ItIVRSZaLG6Rl3gZyxuXxDDGKUkGvXkQmMRUF8C2mwuWqyuKbv63rG3gX99voru/tRLOGJtjU409k7JOQjCTBKRZf5n50mC4QjGdA2Hzo6sgx5OlmWO1fdQOdccn4meZ9ZTUWTitf2NfPfnVTzzcjUWUxpf/czycW9TqpQKQuHBgNoXiE5sFDXUwkyXbYoG1OuW5AFw+zXlfO2zKxL2yTfrk2ao9x3roLvfx6Gz09sRRJZlGjsdE27zNhHvHm6j2epCpZJ47o1TCe/9mUKWZfpcyTPUAOVFJmQZGjpSu3iQZXncDPWiOaPf6UtGBNSXAK8/jCwPrgx0LlzeIH8+2IoM1LennqWONd4/3iACauHS0+8KEAhF+OSaYi5flMvpJvuY3Tpi2rvd2J1+ls3LTtj+uesruGy+BZVSwQ2ri3no7tXkjDIZcajhAbV/IKAWJR/CTLeiPJu7PrmA1QstY+6Tn62ny+4ds9QqEpF5+6MWINrD2uNLPdM5UfuOdfKd56s4keJ3oSzLHDjeyc93nSYcSR4YOzwBtr9fz+I5Wdy7uZJWm4s3P2g619M+75zeIKGwPG6Hj5jyomi9fG2Kd8D9wTARWUY/RtvQq5cXpH6iQ4iA+hLgGQikh8/yn4w/VbXgC4RRKqT4alTJBENhnJ4gOq2K5k7niPZdgjDbxe7m5Jh0rCjPJhCKcLq5b9xjYv8+fMnk8kITX9q8hAfvuow7P7kgpQk0KqVEMDwYTPgGgnnRNk+Y6TRqJdevLh53Bc88s55gKEKvwzfqvx86a8Pa5+W6y4qQZTjbOv57c7JC4Qiv728A4MNTXUn39wfD/PvL1fz3zpPsPtKeUgZ2/7FOPP4Qd31yAasWWFizKJc3DzQlzLuYCqcae3lrf8OEyiwmwu5I3oM6Jj1NTUG2PuWS0thcLrVqakNgEVBfAjz+6At++KSkifL6Q/yxqpXLFlgozcugMcVJALE38ieW5CEDpxrt53Qe4zne0EPbBGf7CsJkyLLMj7Yfi9czj6e7P/pFbslMY2FpJhqVgmN1PfF/33esg/9981TCMWea7WQb05Jmn1OhUioIJ5R8RD8TREAtXAoKBlrntdlG/27408FWcjN13HFNBSqlgtNNqX1HybKM3elPuXzjgxNd2Pp85GbqOHy2O2kpRtVpK9V1PXzqirkAKZ1XQ4eDHFMahTnR/sx3XFNOOCKzfU99SueYql//uZZnX6nmH360n+q6qS+TsbsGAuoUaqghWkdd19af0oTv0EByQSUCamE84UiEdw+38cQvD8XbZMUC6v5RMsPBUIT3jrSlVGN1stGO1x/ik2uKKSvIoLHTmdKLN1Y/vWpBDulpKo439CQ5YvL+Z+cpfvtOzbQ9viDEtPd4OHjGxt5jyScYdg+0jMw2pqFWKVk8J4vq+u74F/Ge6g72HesgGIpmjmVZ5nRzH4tKR7bBmwyVUkFwtJIPUUMtXAJKcg2kp6n46esnOTDsAtjjC1Lb2s/aJXloNUoqiowpBa7vH23nkf/5kG/8aN+IxxxNJCKzc38jc/Iy+Nz1FXj8IU4mSS6darJj0Km57eoyii3pnG5Ofl6NnQ7m5mfEf7Zk6rh+dTH7jnXQanUlPT4VwVCYjh43V64oRKtR8s6htil53KH6UlglcaiKIhNuXyjp5FMg/jmrHueuxmSk/Gjf//73efDBBwE4ceIEn/nMZ7j11lu59957cTiimUqHw8Hf/M3fcPPNN3PXXXdhs0Un3gQCAR544AFuvvlmPv3pT1NXVzelgzgXZ5rtPPNydUq1SRe7fpeff32+ihffPsPZlj4aBjLI3niGemQN9Z8PtvLCrjMcrU1+hdnc5USSov09ywqM+ALhlHp7xuqnc0w6KsvMHK3tmZbnOxiK4HAHqGntnxV/T+HiFsvK1LU5kr7ebP0+TOmaeHnG8vJsbH0+Ons90a45HQ5keXC1r/ZuNy5vkIWlWeM9bMpUSonwkJKPWEAtunwIlwJ9mppvfvFyiizp/PfrJxM6X5xq6iMiyywdaDm5eE4WzVYXLu/Yc4667B6ef+s0GrWSDL06paXNO3o90bKS1UUsLctGp1VSddo65v6yLHOqyc7iOVkoJIlFpVnUtvaPm/xyeYPY+nzxHtwxn7piLjqNihd2nZ6SCYqtNjfhiMzVK4pYVJpFU9fUdxKxO/1IEpgMmpT2Ly+KdkJKpVVgrPztgpR8HDhwgO3bt8d/fuyxx7jvvvt47bXXKCsr47nnngPghz/8IWvWrOGtt97ijjvu4LHHHgPgxRdfRKfT8dZbb/HQQw/xz//8z1M6iHNx8KyNI7XddPXO7EVHPL4g//bSUTrtHj57bQUQXQkoEpHx+qNfnsNrlwPBMLs+agZI6Q3R3OWkIDsdrVoZvwJuSKHsI5ahzsrQsnZxHi5vkFMp3lKbiNgtIn8gTFPn1FyJCxdel93D2x81T9useKcnMKlV1I7V9SARrXNs7hr/9dbT7yNnoBsBwLLy6ETD6roe2mxuAgM1fbFb0rH66enKUMe7fIiSD+ESkZup48u3VgKJk9dONPSQplEyrzAahC4amLNwZpxs8HuH21EqJO77zHJWVORwqtGetIuWdeAzpthiQK1SsLIih8M1tjED3M5eD3annyVzo+ezsDSLQCgybjOA2Pf40Aw1gEGnZttNC6lrd7D9/XMv/Wge+D3lxSbm5BnodwVGLBw1Go8vmPJaFHanH1O6BqUitaA336xHkqLJi2RCsRrq852h7uvr4+mnn+bLX/5yfFskEsHtjn7we71e0tKiXxS7d+9m8+bNAHzqU5/i/fffJxgMsnv3bm699VYALr/8cnp7e2lvb5/SgUxW55CM0Ez2k9dO0t7t5u8/vYwNK6IzVF3eIN4hEwaGT0rcU92Bwx0gTaNMaYJhs9UVb5YfC6wbU5gk0ev0Y9Cp0aqVLJtnRqdV8tHJsa/MJ8s+ZMLJmZbpq9MWzq/3jrTz23dqp3xSTcyv/lTDt5//mJ4UPohjPL4QNa39rF+aD8DZlvEnMdn6vAm10DkmHUU56VTX9cS/3CWgtTsamE9l/TQMdPkY8kUWm5QounwIl5KsDC3GdE18/o8syxxv6GXxnKz4pMayAiNatXLMpE8gGGZPdTur5ueQlaFlaZkZjz9EQ5K1Gbrs0aRdXlb0PX3ZAgtu39hrM8TKQRbPjWbOF5ZmIsG4ZR+xcc0ZFlADrF2cxzUrC3nrw+akn1fJNHW50GlV5Jn1lA4s8d6cQlLusRcP8s3/+TClRJzd5ScrhQ4fMQqFRIZOnVLzhVhyYaprqJPe73v00Uf52te+RkfHYJ3ggw8+yD333MPjjz+OTqfjpZdeAsBqtWKxWAZOVIXBYKC3tzdhO4DFYqGzs5PCwsKUTzQ725DyvhPRNVDb2OcNYbGMfBGOprvPi93pY37J1NyOjUn19w8XCIY53tDD1msquHbdXGRZRqGQiEgSafrBF6QvFIn/jnA4wtsfNbN4rpkii4GPT3WSk2MYc4nPfpc/erU8Lyf+GBUlmbR2u5Oet8sXIjdLH99v/bJCPjzeQWbWatSTXJFoNCdaoh9MWo2Shk7XpJ/PiTpfv+dCuBjG1jMQSLuDMgun+HwiEZnTzXb8gTC/3V3Ho3+1btxlbmP2HW0nHJG5dWMF9R0Omm1jvw/C4Qh2p5/SAmPCPuuWFvDanjqMBi1ZGVpMBi22fj/mbANnWvq5fEneOT//seNNxjTCETn+Hlepox/9RQUm9GMsvztTXAyv0ek2m8d4vse2oDQr/n5t73bR3e/j9usXJJxH5bxsatoco57bO1XNuH0hPn3dfCyWDK7SafjJaydosrr5xIriUX+nxZJBvzeIMV3DnJJogHy1IY0f7zhBg9XNVatLRxxT1+Eg16yncn5u9DGAskIT9R3OMZ+zDruXgux05paM3kf53ttXsvtIO139Pq68bPLPe0ePh/JiE5IksWpJNKnQ4wqM+7f0BUJ09HiQJHj8xYPccf0C7rh+frwMrt3morHDwRXLo3Gh0xukMCd9Qq8Ps0mHf0icM+b5xyaJZxum9PU3bkD9u9/9joKCAtavX8/vf/97AHw+Hw8//DDPP/88y5cv52c/+xn/9E//xE9/+tMRx0cDOwWyLCd8ScW2T0RPz9QtTBLjC4SwDVw11jT1YrMlv8IKRyJ85/kquvt9/Pt9V43bqmciLJaMlH7/aFqtLmQZcjK08cdIT1Nh63HT1jF49dtt98b/vaHDQXe/j60b5+H2huh3BThb343ZmDbq74hNJMxOV8cfoyhbz+7DbXRZHSjGCEIslgw6u93kmNLixy0vM/NOVQvvftTEqvlj9w7dc7Sdfcc7CUcifHJNCWsX5437PDS3Ra+6V83P4UhNN51d/SnfLpqsc/m7XewulrE1tkdfwyfrbJRkT03GNsYVjNDvClBeZKTqVBc736vlE5X5SY/be7gVvVZFdrqK8gIj1XXdWK2OUYPx7n4v4YiMXq1IeD4rCjIIhWU+PNHJZQssaFQKalr7+eBIK05PgAVFxnN6/of+/fwDrTM7Oh2oVQp67NE7ck6HF7cz9cz8xeZieY1Op9k8xgsxtqJsPQdPddHSZmffsehkwrkWfcJ5lBdkcOiMldqGbkzDJsXt3FNPQbaefOPg921pbgYfHe/gupUjk4SxMTa192PJTEv4PfMKjXx0vIOb1hTT0OFAqZAozcsgEpGprulmzSJL4nkVGtl9pI2Ozn5USgWdvR6Meg36gRX/zjTaKR/nc0OWZZQKia5u96Sf93AkQkN7P9esKgLA6/aTY0rjZH0PtuVjP2arLXr37f/75AJq2vr5zR/P8G5VMw/edRkmg5bndhzn0Fkb//UP16CQJGx2D/MLTRM6T71WSbfdk/SY7p7o55/L5Rt1X4VCmlQSd9xo480332Tfvn1s2bKFZ555hnfeeYcvfelLaLVali9fDsDnPvc5Pvoouqx1bm4u3d3R4vxQKITb7SYzM5O8vDys1sFb/N3d3eTm5k74ZKdarG5SpVTQ3pNaycc7h9posbrw+kMpFb+fDx0D4yjI1se3GXRqXN5gvEF9VoYW55Aa6tht5gXFmfHbQ+MtURqrES3JG7yayzPrCYSiAcl47E5fQuubJXOzSE9TJV0t7p1DbXT0uHG6g/zszdNJb/n3Ov3otCpWVuTgCySvax1uvEkowoURCIbp7osGfNPRDrF6YDLuvZsrKc0z8Pr+xpRqtes7HCwoyUSpUDC/JBOnJzhmHXaslGR4+UZFsQmdNpqdKS80UmRJp8fh44MTnSgV0ogFXc5F7MI/Vq/pC4TRqpVjXggLwmw1Nz8Dmeh32senusjL0pGbpU/YJ1ZHPbxXfL/LT21rP+uW5CVcPFeWmalrd8QbAIymy+4lb9jvWVpmpqnTSZfdw9MvHeU/Xok2SDha243HH2LZvJyE/ReUmAiGIjR2OgmFI3z3hSp+9la03abTE6DH4Ru13CNGkiTSdWrc57DIW2ePh0AoEi//BCjNy0ha8mEdSF7OLTDyN5sruf+OFXTZvRw40UVkYAJmKCzT7wrg8YXw+sPxVTBTZdRrUqrlDl6IGuqf/exn7Ny5kx07dnDfffdx3XXX8Z//+Z90dnZSXx8tbP/zn//MsmXLANi4cSOvvvoqEA3G16xZg1qtZuPGjezYsQOAqqoqtFrthMo9pktsRv3SMjOdPZ6ks1/7XX5e3VPPwpJMlAqJ6vrU2789/9YpXnlverqbdHS7kYgGuDHpsYB64A2eb9bjcAfiwUJdWz9moxazMY2SXAOSxLh11M1dTrKNaRiGLNWZO1ALZrWPPaHL5w/h9oUwD6mFUikVLCjJpGacCxJZlum0e1i7OI+vf34l4YjM73bXjvs82J1+zBlaFpZEJ3IdPJN8eeeY5i4nX31mj1ga/SLT2etBJlpfPB3zHKprusnN0pGTqeOaVUV09HgSLsQ6ez08+tyHCRdzsixj6/PGX/+x19vR2tE/D2wDFwSWYV8OKqWCyoH6yPIiE0U50S+ovcc6WDQnC90Yq3hNRmw2e+wzzh8Mi/pp4ZIU64Cx+0gbZ1v7uXYg0zpUaZ4BnVY1oo76SG03MnDZsDury8uzCUdkqs6MPjfIHwxjd/rj9dMxlWVmZOA/XjmGyxukx+Gn6rSNP1a1YDZqWTk/8aK6ojj6WVPT2kddWz8ef4hDZ2xY7R6qBr7v5g3r8DFcepoK9zmsBBn7fJwzJLlWmmfAaveOe0ERC6gtA4mF5eXZFFnSqa7rpt3mxumJBvk9Dh89A/OhzCn2oI4xpmtSWjguNE011BN+NJPJxPe+9z3uv/9+Nm/ezCuvvMLjjz8OwFe/+lWOHDnCLbfcwq9+9SseffRRAO6++24CgQC33HILjz32GE8++eSUDmKyOnEK8FwAACAASURBVHrcKCSJlfNzCEeiX5LjefvjFvyBCH958yIWlGQmLMwwnlNNdt4/2pHSAhAQfdP++k81vL6vYdxgNT6OXg/ZprSEnrKGNDUubyieoY5lk/0Dk5Fq2/qpGGgzo1UrKcxOH7fTR1OXK+GKFCBv4I0Rm2wxmthzOryUpKLYhNXuHfNqst8dwB8Ik2/Wk5up46Z1pXxwomvcpUXtTl+8FvXyRbn86WAL/a7UJrIdqelGluHI2alvUC9MXuyid0FJJm3d7int9BGORDhe3x1fifDyRbmolFJCT9mjtd202twJE4f6XAGCoUj8iyHPrGfxnCx2fdQcf38N1d3vRWLkewDgquWFlOYZmJufQaEluhBDKCyzan7OiH3PhVIpxR8bop1w0kQPauESZErXYDZq+eBEF2kaJVevGJncUyoULCzJjM6vCIbjGd1DZ7vJzdRRNPBejZlfbKLYYuAPH7eM+hkVCyaHJr0AyvKNpKepaO92s74yn3yznt/truV0cx/Xry4eUbJoSteQl6WjpqWfY/W9KBUSCoXEq3sa+P17dSwsyWRByfidgdLT1LjP4W5sU5cTtUpB/pA74rGJiS3j9Lm29XlJT1MlJOWWl2dT09qfcLe6d0hAPeEMdbqGQDASbws6lgu+UuLWrVt54okngGgm+rXXXuP111/n+eefp6SkBIDMzEz+67/+izfeeIPf/OY3FBdHC/S1Wi3f//73eeONN9i+fTuVlZVTOojJ6uj2YMnSxQPFsVZRgugVzf7jnayoyCbfrGd5eTZt3e74ksJjicgyL70Tzaz2OPwJZRejqWvv50e/P8Y7h1rZvqeBF98+k3wcPe6EFzdESz7cvmD8ijF/4MrY4Q7Q6/DR6/DH+zZCdFZwY4eDxk5HwhVed5+XX/3xLNZez4hbSWZjGiqlRNc4QX9sQQvzsNm6sWB+rBnOsebssSv6Wz4xB0li3IuYXsfgrOCtG+cRDsvs2Nc45v5DHRuoERcZ6otLe7cbSYLLFlrwB8LxD9qp0NTpwuMLxQPq9DQ1y8tz+PBkV7yvdGw2eseQco7YRWLukGzTlqvKcLgDvHd45AIH3f3RkqfR5lssL8/mW/9nLRq1khxTGhp1dJ+VFVMbUKtHKfkQLfOES9Xc/GgW96rlBWPeCVo0Jwur3cvfP/0+9z+zlw9OdHKqqZdVC3JGzJWQJIlNa0tos7k50TDyO2Tw+yzxe1qhkFg6Lxu1SsFnNs7jxrUl9Dr8aNQKNowS6APML8mkprWP4/U9lBeZWLckjw9OduH1h7nrxgVJJ1Xr01TnVPLR0eOhIFufEOzHstV17WMnvKx2TzwJEbOiPJrM3PVRc7zfdE+/L14mlz3GnK6xGPXRxxhtEbuhYl0+LtjCLrNRe4+bwmw9Bdnp0VvK49RRH6/vxeEOcNXyaEu65QN9ZI/Vjx+AfXiyi6YuZ/y48bLAHl+In+w4QaZBww/vu4pPX13GiUY7HeOcV0SW6ezxUGBOvGI26BNLPnIHrowdnmA8y1sxJKAuLzTi8AT5zvNVfPtnH8evsp/6zRHePdzGuso8rl+dOINZoZCwZOriV9/Ddfd5+f1AmUaOKfGNNDc/A5VSGrMOvWvYFb1Wo8ScoY0v4TxcKBxd1CUWUOdl6dm4spD3j7QnXTnJ5Q1S3+7AlK6hs9dD7wSDtlA4guccPqCEsXX0uLFk6uIf2FNZ9hF7HywcktFZX5lHvzsQv9UbC6iHLmAUC6iHfjksKMlk8Zws3vwwMUstyzLNXa4Rr//RKKTohKSyAuOYk4MnazBDHQuoQ6LkQ7hkVRSZUCokblhTMuY+ly/KZdX8HDatLaUk18BPXz85cPdo9In065bkYTJo+H/s3Xl41OXV8PHv7JPMZM9kD/u+CNUooBYUF3YooXSBYuvTlgds1fo+9m1qFetTqdW6VS+X16dPrdraWmVVA7ZqKUpRwA3Zl7CFbJN1ksky2+/9Y/IbMslkMgmEJMP5XFevyjAh953JzJw5v3Of807rbIe21KRTWlLH14Fv3zCSe2/JIznezNXjM0iON3Hd5GwsnXTfGZnjnwh4uqKBCUOTufnKXDTAjXk55Ni6PkhnMRsCV657orTKSWZKcLyRFGdiULqVPYc6L7OsaFMmpxqe7c/QN7u8TB6RSqxJT6WjmWpHM3qdhnhLZENdVOr9u2qd1+cZ6mjj8fqoaG0xYzLoSEkwh32z/mBvCfGxhsBBoYzkWFITzOwLU0etKApv7zxFjs0aGLYS7uDf2ztPUu1o4T8XTsBiNjBjcjZ6nYZ/hhnrWe1oxuXxkZnaMUPt9viobWjBbNQFxnc6nC6Ona3DqNeSm3buyffVSVnc/a3JzLw8m5r6FhxOFw6ni4raJr5+3XBWLhgf8gme1klAXe1o5r7/3cWhk9Usu3Fkh0s3Br2OIRnxHD0buh9mWXUjep0m6BNqSkIMVZ1cEahtaEEh+LL6gmuGotXCP/acCfk1qgMnq1EUWDx9GEDIDEM4/9h9hnte+OiCd6ER/mxIVoolcIk10oOJlXVNXfY6ra1vwajXBr1oXzY8hViTnp37yqlvdAXqn9tehbHX+ks4Utv9Ts+ZMgiH0xU0EOLzY5UU2xuYNj58hxrVfy4Yz48WT4jovt1xLkPdWvIhNdTiEnbDFTk8+MMppIXp854UZ+L2JZfx9euG83++OZlsm4XkeFNQIqotvU7L9ZOz2X+ypkNAV17dRILFGDIbHm8xBt6LjQYdv/7h1EC8EMqonHMJgAnDkhmUHscD/3EVX79ueNg9qywxPc9Qu9xequqayWxXugIwZWw6J0odVIQonfV4fVTVtXQIqHVaLePbTKhMSTD7M9SOZpLjzN0+NB1v8ccoXQXUgRpq3YU9lH3JBtT2Wn8rK7UzRlaqpdM3a4fTxd7jVVw9ITNw2Vaj0TA8OyHsydb9J6opqXQy66pcrDEGbInmsAF1UYmDoVlxjMjxP2HjLUauHJPGjn2lnRb7q5mz9r/gap1SRU0TsWY9cbGtv2iNLo4V1zE0Mz7oErRep2XckGSuGO3vvlJc6Qz8PHLSOv/Um5YUS0VNU4e6sROl9bS4vfzyh9M6zQKMyEngVFk9bk/Heqfy6kbSkmLRas/9wqcmmKnsJHusHhprW1qSYDEyZWw6O74sC5tB/rKoCotZzzUTM0iwGrtd9nHG3oCj0d3tzLYIz+vzUVbdSGZqLBazgUSrMWxZVltvbDvOE3/7ImzNdZ3TRWKcKegSqUGvI29MGp8esXO49YR/VqqFsqrGwL9lr20iOUQJhzppTa0j9PkU1m8vIj0pJnCFqispCeYLnp0G0IUs+ZCx4+LSZNBrO5RfhGONMbDmu3ms+e6VQe9J7alllOp7Z7WjmUMnqympcnY4kNgZo0EX9nukJcUQbzESF2sI1C7npFkjbuFrMRtoavEGytq6Qz0k3r7EFODKsf7YYdeBcj7cW8o9L3wUyIRXOZrxKUqHkg+Aqydkkmg1Mm5IMinx5kANdXfrp+FcyUdXBxMlQ32BqZd01U+GuWlWfzuYEIeK3t55Cp+i8NVJwW+KOTYLVY6WTj/t/X3PGX9QN86fnRqcER+2k8bZSifZqcGXUmZenkNTi5fdh0KfHlYPbbW/BKNmk+21TcSY9IEs3N5jVZwsq2fCsNCN3wOZQLuTs619I9uvqa20pBha3N4OhwvV4LJtm732RmQn4PEqbPushM+PVgbVl/tbDAU/+VITzNTUt4TsxqIG1O0nK92Yl0uL28uHe0spr2nskLX0KQr7iqoZPzQZnVbLuMHJHDhZgy9MIKYoCp8cPjcyVh2t3pPx1aJzFTX+D71Zrb/b2akWikocgedoU4un06sCZyoaaGhyhx1D62h0degxC/6yjxa3lw0fFKHR+C/lNrZ4AqfQ7bXNId8YYs0GUuLNgYD644PlnLU7WTx9WK/3Q+9KyBpqOZQoRMQMel2XJQhZre+V6tXuZzbs46dPf0BRiSNQdnm+NBoN86YNZuE1Q3vU9tLS2rO6J2UfZdWh4w3wl3WOyE7gvU+K+eOWQ5RVNwbiLHXeR6grApcNT+HxH1+LNcb/+lnVesarux0+IPKSD4/Xh0bDBX9dviQDaq/Px9aPTzMsKz4QUA/JiMPrUzhjDz6lWlbdyPufFvPVy7I6/BLlpvmDxeIQJ1vPVjrZV1TNzMuzA58cB6dbqaxrDtnvuLa+hYYmN1mpwdngYVnxJMWZOi0tKa1yYmmTgVZZY/xPmipHM7EmPXqdlliTns+PVRIXa2Dm5aEnOsXHGomPNXDW3sDZSv+/nRDmRSQ90Dov+DJPlaMZo17bYV1tjcjx17H95b2jPLVuLz95+kOefP0Lmlo8VNQ0djgRnZJgRlH8/aZPldXz2798Fsjcq0FtUlzwp9rBGXGMzElgw4cnuOeFj3joT58GnQD+5LCdOqcrkJmfMCyZhiZ32HKBw6dreWbDl3x+tLL1e/uDtlIJqC8oNTBV36SuuSyT8upGHnvtc157/yh3PvUB67cXdfg6t8dHebX/9/FkmMexvjVD3d7I3ERS4k3+cpNUC0NaD+OqbyYVtU0hA2rwfzBX1/3vfWWkJ8WQN6bve+6rlzbV8eMtLin5EOJCS7T6yzpKKp24PV5Ol9czbWIm+dOHMWdKx2mIPXVTXm6HM02RsrReve5J67zSqkY0QEZy6Ne/q8amUed0kZXqL6U9cMp/tbcicJA7/IeKlAQzTS3+FoPdPZAI/ivtFrM+ohrqC52dhigLqBVF6fIAGsDuQxVU1jUzd+rgwOXezoab/O39Yxj02kB9bVtqMF7c7jK0x+vjpS2HMBl1zGjT41I9WRyqTOR0uf+Nv302WKPRMH5oa9Y0RDbujL3Bf6iy3SdVteRDUSC2tW5L/fQ2/+ohYXvcZtusnK10ctbuJNvW+ThyOHfYsf3Pvbq+heR4c9ivjY818t/fv4pfrLiCguWXM2fKYPYer+K194/h8SpktAuo1YNdVbVN7DlcwcFTNRxoLc+oqW/BZNQFBmW0teDqIZgNOiaPSMXj9XGs9SSyT1F4c8cJMlNiuWKU/6DJZcNT0Gk1YXtYHyn2lwKUVTfi8ymB7Hgkv3vbPj/Lb/786QVt/xat9hVVYzHrA114po7L4D8XjedEqYO/7zpDvMXIPz8rptkV/MZQVt0YuMIQ7oNRXaOLxBAZaq1GE5iWOCwzPvDBrqy6kRaXF4fTFTagLqtupLHZw9EztUwYmtIvhqeoH+rdXgVFUWhxS5cPIS40jUZDdmv56JkKJ16fwozLc5h/9ZCQWd2+oGaoe1JHXVrlJDXRjEEf+rXjmomZzJ06mJ8svYzRgxI5cNKfoa6oacKo15JoDZ/hb1vm0ZOAGlp7UXeVofYoF7zDB0RZQP3JYTs/f+GjsHXNiqJQuPM0mSmxTG7T61UdWtK2JKO0ysnnxyqZM3VwyCxtotWIxazv0HvxjW3HOXa2jlvnjAnU9EDnQTvA6dbbskKUV0wYmkxji4cTZcHBQX2ji6ISR6DtV1ttez2qY0lT4k2kJpi5bnLHRvZtZada/CUflQ0d+m22lxJvQqfVdDiIUO1ojuiSTWaKheHZCYzKTeTr1w1n8ohUtn9RAhCy5AP8bcjUQEntsuI/xGAKGcBPGJbCE7dfyw/mj0OjgSOttbGfHfH3GJ5/9ZBAzZrFbGDckGT2HKroNOhVO5NU1DZR29CCt/WDTlclH5V1Tfz1vaMcOVMbtnd3ex/tL+vyYOVAUNvQEvEHCZ+i8GVRVaAUR3XV2HTu++6V/Pf3r2L11ybQ1OLl3/uC+7urpUoWs56TpfX4FIVnN3zJJ22GLvgUhXqnO2SGGmDa+Aw0Gn/3jtR4c2DMr72uY4ePtnLTrCgKfLi3BJfHx9ghHZ+bfUENqL1eHx6vD69PkYBaiF6QlWqhpNIZeI8a2UVf6ItNLQd1NvUsQx3ug0GMSc/XrxtOcryZcYOTKK9upKqumTMVDdgSY7ps6dc2ZkjuQQ01QFysEUdj+A8Lbq/3gg91gSgLqNU31nBZqU+P2Cm2NzB36uCgzJFGo2FwRlxQsKt+upoyNvQlW41GQ26aNTCjHvytuP6++ww3XJHDVWODT/ZbYwykJpg5XtJxfafL6ok16UN+ghs7OAkNHbtP7D1ehaIQ9MFAZWkTUKvZ6FvnjqVg+eVdXurItllocXtpavGSE6Z+Gvw1SKkJZvYctvP8pn3sa+3n7A+ou/+EyJ8+DPVRaV/ykRRnQqMBe10zJ0v9j9O+E1U0tXjYf6Ka4VmhT1+rYkx6BqfHcfhMLYqi8Oa/T5CeFMNV7R7fvNE2KuuaOV3ewOdHK9nTpn7d51MCvTYrapoCfbYtZn2XGepX/3EUr/fcpMpIVNY28ccth1i37XjI+v6Boqy6kbuf+XfQ0JRwzpQ3UOd0hRy/nZtmJdtmZXhWAkMz43jvk+KgmvezlU50Wg1XjE7jZHk9e49Xseewnc+Onhva42xy41OUkBlq8L8p/nrlVKaNz0Cr1ZCeFEN5deO5WsBODhjltmbT3/2kGA0welD/eDNVSz7cXh/NrSVPRqmhFuKCy0q10NDk5suiKuJiDZ1++O4r50o+upeh9ikKZdWNHa4cd2Zc6xTY194/ysFTNR3eZ0NJbRMzpPZihtotGerw1F9ggOKK0J0AvD4f6/5VRGZKLFNDtLEakhFHSaUzELgcOlVDSrw57BMipzWgVt/Q9xyqQK/zN2kPZWSOvyl7+0zd6fJ6smwdSzfA/4lrcEZch4D682OVJFqNHQaugD8jpWag1Ax1cnxkHQSy2/SyzI6gr+XYIcnUOJr59Iid9/YU4/H6qGtwdRjmEomcNCtTx2cQH2vocFVAr9OSHGfi4MlqGls8DM2Mp9rRwvp/FdHi9nJdiBGy7Y0elEhRiYMvjlVxuryBOVMHdziY8JVRNrQaDS+/c5in1+3lf946EHiCllQ6aWrx15/aa5sCAdaYQUlUOVpCTsoD/8CYz49Vkj99GDEmXcgPVaH85b2juDw+XB4fh06HbjEYyudHK8M22T9f739a3DpdMrKM82dH7fgUhe1flEZ0/73H/cFvqIC6rRuvyKW0qjHouXHW7iQ9OZaROQm0uLyBwUptJ6GqGYzOMtTg72WuXrlIT471Z6hD9KBuy5YYg8moo7KumUEZcZ32kr3Y1GyM16vgcvvrqE0SUAtxwallm/uKqhmaGd9lVvZii+3hocTqumbcHl+gM1pXsm0W4mMN7DlsJ9tmYc7UwV1+TZzFGLia1pNDiQAJsREE1F6poQ5r96EKvD4Fa4whKGPc1o4vyyirbmTJjOEhT3cOTj93MNHnUzh0uoaxQ5LCPiFybVZcbh/2Wn/ruM+O2hk3JKnTllSjchOob3QHXfJXFIXTZY6w3TTGD03m+FlH4BCe2+NlX1E1k0ekdlqjqZZ9xJq696bedh2hSlDau2XWaJ77rxlcOSaN0xUN1NT7e0L3tAbqe3PGcP+tV4X8uackxASC0flX+5+g731azOD0OIZmdt5RRDU6N8lf4/7OIRKsRqa11sq2ZY0xMGZwIidKHQzLisfj8fHeJ8UAHG3NLOeNslFT30JJpf93bVzrpf3y6kY+OlDWIQP9yaEKTEYdN+blMiwzPqIM9b4TVXx2tJJF1w7FZNDxxfHIxqI7Gl08t2kff/77kYju310tLi9/+vsRnlq3l6fe2Nvl9E/wd5cBOHKmNiiwPV5Sx8EQbQr3FlUxNDOuy1P1V45NIznexOYdJwLB/dnKBrJTLQzJ9J9ZKKtuRKfVBAfUrS+44QLqtjJaA+rNO04SY9IF6hDb02o05LSWSYUqxeoreq1aQ+3D5VEz1FHz8i9Ev6G+Z/oUJXCguT8J1FB3c/x4aZgOH6FoNBrGDU1Gg/89PZK2flqNhuR4E/EWY6d12l2JtxhobPEEWuOF4vH4JEMdzsf7y8hMieXyUTaK7Q0dMmdK6wG04VnxfCVEiQTAkMxzNc6nK+pxthlL3Bm1R3NxRQMllU7stc1hxwaPbG3KfuTMuWxjndNFfaM70B4slMuGp+BTFD4+WA7AodO1tLi9Ics9VOqlndhO3vw7E2PSkxJvItFqDKrFDsdf/hJHTWsHDqDH/XQNem2H9ncqtY7aqNdy2fCUwKfl676SFVEmYFRuAhqgrsHFjVfkdPop9WvXDuPGvBx++u2vMHlkKu9/6j/8dqy4lvhYQ6AZ/b7jVRgN2kD/0b3Hq/j9mwd5898nA/+Woih8cbyK8UOSMej99y22N3Q4TNfe3mNVmAw65k0bzLghSew9FllG+J+fnsXt8XGyrD4wwvVCUnuBTxqewv6T1fxxy6Gw62psdnO0uC7QPnLn/jIq65p4ZsOXrH35Ex577YtAeyVoPRtw1tFldhr8Vy3mTRvC8bMO9p+opsXlpbK2mexUC5nJsZgMOnRaDTMmZ1Hb4ApcfQoE1J2UfLQ3ZVw6l4+0MWlEKt++Ifx4X7X7z7j+FFDrz7XNUzPUxh6+YQkhOpdoNQYaAagf6vsTndZ/9bqhmyUfpa2tACPNUAN8fcZw/s+3JndZjtlWVool4rKSUNQkTLhEj9vrkxrqztQ2tHCk9Q07N82Ks9lDbUPwD9PR6KbK0cJV49I7fTNUDyYePFXDwdb66a4C6qxUCxqNfziIWqM5KUxAnZkSizXGwNHicwG12gQ+K8wBwBHZ/nrRLR+dwuvz8c9Pz2Iy6MKuTw2Gw3X06MxXRtr4yqjQI1Y7M7i1fvTzY/6fQ08v2YSjBtSD0uPQabVcPsqGNcYQCNa6Ems2kJtmxWTUcX2YEpEROQksu3EURoOOOVMH42z28MLmA+w/WcOInMRA+58DJ6pIjjMHhgRs3nESn6IEXYEotjupqW8JjKsfnp2AoviH34Tj75TiHyAyaUQqVY6WLgebuNxe3vukONAZ49MjnXcr6Sl1WuW8aUPInz6cz45WdjgY2Nb+1r7e138lmzGDEnn/k2Lu+99dfFlUxcJrhpCeHMNzG/cF6tH3n6hGAS4b3vnzqK2vXpZJSryJjR+e4Iy9AQX/5UatVsNVY9O4/vLswLAktS+1GlCH6kMdSm6alR/lT+SHC8Z1OaTlsmH+D3ojc/pH/TSAITB6XJEMtRC9SKPRBLLUQ/thhhp6Nn68tLoRa4yBuNjIx4Enx5sZPyT0zIvOfG/uGFYvGt+tr2kr0Is6XEAtGerOqZfPxw9NDlxubV/2oTZaD1fCoNFomDIunU8O29n44QmyUi1dZrBMBh3XTc5m+xelvP3RKYZmxnWaXVW/x8icBI6eOXfJ/2xrl5BwBwA1Gg3zpw3BXtvM0+u+5PNjlSy8ZkjYyyLnSj66H1Avu2kUK24e3a2vUYe4fBEIqC/8xDe1rY56KW3RtUP59cqp3Zr6tuymUaxaOJ7YCOtbR2QncN1XsjlaXIvD6eKy4SmBQ2nNLi/J8SZMRh3J8SY8Xh8xJh2VtU2BIRpqPbAaUKsT9Yq6qHFue7BTzdZ2Vfbx731lNDS5+fYNI8mxWfgkgoD64MnqsBM826tsDUpTEszcfGUuo3ITefXdIx2G+6j2HqvEYtYzPDueayZm4mh0Mywzngd/MIWvfXUYty+5DK9P4ZXWEpW9rYd5hkRQwgP+LPWCa4ZSVOLg4T9/Cpyr/b917liW3TgqUPOsln04Gl1oNZpuvTlEavLIVNb+cGq/6vPcdlKiyyMZaiF604icBLJSLRF/YL/YLDH67pd8VDWGnJB4ocXHGs/r5xaYlujsfH+eXspQR8Xs2aJSBzqthkFp1sCbRXFFQ9Al40BA3UX9z7IbR5KaYOaNbccDAVBXlt88Cq/Px/YvSpk8suus7sicRD47WkltQwuJVhNFpQ5SE8xd/hJNGplKjs3C3uNVjB+SxKwuGsVbzT0r+eiptpOOrDGGXjn0ZGvtRT209VKaXqfFGtO9J8aoHrQxumXWaG6ZNdr/RNRpURSFWJOexhYPya3DZDKTY2ls9rDwmqG89v4xquqaSU+OZe/xKganxwU+nFnMBjJTYjl+NvzBxOr6lkCv86Q4E4PSrXxZVM28aUM6/ZrdhyrItlkYlZvI5aNsvLnjJA6nK2wt8h8KDxIXa2TN964M3FZa5eTZTfu55eZRHcp+Kuua0es0JFiNaDUabpk1mnt//zE7vixlbruDJx6vj71FVUwYloJOq+XqCRlk2ywMSo8L1P5nJMdyU14Ob+44SUVtE/uKqpk4rHv9m796WSbxsUY+PWKn2eXpMJGrQ0DtdBFnMYQd8RtN2k5KVMteJEMtRO9YMmMYi64d2tfL6JTFbMDZ7OGsvYE3/32SIRn+AXJnKhoYnh3PV0LEMWVVzrBX3/sLawRdTNweH/GxElCHdKLEQW6aFYNeh0GvIynOFDJDHdNJW7q2NBoNs64axNRx6RFnMLUaDbfMHsPkkTbGR9B3dmSu//LzkTO1XDU2naISB6MiqLfUajR8c+ZI3vz3SX4wf1yXAYeldVpiT0o+empQujXQE7o3jMpNZPlNo8gb071ylAtFPVih0WiwJcVwqqw+UNryzRtG0tTiCZQUlVU3YokxcOxsXYcgePSgJHbuK6PF7Q35wcPt8eFwuoIOdo4bksw/dp/pdMqdT1E4Uepg2oQMNBp/27jNO07y+bFKpk/KCrmfphYPVY4WqhwtQRnxf+wpZs/BcoZmWJkzJThIrqxrJiXeHPj9y0r1B/DbvyhhzpRBQSVVO/eVUd/o5poJGYGfmzrgqK2vXpbFmztO8so7h2lockf8YVal0WiYPDK10zMFcTGGQGcW8AfUCb2Qne6vtFoNGo3UUAtxMei0WnqhouCCsZj1nK108s/PzrLrYAW7Dp5rDZueFNMhoG5ocuNodPeb4TThBNoChsnA91aGuh8/5JHx+RROlNUHLqMD5NisnGnXOq+k0klWamzELWwSrKZutVXRajRMHpEaiHglWQAAIABJREFU0cnUwelxxJr07CuqxuF0UVnXzOhBkdUZjR+aTMHyyyO6JJKaEINepyE+zPjvC21Qa9lHb5R7gD8wuOGKnB6fAL6Q1Cyoutccm5WROYmBgTTl1Y0cPl2LoviH87R11Zg0WtzeQHlMezUNraPU29ShjxuShNencLS4Fp+isOdQRdBJ5rKqRppdXoa1Zu9zbBYSrEYOn66hM22H0aj11i63l48P+A+/bv+8pMOBw6q6pkAtu2r6pEwqapqCDtv6fAqFH59mULo1cIizMykJZiYMS2H/iWo0Grq8f3dpNBpsCTGBNoeORhdxXXQQiTYGnRaPR2qohbjUWWL8Gep9RdVcNjyFx350Db+89UoWf3Uo5TVNNLQLRsuq1A4fvV/ycb7UEtdwo9WlhroTpVVOWlzeQAkAQE6ahdIqZ6CGFaCkytllucfFotdpmTAsmb3HKwP136N7oSPA1PHp/OoHUyLOtF8I6mG43jiQ2N+oddTt92qNMWAx6ymraeLwmRqMem3QBz7wZ9qT4kx8tL885L9d09pJo+0Hk5E5ieh1Gg6crGHPoQqe3biPHfvO9XVWBxqpzwWNRsPwrISwpSVqKVSsSR8IqD89aqepxcPMvFzKa5o43K7/dVVdMykJwSUVV4xOI8akD0y5BH+AXl7dyLxpQyL6IKtm0YdnJ0TcXaY7bInmoEOJ8ZdQhhr8ddSSoRZCxJr1OJwuKmqbmDgspbWkMC5QDtm+rWtpVfc7fPQVrVZDrCl8jbi/D/WFL/cb8AF1UWtP4rYBS1aKBa9PCRyecjS2tqWLoKfyxTJpRCqORjfvfVqMVqNheE7kbWUipddpA90nLpbBrRnq1IT+NR2qN6gf0NrX62o0GtKTYymvbuTI6VqGZyd06MGpdqD4sqiqQzYAoNrhz1C3LZ0xGXSMyE7gwMlq3t55CgienllU6sBs1AUdHBmeFU9FbVOnJ55LqvxTBWd8JYsjZ+qob3SxY28pqQlmVudf1iFIbnF7cTS6O2SoTQYdU8ens/tQBcfO1uFodLHuX8dJT47ligi7xUwakcLQzHiunRi+i0ZP2RJjsNc24VMUHI3uDoODop1Bp2k9lCgZaiEuZdY2SbYJw85dDRySEY9Wo+kweKy0uhG9Tjtg3tctMfqwNdT+PtQXPqHQrVfUhx9+mIKCAgCKiopYsWIFCxcu5Pvf/z51df5PNCUlJSxfvpzZs2ezevVqnE7/JxuHw8HKlSuZM2cOy5cvx26/MO28TpQ6iDHpg8ZUqz0M1cvZav/EcINTLraJw1LQaPzjzXPTrN3qUtGfJceb+em3JjNjcuia3Why1bg0HvnxVwMt9NpKT4rldHk9ZyoaOj0EOWVcOl6fwuYdJ9h7vDKofKO6vmOGGvxtHE9XNHCmooEEi5FDp2rw+fwlGSdKHAzNjA+qrVf7Yxd1MpmxtNI/SvbKMWn4FIVfvrib/SdruHpCBmaTnmnj09lzuCIwUKiqTYeP9hZdM5TkeDO/e/0LfvuXz6iub+F7s0dHfPBPr9Ny33fzOq33Pl+2xBjcHh/l1Y24PT7iLP1jiuHFotdr8XgV3JKhFuKSpjYqSEuKCUq6mYw6ctIsHTpQlVY6yUiOGTCHuNVDl53x96Huwwz1zp072bBhA+AfVLF69Wp++MMfsnnzZsaOHcsLL7wAwAMPPMCyZcvYunUrEyZM4NlnnwXgySefJC8vjy1btrB06VLWrl17QTZQVOJgWGZcUBChBtflrQF1SWv9T3/KUFtjDIxsDXaGZfe/5u/nY+yQ5It6ELKv6LRaxnZS65uRHIOz2YMCjO4koB6cHkeOzcq7e4p58vW9PL9pX6BeucrRgsWs73BgcVxrT8+kOBNLrx+Os9nDybJ63B4vZyoaOrSaG5zhf24UldShKArlNY1Bf19S5SQzJZbB6XFcOzGToZnxzL96MLNbO8hcOSYNj1fhQOs0Q/WqT/sMNfj7f979zcno9VrKqxu5PX8iowf1n+EmaqcPtQTmUiv50Gv9JR8tHi96nWbAvDkKIS4sS2uGeuLQjoe/h2clUFTiCCRqwJ+hzugnJbORsMQYwpd8ePpw9HhtbS1PPPEEq1atAmD//v3ExsYyffp0AFatWsXy5ctxu93s3r2bWbNmAZCfn8/WrVsB2LZtGwsWLABg/vz5bN++Hbe7e30Q26usa+KMvaHDAAVrjAFrjOFcQF3pxGTUhe0P3RcmtXYkGNYPpymJ86N+qNPrNB3qp1UajYZf3HIFv145lUXXDuWzo5X863N/eUXbjhttDcn0j1jPnz6MCa1tIfefqOJ0RQNen9Lhd8lk0JGbZuX4WQdv7zzFz//fR7y54wTgH19vr21qHU6k4T/mjeXH+RPJnz48cMVkeHYCMSY9Xxz3jw5Xh7p0dukvNTGGNd+9kvu+e2Vgff1FerJ/zX997yjAJVfyoddr/aPH3T7JTgtxCVPfWyaN6PgaPSwrnmaXN1A37fb4sNc2kXke0wsvNotZT0MnGWqvz4eiENEo9O6KKI24Zs0a7rrrLkpL/QegTp8+TWpqKvfccw8HDx5k2LBh3HfffdTU1GC1WtHr/f+szWajvNx/6KqiogKbzV9LqdfrsVqtVFdXk54e2YS7lBRrh9ve/vg0Go2GhdeNxJYU/Aafk2alqt6FzRaHva6ZwRlxpKX1r8B1/vQRlFQ1cf1V/rZkNlv/nKp0vqJ1X6pQ+xvr8l9WHz04meys8H2vc4DxI9M4VdHAX98/xjWX5+BodJORagn5bz9198zAfw/LTuDQmTqavf5sQt6ELFLb1XSPH57Cu7vPcLS4jniLkQ0fnMBiNXHVuAwUBcYMTe30McrMSCBvbDpfHq8kJcVKo9uHXqdhxJCUTjOc/fXxttni+K/lV7Dji7OcKa9n0tiMwO3Rqu3ezEY9Op0OnV6H2aSPmn1Hyz7CieY9RvPeVP1tj6mpVp6++/qQo9GvnKjhf98+SLnDxeRxcZwqc6AoMGpoSsh99Le9AaQmxXLodG3Itanli0kJMRd87V0G1K+//jqZmZlMmzaN9evXA+DxeNi1axd/+tOfmDhxIk8++SS/+c1vuOuuuzqc5u/sdL+iKGi1kX9CqKpqCLoE4XJ72brzJF8ZmQoeD3Z78KS3lDgT+09WU1pWx+FTNVw9MaPDffqD788dg6vJBVZTv1zf+bLZ4qJyX6rO9mdEQa/TMiIrPuL9r7hpFAX/byevbjmIvaaRoRld/+xG5ySw5ePTHDxZzbTx6fhcbuz24E/mWckxuNxeLGY9v7z1Sl7/5zH+tOUQ+1tb9lmM2pDfR93b6Jx4Pvj8LHv2lXC61EFyvJmqqoYO9x8IxucmML61D7y3xQ2Yo/b3s+PvpoKzyYUWBb1WExX7jvbXF4juPUbz3lT9dY8WfejXAIOiYDHr2XuknMuHJ7PviL9HtdXQ8X2iv+5NC9Q3uiivcHSY16E2AWhudne6dq1WEzKJ25UuA+rCwkLsdjuLFi2irq6OxsZGDh06xODBg5k4cSLgL+G44447SE5Opr6+Hq/Xi06nw263k5aWBkBaWhqVlZVkZGTg8XhwOp0kJnZ/Yp3qowPlOJs93HhFTsi/z0iJZce+Mg6frqXF7WVsP6rlFNHPZNRx/61XYgtRa9yZpDgT08ans+PLMjxeX0StB6+emMnxEgc3X5nL5Z100xgzKAmTUcd3bh5NotXErXPHUtvg4rOjlWg0/nrvcCYOS0EDbPn4NMfP1gUO/YqBRa/V4PX6aHF7pcOHECIkjUZDbtq5WR5n7U40GshKHTiv+1azHkWB5hZvh0nR6uH/PqmhfvHFF3nrrbfYtGkTd9xxBzNnzuSvf/0r1dXVHDp0CID333+f8ePHYzAYyMvLo7CwEICNGzcG6qxnzJjBxo0bAX+QnpeXh8HQ81P2H+4tDYxYDkU9ufqvz88CMHpQz4N3IXoiO9WCsZvj12/Myw30T49kOE52qoWC5Zd3Gkyr/84zP5nOlHH+8iq9Tsvqr00gLSmGzBRLl0Ny4mKNDMuOZ8+hChRFYe60wWHvL/ontYba7fF1+/dSCHHpyEmzcrbSXxVwttJJelJsvximFilLmPHj7tb3194Y7NKjVgxms5lnnnmGe++9l6amJjIyMnjkkUcAuP/++ykoKOC5554jMzOTxx9/HIA777yTgoIC5s2bR1xcHI8++miPF+3x+jhZVs8NV2R3WlKiZtE+O1pJjs1C3CV2ol8MTDk2K+OGJHHgZM0FHd/evt7ZGmPgFyuuoMXljejrv3PTaIrtDVw1Nm1AvbCKc/Q6Lc5mDy63F2MvZGeEENEhN82Ky+2joraJYnsDg9K6X/7Ql9QuJs5mNzaCr8D2Zoa6WwF1fn4++fn5AEyaNIk33nijw32ys7N55ZVXOtyemJjI888/38NlBiurasTj9QWGiISiTrHz+hTG9MIUQiF6y8JrhlLlaCHb1rsvYnGxRuIivIo3OCOOwRn97/CJiJy+dVIiXHodToQQkcttDaCPn63DXtPEtPEZfbyi7rHEtI4fb+rY6cPTGlD3RpePAZmmOFXuLyQP9wZvNOhIaa1BlfppMZCMyk3koZVTe2X8trh06XUaPJ7Wkg/JUAshOpGVYkGjgd2HKlDoX0PxItE2Q91eoOSjr/pQ9zenyuoxGXRdjtXOSI5FA4yS+mkhxCXOn6FWcLm9UrYjhOiU0aAjIzmWfUX+gV45A63kQ62hDjHcpTcz1ANynN2p8npy061dTvq6alw6aUmxgU8rQghxqVJLPhTAJF0+hBBh5KZZKa1qxKDXkpYYvhNUf2Np7ewRariLZKjb8CkKp8sbwtZPq756WRYrZo2+CKsSQoj+Ta/T4PH6/IcSpcuHECIMtY46K8XSZfKyv9HrtJiMurAZ6n7T5aMvlVc30uL2RhRQCyGE8FNLPtweX69kZ4QQ0SOn9VB8tm1g1U+rrGZ92BpqfV93+egPIjmQKIQQIphep8Xl9qKAZKiFEGENSo9DAwOuZZ7KYjaE7PIRaJunu/BZ9wEXUJ8ub0Cv05KZMnCm9gghRF/T6zQorf9tkgy1ECKMpDgTP//OFQxKH6ABdYyhiy4fFz6pMOBeVasdzaQkmHvlhKYQQkSrtmUeBslQCyG6MCInYcBezbKY9ThDHEr09OXo8f6mqcVLrGlgPsBCCNFXdNpzL/fSh1oIEc0sMYaQhxIDNdS9UPIx4F5Vm1wezMYBV6kihBB9qm1GxjRAs05CCBEJi9lf8qEoStDtkqFuo7nFQ4xJAmohhOgOXZuMjHT5EEJEM0uMvnWQlS/odrfXh0YTfMXuQhlwr6pNLV5ijJJdEUKI7mjbd3Wg1kUKIUQkOhs/7vEovZZQGHABdbPLg1ky1EII0S1tM9RGmZQohIhiallbi9sbdLvb4+uVoS4wwAJqRVFoavFilgy1EEJ0S1CGuhdaRgkhRH+hJg1ClXz0xlAXGGABtcvjw6coUkMthBDdpA8q+RhQL/1CCNEtkqHuQrPL/4ORGmohhOgevWSohRCXCPWciKtdQO3x+qSGGvwdPgCpoRZCiG7SSw21EOISofbab2lf8uHx9dpgwIj/1YcffpiCgoKg27Zt28bMmTMDf3Y4HKxcuZI5c+awfPly7HY7AC6Xi5/+9KfMmTOHxYsXc/z48R4ttsnlD6hjpA+1EEJ0i2SohRCXCpMxdIba3dcZ6p07d7Jhw4ag2yorK3n44YeDbnvyySfJy8tjy5YtLF26lLVr1wLwyiuvEBMTw5YtW7jnnnv4+c9/3qPFNrW0lnzIpEQhhOgWNaDWaHpnSpgQQvQXatKgxdOu5KMvM9S1tbU88cQTrFq1Kuj2e++9lx//+MdBt23bto0FCxYAMH/+fLZv347b7Wbbtm0sXLgQgCuvvJLq6mpKSkq6vdhAyYdkqIUQolvUINqo16HRSEAthIhepjBdPvosQ71mzRruuusu4uPjA7e9/PLLjBs3jkmTJgXdt6KiApvNBoBer8dqtVJdXR10O4DNZqOsrKzbi1VLPsySoRZCiG5RW0VJ/bQQItoZO+ny4enFLh9hU72vv/46mZmZTJs2jfXr1wNw5MgR/v73v/PHP/6xy6BYURS0Wi2KogRlRNTbuyMlxYre4F9uTmYiiXGmbn39QGCzxfX1EnpFtO5LFc37i+a9qaJ5j0F70/tfP2NM+qjaczTtpTPRvMdo3psqmvfYn/em02rQG3RBa/QBFouxV9YdNqAuLCzEbrezaNEi6urqaGxsJD4+HrvdzpIlS3C73VRUVLBs2TJeffVV0tLSqKysJCMjA4/Hg9PpJDExkfT0dCoqKhg0aBDgr79OS0vr1kKrqhqwVzsBaGxowt3s6uGW+yebLQ67vb6vl3HBReu+VNG8v2jemyqa99h+b3VO/2umTquJmj1H8+OniuY9RvPeVNG8x/6+N6NBS21dc9Aam1s8+DzesOvWajWkpFi7/f3CBtQvvvhi4L/Xr1/Prl27uOeee7jnnnsAKC4u5pZbbuHVV18FYMaMGWzcuJFVq1ZRWFhIXl4eBoOBGTNmsGnTJvLy8tizZw8mk4msrKxuL7bZ5fV/4uildL0QQkQrQ5saaiGEiHZGg65jyYe39w4lXtDTfXfeeScFBQXMmzePuLg4Hn30UQBWrFjBmjVrmDdvHkajkUceeaRH/35Ti4cYk14O1AghRDfpdFJDLYS4dJj0Olztu3x4lV4bPR5xQJ2fn09+fn7QbTk5Obz//vuBPycmJvL88893+FqTydShxV5PNLV4McuURCGE6Db1II6xl95MhBCiPzEatB26fHi8PvTdPMMXqQH1ytrs8kjLPCGE6AGtVoNGc+70uxBCRDNTyJIPBb2+d6ocBlRA7S/5kDcDIYToCYNOKwG1EOKSYDTogiYlKooiGWpVk8tLjEky1EII0RN6nbbXhhoIIUR/YtQHl3x4fQpAr9VQD6hX1uYWj9RQCyFED00cnsLo3MS+XoYQQvQ6kzG45MPj9QfX6tTYC21ApXubXV6poRZCiB76z4Xj+3oJQghxURjbdfnweFsz1L3UNm9AZaibXFJDLYQQQgghwmvf5eNchvoSD6h9Ph8ut48YyVALIYQQQogw2nf58Hh6t+RjwATULa2fMsxyKFEIIYQQQoRhNOhwe3z4FH+ph8cnJR+Avwc1QIwcShRCCCGEEGGoU2HdrQlZNUNtuNQD6haX/wchbfOEEEIIIUQ4ptae+2rZh8fnjyN1l3rJR7Pbn6GWtnlCCCGEECIco94fL6rDXTwef8mHZKhdUkMthBBCCCG6ppZ8tLSWeqhdPnSXekAtNdRCCCGEECISRkO7DLVXaqiBczUwUkMthBBCCCHCMXUIqP0lH1JD7fL/QKSGWgghhBBChBMo+XAHl3xc8hlqteRDRo8LIYQQQohwTO0PJXqlywcAzS4fZqMOrbZ3fhBCCCGEECI6GI3BbfPckqH2a3Z5pH5aCCGEEEJ0yaT3h7iu1i4f3kANdT8IqB9++GEKCgoAePfdd1m0aBELFy7ktttuo66uDoCSkhKWL1/O7NmzWb16NU6nEwCHw8HKlSuZM2cOy5cvx263d2uhLS1eCaiFEEIIIUSX1C4fLa52GWp9HwfUO3fuZMOGDQA0NDTwy1/+khdeeIHNmzczevRonn76aQAeeOABli1bxtatW5kwYQLPPvssAE8++SR5eXls2bKFpUuXsnbt2m4ttMnlIcYkBxKFEEIIIUR46qFEl8cfUKsZan1f1lDX1tbyxBNPsGrVKgDcbjf3338/6enpAIwePZrS0lLcbje7d+9m1qxZAOTn57N161YAtm3bxoIFCwCYP38+27dvx+12R7zQFrdkqIUQQgghRNd0Wi16nQZXa5cPNUOt76WSj4gi1DVr1nDXXXdRWloKQFJSEjfddBMAzc3NvPDCC6xYsYKamhqsVit6vf+ftdlslJeXA1BRUYHNZvN/U70eq9VKdXV1ICjvitvrIzHOjM0W170dDiDRurdo3ZcqmvcXzXtTRfMeo3lvKtnjwBbNe1NF8x77+95MRj06gw6bLQ6TyQBARno8Gs2Fz1J3GVC//vrrZGZmMm3aNNavXx/0d/X19fzoRz9izJgxLF68mPLy8g6L7GzRiqKg1Ub+KaGhyUOixYTdXh/x1wwkNltcVO4tWveliub9RfPeVNG8x2jem0r2OLBF895U0bzHgbA3o15LbV0Tdns9dfVN6HUaKisbwn6NVqshJcXa7e/VZUBdWFiI3W5n0aJF1NXV0djYyK9//Wt+8IMf8P3vf5+pU6dyzz33AJCcnEx9fT1erxedTofdbictLQ2AtLQ0KisrycjIwOPx4HQ6SUxMjHihzS4PsVLyIYQQQgghImDUa4O6fPRWuQdEEFC/+OKLgf9ev349u3bt4mc/+xlLly5lzpw53HbbbYG/NxgM5OXlUVhYyIIFC9i4cSPTp08HYMaMGWzcuJFVq1ZRWFhIXl4eBoMh4oV6PD45lCiEEEIIISJiNOgCg13cXl/fBtShvP/++xw4cACv18s777wDwIQJE1i7di33338/BQUFPPfcc2RmZvL4448DcOedd1JQUMC8efOIi4vj0Ucf7fb3NUuGWgghhBBCRMBk0AUGu3i9vl7r8AHdDKjz8/PJz88H4NChQyHvk52dzSuvvNLh9sTERJ5//vkeLPEcKfkQQgghhBCRMBq057p8eHq35GPATEoEpG2eEEIIIYSIiFF/ruTD6+vdkg8JqIUQQgghRNQxGc+VfLg9vVvyMcACajmUKIQQQgghuhbU5cMnJR8BkqEWQgghhBCRCOry4ZGSjwAJqIUQQgghRCRMBh3NrovT5WNgBdRGCaiFEEIIIUTXzEYdXp+Cx+vD3cuDXQZMQK3TaTHoB8xyhRBCCCFEHzIZ/Wfvml1ePL082GXARKgxRjmQKIQQQgghImMOBNSe1oBaSj4CPxQhhBBCCCG6Ym4tFQ5kqHux0mEABdRSPy2EEEIIISKjJmNbXF48XgW9VgJqyVALIYQQQoiImQztaqglQy0ZaiGEEEIIETlz0KFEBb1WaqgxmwbMUoUQQgghRB/rcChRMtRgkgy1EEIIIYSIkBo7tri90uVDZTZIDbUQQgghhIiMmqFubPagKEgfapAaaiGEEEIIETmjXotGAw1NbgAMElBDjEky1EIIIYQQIjIajQazUYezNaDWSUAtGWohhBBCCNE9JoOuTYa6n9RQP/zwwxQUFABw8OBB8vPzmTVrFr/4xS/weDwAlJSUsHz5cmbPns3q1atxOp0AOBwOVq5cyZw5c1i+fDl2u71bC5U+1EIIIYQQojvMRj0Nzf0oQ71z5042bNgQ+PNPf/pT1qxZwzvvvIOiKPztb38D4IEHHmDZsmVs3bqVCRMm8OyzzwLw5JNPkpeXx5YtW1i6dClr167t1kIlQy2EEEIIIbrDZNTR0ORP+vZ5DXVtbS1PPPEEq1atAuDs2bM0NzczefJkAPLz89m6dStut5vdu3cza9asoNsBtm3bxoIFCwCYP38+27dvx+12R7zQnDRL5LsSQgghhBCXvJigGureK/mIKO27Zs0a7rrrLkpLSwGoqKjAZrMF/t5ms1FeXk5NTQ1WqxW9Xh90e/uv0ev1WK1WqqurSU9Pj2ihttS4yHc1QNls0bnHaN2XKpr3F817U0XzHqN5byrZ48AWzXtTRfMeB8Le4qwmGs/UApCSbOm1NXcZUL/++utkZmYybdo01q9fD4DP50OjORflK4qCRqMJ/H9b7f/c9mu02shT71VVDfh8SsT3H2hstjjs9vq+XsYFF637UkXz/qJ5b6po3mM0700lexzYonlvqmje40DZm0ZRUMNHZ0NLl2vWajWkpFi7/X26DKgLCwux2+0sWrSIuro6Ghsb0Wg0QYcKKysrSUtLIzk5mfr6erxeLzqdDrvdTlpaGgBpaWlUVlaSkZGBx+PB6XSSmJjY7QULIYQQQggRibZn8Pq0y8eLL77IW2+9xaZNm7jjjjuYOXMmDz30ECaTiU8++QSATZs2MX36dAwGA3l5eRQWFgKwceNGpk+fDsCMGTPYuHEj4A/S8/LyMBgMvbUvIYQQQghxiWvbJa5fdPlo79FHH+Whhx5i9uzZNDY2cssttwBw//3387e//Y25c+eyZ88efvKTnwBw55138vnnnzNv3jxeffVV1qxZc2F2IIQQQgghRAhtA2qDvvcC6m71osvPzyc/Px+AMWPG8MYbb3S4T3Z2Nq+88kqH2xMTE3n++ed7uEwhhBBCCCG6x9Q2Q63tJ4NdhBBCCCGEGCiCaqh7MUMtAbUQQgghhIhKZkM/r6EWQgghhBCiPwuqoZaAWgghhBBCiO4JqqHuy7Z5QgghhBBCDEQmyVALIYQQQgjRc20PJeolQy2EEEIIIUT3yKFEIYQQQgghzoPZ5A+odVoNWo1kqIUQQgghhOgWU2uGWt+L2WmQgFoIIYQQQkQpvU7b+r/ey05DN0ePCyGEEEIIMZCYjbpeHTsOElALIYQQQogoZjbqUBSlV7+HlHwIIYQQQoioZTLqer2GWjLUQgghhBAiapkNOpp7+XtIQC2EEEIIIaJWjEmP19e7JR8SUAshhBBCiKi18NqhuNzeXv0eElALIYQQQoioNSI7ode/hxxKFEIIIYQQ4jxEFFD/7ne/Y+7cucybN48XX3wRgA8//JCFCxcyf/58/u///b+4XC4ASkpKWL58ObNnz2b16tU4nU4AHA4HK1euZM6cOSxfvhy73d5LWxJCCCGEEOLi6TKg3rVrFx999BGbN29m3bp1vPLKKxQVFfGLX/yCJ554grfeeovm5mY2bdoEwAMPPMCyZcvYunUrEyZM4NlnnwXgySefJC8vjy1btrB06VLWrl3buzsTQgghhBDiIugyoL7qqqt4+eWX0ev1VFVV4fV6iY2Nxev10tDQgNfrpaWlBZPJtwhEAAARi0lEQVTJhNvtZvfu3cyaNQuA/Px8tm7dCsC2bdtYsGABAPPnz2f79u243e5e3JoQQgghhBC9L6JDiQaDgaeeeoo//OEPzJ49m/T0dH75y1+yYsUKrFYrOTk5zJ49m5qaGqxWK3q9/5+12WyUl5cDUFFRgc1m839TvR6r1Up1dTXp6ekRLTQlxdqT/Q0oNltcXy+hV0TrvlTRvL9o3psqmvcYzXtTyR4Htmjemyqa9xjNe+uuiLt83HHHHfzwhz9k1apVPPPMM7z11lu89dZb5OTk8NBDD/HQQw+xatUqNJrgWent/6xSFAWtNvIzkVVVDfh6uYdgX7LZ4rDb6/t6GRdctO5LFc37i+a9qaJ5j9G8N5XscWCL5r2ponmP0bo3rVbToyRulxHt8ePHOXjwIAAxMTHcfPPNbNmyhVGjRjFo0CC0Wi3f+MY32LVrF8nJydTX1+P1+nv92e120tLSAEhLS6OyshIAj8eD0+kkMTGx2wsWQgghhBCiP+kyQ11cXMxTTz3FX/7yFwDee+89Fi5cyF/+8hcqKytJTU3lvffeY+LEiRgMBvLy8igsLGTBggVs3LiR6dOnAzBjxgw2btzIqlWrKCwsJC8vD4PBEPFCtdrQme5oEq17jNZ9qaJ5f9G8N1U07zGa96aSPQ5s0bw3VTTvMRr31tM9aRRF6bKO4umnn2bLli3odDpuvvlmbr/9djZs2MD//M//oNPpGDx4MP/93/9NcnIyZ8+epaCggKqqKjIzM3n88cdJSEigtraWgoICzpw5Q1xcHI8++ig5OTk9WrQQQgghhBD9RUQBtRBCCCGEECI0mZQohBBCCCHEeZCAWgghhBBCiPMgAbUQQgghhBDnQQJqIYQQQgghzoME1EIIIYQQQpwHCaiFEEIIIYQ4DxJQCyGEEEIIcR4koBZCCCGEEOI89LuAev369RQUFPT1Mi644uJiJkyYwKJFi4L+V1paGvL+Tz/9NE8//fRFXmX3FBcXM3r0aNasWRN0+8GDBxk9ejTr16/vo5X1jiNHjjB69Gjeeeedvl7KebvUHjuI3teWtrraY0FBwYB7bKPpeRfK1q1byc/PZ+HChSxYsIDf//73fb2kC8rpdPLAAw9w0003sXDhQpYtW8bOnTs7vX99fT0/+tGPLuIKe059Hd2xY0fQ7TNnzqS4uLiPVnVhtI9ZZs2axc9//nMqKyv7emn9lr6vF3ApSUtLY9OmTX29jAsqMTGRDz74AK/Xi06nA6CwsJDk5OQ+XtmFt27dOmbPns1rr73GrFmz+no55+1SeuzEwBVtz7u2ysvLefjhh1m/fj1JSUk4nU5WrFjB0KFDueGGG/p6eedNURRWrVrF2LFjefvttzEajRw4cICVK1fy2GOPMWXKlA5fU1dXx8GDB/tgtT1jMBi477772Lx5M1arta+Xc0G1jVkUReHxxx/njjvu4NVXX+3jlfVP/S5Drdq1axff/va3Wbx4MTfccAPvvvsu4M+wPPjgg3z7299m5syZrFu3ro9Xen4qKyu57bbbyM/PZ8mSJfz73/8O/N3evXtZunQp8+bN46WXXurDVXbOYrEwduxYdu/eHbhtx44dXH311QD86U9/YunSpcyfP5/FixdTVFQE+D/B/+QnP2HWrFlUVVX1ydq7w+128+abb/KTn/yE/fv3c/r0acC/j0ceeYSvfe1rfO1rX+PAgQMArFixgh//+MfMmjWr37459OSx27lzJ9/61rcC91+/fj3333//RV/7+VixYgUff/wx4M/CzJw5E4iu15bO9jjQhHveqRnAjz/+mBUrVgD+bHZ+fj6LFi3iV7/6FTfddFOfrT0SNTU1uN1umpubAf9z8je/+Q0jRoxg7969gffA//iP/+DMmTOA/7Fdu3YtixcvZu7cuXz44Yd9uYWwdu3aRUlJCT//+c8xGo0AjBs3jtWrV/Pss89y8OBBli5dyoIFC/jOd75DWVkZDz74IBUVFQMmS52WlsbVV1/Nww8/3OHvnn/+eebOncuCBQv4zW9+g9fr5aGHHuIPf/hD4D633347//jHPy7mkntEo9Fw++23c/ToUQ4dOsQLL7zA4sWLWbhwIY888giKogDwxz/+kVmzZjF37lx++9vf9vGqL65+G1D/6U9/4sEHH2TDhg08+OCD/O53vwv8XVlZGa+++irPPfccjzzySB+usnsqKiqCyj1+//vfs3btWpYsWcL69et57rnnWLNmDQ0NDQDY7XZeeuklXnvtNf785z/328Bszpw5gcuxe/fuZfTo0RgMBhoaGnj33Xd55ZVXeOutt7juuuv485//HPi66dOn884775CSktJXS4/Yv/71L7Kyshg6dCg33ngjr732WuDvYmNj2bhxI3fccQc/+9nPArerl6nHjh3bF0uOSHcfu6lTp2K32wOBzcaNG8nPz+/LLVxQA/W1JVqFe96FUlBQwJ133smmTZvIzc3F6/VepJX2zJgxY7jhhhu48cYb+frXv85vf/tbfD4fmZmZ3HvvvTz22GNs2LCBW2+9lfvuuy/wdQ0NDWzYsIHHHnuMgoICXC5XH+6ic19++SUTJkxAo9EE3X7llVfy5Zdfcvfdd3Pbbbfx5ptvMnfuXF566SXuvfde0tLSeOaZZ/po1d1XUFDAhx9+GFT6sX37dt5//33WrVvHhg0bOHXqFH/9619ZtGgRb731FuB/HD/77DNmzJjRV0vvFqPRyODBgzl06BD79u3jjTfeYOPGjZSXl7N582b27t3Lq6++yhtvvMHmzZvZv38/+/bt6+tlXzT9tuTjt7/9Lf/85z/ZunUrX3zxBU6nM/B311xzDRqNhlGjRlFbW9uHq+yeUCUfU6ZMoaioiKeeegoAj8cTyETMnTuX2NhYAK6//np27drVL4OzmTNn8uSTT+Lz+diyZQtz5syhsLAQq9XKY489xttvv83Jkyf54IMPgtY/adKkPlx196xbt4758+cD/sfl7rvv5s477wTgG9/4BuD/ORQUFFBdXQ3AZZdd1jeL7YbuPnYajYbFixezefNm8vPzqaqqGlCPY1cG6mtLtAr3vGuvtraWs2fPBoKTJUuW8PLLL1+0tfbUAw88wG233caHH37Ihx9+yDe+8Q1WrlzJmTNnWL16deB+aqIFzr3mjB07FpvNxuHDh5k4ceJFX3tXNBpNyA81brcbn8+H3W7n+uuvB2DZsmUAA7L22Gq18qtf/SpQ+gHw0UcfMW/ePGJiYgD/7+PGjRtZvnw5LpeLU6dO8dlnnzFz5sxA9n4g0Gg0vPzyy1RXVweSKc3NzWRlZVFZWcn1119PXFwc4M9WX0r6RUC9Z88ecnNzSU9PR1EUdDody5YtY8qUKUyZMoVp06Zx9913B+5vMpkAOnzqHYh8Ph8vvfQSiYmJgD+LnZKSwrvvvoterw+6X9s/9ycWi4UxY8bwySef8NFHH/Ff//VfFBYWUlpayje/+U2+853vMH36dFJTU4Oy7Orj2N9VVVXxwQcfsH//fl5++WUURcHhcAQu07V/nNR6ZLPZ3Cfr7Y6ePHaLFy/mBz/4AUajkUWLFvXxDsIL9doCBC5PejyeoPsPxNeW7u5xoOjqedd+fzqdLnDbQLFt2zYaGxuZO3cuS5YsYcmSJfztb3/jzTffJCcnJ5CA8Xq9QYfB1McY+vd7w6RJk3jllVdwu90YDIbA7Z9//jkTJ07kyJEjgdtaWlqoqKgYUM+9tq699tqg0g+fz9fhPurv6sKFCyksLOSzzz5j5cqVF3Wd58PlcnHixAmmTJnCggULuPXWWwFwOBzodDreeOONoMevvLycmJgY4uPj+2rJF1W/KPlYt25doEb68OHD5ObmcvLkSe68806mT5/Oe++91+8v3fXU1KlTAwX+x44dY8GCBTQ1NQHwzjvv4HK5qKurY9u2bUydOrUvlxrWnDlzeOyxx5gwYULgxT02NpbBgwfzve99j4kTJ/Luu+8OyMdx06ZNTJ06NXAJ75///CerVq3ir3/9KwBvv/02AP/4xz8YPnw4CQkJfbncbuvuY5ednU1GRkbg8mV/Fuq1JSkpiWPHjgEE/m4gi9Y9hnvetd3fe++9B0BcXBy5ubn861//AuDNN9/ss7VHymw289hjjwWysoqicPDgQSZPnkxdXR179uwB/I9x26RSYWEh4C+pcDgcjBo16uIvPgJ5eXmMGDGCX//617jdbgD27dvHc889x49//GPS09MDNeCbNm3id7/7HXq9fsB+CFRLPyoqKpg6dSpvv/02zc3NeDwe1q1bF3gPX7BgAYWFhZw6dYorrriij1cdGZ/Px9NPP82kSZNYsmQJmzZtwul04vF4+NGPfsQ777zD/2/v/kKa6uM4jr9HmxBIsguXDcULk+qqgiIJ1x81UdeIUcogozBJoSTEPIhFdOFgbOJ2caiL6qa6rCySCsGLILyzq5TAi5QIES+CoGgyty7EwzOeHqn25Jn5ed3tnO3H5zA2vvzO+X1/+/bt49WrV9bxnp4ePfKx1i5cuIBhGDx48ICSkhISiQSfPn3C7/fjdDqpqqri27dvfP361e6o/7tr165x/fp1AoEAANFo1Fop7PV6CYVCJJNJOjo6qKiosDPqqo4ePcrVq1ezbse6XC7S6TRNTU1kMhn279/P9PS0jSl/z/DwMN3d3VnHTp8+zZ07dygsLOTNmzc8fPiQzZs3E4lEbEr5+37nu2tqamJ0dJStW7faEfmn/ei/ZXZ2lr6+Ph49evRXdFL4W69xtd+dYRiEw2FM06S6uto6H41G6e/vJ5FIsGPHjry/S1RVVcWlS5fo7Oy0Ck6fz0dXVxc1NTWEw2GSySSFhYVZi94+fPhAMBgEIB6PZ81Y5xvTNInH4xw/fpxNmzZRVFRELBbjwIEDxGIxbty4QSwWw+12E41GcbvdeL1ezpw5w/379+2O/0tWHv04f/48R44c4fPnz5w8eZJUKkV1dTWtra0AbNu2Dbfbzd69e/N6Rn5l3RcsF9S7du1iaGiIoqIi3r17R0tLC0tLS/h8PoLBIA6Hg9bWVkKhEOl0mmPHjlmL3DcCR2a93SMTySM1NTXcu3eP0tJSu6OsmVQqhWEYNDQ0UF9fb3ccEYtpmrS0tODxeBgdHeXZs2d538//V610EPpRyzkRsU9ezFCLyPqQyWTw+XwcPHiQuro6u+OIZPF6vbS1teF0OtmyZQvhcNjuSCKyQWiGWkREREQkB3mxKFFEREREZL2ytaA2TRO/34/f77c2URgfHycQCFBfX088Hv/XZwzD4PHjx9briYkJTp06xYkTJzh79iwfP35cs/wiIiIiIrYV1OPj47x+/Zrh4WGePHnC5OQkIyMj9Pf3c/PmTZ4/f87bt2+tFkjz8/N0dnZau7qt6O3tZWBggKdPnxIIBBgYGLDjckRERERkg7KtoC4uLqavr4+CggJcLhcVFRXMzMxQXl5OWVkZTqeTQCDAy5cvgeWeorW1tTQ2NlpjLC4ucvnyZXbu3Aksb/U8Nzdny/WIiIiIyMZkW0FdWVnJnj17AJiZmeHFixc4HA6Ki4ut93g8Hubn5wFob2+nubk5a4x/7tSWTqcxTVOdB0RERERkTdm+KHF6epq2tjYMw6CsrCyryXkmk/mppueLi4tcuXKFVCpFR0fHn4wrIiIiIpLF1oJ6YmKCc+fO0dPTQzAYpKSkhIWFBev8wsICHo9n1TG+fPlCe3s7qVSKW7du4XK5/nRsERERERGLbQX13NwcFy9eZHBwEL/fD8Du3bt5//49s7OzLC0tMTIywqFDh1Ydp7e3l/LychKJBAUFBWsRXURERETEYttOiXfv3iWZTBKJRKxjoVCISCRCV1cXyWSSw4cP09DQ8J9jTE1NMTY2xvbt2wkGg8Dyc9e3b9/+4/lFREREREA7JYqIiIiI5MT2RYkiIiIiIuuZCmoRERERkRyooBYRERERyYEKahERERGRHKigFhERERHJgQpqEREREZEcqKAWEREREcnBd/hwd0uhsGl1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,4))\n",
    "births_by_day.plot(ax=ax)"
   ]
  }
 ],
 "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": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}