Course_workbook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../assets/packt-banner.png\" alt=\"\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing Data for Predictive Modeling\n",
    "\n",
    "We explore methods for processing data with Python using Pandas, by running through various examples on a sample dataset.\n",
    "\n",
    "Then, we introduce the Human Resource Analytics and prepare it for use in training machine learning models.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import time\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "%config InlineBackend.figure_format='retina'\n",
    "sns.set() # Revert to matplotlib defaults\n",
    "plt.rcParams['figure.figsize'] = (9, 6)\n",
    "plt.rcParams['axes.labelpad'] = 10\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-02-12 \n",
      "\n",
      "CPython 3.7.5\n",
      "IPython 7.10.1\n",
      "\n",
      "requests 2.22.0\n",
      "numpy 1.17.4\n",
      "pandas 0.25.3\n",
      "matplotlib 3.1.1\n",
      "seaborn 0.9.0\n",
      "sklearn 0.21.3\n",
      "\n",
      "compiler   : Clang 4.0.1 (tags/RELEASE_401/final)\n",
      "system     : Darwin\n",
      "release    : 18.7.0\n",
      "machine    : x86_64\n",
      "processor  : i386\n",
      "CPU cores  : 8\n",
      "interpreter: 64bit\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -d -v -m -p requests,numpy,pandas,matplotlib,seaborn,sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing data for modeling\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging data\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'inner'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mleft_on\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mright_on\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mleft_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mright_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msuffixes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_x'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mindicator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Merge DataFrame or named Series objects with a database-style join.\n",
       "\n",
       "The join is done on columns or indexes. If joining columns on\n",
       "columns, the DataFrame indexes *will be ignored*. Otherwise if joining indexes\n",
       "on indexes or indexes on a column or columns, the index will be passed on.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "left : DataFrame\n",
       "right : DataFrame or named Series\n",
       "    Object to merge with.\n",
       "how : {'left', 'right', 'outer', 'inner'}, default 'inner'\n",
       "    Type of merge to be performed.\n",
       "\n",
       "    * left: use only keys from left frame, similar to a SQL left outer join;\n",
       "      preserve key order.\n",
       "    * right: use only keys from right frame, similar to a SQL right outer join;\n",
       "      preserve key order.\n",
       "    * outer: use union of keys from both frames, similar to a SQL full outer\n",
       "      join; sort keys lexicographically.\n",
       "    * inner: use intersection of keys from both frames, similar to a SQL inner\n",
       "      join; preserve the order of the left keys.\n",
       "on : label or list\n",
       "    Column or index level names to join on. These must be found in both\n",
       "    DataFrames. If `on` is None and not merging on indexes then this defaults\n",
       "    to the intersection of the columns in both DataFrames.\n",
       "left_on : label or list, or array-like\n",
       "    Column or index level names to join on in the left DataFrame. Can also\n",
       "    be an array or list of arrays of the length of the left DataFrame.\n",
       "    These arrays are treated as if they are columns.\n",
       "right_on : label or list, or array-like\n",
       "    Column or index level names to join on in the right DataFrame. Can also\n",
       "    be an array or list of arrays of the length of the right DataFrame.\n",
       "    These arrays are treated as if they are columns.\n",
       "left_index : bool, default False\n",
       "    Use the index from the left DataFrame as the join key(s). If it is a\n",
       "    MultiIndex, the number of keys in the other DataFrame (either the index\n",
       "    or a number of columns) must match the number of levels.\n",
       "right_index : bool, default False\n",
       "    Use the index from the right DataFrame as the join key. Same caveats as\n",
       "    left_index.\n",
       "sort : bool, default False\n",
       "    Sort the join keys lexicographically in the result DataFrame. If False,\n",
       "    the order of the join keys depends on the join type (how keyword).\n",
       "suffixes : tuple of (str, str), default ('_x', '_y')\n",
       "    Suffix to apply to overlapping column names in the left and right\n",
       "    side, respectively. To raise an exception on overlapping columns use\n",
       "    (False, False).\n",
       "copy : bool, default True\n",
       "    If False, avoid copy if possible.\n",
       "indicator : bool or str, default False\n",
       "    If True, adds a column to output DataFrame called \"_merge\" with\n",
       "    information on the source of each row.\n",
       "    If string, column with information on source of each row will be added to\n",
       "    output DataFrame, and column will be named value of string.\n",
       "    Information column is Categorical-type and takes on a value of \"left_only\"\n",
       "    for observations whose merge key only appears in 'left' DataFrame,\n",
       "    \"right_only\" for observations whose merge key only appears in 'right'\n",
       "    DataFrame, and \"both\" if the observation's merge key is found in both.\n",
       "\n",
       "validate : str, optional\n",
       "    If specified, checks if merge is of specified type.\n",
       "\n",
       "    * \"one_to_one\" or \"1:1\": check if merge keys are unique in both\n",
       "      left and right datasets.\n",
       "    * \"one_to_many\" or \"1:m\": check if merge keys are unique in left\n",
       "      dataset.\n",
       "    * \"many_to_one\" or \"m:1\": check if merge keys are unique in right\n",
       "      dataset.\n",
       "    * \"many_to_many\" or \"m:m\": allowed, but does not result in checks.\n",
       "\n",
       "    .. versionadded:: 0.21.0\n",
       "\n",
       "Returns\n",
       "-------\n",
       "DataFrame\n",
       "    A DataFrame of the two merged objects.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "merge_ordered : Merge with optional filling/interpolation.\n",
       "merge_asof : Merge on nearest keys.\n",
       "DataFrame.join : Similar method using indices.\n",
       "\n",
       "Notes\n",
       "-----\n",
       "Support for specifying index levels as the `on`, `left_on`, and\n",
       "`right_on` parameters was added in version 0.23.0\n",
       "Support for merging named Series objects was added in version 0.24.0\n",
       "\n",
       "Examples\n",
       "--------\n",
       "\n",
       ">>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],\n",
       "...                     'value': [1, 2, 3, 5]})\n",
       ">>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],\n",
       "...                     'value': [5, 6, 7, 8]})\n",
       ">>> df1\n",
       "    lkey value\n",
       "0   foo      1\n",
       "1   bar      2\n",
       "2   baz      3\n",
       "3   foo      5\n",
       ">>> df2\n",
       "    rkey value\n",
       "0   foo      5\n",
       "1   bar      6\n",
       "2   baz      7\n",
       "3   foo      8\n",
       "\n",
       "Merge df1 and df2 on the lkey and rkey columns. The value columns have\n",
       "the default suffixes, _x and _y, appended.\n",
       "\n",
       ">>> df1.merge(df2, left_on='lkey', right_on='rkey')\n",
       "  lkey  value_x rkey  value_y\n",
       "0  foo        1  foo        5\n",
       "1  foo        1  foo        8\n",
       "2  foo        5  foo        5\n",
       "3  foo        5  foo        8\n",
       "4  bar        2  bar        6\n",
       "5  baz        3  baz        7\n",
       "\n",
       "Merge DataFrames df1 and df2 with specified left and right suffixes\n",
       "appended to any overlapping columns.\n",
       "\n",
       ">>> df1.merge(df2, left_on='lkey', right_on='rkey',\n",
       "...           suffixes=('_left', '_right'))\n",
       "  lkey  value_left rkey  value_right\n",
       "0  foo           1  foo            5\n",
       "1  foo           1  foo            8\n",
       "2  foo           5  foo            5\n",
       "3  foo           5  foo            8\n",
       "4  bar           2  bar            6\n",
       "5  baz           3  baz            7\n",
       "\n",
       "Merge DataFrames df1 and df2, but raise an exception if the DataFrames have\n",
       "any overlapping columns.\n",
       "\n",
       ">>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))\n",
       "Traceback (most recent call last):\n",
       "...\n",
       "ValueError: columns overlap but no suffix specified:\n",
       "    Index(['value'], dtype='object')\n",
       "\u001b[0;31mFile:\u001b[0m      /anaconda3/lib/python3.7/site-packages/pandas/core/reshape/merge.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.merge?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build a simple set of tables\n",
    "\n",
    "df_1 = pd.DataFrame({\n",
    "    'product': [\n",
    "        'red shirt', 'red shirt',\n",
    "        'red shirt', 'white dress',\n",
    "    ],\n",
    "    'price': [\n",
    "        49.33, 49.33, 32.49, 199.99,\n",
    "    ],\n",
    "})\n",
    "df_2 = pd.DataFrame({\n",
    "    'product': [\n",
    "        'red shirt', 'blue pants',\n",
    "        'white tuxedo', 'white dress',\n",
    "    ],\n",
    "    'in_stock': [\n",
    "        True, True, False, False,\n",
    "    ],\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>32.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       product   price\n",
       "0    red shirt   49.33\n",
       "1    red shirt   49.33\n",
       "2    red shirt   32.49\n",
       "3  white dress  199.99"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>in_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>blue pants</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>white tuxedo</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product  in_stock\n",
       "0     red shirt      True\n",
       "1    blue pants      True\n",
       "2  white tuxedo     False\n",
       "3   white dress     False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>32.49</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.99</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       product   price  in_stock\n",
       "0    red shirt   49.33      True\n",
       "1    red shirt   49.33      True\n",
       "2    red shirt   32.49      True\n",
       "3  white dress  199.99     False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inner merge\n",
    "\n",
    "df = pd.merge(left=df_1, right=df_2, on='product', how='inner')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>32.49</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.99</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue pants</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white tuxedo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product   price  in_stock\n",
       "0     red shirt   49.33      True\n",
       "1     red shirt   49.33      True\n",
       "2     red shirt   32.49      True\n",
       "3   white dress  199.99     False\n",
       "4    blue pants     NaN      True\n",
       "5  white tuxedo     NaN     False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outer merge\n",
    "\n",
    "df = pd.merge(left=df_1, right=df_2, on='product', how='outer')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### De-duplicating\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>32.49</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.99</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue pants</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white tuxedo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product   price  in_stock\n",
       "0     red shirt   49.33      True\n",
       "2     red shirt   32.49      True\n",
       "3   white dress  199.99     False\n",
       "4    blue pants     NaN      True\n",
       "5  white tuxedo     NaN     False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard method\n",
    "\n",
    "df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using masks for more fine tuned control\n",
    "\n",
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     product  price  in_stock\n",
       "1  red shirt  49.33      True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>32.49</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.99</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue pants</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white tuxedo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product   price  in_stock\n",
       "0     red shirt   49.33      True\n",
       "2     red shirt   32.49      True\n",
       "3   white dress  199.99     False\n",
       "4    blue pants     NaN      True\n",
       "5  white tuxedo     NaN     False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~(df.duplicated())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.99</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue pants</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white tuxedo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product   price  in_stock\n",
       "0     red shirt   49.33      True\n",
       "3   white dress  199.99     False\n",
       "4    blue pants     NaN      True\n",
       "5  white tuxedo     NaN     False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = ~(df['product'].duplicated())\n",
    "df[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>32.49</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.99</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue pants</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white tuxedo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product   price  in_stock\n",
       "0     red shirt   49.33      True\n",
       "2     red shirt   32.49      True\n",
       "3   white dress  199.99     False\n",
       "4    blue pants     NaN      True\n",
       "5  white tuxedo     NaN     False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's overwrite the DataFrame using drop_duplicates\n",
    "\n",
    "df.drop_duplicates(inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling missing data\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>32.49</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.99</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       product   price  in_stock\n",
       "0    red shirt   49.33      True\n",
       "2    red shirt   32.49      True\n",
       "3  white dress  199.99     False"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the incomplete rows\n",
    "\n",
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>in_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue pants</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white tuxedo</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product  in_stock\n",
       "0     red shirt      True\n",
       "2     red shirt      True\n",
       "3   white dress     False\n",
       "4    blue pants      True\n",
       "5  white tuxedo     False"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the incomplete columns\n",
    "\n",
    "df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdowncast\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Fill NA/NaN values using the specified method.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "value : scalar, dict, Series, or DataFrame\n",
       "    Value to use to fill holes (e.g. 0), alternately a\n",
       "    dict/Series/DataFrame of values specifying which value to use for\n",
       "    each index (for a Series) or column (for a DataFrame).  Values not\n",
       "    in the dict/Series/DataFrame will not be filled. This value cannot\n",
       "    be a list.\n",
       "method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None\n",
       "    Method to use for filling holes in reindexed Series\n",
       "    pad / ffill: propagate last valid observation forward to next valid\n",
       "    backfill / bfill: use next valid observation to fill gap.\n",
       "axis : {0 or 'index', 1 or 'columns'}\n",
       "    Axis along which to fill missing values.\n",
       "inplace : bool, default False\n",
       "    If True, fill in-place. Note: this will modify any\n",
       "    other views on this object (e.g., a no-copy slice for a column in a\n",
       "    DataFrame).\n",
       "limit : int, default None\n",
       "    If method is specified, this is the maximum number of consecutive\n",
       "    NaN values to forward/backward fill. In other words, if there is\n",
       "    a gap with more than this number of consecutive NaNs, it will only\n",
       "    be partially filled. If method is not specified, this is the\n",
       "    maximum number of entries along the entire axis where NaNs will be\n",
       "    filled. Must be greater than 0 if not None.\n",
       "downcast : dict, default is None\n",
       "    A dict of item->dtype of what to downcast if possible,\n",
       "    or the string 'infer' which will try to downcast to an appropriate\n",
       "    equal type (e.g. float64 to int64 if possible).\n",
       "\n",
       "Returns\n",
       "-------\n",
       "DataFrame\n",
       "    Object with missing values filled.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "interpolate : Fill NaN values using interpolation.\n",
       "reindex : Conform object to new index.\n",
       "asfreq : Convert TimeSeries to specified frequency.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],\n",
       "...                    [3, 4, np.nan, 1],\n",
       "...                    [np.nan, np.nan, np.nan, 5],\n",
       "...                    [np.nan, 3, np.nan, 4]],\n",
       "...                   columns=list('ABCD'))\n",
       ">>> df\n",
       "     A    B   C  D\n",
       "0  NaN  2.0 NaN  0\n",
       "1  3.0  4.0 NaN  1\n",
       "2  NaN  NaN NaN  5\n",
       "3  NaN  3.0 NaN  4\n",
       "\n",
       "Replace all NaN elements with 0s.\n",
       "\n",
       ">>> df.fillna(0)\n",
       "    A   B   C   D\n",
       "0   0.0 2.0 0.0 0\n",
       "1   3.0 4.0 0.0 1\n",
       "2   0.0 0.0 0.0 5\n",
       "3   0.0 3.0 0.0 4\n",
       "\n",
       "We can also propagate non-null values forward or backward.\n",
       "\n",
       ">>> df.fillna(method='ffill')\n",
       "    A   B   C   D\n",
       "0   NaN 2.0 NaN 0\n",
       "1   3.0 4.0 NaN 1\n",
       "2   3.0 4.0 NaN 5\n",
       "3   3.0 3.0 NaN 4\n",
       "\n",
       "Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1,\n",
       "2, and 3 respectively.\n",
       "\n",
       ">>> values = {'A': 0, 'B': 1, 'C': 2, 'D': 3}\n",
       ">>> df.fillna(value=values)\n",
       "    A   B   C   D\n",
       "0   0.0 2.0 2.0 0\n",
       "1   3.0 4.0 2.0 1\n",
       "2   0.0 1.0 2.0 5\n",
       "3   0.0 3.0 2.0 4\n",
       "\n",
       "Only replace the first NaN element.\n",
       "\n",
       ">>> df.fillna(value=values, limit=1)\n",
       "    A   B   C   D\n",
       "0   0.0 2.0 2.0 0\n",
       "1   3.0 4.0 NaN 1\n",
       "2   NaN 1.0 NaN 5\n",
       "3   NaN 3.0 NaN 4\n",
       "\u001b[0;31mFile:\u001b[0m      /anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DataFrame method for filling missing values\n",
    "\n",
    "df.fillna?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>32.49</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.99</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue pants</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white tuxedo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product   price  in_stock\n",
       "0     red shirt   49.33      True\n",
       "2     red shirt   32.49      True\n",
       "3   white dress  199.99     False\n",
       "4    blue pants     NaN      True\n",
       "5  white tuxedo     NaN     False"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.330000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>32.490000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.990000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue pants</td>\n",
       "      <td>93.936667</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white tuxedo</td>\n",
       "      <td>93.936667</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product       price  in_stock\n",
       "0     red shirt   49.330000      True\n",
       "2     red shirt   32.490000      True\n",
       "3   white dress  199.990000     False\n",
       "4    blue pants   93.936667      True\n",
       "5  white tuxedo   93.936667     False"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill with the average\n",
    "\n",
    "fill_value = df.price.mean()\n",
    "df.fillna(value=fill_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>32.49</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.99</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue pants</td>\n",
       "      <td>199.99</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white tuxedo</td>\n",
       "      <td>199.99</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product   price  in_stock\n",
       "0     red shirt   49.33      True\n",
       "2     red shirt   32.49      True\n",
       "3   white dress  199.99     False\n",
       "4    blue pants  199.99      True\n",
       "5  white tuxedo  199.99     False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill with the previous value in that column\n",
    "\n",
    "df.fillna(method='pad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's fill the data using the average\n",
    "\n",
    "df = df.fillna(value=df.price.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning for Machine Learning with Pandas\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.330000</td>\n",
       "      <td>True</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>32.490000</td>\n",
       "      <td>True</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.990000</td>\n",
       "      <td>False</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue pants</td>\n",
       "      <td>93.936667</td>\n",
       "      <td>True</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white tuxedo</td>\n",
       "      <td>93.936667</td>\n",
       "      <td>False</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product       price  in_stock  rating\n",
       "0     red shirt   49.330000      True     low\n",
       "2     red shirt   32.490000      True  medium\n",
       "3   white dress  199.990000     False     low\n",
       "4    blue pants   93.936667      True    high\n",
       "5  white tuxedo   93.936667     False    high"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, let's create a new column. This will allow\n",
    "# us to explore more data transformation techniques\n",
    "\n",
    "ratings = ['low', 'medium', 'high']\n",
    "np.random.seed(2)\n",
    "df['rating'] = np.random.choice(ratings, len(df))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.330000</td>\n",
       "      <td>1</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>32.490000</td>\n",
       "      <td>1</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.990000</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue pants</td>\n",
       "      <td>93.936667</td>\n",
       "      <td>1</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white tuxedo</td>\n",
       "      <td>93.936667</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product       price  in_stock  rating\n",
       "0     red shirt   49.330000         1     low\n",
       "2     red shirt   32.490000         1  medium\n",
       "3   white dress  199.990000         0     low\n",
       "4    blue pants   93.936667         1    high\n",
       "5  white tuxedo   93.936667         0    high"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert in_stock to binary\n",
    "\n",
    "df.in_stock = df.in_stock.map({False: 0, True: 1})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.330000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>32.490000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.990000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue pants</td>\n",
       "      <td>93.936667</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white tuxedo</td>\n",
       "      <td>93.936667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product       price  in_stock  rating\n",
       "0     red shirt   49.330000         1       1\n",
       "2     red shirt   32.490000         1       2\n",
       "3   white dress  199.990000         0       1\n",
       "4    blue pants   93.936667         1       0\n",
       "5  white tuxedo   93.936667         0       0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encode ordinal labels\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "rating_encoder = LabelEncoder()\n",
    "df.rating = rating_encoder.fit_transform(df.rating)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['low', 'medium', 'low', 'high', 'high'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert back if needed\n",
    "\n",
    "rating_encoder.inverse_transform(df.rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.330000</td>\n",
       "      <td>1</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>32.490000</td>\n",
       "      <td>1</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.990000</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue pants</td>\n",
       "      <td>93.936667</td>\n",
       "      <td>1</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white tuxedo</td>\n",
       "      <td>93.936667</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product       price  in_stock  rating\n",
       "0     red shirt   49.330000         1     low\n",
       "2     red shirt   32.490000         1  medium\n",
       "3   white dress  199.990000         0     low\n",
       "4    blue pants   93.936667         1    high\n",
       "5  white tuxedo   93.936667         0    high"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's apply the backwards conversion and then try a different mapping technique\n",
    "\n",
    "df.rating = rating_encoder.inverse_transform(df.rating)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'low': 0, 'medium': 1, 'high': 2}\n"
     ]
    },
    {
     "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>product</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>49.330000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>red shirt</td>\n",
       "      <td>32.490000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white dress</td>\n",
       "      <td>199.990000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue pants</td>\n",
       "      <td>93.936667</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white tuxedo</td>\n",
       "      <td>93.936667</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        product       price  in_stock  rating\n",
       "0     red shirt   49.330000         1       0\n",
       "2     red shirt   32.490000         1       1\n",
       "3   white dress  199.990000         0       0\n",
       "4    blue pants   93.936667         1       2\n",
       "5  white tuxedo   93.936667         0       2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encode the odrinal labels properly using a custom mapping\n",
    "\n",
    "ordinal_map = {\n",
    "    rating: index\n",
    "    for index, rating in enumerate(['low', 'medium', 'high'])\n",
    "}\n",
    "print(ordinal_map)\n",
    "df.rating = df.rating.map(ordinal_map)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "      <th>rating</th>\n",
       "      <th>product_blue pants</th>\n",
       "      <th>product_red shirt</th>\n",
       "      <th>product_white dress</th>\n",
       "      <th>product_white tuxedo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.330000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32.490000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>199.990000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>93.936667</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>93.936667</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  in_stock  rating  product_blue pants  product_red shirt  \\\n",
       "0   49.330000         1       0                   0                  1   \n",
       "2   32.490000         1       1                   0                  1   \n",
       "3  199.990000         0       0                   0                  0   \n",
       "4   93.936667         1       2                   1                  0   \n",
       "5   93.936667         0       2                   0                  0   \n",
       "\n",
       "   product_white dress  product_white tuxedo  \n",
       "0                    0                     0  \n",
       "2                    0                     0  \n",
       "3                    1                     0  \n",
       "4                    0                     0  \n",
       "5                    0                     1  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One-hot-encode the product feature\n",
    "\n",
    "df = pd.get_dummies(df)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Shapes\n",
      "--------------\n",
      "X_train (3, 6)\n",
      "X_test  (2, 6)\n",
      "y_train (3,)\n",
      "y_test  (2,)\n"
     ]
    }
   ],
   "source": [
    "# Split into training and testing sets\n",
    "\n",
    "features = [\n",
    "    'price', 'rating',\n",
    "    'product_blue pants', 'product_red shirt',\n",
    "    'product_white dress', 'product_white tuxedo',\n",
    "]\n",
    "X = df[features].values\n",
    "\n",
    "target = 'in_stock'\n",
    "y = df[target].values\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = (\n",
    "    train_test_split(X, y, test_size=0.3)\n",
    ")\n",
    "    \n",
    "print('Data Shapes')\n",
    "print('--------------')\n",
    "print('X_train', X_train.shape)\n",
    "print('X_test ', X_test.shape)\n",
    "print('y_train', y_train.shape)\n",
    "print('y_test ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Activity: Preparing to train a predictive model for the employee retention problem\n",
    "\n",
    "_Note: If desired, the following code can be removed from the student version of the notebook and replaced with empty cells._\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "satisfaction_level,last_evaluation,number_project,average_montly_hours,time_spend_company,work_accident,left,promotion_last_5years,is_smoker,department,salary\n",
      "0.38,0.53,2,157,3,0,yes,0,,sales,low\n",
      "0.8,0.86,5,262,6,0,yes,0,yes,sales,medium\n",
      "0.11,0.88,7,272,4,0,yes,0,,sales,medium\n",
      "0.72,0.87,5,223,5,0,yes,0,,sales,low\n",
      "0.37,0.52,2,,,0,yes,0,no,sales,low\n",
      "0.41,0.5,2,,,0,yes,0,,sales,low\n",
      "0.1,0.77,6,247,4,0,yes,0,,sales,low\n",
      "0.92,0.85,5,259,5,0,yes,0,,sales,low\n",
      "0.89,1,5,224,5,0,yes,0,,sales,low\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "head ../data/hr-analytics/hr_data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "satisfaction_level,last_evaluation,number_project,average_montly_hours,time_spend_company,work_accident,left,promotion_last_5years,is_smoker,department,salary\n",
      "0.38,0.53,2,157,3,0,yes,0,,sales,low\n",
      "0.8,0.86,5,262,6,0,yes,0,yes,sales,medium\n",
      "0.11,0.88,7,272,4,0,yes,0,,sales,medium\n",
      "0.72,0.87,5,223,5,0,yes,0,,sales,low\n",
      "0.37,0.52,2,,,0,yes,0,no,sales,low\n",
      "0.41,0.5,2,,,0,yes,0,,sales,low\n",
      "0.1,0.77,6,247,4,0,yes,0,,sales,low\n",
      "0.92,0.85,5,259,5,0,yes,0,,sales,low\n",
      "0.89,1,5,224,5,0,yes,0,,sales,low\n"
     ]
    }
   ],
   "source": [
    "with open('../data/hr-analytics/hr_data.csv', 'r') as f:\n",
    "    for _ in range(10):\n",
    "        print(next(f).strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/hr-analytics/hr_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
       "       'average_montly_hours', 'time_spend_company', 'work_accident', 'left',\n",
       "       'promotion_last_5years', 'is_smoker', 'department', 'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>is_smoker</th>\n",
       "      <th>department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                 157.0   \n",
       "1                0.80             0.86               5                 262.0   \n",
       "2                0.11             0.88               7                 272.0   \n",
       "3                0.72             0.87               5                 223.0   \n",
       "4                0.37             0.52               2                   NaN   \n",
       "\n",
       "   time_spend_company  work_accident left  promotion_last_5years is_smoker  \\\n",
       "0                 3.0              0  yes                      0       NaN   \n",
       "1                 6.0              0  yes                      0       yes   \n",
       "2                 4.0              0  yes                      0       NaN   \n",
       "3                 5.0              0  yes                      0       NaN   \n",
       "4                 NaN              0  yes                      0        no   \n",
       "\n",
       "  department  salary  \n",
       "0      sales     low  \n",
       "1      sales  medium  \n",
       "2      sales  medium  \n",
       "3      sales     low  \n",
       "4      sales     low  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>is_smoker</th>\n",
       "      <th>department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>151.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>160.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>143.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6</td>\n",
       "      <td>280.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>158.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "14994                0.40             0.57               2   \n",
       "14995                0.37             0.48               2   \n",
       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  work_accident left  \\\n",
       "14994                 151.0                 3.0              0  yes   \n",
       "14995                 160.0                 3.0              0  yes   \n",
       "14996                 143.0                 3.0              0  yes   \n",
       "14997                 280.0                 4.0              0  yes   \n",
       "14998                 158.0                 3.0              0  yes   \n",
       "\n",
       "       promotion_last_5years is_smoker department salary  \n",
       "14994                      0       NaN    support    low  \n",
       "14995                      0       NaN    support    low  \n",
       "14996                      0       NaN    support    low  \n",
       "14997                      0       NaN    support    low  \n",
       "14998                      0       NaN    support    low  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many lines in the CSV (including header)\n",
    "\n",
    "with open('../data/hr-analytics/hr_data.csv') as f:\n",
    "    num_lines = len([\n",
    "        line for line in f.read().splitlines()\n",
    "        if line.strip()\n",
    "    ])\n",
    "    \n",
    "num_lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14999"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many records did we load into Python?\n",
    "\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Assess the target**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     11428\n",
       "yes     3571\n",
       "Name: left, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.left.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: `Series.plot()` should not be called with positional arguments, only keyword arguments. The order of positional arguments will change in the future. Use `Series.plot(kind='barh')` instead of `Series.plot('barh',)`.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABD4AAALQCAYAAAB4ybAdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzde7DXdZ3H8RcXMRFSEUQUNcl+aCkkhYNMrpc1ay1djZLNS5J5ydW2aVZdTWfXzbRt11uh5prQ6mbmZUrDDEt3mzVSQ/PCesEQtdUEURS5iALnu38056wnsFy5nMObx2OGYfx+P+d3Pr/j28Ph6ff3/fVomqYJAAAAQEE9u3oDAAAAAGuL8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACU1burN9AdtbU1Wb58RVdvAzr06fP7/1Rff315F+8EOjObdGfmk+7KbNKdmU+6q969e6Vnzx5v72PX8F5KWL58RRYseLWrtwEdBg3qnyTmkm7HbNKdmU+6K7NJd2Y+6a4222yTjjD3/+WlLgAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQlvABAAAAlCV8AAAAAGUJHwAAAEBZwgcAAABQVu+u3kB31KdP7wwa1L+rtwErMZe0mzdvYVdvAQAA1guu+AAAAADKcsXHKsyY9UK+/K1pXb0NgJVMueAvu3oLAACwXnHFBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZa1W+Jg0aVKGDx+eL3/5y6s8f/vtt2f48OE544wzOo5NmTIlhx9+eEaNGpX3v//9+eQnP5kbbrghTdOs9PH3339/TjzxxOyzzz7Zdddds/fee+e0007LrFmzVmfbAAAAwAZitcLHIYcckt69e+e2227L0qVLVzp/0003JUkOPfTQJMmZZ56ZU045JY8++mh22223jBkzJk8++WTOOuusnHrqqZ3ixwMPPJAJEybk5z//eYYOHZr99tsv/fv3z80335xPfepTefzxx1dn6wAAAMAGYLXCx5Zbbpm99947ixYtyn/8x390Ovfyyy93RIvRo0fnhhtuyI033phddtklt956a6666qpcfvnl+dnPfpaRI0dmypQpuf766zs+/qKLLsrSpUszadKkfPe73803v/nN3HLLLTn22GOzZMmSTJ48eXW2DgAAAGwAeq/uA3ziE5/IHXfckR/96Ec58MADO47/+Mc/zrJly3LooYemR48emTRpUpLkn/7pnzJkyJCOdQMGDMi5556bj3/845k8eXLGjx+fJJk3b16SZOutt+70+Y477rhsvfXW2XnnnVd36wDrrUGD+nf1FjrpbvuBNzKfdFdmk+7MfFLJat/cdJ999snAgQNz5513Zv78+R3Hb7rppvTo0SOHHHJInn/++Tz55JPZfPPNVxks3vOe92Tw4MF56qmnOoLHBz/4wSTJZz7zmVxwwQW59957s3z58my++eY56qijMnr06NXdOgAAAFDcal/x0bt37xx88MGZPHlyfvzjH+eoo47K7Nmz89BDD2WPPfbI0KFD89BDDyX5/ctfhg8f/kcf77nnnsugQYNy6qmn5umnn87dd9+dK664IldccUX69++fvffeO5/85Cez5557ru7WAdZb8+Yt7OotJPm//xvUXfYDb2Q+6a7MJt2Z+aS72myzTdKnz9tLGKsdPpJk3LhxmTx5cqZMmZKjjjoqN998c5LfvwwmSVasWJHk9/cEGTt27B99rE033TRJ0r9//1x11VV58MEH87Of/Sy//OUv8+ijj+aWW27JLbfcks997nM57bTT1sT2AQAAgKLWSPjYaaedMmLEiDz00EOZM2dOfvrTn6Zv3775yEc+kiQZNGhQkt9HjfPPP///9dgjR47MyJEjkyTz58/PTTfdlPPPPz/f+c53cvTRR2fw4MFr4ikAAAAABa32PT7ajRs3Lk3T5Morr8zs2bPz0Y9+NH379k2SDB06NEOGDMkzzzyTJ554YqWPffHFF/PRj340EyZMyOLFi7No0aKMGzcuBx98cKd1AwYMyDHHHJNddtklbW1tmTt37praPgAAAFDQGgsfH/vYx/KOd7wj11xzTZLk0EMP7XT+6KOPTltbW0499dT87ne/6zj+6quv5owzzsiTTz6ZTTfdNJtuumn69euXpmkyc+bMXH311Z0e57HHHsusWbPSt2/fDBs2bE1tHwAAAChojbzUJfn9PTk+/OEPZ8qUKRk6dOhK77py9NFH5/77789tt92WAw88MLvttlv69euXBx54IPPnz88OO+yQf/zHf+xYf/bZZ+fII4/Mueeem+uvvz7Dhg3Lyy+/nPvuuy/Lly/PP/zDP6Rfv35ravsAAABAQWvsio8kGTVqVJLfX+3Ro0ePzp+oZ89cfPHFOe+887Lzzjvn4Ycfzt13352BAwfmxBNPzA033JCBAwd2rB8xYkS++93v5oADDshLL72UO+64IzNnzszYsWPzne98J4cffvia3DoAAABQ0Bq74iNJbrvttvTs2bPj3Vz+UM+ePTNu3LiMGzfuLT3eiBEjMnHixDW5RQAAAGADstpXfCxdujRJ8oMf/CB333139tlnn2yzzTarvTEAAACA1bXaV3xMmDAhjzzySF577bVstNFG+eIXv7gm9gUAAACw2lb7io/3v//96dWrV4YNG5ZLLrkkO++885rYFwAAAMBqW+0rPk4//fScfvrpa2IvAAAAAGvUGn1XFwAAAIDuRPgAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKCs3l29ge5ot50GZsoFf9nV2wAAAABWkys+AAAAgLJc8bEKr7++PAsWvNrV24AOgwb1T5LMm7ewi3cCAACwfnHFBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFnCBwAAAFCW8AEAAACUJXwAAAAAZQkfAAAAQFk9mqZpunoTAAAAwNo3b97Crt7C27LZZpukT5/eb+tjXfEBAAAAlPX2cklxM2a9kC9/a1pXbwMAAADWiCkX/GVXb6HLuOIDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgrHUWPiZOnJjhw4fnP//zP3P77bfnr/7qr7L77rtn9OjROemkkzJz5syVPubXv/51TjrppIwZMya77rpr9ttvv5x99tmZM2fOuto2AAAAsB5b51d83HDDDTnppJOycOHCfOhDH0r//v1z++235/DDD8/cuXM71l1zzTU54ogjcvvtt2eHHXbIfvvtl969e+faa6/NIYcckocffnhdbx0AAABYz/Re15/wjjvuyNlnn51Pf/rTSZLXX389xx13XO6+++7ceOONOemkk/LII4/kq1/9ajbeeONcdtllGTt2bJKkra0tl112WSZOnJgvfOELmTp1avr06bOunwIAAACwnljn4WPUqFEd0SNJ+vTpk8MOOyx33313ZsyYkST593//97S1teXEE0/siB5J0rNnz5x88sm555578qtf/Sq33nprDjnkkHX9FAAAAGC9NGhQ/67ewjq3zl/qMnLkyJWODRw4MEmyZMmSJMn06dOTJH/xF3+xysf42Mc+1mkdAAAAwKqs8ys++vdfuS716tUrSdI0TZLk+eefT5Jsu+22q3yMoUOHJkleeOGFtbFFAAAAKGnevIVdvYW3ZbPNNkmfPm8vYazzKz569OjxJ9e0B5A3s2LFiiRxfw8AAADgj1rn4eOt2GqrrZIkzzzzzCrPtx/fcsst19meAAAAgPVPtwwfo0ePTpJMnTp1led/8pOfJEn22GOPdbYnAAAAYP3TLcPHkUcemV69euVb3/pW7rrrro7jTdPkkksuyfTp07Pttttm33337cJdAgAAAN3dOr+56Vux66675owzzsi5556bCRMmZPfdd8/gwYPz2GOP5amnnsqAAQNy8cUXZ5NNNunqrQIAAADdWLcMH0ly1FFHZZdddsmVV16Z+++/Pw8//HCGDBmSCRMm5JhjjsngwYO7eosAAABAN9ej+VNvobIBmjHrhXz5W9O6ehsAAACwRky54C+TeDtbAAAAgFKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoS/gAAAAAyhI+AAAAgLKEDwAAAKAs4QMAAAAoq0fTNE1XbwIAAABY++bNW9jVW3hbNttsk/Tp0/ttfawrPgAAAICy3l4uKe7115dnwYJXu3ob0GHQoP5J1t86S11mk+7MfNJdmU26M/NJRa74AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgLOEDAAAAKEv4AAAAAMoSPgAAAICyhA8AAACgrB5N0zRdvYnupq2tyfLlK7p6G9ChT5/eSZLXX1/exTuBzswm3Zn5pLsym3Rn5pPuqnfvXunZs8fb+ljhAwAAACjLS10AAACAsoQPAAAAoCzhAwAAAChL+AAAAADKEj4AAACAsoQPAAAAoCzhAwAAAChL+AAAAADKEj4AAACAsoQPAAAAoCzhAwAAAChL+AAAAADKEj4AAACAsoQPAAAAoCzhAwAAAChL+AAAAADKEj4AAACAsoQPAAAAoKzeXb2B7uCXv/xlLr/88sycOTPLli3L+973vhx//PHZa6+9unprrIdWrFiRa6+9Nj/84Q8ze/bsrFixItttt10OPPDAHHvssdl44407rZ8xY0YuvfTSzJgxI0uWLMlOO+2Uz3zmMznooINW+fhPPvlkJk6cmPvuuy8vv/xytt9++4wfPz6HH354evZcuWXOnTs3l156aaZNm5Z58+ZlyJAhOfjgg3PcccelT58+a+VrwPrh5Zdfzsc//vHMmzcvM2fOXOn82p61V155Jf/6r/+a22+/Pc8991wGDhyYAw44ICeffHL69eu3Vp4z3duzzz6bSy+9NL/4xS8yf/78bLHFFtlnn33yN3/zNxk0aFCnteaTde3mm2/ONddck8cffzxtbW3Zcccd84lPfCJHHnlkevXq1Wmt+WRt+8EPfpAzzjgj11xzTT74wQ+udL67zeDSpUtz1VVX5Uc/+lGeeeaZ9O/fv+P7+1ZbbbVmvijwR/Romqbp6k10pfZvGn369MmYMWPS1taWe+65J8uWLctXvvKVjB8/vqu3yHpkxYoV+eu//uv8/Oc/T9++fTNy5Mj07t07Dz74YF555ZWMHDkyV111VTbZZJMkybRp03LCCSekra0to0ePziabbJK77rorS5cuzec///l86Utf6vT4jz32WI444ogsWrQoo0aNypZbbpl77rknr7zySg466KCcf/75ndbPmTMn48ePz5w5c/Le97432223XX79619n3rx52WOPPTJ58uRstNFG6+zrQ/fypS99KbfeemuSrBQ+1vasLVq0KIcffnhmzpyZHXfcMa1WKw8//HCeeeaZ7LTTTvn+97+f/v37r/0vAt3GjBkz8tnPfjYLFy5Mq9XK9ttvn//+7//OnDlzsv322+fGG2/MZpttlsR8su798z//cyZNmpQ+ffpk9OjR6dWrV+69994sWbIk+++/fy655JL06NEjiflk7bv//vtzzDHHZMmSJasMH91tBpctW5YTTjgh06ZNy5AhQzJixIjMnj07v/nNbzJo0KBcf/312WabbdbuFw2aDdjcuXObXXfdtfnABz7QzJw5s+P4gw8+2IwaNarZbbfdmjlz5nThDlnfXHvttU2r1WoOOuigTrPz4osvNuPHj29arVZz/vnnN03TNK+++mqz5557Nu973/uau+66q2Pt008/3fzZn/1Z02q1mhkzZnQcb2traw466KCm1Wo1N910U6fHbj8+derUTvs54YQTmlar1Vx66aUdxxYvXtxMmDChabVazaRJk9b414D1w5QpU5pWq9Xx643Wxaydc845TavVas4666xmxYoVTdM0zbJly5pTTz21abVazVe+8pU1/ZTpxl577bXmgAMOaFqtVnP11Vd3HF+6dGnzhS98oWm1Ws0555zTNI35ZN177LHHmuHDhzdjxoxpZs+e3XF8zpw5zb777ttp5oXZv34AAA2nSURBVMwna9ttt93W7L777h1/fk+fPr3T+e44g5MnT25arVZz/PHHN6+99lrH8QsvvLBptVrNCSecsHpfFHgLNujwccEFFzStVquZOHHiSucuu+yyptVqNd/4xje6YGesrw477LCm1Wp1ChntHn300abVajX77rtv0zRN8/3vf79ptVrN6aefvtLam2++uWm1Ws1pp53WcezOO+9sWq1Wc+SRR660/t57721arVZzxBFHdBx74oknmuHDhzf7779/xx9K7Z599tlml1126dgLG5Y5c+Y0e+yxRzN+/Phml112WSl8rO1ZW7BgQTNixIhm1KhRzcKFCzutX7RoUTN69OhmxIgRzeLFi9fE02U98MMf/rBptVrN3/7t36507ne/+10zduzY5phjjmmaxnyy7rX/pe28885b6dy3v/3tTmHOfLK2PPfccx1hYeTIkc3YsWNXGT662wy2tbU1e+21VzN8+PDmf/7nfzqtX7FiRfORj3ykabVazW9/+9u394WBt2iDvrnpnXfemSTZf//9Vzr34Q9/OEnyX//1X+t0T6zftthiiwwbNiwjRoxY6dy73vWuJMnzzz+f5P/m78///M9XWrvffvulV69enebvj83rBz7wgWy55Za57777smjRoiTJL37xizRNk3333Xel13Jus802ee9735tnn302s2bNehvPlPXZmWeemddeey1f//rXV3l+bc/a9OnTs3Tp0owZM2al1wFvuumm2XPPPbN06dJMnz59tZ8r64ef/vSnSZLPfvazK50bMmRIpk2blkmTJiUxn6x77S9hmTt37krnXnrppSTJ5ptvnsR8svZcfPHFufnmm7Prrrvmuuuuy7Bhw1a5rrvN4MyZMzN37tzsvPPOGTp0aKf1PXv2zH777ZfE37lY+zbY8NE0TWbNmpWePXuu8hvHu971rvTs2TOzZs1Ks2HfBoX/h8svvzw/+clP0rdv35XOzZgxI0my9dZbJ0l+85vfJElardZKa/v165etttoq8+fPzwsvvJAkHX/grGp9kuy4445pa2vLE0880Wn9e97znlWub5/7xx9//K09OUr43ve+lzvvvDOnnHJKdthhh1WuWduz9lbXr+qGq9T0yCOPZKONNsrOO++c5557LldccUXOOuusnH/++XnooYc6rTWfrGt77bVXevTokalTp+aKK67I/Pnz88orr+TGG2/M1Vdfnc022yzjxo1LYj5Ze4YNG5avf/3rueGGGzJ8+PA3XdfdZtDPo3QXG+y7uixYsCCvv/56BgwYsMo7Fffu3TtbbLFFXnzxxSxevNgdslktTdPkm9/8ZpLkgAMOSJLMmzcvSVZ6p4J2gwYNynPPPZcXXnghAwcO7LhS5I+tT9IRStrXv9mdsv9wPfX99re/zb/8y79kzJgxOeKII9503dqetbcy+0ny4osvvvmToYzXX389zz33XLbeeutMnTo1Z555Zl599dWO89/+9rfzuc99LqeddloS88m69+53vzvnnHNOzj333FxwwQW54IILOs7tvvvu+drXvpYhQ4YkMZ+sPccff/xbWtfdZtDM0l1ssFd8tP9Q1f7uGqvyjne8I0myePHidbIn6rrwwgvzq1/9KgMHDsyxxx6b5P9msH3O/lD78SVLlqyT9dS2YsWKnHbaaenRo0e+9rWvdVy6vSpre9baf3+z779mc8PSfrn1ggUL8nd/93fZf//9M3Xq1EyfPj0XXXRRNt9880yaNCnXXXddEvNJ1xg1alT23HPP9O3bN2PGjMnYsWOz6aabZsaMGfne977XcXWw+aSrdbcZNLN0FxvsFR+rev/qP+QlLqwJ3/jGN3LFFVekT58+ufjiizNgwIAkSa9evdI0zZv+BbR9/tp/b5/ZtbWe2q688srcf//9+epXv/on3zJubc+a2eSNXnvttSS//+H7Qx/6UKe3WTzwwAPTt2/fnHDCCbn00ktz2GGHmU/WuQceeCDHHHNMtt1220yZMqXjPgVz587NySefnKuvvjr9+vXLF7/4RfNJl+tuM2hm6S422Cs+2u/B0P4D16q0n/tjV4XAm1m+fHn+/u//Ppdddlk23njjXHLJJRk9enTH+U022SRN07zpDLYfb5/V9t+XLl26Rteb7/oee+yxTJw4MXvvvXc+9alP/cn1a3vWzCZv9MZ/z5/+9KdXOr/PPvtk8ODBmTt3bp5++mnzyTp33nnnZfHixTn33HM73Zxx8ODBufDCC9O7d+/827/9W1599VXzSZfrbjNoZukuNtjw0a9fv/Tt2zcvvfRSli9fvtL55cuX56WXXsrGG2+cd77znV2wQ9Znixcvzuc///lcd911eec735lJkyZl77337rSm/bWU7a99/EN/+JrI9vVvdk+Ot7v+zV7TSR0XXXRRli1bluXLl+eUU07p9KutrS1JOv55/vz5a33W/r+PT239+/fPRhttlCQr3fG/XftVSi+99JL5ZJ1aunRpHnroofTv33+V79i23XbbZccdd8ySJUvy9NNPm0+6XHebQTNLd7HBho8ePXpkp512yooVK/LUU0+tdP7JJ59MW1vbm94RGd7MggULctRRR+XOO+/MkCFDcs0113S60qNd+92t2++q/UaLFi3K888/nwEDBmTgwIGd1q/q7Webpsns2bPTq1evvPvd7/6T69/4ec14fe2vm502bVqmTJnS6Vf7paXt/7xkyZK1Pmtvdf0fu2s9dbxxllb1dqHJ//3APGDAAPPJOrVw4cI0TZNevXq96Zr2c8uWLTOfdLnuNoP/2979hTTVx3Ec/0zSyUYQiCOz9cdWwbwosoVB2JVEEBj9XUU4ocIggqCu9Ea76qILRyHUoAszqpuKIrrQRUKYg4b2Z2KUC0YZSGszmjW3+VzERnuseIjHPx3fL/DC7/keOOfwRbbP3O+XPY/Xo5ht8zb4kL5vTyZJXV1dU45la//+lB74nWQyqWPHjunly5dyOBy6fv36L/+Q/27+/H6/0ul03vxl+7u7u6f0B4NBRaNRVVVV5XYgyvb7/f7cp/pZ79+/1+DgoMrLy+VwOP7gTvE36ejo0NDQ0E9/si/Ys78vXbp02mfN5XKpuLhYvb29UxYz+/Lli3p7e2WxWFRVVfX/PgjMWTU1NZKkBw8eTDk2PDysd+/eyWazyW63M5+YUSUlJVq0aJFisdiUrZWl72HdmzdvVFhYqIqKCuYTs26uzeCqVatUXl6uUCikkZGRvP5MJiO/3y+TyZS7DmC6zOvgY9euXTKbzbp8+bJevHiRqz9//lw+n0/FxcU6ePDgLF4h/jZer1f9/f0qKytTR0eHFi9e/Mvebdu2qaSkRLdu3dKjR49y9UgkovPnz8tkMsnj8eTqmzZt0urVq/X48WPdvHkzV49Go2ppaZEkNTQ05OrZNwjhcFhtbW25eiKRUHNzs9LpdF4/kDXds2axWLRz507F43G1tLTkvm6YSqXU2tqqsbEx7d+/n23E5xG32y2LxaLbt2/r7t27uXo8Hldzc7MymYwOHTqkgoIC5hMzqqCgQHv27JEkNTU15f1XUjQa1enTpzUxMaHdu3fLarUyn5h1c3EG3W630um0mpqa8sKStrY2vX37VrW1tVq2bNn//zCAH5gm5/kSup2dnWptbVVhYaGqq6s1OTmpvr4+pVIpnTt3TnV1dbN9ifhLxGIxbd26VV+/flVlZaUqKip+2ZvdtaC7u1snT55UOp2Wy+WS1WrVkydPND4+rlOnTqmxsTHvvGfPnqm+vl6JRELr1q2TzWZTIBBQPB7Xvn37dPbs2bz+SCSiAwcOaHR0VGvWrNHKlSsVDAY1Ojqqmpoatbe3a8GCebu5EyQ5nU6l02kNDQ3l1ad71mKxmNxut8LhsOx2u5xOp0KhkCKRiJxOp65evSqr1TojzwBzw/3793XmzBmlUilVVlbKZrOpv79fnz59UnV1tXw+X24tEOYTM+nbt286cuSIAoGAzGazXC6XTCaTBgYGNDY2pvXr1+vKlSu5RRyZT8yEw4cPKxAIqLOzUxs3bsw7NtdmMJlMyuPx6OnTpyotLdWGDRsUDof16tUrLVmyRDdu3GDNOUy7eR98SNLDhw/l8/kUCoVUVFSktWvX6vjx49q8efNsXxr+Ij09PTp69Oh/6v3xTWYwGNTFixc1MDCgyclJORwOeTwebd++/afnvn79Wl6vV319fUomk1q+fLncbrf27t370+8gj4yMyOv1qqenR58/f5bdblddXZ3q6+tlNpv/7GZhGL8KPqTpn7VYLKYLFy6oq6tLHz9+VFlZmWpra9XY2KiFCxdOy/1ibhscHFR7e7sCgYASiURuhhoaGnKhRxbziZk0MTGha9eu6c6dOxoeHlYmk9GKFSu0Y8cOeTweFRUV5fUzn5huvws+pLk3g+Pj47p06ZLu3bunDx8+qLS0VFu2bNGJEycIPTAjCD4AAAAAAIBhzes1PgAAAAAAgLERfAAAAAAAAMMi+AAAAAAAAIZF8AEAAAAAAAyL4AMAAAAAABgWwQcAAAAAADAsgg8AAAAAAGBYBB8AAAAAAMCwCD4AAAAAAIBhEXwAAAAAAADDIvgAAAAAAACGRfABAAAAAAAMi+ADAAAAAAAYFsEHAAAAAAAwLIIPAAAAAABgWAQfAAAAAADAsAg+AAAAAACAYf0D5YdVHiR9WqsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 543
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How is it distributed?\n",
    "\n",
    "df.left.value_counts().plot('barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How much missing data?\n",
    "\n",
    "df.left.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Assess the features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       float64\n",
       "last_evaluation          float64\n",
       "number_project             int64\n",
       "average_montly_hours     float64\n",
       "time_spend_company       float64\n",
       "work_accident              int64\n",
       "left                      object\n",
       "promotion_last_5years      int64\n",
       "is_smoker                 object\n",
       "department                object\n",
       "salary                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print datatypes\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 381,
       "width": 544
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 381,
       "width": 550
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABEwAAAL7CAYAAADnHhBNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdfXSX9X3/8VcIRQgoDoUJDloUZId2dCtHqj0HRatyuo3S02Oh2Fq12tZj65zi6GmHg8hPXWldMcJqu96cU+i8gTPLmG70VKTF1AqD3h2rUYRjOdpoNEcwBNRAfn9sSZdPQgUTSAmPxzk9Ob2u9/fKdcl1TuLTD9dV0dra2hoAAAAA2vXr7RMAAAAA+EMjmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQKF/b59AX7J/f2taWvb19mkctAED/ueP//XXW3r5TKDnuK/pq9zb9FXubfoi9zV91dF4b/fvX5l+/Sre2md7+FyOaS0t+7Jz557ePo2DNnz48UlyVJ0zvBn3NX2Ve5u+yr1NX+S+pq86Gu/toUMHtYeeQ+Wv5AAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUOjf2ycAAIdq+PDje/sUes2xfO1HWkPDq719CgBAL7LCBAAAAKBghQkAR60Zc1f39inQB625fWZvnwIA8AfAChMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgEL/3j4BoG8aPvz4Y/r7AwAAR7fDEkxeeeWV/PVf/3UaGhpSV1fXaf/27dtz5513ZvPmzXnllVcyZsyYzJ49O5dcckn69eu86OWFF17IsmXLUltbm4aGhowcOTIf/OAH86lPfSoDBgzoNL9r1658/etfzw9/+MP89re/zcknn5yLLroon/vc5zJkyJDDcckAAABAH3JYgkl1dXUaGhq63Pfkk0/mYx/7WJqamvKe97wnf/Znf5bHHnssixYtys9//vN85Stf6TBfX1+f2bNnp76+PhMnTsw73/nObNmyJTU1NfnpT3+ab3/723nb297WPt/U1JSPf/zjqaury9ixYzNt2rQ8/vjj+c53vpMNGzbknnvuyfHH+y/PcKTMmLu6t0+BPmjN7TN7+xQAAOjjevwZJv/xH/+RBx98sMt9ra2tmTdvXpqamrJ48eLcfffdWbp0adauXZsJEyZkzZo1Wbt2bYfPLFy4MPX19bnuuuty//33p6amJj/4wQ/yvve9Lxs3bszy5cs7zC9ZsiR1dXWZNWtWHnzwwdTU1GTt2rWZOXNmtm7dmiVLlvT0JQMAAAB9TI8GkxdeeCGLFi3KX/zFX6SysrLT/tra2tTV1WXKlCmZOfN3/3Vw2LBhWbBgQZJ0CCDbtm3L+vXrM2bMmFx99dXt26uqqnLLLbeksrIyK1asaN++a9eurFy5MkOGDMnnP//59r/e079//yxYsCBDhw7NqlWr0tzc3JOXDQAAAPQxPRpM/v7v/z6vvfZavvSlL3W5f8OGDUmSCy64oNO+yZMn56STTsrmzZvT1NSUJHnkkUfS2tqa8847r9OzTUaNGpWJEyfmueeey9atW5MkmzZtyt69e3PWWWd1elbJ4MGDc/bZZ2fv3r3ZtGlTt68VAAAA6Lt6LJj867/+azZs2JAbb7wxb3/727ucaQsbZ5xxRpf7x44dm/379+eZZ57pMD9+/Pgu50877bQkyVNPPXVI8109iBYAAACgTY8Ek9/85jf58pe/nLPOOisf+9jHDjj34osvJkmGDx/e5f627S+99FKH+REjRhzUfNuDZt/s+C+//PKBLwYAAAA45nX7LTn79u3LvHnzUlFRkdtuuy0VFRUHnN2zZ0+SZODAgV3ub9ve9oyRQ51v+zpo0KCDmu9pAwb0z/DhR98beI7GcwaAw83PxyPLP2/6Ivc1fdWxcm93e4XJN7/5zfzsZz/LF77whYwaNer3f7P/fQ7JgaJKa2trh6+Hex4AAACgK91aYfLkk0/mzjvvzLnnnpuPfOQjbzpfVVWVJNm7d2+X+1977bUOcwc737ai5FDne9rrr7dk5849h+XYh0NbFWxoeLWXz4S+6FipzkDf5efjkeH3Efoi9zV91dF4bw8dOigDBry19NGtYPLVr341b7zxRlpaWnLjjTd22Ld///4kad/+xS9+MSNGjMgTTzyRl156Kaeffnqn45XPIGl7dknbM0oONN82d7DzB3rGCQAAAEDSzWDS9iyQ2traA86sWbMmSfK3f/u3GT9+fH70ox9l69atee9739thrrW1Ndu2bUtlZWV7TGl7203b229KbW/TaXvrzsHOT5gw4c0vDgAAADhmdesZJsuXL09dXV2X/6usrEyS9v//J3/yJ5k6dWqS5KGHHup0rC1btqSxsTGTJ0/OkCFDkqR9ft26de0rVto8//zzeeKJJ3Lqqadm3LhxSZIzzzwzAwcOzKOPPtrpwa67d+/Oo48+mqqqqkyePLk7lw0AAAD0cT3yWuGDNWXKlIwfPz61tbW577772rc3Njamuro6SXLFFVe0bx89enSmTp2a7du354477mjf3tzcnPnz52ffvn0d5quqqvKhD30oO3fuTHV1dVpaWpIkLS0tufnmm7Nr167Mnj27PcgAAAAAdKXbrxU+FP369cutt96ayy67LDfddFNWrVqVESNGZOPGjdm5c2dmzZqV888/v8NnFixYkDlz5uSuu+7KunXrMnbs2GzZsiUNDQ0555xzMmfOnA7z119/fR577LF8//vfz+bNmzNx4sT8+te/zo4dOzJx4sRce+21R/KSAQAAgKPQEV1hkiSTJk3KypUrM3369Dz77LOpra3NqFGjUl1dnYULF3aaHz16dFauXJkPf/jDaWxszPr16zN06NDMnTs3S5cuTf/+HZvPiSeemHvuuSeXXnppWlpa8vDDD6dfv3656qqr8t3vfjeDBw8+QlcKAAAAHK0qWltbW3v7JPoKrxWG32m7v2bMXd3LZ0JftOb2mUncXxwebfeXn49Hht9H6Ivc1/RVR+O93Z3XCh/xFSYAAAAAf+gEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACj074mD7Nu3L9/73veyatWqbN++PYMGDcq73vWufOITn8i0adM6zW/fvj133nlnNm/enFdeeSVjxozJ7Nmzc8kll6Rfv84N54UXXsiyZctSW1ubhoaGjBw5Mh/84AfzqU99KgMGDOg0v2vXrnz961/PD3/4w/z2t7/NySefnIsuuiif+9znMmTIkJ64ZAAAAKAP65EVJl/4whdyyy235LnnnsvZZ5+dd77zndm4cWM+85nPZNmyZR1mn3zyyVx88cV54IEHMmrUqEydOjX19fVZtGhR5s2b1+nY9fX1mTVrVu69996ccMIJmTZtWnbv3p2amppceeWVeeONNzrMNzU15eMf/3i++c1vpqKiItOmTUtFRUW+853vZPbs2Xn11Vd74pIBAACAPqzbK0wefPDBrF69OmPHjs2KFSty8sknJ0mefvrpzJkzJ0uXLs1f/dVf5R3veEdaW1szb968NDU1ZfHixZk5c2aSpLGxMZdffnnWrFmTCy+8MNOnT28//sKFC1NfX5/rrrsu11xzTZKkubk5n/3sZ/OTn/wky5cvzyc/+cn2+SVLlqSuri6zZs1KdXV1+vXrl5aWlnzxi1/M6tWrs2TJktx0003dvWwAAACgD+v2CpN///d/T5LceOON7bEkScaPH58ZM2Zk//79qa2tTZLU1tamrq4uU6ZMaY8lSTJs2LAsWLAgSbJ8+fL27du2bcv69eszZsyYXH311e3bq6qqcsstt6SysjIrVqxo375r166sXLkyQ4YMyec///n2v97Tv3//LFiwIEOHDs2qVavS3Nzc3csGAAAA+rBuB5OampqsWbMm55xzTqd9u3fvTpJUVlYmSTZs2JAkueCCCzrNTp48OSeddFI2b96cpqamJMkjjzyS1tbWnHfeeZ2ebTJq1KhMnDgxzz33XLZu3Zok2bRpU/bu3Zuzzjqr07NKBg8enLPPPjt79+7Npk2bunnVAAAAQF/W7WAyYMCAnHHGGZ0evvrwww/nv/7rv1JVVdUeSNrCxhlnnNHlscaOHZv9+/fnmWee6TA/fvz4LudPO+20JMlTTz11SPN1dXUHd3EAAADAMalH3pLTZu/evZk3b162bt2aZ555JqNGjcrixYvb/6rOiy++mCQZPnx4l59v2/7SSy91mB8xYsRBzTc0NBzU8V9++eVDuzAAAADgmNKjweT555/P2rVrO2yrq6vLmWeemSTZs2dPkmTgwIFdfr5te9szRg51vu3roEGDDmq+pw0Y0D/Dhx9/WI59OB2N5wwAh5ufj0eWf970Re5r+qpj5d7ukdcKtznllFPy05/+NBs3bsySJUvyxhtvZNGiRfnGN77xP9/sf59DUlFR0eXnW1tbO3w93PMAAAAAXenRFSZVVVWpqqpKknzgAx/IyJEj89GPfjRf//rXc9lll7Xv27t3b5eff+2119qP83+/vtl824qSQ53vaa+/3pKdO/cclmMfDm1VsKHh1V4+E/qiY6U6A32Xn49Hht9H6Ivc1/RVR+O9PXTooAwY8NbSR4+uMCn9+Z//ecaMGZOmpqbs2LGj/Vkkbc8cKZXPIDnY+ba5Qz0+AAAAQFe6FUxaW1uzePHiXH/99Wlpaelypu3tOS0tLe1vr2l7m015rG3btqWysjKnn356kvze+STtb9Npe+vOwc5PmDDhzS8OAAAAOGZ1K5hUVFTkoYceyoMPPpja2tpO+3fs2JHt27enqqoqY8eOzdSpU5MkDz30UKfZLVu2pLGxMZMnT86QIUOSpH1+3bp12b9/f4f5559/Pk888UROPfXUjBs3Lkly5plnZuDAgXn00Uc7Pdh19+7defTRR1NVVZXJkyd357IBAACAPq7bfyVn1qxZSZL/9//+X+rr69u3v/DCC7nhhhvS0tKSSy65JMcdd1ymTJmS8ePHp7a2Nvfdd1/7bGNjY6qrq5MkV1xxRfv20aNHZ+rUqdm+fXvuuOOO9u3Nzc2ZP39+9u3b12G+qqoqH/rQh7Jz585UV1e3r3ppaWnJzTffnF27dmX27NntQQYAAACgKxWt3XxlzBtvvJHPfvaz+dGPfpSqqqq85z3vyb59+/KLX/wizc3NOffcc7N06dL2v5rzy1/+Mpdddlmam5vz7ne/OyNGjMjGjRuzc+fOzJo1K4sWLepw/B07dmTOnDlpaGjIGWeckbFjx2bLli1paGjIOeeck6997Wvp3/93D3B55ZVX8tGPfjTbt2/P6NGjM3HixPz617/Ojh07MnHixKxYsSKDBw/uziUfkIe+wu+03V8z5q7u5TOhL1pz+8wk7i8Oj7b7y8/HI8PvI/RF7mv6qqPx3u7Vh76+7W1vy9e+9rXMnz8/73jHO7Jp06b8/Oc/z/jx41NdXZ277rqrPZYkyaRJk7Jy5cpMnz49zz77bGprazNq1KhUV1dn4cKFnY4/evTorFy5Mh/+8IfT2NiY9evXZ+jQoZk7d26WLl3aIZYkyYknnph77rknl156aVpaWvLwww+nX79+ueqqq/Ld7373sMUSAAAAoO/o9goTfscKE/gdK0w4nKww4XCywuTI8vsIfZH7mr7qaLy3/2BfKwwAAABwNBJMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAQv/ePgEAgD9Ew4cf39uncEw51v55NzS82tunAMCbsMIEAAAAoGCFCQBAF2bMXd3bp0AftOb2mb19CgAcJCtMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEChf2+fAL1v+PDje/sUAAAA4A+KFSYAAAAABStMyIy5q3v7FOiD1tw+s7dPAQAA4C2zwgQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACv174iD79u3L3Xffnfvvvz/btm3Lvn37Mnr06PzlX/5lrrrqqhx33HEd5n/1q19l2bJl+dWvfpXm5uaMGzcun/jEJzJjxowuj799+/bceeed2bx5c1555ZWMGTMms2fPziWXXJJ+/To3nxdeeCHLli1LbW1tGhoaMnLkyHzwgx/Mpz71qQwYMKAnLhkAAADow7q9wmTfvn255pprsmjRomzbti3vfve7M2XKlLz44oupqanJpZdemj179rTP19bWZs6cOfnxj3+ccePG5b3vfW+eeuqp3HjjjfnqV7/a6fhPPvlkLr744jzwwAMZNWpUpk6dmvr6+ixatCjz5s3rNF9fX59Zs2bl3nvvzQknnJBp06Zl9+7dqampyZVXXpk33niju5cMAAAA9HHdXmGycuXKrF+/PhMmTMi//Mu/5I//+I+TJI2Njbnmmmvys5/9LP/8z/+cuXPnZu/evfm7v/u7JMm3v/3tnHXWWUmS3/zmN7n00ktz11135cILL8y73vWuJElra2vmzZuXpqamLF68ODNnzmw/9uWXX541a9bkwgsvzPTp09vPZ+HChamvr891112Xa665JknS3Nycz372s/nJT36S5cuX55Of/GR3LxsAAADow7q9wuT+++9Pknzxi19sjyVJMmzYsCxcuDBJ8sADDyRJVq9enZdffjkzZsxojyVJMmbMmMydOzdJsnz58vbttbW1qaury5QpU9pjSduxFyxY0Gl+27ZtWb9+fcaMGZOrr766fXtVVVVuueWWVFZWZsWKFd29ZAAAAKCP63Yw+aM/+qOcdtppmTRpUqd973jHO5IkL774YpJkw4YNSZL3v//9nWbPP//8VFZW5sc//nH7trb5Cy64oNP85MmTc9JJJ2Xz5s1pampKkjzyyCNpbW3Neeed1+nZJqNGjcrEiRPz3HPPZevWrW/hSgEAAIBjRbeDyV133ZX//M//TFVVVad9v/rVr5Ikp5xySpLk6aefTpKcccYZnWaHDBmSESNGpLGxMS+99FKStIeNruaTZOzYsdm/f3+eeeaZDvPjx4/vcv60005Lkjz11FMHd3EAAADAMemwvVa4tbU1NTU1SZKLLrooSdLQ0JAkGT58eJefadveFkzaVqYc6vyIESMOah4AAACgKz3yWuGu/NM//VM2btyYk08+OVdddVWStL8tZ+DAgV1+pm17c3PzEZnvaQMG9M/w4ccflmMDANB3+J3x2ODPmb7qWLm3D8sKkzvuuCPf+MY3MmDAgCxZsiTDhg1LklRWVqaioiIVFRVdfq61tbXD17bnkByueQAAAICu9OgKk5aWltx888259957c9xxx+XOO+/MmWee2b5/0KBB2bVrV1577bUcd9xxnT7/2muvJUn781Davu7du7fL7/dW5wcNGnTI13YwXn+9JTt37jksxz4cjpUqCADwh6ah4dXePgUOo7bfs/0509ccjff20KGDMmDAW0sfPbbCZPfu3bn66qtz77335oQTTsi3vvWtnHvuuR1m2p4t0vYsk1L5jJO2+QM9c+Stzh/oGScAAAAASQ8Fk507d+bSSy/Nhg0bMnLkyHzve9/rsLKkTdvba9reavN/NTU15cUXX8ywYcNy8sknd5jv6jXAra2t2bZtWyorK3P66ae/6fz//b4HeusOAAAAQNIDweT111/Ppz/96Tz++OMZN25c7rnnngMGialTpyZJfvjDH3bat27duuzbt6/DqpS2+YceeqjT/JYtW9LY2JjJkydnyJAhHebXrVuX/fv3d5h//vnn88QTT+TUU0/NuHHj3sKVAgAAAMeKbgeTmpqa/PznP8/IkSOzfPnynHLKKQecnT59ek466aTcf//9+dGPftS+fceOHbn99ttTUVGRyy+/vH37lClTMn78+NTW1ua+++5r397Y2Jjq6uokyRVXXNG+ffTo0Zk6dWq2b9+eO+64o317c3Nz5s+fn3379nWYBwAAAOhKtx76+sorr2T58uVJkmHDhuXWW2894OxXvvKVDBkyJIsWLcrf/M3f5DOf+UzOPPPMDB48OD/96U+zZ8+eXH/99fnTP/3T9s/069cvt956ay677LLcdNNNWbVqVUaMGJGNGzdm586dmTVrVs4///wO32fBggWZM2dO7rrrrqxbty5jx47Nli1b0tDQkHPOOSdz5szpziUDAAAAx4BuBZNf/vKX7W+kefzxx/P4448fcPYrX/lKkuT9739/li9fnmXLluUXv/hFWltbM2HChFx++eX5wAc+0OlzkyZNysqVK1NTU5PHHnssTz/9dN7+9rfnhhtuyEc+8pFO86NHj26f//GPf5xnn302o0ePzic+8Ylcdtll6d+/R18MBAAAAPRB3aoH55xzTurq6g75c+95z3vyrW9966Dnx40bl5qamoOeHzlyZG677bZDPi8AAACApAdfKwwAAADQVwgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAACtiOKQAACAASURBVAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABT69/YJAADAsWb48ON7+xQ4Anrzz7mh4dVe+97QV1hhAgAAAFCwwgQAAI6wGXNX9/Yp0EetuX1mb58C9BlWmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgclmDyb//2b5kwYUL++7//u8v927dvzw033JBzzz037373uzNjxoysWLEi+/fv73L+hRdeyD/8wz/k/e9/fyZNmpTp06dn2bJlef3117uc37VrV7785S9n+vTpmTRpUs4///z84z/+Y5qamnrsGgEAAIC+q8eDyc9+9rMsWrTogPuffPLJXHzxxXnggQcyatSoTJ06NfX19Vm0aFHmzZvXab6+vj6zZs3KvffemxNOOCHTpk3L7t27U1NTkyuvvDJvvPFGh/mmpqZ8/OMfzze/+c1UVFRk2rRpqaioyHe+853Mnj07r776ak9fMgAAANDH9Ggw+cEPfpArr7wyzc3NXe5vbW3NvHnz0tTUlMWLF+fuu+/O0qVLs3bt2kyYMCFr1qzJ2rVrO3xm4cKFqa+vz3XXXZf7778/NTU1+cEPfpD3ve992bhxY5YvX95hfsmSJamrq8usWbPy4IMPpqamJmvXrs3MmTOzdevWLFmypCcvGQAAAOiDeiSY1NfXZ968ebn22muzf//+nHzyyV3O1dbWpq6uLlOmTMnMmTPbtw8bNiwLFixIkg4BZNu2bVm/fn3GjBmTq6++un17VVVVbrnlllRWVmbFihXt23ft2pWVK1dmyJAh+fznP59+/f7n8vr3758FCxZk6NChWbVq1QGDDgAAAEDSQ8FkyZIlWb16dd71rnfl3nvvzWmnndbl3IYNG5IkF1xwQad9kydPzkknnZTNmze3P2vkkUceSWtra84777z2+NFm1KhRmThxYp577rls3bo1SbJp06bs3bs3Z511VoYMGdJhfvDgwTn77LOzd+/ebNq0qdvXDAAAAPRdPRJMTjvttHzpS1/KypUrM2HChAPOtYWNM844o8v9Y8eOzf79+/PMM890mB8/fvwBv2+SPPXUU4c0X1dX93uvBwAAADi29e+Jg3z6058+qLkXX3wxSTJ8+PAu97dtf+mllzrMjxgx4qDmGxoaDur4L7/88kGdLwAAAHBs6pFgcrD27NmTJBk4cGCX+9u2tz1j5FDn274OGjTooOZ72oAB/TN8+PGH5dgAAAAHy7+XcDgdK/dXj79W+Pd+s/99DklFRUWX+1tbWzt8PdzzAAAAAF05oitMqqqqkiR79+7tcv9rr73WYe5g59tWlBzqfE97/fWW7Ny557Ac+3A4VqogAAAcaxoaXu3tU6APavt3yKPp/ho6dFAGDHhr6eOIrjBpexZJ2zNHSuUzSA52vm3uUI8PAAAA0JUjGkza3l7T9jab/6u1tTXbtm1LZWVlTj/99DedT9L+Np22t+4c7Pzve5MPAAAAwBENJlOnTk2SPPTQQ532bdmyJY2NjZk8eXKGDBnSYX7dunXZv39/h/nnn38+TzzxRE499dSMGzcuSXLmmWdm4MCBefTRRzs92HX37t159NFHU1VVlcmTJ/f4tQEAAAB9xxENJlOmTMn48eNTW1ub++67r317Y2NjqqurkyRXXHFF+/bRo0dn6tSp2b59e+6444727c3NzZk/f3727dvXYb6qqiof+tCHsnPnzlRXV6elpSVJ0tLSkptvvjm7du3K7Nmz24MMAAAAQFeO6ENf+/Xrl1tvvTWXXXZZbrrppqxatSojRozIxo0bs3PnzsyaNSvnn39+h88sWLAgc+bMyV133ZV169Zl7Nix2bJlSxoaGnLOOedkzpw5Heavv/76PPbYY/n+97+fzZs3Z+LEifn1r3+dHTt2ZOLEibn22muP5CUDAAAAR6EjusIkSSZNmpSVK1dm+vTpefbZZ1NbW5tRo0aluro6Cxcu7DQ/evTorFy5Mh/+8IfT2NiY9evXZ+jQoZk7d26WLl2a/v07Np8TTzwx99xzTy699NK0tLTk4YcfTr9+/XLVVVflu9/9bgYPHnyErhQAAAA4Wh2WFSbLly//vfvHjRuXmpqagz7eyJEjc9tttx30/Iknnpj58+dn/vz5B/0ZAAAAgDZHfIUJAAAAwB86wQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAAAUBBMAAACAgmACAAAAUBBMAAAAAAqCCQAAAEBBMAEAAAAoCCYAAAAABcEEAAAAoCCYAAAAABQEEwAAAICCYAIAAABQEEwAAAAACoIJAAAAQEEwAQAAACgIJgAAAAAFwQQAAACgIJgAAAAAFAQTAAAAgIJgAgAAAFAQTAAAAAAKggkAAABAQTABAAAAKAgmAAAAAAXBBAAAAKAgmAAAAPD/27v3+J7rxv/jz80O9skxY0O0zezjfLyIb7TdnCra1ZWkVNSQyyHSVShJB+GGLrqQErpU2BXZwamJZFvlVKbGLGbH2IYRY+z8+8Pt8/m192cTxj42j/tf9X69D8/Pm9tuH8+93q83AAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMnewcAAAAAANxc9evXtHcEoNJjhgkAAAAAAIABM0wAAAAAoIoJfCXc3hFQBW3896P2jlChmGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgQGECAAAAAABgUKULkx9//FHDhg3Tfffdp06dOmno0KGKjo62dywAAAAAAHCbq7KFSUhIiIKCghQTE6N27dqpY8eOiomJ0ciRI/Xll1/aOx4AAAAAALiNOdk7wK1w8uRJvfXWW6pZs6bWrFkjPz8/SdKvv/6qoKAgzZw5UwEBAfLw8LBzUgAAAAAAcDuqkjNMVq1apby8PD3//PPWskSS2rVrp5EjRyo3N5dZJgAAAAAAoExVsjCxrFPSp08fm7G+fftKkqKioio0EwAAAAAAqDyqXGFSXFyshIQEOTo6ysfHx2bcy8tLjo6OSkhIUHFxsR0SAgAAAACA212VK0zOnTunvLw81alTRy4uLjbjTk5Oqlu3ri5duqSLFy/aISEAAAAAALjdORRXsWkW6enpCggIUOPGjbVjx45S9+nVq5eOHz+uqKgoFn4FAAAAAAA2qtwME0fHv/5IVawjAgAAAAAAN1mVK0xMJpMkKTc3t8x9LGNubm4VkgkAAAAAAFQuVa4wqVGjhkwmk86ePauCggKb8YKCAp09e1aurq6qVauWHRICAAAAAIDbXZUrTBwcHOTr66vCwkIlJyfbjCclJamoqEh+fn4VHw4AAAAAAFQKVa4wkaSePXtKkrZv324zZtnm7+9foZkAAAAAAEDlUSULk4EDB8rV1VXLli3TwYMHrdtjY2O1fPlyVa9eXU8//bQdEwIAAAAAgNtZlXutsMXq1av17rvvytnZWd26dVNxcbH27NmjgoICzZkzR48++qi9IwIAAAAAgNtUlS1MJOm7777T8uXLFRcXJxcXF5nNZo0ZM0bdu3e3dzQAAAAAAHAbq9KFCQAAAAAAwI2okmuYAAAAAAAAlAeFCQAAAAAAgAGFCQAAAAAAgAGFCQAAAAAAgAGFCQAAAAAAgAGFCQAAAAAAgAGFCQAAAAAAgAGFCQAAAAAAgAGFCQAAAAAAgAGFCQAAAAAAgIGTvQOgYhUWFio4OFihoaFKTExUYWGhmjRpov79+2vkyJFydXW1d0TghhQWFmr16tX66quvlJSUJDc3N7Vp00bDhg1TQECAveMBN8Uff/yhRx55RKdOndJvv/1m7zjADQsLC9OUKVPKHB89erRefvnlCkwE3DzHjx/Xhx9+qO+//15nzpxR3bp1FRAQoAkTJqh+/fr2jgdcF7PZfE37ff7557rvvvtucZqKR2FyByksLNTYsWO1c+dOmUwmtW/fXk5OTvrll1+0cOFCRUZG6rPPPpObm5u9owLX7fXXX1d4eLhq1Kih7t27Kz8/X3v37tUPP/ygCRMmaNy4cfaOCJTbO++8o1OnTtk7BlBuhw8fliTdf//9uvvuu23GW7ZsWdGRgJsiNjZWQUFBys7Olp+fn9q2bauDBw9q7dq12r17t7766ivVrl3b3jGBaxYYGFjmWFpamg4cOKAaNWqoSZMmFZiq4lCY3EHWrVunnTt3ymw2a9myZfLw8JAknTlzRmPHjlVMTIyWLFmiV155xc5JgeuzZcsWhYeHy9vbW6tWrZK7u7sk6ejRoxoyZIgWL16sAQMGyMvLy75BgXLYtGmTtmzZYu8YwE0RFxcnSZo9e7b1+whQ2eXl5enVV19Vdna2pk2bpqFDh0qScnNzNWnSJG3dulWLFi3StGnT7JwUuHbvv/9+qdsvXbqkxx9/XJI0d+5cNWrUqCJjVRjWMLmDhIaGSpKmTp1a4svJ3XffrbfffluStHnzZntEA8plw4YNkqRXX33VWpZIUvPmzRUYGKiioiL98MMP9ooHlFtmZqZmzJihjh07qlq1avaOA5RbfHy83N3dKUtQpWzZskXJyckKDAy0liWS5Orqqtdff13u7u5KSkqyY0Lg5pk1a5aOHTumwYMHq3fv3vaOc8sww+QOUrduXfn4+Khdu3Y2Y5bfvJ88ebKCUwHlt3DhQiUnJ5c6g+TixYuSxD8yUam98cYbys3N1Zw5c/Twww/bOw5QLmlpaTp//rz8/f3tHQW4qb755htJUlBQkM1Yw4YN+eUNqoxff/1V69atU7169TRp0iR7x7mlKEzuIB9//HGZY7GxsZIkT0/PiooD3DQuLi7y8/Oz2f7dd98pIiJCJpNJffr0sUMyoPzWrFmj6Ohovfnmm7r33nvtHQcoN8v6JfXq1dOMGTMUFRWljIwMNWrUSH//+99ZhB6VVlxcnJydndWiRQulp6dr48aNSk1NVZ06ddSvX79Sf2kJVEazZs1ScXGxxo8fr1q1atk7zi1FYQIVFxdr4cKFkqR+/frZOQ1QPpcvX9bkyZOVkJCgY8eOqVGjRpo7d26JR3WAyiI1NVXz5s1Tt27d9Mwzz9g7DnBTWNYvCQkJUe3atdW5c2d5eHjo4MGDWrhwoaKjo7Vy5UpVr17dzkmBa5eXl6f09HR5enoqIiJCb7zxhi5dumQdX7ZsmUaMGKHJkyfbMSVQfpGRkYqJiZGnp6cGDRpk7zi3HGuYQPPnz9fevXvl7u6ukSNH2jsOUC4nTpzQ1q1bdezYMes2Xr+KyqiwsFCTJ0+Wg4ODZs+eLQcHB3tHAm4KywyThx9+WDt37tRHH32kVatWadOmTWrRooViYmL0wQcf2DklcH0uXLggSTp37pymTJmiPn36KCIiQvv27dOCBQtUp04drVixQl9++aWdkwLls3LlSklXHj1zdna2b5gK4FBcXFxs7xCwn//85z9asmSJXFxc9Omnn6pLly72jgSUS05OjnJzc+Xo6Kgff/xRM2fO1KlTp/TKK69o1KhR9o4HXLOlS5dq/vz5eu+99/TEE09Yt7dq1UqFhYUUgai0cnNzlZaWpqZNm8rFxaXE2OHDh/XYY4/Jzc1Ne/fuvSO+jKNqSE9PV0BAgCSpR48eWrFiRYnxnTt36p///Kc8PDwUGRlJCY5KKSEhQQMGDFDNmjUVFRUlk8lk70i3HDNM7lAFBQWaPn26lixZIldXVy1evJiyBFWCyWRS3bp1Vbt2bT388MNavHixHBwctHTpUuXm5to7HnBN4uPjtWjRIvn7+5coS4CqwNXVVb6+vjZliSS1bNlSnp6eysnJUXJycsWHA26Qm5ub9b+HDBliMx4QECAPDw9lZmYqJSWlIqMBN82WLVskSX379r0jyhKJNUzuSBcvXtRLL72k6Oho1apVS0uWLKEsQZXVoUMHNW3aVCkpKUpLS5Ovr6+9IwF/acGCBcrPz1dBQYFeffXVEmNFRUWSZN0+depU3X333RWeEbhV3N3dlZ6eXmL9B+B2V7NmTTk7Oys/P1/33HNPqfs0atRImZmZOnv2bKlv9gNud9u2bZMk9e/f385JKg6FyR3m3LlzCgoK0qFDh9SwYUN98sknpb5dBKgsiouLNW/ePKWnp2vevHlycrL9sWb5LWZBQUFFxwNuSE5OjiRd9RWUGzdulCRNnDiRwgSVxoULFzRnzhydO3dO8+fPL/Vn9u+//y5J8vDwqOh4wA2rVq2amjVrpvj4eGVmZqpFixY2+5w+fVqS+JmNSik9PV1HjhxRzZo11b17d3vHqTAUJneQvLw8jRo1SocOHZKvr69WrFjBa4RR6Tk4OOjbb79VcnKy/vGPf8jf37/EeFpampKSkmQymeTt7W2nlMD1+eKLL8ocYw0TVGZ33XWXtm3bprNnz2rfvn02X7qjoqJ09uxZ+fn5UZig0nnggQcUHx+viIgIm+8jiYmJOn78uBo0aKAmTZrYKSFw43755RdJUrt27Uotu6sq1jC5gyxcuFAHDhxQw4YN9cUXX1CWoMoYPHiwJOm9995TRkaGdXtmZqb+9a9/qaCgQE8//bRcXV3tFREAoCslt+Vn9owZM5SZmWkdS01N1TvvvCNJGjNmjF3yAeXx1FNPyWQyKSwszDoLULoyw3vatGkqKirSM888I0dH/gmGyufgwYOSpLZt29o5ScXiLTl3iD/++EP+/v66fPmyWrduLR8fnzL3ff/99yswGVB++fn5GjdunCIjI2UymdSpUycVFhbql19+UU5Ojvz9/bV48eJSFxgEKhtmmKCyu3z5soYPH66ff/5ZJpNJnTt3liTt2bNHeXl5CgoK0muvvWbnlMCN2bJliyZNmqSCggK1bt1aDRo00IEDB3T27Fl169ZNy5cv5+1PqJQmTpyor7/+WjNnztSgQYPsHafCUJjcIaKiovTCCy9c0758CUdlVFhYqDVr1igkJETHjh2To6Oj/Pz8NHDgQA0ePJjf5qDKoDBBVZCXl6eVK1dq48aNSk5OlouLi1q1aqWhQ4eqX79+9o4HlMvhw4f10Ucfae/evcrJyVGTJk306KOPKigoiLIEldawYcO0Z88erVixQj169LB3nApDYQIAAAAAAGDAr1wBAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAAAAAAAMKEwAAIDdLVq0SGazWRMmTLB3lEpv6NChMpvNWrVqlV2uX1RUpKSkJLtcGwCAm4nCBAAAADdFbGysBg0apLVr19o7CgAA5UZhAgAAUIU0bNhQ3t7eqlWrVoVfe/Xq1Tp06FCFXxcAgFvByd4BAAAAcPPMnTvX3hEAAKgSmGECAAAAAABgQGECAEAVEhISIrPZrHfffVdnzpzRu+++q4CAALVp00b+/v566623dPLkSev+v//+u8xms8xmsy5evGhzviNHjljH/+y1116T2WzW9u3bFRsbq9GjR6tr167q2LGjhgwZou+//16SlJOTo3nz5qlXr15q06aNevXqpQULFig/P7/Mz5CYmKgXX3xRXbp0sZ5vw4YNZe5/4cIFLV68WIGBgWrfvr06deqkp556SmvXrlVhYaHN/r169ZLZbFZqaqpefvlldejQQV26dNHkyZP/8v6WxXLfp0+frszMTL3yyivq1q2bOnbsqIEDB2rt2rUqKioqcYzl3j/yyCNKSEjQk08+qbZt26pHjx4lFmw9f/68Fi1aZP18lnMuX75cly9ftslytUVfs7KyNGfOHD344INq166dunTpoueee04RERFlfrb8/HytXr1aTz75pLp27ap27dopMDBQS5cuVW5ubonPEhoaKkn69NNPZTab9dprr93Q/QQA4HbAIzkAAFRBJ0+e1MCBA5WRkaHGjRvLy8tLR48e1f/+9z9FR0crLCzspqxxsXPnToWFhcnZ2VleXl5KS0vT/v37NWrUKH300Uf697//raNHj6pp06Zq2LChUlNT9fHHH+v06dOaOXOmzfmSkpI0ePBgXbx4Uc2bN1dOTo7279+v/fv3a/fu3Zo1a1aJ/X///XcNHz5cKSkpcnJykpeXl4qKihQTE6OYmBh98803WrJkiVxcXGyuNWnSJMXGxsrPz08ZGRlq1KhRue/HqVOnNHjwYGVkZKhZs2YqKirSoUOH9Oabb+r777/X/Pnz5eRU8utXdna2RowYofPnz8vX11eJiYlq1qyZJCk5OVlBQUE6ceKEqlWrpubNm6uoqEhxcXE6dOiQNmzYoBUrVqh+/fp/me3QoUN64YUXlJWVJRcXF3l7e+vSpUvavXu3du/erYEDB2rWrFlycHCwHnPu3DmNHj1a+/fvlyT5+PioWrVqOnbsmObPn69du3Zp2bJlcnV1VadOnZSSkqKsrCx5eHhY/94BAFBZMcMEAIAqaNu2bXJ1ddX69ev17bffatOmTQoODpabm5uOHz9+095ism7dOvXq1UvR0dEKDQ3Vzp071apVKxUWFmr06NG6cOGC1q9fr61bt2rbtm2aMmWKJCk0NFR//PGHzfmOHDmiunXratOmTdqwYYO2b9+uhQsXysXFRevXr9fmzZut+xYWFmr8+PFKSUlRr169FBkZqc2bN+vrr7/W5s2b1axZM0VHR2vevHmlZo+Li9Pq1asVFham6OhojRw5stz3Y8eOHbp8+bJWrVqlLVu2KCIiQitXrlSNGjW0detWBQcH2xyTkZGh6tWra/v27QoNDVVkZKS6deum/Px8jR49WidOnFDXrl21Y8cOhYeHa+PGjfr6669lNpv122+/aeLEiX+ZKzs7W+PGjVNWVpaeeOIJ7dq1Sxs2bNC2bdu0Zs0aNWjQQCEhIfrss89KHDd79mzt379fXl5e1utu2rRJoaGhql+/vnbt2qXly5erfv36Cg4O1gMPPCBJGjBggIKDgzV69Ohy31MAAOyFwgQAgCpq7ty5at26tfX/O3XqpAEDBkiSDhw4cFOuUbt2bc2aNUs1atSQJNWoUUNDhgyRJBUVFentq1//JwAACNdJREFUt99Wq1atrPsPGzZMLi4uKiws1NGjR23O5+DgoMWLF1tnWEjSgw8+qDFjxkiSVqxYYd3+zTffKC4uTl5eXvrggw/k7u5uHfP19dUHH3wgR0dHBQcHKysry+Za/fr1U8eOHSVJzs7O1s9QXrNmzVKXLl2s/9+9e3dNnTpVkvTJJ5+ouLjY5pjhw4erXr16kqQ6derIwcFBmzdvVlJSktzd3bVkyRJ5enpa9/f29tYnn3wik8mkn376SZGRkVfN9OWXXyo9PV1du3bVjBkzSnzWzp0767333rPmszwulZmZqbCwMOufiZ+fn/UYs9msadOmSZLCw8Ov6/4AAFBZUJgAAFAF1alTR+3bt7fZ7u3tLenKuh83Q6dOnWyKBsujLU5OTurWrVuJMScnJ9WpU0eSSl0zpXPnzjbrpUjS448/LunKYyWW8mPHjh2SpL59+8rV1dXmGD8/P/n5+Sk/P1+7d++2Ge/QocNffr7r1bhxY/Xu3dtme2BgoNzc3HTy5EnFxcVdUxZLCRIYGKiaNWvajHt6eqpv376SrjwadTWWe9W/f/8Sj9xYPPDAA6pdu7aysrKsrwWOjIxUcXGx2rdvr+bNm9sc07t3b4WHhyssLOyq1wYAoLJiDRMAAKqgBg0alLq9evXqklTqYqg3wsPDw2abs7OzpCuzTUpbO8QyXtpMi5YtW5Z5nZo1ayo7O1tJSUmqV6+ejh07JkmKiIjQzz//XOpxGRkZkq6sjWJ0Let+XK82bdqUut3FxUX33nuv4uPjlZKSUmLmT1lZkpOTJZV9TySpVatWCg8Pt+5bFsu9+uKLL8pcQNcysyQpKUkdOnRQamqqJJValkhX/hxbtGhx1esCAFCZUZgAAFAFWUqJW81kMlXY+Uwmk7Kzs61vhrHMkklLS1NaWtpVz5udnW2zrbRZKeV1tYV077rrLkmlz+4pLYtlBo7luKuds7TZOn9muaalOLkay72yrDFzs/+MAQCoLChMAACApNJnfJT22tpbKScnp8wxSylgKSXc3NwkSQsXLtSDDz5468Ndg0uXLpU5Zikt6tate03nshQVV3t8ylJu/FWp4ebmpuzsbK1fv77MWTBGltlIV/tMAABUZaxhAgDAHezPr7jNy8uzGT958mRFxinz0ZK0tDRduHBBjo6O8vHxkSTde++9kq4+ayImJkZHjhypsOKnrCy5ubnWz/bnBW2vxrLezOHDh8vcx7LeSNOmTa96rmu5V3v27NGxY8esfw8s109ISCh1/4KCAj311FOaMGGCTp8+fdXrAwBQGVGYAABwB/vzIySlrfNhWSy0ouzZs0fp6ek22y2v4/3b3/5mXWQ2ICBAkhQWFqbc3FybY9LS0vTss88qMDBQMTExty70nxw+fFjx8fE22zds2KDc3Fz5+PhYC5+/4u/vL0natGlTqY8UZWRk6Ntvv5Uk9ejR46rnstyrtWvXljqT6KefftKwYcM0YMAAnThxQpJ0//33y8HBQQcOHCi1yNq1a5diYmK0Z88e66wZy4KypV0DAIDKhsIEAIA7mMlksr6VZsGCBdZ/mBcUFOjzzz9XaGhohebJy8vTiy++qMzMTOu2devWaeXKlXJwcNC4ceOs2x955BF5eXkpJSVF48eP16lTp6xjycnJGjt2rAoKCtSyZUt17969wj7DxIkTS5RPUVFRmj17tiTppZdeuubz9O/fX97e3jp9+rTGjh1rXcBWulJujRo1SpcuXVLHjh1LfTPPnz399NOqW7eufvrpJ02dOrVEARMbG6uXX35Z0pU333h5eUmSvLy89NBDD6moqEjjx49XSkqK9Zj4+Hi99dZbkqShQ4eqWrVqkv7/miqW0gUAgMqMNUwAALjDTZgwQePHj9e+ffvk7+8vLy8vpaen68yZM3ruuecUEhJS6gyHW6Fnz57at2+fevfurebNm+vs2bPWGSeTJ08u8ZpiFxcXffjhhxoxYoQiIyMVEBAgX19f5efnKzk5WYWFhfL09NSSJUsqJLt05W03Z86cUf/+/eXn56fLly9bZ2cMHz5cDz300DWfy/L5Ro4cqb1796p3797y9fVVUVGRjh49quLiYpnNZi1YsMBaWJSlXr16WrRokcaOHauQkBBt3rxZvr6+unDhgrUIMZvN1mLH4p133lFaWpoOHjyohx56yHp/U1JSVFRUpJ49e2rUqFHW/S3l29atWxUYGKiuXbvqzTffvObPDADA7YQZJgAA3OH69Omjzz77TD179pSjo6MSExN1zz33aO7cuZo6dWqFZmndurWCg4N13333KTk5WefPn9f//d//6b///a9GjBhhs7+vr6/Cw8M1ZswY+fj4KDk5WampqWratKmGDx+u0NBQNWrUqMLyN2jQQOvWrVOfPn10/PhxnTlzRt27d9fHH3+sKVOmXPf5mjVrprCwMI0ZM0be3t5KTk5Wenq62rZtq6lTp2rdunVq2LDhNZ2rS5cu2rhxo4YNG6aGDRsqISFBGRkZ8vPz0/jx4xUcHGzzlp/atWtrzZo1mjJlilq2bKm0tDSdOHFCLVu21PTp07V06dISr45+7LHH9Pzzz6tevXpKTk4u9fEkAAAqC4diHjIFAAAol5CQEL3++utq3bq1QkJC7Jrl2Wef1b59+zR9+nQ988wzds0CAEBlxgwTAACAKsTyGmDLa5cBAMCNYQ0TAACAKqC4uFhZWVlKTEyUJDVu3NjOiQAAqNwoTAAAACTNmDFDcXFx133c7bCoaWJiogYNGqT8/Hzl5eWpfv366tixo71jAQBQqVGYAAAASDpy5Ij2799/3cdV1BuErubEiRMymUw6d+6c2rdvr+nTp5dYjBUAAFw/Fn0FAAAAAAAwYNFXAAAAAAAAAwoTAAAAAAAAAwoTAAAAAAAAAwoTAAAAAAAAAwoTAAAAAAAAAwoTAAAAAAAAAwoTAAAAAAAAAwoTAAAAAAAAAwoTAAAAAAAAAwoTAAAAAAAAAwoTAAAAAAAAAwoTAAAAAAAAAwoTAAAAAAAAg/8H72ydxZLGW1gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 381,
       "width": 550
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 381,
       "width": 550
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 381,
       "width": 550
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 381,
       "width": 556
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 388,
       "width": 556
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 381,
       "width": 556
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 388,
       "width": 544
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 435,
       "width": 550
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 410,
       "width": 550
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print the distibutions\n",
    "\n",
    "for f in df.columns:\n",
    "    fig = plt.figure()\n",
    "    s = df[f]\n",
    "    if s.dtype in ('float', 'int'):\n",
    "        num_bins = min((30, len(df[f].unique())))\n",
    "        s.hist(bins=num_bins)\n",
    "    else:\n",
    "        s.value_counts().plot.bar()    \n",
    "    plt.xlabel(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level        0.000000\n",
       "last_evaluation           0.000000\n",
       "number_project            0.000000\n",
       "average_montly_hours      2.453497\n",
       "time_spend_company        1.006734\n",
       "work_accident             0.000000\n",
       "left                      0.000000\n",
       "promotion_last_5years     0.000000\n",
       "is_smoker                98.433229\n",
       "department                0.000000\n",
       "salary                    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many NaNs?\n",
    "\n",
    "df.isnull().sum() / len(df) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove is_smoker\n",
    "\n",
    "del df['is_smoker']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill time_spend_company\n",
    "fill_value = df.time_spend_company.median()\n",
    "df.time_spend_company = df.time_spend_company.fillna(fill_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       0.000000\n",
       "last_evaluation          0.000000\n",
       "number_project           0.000000\n",
       "average_montly_hours     2.453497\n",
       "time_spend_company       0.000000\n",
       "work_accident            0.000000\n",
       "left                     0.000000\n",
       "promotion_last_5years    0.000000\n",
       "department               0.000000\n",
       "salary                   0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() / len(df) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 381,
       "width": 565
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How does average_montly_hours depend on number_project\n",
    "\n",
    "sns.boxplot(\n",
    "    x='number_project',\n",
    "    y='average_montly_hours',\n",
    "    data=df\n",
    ")\n",
    "plt.savefig(\n",
    "    '../figures/hr-analytics-hours-num-proj-boxplot.png',\n",
    "    bbox_inches='tight',\n",
    "    dpi=300,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: 160.16353543979506,\n",
       " 3: 197.47882323104236,\n",
       " 4: 205.07858315740089,\n",
       " 5: 211.99962839093274,\n",
       " 6: 238.73947368421054,\n",
       " 7: 276.015873015873}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate fill values for average_montly_hours\n",
    "\n",
    "mean_per_project = (\n",
    "    df.groupby('number_project')\n",
    "    .average_montly_hours\n",
    "    .mean()\n",
    ")\n",
    "mean_per_project = dict(mean_per_project)\n",
    "mean_per_project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill average_monthly_hours with the appropriate values\n",
    "\n",
    "fill_values = df.number_project.map(mean_per_project)\n",
    "df.average_montly_hours = (\n",
    "    df.average_montly_hours\n",
    "    .fillna(fill_values)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make sure we are all clear of NaNs\n",
    "\n",
    "assert df.isnull().sum().sum() == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert categorical features to binary integer representation\n",
    "\n",
    "df.left = df.left.map({'no': 0, 'yes': 1})\n",
    "df = pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
       "       'average_montly_hours', 'time_spend_company', 'work_accident', 'left',\n",
       "       'promotion_last_5years', 'department_IT', 'department_RandD',\n",
       "       'department_accounting', 'department_hr', 'department_management',\n",
       "       'department_marketing', 'department_product_mng', 'department_sales',\n",
       "       'department_support', 'department_technical', 'salary_high',\n",
       "       'salary_low', 'salary_medium'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to file\n",
    "\n",
    "df.to_csv('../data/hr-analytics/hr_data_processed.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "hide_input": false,
  "kernelspec": {
   "display_name": "packt",
   "language": "python",
   "name": "packt"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "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"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}