Data Visualization Using Python Workshop | Session 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization Using Python Workshop\n",
    "## Session 2\n",
    "\n",
    "Author: G. Gordon\n",
    "Overview: Exploring the HR Analytics Data Set (source:)\n",
    "\n",
    "Not to be re-used without written permission from author."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np # import numpy package as use the alias np\n",
    "import pandas as pd # import  pandas package as use the alias pd\n",
    "import matplotlib.pyplot as plt # import pyplot from matplotlib package as use the alias plt\n",
    "# plot all graphs and images in the notebook\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_file='./hr_data.csv' # store path of data file in variable `data_file`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import data from file into python pandas.DataFrame and store in variable `data`\n",
    "data = pd.read_csv(data_file) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>sales</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</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.77</td>\n",
       "      <td>6</td>\n",
       "      <td>247</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5</td>\n",
       "      <td>259</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.89</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>142</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "5                0.41             0.50               2                   153   \n",
       "6                0.10             0.77               6                   247   \n",
       "7                0.92             0.85               5                   259   \n",
       "8                0.89             1.00               5                   224   \n",
       "9                0.42             0.53               2                   142   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "5                   3              0     1                      0  sales   \n",
       "6                   4              0     1                      0  sales   \n",
       "7                   5              0     1                      0  sales   \n",
       "8                   5              0     1                      0  sales   \n",
       "9                   3              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  \n",
       "5     low  \n",
       "6     low  \n",
       "7     low  \n",
       "8     low  \n",
       "9     low  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10) # view first 10 records from data frame "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renaming Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'satisfaction_level', u'last_evaluation', u'number_project',\n",
       "       u'average_montly_hours', u'time_spend_company', u'Work_accident',\n",
       "       u'left', u'promotion_last_5years', u'sales', u'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns #View columns within data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['satisfaction_level',\n",
       " 'last_evaluation',\n",
       " 'number_project',\n",
       " 'average_montly_hours',\n",
       " 'time_spend_company',\n",
       " 'Work_accident',\n",
       " 'left',\n",
       " 'promotion_last_5years',\n",
       " 'sales',\n",
       " 'salary']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns.tolist() #View columns as python list within data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4391       technical\n",
       "11230      technical\n",
       "5385       technical\n",
       "12986          sales\n",
       "9075     product_mng\n",
       "11113          sales\n",
       "5561              IT\n",
       "6294       technical\n",
       "Name: sales, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#retrieve all values from a column within a dataframe as a pandas.Series object\n",
    "data[\"sales\"] \n",
    "# Only display the last 5\n",
    "data[\"sales\"].tail(5)\n",
    "# Randomly sample 8 items from pandas.Series\n",
    "data[\"sales\"].sample(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "Since the ```sales``` column contains the names of department, we will rename the column to ```department```. We will rename the column using the following steps\n",
    "\n",
    "-----------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'satisfaction_level', u'last_evaluation', u'number_project',\n",
       "       u'average_montly_hours', u'time_spend_company', u'Work_accident',\n",
       "       u'left', u'promotion_last_5years', u'department', u'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# retrieve all column listing as a list\n",
    "column_listing = data.columns.tolist()\n",
    "# Change the name of the 9th columns (index 8) from `sales` to `department`\n",
    "column_listing[8]='department'\n",
    "# update the column names for the dataframe\n",
    "data.columns = column_listing\n",
    "#show the updated columns\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the Data Set\n",
    "\n",
    "In this section we will utilize aggregate methods available on each pandas.DataFrame or pandas.Series along with visualizations to answer questions about our dataset. Each question will be supported by sub-questions that will guide the exploration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 1 : How Satisfied are employees?\n",
    "\n",
    "1. What is the average satisfaction level?\n",
    "2. What is the range of satisfaction for all our employees?\n",
    "3. What is the range of satisfaction for each department?\n",
    "4. On average, which department is the most satisfied?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Storing the satisfaction levels for each employees `emp_satisfaction`\n",
    "emp_satisfaction = data['satisfaction_level']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    14999.000000\n",
       "mean         0.612834\n",
       "std          0.248631\n",
       "min          0.090000\n",
       "25%          0.440000\n",
       "50%          0.640000\n",
       "75%          0.820000\n",
       "max          1.000000\n",
       "Name: satisfaction_level, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using the describe method to view the mean/average satisfaction along with quartile ranges\n",
    "emp_satisfaction.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The employee satisfaction `count` is 1499900.00\n",
      "The employee satisfaction `std` is 24.86\n",
      "The employee satisfaction `min` is 9.00\n",
      "The employee satisfaction `max` is 100.00\n",
      "The employee satisfaction `50%` is 64.00\n",
      "The employee satisfaction `25%` is 44.00\n",
      "The employee satisfaction `75%` is 82.00\n",
      "The employee satisfaction `mean` is 61.28\n"
     ]
    }
   ],
   "source": [
    "# Using a for loop to print the values as a percentage instead of decimal numbers\n",
    "\n",
    "# Retrieving statistics as a dictionary\n",
    "emp_satisfaction_dict = emp_satisfaction.describe().to_dict()\n",
    "#looping through each \n",
    "for aggregate_value in emp_satisfaction_dict:\n",
    "    print(\"The employee satisfaction `%s` is %.2f\" % (aggregate_value, emp_satisfaction_dict[aggregate_value] * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although quick, this may not be the best way to present these quick statistics. For example, multiplying the count by 100 gives us 100 more than the actual number of employees and all sentences may not be considered as immediately meaningful.\n",
    "\n",
    "Another alternative is writing each statement individually, using the methods available on the pandas.Series object (The dictionary may be used if so desired. Can you use the dictionary to produce the same output as below?). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of employees is 14999. \n",
      "The minimum satisfaction level of employees is 9.0 %.\n",
      "While, the maximum satisfaction level of employees is 100.0 %.\n",
      "The satisfaction level for most employees is between 44.0 % and 82.0 %, i.e. 25-75% of responses.\n"
     ]
    }
   ],
   "source": [
    "print(\"The number of employees is %d. \" % emp_satisfaction.count() )\n",
    "print(\"The minimum satisfaction level of employees is %.1f %%.\" % (emp_satisfaction.min() * 100) )\n",
    "print(\"While, the maximum satisfaction level of employees is %.1f %%.\" % (emp_satisfaction.max() * 100) )\n",
    "print(\"The satisfaction level for most employees is between %.1f %% and %.1f %%, i.e. 25-75%% of responses.\" % \n",
    "      ((emp_satisfaction.quantile(0.25) * 100), (emp_satisfaction.quantile(0.75)*100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While this is more meaningful, we spent a considerable amount of time preparing this information and our reader will spend a considerable amount of time perusing it. This questions could be answered using a boxplot (https://en.wikipedia.org/wiki/Box_plot)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f344e50>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD9CAYAAABHnDf0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f585ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a box plot using matplotlib methods made available on the pandas.Series object\n",
    "emp_satisfaction.plot.box()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10fad06d0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD9CAYAAACyYrxEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fbe6b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Multiplying the decimal number for employee satisfaction by 100 before plotting boxplot\n",
    "# Creating a box plot using matplotlib methods made available on the pandas.Series object\n",
    "emp_satisfaction_percentage = (emp_satisfaction*100)\n",
    "emp_satisfaction_percentage.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x115ae5a90>,\n",
       " <matplotlib.text.Text at 0x115aee690>,\n",
       " <matplotlib.text.Text at 0x115a98a50>,\n",
       " <matplotlib.text.Text at 0x115c5e190>,\n",
       " <matplotlib.text.Text at 0x115c5e890>,\n",
       " <matplotlib.text.Text at 0x115c5ef90>]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5/jFJ0yTtkyedBGwl6XFgyzwOcC2pr/knSH3IH1hY1wOFVR8InJXn+zfwlzrbHw0QEZV+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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11453e790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Removing name from series before plotting\n",
    "emp_satisfaction_percentage.name = ''\n",
    "# The name of the series is used as the ticklabel / series name by default\n",
    "# we will re-purpose this by placing the average here since the box plot does not show the average\n",
    "emp_satisfaction_percentage.name = \"Average : %.2f %%\" % (emp_satisfaction_percentage.mean())\n",
    "# setting the title of the boxplot and storing the boxplot axes in a variable `emp_satisfaction_boxplot_axes`\n",
    "emp_satisfaction_boxplot_axes = emp_satisfaction_percentage.plot.box(title=\"Range of employee satisfaction across all departments\")\n",
    "# Changing the label on the x and y axis labels respectively\n",
    "emp_satisfaction_boxplot_axes.set_xlabel(\"Employee Satisfaction Level\")\n",
    "emp_satisfaction_boxplot_axes.set_ylabel(\"Percentage Satisfied\")\n",
    "# Changing the tick labels on the y axis from 20,40,60,80,100 to all end with percentage\n",
    "\n",
    "# First format tick labels\n",
    "emp_satisfaction_boxplot_axes_yticklabels = [ \"%.1f %% \" % label for label in [0,20,40,60,80,100] ]\n",
    "\n",
    "# Apply updated tick labels\n",
    "emp_satisfaction_boxplot_axes.set_yticklabels(emp_satisfaction_boxplot_axes_yticklabels)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations, you have successfully showed the range of satisfaction levels across the company. Let's see if we can do the same for each department within the company?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Retrieve satisfaction level and department data only\n",
    "department_satisfaction_level_data = data[['department','satisfaction_level']]\n",
    "# Group satisfaction_level data by department\n",
    "department_satisfaction_levels_groups = department_satisfaction_level_data.groupby('department')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We will be utilizing some methods from the python math library henceforth\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of department rows and columns in the format (rows, columns) - (5,2)\n"
     ]
    }
   ],
   "source": [
    "# get number of departments/groups\n",
    "no_depts = data['department'].nunique() # `nunique` method returns number of unique values\n",
    "# We would like to plot the graphs in two columns, we will calculate the number of rows required\n",
    "no_depts_rows = no_depts / 2\n",
    "no_dept_columns = 2\n",
    "# in the event that this is a decimal value, lets round the number up\n",
    "no_depts_rows = math.ceil(no_depts_rows)\n",
    "#let's also convert this to an integer\n",
    "no_depts_rows = int(no_depts_rows)\n",
    "\n",
    "print(\"Number of department rows and columns in the format (rows, columns) - (%d,%d)\" % (no_depts_rows,no_dept_columns ) )\n",
    "    \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x113b82490>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1127b80d0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1141a2f50>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1117a4490>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x113d23550>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x10b3cdfd0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1141f3950>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x11438ac10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1146fd810>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x114d8d910>], dtype=object)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3GW3Yy+y06P7u/uxOFrYF8zwvlya5tKq+WFUnqurQjlW3OfP08r4kb66q00mOJ3nnzpS2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\nWwAAAwlbAAADCVsAAAMJWwAAA/1/GEbKsv8ORScAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1142bc950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a special figure with axes for subplots using `no_depts_rows` and `no_dept_columns`\n",
    "# set the width to 10 units\n",
    "# set the hight to 25 units\n",
    "#save the figure and axis in variables for later use\n",
    "dept_satisfaction_levels_figure, dept_satisfaction_levels_axes = plt.subplots(no_depts_rows, no_dept_columns,figsize=(10,25))\n",
    "\n",
    "# since we get a 2 dimensional array for axis (4 rows, 2 columns) we will convert it to a 1 dimensional\n",
    "# array for easier use (8 rows) i.e. we can index the array from 0-7\n",
    "dept_satisfaction_levels_axes = dept_satisfaction_levels_axes.flatten()\n",
    "\n",
    "dept_satisfaction_levels_axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ANDMzM7MSbiCamZmZWQk3EM3MzMyshBuIZmZmZlbCDUQzMzMzK+EG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pmZlVLOHkSAjYFNgFWALSQREZe2X1hmZpUzbdo0nn32WRYuXMjjjz8OwCGHHFLlqMzMalc5t7k5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cCB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\nmVme/wdhSoS0hoK2cAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1142bc950>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let us save these labels since each graph will use them\n",
    "yticklabels = [ \"%.1f %% \" % label for label in [0,20,40,60,80,100] ]\n",
    "\n",
    "# We would like to plot on each department on each axis\n",
    "# The number of departments and the number of axis are currently the same\n",
    "# We will `zip` these together and extract the (group_name,group_data),axes in a for loop\n",
    "for (group_name,group_data),axes in zip(department_satisfaction_levels_groups,dept_satisfaction_levels_axes):\n",
    "    # for each group\n",
    "    \n",
    "    # Create a custom title\n",
    "    title = \"Range of employee satisfaction for %s\" % group_name.capitalize()\n",
    "    # Plot the graph on the correct axis\n",
    "    group_data.plot.box(title=title,ax=axes)\n",
    "    \n",
    "    #Perform modifications as before\n",
    "    axes.set_xticklabels([\"Average : %.2f %%\" % (group_data.mean() *100)])\n",
    "    axes.set_xlabel(\"Employee Satisfaction Level\")\n",
    "    axes.set_ylabel(\"Percentage Satisfied\")\n",
    "    axes.set_yticklabels(yticklabels)\n",
    "# show plots\n",
    "dept_satisfaction_levels_figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>department</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>0.618142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandD</th>\n",
       "      <td>0.619822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accounting</th>\n",
       "      <td>0.582151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>0.598809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>0.621349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>0.618601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_mng</th>\n",
       "      <td>0.619634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>0.614447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>0.618300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technical</th>\n",
       "      <td>0.607897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             satisfaction_level\n",
       "department                     \n",
       "IT                     0.618142\n",
       "RandD                  0.619822\n",
       "accounting             0.582151\n",
       "hr                     0.598809\n",
       "management             0.621349\n",
       "marketing              0.618601\n",
       "product_mng            0.619634\n",
       "sales                  0.614447\n",
       "support                0.618300\n",
       "technical              0.607897"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How about determining which department on average has the highest satisfaction level?\n",
    "department_satisfaction_levels_groups.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1148dbed0>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NjgNA0hH1rLPySeojaZlGx9Hb9JgTqpJujIhPNkEc2wBfI11hCzAV+EVEjC85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\nVw0RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b51790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting average satisfaction level for each department using a bar plot\n",
    "department_satisfaction_levels_groups.mean().plot.bar(title=\"Average Satisfaction in each Department\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visually, it seems the satisfaction level, may not vary that much between departments. We will continue our investigation with other questions, but before we do so, can you carry out a similar investigation for the employee last evaluation score (`last_evaluation`) ?\n",
    "\n",
    "----\n",
    "#### Exercise Questions:\n",
    "\n",
    "1. What is the average evaluation score on the last evaluation?\n",
    "2. What is the range of evaluation scores on the last evaluation for all our employees?\n",
    "3. What is the range of evaluation scores on the last evaluation for each department?\n",
    "4. On average, which department has the highest evaluation score on the last evaluation?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 2 : Are work accidents impacting our employee retention?\n",
    "\n",
    "1. How many employees do we have remaining?\n",
    "2. What percentage of employees remaining experienced a work accident?\n",
    "3. Can you show me the number of employees who are `remaning and experienced an accident`, `remaning and did not experience an accident`, `not remaning and experienced an accident` and `not remaning and did not experience an accident`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7813</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11473</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6853</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8850</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14942</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14661</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13334</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Work_accident  left\n",
       "7813               0     0\n",
       "11473              0     0\n",
       "6853               0     0\n",
       "8850               1     0\n",
       "14942              0     1\n",
       "1995               0     1\n",
       "14661              0     1\n",
       "13334              0     0"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The fields we will focus on include\n",
    "fields = ['Work_accident','left']\n",
    "# we will extract data from these columns and store in a variable called `retention_data`\n",
    "retention_data = data[fields]\n",
    "#Let's look at a sample of 8 items from this extracted data set\n",
    "retention_data.sample(8) # NB This wil return a different sample each time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "Interesting, it seems as if they have used \n",
    " - 0 to represent NO\n",
    " - 1 to represent YES\n",
    "\n",
    "How about we relabel these values to YES and NO accodingly. \n",
    "\n",
    "We will create a function, that will accept a numerical value, if the value is 1 it will return \"Yes\", if the value is 0 it will return  \"No\". We will call it encode_responses_to_yes_no."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def encode_responses_to_yes_no(number_value):\n",
    "    if number_value == 1:\n",
    "        return \"Yes\"\n",
    "    else:\n",
    "        return \"No\"\n",
    "    \n",
    "# NB this could also be done using a lambda expression as denoted below\n",
    "encode_responses_to_yes_no = lambda number_value : \"Yes\" if number_value == 1 else \"No\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         No\n",
       "1         No\n",
       "2         No\n",
       "3         No\n",
       "4         No\n",
       "5         No\n",
       "6         No\n",
       "7         No\n",
       "8         No\n",
       "9         No\n",
       "10        No\n",
       "11        No\n",
       "12        No\n",
       "13        No\n",
       "14        No\n",
       "15        No\n",
       "16        No\n",
       "17        No\n",
       "18       Yes\n",
       "19        No\n",
       "20        No\n",
       "21        No\n",
       "22        No\n",
       "23        No\n",
       "24        No\n",
       "25        No\n",
       "26        No\n",
       "27        No\n",
       "28        No\n",
       "29        No\n",
       "        ... \n",
       "14969     No\n",
       "14970     No\n",
       "14971     No\n",
       "14972     No\n",
       "14973     No\n",
       "14974     No\n",
       "14975     No\n",
       "14976     No\n",
       "14977     No\n",
       "14978     No\n",
       "14979     No\n",
       "14980     No\n",
       "14981     No\n",
       "14982     No\n",
       "14983     No\n",
       "14984     No\n",
       "14985     No\n",
       "14986     No\n",
       "14987     No\n",
       "14988     No\n",
       "14989    Yes\n",
       "14990    Yes\n",
       "14991     No\n",
       "14992     No\n",
       "14993     No\n",
       "14994     No\n",
       "14995     No\n",
       "14996     No\n",
       "14997     No\n",
       "14998     No\n",
       "Name: Work_accident, Length: 14999, dtype: object"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets transform the data in work accident\n",
    "\n",
    "# We map apply this method to a column/series in the data set and update it's values accordingly\n",
    "retention_data[\"Work_accident\"].apply(encode_responses_to_yes_no)\n",
    "#retention_data.sample(8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1217</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4901</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9718</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13295</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11158</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12345</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10287</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Work_accident left\n",
       "1217             No  Yes\n",
       "4901            Yes   No\n",
       "9718            Yes   No\n",
       "13295            No   No\n",
       "11158            No   No\n",
       "12345            No  Yes\n",
       "10287            No   No\n",
       "1951             No  Yes"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since we would like to apply it to all columns we will use the `applymap` method\n",
    "# We will also store the result in the retention_data variable\n",
    "retention_data = retention_data.applymap(encode_responses_to_yes_no)\n",
    "# Viewing a sample of 8 employee responses\n",
    "retention_data.sample(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of employees : 14999\n",
      "Number of employees remaining: 11428 \n",
      "Percentage remaining: 76.2 %\n"
     ]
    }
   ],
   "source": [
    "# Let's determining, how many employees we have remaining\n",
    "# i.e. how many have responded `No` in the `left` column\n",
    "num_employees = retention_data['left'].count()\n",
    "print(\"Number of employees : %d\" % (num_employees) )\n",
    "# we will use the `query` method and the logical operator `==` to compare responses\n",
    "# The `query` method filters our datasets based on the condition `left == \"No\"`\n",
    "num_employees_remaining = retention_data.query('left == \"No\"')['left'].count()\n",
    "print(\"Number of employees remaining: %d \" % num_employees_remaining  )\n",
    "#converting to a float\n",
    "#because an int/int gives an int and we would like decimal places and not just 0/100\n",
    "num_employees_remaining = float(num_employees_remaining)\n",
    "\n",
    "print(\"Percentage remaining: %.1f %%\" %  (num_employees_remaining/num_employees *100 ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although, we could do the same to answer how many employees remaining experienced a work accident, let us use a pie chart. Pie charts are useful for expressing percentages of a whole."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     11428\n",
       "Yes     3571\n",
       "Name: left, dtype: int64"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This time we will save the remaining employees\n",
    "employees_remaining = retention_data['left']\n",
    "# We will use the `value_counts` method to count the number of Yes/No response in each group\n",
    "# NB. This method is only available on Series objects\n",
    "employees_remaining_counts = employees_remaining.value_counts()\n",
    "employees_remaining_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11419c7d0>"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EDtBfhdniTtfafgkYFTqLiAS3UxJHM0OH6KsibXEfhkpbRLxcTw0sUnH/fegAIpIZue6D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\nRHJGxS0ikjMqbhGRnFFxi4jkzP8CWZd9wrIFtlgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111891810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the responses on a pie chart\n",
    "employees_remaining_counts.plot.pie(title=\"Employees Remaining\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115c5ed10>"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Oxu8HdgPKdkKcqPjIDVrlO0MZmQ0cTH3mZ9Rn2nyHiRoVdwGkk/HgPykkQJMZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1153f0e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Similar options are available on the pie chart\n",
    "# Let us also include the percentage\n",
    "employees_remaining_counts.plot.pie(title=\"Employees Remaining\",autopct=\"%.2f %%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "left  Work_accident\n",
       "No    No               9428\n",
       "      Yes              2000\n",
       "Yes   No               3402\n",
       "      Yes               169\n",
       "Name: Work_accident, dtype: int64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets group the employees based on those who are remaining\n",
    "retention_data_grouped = retention_data.groupby('left')\n",
    "# We will use the `value_counts` method to count the number of Yes/No response in each group\n",
    "# NB. This method is only available on Series objects\n",
    "retention_data_grouped['Work_accident'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1139625d0>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/PueWhh9vZ8VhcOptsly5exc42IpD6ViBGKJ0MvY3YCawynWWXlPl1WVLIrNe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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113962310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's save this data\n",
    "retention_data_grouped_counts = retention_data_grouped['Work_accident'].value_counts()\n",
    "# plotting the data using a pie chart\n",
    "retention_data_grouped_counts.plot(kind=\"pie\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems we are now experiencing a common dilemma, Should we relabel our values to show more distinct groups? Should we try a bar graph instead?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115f6ba10>"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L8zLgVcA/VNXVSVYAnxq4pmn2CeD8JJ9tyy8EThiwnmn3RuDdwJer6vIkjwTOHbimiXAI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\nnTIAJKlTBoAkder/AxvdzokOMZsIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115f980d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Re-labelling is an option, similar to how we did before, however let's explore the \n",
    "# bar chart option\n",
    "# We may plot a bar chart using a pandas.Series or pandas.DataFrame by passing 'kind=\"bar\"'\n",
    "# to the plot method as denoted below.\n",
    "retention_data_grouped_counts.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Work_accident</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>9428</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>3402</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Work_accident    No   Yes\n",
       "left                     \n",
       "No             9428  2000\n",
       "Yes            3402   169"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This is still ambiguous, the key is to unstack the value counts first\n",
    "unstacked_retention_data_grouped_counts = retention_data_grouped_counts.unstack()\n",
    "unstacked_retention_data_grouped_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1160dafd0>"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2mTr44IO54YYb1vxi2+LFi3nhhReoq6sjIjjyyCM5//zzefjhh2tcU2suX6lv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\nzMxKwkHdzMysJP5/LYj4aiZH6uoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116313610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's attempt another plot\n",
    "title=\"Number of employees remaining separated by those who experienced accidents\"\n",
    "unstacked_retention_data_grouped_counts.plot(kind=\"bar\",stacked=True,title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other charts are also possible, including:\n",
    "1. 'line' : line plot (default)\n",
    "1. 'bar' : vertical bar plot\n",
    "1. 'barh' : horizontal bar plot\n",
    "1. 'hist' : histogram\n",
    "1. 'box' : boxplot\n",
    "1. 'kde' : Kernel Density Estimation plot\n",
    "1. 'density' : same as 'kde'\n",
    "1. 'area' : area plot\n",
    "1. 'pie' : pie plot\n",
    "1. 'scatter' : scatter plot\n",
    "1. 'hexbin' : hexbin plot\n",
    "\n",
    "Simply change the value for the kind paramter, as denoted below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ee28510>"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mp9f3kAuM1enSEel785/tWg4cnrf+YamtpXazbmnD1p2cfNlknlnmM2UrT60GRESsApZK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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ee07510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[\"salary\"].value_counts().plot(kind='area',title=\"Distribution of Wages\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14999 entries, 0 to 14998\n",
      "Data columns (total 10 columns):\n",
      "satisfaction_level       14999 non-null float64\n",
      "last_evaluation          14999 non-null float64\n",
      "number_project           14999 non-null int64\n",
      "average_montly_hours     14999 non-null int64\n",
      "time_spend_company       14999 non-null int64\n",
      "Work_accident            14999 non-null int64\n",
      "left                     14999 non-null int64\n",
      "promotion_last_5years    14999 non-null int64\n",
      "department               14999 non-null object\n",
      "salary                   14999 non-null object\n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Let's take another look at the columns, but this time view their data types\n",
    "data.sample(100).info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119c7ad90>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pTI6CuIEHYlwm4ai3EEWUOeNMYYqFb0BkCRKkzyObUcFlHizUYpsgXOyLccB3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izRaha8DB6UB24AOMmKg4W+Z841Y4z5mDCoS8T34vOorBDI5qq987HuH1rJ2b0ZOZB2NdpqH2O+\nd6Xtd8yTtu5bqJ+17A+av7mnA7IRSST0yJKffROlny7LZtcBpe8G6/tktM85dGL5TrFQZO2d2yR+\nn5uVVrJjwS8+yPQy09efecFjH5o0VTKvQKlshjN0uTqjR1U/x15VPQjPji/uXR6rgT77lat77Vmp\nut8+Ql+tIOw6o2K1zuVOdZhU7a4ankkfVFO3kuE0VRltWW1edLGqgzEb7NmOCI1aPleqKjhfoaCi\nzdX1ZtWWFutsSkJr61RxcEyFKnhMqW7KbVO3seTbVBdmK5ALfa+0b9XOi3dk1D+DrOr2oOrjNSXF\n+uMx2lxzRHDKFB1rqqBtOA7Km5Z3WSafur3Myl72C6OtSpxVLZvSKe3+ug1rmmZ5J1UbVPdYv1v2\nN1dNDGlxvQV/rp9V15lQ3svyPduzhWU1p10y5pPMPvI1Pv3rTmaQfFNRBYnoNwP4RwG8ucC9zwB8\nuECgRVqOsY13GGICE+EQ5YOYImPTyBJuUEcB+xiso1rHNkW0ebxKsbpG/eA9ChY3wyBJguma5Z7x\nzqqcjVrkNsA6yHJzSmpJAhg54mIaEJoOkT0iAyehwcQJwYkYk3m1CSZOeD5M+sGnjIuvVFjoaooY\nY4Ij4Ew9KQkKk4A1GO+EJ90azMD7ww6RE95an6CPCZN6/rUZ6pByjEoxdAA2QXWzBxmorsYD+jTg\ny7sLfOfpG1iFBisfskCR8wSnOCiRsDKSTjAW0Je5QF+CKRulTw5ejhHDlMC6F7D2hEaDGBzUEaqf\nEhISdlPESdNI9JoQMCV1pnHilXoaGhCJEl9EgSoA+QDs+WbxP2hctnItSs6QKgsSElR4d+ixTxGu\nT3iy2mDTeKz0vU/qkv58jFh7J5ruLJDTqmkwRMY+ypsOBDxsG1xOAq8Fkt61m0YkTjhrRUzLaJ0J\nMhEG7ZuHJBNGcJzlIozOuAllMtlPUWmeCjmhcLKzJzCxWuxicRhcCBbI5v2xx24asfIejzQupjGy\n+iR7DBNHvLnaACAEhRgdig59NkDu0xKmb0LYBEALYQUFzHHvcwD/3Oso1KtKx6iCNshY6nR6H6oI\nK75a8hnkUQegPfYKgy5x7R65dj6B2NL6GkZOc73munzGyY0QLZEv7J6BAZy1LTwRBo44nwYAwCNa\nzWiJAPC0F0/IrScJuMtlidtPDMCh84STRubh8ykqHi6ebieuxQP9+L+4e46vHi4RyOGdzVnm4gIy\nYRxM8RByr+m+EQjnY9QBlrDyK0Tu4KmVKO4x6sBKRbu8akfDQ/PKiVWHJrc9wIJFgSGBlYMjbA0q\ngoj9gwSDF7hCohkBDiAnUW6Uy07QaDjQyQLybOfcjEo4MdAn5DJSbnvrUwwLZG2+AAbfvHu4wsQR\nG9/gyWqTt2dsgAheOOwnJJ/olBi7FJFiwmkjmPHKl7Yfk7j4r3SCuhh7vD/swAC60OpqSd31WSIX\nTZCNY3mubHADgPPFuq4DcTfOo0nqN2DlzG0v+Vjwj0ZhqYkLbXaMjM63OAkdVsHpxyH3iveqwyY0\n2ISiYMj6/jL1VJ9jDk33me47zNld6UUw758D8HNE9F8w86/eQ5leaaoQBz3wjRONjt1PdN3Sh9Lj\n7sqnXm4do6YBxyEce+6yjvZJHIN36EhB64lqmdkME/96W46u/1GsGBuEy0XHyljfTYtC3ryALmXn\nG14EzcpxvF3qxNfOLyAqG8hmcAEdvb66/MXeywskQl1rzH+rn3VDq9WY9/HzL9kPrj2rkpe4q843\nlPH5sEe33sw02V93ktXoh2vwfpnS/AUiemh/ENEjIvorr6FMryyZ2/ddSTBTEVYyZbj6HFiXkXbw\nSMe2jTVFAG99Xm1BLNNRq16f3fmAT2wf4EHT6QaOOK886VbYBvHwez4c8IWrZ/jK/gJEhIdtEEpd\nLjplq2XjRUCodvh50AasHGVP0FIwwjubM3xs/QAP2zViihjTPHhu5yjjaHl5q224bUR10AwmR4Q3\nVh1W3gn7BPPOOOuYPJtNNOhC0UkfE2MflSIJsUpLvJfSrsyMbRCdDledW+v1W+9UN6NsrolXoViY\nKSUcYsrwjwcLHGNjUDVwm3VIVTk8EbZB2uGdzQketit0ldjRjXUGcNY0OA0S9DqxQBjP+gGDBn8u\nQa2lHKdNizdXW5w2rfRrIAc2kH4jdV5rHwhVv6/LkVgYPBamz2teZRWkutu8bHvoxnAJkL3RtsrT\ntfZFB+iqYO7sZucIxhjq8YXdOZ72B1wMA+47fdNh3lV6g6ugwMz8PhG99RrK9MrSLZM3gMLGsIsF\nQy1d16iFgA5yRzajgGNUwdtf4m1De58i3t1LgNd31lu03uMQI54Po+60B3R+A4KrNiAlXiSB8KBd\n4aRpAcjS92l/wD5O2IaAzq+RmPFru3McYsQ2dNiEFYiFJ06QpW2E4LdedpHQaEMyMx52WzAzng8j\nRmZsvMfKN2AudLwt5CO2embGQgWzMAtFzFHA5TDgYtqDAHzy5AECeQyRcdDBuFNCsGh4lP0HQMrY\nOMr4rWGs9YcUmfHF3TnGlHDWrNH5Viab6n003mE3Dnj3sEMC45P+DN4HXE0jnvYH7SsyUT1u1/C0\nUhVChWFYePKHOOFL+0tRSGw2IPLonMBStvHYBYcpJaxDV1nKJehG3VO2QeC4MTEi5CURxPO19S7D\nMU97UTd80DRYBwkKHFzAiQtgtZbtfTD/f+y9ebRmyVEf+Iu8937L22vpql6qu9V7a2ukVkkCpEGS\nWcwyw2IMB+zBjMEHH/vgwwzGc1gOGMPBRmPsGXuwMcJwLBgQZh0ECAmMrBFIIKm10C211FKr1UtV\nV3dVdW1v/b7v3oz5IzPyxs0v77dUvfeqVCj6vK737pIZGZk3MjLylxGM7dKHVgUQG5SS+KG0Fc4N\nNlEx42h/CQR3XH7gx7vhOsCWk7vP4kRuFcPs0pt95tJZDGyJYwtrWCr6DT6G/hvLfB9AyUOnMcyJ\nsFx0UZgMp7bW0cn2d7vNOdj231UzieaxvC0R3SZ/ENHtmGZiXmXKzXgW+DZyPstm54i7/ErKSJEI\nLfXkdlnCwoVLFUTCto82JxYIUEde03AugWflJvNHtd0BHaCGkI1shYGHAxbGzd0aVTAMvn2Nbqg3\n5ADvY/X3erkkoVUR5fwhlEkyYtSJcXfsSMHmTIMPUnyYiA/NW5C92kyUTcNBVYZgT9Lm1AGPrcrJ\nPveJcAFgYzTyK4g6GNKyhwrqlZ1EIXSbhOLmMo2+0lTDVuvrTcXtSPZCJMKjUW0Osb+55k2iG0p8\nEwBhwIWok6p+6XhT/6qSLtdREwUSKnLU5QH1uI9lv1UOMbRuDPazbqNNWg6p8RJ/f7LSvHPlABby\nYuz5vaZrzfKeR3n/CIA/J6JfIaL/B8B7AfzQ3rC1OzQoK2yrSHVADUcaP4DT9FM24HtRuU1IYNP3\nGl9LkZSrLRD5Wchzv7POLlQqc4j7DdQDX+6l4FyjqgoZviX+iMAIczLI/Umx0pYA6hyQ7r7wVPM2\nrKqG/AwQQnJulaUPEiQn5hib/hqjmUBYyx5KTn1TwMBNLBU3Ixhari3qYWWDnCzbcF9WUOLikucF\nSieZ7J2MRgBzA5YotJgXPmCUC0Pr3CxFsLoke8vmaNiAsjlZurL6eRGCbpFHfezIGEQ9+YZkF7aG\naXbU16gVKXMdJVCSbej+KEwdL1yggo0P2wmkbq8a2zIIKz8B6DHQMflYm5vJn8cjY8Z9tZB30PET\n5lY5qNvkn8uisnRfMuq/pY6Kgc3ShhDO+0ZEMD7D/aSf/aR5Ypu8k4gehEtXD7jca2f3hq3dofpw\nTn0qzWWUEcu2PjUWnvcDX2Z9Sdwr5YhfMSzP4XbChcRCjkljY/V9jv7NTYaj/UV/Ug04u7OJFwZb\nWO0sYK3oAYawNXJJI0r/IVp2iWb7WQcXRyOsjwYua0t/GQc6PfSNy5LjMq1YLBY9LGQZCmP8MXCX\nYMF6zsSnWVpguxqhYovVTjckFy6McdC0kUvWMLIWBRFWigyXhiNsVyWIGNa6Q0TdLGu4UZw7yi3L\nl/IcC90uVjodhyoAedSH8+PmhkAMDNliq7Ju5eCz6vTzAlnukuHK0X0J8j/wWo7IWYE3LiyjrKxz\nj5SSpYaxWOTheHrX5Lh1YSUcq3dHvDMc7C4EBI0ceir9wZ8cQDd3VvDQMipLWC0WkRtCP89Cgo9h\nxTDGKetO5lwhQ4EsWkbPo0vIWmQgdL0GzYLLhNATKB8zNkoXHbDr0SWHel0MPNpHh2ol1FBWMIOt\nYNxdSAACsD6q/MrJYtkfY4d1ZwiOLSyHVZVY5IUf88avgip2IQMAoMgIl0YlwIzlToGMDO5aPoyM\n3Iafw63XYSZyA5B142rgE54Uxk3sO1WFfpbDwPXvUC259hnm7eV4bW1Yzpt+uYLLrdYD8BJ/WOC9\nu8/W7lA3M2C2YZkp6ZgyrperbfN3ZghGDZD4sI+zWb2y9r5MqnEFyTKDBeH/lY+LSC1jpW5yH91C\nvoilou+SAQAYhMS7ztrarlxYTQZwkHpYLXpYLnrOcrEWm5WrqfCbQ86d4rOkMzs8NWro1bZYzJ6X\nTpZjpRBLnUMslJKdy8SdVKzhlAtFgVXjogMOPNbb4cDZL/VdtnFm9rv33rIiClljejmh4DqeiSGg\nhywcmDGVw2YPLWOZ6k00seyHPq6GpHOzftKGV7C5MegaU8MBxUUDV1aOenw4v6tY0hSMAOM3Z62p\nEUUuXIFDtowYyKx1x8TJTUIMIPMm95ZX3LKhBwDrQ+ce63r8qBgNhSEfYc9BPC8NxQ1Re2EZQCer\nDxKVDEjwA2lfYdw4YHhIpp8EJD7NilfcmyN3uC2H2+dpxE8Hat8NgMwAm8M6kffAuvC5q90spJXL\n2AR3VmZMwO/DG0AgN2FLyOWedzO50LjNNgBAl+rsUPtJn484bwAAEf0DAN8H4BiAj8FZ4H+BazgB\nMdC0ioVm8ktz+9TurOtoN9R/LPP4vUQ5papqQvS8n5ib9YGabWm4MYPvt1l4CoLlyq3ri95Q78bT\nEiG+kmqDYrd5j+Ormp/m+43fx/iiGt7WEKYrP9mTka/WPR2EGu4lxFW/4ydtzdfYuGiy2koiyZTT\nrTGmtGsvWcqE6qR58u5Y2335URMasmeOYJdpOCzruuIH5O+x/hqnsXEPXCXIXm2aXSs0j+X9fQBe\nDeAvmflNRHQ/gH+5N2ztDsUHdEI8YP83kU/ThOZ41X8T/OaL+CzDNWetWB/1zVmljMw0Y1FLPXFd\nstkliXYrZn+qsUkWfjPMK6eeMVgfVdgsLZYKZ0He0Os7nzaUIoP7aJZytxzVAbP6mcHI2npJzi4F\nWEcsftQ+2ZF1FrQgG5xcOJyuk1UNAchJTj8yjHEQu5UiC24VOSEnSQ46/jSfUSsPkUeQXaJfe5kJ\nMg99JP1FhJ7xSZP9BObqctHtcuNcCEb4gLNgJXpfPNd3jUlGp9T8hXtE6Ocu9Vpp2efPdC66sAfA\nrj8XM3KnLsn7d4mw3HHJL1x+07SiyMjBPwc+qJUoVhlbMmYMuYMy7NssSXuB5rhfzg0GVoUKIHca\nVZItpNobT09LeYacnDsk9+45vZmpx32jLN8vzgVGPoxAfV/6VAKMVd6/D++u2c/8w6IHriWaR3nv\nMPOOV0RdZv4UEd23Z5ztAsVWUzgcgOa1gE2OZniKfo8txwz1MrgwdcB8bT2RKzgo8ICBjYyHTH1Y\nDWI5pVd/DAcMNSahTpajk+Vhs0hbhgPrMpdU4BCA32Xezny9tX1OQJhAxOLpZPU9cXtIzboJzBzQ\nI0Dt8uhk6tSkkm9wCWT1B66Jouca94h8goDmNar/GOvTnMglQvCKSz0KguMzRUQUeIyJoZJuUB2g\nSfMmH5hUGRIVGIOeGecxz7NkmzXlhpCb5qerx5aMNwOAcjdyY2BdeJ4I/YiPjAhZlkZP6BWhvqaT\ndnQiSG2Dt0R5It8Uj1KPIWooq6uhR6815T3P+uMEuUM6/y+APyGi34PLcnzNUvfRDOYAACAASURB\nVDZh0KRoHPqjBmviWuu7+jrq5zmeSHTZGC9XK+LYhRAUbEtdQo1IboGPphLV5RnQWJsb0ehaSPNh\nJskowWODD2rK8kopBeVq/n35dTSsxJY2j/Mx/lyKxyshXdc87dttPvaK9J7EfpL7Oib/zFQO0S8R\n0Wki+ri69uNEdJKIPuZ/vnZaObMEprqDmT/HzN/kL/04Ef13AKsA3jkTt1eJSm5PQBwgTkgrvVrB\n1ckA6ufHfdttPnILZ1U6d0GdvEFDouRUmbhGpKyRdUHph7ZCTnWwI2bGVjXy6by03xIOUeCvCuxr\nZBlbZYV+7sKPjqoqhF7VEf7qcKyedx/y9OJgBwe6/XA9pRT0crrybUrJSHiMLbem/JryndZXKUpZ\n+bPQ2OprAh+GAGLlCppQ16T2zcvHNErV1TY+r2Ul3UYl3LeyrzksiZDtnq/9vwD4WQC/HF3/P5n5\nZ2YtZBa3yW8BeBUR/SkzfzkAsIt3cu1TNF5FYVL4Gz6jSB1iE/CBqYCGEqws+4hyGIMd6nyC8lFn\nynXgotRZnBlsYavcRlUN8QdPfgDLnSV8691fhrVuz+dDPIvFvIv10RDnB9t4+YFbsNrpYH00wKAq\n0TEGI1T49MUzWOv08ZLVo+hmOZ7dvoTzg23cvrSGpbyHii3WR0NUbLFTDfHwuVM40FnAi9eOojAG\n7z31STxy9rN41dF7MLQZhrbCqw7fhlsXVzGyFhvDERgWHz77JB459yxefOAmfPnN9zqoFBHACC4C\nTRlR8DWLwmAlx9LLK4ODxdXJkm1AsQREjOozOYgksk92tfL5am9ZbFvr50KER9+/zrNCgecxt5A/\neZh71AbgXBjiw5+mCDViifwENumdFB+zULwai2Wjn5mFj0bZqk8nTVZ7Tum97j2l3bL2mfm9RPSi\nKy1nFuVtiOiHAdxLUSo0z8g1mwYtN27DpoHj9iRKNgTCV0kbJF2WoAks19A5CSQ/Yg6nDHNOQwVl\nYOcEbNsRTu9cBAP4Tw//Ds7vrOMNN78Myx13MORDZ5/24VLPY7saITcZblzow5DBpdEORmyxMRzg\n6U2X5PeelcPo5wVe2NnEk+vnYcG4PzsCQ8BGWWLDhwP9s+c/CwZw3+oNWOv28PzWBfzGZ/4/lFwB\nWR8r3WXcuriGmxdWQEQ4t7MDy8Cj50/gL08/gYotvuqW+x2md4KsGTUOmICwAmB1vwnT5IDVLbn+\nFrNICVpErp+W+htp5fy/KaUXKzGg9ltLoCOJBhnzMdT+c9TKNTzH3OqMJc+Prn+SKnAKdZyPWcho\nywN1FEtdZ6zgZyXdp1eLHKR0f+t0/bfnlX4vEf09AA8B+KfMfH7Sw7NM6t8Gp7skJGz8c82SxC2O\nl4x6+av/Tj4TW+/y3tgr4x0bTk6i6Z/uZy5Gw1Y5hGzu5ZQ5rxm5KcAdAnInCWXGbx6lrlDaChW7\nH8AdlgiceKyWITfktqsRKlshNy4+Rk4GpS2RwZ1sJLjTkOTL6WaFV6YmHG9uk1WK2mSb8v+3yk25\nV7TFx1Gfxqf8Yh4m8Ze+qlYOER+a97jHJ0omoYC1jOo2sXp+nI/ZiML7mum291N8TPomdoOSbW69\n36R9PlsZaHxPLHk8/jARPaR+vmfG4n8OwF0AXgHgFIB/M+2FWULCPgbgzUT0MDP/0YyMXBNU+uQK\nDOd3liD34pc15P52S3X3DnMNV6t9m4ScOMQmJrhYFwRnVQcIFrRyYn/yj3FxuIXzw02cH2zipWs3\n4ae++Dvxu0/8JYq8g2e31nFTfxnHD9+OT194Fi9dO4LTO1t49MJz+J2nHsFX3XwfKsvomRwrRQfH\nFlbwrmc/jT8++WnQ5hk89tyn8MTFZ/G/vvEfgQA8v72BFwabICLc3F/FN932UvzsJ/4UHz/7BF53\n4724a+UovvL2L0GfKrzmyD14avMSNsoBHj53EpvlAH9+6tP423e+Bq88dAsOd/v4xPlTeH5rBzcv\nZnDKJG2puXjREnjf5eOUBAqdrIl+tup3A4/AIAk55Y6Tb1cWS3nm+gbAgIH1kQ0HWggICXRLL2eJ\nPS11adcJVH0xbK0ghNOy2sqVQEsuRnoNKdSbjjpZ8LRlNRGF/Q/5WyhY8ez2VOSajMPg72/xF6R8\n/PrIesWu3RlYQT4n8OEKSN67Eqrjvii3EMsqgwM/zTanytlfqKAYQjPQWWY+Pm/pzPx8qInoFwD8\nwbR35oEK3ktE7wOwDuA/A3glgB9k5j+el9H9IuPXnfWBMIIKbBd8gTsea1wQNYK+a6sP5DPhUNM1\nAqQ35sRH2yXCjf0lHOkv4t6VI8iI8K4Tn8TZUYUjWYZbFpaRkcG//PCv47Hzz+DY0o140+2vxeHu\nIl539A4sFh1cGG7hUxdP+1oJC3mBVx66GbctPIBnbn8AP/7Bt+H/fvj38AOv+Ea85OBtuKG/6K13\nix/94K/hxMYLeP1NL8bX3vZyt0FqXNS5Z7Y3sF4O0M8KvOzgzQCAmxcOYKsq8eGzT+PPTj0GC8aX\n3njHmF8/tiQFIyzL2Tx6Rp8uNQBYuUBqFIqro+cx5e60pHsmI6CXURIDnhsKMEjhrc1KFP85oT5V\nqvnWCB/Hg8XAZ8HpK9gjoE5uok7sO83FQZR2/egJRyYNV26swNKKu+EO8m3Xvv1c+xmYMfLly2QX\nJiPFhxg9+h5w+QpcXJMie90uHWvIiB9+Qln77Wr366C9K5/oJmY+5f/8JgAfn/Q8MJ/y/i5m/ndE\n9DcBHATwHQB+BcA1q7xzIjABVlkr9cflrsmQ0RC5+jn/jLIIKFFGGxHVCYsz1Al8n9p4ARVbHO4t\nOT85ET55/mmUtsLB/goAoMgyLBVdGCKcHWwp37H77WhvGUWW4/ntSzAmw8hWuHP1Rq/c/AYsWzy9\nfgYM4KUHb0NhMuxUtQtkqxyCgVBPRgYj76Y5P9z08U4YN/ZXGvKYRkkZab+rn1RZFRXLPj6tShRD\nFqM+jfzNbfwGJZdoRmx5ExHqqHfjz+v5IX53Hmr4zFt5mk/2NiqrMZ5VXanIgLqv4jLicuehMJnQ\nOE+T+G0Qy2Gq/dfeu3Wyk4jeBuCNcC6WEwD+OYA3EtEr4Br9JIB/OK2cebgRaX0tgF9m5k9gltF0\nFakMFkna1iGqUQNpSy1h5TSend58Fx/JoRkk6t8bb7oHi3kH5wZbbuMQwHfe9xVYzHuoqiEWshwM\nxvpoBwBw9/JhLOYddCjDoU4fGRFO72yAmfHyQ7fhgUMvQjcr8PDZJ7E1GuBzl07hmY3TKEyOv3Xn\nl6CXdfCZC8+ishW6JsNapwcCcMvCKnpZju1yGEJ9Hu0tISPCHUs34Nalg+iaHJ9bl/hjs3W3Tkwb\nFGxSfu3XclWV/CoBl2Jegj98zH86zm+W0JOTeOv4+CHM9f2QPJciy3GKH3cakfpXl5tqi+ajcd3f\no+haXIZ8+NaOlzHJwm4gcCbwkSIdKTKuLa2Ioj5m57rcrvY/qiDN+N8sxMzfzsw3MXPBzMeY+ReZ\n+TuY+eXM/AAzf72ywltpHsv7w0T0xwDuAPBDRLSMq7d3MBtF4ykFE8sMAX5ZHEOf9POaZlkawx+X\nNyRLYbgwmTA42l/F62+8Fx2ThUhlD9xwD3bQwYGOS5iwMdwJG5ArnR5eefB2WMsY8RDrowEOdhzu\nejHv4R+/7H/CiBk9Qzi5eRqfuXASS3kfx5ZuwJfd8gBetHYbDncXgltiZCu8MNjErYtr+JIb7gRR\nnc380nAL7zn5CRzqLeFrbn0ADOBgtzcmD20XUeJaTClLadIBqsyY1vtxP0LVW2/0IgT30v2VGzM2\n6I2awIfWhkS/hiTLi4eGVtbt3Ht5ETVPZjbGC6fhlELW1wW48LoNa/IyrEqRvXvdnyPw+ztZBM0g\nqk81Bp65xqlreVS+nMzUp3x1v8Qbx7GvOl7JWGu95R3Vj+nQQ1f2lXrdL5+utROW8yjv74bbCX2C\nmbeI6BCAv783bO0OtUEFg2XBCtud6Jik4nYvTvzAnIWglqXwi1XvL/3UxTMY2BJHe0thE+Qdz3wC\nJVs8t30JANA1Oda8gt4cuQh6I1vi/HATgEuuAEgSXN8GQ3jRyo24bblOcPSJC24f5Ob+CgjAZjnE\noxee9/j0IoTLlda89dPvx6XRDvp5D9tVCUMGHR8LOwXHk4+UE9f2iuLPl8jFfnE81tC4eT82nYRa\n3hzZWiEFuGjL8ln4mEW97FSSVuzyZBXaTPWqAKjHp2WMTXApmmR9uXCvk8uYxEfcLklQbRKKe5oM\ngssSAJl2vP9eEl1jIWGnckMuABXgFDcA3EkurvftmO2E5lcT0WNE9DgR/eCE576ZiJiI5t6pbSPG\n5MHZsBqiT27SMnCeuV+WiKRe7Po4JDs+cH5pKxfE3w9Ql5igDHBAsSh15voRVw3fpfxaectRVu45\nuazsO1XZsIoJwKAaAagTJpS2wkqnj4wMtj2MEZANtHSr2xwEsqRug5zND3+r39X/hvJ8nRTJQ9cH\n9W7KvSHSnex4aY8lLXzMQvUG6YwvtNSVep/V/xvPzyDvBjxSNT7lUgpE46o6NS4muWNmJUrUtR8k\n3+W0n/2kWSzv7wfwPUjjDhkTQsISUQbgPwD4SgAnAHyIiN7OzI9Gzy3DRS38wIx8z0QSVZDrrzhY\nzFqxSzLbGBYlJBHoQHWUOl1uQyn6pWXHICzdh9ZiaF3OxuUiwwNrR/Ebj7wPj/IIJ7cu4u6VQ1jK\nerh14QDuWjmExy8+h/c8+yi+772/gJ/84u/ATmlhYdHPCrxocQ2PXHgeT25cwC0Laygox6AqQbBA\n3sPD507gk+efw2qnj79xy/14zaFj+PT6ORSmg2c2LuGFwTrO7GzgFQdvxg29PjZG27g42sF2NQQx\nYylfwBcfOYx7Vo/gULeHF3a28dTGBdy2tNaARIrVRF4ehhzkS+QWnybUsrfskRrMISa13hQO/ed9\ntya6JspTkkWPfPLhwjg3RgF3GpbBqGzd1x1JvmHrsAmFP9HC/nfjWRXkiMThZhAKa1FCnh/3cE5T\n+nrMdIhgW5Ans5B2T+iol0FpenQU4OF5VIMMNdxQHxxi9a+UWxjfdZjNXShQyNTz/cy4hBHMKG2d\nWFt/P/E4SE0YbuN0n6GC4mK9hmgWnLeAzL+GmXf0PSLqTXn9NQAeZ+Yn/PO/DuAbADwaPfeTAN4M\n4J/NwvSsRISG26T0popAp9xOO4Ji0VAvPb+zejenGqESuxFkySoKXAZXxxjk5NKZjZjxnW/+J3jX\nB/8bHrz3i/CH/+pXkZkMH3vhBB679Dw+t3EOq50+Xn74bvzAK/5H9LMCH7r0FP7i9BMgkEtaS4Q3\n3HgX1jpdXBxu4/2nPwuA8Pqjd+CBg8dw18oRPLe9iZObl/Dk5gso2YJgcf/aUdzQ6+GZzQt4/NIL\n2CxLHO4toWty3L1yCJYZT22ex7ObFzECsFgswhjCLf3VcGwc8BuwSoGKxEi5AEykTFzYUZferGsc\nFFCiMI7BxIiwU1UhQW5P5YDM/EQhKwECfOTCelJmQfkg7d2K4aBDFVo2M812aGgfG4POeHGB9JhJ\nKbqGm8D7ji8boaLq0pDF3MtHFHiou/FOczKsc4/Gd/3EOQeLbVBIuddVUEvyPBKiMaB4S5FhBu2z\nIhXL+1qieZw475/xmqZbADyj/j7hrwXyLphbmfkP5+BlJspaBlLYF4ou6KGuYYENK5zGr02jeKn3\nkcc+hu3BDu666XYQu4TBF4ZbjcNB3azAYt5FbjKc2rrkM7qwT4RrcaS3DEOETQ/3c6nKeshMHZva\nwp3EtMxY7fRB8AgcX9ZC7lSRCydLKEyGF3Y2G/cAlwi5zX/cFtNjDPuunpf7KfkKBXdTVM4YrC3+\nXT8r9ai6mnUjrKYgf0/oWFJtbWszUXrCGHu2pYxZadr4nJUabr2oXE27dboyln0of44y3HGx/acw\nlib87CfN4vO+kYheBaBPRK8kogf9zxsBLFxJ5eR2AP4tgH86w7PfQ/7I6ZkzZ2Yqf2htI6pggEfp\n5b1yfYhfLyR4pXppKe9KotlUN3HiN8D5lEtrMfQY6//rn/wU7j12F06fP4utwRYsM1535G4s5V0c\n6i5gOe9guxzhQ2eewk45wj2rR7DW6WOt6OPOpUOwbPEnJz+JzdEAR3pLuHVxDb0sx+ntDVTWopfl\nKChDxxjc6eF+l0Y7WB/tYHO0jfM7F9AxGRgWBsDmaIALw23sVCXuXT3iFDe7qCKbowE+cPrJ4Hsf\nk2UCaiakny+8AdfwnSv5askxM7rKndLmO5Xnrd8glpObwSKFUjrxh6Um1LD81i8nSGBxMgGmqTlR\nt1ETTnmZlBifk2B7qfEph3ecAdAc90DtA68mlHt5fDdllFJEbcpphGbawP0iQQRN+tlPmsXn/TcB\n/C9w6c90EKp1AD885d2TAG5Vfx/z14SWAbwMwHv8rHUjgLcT0dcz80O6IGZ+C4C3AMDx48fn6joZ\nKkyESkGVgm8PCPCq5PtUn8xkdS2GTGk/ZIN3AEwuPOtCZvCmB/8HvPPf/z4yMlju9sAM3LN6BC89\ncBREhO1yiK1yhJNbF1CyxYHOAr7tzldjqeiAiHB+sIknLp11MUjIoVIqZvSy3NVPhBv7i+hkGS4O\nd5D7o/UVWwxtiTuWDuANt7wYhgjDkNS3RGUtji2s4dWHb0dhDAbVCBvlEJeG601/rncJxbBK3X6x\n3kRGWZa1uhzEV6rl6/J3To/G3jCWvd6Z1wJKwQcn0hQlLxS7AuIxs5skrqCxlQfqsZ0an5JlSCbB\nVMP2Q09Ok42GhF4FvX1Nuk1m8Xm/FcBbieibmfm35yz/QwDuIaI74JT2twH4O6rsiwAOy99E9B4A\nPxAr7suljjGAz1YNIgwqZ4cb1EvDxnLaMeGhTy3L9cQ9uS+QqfgDyImQUYaucVlSXtjZRsmMRR9f\n2ymvuo5+VqCfd3CwuwAiwqJ3Ycj9A91FPHjY3Tuzs4FTW+uwYKx2esi9Pxlwg/zZ7XUvixyrnR4O\ndBdx29LhUFbHGBTGYAEFiAgL+YFwr5d30Ms7ONRdBBG1+iCFwsSG+RXoJNm31SOkUTh7uXQN48VM\nr0evGGazxXeHwriOVhyTxmfqWwj35Hri3l5TjBSTNhii/d2sdLXve/un0cwGBzP/NhF9HYCXwmWP\nl+s/MeGdkoi+F8C74FxVv8TMnyCinwDwEDO//fJZn07O8qEwVmVnPX1cHrXSmNRJkYU+tivOAJEr\nvwlFRHADFCZDWZUY2cpbPBYSSTBsNvl3R8pdkcFt3A2rkct7aRk5GbijP4TtcoTFgmpgB/vogWyx\nXY2wjC5g60S95P3ipS2RURbgiIEPrnEVElBIK85WGKAqP0VjJ/qoeWR7nndnuXa5H11bGfPwFu65\nB/z7gFagKXlcCTXK07IMYzdq1wQ/vn5uVpnuhuwnlVuyS+m3n6qUgHCg7lqhebLH/yc4H/eb4AJT\n/W0AH5z2HjO/A8A7oms/1vLsG2flZxaK8bhdQygxexD5WPlWPpmrtghMULIuoUBG5ILuUL3E2ylH\n2KkqXBhso5sRDnUX8ZFnP4EShDsP3YqD3QWc3DyP5byDQ71FVJaxXu7gtqVD+LNnH8V7n/0kjh+5\nG68+cjcOdhfxtk+9G+944n34ey/7Bqz2D+LZzQt40fIaCpPhs+vn8NT6OWyXO7hraRV3r96Mvzr/\nHE5tXcDp7UUsFz08vXkedywdwKHeIj7+wjN4+IWncWrrPP7hS78Kz21v4O5lFyv83GADp7YvIaMM\n963cgDzLGvBIbVlKVhkLd7Al99ZRCk4JNH2+Bk6vWe+37kxAEsTWWMwHwSV40Ad1Gr5zP6ETxScr\nKxgyjQBceh/E+Els0ripVNArgcFZNHkE6ih6qXYE98AcClAsUimjUoeNCnGnqNWYlldqomyr26r7\nYgEnUTWI0S2XRxmNG0DyV5yoes9JjZdrheZx9X0pMz9ALjTsvyCifwPgmg4Rq7OSA3XQf/kgp30U\nMlgrZmz77LqL0ZJN3AmylAvwQ9SDdyEvsFON8PanPwoiwts/8DY8f/4EXnXsZfjFb/lXMMbgLY++\nG+cGbsORiNDPCvzYq74RX3bzS9ArFvGxcydw5sSjeNnBW3D/kfvwdXe+DstFFyc2z+E3Pvs+fPB5\nwrEHVnHn8kGcWj+Ff//wb6JjcvzIa78LB7oLeNHSQRzs9bE5HODXP/sBvA/Aq2+4E3cs34CvOHYQ\nty2vufbliyiZ8eiFM/iTk48AAL77vi+F8UpN+091G+HWDu7km29/47g4In84M3YkIh95JTPDxyG4\n4DaXBLOD/gE12kjzEUMWiQgntzZgmbGYFzjY7TXcaKKQKwY6UwyvzKgE0966jhMr1/eia6od8ZHx\naRT3R24MDLs4ICU7+QYIq7wDvVfASSUNNKMKGkTQwpbu2k03kV7ZSrmM9InovaZrzec9zzpAMN5b\nRHQzXCq5m3afpd0j7QsF9GZjywvhQ+P0ZbRDsZpQofFl6GY5AODcIOc2zmJ7tIMblw57RWJwbrCB\nka0CFJAB9LICuXGbjtrnlpscC0UHnSzHxeE2CIShrXCot4SMDJ7fOgcGY8glFos+AEI3y2FAGHDp\nccEVVoo+iIBe0YEhg9yHiiUirI+2PcrAYrnotSpWfTVApqBcIS2kZaOjzNW+1dTb6QlX+2UbFm6C\n5VrJ1TcrlkM6yupmbrRhls9W85EaA7XyDLObbxNmltssNOa3Vtco+iZqfse3AuM2kywnx2jc7RPK\nbUOptHxrbaTHVkH76zIBRIbTf/aT5lHev08ue/y/BvARAJ8D8Gt7wtUukSReEJIYwm2uSbHI5QRg\n5WFwGdVQt0FlYa08I0vq+n1NAiurGLipv4oHD92OA50F/MCX/2N80U0vxuZoBxuDHZSW8Xfuej1u\nWljDg4dux90rR7FdjvAbT3wIw6rCKw/dhlsW1rBSdLFW9GBAOLV1CZdGO7ihv4I7V47iSG8Fn7t0\nBtvlCA8euR+vvOE+3LJ4GBuDSyjIYGgrVMxYLRbwpUfvxcHuIkoegUBYH+7g9PY6dsoR1kebYLa4\nf/UoXnrgZhzoLOLJ9QtjS2kZpw3L2n+o4hKJ5SGQM4FqCjoihg824Jq6XK4tsLjcGibIYWWk+1/e\nCacsxc3AjEPdHnIyIbRAadlZ6L7vtbU6iYQPq56uLGNoOQ3Dg7iLmvdig2MWiF7DovYrwVq+0bNR\n/8V8tI1nwK0upFz9vWhfupZDBTT6Mm5X6t7khpIrc/Y3dommwwT3261CswqOiL4FwDuZeZ2IfhTA\ngwB+kpk/spcMpuj48eP80EPTASnihxZKQbc0xf7ZUikKrXDkmCzz5EMRoqzkY7HMITKbw+MCDPeh\nDSrrfeVuQ3JQlXjy0lm87NCx4GsXH2NlLbbLEYwhDKoSw6rCkf6Sf7fCVjmEIcJy0W24OaSrR8zo\n+rpGtsLIVhjaCot5BxeGO1jKu8jIhM1Ty07pMWqFWFBtJaYUeRztT/jQgYhSfsu29/Q1Burof14u\nMR/yThxVMOaBorpkIpEMS/reNEopKDmNKicfpZw4kYOMD+2rjuWwVxTLd566ZGyJZQo03WrumfHv\nZJY6LdeB4+TbEIoTVU8iIvowX0Z2G033PPAy/rd/+FtTn/v62158xXXNSvP4vH+UmX+TiF4PF8/k\nZ+Dyrr12TzjbBcojhTFtCezGXg1Xaxx8kDJlEDHrEZsuj5pHoPUgdQrHDV2iOkMLoYYKftHhW10Z\n1HQXZMZg0WO+F/JOw3/fyTIUphfql3/DYpyasMTCZChMhr4vY6XoN9rgUsC550sfCS+UGcptbizp\niVGrVSJCpj7aVleM6oNGO/w1PSmbBh/1BEVEyQk6VtxxXcbLqDBotG8W0u4e9pM9EaGg8baKUqqP\n8VN9TbuRImt9LyiW77zvxjKK+Y4Ta8xapxgKsXsgT1zbD/p89nnLYcWvA/AL/jj7pFAPV52qaMNM\nqHWtQdT8Vy6r3xtL3BkG+iSIWdNPXvPG0fOtKwT/b8qiiX8XNRq/G/PWNiDcBDF+jScsiaXeqKLZ\nFETUpnr1wI2PSDlzxupi/S5z4+kGx5qfMZnP+8GO8yFtiJ+K+dCrvCRve02XXVfivQnjftY6YxlJ\nPw4SLrn9oGCwTPjZT5rH8j5JRD8PFyHwzUTUxdWZAGem2COkmZ3mMnEWLwVFbSAwMIbE9W0uGedb\nao6i5eAsvIW/NZ8RH5KMl8gfUlJWuSAFSmYsZCapwHPj0TOAw2ujtpJzbzlL/YLEkCTMjhfHSKoN\n4kISVwZp+UVtjyFigQ/4I91MLmZ6ZLnJqqjyCQR0Cw3XWH/NnfheKWEhT6O2MaDbB4zLo8hMHVzL\n38sJgQ+GCz0wa6ySSWMx1b4YoaL5nlbu5Y77efnOzfj5Avl3v6GCRFch9doUmkd5fyuArwbwM8x8\ngYhuwi5HAdxtygyFsLDA5IHWsHjV9eAzJmooS6C5BJ8Feih0cVihZOdTLXyig0nvxr5krexiH25u\nXL5Mqz4KIcmu3vFXUz5qK21W93T7RDlKgmWdiTxMSDSejHfbu1xyAgTUEVtP+vkUb3J3WFlse79J\nJzpqZ5mxMXJv91H7jqV8wX9r2QRoIeZTSLHsdZvH/b7j42PMleJdLXqDojOj4p40FuP2AenEGrEl\nliq37Tu5HIqhpEnIoh+L8Ri4GlDB/ce4TKZ5TlhuAfgd9fcpAFPzrF1NCso2ddNbbDEkrPlIOgAV\nEmVO/ODFP+65Eovrcpctweee4gO1rzX5XuCnLmPWOsc2t2lC+M9Qj/trpg9eJpw23qXPImdJowj1\neyP+eIq/NNczU3IMNPp6PtotV8A0R8Ss9aTaNzsyhCMtT43rl9NWqT+GAO8XXWuW9zXt9rhSiqGC\nmsQfKsiEOLKdjlQnpHe/057N9ro0tHClk4cg/7Mkb012EpGCpTWvT+VrVHnUeAAAIABJREFUgn+7\nbUAIaiIOIRpD9Aj1SUmXfNm1ueejBKbiNsOXUbF37Vgn57HIfZ7vjnHBlDIah8EZAP3MITtK66z9\nGg7XhMGFa9RcUc1K8RgIEDkeb98sJKgTXa5EsZyVpK2pcsV9IvlUp1FqjM8y5gPcFs0x4O41T51W\ncQcqSkWbZCDAb/eTCO54/LSfmcoi+iUiOk1EH1fXDhLRnxDRZ/y/B6aVc10r73ikWe931QGWtK9V\nQmuGQcvOFTD0ecXckrour2xTMi0kioQIWC0yLBdZmM1LazGoLAZlhe2ywmZZhYEtfmIDYLu0uDis\nMChdzBPht+3I/yTOWLUhyMPXpX9IXEYEcPD9xy4cn4DCqOeVfDMi9DMTQr26fUtSpyBd2QSflUcp\n1JFlDCoXVleUQCcz6Ocmud9FcAo+U3yk2ifXTeDbuRgGlQ19atn9PagsKmvD+JEVm1EyivtZ2i2I\nGFbjT/+wqkt6ojCEjqHkQaOx9kZjV8raGFW4OKwwrNSESW7Sy41xsNeon8fKje7RhOdT/QBmbJUW\nl0Y2TBik7od0cC3tivvqapKMy0k/M9J/gXNBa/pBAH/KzPcA+FP/92R+5uD9845yorHZG4gUNqEx\nMN1P0++rXREyaCu19MtaEtIKUTT4RNHqusSS0AcQ4sFQMTDyz+X+aHfcBt1O3dYUabjaNEohX9rg\neKn2oXGtqVKtZ1S3JSg81Z5MXWtrX0Nxmrq+FJ8xQkAbgdrylb/1iqHNtTOvQahLqeGPdXlt8UNS\ndWv5jay2Tjl6poZ/7gVpPirUYzvX/aHaCGDqXoN+r0sU4pDvH9FM/81CzPxeAOeiy98A4K3+97cC\n+MZp5cwVxvjzjSZBBZvL5HgzybsVCA0lLe/CL7VlUNpglc/Yeb4MrcTCBIG6PnlOKFijcEvHGq8d\nKSWkLZkYUSNtsNF9IO3fjMsN9xNoGDTKkhrTfLSWG7XMMnyAqPYVRfP5aM8i2hCb1j5gfAzI5nVc\nltwjqTdxL9kniucQ9ZLrBBFtYyruK92KlBvQbTIzmE3inb0gv5+kWl1ZVuF0x6XdBl+N7w3AMPuN\nNsGe+7yP+n1EAHgOwNFpL1zXyjv2ZmiYmFaQbZZnbkzjUIm2At1y0lk3syhuomaQovj5joeOyezN\nvg6NGjEELBcuzZlzYbjrFTOsh8bV8cHrukSJld4aG1rGcmEa7YO/L6cvdUxwkZFRCp2IMKpsQM2I\nW0eoISunPTCsPHyPaz6WCpdiTVxZ1telYZqZaqvIzSg+YjkXpKCTwVfvkh3HpFdXOquMlBuPATkZ\nWfhVhSY5+u8s88mroYa69c8VYQxMH0/SVx3jIJyNe8yowOgSkGeubwaVc/9ZuP69XJJJSJ8WbiOJ\nGdMzPmhX9Hw8Tqd9k3pMXcNRBQ8TkT7+/RZ2iWRmJmZmchbPRLqulXcMFWxkNFf/TuoTjdCo3S3e\nR+3L7mAcGjexrBbSg0NbY9oOJULI6gM4a2bHCjSOAiJDLGfd5twQcgDdrPkBueaxvz9uxWsZ6Tbo\n58eUKDcxzpeGLnZ5B0AvG+cjU+1MumjQpEkWKWse4eCJbQ6kZvvqVVCqLnm2LRGAQDFT/Ek/TlPM\nsyqkVF+Fe+QVuqnb1c2AbktfzUOW3eQATM/eHuB8GTXkq6nRxzwZP64nvXjC2g+a0S1y9jKPxz9P\nRDcx8ylyMOzT0164rpU3oba2k/fjARApnEnPxovgtm5NnYJsqzM85+Fm7N0irYqLeSJsLkWkypUy\ndLl6EpLvjVkea7ZlohIggLhpTWke2z7OSeZG+7Ka47M6obz6iebzCM/PpwSaZTShjZPlkfj0tfto\nTmU0afJqyjHijZ39mhoDk/ioIbVTDcKpPIY647GIenxOKutqbNQRaOre1hXS2wF8J4Cf9v/+3rQX\nrmvlXdqm5SnxiMUlIX5LGZgSfMoyIze1MgPGB2JOBPZuE1nqx37Qyi9TsxYfKeAjr1m3FO/l7kSk\nq9MfTphg0YfA/1RDETOjPwTycZ3dx6oP1Biu8e3hW/GWOokCUHyIq8OQGzTTlE0DAcaMpdxgZwI8\nTbuljOoXXZ71S2VRIBI3Rury+qA1CQJUP0ulNLbfMZk4LkOVq58BZpBRgrdZ303xFdobJk1uxNbx\ntzyyp30MxG3R/eKsaQ5lzMplvDJt5SO6lqJ4X2C/aLdimxDR2wC8Ec7FcgLAP4dT2r9BRN8N4Cm4\nQ5ET6bpW3rEpJ8fD66Wvh6CFa65zZEkmGUkAF4g/HkyZMcjglK9k6MnVc26DcRyuFrOoMd+OTwDc\nTGzQ9i4RhVN4bQiIHACrosYs+ehvhpPVTukmn4WMPFIAwRUz6eNKlZkZwqKZnH5Xy0nD5hxPHOBY\n9c5A/V5D+aPuyxRG+nI+QWbGTmVDpp+2ZXszWmC7GySFd29cm8ENp3kb+TMNGfn9AiBgrWUyjsuz\n7uWxxMVt5Yo7bh6XReOUZsRH6ruY9K2MPbiPRNi9DUtm/vaWW18+TznXtfIujAEqGyJqxVAsgIL/\nTiu2AHOaAKXjxO8CZ4L+O7WeVyTPy16a9tFPi2sx68edcr20luHlUdraMtSRFOepcx4aUywJF0mA\nICbebcPat01o85IoQmA8zKymRjiGCeWl3Eg2Gosz86bKC3DKyP3VcO1EE0erhcvj5V4JjfER35/V\n34+rdTz+2qLrWnmXtlbcwDgUSyBVevCL9SZKXEPpNGmjPlh+3PQlxlA6KTd2CTTKVT6/acvSJlzM\ncZK6Nona+MjUZkHFHOJiNL0F89U1D8V1Qf5umUBSz7vHxyeBy6EGTNMyCjWja9kbql1Guv/aYImp\nPo6vTWuDNi5ScMpJcpN7KSJViJTbuNfS33FQsUYIioRLLNWmSVQC+w4VdHRtqe/rWnnHY7LIDEY+\nQFLpoXWdjMYGlHSRhom1Wic+aL9Qw+fNtQUpbpGeN7HlCDm4maw2ztE4sX3qX0lqm7rW+j6P7wkY\nuFOlRMCyO8nRjBGi5CGnSzMSfyCHNoxt8HJ9L5ZTHOGOfJ0aD96m6ALvkUIIkRG1PNpFMZWI3AlR\n4UGUkgUA69xl7B5E7pWnVi4xHzHsEmgm3NXycXBH529OuSycK8O7BNVqbRKcMkPdl87CdjBHceEJ\n/5K3U1YGoQ8mjK3QX+IvZ7cvo+Wmn72cfuFpls0e0LUW2+S6Vt4xVBBAyI4iRADI+FNgcyzlNGRK\nyonfF/+jro3CPY+LFheNL2vEKsHrlNGZgp/NCkkDEtEKibBVOkhfDqrdJboF6u+4ffEGHBL3Ut9c\nCsLZqMtbtFM8UGECjts1qzymkXbZEHNI8RXXJb5hTSk+ZnUdBOhfy33JwBPIC2pae8OkAReYIMAc\nUcs5Rnkwxq/HpOUgBeoxTuqn5bObzDfmOoq+KzTPCcr9outaeesBUl9s64JZbN0561fKJ9RQfxVX\nPhRSZUwqN7Jwd6H6oFBTK+/LRU6M1XNFb195/U1SsEQed9a01jVvf3vojHbDzff6HLKflber9Vz8\n2mW8c8VE808ye03XtfJORRVsG9ST8OApcpA5aqIiWvyK2uqMYXDaYnaJDVzih2luglnaFFMl1qJl\ndDOTTEzQywxG1s5Ur2xoEaMB0QPGrfA2+eqwu23Tpyy9G+6hCS4Bo6zv4OZQz0+S17R70i+Gx8fA\nbkwSDZcCTx4zQgTnlgjhGpoFXpHyD3VcQdukrEljoK2uVJuvFlTwCz7vfSS94QKgEU0whmLNa3cT\nuWVmBgrY8VS5QK08ZGlrwDCGGs8LJp3gUDLzwMTaoFgxSUZxzuoleAzeM0ToZpMhfbpdwY0QluCB\nseazUV1Jf/uENuu64jab6GPfLF2Oy44h9DJqQN5SsheaBaoXt1nGwG5QY3xGdU3ljZpuD3V5bpK6\nBNsPSLak+ZV4o6/Q3A+Jx4BQ/U3UuUpjqO7VsIK/4DbZR8qNgfXYXE2pLtgNp0mb4tQ77e7vcT7q\nSILzQbLG/PeTFGC4FyOlL48ozI6z+W5by5ny/CQYYfyWRW2BSoo5PbmmZC80bQzsrvtlQj26Pu+j\nmTXs8DRZzkIc/QtcnuKOaV7e2uKiF7g6m4fXluq+zpV3accVN5BWN+Pey+idGZaRYq2kkBbaEnW/\nNk961jDG6OjyFNJ8ywSRrF/xvVsWhLgR3ClF4Wby88nriXttm4yp5xBZrJl3IYysIGFqOQUfvVxT\nCIp5x4Cm3XBNhOsxbzMqbnl3ljGQuqe/C+3OAhQEcYZ2TmpX4ztoeVeiNrZBdUcYX3HtB+3X5D0r\nXdfKOx5DKShWWLox1zN9y1Z28HfKe+yjqxlqWkuJ90A+hrLXHjsespgR0DWEwicEZg8LY8tJtMfQ\nJwboZgadzAQfb9wm7TPXm5TkIWGVRCb0ykwf7pCohYbq/IFyr/KTi0sOK0fWnd8bhJD4V6Bwwgdb\nxlZlkZHzq0sqq8o6N4bVUBIGRtZ65Uvo5aZOnKtkQagjLwoZApaKDJW1Y2vrirkRn0KUecVcZ5uJ\n+y64XFwkQRCNyRVo+qVj2cvEoNEwBjVET+RJgN9vIID8+QPGzLGrJbJkaqUS+hR1BiIZA+JSysll\ni6lkTPu2WI8LnEV5DaoKW2WJbpah791vWlbahQL1O0EsbfI4epoI1eX0gm/PyPH4BeW9b5Qbgo02\nLePNEBlMcdzumJzFWiMNKltDBXMgCRUU0qm3yJgAx8uI0FUfmyzx21I8lZZxaeSOHfXy2sofG9gY\nX/oGy5LrRMFyk9TmjySrJb860IiZOFZMRmgoVSk3889rPi6OXJv7Wa2Iaz7q+NMSTnXdhy0ojEsk\nrGUZW+VNGKG75kLPumulRUhYXBgOoC/ydYWJZFzkGHi/qwFAIUSAVzaIxkzU5tRE0PDzC7/eJbEd\nxgXDwE2AkzLNhHJRJ40Q2euxKHJ2ZTbHQBX2AvyJZNR9WoEwZMdxJ1FuTCNrcW4wBAAs5rVqaRuL\ntfHg+wwcvovg3krU5/ZHJghkT+jKT5juNl3Xylt/SPO/LAgCQHkh1dido2RRzkkOKaprnI+5dmfm\nWGLP+268ATyxKExxQ6TqkrYGmetnWN2O5MHN1z23AZUSHwrRFGrxq4epzVMaKH4+rDJaNjtb655C\nwfety7qsfna9MrP7Z846dIsnTjZBPlFMdJrNtr2CEX5FdG2p7qsTXXHfKJXcVH/MRHUCgZyc1SIW\nqE6emiJD9clIX9jMfPUz43bvUVtjskTOlLU1UqsGZue/Xe1kKAgYVbWbR7cptgYp/pcIhefbLZPF\ncnV8iI9Yci/WBVE44RfLiL08CiUPDYEEHN99H+BK/JpSl3Ox1H5qZmClk2EhM+j45yW2CLe0uW53\ns8NGtkb5FOSQIdIq1u9ybREGOTKjawyKSEZtSYZLZox8nkst83GuVB2qrn5mUHgZx/7cGH0isrLs\n5FdMGIsdf4/gXERDCwwr59PuGnJySfjBDVz7c6pdf5MoNwaHuh0siLskGgMEtw81ZMZQ5SONJ9iY\nj9Q+x4S8xXtGu5UGbbfoura8Y0sxBRUMEDZqZoJBtGEzXrZ7L7sMsCnDDXQgrVxzfQFNmFhOhJVO\n3tjUEldHgHOFsqKCPBkimGz8YwX8R0zp5SoFxV27UuqnfOLgRKIHka+0OVUXGzSghIYIC0XWeD4o\nXeV+MJ4HzYemwhAKuMQP2tWSKlcsXF2CQ6pQoz2N51Hz7RI/UCi75Hpiy1PvJurvtIwnfbrT1cnB\n3QOk+1TIot7HcWMW6Ch5xMDQMN78vXxKNEghZsagAkAZmJvBsaSETrTnYBPfpNC1BhW81ui6Vt65\nMeDKjvkyZ+n3vfRvad9fqq7az+weSkXY0xuEQg0415w7OpfT3klQwdnqqt/Vm62t8vCUksfUumYo\nN1mXTSj4aXVBqqx9zRPrmtc1BlHmc/YxJstDKB6fs5JGhhSJjVMgkpEySuKJFWiHCubYf6ggATB0\nbTkqrmvlPYqiCoqqmV/dXB61wbNm5oMm+Db9PR2pxXINOSTAHdqhGgERq0x9ai/F56TIh03fafME\noPZ3M9eTShyAqu1dKH5ngQqmZKjbLBZenJhgGglcjjHeX7NAR6UvKtbJosfbwsDUFGAp3vxvSdkn\n34nqBLe8y3Bbh96yF7eV3iRMreoycqu/koGBZfQTK7h54KKZLyu4xLyMBgDII2v2k641Y3/PlTcR\nfTWAfwe3cvrPzPzT0f3vB/AP4CI9ngHwXcz81K5Unhgn1sOjxNJJQQU11C0szzGu2MJHzS5bjQEF\niyM+WaatpFREuVmbIIpIB/8p3BcGY9zR9sqHKywy4wIoMXsl6j5GlykoxWPTJaFJ7oVkFia+Z30U\nxdpdVcGtDApDyIzKLARyaBB5P/g+62w5lZ943d6CCX0Q+/NTFpj0B3u3RWVd0o1OZgIkEKjjlEub\nRQbSJ9Z3mRzJ1zXpfrfsEkhn1IQKCtxODmnFlrKEF2A4SGTJjFHlfi+8C2RShEaRAdi1b8cy+h52\n2pARmiEaRM464bLAFg207B08kuHgmOGwmX9JcN/sfdgA0MuNg2lifCJJjTcN302duM1Uj4tc4WW2\nr+qU6Jrz1ezpOoCIMgD/AcDXAHgJgG8nopdEj30UwHFmfgDAbwH4P3ar/saGIvwGj/o7hgrKZpR0\n0aRYJ3ogDXxW9toVPD5I44GWgvilKFZOWnlJCFcCYLyCK3yYW/1hGnJ/y+akKK04ml98La4npIlD\nrThlHmRQgFtmvr6c6hCjzIzMwG3ImfGtHVeeez4z5GKwSJ3aLwr56NsVt1Vt7hi/mWcMSvYZ3qVC\nT239LJO4SbgABHInlqGkwWsqmNpXnRoDLiojwgblTsm+vMkbeA2DAm5cb5QWpWXkxgSFLSueeCzq\nMSDlCB+ZIZQQa5fCPkeqz4QGPuNUxdLPJvl8zIeGyKYiXFrU34mMN7fRTlctGcO0n/2kvXbivAbA\n48z8BDMPAfw6gG/QDzDzf2fmLf/nXwI4tluVi6JJXdf/zlye3/nWu+TJgi+7TB67lvqIG1b/2E33\nz1T3R0u5uoxEscnnWz07Yx/Y5Alr1glNYGaajzFIoHZZTSgr1eapsvdWvS64vT9mHxS1S6atznGX\nEdA0CLkeAGhr+SQ5i6uoUYMoUEoocGZ/JqB5Lea9ra62fkt9Y8FCn0Omu0l/3dAmtwB4Rv19AsBr\nJzz/3QD+aLcqj6GCHeMOwQQ/nxrAOQEc+YDjYP7yr3y8zsoE+oZQcp38V049yvc9i2uEgWRCBf2m\n/F6yQ5gUhkK+wpA0GOMKpLSMEbOzhL1FI/AxsaLFHVOxe4ZUOQJlk8WwDNLSOreB8CH+zpF1y+3M\nEMjUrhDdhlhGIld5rpeZxknPWgjjsqyUT1aSLIuM9AfVMa7dYhELqqJi148Easgjln3cL0SEgry7\njf2x7jHevDVKGPO5hxR8XhZLhcGgsg0UiebH+ajdBmiTB8JqgZDgeWyVgNpvzIB3RSj3jfzrx7Ws\nJFLWbZhg1LvOPeTcQhS1OZzS5WbWKuv7bMTO1UPkTlZWzNiyLm+qrKBi2Yvsin20vsXyv5bomtmw\nJKL/GcBxAG9ouf89AL4HAG677bYZy0RDkxHVQe2D1SL/CoRJbyyqj6gBK4scoGSMO4HW5HfmeVjb\nSHpTT+NjpUxgHG4Vt0fXa+GWvKlgPvpd+VhDZLpoIgMRmC2GTouEZA15QxCUhrC1DHqR0ahy2F8A\nWPBH/kn5faeR5lvaI5NJPRnUE2vc/pFf9mfkcM2AkrniNQVZdK4FN6XFiXyd8lEuJnVzY1Rip3IK\nbq2Tg8gpPfFXx0vi0lqMPMP9rCnTyp+8ZQAFMzJuIklkcqipKVf5S6CbBk2YX0xxSbX8KcBWdaIO\nN8m43wIfJKGPCSN2/nMH6yR0s3oci4z1tyBKff/pr5fyPgngVvX3MX+tQUT0FQB+BMAbmHmQKoiZ\n3wLgLQBw/PjxmdZNGfmIcpGV7euUkjFLp6Sfn+3decqtmBP3m/WIIokVbNPKZ9TfSYxxduWlIXd1\nXWPQLV1PCx9xXROVgL9XeXVwuT48InJL9YiPxmbfhDEg7Wok2Q3Pp+Thr7H8Ph51b5o8QiiB8E5t\naTQna99X6qlYmjpqYqcRTjjmPT1ex9vFY8aH8D0WoVPJvhljJpJly5iSQdqAU8qYSUxCzHxVfd67\nUg7RkwDW4fb0S2Y+fjnl7LXP+0MA7iGiO4ioA+DbALxdP0BErwTw8wC+nplP72blg8pix+O8WyF3\nc3cJtfx+peTKSndIZDH7JWfTP6ldOkpxR/f01RZ7uJVD2Qxzy17hg5sJKVRdVvGpSd7ZqUows4tf\njkSskBkp7AHE9ehrEz52WX5XVu01hHdT7/m+UgswO8m3i1oeYkwsZK7NElukLjWWgbtaRLLXJBuz\ngBvz7bKfNl6b4yKszBTf8fjUsiLU49cqWTb6QdemVkwiP+3rTtU1tIwLwwqDap7UKbtDwe8/4WcO\nehMzv+JyFTewx5Y3M5dE9L0A3gXn8vslZv4EEf0EgIeY+e0A/jWAJQC/6Rv/NDN//a7UX/Mx8eOV\nZ9pggbtNljn441ORAyWYFpka2jeyLgphYUwjbKwsLS0jRDcU37WUr60UZsbAMnZKi25G6GTp6UJv\nioo8MiIs5FloQ+mVRA4ftAuS4cT9VTFC4lxdjYtyZ7FTVehmuQuKNIe8mTlkSRJUQwqtkGqLuDA0\ndTLn9rLM2C5dGOFeNi63YeX2DgTBIvLVMhIiap5crJhh/R6BJddXa133+WllOawsDDVdYwDGZF9x\nfbKU4CIpSvsYAsv0bg1V1KxjOxxOIngkUdMd1nhW/W7UUXxR6jIWU3xIEKp63YGGTDQfFhTKarXF\n9oz2f0NyGu25z5uZ3wHgHdG1H1O/f8Ve1d3LDMAVMMOsuJ/zeIjylmCp4gjDC2DojwmLotbR5nTy\ngcZHE5UhZBnYkMiE1O7b1GWM3eM6FrlsEDLqZM9Esqnm/JXBMvPP5kTIswy9LHMuBJ8wI+USSJE+\nyRdcElO+5oY8WibzQeX8yoTIheLr3IlcHZNkFFPmK5fNxhTJhqOZIAW9JyLyivubAFh/I5b9LCQT\no7iCDBEMm7DJq6lN9sJHpXiL+dAGU3gneGEIQx82WfpY4up0DaG7/znQZpXfYSJ6SP39Fu/y1cQA\n/piIGMDPJ+7PRNfMhuVeUMUO/ZA7rTfR+o5n/oYvOfIty7VZKS6rtkowdUQwp6FYLB+BKnsWRaLZ\nlkMosjKJ/efBddCi5Kdtak1qWtuEkXp7TPap8jC7InWu6tonXZfhSomVjXsuxWfzmbifx65FPDT+\n5nG0ULurzyvxRH/ItUmynza26+dQj48WfvS91GbwLBNrk1Q4WIz3qXa17CcFI2E6nZ3BFfJ6Zj5J\nREcA/AkRfYqZ3zsvT9e18h75TDojy42TdSnSVgxQf0TaGoyviRU0rWP1B2nY8SLL3phyY0Ceb1mq\nZmop6k4fcvOUJbwVzz7BsCxTafyjMkQ40MkwsG75X1qLkY9NvlpkKouJe6f0bpZ+VtuDgl7YsS5R\ngUQLLAi+fokCyKgsguzDj1JscSLZit0JTCJXjrhERta5LRZzE6BpWifEfaSVM5E76WgZLuF71B/y\nt7PmGCMGNkuLxbwOpGUALOWEkXXqfWRd+6z1Pnt1KEqXO/KuEhfBsVY8JqHoOj7BhaFahpIoQkxS\n52JwqA6RvZyklb6SGPFyOlHi4AD1ScqKXZRFAGPWrxxAEj4q346e8KHk7SIpOvnkBqG/ZKwacnDK\nCgiJSGTVBn+t8c2psZeb9vRv8bjZD9ottwkzn/T/niai34U7D/MF5a3JLek5+CcnEcUWS0K5akVu\n/aAFnHKKd8bb3iPy4VxRfygxyUky3Y4Gj+ome17Eos9Qf3SppTIze1ic46qbZegq56wsmV2aKefi\n6KRORBKhlxn0srqNIAoDKiOXBGHAjNIr3Tj57PpI/PhOcZFq90jlHpWYGZ2inkCMOEo96SQWGdUr\nHPil+cbIH9/OHIQQVKNRJDlGLX8XthdoRnR0yZlFAMDIAhuVBaoKq50s4OOFHGyOw6lSTbGPXmTq\nUrjVY6uvNjYrAGAHaSy86yCMKzg/uUBW2fcH4PHktu5PZjfJjtj1c6bk4UUWwuRKqNkYtin9mAOo\nwBhYd9LYz3cO++/lJxDHIQPw2ZTkRLCMN42YEbioVW2IaQ+3pPaUiGgRgGHmdf/7VwH4icsp67pW\n3oVxxwbc8m++3tYQqPA3EK7JRgow3fIeg+iJRdTyyjjMrrnIjqFxKTjVJHeG0FgUQpJtxyYvbe0b\nk1GoZLysjJpl6NVI7F8m9a629ifJOWWgyfO6r4xXVLqUsaW5fjfhLguwOXUvD20QN0Jd2hhsLuFK\n0O6FeEVBM/TLWD8m/dDqWVZjMJJ9PD6bY2qcD535SZonfZqynLVPPyX7aZTN+Nxu0y7VeRTA7/r+\nygH8GjO/83IKuq6VtwTiJyAECJqVgkskfs//bsidiHTxN3hG3Gm9HCzt+BFrXTdQB6BK8q0Upl52\nggX1weF0m36bUJ+4s5ZBRpSDe1cHMQqTSJrNiTJib9FJvsSR5RCrmtn5PDrGWa82DlmnZBTa6Jfv\nqciA7FcJoqPlvoVzX2RwbouKAWsBkzXb1CHCSK20RH4ZUUMegCBlnIXZ8RA9cel0c73G4pAI2bKK\nKijjB3V8eQt3YEX6UmSm+cgMgWVV5OWblEdCcYeTjWqiFd7AtZdf+NDjk+HcQ8Oq8hvM0iO17DsZ\nwfrANhn5TXRrnXWteMngg5WxIJRqPnK4yHSs+i2WvVDl2zRblPHdo92w9pn5CQBfdOUlXefKO7ZW\nJsEB43uzkrgLYmost6O6MqKpSRy0ZToPsf+fAYVgUkNrHcrDNE8u1q4fDq4EIqfM5oVKChZY9LBY\nmwt5beWKshJ5dDNCL0tPToaooexFkQnfl4YjjKzFaqcTIjl2xPj9E3knAAARWElEQVREUzkTEfp5\nu+VeZAZFS7sqBjb96cXlwu08aN3Y966juDvFBTRpdeUyKdXXSsvYLCvkRFgqsrG9Cp2oQYwGPWFL\nm7UrheDgew2/Njnkhnz8Tfn64E9qMhVYqIwtsczlO8mIsJTXE73wIVNvSBJCpoGWkXbJhCB9IPdj\nuOUXqEnXtfLuGANYG9wmqfRUQqmTY26DJV22pOUCxpU9x4p7Tr5FyUxzx7S9pz8PiZ+RWhkESJjU\nN2edWkZuc81dD0fiRTMlZF+/O72uwJf/fXM0wnbl4I6iuIUPsRaDPNTvl2M6bY4qHxemdn+Iu0nD\nOsdCD6gxoPnQZFBvoBIR1kclGAgHbiaRUTLRE30YOxNkH5OWLUXPE9zZAnELpRODtMtet6/xfIpv\nz++0cZFh708XpuivHc77apKzTGhs1AbLQA1E7X6oLzYt8+a95guTTtfJ443rLc/rpXUM40q5CxhN\nREmjXPW74GLkPtH4hx/DviZB1QIvgSdVl1qKA8qREOpWZSfqSi3N9e8Z1fFHZIkdsZD6ExTJINUm\njSACnEU9UtZmEzYnh6Vql1UbxfeDq0q1y3gXQhwiYQxumPJzR89PRD9pfzfG+dA868l2UlnTZJ8q\nf+yee0AeDNfj8TFCMxfpfpD6LK8Zus6Vd/NvPVsznK+1go+wN0VZyTsyoDMiGNOM1gZ/T/zhYRmb\ncquo30WJ1rv9zQ8oWPFcx2kh1Dn+5EQg/LXSK5SOMchRW9fNstzSWBSFRPCTqIIZNWVkLWNgLQpD\n6OcZBJcM3z6BhDkUDcNCTSb+2dKf+su8XzVsHKplv/AWO70EktYzBv08Q04dDJl9sgnUiRuo+VE3\nUB1eLiExgTcVS65PgZbWHcHeqSzWOhmKzCA37PcYORyKEp9zZT3EkBlW9XPdLvJydW6lkh2aInVA\nZbnIUFkXD1uPmWFlMfSQS6lLjy2Bbmp3A6KyhRpjlaWvms/qSRaoIZvSz63jM6qvUS7XsE+o5wn6\nvIGHSKL2hUuAMS0vWR1ca1H+9puua+UtloxQY1CzS6OkfWrT3AXx8ld8cvEyE6iP/LbyligbSPjK\nUX8cAQ7nl/G5MY0OJH8t0+UoZdZwI3irK7Tf39C+WwJqGRlCB02fZbDMxBpVdcmG3CCaQTOiEJY1\nuWz2vGk4ZWFc2FPx1Tt3BYWlvIRbFWpMpA351LyFCwRsjdxZ0Nyy98MTFvM8POLmyxqKKRH0SsvY\nAmOnYuQeCqnDqw4UVFDC5gqULwUVlL2QeBx2MoOONJDZR3b0z6NOpN023lIyjuWv+ZDNUm39ajhs\n/B3JUzpbj8he1xP3B+BOlTJqSKpWyDruyUgxmqNefe0nXWtTxdVwHe0btcViANAY8OPLz3ELfPz5\neOjPR7EfV/4dW+q3TChuGdcsA4l/2+qF+n+Kj/Bs4lqjvFQd0paEeMSdG7+n69dLeW3dt5Vb30uU\nmeA2lluYLFtkqNvc1ldxVMFkm+PxlmxDfI8b7dOnKmdwjSdp0mupMSjvtPHdVkdqQon7qpZ9fD/d\np8Dl7SPtBtEMP/tJ17Xylg25iaRGhzNq05Hw9PPyTBzZb25VTnXcD4FyxR0iQaiEH7kvEfBSzrjU\nIGrwFrcZ41CyMT5b5JGCcgkMUfOTQ5bI9ZI8tgJ1XRKxL8V3RuOrEndPT0sIae0YGO9TJbd+5pEf\naPZlqj80H4UhdPypQoE16gM3coKxTgpSyzd24ekxAP9vYyz69hFqi3Sm8a3kEa90YhI+4tUXgLGN\nyvDOhL2eGFkS95VDCFFjXNT30Wiz8eVJsoZR7BPdB5IhM+lnP+m6dpvsJWnFsZu+tzF4lPI3EgAy\nNPZRTC2jhRp+xFl4kx9yfzUQFUSNo+YgB1fs0vghHKFJcjPeHdJm7bUlaoitvWmyAppuCdZtmEIO\ngpihH10T2T9y4RQ+du4k7ls5gtfccHsD8fPxc0/iPz78dqx2FvFDx78dK50FXBhu4+TWRSzmHdy+\neKAhn9Pbl/DbT3wE66MdfNd9r8Oh3lIIVjasSvzXx9+Pj559Et9615fgtUfvbsjt9z/1bvzcB9+G\nL7v9OH7g9d+NIsvwxPo5PLVxDrctHcBdy4cbvuwn18/inc98AmvdPr75jlehn9dAygvDLfzhU4/g\nzM46vvXO47hpYTWMCwbjj57+OD589kn8jZtfjNfdeHfDZffs1iU8cv4U1jp9HD98DLnJ8Nil5/Hh\ns0/j7pUb8MU3vCidvYcIuXGblJIV6urQ1bD32+m6trwnJU1NkSjJaWEzRUFMWirPXCfRxDoFmqbv\nz8LjLJRKQDyJzzq0RZoP3RaJfNimuCfRbrWv5ptm7qt4DFxutMmdaoS/OP0ktsohblxY8Yq4VpD/\n8eG346n157HSWcBC3gUR4enN8xjaCh2T1bBEz8e7T34KT6yfAQCsdhcgvn4iwqcunMT7n/s0Lg63\ncP+BW8ba+S/e/bN4fuMs7jh4DEWWY6eq8JlLZzGwFQ50FhqTBBHhD59+BGcHG1jMuyiMm4qE74+c\neRqfuXQaO1WJowsrjcny2c2LeN9zj2N9NMDdq0fG3G4fPvsMNsshFvMOMnJZ5t/73OPYLIc43F2c\nOJmHTXFDWMzNTCEvdpu+4DbZR4qTBEylxnJtlmd3sbumDNxxh+7V9PpN4SNS5pdX1e62b2Y+ojEw\nDxfOzeHcHTkMFvMOCjI4vb0OhrOQK7awzLj/wK3oZgXObF8IvC1kBQwIO1UZyhP3w61LB5CTwaAa\nobIWw2qE0lYY2RKHu8sgInSzHCc2ztYJMvzPnQdvRT/v4TNnn4RlRmEydIxBRoSLw21Ytq5ctrBs\nccviGgqT4cJgCwRgZCu3WW4tjvaXkfvNwu1yiNJWoU0rRQ+5MeiYHCc2zvu49RYMx8dat4+MCOuj\ngdvAJMJq0XMy2tmo2yyoqtZ+bA+r+9eJaBo87lqk48eP80MPPTT1uZHPovMFShPzeDCnz2eahInf\njXKnlXl+sIWLowHObJ1Hh4D71m7Fs9uXcOPCCk5snMNnLj6H9z33OP7+fa/H0YUVfPbiKfTyAvev\nHQMRYViN8IHnP40beqs4tnQE21WJ3HSw2nGW+J+f/DjObF/E6296MX76L34Fn954Dj/2Jd+JVx+9\nH89vXcT7n38cX37LSwAYbJZDXBhso5sZHO4t4L8++sd4zc0vxRcduQcAMKoqnNi6iCO9Rfy3E4/g\n4ReexnY5wE+99tvAAP7y9OewUvRwqLuET116Do9fPINvv+s4loounl4/h+e2L+HBw7fio2efwUOn\nn8TBbgf3HziKW5aO4H3PPY7X33gPtsoSz22v4+zOBl5+8CYc6izgxNYlrHS6ONpfBgBslUO859lH\n8eK1m3HL4mEAmGnFJgmuZyEi+vAMYVon0itf9Sp+9/s/MPW5g73iiuuala5rn3dmaCyD/BeopoHH\niRsAWUtGnc8XiiGWu0mzTgRrnT4eev4x/OIn34VuVuDNX/qPcKi3BkOEe1aP4EB3AR954QR+/YmH\n8ODh23HT4hqM6QYY3f/2Zz+HU5vncP/BO/C37nkTDGV44MCNIABvfeQP8KsffycqW+F/P/EZGDJ4\n89f+Mzxw6E6c2ryIX3zsz0FEePUNd+KWxTU8v30Rf3TiERgQPnjqIQCMTncRLzt8F0DAZ9fPgcH4\n5U+/H49dfBYHOgv48Vd/CxiM3/rcR/Hs1kUs5V3cs3ojCBn+7t2vRtfk+Nz6C/irc8/CkAEDePDw\nbfir04/iVx/7U3SzLl504F4UJsfdK0fw4gM3oTAZPnnxNN7z3GdxtLeCXpajZIsjvSUwGN//57+I\noS3xFccexNfcfgAZGSwXZurC6yp4Ta7OYncCXdfKu95I+QKlSAzV/RyUs1qxc5e7q6VdHhERXti5\nhMpWqEyGjnEbfSHMra0cMoUtFvIOGD5lnEeTnN2+iJ1qiKVOX85zIiN3YOfUxlkMbYnBcAeWLSpb\n4da1m9DJCmyWF4J7Y7nTAwBsjAYAGCO2GFRDAMDB3gqIBCfvarg02kZpKywVPb+PY3BpuIORrZB5\nBU1E6JocmTHYLkf+0I7jzRDh9PYFVNaiMs7dMbIV1roLIBAG1cift3DnKiwYHePyxltmbI52YME4\n2FuGIEu8NFvlfLXinVxjuvv69nnPCqX660rdzARY2H7IqfJIgWqCT/Ny6VoYyBujAR48ci+OH70f\nty/fgAuD86hshQvDITbLEdY6i3jtkbtwY38F/azAqCrx2Utn8NGzz6CyFj/86r+Llx58EdaKPpYy\np9w/u/4CtsohvvH+r8Tr///27uW3jSoK4PDvzIzH7zgvt4kd+iIENYKKlqoSCFViAbQbEDtYgdQt\n7PlH2HTBmjUSlSpWLEAgCFLVoqqiCmpp0yQteTSJnzO+LGZsUtTGnsQ4dno+KQuPRnfOJM7JzfGd\nc6dOc674Kp+/9Skz+Re5tjhPvdHgeHaM85MzDLspbqwssOXVKaSHmR46xERyiEuzFymkx7m9vszD\n8hYgjMUzxC2HD4+9zsmRIj5wd3ONklfjbP4ohVSOfCLD4URw3kJpk5rvM5nMUUjmyLkJNmoVNutV\n3jv2BqfzM5wcKXJ+YppMLMEPS/MsVzZxLItcLMGom2IymaXue1xffcDN1SV8A5dmL3J86DAVr4wd\nPqFb9Xd+f/j0dtvCfnWwa95hWUD1h4rvt8o0Cdsa+Br7f91YXWS1VibrxDk1OoklwsLWJp5ptP44\nCjCVzmKJ8OPSPD8v/0nccvhk5k0cK9jQwbaEslfn5voSAK7lYItFxnGZSucQEZZKZRpA2rHJxmIY\n4NZ6sBqlWYZxLZsT2VFEhGsrCzyuVUnH4pzIBCtBMrHgQ8vVapnF8gbGGMqNOgIUkzkOJTM0DCxX\nKq17FIL17aPxYIXMr4/uUfbqjCfSvJzLY4nw3f1b4eYKBkFwxOLtwjS2WHx79zp/rC+Tc1O8O3UK\nSyzG4i6ubeOFLRgg6Na40+qT5hOrnehOzfus+f6n9jXvnOtozbsbnHCJWMOYVi/hZkuj5gMZDf59\njLe5h2HQfyM4v/n2MNuO7WaM5nlPjBH+y2xtO7b9l7w5buMZY3Qrtl7dX9axn9h96KDd32ujEyyW\nNwBaLQCOZDI8rtfCfjEWFd8nJgIC5ydOUEhlWauVSTl2mOiCD+qcWIxTI4dZqZYZi6fY8urhSo7g\n+1ZMJ9mse7i2FW46ArPD46xUK2SdGDXTwG80WuefGZtksbSJbVmMxB3qDUNcBCOQTyRJOTZlz2PI\njfN3tcRQLI4b9mApphJseT5J2wp3B6I17rnxIg9KGyQch3h47J3CNHe21hiOJfCMoeTViEnQMuL9\no69wc20Jr+EzmUpQ9Q0J28aSoDWvawmeMa2xnvbzs0V2TOzPiwM981ZKqW7MvM90OPMe6uHMux9K\nhUoppSI60GUTpZTqlv1YnrgTnXkrpdQA0uStlFId6FZvExG5ICK3ROS2iHyx23g0eSulVEf2nr5F\nxAa+BC4Cs8DHIjK7m2g0eSulVDthb7h2Xx04B9w2xswbY2rA18AHuwlJk7dSSrXRyZy7w7JJEfhr\n2+t74bHIBnK1ydzc3CMRudOjyx0B7vboWlFoXNH1a2waVzRR4zq61wv+Njd3NenY4x2cmhCR7Q+h\nXDbGXN7r9Z9mIJO3MSbfq2uJyMNeLbqPQuOKrl9j07ii2Y+4jDEXujTUfeCFba+nwmORadmkvbX9\nDuAZNK7o+jU2jSuafo2rE78AL4nIcRFxgY+Ab3Yz0EDOvHtsfb8DeAaNK7p+jU3jiqZf42rLGOOJ\nyGfAVYJ2LV8ZY37fzViavNv7X+pVXaBxRdevsWlc0fRrXB0xxlwBrux1nIFsTKWUUs87rXkrpdQA\n0uStlFIDSJO3UkoNIE3eSik1gDR5K6XUANLkrZRSA0iTt1JKDaB/AJ3zACnEdNNOAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118141e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We may also plot a hexbin \n",
    "data.plot(kind=\"hexbin\",x=\"last_evaluation\",y=\"satisfaction_level\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a22c7d0>"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LtGoLSFpCq2npPqxvdRtt1A8vnVA/KZyzfmMYh+9UuyJNSY9GxNeKvHlEPCnpPOB6Wv0H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\nmXVwYjAzsw5ODGZm1sGJwczMOvx/9EB3nIcVsrEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a30bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plots, similar to hexbins, require that you specify the x and y values\n",
    "data.sample(100).plot(kind=\"scatter\",x=\"last_evaluation\",y=\"satisfaction_level\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11c830410>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11d8dd450>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x114ee98d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x114f4eed0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x114fd6f10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115047890>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1150cea90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115142350>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1151c8450>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115224e90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1152c2350>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115275b50>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11e13df50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11e1c4dd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11e235ad0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11e2bea50>]], dtype=object)"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k+fMzbGj28VAZV1usFsNzMX58coJGn5P37OnEaa/Ma7nJTJbHj44SSWZ4ZFcH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\n5za2H4nIu3Cu7r/a/f0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c817790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We may also use a scatter matrix to plot multiple columns as x and y simultaneously\n",
    "\n",
    "#Let's sample 100 employees first and retrieve only the 'last_evaluation','satisfaction_level',\n",
    "# 'average_montly_hours' and 'time_spend_company'\n",
    "\n",
    "# Are there any related columns here?\n",
    "possibly_related_columns = ['last_evaluation','satisfaction_level','average_montly_hours','time_spend_company']\n",
    "sample_100 = data.sample(100)[possibly_related_columns]\n",
    "\n",
    "# using the scatter_matrix method\n",
    "pd.plotting.scatter_matrix(sample_100, figsize=(15,15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next session we will continue to create an interactive dashboard. \n",
    "\n",
    "### Exercises\n",
    "\n",
    "1. Using the data create an appropriate chart for each of the chart types. \n",
    "\n",
    "Tip: It is useful to start with a question. Have fun developing this exploratory skill!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}