datapluspeople/ibm_hr_data_w_python_2.ipynb

## ibm_hr_data_w_python_2.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Exploring IBM HR data using Python</h1>\n",
    "<h2>Intro</h2>\n",
    "<p>This tutorial series explores the IBM HR data set. This data is typically used to demonstrate the ability of various machine learning algorithms applied to HR data.</p>\n",
    "<p>In this series, I'll use it to demonstrate the awesome power Python can bring to HR data</p>\n",
    "<p><b>Sections</b></p>\n",
    "<ul>\n",
    "    <li>Statistics</li>\n",
    "    <li>Matplotlib</li>\n",
    "    <li><font color='#1569C7'>Pandas</font></li>\n",
    "    <li>Seaborn</li>\n",
    "    <li>Plotly</li>\n",
    "    <li>Findings</li>\n",
    "   </ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "__author__ = \"adam\"\n",
    "__version__ = \"1.0.0\"\n",
    "__maintainer__ = \"adam\"\n",
    "__email__ = \"adam@datapluspeople.com\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if continuing on from the previous section, read the data from saved file\n",
    "\n",
    "# empl_data = pd.read_excel(\"WA_Fn-UseC_-HR-Employee-Attrition.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the data directly from IBM Watson Analytics\n",
    "# using pandas read excel file into dataframe\n",
    "url = \"https://community.watsonanalytics.com/wp-content/uploads/2015/03/WA_Fn-UseC_-HR-Employee-Attrition.xlsx\"\n",
    "empl_data = pd.read_excel(url)\n",
    "\n",
    "# save data for later\n",
    "# empl_data.to_excel(\"WA_Fn-UseC_-HR-Employee-Attrition.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>pandas</h3>\n",
    "<p>Pandas? We've already looked at pandas. Yes, but we've not explored the plotting capabilities of pandas.</p>\n",
    "<p>This section, let's explore Gender.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e9a422d30>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "empl_data['Gender'].value_counts(normalize=True).plot(kind='bar', title='Employee Gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "~60% of the employees are Male, ~40% Female."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Salary</mh4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e9a593c88>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "empl_data.pivot_table(values='HourlyRate', columns='Gender', aggfunc='mean')\\\n",
    "    .plot(kind='bar', title='Average Hourly Salary by Gender and Job Level')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Positively, there does not appear to be <u>any</u> variance in the overall average pay between gender.</p>\n",
    "<p>Let's explore further</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e9a858668>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# by job level\n",
    "empl_data.pivot_table(values='HourlyRate',index='JobLevel', columns='Gender', aggfunc='mean')\\\n",
    "    .plot(kind='bar', title='Average Hourly Salary by Gender and Job Level')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, no significant findings. Let's look at one more..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e9a8bb470>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# by department\n",
    "empl_data.pivot_table(values='HourlyRate',index='Department', columns='Gender', aggfunc='mean')\\\n",
    "    .plot(kind='bar', title='Average Hourly Salary by Gender and Department')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, it would initially appear that within Sales there is a a significant pay gap for Female employees."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Performance</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e9b90fe48>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "empl_data.pivot_table(values='PerformanceRating', columns='Gender', aggfunc='mean')\\\n",
    "    .plot(kind='bar', title='Average Performance Rating by Gender and Job Level')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performance also appears equal between Genders. What about by Job Level?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e9a3db978>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "empl_data.pivot_table(values='PerformanceRating',index='JobLevel', columns='Gender', aggfunc='mean')\\\n",
    "    .plot(kind='bar', title='Average Performance Rating by Gender and Job Level')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now, what about by Department? Will we see the same as with Average Salary?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e9a3daf98>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "empl_data.pivot_table(values='PerformanceRating',index='Department', columns='Gender', aggfunc='mean')\\\n",
    "    .plot(kind='bar', title='Average Performance Rating by Gender and Department')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average Performance appears equal - even in Sales; this does not explain the salary difference between genders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n",
       "       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',\n",
       "       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',\n",
       "       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
       "       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n",
       "       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n",
       "       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n",
       "       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
       "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
       "       'YearsWithCurrManager'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "empl_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e9a321550>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fqH0fM+uZtypERKTFNHnEb2YHmtkJZvbFMNzfzO4G5qZenYjIFlqzZg2nnnoqu+22G/vvvz8HHnggM2fO3OrlVlZWcvTRR+ehwsJpNPjN7P8BtwLHA4+Y2U+Ax4DngD3TL09EpPncnWOPPZaDDz6Yv//97yxatIh77rmH6urqFq9l8+bi6+SgqSP+McBAdz+F6EdYLgFGuPs17q7rrUSkKM2ZM4d27dpxzjnn1I7r2bMn5513Hh9//DEXXXQRQ4YMoX///tx4441AdCQ/cuRITjjhBPr06cNpp52Ge9Qb/aOPPkqfPn0YMWIE99//6e9PffDBB0yYMIEhQ4YwcOBAHnzwQQCmTZvGiSeeyDHHHMPhh8d/v6o4NNXGvzEX8O7+jpm96O6rWqAuEZEttmLFCgYNGlTvtFtuuYXOnTuzYMECPvroI4YPH14bzosXL2bFihXsvPPODB8+nHnz5jF48GC++c1vMmfOHPbYYw/GjRtXu6zLL7+cUaNGceutt7J+/XqGDh3KoYceCsAzzzzDsmXL2HHHHdPf4GZqKvh3N7NZ4b4RfYM3N4y7j02tMhGRPDn33HOZO3cu7dq1o2fPnixbtoz77rsPgA0bNrBq1SratWvH0KFDKS8vB2DAgAFUVVWx/fbb06tXL/bcM2rdPv3007npppsAmD17NrNmzWLKlClAdDnqa6+9BsBhhx1WlKEPTQf/1+oMT0mrEBGRfNlnn32YMWNG7fB1113H22+/zeDBg9l1112ZOnUqo0eP/sxjKisr2W677WqHW7duXds+39CllO7OjBkz6N2792fGP/fcc3Ts2DFfm5N3jbbxu/tTuRswH1hdZ5yISNEZNWoUNTU1XH/99bXjPvzwQwBGjx7N9ddfz6ZNUb+TL730Eh988EGDy+rTpw+vvPIKL7/8MgDTp0+vnTZ69GimTp1aey5g8eLFed+WNCT6ApeZHQMsAR4NwwPiTT4iIsXEzHjggQd46qmn6NWrF0OHDmX8+PFceeWVTJw4kb59+zJo0CD23XdfvvWtbzV65U1ZWRk33XQTY8aMYcSIEfTs+elXmCZNmsSmTZvo378/++67L5MmTWqJzdtqlnunanQms0XAKKDS3QeGccvcvX++Chk8eLAvXKif9hXJspUrV7L33nsXuoyiVd/zY2aL3H1wc5aTtMuGze6+oTkLFhGR4pS0d87lZnYq0NrM9iTqs+fP6ZUlIiJpSXrEfx6wD/ARcDewgajjNhERKTFJO2n7EPhRuImISAlLelXPY2bWJTb8BTP7Y3pliYhIWpI29ezk7utzA+7+DvohFhGRkpT05O4nZraru78GtX3xN30dqIjIVqi45JG8Lq/qijFNztO6dWv69etXO/zAAw9QUVGR1zpypk2bxsKFC7n22mtTWX5Dkgb/j4C5Zpb7tu7BwNnplCQiUjjt27dnyZIlhS4jVYmaetz9UWAQcC/wW2B/d1cbv4hkQmNdOX/lK1/hpJNOYq+99uKSSy7hrrvuYujQofTr16+2m4eHHnqIYcOGMXDgQA499FDWrFnzuXWsXbuW448/niFDhjBkyBDmzZuX2vYkbeMH2A74J9GlnH3N7OB0ShIRKZyNGzcyYMAABgwYwHHHHQd8tivnBQsWcPPNN/PKK68AsHTpUq655hqef/557rzzTl566SXmz5/PxIkTmTp1KgAjRozg2WefZfHixZx88slcddVVn1vv+eefz4UXXsiCBQuYMWMGEydOTG0bEzX1mNmVwDhgBfBJGO3A0ynVJSJSEPU19cyePbvBrpyHDBlC9+7dAdh9991r+/bv168fTz75JADV1dWMGzeON998k3/961/06tXrc+t9/PHHeeGFF2qH3333Xd577z06deqU921M2sZ/LNDb3T/KewUiIkXO3RN15dyqVava4VatWtV2/nbeeefxve99j7Fjx1JZWcnkyZM/t45PPvmEZ555hvbt26e3Ibk6E873d6BtmoWIiBSr5nblXNeGDRvo0aMHALfffnu98xx++OGfubonzRPMSY/4PwSWmNkTRN02AODu302lKhERkl1+2RImTpxIVVUVgwYNwt3p1q0bDzzwQOLHT548mRNPPJEePXpwwAEH1J4fiPv1r3/NueeeS//+/dm8eTMHH3wwN9xwQz43o1bSbpnH1zfe3et/69oC6pZZRNQtc+Py1S1z0r568hbwIiJSWEmv6tkT+DnQFyjLjXf33VKqS0REUpL05O5twPXAZuAQ4A7gzrSKEhGR9CQN/vbu/gTROYFX3X0y0U8xiohIiUl6VU+NmbUCVpnZd4B/oN45RURKUtIj/guADkQ/ubg/cAZQ75U+IiJS3JJe1bMg3H0fOCu9ckREYiZ3zvPyNjQ5i5lx+umnc+ed0WnMzZs30717d4YNG8bDDz/c4OMqKyuZMmVKo/MUi6RX9Qwm6pq5Z/wx7t4/pbpERAqiY8eOLF++nI0bN9K+fXsee+yx2m/dbiuSNvXcRXRlz/HAMbGbiMg258gjj+SRR6IfgZk+fTqnnHJK7bT58+dz0EEHMXDgQA466CBefPHFzz3+gw8+YMKECQwZMoSBAwfy4IMPtljtSSQN/rXuPsvdXwlX9bzq7q+mWpmISIGcfPLJ3HPPPdTU1LBs2TKGDRtWO61Pnz48/fTTLF68mMsuu4wf/vCHn3v85ZdfzqhRo1iwYAFPPvkkF110UbP69klb0qt6fmJmvwHq9tVzfypViYgUUP/+/amqqmL69OkcddRRn5m2YcMGxo8fz6pVqzCz2o7b4mbPns2sWbOYMmUKADU1Nbz22mtF0x1F0uA/C+hD1ENnvD9+Bb+IbJPGjh3LD37wAyorK1m3bl3t+EmTJnHIIYcwc+ZMqqqqGDly5Oce6+7MmDGD3r17t2DFySUN/v3cvV/Ts4mIbBsmTJhA586d6devH5WVlbXj410sT5s2rd7Hjh49mqlTpzJ16lTMjMWLFzNw4MAWqDqZpMH/rJn1dfcXmp5VRCRPElx+mZby8nLOP//8z42/+OKLGT9+PFdffTWjRtXfgcGkSZO44IIL6N+/P+5ORUVFUV3mmbRb5pXA7sArRG38Bng+L+dUt8wiom6ZG9ei3TIDRzRnoSIiUryaDP7QR88j7r5vC9QjIiIpa/I6fnf/BFhqZru2QD0iknFJmp+zKJ/PS9Kmnu7ACjObD9R+C8Hdx+atEhHJvLKyMtatW0fXrl0xs0KXUzTcnXXr1lFWVtb0zAkkDf6f5mVtIiKNKC8vp7q6mrVr1xa6lKJTVlZGeXl5XpaVtHfOp8zsS8CQMGq+u7+VlwpERIK2bdvSq1evQpexzUvUV4+ZnQTMB04ETgKeM7MT0ixMRETSkbSp50fAkNxRvpl1Ax4H7stbJW8szn/f2yIixaaAX0rLSdo7Z6s6TTvrmvFYEREpIkmP+B81sz8C08PwOOD36ZQkIiJpajT4zWw7d//I3S8ys68DI4i6a7jJ3We2SIUiIpJXTR3xPwMMMrM73f0M1A2ziEjJayr425nZeOCgcMT/GfohFhGR0tNU8J8DnAZ04fO/sasfYhERKUGNBr+7zzWzPwPV7n55C9UkIiIpStpJ29EtUIuIiLSApNfizzaz4029JomIlLyk1/F/D+gIfGxmG/n0F7h2SK0yERFJRdJO2jqlXYiIiLSMpJ20mZmdbmaTwvAuZjY03dJERCQNSdv4/xc4EDg1DL8PXJdKRSIikqqkbfzD3H2QmS0GcPd3zKxdinWJiEhKkh7xbzKz1kRf2sp1y/xJalWJiEhqkgb/r4GZwBfN7HJgLvA/qVUlIiKpSXpVz11mtgj4KtGlnMe6+8pUKxMRkVQ01S1zGVF/PXsAzwM3uvvmlihMRETS0VRTz+3AYKLQPxKYknpFIiKSqqaaevq6ez8AM7uF6AfXRUSkhDV1xL8pd0dNPCIi24amjvj3M7N3w30D2odh9dUjIlKimuqPv3VLFSIiIi0j6XX8zWZmZWY238yWmtkKM/tpWusSEZHkknbZsCU+Aka5+/tm1haYa2Z/cPdnU1yniIg0IbXgd3cn6swNoG24eVrrExGRZNI84if077OI6Atg17n7c3Wmnw2cDdB6h25U1NyWZjkiIoV3ySMAVF0xpmAlpNbGD+DuH7v7AKAcGGpm+9aZfpO7D3b3wa07dE6zFBERCVIN/hx3Xw9UAke0xPpERKRhaV7V083MuoT77YFDgb+mtT4REUkmzTb+7sDtoZ2/FfBbd384xfWJiEgCaV7VswwYmNbyRURky7RIG7+IiBQPBb+ISMYo+EVEMkbBLyKSMQp+EZGMUfCLiGSMgl9EJGMU/CIiGaPgFxHJGAW/iEjGKPhFRDJGwS8ikjEKfhGRjFHwi4hkjIJfRCRjFPwiIhmj4BcRyRgFv4hIxij4RUQyRsEvIpIxCn4RkYxR8IuIZEybQheQ069HZxZeMabQZYiIbPN0xC8ikjEKfhGRjFHwi4hkjIJfRCRjFPwiIhmj4BcRyRgFv4hIxij4RUQyRsEvIpIxCn4RkYxR8IuIZIyCX0QkYxT8IiIZo+AXEckYBb+ISMYo+EVEMkbBLyKSMQp+EZGMUfCLiGSMgl9EJGMU/CIiGaPgFxHJGAW/iEjGKPhFRDJGwS8ikjEKfhGRjDF3L3QNAJjZe8CLha5jK+wEvF3oIraC6i8s1V84pVw7QG9379ScB7RJq5It8KK7Dy50EVvKzBaq/sJR/YVVyvWXcu0Q1d/cx6ipR0QkYxT8IiIZU0zBf1OhC9hKqr+wVH9hlXL9pVw7bEH9RXNyV0REWkYxHfGLiEgLUPCLiGRMUQS/mR1hZi+a2d/M7JJC19MUM7vVzN4ys+WxcTua2WNmtir8/UIha2yMme1iZk+a2UozW2Fm54fxRb8NZlZmZvPNbGmo/adhfC8zey7Ufq+ZtSt0rY0xs9ZmttjMHg7DJVO/mVWZ2fNmtiR3KWEp7Ds5ZtbFzO4zs7+G18CBpVK/mfUOz3vu9q6ZXdDc+gse/GbWGrgOOBLoC5xiZn0LW1WTpgFH1Bl3CfCEu+8JPBGGi9Vm4PvuvjdwAHBueM5LYRs+Aka5+37AAOAIMzsAuBL4Zaj9HeAbBawxifOBlbHhUqv/EHcfELv+vRT2nZxrgEfdvQ+wH9H/oSTqd/cXw/M+ANgf+BCYSXPrd/eC3oADgT/Ghi8FLi10XQnqrgCWx4ZfBLqH+92JvpBW8DoTbsuDwGGltg1AB+AvwDCib162qW+fKrYbUB5enKOAhwErsfqrgJ3qjCuJfQfYAXiFcGFLqdVfp+bDgXlbUn/Bj/iBHsDrseHqMK7UfMnd3wQIf79Y4HoSMbMKYCDwHCWyDaGZZAnwFvAY8DKw3t03h1mKfR/6FXAx8EkY7kpp1e/AbDNbZGZnh3Else8AuwFrgdtCU9tvzKwjpVN/3MnA9HC/WfUXQ/BbPeN0jWkLMLPtgRnABe7+bqHrScrdP/boo245MBTYu77ZWraqZMzsaOAtd18UH13PrEVZfzDc3QcRNc+ea2YHF7qgZmgDDAKud/eBwAcUabNOY8I5oLHA77bk8cUQ/NXALrHhcuCNAtWyNdaYWXeA8PetAtfTKDNrSxT6d7n7/WF0SW2Du68HKonOU3Qxs1zfU8W8Dw0HxppZFXAPUXPPryid+nH3N8Lft4jal4dSOvtONVDt7s+F4fuI3ghKpf6cI4G/uPuaMNys+osh+BcAe4arGtoRfXyZVeCatsQsYHy4P56o3bwomZkBtwAr3f3q2KSi3wYz62ZmXcL99sChRCfnngROCLMVZe0A7n6pu5e7ewXRvj7H3U+jROo3s45m1il3n6ideTklsO8AuPtq4HUz6x1GfRV4gRKpP+YUPm3mgebWX+gTFOFkxFHAS0RttT8qdD0J6p0OvAlsIjqC+AZRO+0TwKrwd8dC19lI/SOImhKWAUvC7ahS2AagP7A41L4c+HEYvxswH/gb0cff7Qpda4JtGQk8XEr1hzqXhtuK3Ou1FPad2DYMABaGfegB4AslVn8HYB3QOTauWfWrywYRkYwphqYeERFpQQp+EZGMUfCLiGSMgl9EJGMU/CIiGaPgFxHJGAW/iEjG/H9KekC2VSOYpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "empl_data.pivot_table(values='HourlyRate',index='PerformanceRating', columns='Gender', aggfunc='mean')\\\n",
    "    .plot.barh(title=\"Average Performance Rating by Gender\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With pandas, we can even strip away some of the transformations and call methods directly on the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e9b95ad30>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEcCAYAAAAoSqjDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAGw9JREFUeJzt3XucJGV97/HPF5a7hOtAuLisxlUxKmhWMMfEEFHjHVRUiDFLDsfVHMWYeF66mpOAUSIkOTHGnBjxut4QRAkEPVEE0agRXC4qAgblui6BlZsgRAF/5496prYZe1h2u2dml/m8X69+dVfVU/U83dNT36qnLp2qQpIkgM3mugGSpI2HoSBJ6hkKkqSeoSBJ6hkKkqSeoSBJ6hkKmhVJPpzk7XPdjrl2f59DkiOTfHW22zQOSQ5Ksmqu26HRGQrzTJKrk9yV5I4ktyT5bJKHznW7BiWpJI+Y63Zs6pI8I8mXktye5KYkFyd5U5Kt57pt2ngZCvPT86vqIcAewA3Au+e4PTMmnXn3PU/yEuBU4BPAPlW1C/AyYG9gY9sIWDDXbdBa8+6fRWtV1X/RrTgeMzkuyQ5JPpJkTZJrkvzvyZVqkvckOXWg7AlJzm4r3oOSrEryliQ/anskL5+u7iSvTPL9JDcnOSPJnm38V1qRb7W9mZcNmXfzJP+n1XNVkte2vYsFbfq5SY5L8jXgTuDhSfZs9dzc6n3lwPLu06UztSukvZc3J7m07V19aHBrO8nz2lb4rUm+nuTxA9OekOTCtrV+MrCurfQkeXeS25JcnuTgNvIlSS6YUvANSf552AKAvwX+oqreV1U3A1TV96rq6Kq6opXbLMnyJD9oexKnJNm5TVvUPtOlSa5tn/WfDtSxTfvcbklyKfCkKW3YM8mn2/foqiSvG5h2bJJTk3wsyY+BI9fxmWg2VZWPefQArgae3l5vC6wAPjIw/SPA6cD2wCLgP4CjBsr/B90/8W8CPwL2btMOAu6hWxltBfwW8BPgUW36h4G3t9dPa/M+sZV9N/CVgTYU8Ij7eQ+vBi6l2+rdCfhim2dBm34ucC3wq8ACYAvgy8A/0q2U9wfWAAdPbdvAe1k15TO7hG4Le2fgawPv5YnAjcCBwObA0lZ+K2BL4Brgj1sbDgPuHqxryvs6sn2Gk+VfBtzW6twKuBnYd6D8RcCLhyzn0e3zWLSO78LrgW+0z3Er4L3ASW3aoraM9wHbAPsBP52sHzge+LfWtoe2z2dVm7YZcAHw5+0zeDhwJfA7bfqx7XM4tJXdZq7/L3wMfC/mugE+ZvkP3q2w7gBubSug1cDj2rTN2z/+YwbKvwo4d2D4gLZyugY4YmD8QW152w2MOwX4s/a6X/ECHwD+aqDcQ9pKYlEbXlconAO8amD46fxiKPzFwPSHAvcC2w+Mewfw4altG3gvU0Ph1QPDzwF+0F6/B3jblPZ9jy4Un9o+3wxM+zr3HwpTy58PvGKgruPa618FbgG2GrKc32ifx9YD4z7Z/uZ3DizvMlowtuE92t9hAWtDYe8pbTm8vb4SeNbAtGWsDYUDgWuntOnNwIfa62MZ2AjwsXE97D6anw6tqh3ptg5fC3w5yS8Du7J263bSNcBekwNVdT7dCiF0K/1Bt1TVT6bMu+eQ+vccrKOq7gBuGqxnHfYErhsYvm5ImcFxewI3V9XtU9r2QOuburzB97UP8IbWdXRrklvpQmjP9vhhtTXhwLz3Z1j5ybpWAL/buodeAZxSVT8dsoyb2vMekyOq6vD2N7+QLvwn237aQLsvowvP3QeW9Z8Dr++kC3D4xb/B4PvaB9hzymfylinLHfY300bAUJjHqureqvoM3YrgN+i6dO6m+6eetBD44eRAktfQhclq4I1TFrlTku2mzLt6SNWrB+to8+wyWM86XE/X5TFp2IHTwRXramDnJNtPadtkfT+h6xqb9MtTlrUPXTfR4LyT7+s6uq33HQce21bVSa2de7WV+OC892dY+dUAVfUN4Gd0XXe/C3x0mmVc3t7bi9ZR13XAs6e0feuqeiB/h+u57+c++L6uA66astztq+o5A2W8PfNGylCYx9oB4kPo+uUvq6p76bb+j0uyfZJ9gD8BPtbKPxJ4O/B7dFuqb0yy/5TFvjXJlkl+E3ge8KkhVX8C+IMk+yfZCvhL4LyqurpNv4GuH3o6jwfekmSvJDsCb2rjzx1WuKquo+u2eUeSrduB4KOAj7ciFwPPSbJz22N6/ZDF/F6SvduB2LcAJ7fx7wNeneTAdgD17nSn/N5K1+++GfC6JAuSvIiu+206y+m2pl+XZIt0ZxDtC3xuoMxHgH8A7qmqodc0tD2NNwDHpDugv1P7Wy/mvlvr/0T3t94HIMlE+z48EKcAb27L3hs4emDa+cCP053+uk26EwMem+RJwxeljYmhMD/9S5I7gB8DxwFLq+q7bdrRdFvOVwJfpVuBfzDdmT0fA06oqm9VdwbLW4CPthU7dF0Nt9Bt2X6crh/+8qmVV9XZwJ8Bn6bb4vwV4PCBIscCK1rXw0uHtP92ugOZ36Y72Po5ur2d+3MEXT/5auA04JiqOqtN+yjwLbpjB1+grfBz31Mlz2jTrmyPt7f3shJ4Jd2KejndcZUzW13n0HWzHdk+l5cBn1lHO68CFtPttR0HHFZVNw1M/yjwWKbfS6C162TgpXQBfl1b3inAiawN6ndNvq8kt9MddD5wHe2b9Fa6LqOr6D6Xvj1t4+L5dAf0r2p1vx/Y4QEuW3Nprg9q+HhwPJhycHaG67qadgZVG3423Yrnq214X7q9hluB7wIvGCh7LvA/BoaPnJyvDRfwGuAKui6QyXG/T3fa5Q20A9pt2ouBi9vrY4GPDUx7TJt3og3vRBcYa+hC4kzWnr11HF2w/RfdiQD/0MY/GjiL7uD+94CX04Xi4rn+m/t4cD7cU9CmKMABrUtmL+AYugOoJNkC+Be6rdfd6PZ8Pp7kUeux/EPptpgfMziyqr5JdxD3GQOjf48hW+1JtqQLkpvoAgC6PfMP0R2jWAjcRbeHQVX9Kd0pnq+tqodU1WvbsZaz6PbWdqPb23kvcGm1aw2kcTMUtKl6G91B8euAJ9AdKAd4Mt0ZMsdX1c+q6hy6LfIj1mPZ76iqm6vqriHTVtAFAe34wu/QrbQnvbQdT7iLrlvpsKq6B6CqbqqqT1fVndWdCXUc3amr03kecHVVfagt4zN0ex4Xrsd7kdaLoaCxqKpzq2rvdZccT3V0F0Klqjarqq2A/9mm7QlcV1U/Hyg/yumnk77enj8GPD/JQ+j67P+tqq4fKHdKdad+7k53QdevTU5Ism2S96a7UvzHwFeAHZNsznD7AAcOnNa5I91ekjRjvOeIHmxWAw9NstlAMCykuxIb1n36KdzP6ZJV9cMk/w68kO4MrPdMU+5HSV4FfDPJJ1pwvAF4FHBgVf1nO3PrItau6KfWex3w5ap6BtIscU9BDzbn0a3439hO6zyI7kyYT7bpFwMvalvtj6A7NXV9fYTuGo3H0Z3JNFR1Z159nrXXc2xP1610a+t6OmbKLFNPxT0TeGSSV7T3skWSJyXZdwPaLD0ghoIeVKrqZ8ALWHtG0j8Cv19rT419J90FYDfQHR/4+LDlrMNptKuB675XcA/z18CyJLsBf0d3H6Ef0Z3++a9Tyr4LOKzdZO7v23GHZ9Kdrrua7pTfE+guHpRmRKq8sFBaX0l+QHf/pS/OdVukcXJPQVpPSV5M1/9/zly3RRo3DzRL6yHJuXTXL7xiyhlO0oOC3UeSpJ7dR5KknqEgSeptFMcUdt1111q0aNFcN0OSHrQuuOCCH1XVxLrKbRShsGjRIlauXDnXzZCkB60k6/rVP8DuI0nSAENBktQzFCRJPUNBktRbZygk+WCSG5NcMjBu5yRnJbmiPe/UxifJ3yf5fpJvJ3niTDZekjReD2RP4cPAs6aMWw6cXVWLgbPbMHR3plzcHsuY5l7zkqSN0zpDoaq+Qvej4YMOobvtMO350IHxH6nON+h+VWqPcTVWkjSzNvSYwu6TP0HYnndr4/fivj9luIr1+xlESdIcGvfFa8N+P3boHfeSLKPrYmLhwoVjbsb8kmzYz/Z6M0TNFb+zG68N3VO4YbJbqD3f2MavAh46UG5vul+M+gVVdWJVLamqJRMT67zyWvejqoY+9nnTmdNO859Lc8nv7MZrQ0PhDGBpe70UOH1g/O+3s5CeDNw22c0kSdr4rbP7KMlJwEHArklW0f3Y+PHAKUmOAq4FXtKKfw54DvB94E7gD2agzZKkGbLOUKiqI6aZdPCQsgW8ZtRGSZLmhlc0S5J6hoIkqWcoSJJ6hoIkqWcoSJJ6hoIkqWcoSJJ6hoIkqWcoSJJ6hoIkqWcoSJJ6hoIkqWcoSJJ6hoIkqWcoSJJ6hoIkqWcoSJJ6hoIkqWcoSJJ6I4VCkj9KckmS7yZ5fRu3c5KzklzRnncaT1MlSTNtg0MhyWOBVwIHAPsBz0uyGFgOnF1Vi4Gz27AkaRMwyp7CvsA3qurOqroH+DLwQuAQYEUrswI4dLQmSpJmyyihcAnw1CS7JNkWeA7wUGD3qroeoD3vNnozJUmzYcGGzlhVlyU5ATgLuAP4FnDPA50/yTJgGcDChQs3tBmSpDEa6UBzVX2gqp5YVU8FbgauAG5IsgdAe75xmnlPrKolVbVkYmJilGZIksZk1LOPdmvPC4EXAScBZwBLW5GlwOmj1CFJmj0b3H3UfDrJLsDdwGuq6pYkxwOnJDkKuBZ4yaiNlCTNjpFCoap+c8i4m4CDR1muJGlueEWzJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeqPeJVWShtrvrV/gtrvuXu/5Fi3/7HqV32GbLfjWMc9c73o0nKEgaUbcdtfdXH38c2e8nvUNEd0/Q2ETsiFbXm51SVofhsImZDa2vNzqkuY3DzRLknqGgiSpZyhIknojhUKSP07y3SSXJDkpydZJHpbkvCRXJDk5yZbjaqwkaWZtcCgk2Qt4HbCkqh4LbA4cDpwAvLOqFgO3AEeNo6GSpJk3avfRAmCbJAuAbYHrgacBp7bpK4BDR6xDkjRLNjgUquqHwN8A19KFwW3ABcCtVXVPK7YK2GvURkqSZsco3Uc7AYcADwP2BLYDnj2kaE0z/7IkK5OsXLNmzYY2Q5I0RqN0Hz0duKqq1lTV3cBngP8G7Ni6kwD2BlYPm7mqTqyqJVW1ZGJiYoRmSJLGZZRQuBZ4cpJtkwQ4GLgU+BJwWCuzFDh9tCZKkmbLKMcUzqM7oHwh8J22rBOBNwF/kuT7wC7AB8bQTknSLBjp3kdVdQxwzJTRVwIHjLJcSdLc8IpmSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVJvg0MhyaOSXDzw+HGS1yfZOclZSa5ozzuNs8GSpJmzwaFQVd+rqv2ran/g14A7gdOA5cDZVbUYOLsNS5I2AePqPjoY+EFVXQMcAqxo41cAh46pDknSDBtXKBwOnNRe715V1wO0593GVIckaYaNHApJtgReAHxqPedblmRlkpVr1qwZtRmSpDEYx57Cs4ELq+qGNnxDkj0A2vONw2aqqhOraklVLZmYmBhDMyRJoxpHKBzB2q4jgDOApe31UuD0MdQhSZoFI4VCkm2BZwCfGRh9PPCMJFe0acePUockafYsGGXmqroT2GXKuJvozkaSJG1ivKJZktQzFCRJPUNBktQb6ZiCZtf2+y7ncStm9q4h2+8L8NwZrUPSxstQ2ITcftnxXH38zK6wFy3/7IwuX9LGze4jSVLPUJAk9QwFSVLPUJAk9TzQLGlGzMbZcl094Blz42MoSJoRs3G2HHjG3LjZfSRJ6hkKkqSeoSBJ6hkKkqSeoSBJ6hkKkqSeoSBJ6hkKkqTeSKGQZMckpya5PMllSX49yc5JzkpyRXveaVyNlSTNrFH3FN4F/GtVPRrYD7gMWA6cXVWLgbPbsCRpE7DBoZDkl4CnAh8AqKqfVdWtwCHAilZsBXDoqI2UJM2OUfYUHg6sAT6U5KIk70+yHbB7VV0P0J53G0M7JUmzYJRQWAA8EXhPVT0B+Anr0VWUZFmSlUlWrlmzZoRmSJLGZZRQWAWsqqrz2vCpdCFxQ5I9ANrzjcNmrqoTq2pJVS2ZmJgYoRmSpHHZ4FCoqv8ErkvyqDbqYOBS4AxgaRu3FDh9pBZKkmbNqL+ncDTw8SRbAlcCf0AXNKckOQq4FnjJiHVIkmbJSKFQVRcDS4ZMOniU5UqS5oZXNEuSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeiP9RnOSq4HbgXuBe6pqSZKdgZOBRcDVwEur6pbRmilJmg3j2FP47arav6qWtOHlwNlVtRg4uw1LkjYBM9F9dAiwor1eARw6A3VIkmbAqKFQwBeSXJBkWRu3e1VdD9CedxuxDknSLBnpmALwlKpanWQ34Kwklz/QGVuILANYuHDhiM2QJI3DSHsKVbW6Pd8InAYcANyQZA+A9nzjNPOeWFVLqmrJxMTEKM2QJI3JBodCku2SbD/5GngmcAlwBrC0FVsKnD5qIyVJs2OU7qPdgdOSTC7nE1X1r0m+CZyS5CjgWuAlozdTkjQbNjgUqupKYL8h428CDh6lUZKkuTHqgWbNskXLPzujy99hmy1mdPmaX2b6+wp+Z8ctVTXXbWDJkiW1cuXKuW7Gg86i5Z/l6uOfO9fNkB4wv7MzJ8kFAxcZT8t7H0mSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeiOHQpLNk1yU5Mw2/LAk5yW5IsnJSbYcvZmSpNkwjj2FPwIuGxg+AXhnVS0GbgGOGkMdkqRZMFIoJNkbeC7w/jYc4GnAqa3ICuDQUeqQJM2eUfcU/g54I/DzNrwLcGtV3dOGVwF7jViHJGmWbHAoJHkecGNVXTA4ekjRmmb+ZUlWJlm5Zs2aDW2GJGmMRtlTeArwgiRXA5+k6zb6O2DHJAtamb2B1cNmrqoTq2pJVS2ZmJgYoRmSpHHZ4FCoqjdX1d5VtQg4HDinql4OfAk4rBVbCpw+cislSbNiJq5TeBPwJ0m+T3eM4QMzUIckaQYsWHeRdauqc4Fz2+srgQPGsVxJ0uzyimZJUs9QkCT1DAVJUs9QkCT1DAVJUs9QkCT1DAVJUs9QkCT1DAVJUs9QkCT1DAVJUs9QkCT1DAVJUs9QkCT1DAVJUs9QkCT1DAVJUs9QkCT1DAVJUm+DQyHJ1knOT/KtJN9N8tY2/mFJzktyRZKTk2w5vuZKkmbSKHsKPwWeVlX7AfsDz0ryZOAE4J1VtRi4BThq9GZKkmbDBodCde5og1u0RwFPA05t41cAh47UQknSrBnpmEKSzZNcDNwInAX8ALi1qu5pRVYBe43WREnSbFkwysxVdS+wf5IdgdOAfYcVGzZvkmXAMoCFCxeO0ox5L8n0006Yfr6qoX8aacb5nd14jeXso6q6FTgXeDKwY5LJsNkbWD3NPCdW1ZKqWjIxMTGOZsxbVbVBD2mu+J3deI1y9tFE20MgyTbA04HLgC8Bh7ViS4HTR22kJGl2jNJ9tAewIsnmdOFySlWdmeRS4JNJ3g5cBHxgDO2UJM2CDQ6Fqvo28IQh468EDhilUZKkueEVzZKknqEgSeoZCpKknqEgSeoZCpKkXjaGC0KSrAGumet2PAjtCvxorhshrQe/szNnn6pa55XCG0UoaGYkWVlVS+a6HdID5Xd27tl9JEnqGQqSpJ6h8OB24lw3QFpPfmfnmMcUJEk99xQkST1DYSOV5N4kFw88Fs1gXUcm+YeZWr7mtySV5KMDwwuSrEly5jrmO2hdZTR+I/3ymmbUXVW1/1w3QhqDnwCPTbJNVd0FPAP44Ry3SdNwT2ET0n4T+6+TfDPJt5O8qo0/KMmXk5yS5D+SHJ/k5UnOT/KdJL/Syj0/yXlJLkryxSS7D6ljIsmnWx3fTPKU2X6felD6f8Bz2+sjgJMmJyQ5IMnX2/fy60keNXXmJNsl+WD7Tl6U5JBZave8YyhsvLYZ6Do6rY07Critqp4EPAl4ZZKHtWn7AX8EPA54BfDIqjoAeD9wdCvzVeDJVfUE4JPAG4fU+y7gna2OF7f5pVF9Ejg8ydbA44HzBqZdDjy1fS//HPjLIfP/KXBO+17+NvDXSbab4TbPS3YfbbyGdR89E3h8ksmfO90BWAz8DPhmVV0PkOQHwBdame/Q/RNB95vZJyfZA9gSuGpIvU8HHjPww+q/lGT7qrp9DO9J81RVfbsdFzsC+NyUyTvQ/YrjYqCALYYs4pnAC5L8rza8NbCQ7ieANUaGwqYlwNFV9fn7jEwOAn46MOrnA8M/Z+3f+d3A31bVGW2eY4fUsRnw663vVxqnM4C/AQ4CdhkY/zbgS1X1whYc5w6ZN8CLq+p7M9tE2X20afk88IdJtgBI8sj13IXegbUH+JZOU+YLwGsnB5J4sFvj8kHgL6rqO1PGD34vj5xm3s8DR6ftwib5hZ8C1ngYCpuW9wOXAhcmuQR4L+u3t3cs8Kkk/8b0d6J8HbCkHci+FHj1CO2VelW1qqreNWTSXwHvSPI1YPNpZn8bXbfSt9t3/20z1Mx5zyuaJUk99xQkST1DQZLUMxQkST1DQZLUMxQkST1DQfNOkt2TfCLJlUkuSPLvSV44huV6V09t8gwFzSvt4qd/Br5SVQ+vql8DDqe7Bchst8U7CmijYyhovnka8LOq+qfJEVV1TVW9ex13oT03yalJLk/y8YEra5/Vxn0VeNHkMqe7q2f77YpPJfkX1t6fStpouKWi+eZXgQunmdbfhTbJVsDXkkyuuJ/Q5l0NfA14SpKVwPvogub7wMkDy5q8q+d/T7IjcH6SL7Zpvw48vqpuHucbk8bBUNC8luT/Ar9Bd6fZa5j+LrTnV9WqNs/FwCLgDuCqqrqijf8YsKzNO91dPQHOMhC0sTIUNN98l+53IgCoqtck2RVYCVzLA7sL7b2s/d+Z7j4xQ+/qmeRAul8ikzZKHlPQfHMOsHWSPxwYt217Xt+70F4OPGzyl+3ofitgknf11CbJUNC8Ut0dIA8FfivJVUnOB1YAb2I970JbVf9F11302Xag+ZqByd7VU5sk75IqSeq5pyBJ6hkKkqSeoSBJ6hkKkqSeoSBJ6hkKkqSeoSBJ6hkKkqTe/wdbLnEqqxt5zwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "empl_data.boxplot(column='HourlyRate', by='Gender', grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# suppress warning message from matplotlib\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "\n",
    "# more information on warning here: https://github.com/MichaelGrupp/evo/issues/28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e9b99fd68>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "empl_data[empl_data['Gender'] == 'Male']['HourlyRate'].plot.hist(by='Gender', alpha=0.5, normed=True)\n",
    "empl_data[empl_data['Gender'] == 'Female']['HourlyRate'].plot.hist(by='Gender', alpha=0.5, normed=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Section Findings</h3>\n",
    "<p>Having explored Education, we learned:\n",
    "<ul>\n",
    "    <li>Compensation does not vary between genders significantly.</li>\n",
    "    <li>Compensation variance is not noted across Job Level nor Department.</li>\n",
    "    <li>Performance Ratings generally appear similar across gender.</li>\n",
    "    </ul>\n",
    "    </p>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Pandas Recap</h3>\n",
    "<p>Pandas plotting is powerful; you have the ability to plot directly from your DataFrames.</p>\n",
    "</br>\n",
    "<p>Here's the secret - pandas doesn't do any plotting. As was mentioned in the previous section, matplotlib, other packages build on top of matplotlib. pandas is no exception. In fact, the .plot method is just a wrapper around matplotlib calls.</p>\n",
    "<p>Still, this can be more effective that calling matplotlib directly. When working with DataFrames, it's easy to transform the data and pass to <strike>.plot</strike> matplotlib via the wrapper methods.</p>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}