Employee Exit Surveys.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   },
   "source": [
    "# Analysis of Employee Exit Surveys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will work with the data of exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia.\n",
    "You can find the DETE exit survey data [here.](https://data.gov.au/dataset/ds-qld-fe96ff30-d157-4a81-851d-215f2a0fe26d/details?q=exit%20survey)\n",
    "Using this data, we will try to answer the following questions:\n",
    "\n",
    "a) Are employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been there longer?\n",
    "\n",
    "b) Are younger employees resigning due to some kind of dissatisfaction? What about older employees?\n",
    "\n",
    "The data dictionary is as follows:\n",
    "\n",
    "dete_survey.csv:\n",
    "\n",
    " - ID: An id used to identify the participant of the survey\n",
    " - SeparationType: The reason why the person's employment ended\n",
    " - Cease Date: The year or month the person's employment ended\n",
    " - DETE Start Date: The year the person began employment with the DETE\n",
    " \n",
    "tafe_survey.csv:\n",
    "\n",
    " - Record ID: An id used to identify the participant of the survey\n",
    " - Reason for ceasing employment: The reason why the person's employment ended\n",
    " - LengthofServiceOverall. \n",
    " - Overall Length of Service at Institute (in years): The length of the person's employment (in years)\n",
    "\n",
    "Let's start by reading the datasets into pandas and exploring them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the libraries we need\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "pd.set_option('display.max_columns', None)\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>SeparationType</th>\n",
       "      <th>Cease Date</th>\n",
       "      <th>DETE Start Date</th>\n",
       "      <th>Role Start Date</th>\n",
       "      <th>Position</th>\n",
       "      <th>Classification</th>\n",
       "      <th>Region</th>\n",
       "      <th>Business Unit</th>\n",
       "      <th>Employment Status</th>\n",
       "      <th>Career move to public sector</th>\n",
       "      <th>Career move to private sector</th>\n",
       "      <th>Interpersonal conflicts</th>\n",
       "      <th>Job dissatisfaction</th>\n",
       "      <th>Dissatisfaction with the department</th>\n",
       "      <th>Physical work environment</th>\n",
       "      <th>Lack of recognition</th>\n",
       "      <th>Lack of job security</th>\n",
       "      <th>Work location</th>\n",
       "      <th>Employment conditions</th>\n",
       "      <th>Maternity/family</th>\n",
       "      <th>Relocation</th>\n",
       "      <th>Study/Travel</th>\n",
       "      <th>Ill Health</th>\n",
       "      <th>Traumatic incident</th>\n",
       "      <th>Work life balance</th>\n",
       "      <th>Workload</th>\n",
       "      <th>None of the above</th>\n",
       "      <th>Professional Development</th>\n",
       "      <th>Opportunities for promotion</th>\n",
       "      <th>Staff morale</th>\n",
       "      <th>Workplace issue</th>\n",
       "      <th>Physical environment</th>\n",
       "      <th>Worklife balance</th>\n",
       "      <th>Stress and pressure support</th>\n",
       "      <th>Performance of supervisor</th>\n",
       "      <th>Peer support</th>\n",
       "      <th>Initiative</th>\n",
       "      <th>Skills</th>\n",
       "      <th>Coach</th>\n",
       "      <th>Career Aspirations</th>\n",
       "      <th>Feedback</th>\n",
       "      <th>Further PD</th>\n",
       "      <th>Communication</th>\n",
       "      <th>My say</th>\n",
       "      <th>Information</th>\n",
       "      <th>Kept informed</th>\n",
       "      <th>Wellness programs</th>\n",
       "      <th>Health &amp; Safety</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Aboriginal</th>\n",
       "      <th>Torres Strait</th>\n",
       "      <th>South Sea</th>\n",
       "      <th>Disability</th>\n",
       "      <th>NESB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Ill Health Retirement</td>\n",
       "      <td>08/2012</td>\n",
       "      <td>1984</td>\n",
       "      <td>2004</td>\n",
       "      <td>Public Servant</td>\n",
       "      <td>A01-A04</td>\n",
       "      <td>Central Office</td>\n",
       "      <td>Corporate Strategy and Peformance</td>\n",
       "      <td>Permanent Full-time</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Male</td>\n",
       "      <td>56-60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Voluntary Early Retirement (VER)</td>\n",
       "      <td>08/2012</td>\n",
       "      <td>Not Stated</td>\n",
       "      <td>Not Stated</td>\n",
       "      <td>Public Servant</td>\n",
       "      <td>AO5-AO7</td>\n",
       "      <td>Central Office</td>\n",
       "      <td>Corporate Strategy and Peformance</td>\n",
       "      <td>Permanent Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Male</td>\n",
       "      <td>56-60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Voluntary Early Retirement (VER)</td>\n",
       "      <td>05/2012</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>Schools Officer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Central Office</td>\n",
       "      <td>Education Queensland</td>\n",
       "      <td>Permanent Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Male</td>\n",
       "      <td>61 or older</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Resignation-Other reasons</td>\n",
       "      <td>05/2012</td>\n",
       "      <td>2005</td>\n",
       "      <td>2006</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>Primary</td>\n",
       "      <td>Central Queensland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Permanent Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>36-40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Age Retirement</td>\n",
       "      <td>05/2012</td>\n",
       "      <td>1970</td>\n",
       "      <td>1989</td>\n",
       "      <td>Head of Curriculum/Head of Special Education</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South East</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Permanent Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>SA</td>\n",
       "      <td>SA</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>Female</td>\n",
       "      <td>61 or older</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                    SeparationType Cease Date DETE Start Date  \\\n",
       "0   1             Ill Health Retirement    08/2012            1984   \n",
       "1   2  Voluntary Early Retirement (VER)    08/2012      Not Stated   \n",
       "2   3  Voluntary Early Retirement (VER)    05/2012            2011   \n",
       "3   4         Resignation-Other reasons    05/2012            2005   \n",
       "4   5                    Age Retirement    05/2012            1970   \n",
       "\n",
       "  Role Start Date                                      Position  \\\n",
       "0            2004                                Public Servant   \n",
       "1      Not Stated                                Public Servant   \n",
       "2            2011                               Schools Officer   \n",
       "3            2006                                       Teacher   \n",
       "4            1989  Head of Curriculum/Head of Special Education   \n",
       "\n",
       "  Classification              Region                      Business Unit  \\\n",
       "0        A01-A04      Central Office  Corporate Strategy and Peformance   \n",
       "1        AO5-AO7      Central Office  Corporate Strategy and Peformance   \n",
       "2            NaN      Central Office               Education Queensland   \n",
       "3        Primary  Central Queensland                                NaN   \n",
       "4            NaN          South East                                NaN   \n",
       "\n",
       "     Employment Status  Career move to public sector  \\\n",
       "0  Permanent Full-time                          True   \n",
       "1  Permanent Full-time                         False   \n",
       "2  Permanent Full-time                         False   \n",
       "3  Permanent Full-time                         False   \n",
       "4  Permanent Full-time                         False   \n",
       "\n",
       "   Career move to private sector  Interpersonal conflicts  \\\n",
       "0                          False                    False   \n",
       "1                          False                    False   \n",
       "2                          False                    False   \n",
       "3                           True                    False   \n",
       "4                          False                    False   \n",
       "\n",
       "   Job dissatisfaction  Dissatisfaction with the department  \\\n",
       "0                 True                                False   \n",
       "1                False                                False   \n",
       "2                False                                False   \n",
       "3                False                                False   \n",
       "4                False                                False   \n",
       "\n",
       "   Physical work environment  Lack of recognition  Lack of job security  \\\n",
       "0                      False                 True                 False   \n",
       "1                      False                False                 False   \n",
       "2                      False                False                 False   \n",
       "3                      False                False                 False   \n",
       "4                      False                False                 False   \n",
       "\n",
       "   Work location  Employment conditions  Maternity/family  Relocation  \\\n",
       "0          False                  False             False       False   \n",
       "1          False                  False             False       False   \n",
       "2          False                  False             False       False   \n",
       "3          False                  False             False       False   \n",
       "4          False                  False             False       False   \n",
       "\n",
       "   Study/Travel  Ill Health  Traumatic incident  Work life balance  Workload  \\\n",
       "0         False       False               False              False     False   \n",
       "1         False       False               False              False     False   \n",
       "2         False       False               False              False     False   \n",
       "3         False       False               False              False     False   \n",
       "4         False       False               False               True     False   \n",
       "\n",
       "   None of the above Professional Development Opportunities for promotion  \\\n",
       "0               True                        A                           A   \n",
       "1              False                        A                           A   \n",
       "2               True                        N                           N   \n",
       "3              False                        A                           N   \n",
       "4              False                        A                           A   \n",
       "\n",
       "  Staff morale Workplace issue Physical environment Worklife balance  \\\n",
       "0            N               N                    N                A   \n",
       "1            N               N                    N                N   \n",
       "2            N               N                    N                N   \n",
       "3            N               N                    A                A   \n",
       "4            N               N                    D                D   \n",
       "\n",
       "  Stress and pressure support Performance of supervisor Peer support  \\\n",
       "0                           A                         A            A   \n",
       "1                           A                         A            A   \n",
       "2                           N                         N            N   \n",
       "3                           N                         N            A   \n",
       "4                           N                         A            A   \n",
       "\n",
       "  Initiative Skills Coach Career Aspirations Feedback Further PD  \\\n",
       "0          N      N     N                  A        A          A   \n",
       "1          N      N     N                  A        A          A   \n",
       "2          N      N     N                  N        N          N   \n",
       "3          A      A     A                  A        A          A   \n",
       "4          A      A     A                  A       SA         SA   \n",
       "\n",
       "  Communication My say Information Kept informed Wellness programs  \\\n",
       "0             N      A           A             N                 N   \n",
       "1             N      A           A             N                 N   \n",
       "2             A      A           N             N                 N   \n",
       "3             A      A           A             A                 N   \n",
       "4             D      D           A             N                 A   \n",
       "\n",
       "  Health & Safety  Gender          Age Aboriginal Torres Strait South Sea  \\\n",
       "0               N    Male        56-60        NaN           NaN       NaN   \n",
       "1               N    Male        56-60        NaN           NaN       NaN   \n",
       "2               N    Male  61 or older        NaN           NaN       NaN   \n",
       "3               A  Female        36-40        NaN           NaN       NaN   \n",
       "4               M  Female  61 or older        NaN           NaN       NaN   \n",
       "\n",
       "  Disability NESB  \n",
       "0        NaN  Yes  \n",
       "1        NaN  NaN  \n",
       "2        NaN  NaN  \n",
       "3        NaN  NaN  \n",
       "4        NaN  NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading the datasets we need in dataframes.\n",
    "dete=pd.read_csv(\"dete_survey.csv\")\n",
    "tafe=pd.read_csv(\"tafe_survey.csv\")\n",
    "dete.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Record ID</th>\n",
       "      <th>Institute</th>\n",
       "      <th>WorkArea</th>\n",
       "      <th>CESSATION YEAR</th>\n",
       "      <th>Reason for ceasing employment</th>\n",
       "      <th>Contributing Factors. Career Move - Public Sector</th>\n",
       "      <th>Contributing Factors. Career Move - Private Sector</th>\n",
       "      <th>Contributing Factors. Career Move - Self-employment</th>\n",
       "      <th>Contributing Factors. Ill Health</th>\n",
       "      <th>Contributing Factors. Maternity/Family</th>\n",
       "      <th>Contributing Factors. Dissatisfaction</th>\n",
       "      <th>Contributing Factors. Job Dissatisfaction</th>\n",
       "      <th>Contributing Factors. Interpersonal Conflict</th>\n",
       "      <th>Contributing Factors. Study</th>\n",
       "      <th>Contributing Factors. Travel</th>\n",
       "      <th>Contributing Factors. Other</th>\n",
       "      <th>Contributing Factors. NONE</th>\n",
       "      <th>Main Factor. Which of these was the main factor for leaving?</th>\n",
       "      <th>InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction</th>\n",
       "      <th>InstituteViews. Topic:2. I was given access to skills training to help me do my job better</th>\n",
       "      <th>InstituteViews. Topic:3. I was given adequate opportunities for personal development</th>\n",
       "      <th>InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL%</th>\n",
       "      <th>InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had</th>\n",
       "      <th>InstituteViews. Topic:6. The organisation recognised when staff did good work</th>\n",
       "      <th>InstituteViews. Topic:7. Management was generally supportive of me</th>\n",
       "      <th>InstituteViews. Topic:8. Management was generally supportive of my team</th>\n",
       "      <th>InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me</th>\n",
       "      <th>InstituteViews. Topic:10. Staff morale was positive within the Institute</th>\n",
       "      <th>InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly</th>\n",
       "      <th>InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently</th>\n",
       "      <th>InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly</th>\n",
       "      <th>WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit</th>\n",
       "      <th>WorkUnitViews. Topic:15. I worked well with my colleagues</th>\n",
       "      <th>WorkUnitViews. Topic:16. My job was challenging and interesting</th>\n",
       "      <th>WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work</th>\n",
       "      <th>WorkUnitViews. Topic:18. I had sufficient contact with other people in my job</th>\n",
       "      <th>WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job</th>\n",
       "      <th>WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job</th>\n",
       "      <th>WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT]</th>\n",
       "      <th>WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job</th>\n",
       "      <th>WorkUnitViews. Topic:23. My job provided sufficient variety</th>\n",
       "      <th>WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job</th>\n",
       "      <th>WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction</th>\n",
       "      <th>WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance</th>\n",
       "      <th>WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area</th>\n",
       "      <th>WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date</th>\n",
       "      <th>WorkUnitViews. Topic:29. There was adequate communication between staff in my unit</th>\n",
       "      <th>WorkUnitViews. Topic:30. Staff morale was positive within my work unit</th>\n",
       "      <th>Induction. Did you undertake Workplace Induction?</th>\n",
       "      <th>InductionInfo. Topic:Did you undertake a Corporate Induction?</th>\n",
       "      <th>InductionInfo. Topic:Did you undertake a Institute Induction?</th>\n",
       "      <th>InductionInfo. Topic: Did you undertake Team Induction?</th>\n",
       "      <th>InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted?</th>\n",
       "      <th>InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted?</th>\n",
       "      <th>InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction?</th>\n",
       "      <th>InductionInfo. Face to Face Topic:Did you undertake a Institute Induction?</th>\n",
       "      <th>InductionInfo. On-line Topic:Did you undertake a Institute Induction?</th>\n",
       "      <th>InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction?</th>\n",
       "      <th>InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category?</th>\n",
       "      <th>InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.]</th>\n",
       "      <th>InductionInfo. Induction Manual Topic: Did you undertake Team Induction?</th>\n",
       "      <th>Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)?</th>\n",
       "      <th>Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination?</th>\n",
       "      <th>Workplace. Topic:Does your workplace promote and practice the principles of employment equity?</th>\n",
       "      <th>Workplace. Topic:Does your workplace value the diversity of its employees?</th>\n",
       "      <th>Workplace. Topic:Would you recommend the Institute as an employer to others?</th>\n",
       "      <th>Gender. What is your Gender?</th>\n",
       "      <th>CurrentAge. Current Age</th>\n",
       "      <th>Employment Type. Employment Type</th>\n",
       "      <th>Classification. Classification</th>\n",
       "      <th>LengthofServiceOverall. Overall Length of Service at Institute (in years)</th>\n",
       "      <th>LengthofServiceCurrent. Length of Service at current workplace (in years)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.341330e+17</td>\n",
       "      <td>Southern Queensland Institute of TAFE</td>\n",
       "      <td>Non-Delivery (corporate)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Contract Expired</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Face to Face</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Face to Face</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Face to Face</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Female</td>\n",
       "      <td>26  30</td>\n",
       "      <td>Temporary Full-time</td>\n",
       "      <td>Administration (AO)</td>\n",
       "      <td>1-2</td>\n",
       "      <td>1-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.341337e+17</td>\n",
       "      <td>Mount Isa Institute of TAFE</td>\n",
       "      <td>Non-Delivery (corporate)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Retirement</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Travel</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.341388e+17</td>\n",
       "      <td>Mount Isa Institute of TAFE</td>\n",
       "      <td>Delivery (teaching)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Retirement</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.341399e+17</td>\n",
       "      <td>Mount Isa Institute of TAFE</td>\n",
       "      <td>Non-Delivery (corporate)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Resignation</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Travel</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.341466e+17</td>\n",
       "      <td>Southern Queensland Institute of TAFE</td>\n",
       "      <td>Delivery (teaching)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Resignation</td>\n",
       "      <td>-</td>\n",
       "      <td>Career Move - Private Sector</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Induction Manual</td>\n",
       "      <td>Face to Face</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Face to Face</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Male</td>\n",
       "      <td>41  45</td>\n",
       "      <td>Permanent Full-time</td>\n",
       "      <td>Teacher (including LVT)</td>\n",
       "      <td>3-4</td>\n",
       "      <td>3-4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Record ID                              Institute  \\\n",
       "0  6.341330e+17  Southern Queensland Institute of TAFE   \n",
       "1  6.341337e+17            Mount Isa Institute of TAFE   \n",
       "2  6.341388e+17            Mount Isa Institute of TAFE   \n",
       "3  6.341399e+17            Mount Isa Institute of TAFE   \n",
       "4  6.341466e+17  Southern Queensland Institute of TAFE   \n",
       "\n",
       "                   WorkArea  CESSATION YEAR Reason for ceasing employment  \\\n",
       "0  Non-Delivery (corporate)          2010.0              Contract Expired   \n",
       "1  Non-Delivery (corporate)          2010.0                    Retirement   \n",
       "2       Delivery (teaching)          2010.0                    Retirement   \n",
       "3  Non-Delivery (corporate)          2010.0                   Resignation   \n",
       "4       Delivery (teaching)          2010.0                   Resignation   \n",
       "\n",
       "  Contributing Factors. Career Move - Public Sector   \\\n",
       "0                                                NaN   \n",
       "1                                                  -   \n",
       "2                                                  -   \n",
       "3                                                  -   \n",
       "4                                                  -   \n",
       "\n",
       "  Contributing Factors. Career Move - Private Sector   \\\n",
       "0                                                NaN    \n",
       "1                                                  -    \n",
       "2                                                  -    \n",
       "3                                                  -    \n",
       "4                       Career Move - Private Sector    \n",
       "\n",
       "  Contributing Factors. Career Move - Self-employment  \\\n",
       "0                                                NaN    \n",
       "1                                                  -    \n",
       "2                                                  -    \n",
       "3                                                  -    \n",
       "4                                                  -    \n",
       "\n",
       "  Contributing Factors. Ill Health Contributing Factors. Maternity/Family  \\\n",
       "0                              NaN                                    NaN   \n",
       "1                                -                                      -   \n",
       "2                                -                                      -   \n",
       "3                                -                                      -   \n",
       "4                                -                                      -   \n",
       "\n",
       "  Contributing Factors. Dissatisfaction  \\\n",
       "0                                   NaN   \n",
       "1                                     -   \n",
       "2                                     -   \n",
       "3                                     -   \n",
       "4                                     -   \n",
       "\n",
       "  Contributing Factors. Job Dissatisfaction  \\\n",
       "0                                       NaN   \n",
       "1                                         -   \n",
       "2                                         -   \n",
       "3                                         -   \n",
       "4                                         -   \n",
       "\n",
       "  Contributing Factors. Interpersonal Conflict Contributing Factors. Study  \\\n",
       "0                                          NaN                         NaN   \n",
       "1                                            -                           -   \n",
       "2                                            -                           -   \n",
       "3                                            -                           -   \n",
       "4                                            -                           -   \n",
       "\n",
       "  Contributing Factors. Travel Contributing Factors. Other  \\\n",
       "0                          NaN                         NaN   \n",
       "1                       Travel                           -   \n",
       "2                            -                           -   \n",
       "3                       Travel                           -   \n",
       "4                            -                           -   \n",
       "\n",
       "  Contributing Factors. NONE  \\\n",
       "0                        NaN   \n",
       "1                          -   \n",
       "2                       NONE   \n",
       "3                          -   \n",
       "4                          -   \n",
       "\n",
       "  Main Factor. Which of these was the main factor for leaving?  \\\n",
       "0                                                NaN             \n",
       "1                                                NaN             \n",
       "2                                                NaN             \n",
       "3                                                NaN             \n",
       "4                                                NaN             \n",
       "\n",
       "  InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction  \\\n",
       "0                                              Agree                                       \n",
       "1                                              Agree                                       \n",
       "2                                              Agree                                       \n",
       "3                                              Agree                                       \n",
       "4                                              Agree                                       \n",
       "\n",
       "  InstituteViews. Topic:2. I was given access to skills training to help me do my job better  \\\n",
       "0                                              Agree                                           \n",
       "1                                              Agree                                           \n",
       "2                                              Agree                                           \n",
       "3                                              Agree                                           \n",
       "4                                              Agree                                           \n",
       "\n",
       "  InstituteViews. Topic:3. I was given adequate opportunities for personal development  \\\n",
       "0                                              Agree                                     \n",
       "1                                              Agree                                     \n",
       "2                                              Agree                                     \n",
       "3                                              Agree                                     \n",
       "4                                     Strongly Agree                                     \n",
       "\n",
       "  InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL%  \\\n",
       "0                                            Neutral                                                    \n",
       "1                                              Agree                                                    \n",
       "2                                              Agree                                                    \n",
       "3                                              Agree                                                    \n",
       "4                                              Agree                                                    \n",
       "\n",
       "  InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had  \\\n",
       "0                                              Agree                                                \n",
       "1                                              Agree                                                \n",
       "2                                              Agree                                                \n",
       "3                                              Agree                                                \n",
       "4                                     Strongly Agree                                                \n",
       "\n",
       "  InstituteViews. Topic:6. The organisation recognised when staff did good work  \\\n",
       "0                                              Agree                              \n",
       "1                                     Strongly Agree                              \n",
       "2                                              Agree                              \n",
       "3                                              Agree                              \n",
       "4                                     Strongly Agree                              \n",
       "\n",
       "  InstituteViews. Topic:7. Management was generally supportive of me  \\\n",
       "0                                              Agree                   \n",
       "1                                     Strongly Agree                   \n",
       "2                                     Strongly Agree                   \n",
       "3                                              Agree                   \n",
       "4                                     Strongly Agree                   \n",
       "\n",
       "  InstituteViews. Topic:8. Management was generally supportive of my team  \\\n",
       "0                                              Agree                        \n",
       "1                                              Agree                        \n",
       "2                                              Agree                        \n",
       "3                                              Agree                        \n",
       "4                                     Strongly Agree                        \n",
       "\n",
       "  InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me  \\\n",
       "0                                              Agree                                                      \n",
       "1                                     Strongly Agree                                                      \n",
       "2                                              Agree                                                      \n",
       "3                                              Agree                                                      \n",
       "4                                              Agree                                                      \n",
       "\n",
       "  InstituteViews. Topic:10. Staff morale was positive within the Institute  \\\n",
       "0                                              Agree                         \n",
       "1                                              Agree                         \n",
       "2                                              Agree                         \n",
       "3                                              Agree                         \n",
       "4                                     Strongly Agree                         \n",
       "\n",
       "  InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly  \\\n",
       "0                                              Agree                               \n",
       "1                                              Agree                               \n",
       "2                                              Agree                               \n",
       "3                                              Agree                               \n",
       "4                                     Strongly Agree                               \n",
       "\n",
       "  InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently  \\\n",
       "0                                              Agree                                   \n",
       "1                                              Agree                                   \n",
       "2                                            Neutral                                   \n",
       "3                                              Agree                                   \n",
       "4                                              Agree                                   \n",
       "\n",
       "  InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly  \\\n",
       "0                                              Agree                                  \n",
       "1                                           Disagree                                  \n",
       "2                                            Neutral                                  \n",
       "3                                              Agree                                  \n",
       "4                                     Strongly Agree                                  \n",
       "\n",
       "  WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit  \\\n",
       "0                                              Agree                                                                \n",
       "1                                     Strongly Agree                                                                \n",
       "2                                     Strongly Agree                                                                \n",
       "3                                     Strongly Agree                                                                \n",
       "4                                     Strongly Agree                                                                \n",
       "\n",
       "  WorkUnitViews. Topic:15. I worked well with my colleagues  \\\n",
       "0                                              Agree          \n",
       "1                                     Strongly Agree          \n",
       "2                                     Strongly Agree          \n",
       "3                                     Strongly Agree          \n",
       "4                                     Strongly Agree          \n",
       "\n",
       "  WorkUnitViews. Topic:16. My job was challenging and interesting  \\\n",
       "0                                              Agree                \n",
       "1                                     Strongly Agree                \n",
       "2                                              Agree                \n",
       "3                                     Strongly Agree                \n",
       "4                                     Strongly Agree                \n",
       "\n",
       "  WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work  \\\n",
       "0                                     Strongly Agree                                        \n",
       "1                                              Agree                                        \n",
       "2                                              Agree                                        \n",
       "3                                     Strongly Agree                                        \n",
       "4                                     Strongly Agree                                        \n",
       "\n",
       "  WorkUnitViews. Topic:18. I had sufficient contact with other people in my job  \\\n",
       "0                                              Agree                              \n",
       "1                                              Agree                              \n",
       "2                                              Agree                              \n",
       "3                                     Strongly Agree                              \n",
       "4                                     Strongly Agree                              \n",
       "\n",
       "  WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job  \\\n",
       "0                                              Agree                                                             \n",
       "1                                              Agree                                                             \n",
       "2                                              Agree                                                             \n",
       "3                                     Strongly Agree                                                             \n",
       "4                                     Strongly Agree                                                             \n",
       "\n",
       "  WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job  \\\n",
       "0                                              Agree                                 \n",
       "1                                     Strongly Agree                                 \n",
       "2                                              Agree                                 \n",
       "3                                     Strongly Agree                                 \n",
       "4                                     Strongly Agree                                 \n",
       "\n",
       "  WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT]  \\\n",
       "0                                              Agree                                                                                                              \n",
       "1                                              Agree                                                                                                              \n",
       "2                                              Agree                                                                                                              \n",
       "3                                     Strongly Agree                                                                                                              \n",
       "4                                     Strongly Agree                                                                                                              \n",
       "\n",
       "  WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job  \\\n",
       "0                                              Agree                                    \n",
       "1                                              Agree                                    \n",
       "2                                              Agree                                    \n",
       "3                                     Strongly Agree                                    \n",
       "4                                     Strongly Agree                                    \n",
       "\n",
       "  WorkUnitViews. Topic:23. My job provided sufficient variety  \\\n",
       "0                                              Agree            \n",
       "1                                              Agree            \n",
       "2                                              Agree            \n",
       "3                                     Strongly Agree            \n",
       "4                                     Strongly Agree            \n",
       "\n",
       "  WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job  \\\n",
       "0                                              Agree                                            \n",
       "1                                     Strongly Agree                                            \n",
       "2                                              Agree                                            \n",
       "3                                     Strongly Agree                                            \n",
       "4                                     Strongly Agree                                            \n",
       "\n",
       "  WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction  \\\n",
       "0                                              Agree                                                        \n",
       "1                                              Agree                                                        \n",
       "2                                              Agree                                                        \n",
       "3                                     Strongly Agree                                                        \n",
       "4                                     Strongly Agree                                                        \n",
       "\n",
       "  WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance  \\\n",
       "0                                              Agree                                                            \n",
       "1                                     Strongly Agree                                                            \n",
       "2                                              Agree                                                            \n",
       "3                                     Strongly Agree                                                            \n",
       "4                                     Strongly Agree                                                            \n",
       "\n",
       "  WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area  \\\n",
       "0                                              Agree                                                                                         \n",
       "1                                     Strongly Agree                                                                                         \n",
       "2                                              Agree                                                                                         \n",
       "3                                     Strongly Agree                                                                                         \n",
       "4                                     Strongly Agree                                                                                         \n",
       "\n",
       "  WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date  \\\n",
       "0                                            Neutral                                                                                                              \n",
       "1                                              Agree                                                                                                              \n",
       "2                                              Agree                                                                                                              \n",
       "3                                     Strongly Agree                                                                                                              \n",
       "4                                     Strongly Agree                                                                                                              \n",
       "\n",
       "  WorkUnitViews. Topic:29. There was adequate communication between staff in my unit  \\\n",
       "0                                              Agree                                   \n",
       "1                                              Agree                                   \n",
       "2                                              Agree                                   \n",
       "3                                     Strongly Agree                                   \n",
       "4                                     Strongly Agree                                   \n",
       "\n",
       "  WorkUnitViews. Topic:30. Staff morale was positive within my work unit  \\\n",
       "0                                              Agree                       \n",
       "1                                     Strongly Agree                       \n",
       "2                                              Agree                       \n",
       "3                                     Strongly Agree                       \n",
       "4                                     Strongly Agree                       \n",
       "\n",
       "  Induction. Did you undertake Workplace Induction?  \\\n",
       "0                                               Yes   \n",
       "1                                                No   \n",
       "2                                                No   \n",
       "3                                               Yes   \n",
       "4                                               Yes   \n",
       "\n",
       "  InductionInfo. Topic:Did you undertake a Corporate Induction?  \\\n",
       "0                                                Yes              \n",
       "1                                                NaN              \n",
       "2                                                NaN              \n",
       "3                                                 No              \n",
       "4                                                Yes              \n",
       "\n",
       "  InductionInfo. Topic:Did you undertake a Institute Induction?  \\\n",
       "0                                                Yes              \n",
       "1                                                NaN              \n",
       "2                                                NaN              \n",
       "3                                                Yes              \n",
       "4                                                Yes              \n",
       "\n",
       "  InductionInfo. Topic: Did you undertake Team Induction?  \\\n",
       "0                                                Yes        \n",
       "1                                                NaN        \n",
       "2                                                NaN        \n",
       "3                                                Yes        \n",
       "4                                                Yes        \n",
       "\n",
       "  InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted?  \\\n",
       "0                                       Face to Face                                                          \n",
       "1                                                NaN                                                          \n",
       "2                                                NaN                                                          \n",
       "3                                                  -                                                          \n",
       "4                                                  -                                                          \n",
       "\n",
       "  InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted?  \\\n",
       "0                                                  -                                                     \n",
       "1                                                NaN                                                     \n",
       "2                                                NaN                                                     \n",
       "3                                                  -                                                     \n",
       "4                                                  -                                                     \n",
       "\n",
       "  InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction?  \\\n",
       "0                                                  -                               \n",
       "1                                                NaN                               \n",
       "2                                                NaN                               \n",
       "3                                                  -                               \n",
       "4                                   Induction Manual                               \n",
       "\n",
       "  InductionInfo. Face to Face Topic:Did you undertake a Institute Induction?  \\\n",
       "0                                       Face to Face                           \n",
       "1                                                NaN                           \n",
       "2                                                NaN                           \n",
       "3                                                NaN                           \n",
       "4                                       Face to Face                           \n",
       "\n",
       "  InductionInfo. On-line Topic:Did you undertake a Institute Induction?  \\\n",
       "0                                                  -                      \n",
       "1                                                NaN                      \n",
       "2                                                NaN                      \n",
       "3                                                  -                      \n",
       "4                                                  -                      \n",
       "\n",
       "  InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction?  \\\n",
       "0                                                  -                               \n",
       "1                                                NaN                               \n",
       "2                                                NaN                               \n",
       "3                                                  -                               \n",
       "4                                                  -                               \n",
       "\n",
       "  InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category?  \\\n",
       "0                                       Face to Face                               \n",
       "1                                                NaN                               \n",
       "2                                                NaN                               \n",
       "3                                                  -                               \n",
       "4                                       Face to Face                               \n",
       "\n",
       "  InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.]  \\\n",
       "0                                                  -                                                                \n",
       "1                                                NaN                                                                \n",
       "2                                                NaN                                                                \n",
       "3                                                  -                                                                \n",
       "4                                                  -                                                                \n",
       "\n",
       "  InductionInfo. Induction Manual Topic: Did you undertake Team Induction?  \\\n",
       "0                                                  -                         \n",
       "1                                                NaN                         \n",
       "2                                                NaN                         \n",
       "3                                                  -                         \n",
       "4                                                  -                         \n",
       "\n",
       "  Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)?  \\\n",
       "0                                                Yes                                                          \n",
       "1                                                Yes                                                          \n",
       "2                                                Yes                                                          \n",
       "3                                                Yes                                                          \n",
       "4                                                Yes                                                          \n",
       "\n",
       "  Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination?  \\\n",
       "0                                                Yes                                                            \n",
       "1                                                Yes                                                            \n",
       "2                                                Yes                                                            \n",
       "3                                                Yes                                                            \n",
       "4                                                Yes                                                            \n",
       "\n",
       "  Workplace. Topic:Does your workplace promote and practice the principles of employment equity?  \\\n",
       "0                                                Yes                                               \n",
       "1                                                Yes                                               \n",
       "2                                                Yes                                               \n",
       "3                                                Yes                                               \n",
       "4                                                Yes                                               \n",
       "\n",
       "  Workplace. Topic:Does your workplace value the diversity of its employees?  \\\n",
       "0                                                Yes                           \n",
       "1                                                Yes                           \n",
       "2                                                Yes                           \n",
       "3                                                Yes                           \n",
       "4                                                Yes                           \n",
       "\n",
       "  Workplace. Topic:Would you recommend the Institute as an employer to others?  \\\n",
       "0                                                Yes                             \n",
       "1                                                Yes                             \n",
       "2                                                Yes                             \n",
       "3                                                Yes                             \n",
       "4                                                Yes                             \n",
       "\n",
       "  Gender. What is your Gender? CurrentAge. Current Age  \\\n",
       "0                       Female                  26  30   \n",
       "1                          NaN                     NaN   \n",
       "2                          NaN                     NaN   \n",
       "3                          NaN                     NaN   \n",
       "4                         Male                  41  45   \n",
       "\n",
       "  Employment Type. Employment Type Classification. Classification  \\\n",
       "0              Temporary Full-time            Administration (AO)   \n",
       "1                              NaN                            NaN   \n",
       "2                              NaN                            NaN   \n",
       "3                              NaN                            NaN   \n",
       "4              Permanent Full-time        Teacher (including LVT)   \n",
       "\n",
       "  LengthofServiceOverall. Overall Length of Service at Institute (in years)  \\\n",
       "0                                                1-2                          \n",
       "1                                                NaN                          \n",
       "2                                                NaN                          \n",
       "3                                                NaN                          \n",
       "4                                                3-4                          \n",
       "\n",
       "  LengthofServiceCurrent. Length of Service at current workplace (in years)  \n",
       "0                                                1-2                         \n",
       "1                                                NaN                         \n",
       "2                                                NaN                         \n",
       "3                                                NaN                         \n",
       "4                                                3-4                         "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tafe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us remove the columns we do not need in our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing columns we do not need\n",
    "dete.drop(dete.columns[28:49],axis=1,inplace=True)\n",
    "tafe.drop(tafe.columns[17:66],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column names of dete and tafe data are way too descriptive. Let us rename them to make them look homogenous and and to the point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tafe=tafe.rename({\"Record ID\":\"ID\",\"CESSATION YEAR\":\"Cessation_Year\",\n",
    "           \"Reason for ceasing employment\":\"Separation_Type\",\n",
    "          \"Gender. What is your Gender?\":\"Gender\",\n",
    "          \"CurrentAge. Current Age\":\"Age\",\"Contributing Factors. Career Move - Public Sector \":\"CareerMove_Public\",\n",
    "            \"Contributing Factors. Career Move - Private Sector \":\"CareerMove_Private\",\n",
    "            \"Contributing Factors. Career Move - Self-employment\":\"Self_Employment\",\n",
    "            \"Contributing Factors. Ill Health\":\"Ill_Health\",\n",
    "            \"Contributing Factors. Maternity/Family\":\"Maternity/Family\",\n",
    "           \"Contributing Factors. Dissatisfaction\":\"Dissatisfaction\",\n",
    "            \"Contributing Factors. Job Dissatisfaction\":\"Job_Dissatisfaction\",\n",
    "            \"Contributing Factors. Interpersonal Conflict\":\"Interpersonal_Conflict\",\n",
    "            \"Contributing Factors. Study\":\"Study\",\"Contributing Factors. Travel\":\"Travel\",\n",
    "            \"Contributing Factors. Other\":\"Other\",\"Contributing Factors. NONE\":\"None\",\n",
    "           \"Employment Type. Employment Type\":\"Employment_Status\",\n",
    "           \"LengthofServiceOverall. Overall Length of Service at Institute (in years)\":\"Service_Length_Institute(years)\",\n",
    "            \"Classification. Classification\":\"Position\",     \n",
    "           \"LengthofServiceCurrent. Length of Service at current workplace (in years)\":\"Service_Length_Role(years)\"},axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Institute', 'WorkArea', 'Cessation_Year', 'Separation_Type',\n",
       "       'CareerMove_Public', 'CareerMove_Private', 'Self_Employment',\n",
       "       'Ill_Health', 'Maternity/Family', 'Dissatisfaction',\n",
       "       'Job_Dissatisfaction', 'Interpersonal_Conflict', 'Study', 'Travel',\n",
       "       'Other', 'None', 'Gender', 'Age', 'Employment_Status', 'Position',\n",
       "       'Service_Length_Institute(years)', 'Service_Length_Role(years)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tafe.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dete=dete.rename({\"SeparationType\":\"Separation_Type\",\"Employment Status\":\"Employment_Status\",\n",
    "                \"Career move to public sector\":\"CareerMove_Public\",\n",
    "                \"Career move to private sector\":\"CareerMove_Private\",\n",
    "               \"Interpersonal conflicts\":\"Interpersonal_Conflict\",\n",
    "                \"Job dissatisfaction\":\"Job_Dissatisfaction\",\n",
    "                \"Ill Health\":\"Ill_Health\",\n",
    "                \"None of the above\":\"None\",\n",
    "               \"Dissatisfaction with the department\":\"Department_Dissatisfaction\"},\n",
    "                 axis=1\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Separation_Type', 'Cease Date', 'DETE Start Date',\n",
       "       'Role Start Date', 'Position', 'Classification', 'Region',\n",
       "       'Business Unit', 'Employment_Status', 'CareerMove_Public',\n",
       "       'CareerMove_Private', 'Interpersonal_Conflict', 'Job_Dissatisfaction',\n",
       "       'Department_Dissatisfaction', 'Physical work environment',\n",
       "       'Lack of recognition', 'Lack of job security', 'Work location',\n",
       "       'Employment conditions', 'Maternity/family', 'Relocation',\n",
       "       'Study/Travel', 'Ill_Health', 'Traumatic incident', 'Work life balance',\n",
       "       'Workload', 'None', 'Gender', 'Age', 'Aboriginal', 'Torres Strait',\n",
       "       'South Sea', 'Disability', 'NESB'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dete.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us also remove dete columns that are related to location like **ABOROGINAL, TORRES and STRAIT.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Separation_Type', 'Cease Date', 'DETE Start Date',\n",
       "       'Role Start Date', 'Position', 'Classification', 'Region',\n",
       "       'Business Unit', 'Employment_Status', 'CareerMove_Public',\n",
       "       'CareerMove_Private', 'Interpersonal_Conflict', 'Job_Dissatisfaction',\n",
       "       'Department_Dissatisfaction', 'Physical work environment',\n",
       "       'Lack of recognition', 'Lack of job security', 'Work location',\n",
       "       'Employment conditions', 'Maternity/family', 'Relocation',\n",
       "       'Study/Travel', 'Ill_Health', 'Traumatic incident', 'Work life balance',\n",
       "       'Workload', 'None', 'Gender', 'Age', 'Disability', 'NESB'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dete.drop(dete.columns[30:33],axis=1,inplace=True)\n",
    "dete.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['08/2012', '05/2012', '07/2012', '2012', '2013', '2014', '2010',\n",
       "       '09/2013', '07/2013', '08/2013', '06/2013', '04/2013', '05/2013',\n",
       "       '09/2010', '01/2014', '11/2012', '04/2014', '12/2013', '07/2014',\n",
       "       '10/2013', '11/2013', 'Not Stated', '09/2014', '07/2006',\n",
       "       '02/2014'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dete[\"Cease Date\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The date columns in dete data contain **NOT STATED** instead of NaN. Let us replace those with *NAN*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['08/2012', '05/2012', '07/2012', '2012', '2013', '2014', '2010',\n",
       "       '09/2013', '07/2013', '08/2013', '06/2013', '04/2013', '05/2013',\n",
       "       '09/2010', '01/2014', '11/2012', '04/2014', '12/2013', '07/2014',\n",
       "       '10/2013', '11/2013', nan, '09/2014', '07/2006', '02/2014'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dete[\"Cease Date\"].replace({\"Not Stated\":np.nan},inplace=True)\n",
    "dete[\"Role Start Date\"].replace({\"Not Stated\":np.nan},inplace=True)\n",
    "dete[\"DETE Start Date\"].replace({\"Not Stated\":np.nan},inplace=True)\n",
    "dete[\"Cease Date\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us only extract the year from the date columns and rename those columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dete[\"Cease Date\"]=dete[\"Cease Date\"].str.split(\"/\").str.get(-1)\n",
    "dete[\"Role Start Date\"]=dete[\"Role Start Date\"].str.split(\"/\").str.get(-1)\n",
    "dete[\"DETE Start Date\"]=dete[\"DETE Start Date\"].str.split(\"/\").str.get(-1)\n",
    "\n",
    "dete=dete.rename({\"Cease Date\":\"Cessation_Year\",\"Role Start Date\":\"Role_Start_Year\",\n",
    "            \"DETE Start Date\":\"DETE_Start_Year\"},axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now change these dates columns' type to float from str, so that we can subtract them to get length of the services."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dete[\"Cessation_Year\"]=dete[\"Cessation_Year\"].astype(float)\n",
    "dete[\"DETE_Start_Year\"]=dete[\"DETE_Start_Year\"].astype(float)\n",
    "dete[\"Role_Start_Year\"]=dete[\"Role_Start_Year\"].astype(float)\n",
    "\n",
    "#making new service length columns in dete data\n",
    "dete[\"Service_Length_Institute(years)\"]=dete[\"Cessation_Year\"]-dete[\"DETE_Start_Year\"]\n",
    "dete[\"Service_Length_Role(years)\"]=dete[\"Cessation_Year\"]-dete[\"Role_Start_Year\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now observe Age column in dete and tafe data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['26  30', nan, '41  45', '56 or older', '20 or younger', '46  50',\n",
       "       '36  40', '21  25', '51-55', '31  35'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tafe[\"Age\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us replace space with hyphen to denote the age brackets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['26-30', nan, '41-45', '56 or older', '20 or younger', '46-50',\n",
       "       '36-40', '21-25', '51-55', '31-35'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tafe[\"Age\"]=tafe[\"Age\"].str.replace(\"  \",\"-\")\n",
    "tafe[\"Age\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['56-60', '61 or older', '36-40', '41-45', '31-35', '46-50',\n",
       "       '51-55', '21-25', '26-30', nan, '20 or younger'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dete[\"Age\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us make them uniform and similar to age brackets in tafe data. This will help in making our comparisons between the two datasets relevant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['56 or older', '36-40', '41-45', '31-35', '46-50', '51-55',\n",
       "       '21-25', '26-30', nan, '20 or younger'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dete.loc[dete[\"Age\"]==\"61 or older\",\"Age\"]=\"56 or older\"\n",
    "dete.loc[dete[\"Age\"]==\"56-60\",\"Age\"]=\"56 or older\"\n",
    "dete[\"Age\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look at the Dissatisfaction and Job_Dissatisfaction in tafe data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, '-', 'Contributing Factors. Dissatisfaction '], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tafe[\"Dissatisfaction\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, '-', 'Job Dissatisfaction'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tafe[\"Job_Dissatisfaction\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us replace \"nan\" by np.nan values, \"-\" by False and \"Contributing Factors. Dissatisfaction\" by True. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "d={\"Contributing Factors. Dissatisfaction \":True,\"-\":False,\"nan\":np.nan}\n",
    "tafe[\"Dissatisfaction\"]=tafe[\"Dissatisfaction\"].map(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us replace \"nan\" by np.nan values, \"-\" by False and \"Job Dissatisfaction\" by True. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "d={\"Job Dissatisfaction\":True,\"-\":False,\"nan\":np.nan}\n",
    "tafe[\"Job_Dissatisfaction\"]=tafe[\"Job_Dissatisfaction\"].map(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The corresponding tafe columns are already clean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Job_Dissatisfaction</th>\n",
       "      <th>Dissatisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>702 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Job_Dissatisfaction Dissatisfaction\n",
       "0                   NaN             NaN\n",
       "1                 False           False\n",
       "2                 False           False\n",
       "3                 False           False\n",
       "4                 False           False\n",
       "..                  ...             ...\n",
       "697               False           False\n",
       "698               False           False\n",
       "699               False           False\n",
       "700                 NaN             NaN\n",
       "701               False           False\n",
       "\n",
       "[702 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tafe.loc[:,[\"Job_Dissatisfaction\",\"Dissatisfaction\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us create a column **Dissatisfied** in our tafe data, which will hold True if any of the columns, Job_Dissatisfaction or Dissatisfaction are True, otherwise False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Job_Dissatisfaction</th>\n",
       "      <th>Dissatisfaction</th>\n",
       "      <th>Dissatisfied</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>702 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Job_Dissatisfaction Dissatisfaction Dissatisfied\n",
       "0                   NaN             NaN          NaN\n",
       "1                 False           False        False\n",
       "2                 False           False        False\n",
       "3                 False           False        False\n",
       "4                 False           False        False\n",
       "..                  ...             ...          ...\n",
       "697               False           False        False\n",
       "698               False           False        False\n",
       "699               False           False        False\n",
       "700                 NaN             NaN          NaN\n",
       "701               False           False        False\n",
       "\n",
       "[702 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def diss(df):\n",
    "    if (pd.isna(df[\"Job_Dissatisfaction\"]))&(pd.isna(df[\"Dissatisfaction\"])):\n",
    "        return np.nan\n",
    "    elif (df[\"Job_Dissatisfaction\"]|df[\"Dissatisfaction\"]):\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "tafe[\"Dissatisfied\"]=tafe.apply(diss,axis=1)\n",
    "tafe.loc[:,[\"Job_Dissatisfaction\",\"Dissatisfaction\",\"Dissatisfied\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And similarly a column **Dissatisfied** in dete data based on Job_Dissatisfaction and Department_Dissatisfaction columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Job_Dissatisfaction</th>\n",
       "      <th>Department_Dissatisfaction</th>\n",
       "      <th>Dissatisfied</th>\n",
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       "      <th>3</th>\n",
       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>817</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>818</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>819</th>\n",
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       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>822 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Job_Dissatisfaction  Department_Dissatisfaction  Dissatisfied\n",
       "0                   True                       False          True\n",
       "1                  False                       False         False\n",
       "2                  False                       False         False\n",
       "3                  False                       False         False\n",
       "4                  False                       False         False\n",
       "..                   ...                         ...           ...\n",
       "817                False                       False         False\n",
       "818                False                       False         False\n",
       "819                False                       False         False\n",
       "820                False                       False         False\n",
       "821                False                       False         False\n",
       "\n",
       "[822 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def diss(df):\n",
    "    if (pd.isna(df[\"Job_Dissatisfaction\"]))&(pd.isna(df[\"Department_Dissatisfaction\"])):\n",
    "        return np.nan\n",
    "    elif (df[\"Job_Dissatisfaction\"]|df[\"Department_Dissatisfaction\"]):\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "dete[\"Dissatisfied\"]=dete.apply(diss,axis=1)\n",
    "dete.loc[:,[\"Job_Dissatisfaction\",\"Department_Dissatisfaction\",\"Dissatisfied\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now create groups for Service_Length_Institute and Service_Length_Role in dete data, similar to those in tafe data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dete[\"Service_Length_Institute_yr_grp\"]=np.nan\n",
    "dete.loc[(dete[\"Service_Length_Institute(years)\"]<1),\"Service_Length_Institute_yr_grp\"]=\"Less than 1 year\"       \n",
    "dete.loc[(dete[\"Service_Length_Institute(years)\"]>=1)&(dete[\"Service_Length_Institute(years)\"]<=2),\"Service_Length_Institute_yr_grp\"]=\"1-2\"\n",
    "dete.loc[(dete[\"Service_Length_Institute(years)\"]>=3)&(dete[\"Service_Length_Institute(years)\"]<=4),\"Service_Length_Institute_yr_grp\"]=\"3-4\"\n",
    "dete.loc[(dete[\"Service_Length_Institute(years)\"]>=5)&(dete[\"Service_Length_Institute(years)\"]<=6),\"Service_Length_Institute_yr_grp\"]=\"5-6\"\n",
    "dete.loc[(dete[\"Service_Length_Institute(years)\"]>=7)&(dete[\"Service_Length_Institute(years)\"]<=10),\"Service_Length_Institute_yr_grp\"]=\"7-10\"\n",
    "dete.loc[(dete[\"Service_Length_Institute(years)\"]>=11)&(dete[\"Service_Length_Institute(years)\"]<=20),\"Service_Length_Institute_yr_grp\"]=\"11-20\"\n",
    "dete.loc[(dete[\"Service_Length_Institute(years)\"]>20),\"Service_Length_Institute_yr_grp\"]=\"More than 20 years\"\n",
    "dete[\"Service_Length_Institute(years)\"]=dete[\"Service_Length_Institute_yr_grp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dete[\"Service_Length_Role_yr_grp\"]=np.nan\n",
    "dete.loc[(dete[\"Service_Length_Role(years)\"]<1),\"Service_Length_Role_yr_grp\"]=\"Less than 1 year\"       \n",
    "dete.loc[(dete[\"Service_Length_Role(years)\"]>=1)&(dete[\"Service_Length_Role(years)\"]<=2),\"Service_Length_Role_yr_grp\"]=\"1-2\"\n",
    "dete.loc[(dete[\"Service_Length_Role(years)\"]>=3)&(dete[\"Service_Length_Role(years)\"]<=4),\"Service_Length_Role_yr_grp\"]=\"3-4\"\n",
    "dete.loc[(dete[\"Service_Length_Role(years)\"]>=5)&(dete[\"Service_Length_Role(years)\"]<=6),\"Service_Length_Role_yr_grp\"]=\"5-6\"\n",
    "dete.loc[(dete[\"Service_Length_Role(years)\"]>=7)&(dete[\"Service_Length_Role(years)\"]<=10),\"Service_Length_Role_yr_grp\"]=\"7-10\"\n",
    "dete.loc[(dete[\"Service_Length_Role(years)\"]>=11)&(dete[\"Service_Length_Role(years)\"]<=20),\"Service_Length_Role_yr_grp\"]=\"11-20\"\n",
    "dete.loc[(dete[\"Service_Length_Role(years)\"]>20),\"Service_Length_Role_yr_grp\"]=\"More than 20 years\"       \n",
    "dete[\"Service_Length_Role(years)\"]=dete[\"Service_Length_Role_yr_grp\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some columns in tafe data have ambiguous values. Let us convert them into boolean values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Institute</th>\n",
       "      <th>WorkArea</th>\n",
       "      <th>Cessation_Year</th>\n",
       "      <th>Separation_Type</th>\n",
       "      <th>CareerMove_Public</th>\n",
       "      <th>CareerMove_Private</th>\n",
       "      <th>Self_Employment</th>\n",
       "      <th>Ill_Health</th>\n",
       "      <th>Maternity/Family</th>\n",
       "      <th>Dissatisfaction</th>\n",
       "      <th>Job_Dissatisfaction</th>\n",
       "      <th>Interpersonal_Conflict</th>\n",
       "      <th>Study</th>\n",
       "      <th>Travel</th>\n",
       "      <th>Other</th>\n",
       "      <th>None</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th>Position</th>\n",
       "      <th>Service_Length_Institute(years)</th>\n",
       "      <th>Service_Length_Role(years)</th>\n",
       "      <th>Dissatisfied</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.341330e+17</td>\n",
       "      <td>Southern Queensland Institute of TAFE</td>\n",
       "      <td>Non-Delivery (corporate)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Contract Expired</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>26-30</td>\n",
       "      <td>Temporary Full-time</td>\n",
       "      <td>Administration (AO)</td>\n",
       "      <td>1-2</td>\n",
       "      <td>1-2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.341337e+17</td>\n",
       "      <td>Mount Isa Institute of TAFE</td>\n",
       "      <td>Non-Delivery (corporate)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Retirement</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Travel</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.341388e+17</td>\n",
       "      <td>Mount Isa Institute of TAFE</td>\n",
       "      <td>Delivery (teaching)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Retirement</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.341399e+17</td>\n",
       "      <td>Mount Isa Institute of TAFE</td>\n",
       "      <td>Non-Delivery (corporate)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Resignation</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Travel</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.341466e+17</td>\n",
       "      <td>Southern Queensland Institute of TAFE</td>\n",
       "      <td>Delivery (teaching)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Resignation</td>\n",
       "      <td>-</td>\n",
       "      <td>Career Move - Private Sector</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Male</td>\n",
       "      <td>41-45</td>\n",
       "      <td>Permanent Full-time</td>\n",
       "      <td>Teacher (including LVT)</td>\n",
       "      <td>3-4</td>\n",
       "      <td>3-4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID                              Institute  \\\n",
       "0  6.341330e+17  Southern Queensland Institute of TAFE   \n",
       "1  6.341337e+17            Mount Isa Institute of TAFE   \n",
       "2  6.341388e+17            Mount Isa Institute of TAFE   \n",
       "3  6.341399e+17            Mount Isa Institute of TAFE   \n",
       "4  6.341466e+17  Southern Queensland Institute of TAFE   \n",
       "\n",
       "                   WorkArea  Cessation_Year   Separation_Type  \\\n",
       "0  Non-Delivery (corporate)          2010.0  Contract Expired   \n",
       "1  Non-Delivery (corporate)          2010.0        Retirement   \n",
       "2       Delivery (teaching)          2010.0        Retirement   \n",
       "3  Non-Delivery (corporate)          2010.0       Resignation   \n",
       "4       Delivery (teaching)          2010.0       Resignation   \n",
       "\n",
       "  CareerMove_Public            CareerMove_Private Self_Employment Ill_Health  \\\n",
       "0               NaN                           NaN             NaN        NaN   \n",
       "1                 -                             -               -          -   \n",
       "2                 -                             -               -          -   \n",
       "3                 -                             -               -          -   \n",
       "4                 -  Career Move - Private Sector               -          -   \n",
       "\n",
       "  Maternity/Family Dissatisfaction Job_Dissatisfaction Interpersonal_Conflict  \\\n",
       "0              NaN             NaN                 NaN                    NaN   \n",
       "1                -           False               False                      -   \n",
       "2                -           False               False                      -   \n",
       "3                -           False               False                      -   \n",
       "4                -           False               False                      -   \n",
       "\n",
       "  Study  Travel Other  None  Gender    Age    Employment_Status  \\\n",
       "0   NaN     NaN   NaN   NaN  Female  26-30  Temporary Full-time   \n",
       "1     -  Travel     -     -     NaN    NaN                  NaN   \n",
       "2     -       -     -  NONE     NaN    NaN                  NaN   \n",
       "3     -  Travel     -     -     NaN    NaN                  NaN   \n",
       "4     -       -     -     -    Male  41-45  Permanent Full-time   \n",
       "\n",
       "                  Position Service_Length_Institute(years)  \\\n",
       "0      Administration (AO)                             1-2   \n",
       "1                      NaN                             NaN   \n",
       "2                      NaN                             NaN   \n",
       "3                      NaN                             NaN   \n",
       "4  Teacher (including LVT)                             3-4   \n",
       "\n",
       "  Service_Length_Role(years) Dissatisfied  \n",
       "0                        1-2          NaN  \n",
       "1                        NaN        False  \n",
       "2                        NaN        False  \n",
       "3                        NaN        False  \n",
       "4                        3-4        False  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tafe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us convert them to boolean values so that it makes our analysis easier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Institute</th>\n",
       "      <th>WorkArea</th>\n",
       "      <th>Cessation_Year</th>\n",
       "      <th>Separation_Type</th>\n",
       "      <th>CareerMove_Public</th>\n",
       "      <th>CareerMove_Private</th>\n",
       "      <th>Self_Employment</th>\n",
       "      <th>Ill_Health</th>\n",
       "      <th>Maternity/Family</th>\n",
       "      <th>Dissatisfaction</th>\n",
       "      <th>Job_Dissatisfaction</th>\n",
       "      <th>Interpersonal_Conflict</th>\n",
       "      <th>Study</th>\n",
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       "      <th>Other</th>\n",
       "      <th>None</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th>Position</th>\n",
       "      <th>Service_Length_Institute(years)</th>\n",
       "      <th>Service_Length_Role(years)</th>\n",
       "      <th>Dissatisfied</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.341330e+17</td>\n",
       "      <td>Southern Queensland Institute of TAFE</td>\n",
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       "      <td>2010.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>26-30</td>\n",
       "      <td>Temporary Full-time</td>\n",
       "      <td>Administration (AO)</td>\n",
       "      <td>1-2</td>\n",
       "      <td>1-2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.341337e+17</td>\n",
       "      <td>Mount Isa Institute of TAFE</td>\n",
       "      <td>Non-Delivery (corporate)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Retirement</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
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       "      <td>-</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.341388e+17</td>\n",
       "      <td>Mount Isa Institute of TAFE</td>\n",
       "      <td>Delivery (teaching)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Retirement</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.341399e+17</td>\n",
       "      <td>Mount Isa Institute of TAFE</td>\n",
       "      <td>Non-Delivery (corporate)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Resignation</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.341466e+17</td>\n",
       "      <td>Southern Queensland Institute of TAFE</td>\n",
       "      <td>Delivery (teaching)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Resignation</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Male</td>\n",
       "      <td>41-45</td>\n",
       "      <td>Permanent Full-time</td>\n",
       "      <td>Teacher (including LVT)</td>\n",
       "      <td>3-4</td>\n",
       "      <td>3-4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID                              Institute  \\\n",
       "0  6.341330e+17  Southern Queensland Institute of TAFE   \n",
       "1  6.341337e+17            Mount Isa Institute of TAFE   \n",
       "2  6.341388e+17            Mount Isa Institute of TAFE   \n",
       "3  6.341399e+17            Mount Isa Institute of TAFE   \n",
       "4  6.341466e+17  Southern Queensland Institute of TAFE   \n",
       "\n",
       "                   WorkArea  Cessation_Year   Separation_Type  \\\n",
       "0  Non-Delivery (corporate)          2010.0  Contract Expired   \n",
       "1  Non-Delivery (corporate)          2010.0        Retirement   \n",
       "2       Delivery (teaching)          2010.0        Retirement   \n",
       "3  Non-Delivery (corporate)          2010.0       Resignation   \n",
       "4       Delivery (teaching)          2010.0       Resignation   \n",
       "\n",
       "  CareerMove_Public CareerMove_Private Self_Employment Ill_Health  \\\n",
       "0               NaN                NaN             NaN        NaN   \n",
       "1             False              False           False          -   \n",
       "2             False              False           False          -   \n",
       "3             False              False           False          -   \n",
       "4             False               True           False          -   \n",
       "\n",
       "  Maternity/Family Dissatisfaction Job_Dissatisfaction Interpersonal_Conflict  \\\n",
       "0              NaN             NaN                 NaN                    NaN   \n",
       "1            False           False               False                  False   \n",
       "2            False           False               False                  False   \n",
       "3            False           False               False                  False   \n",
       "4            False           False               False                  False   \n",
       "\n",
       "   Study Travel Other  None  Gender    Age    Employment_Status  \\\n",
       "0    NaN    NaN   NaN   NaN  Female  26-30  Temporary Full-time   \n",
       "1  False   True     -     -     NaN    NaN                  NaN   \n",
       "2  False  False     -  NONE     NaN    NaN                  NaN   \n",
       "3  False   True     -     -     NaN    NaN                  NaN   \n",
       "4  False  False     -     -    Male  41-45  Permanent Full-time   \n",
       "\n",
       "                  Position Service_Length_Institute(years)  \\\n",
       "0      Administration (AO)                             1-2   \n",
       "1                      NaN                             NaN   \n",
       "2                      NaN                             NaN   \n",
       "3                      NaN                             NaN   \n",
       "4  Teacher (including LVT)                             3-4   \n",
       "\n",
       "  Service_Length_Role(years) Dissatisfied  \n",
       "0                        1-2          NaN  \n",
       "1                        NaN        False  \n",
       "2                        NaN        False  \n",
       "3                        NaN        False  \n",
       "4                        3-4        False  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tafe.loc[tafe[\"CareerMove_Private\"]==\"-\",\"CareerMove_Private\"]=False\n",
    "tafe.loc[tafe[\"CareerMove_Private\"]==\"Career Move - Private Sector\",\"CareerMove_Private\"]=True\n",
    "tafe.loc[tafe[\"CareerMove_Public\"]==\"-\",\"CareerMove_Public\"]=False\n",
    "tafe.loc[tafe[\"CareerMove_Public\"]==\"Career Move - Public Sector\",\"CareerMove_Public\"]=True\n",
    "tafe.loc[tafe[\"Maternity/Family\"]==\"-\",\"Maternity/Family\"]=False\n",
    "tafe.loc[tafe[\"Maternity/Family\"]==\"Maternity/Family\",\"Maternity/Family\"]=True\n",
    "tafe.loc[tafe[\"Interpersonal_Conflict\"]==\"-\",\"Interpersonal_Conflict\"]=False\n",
    "tafe.loc[tafe[\"Interpersonal_Conflict\"]==\"Interpersonal Conflict\",\"Interpersonal_Conflict\"]=True\n",
    "tafe.loc[tafe[\"Study\"]==\"-\",\"Study\"]=False\n",
    "tafe.loc[tafe[\"Study\"]==\"Study\",\"Study\"]=True\n",
    "tafe.loc[tafe[\"Travel\"]==\"-\",\"Travel\"]=False\n",
    "tafe.loc[tafe[\"Travel\"]==\"Travel\",\"Travel\"]=True\n",
    "tafe.loc[tafe[\"Self_Employment\"]==\"-\",\"Self_Employment\"]=False\n",
    "tafe.loc[tafe[\"Self_Employment\"]==\"Self Employment\",\"Self_Employment\"]=True\n",
    "tafe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us create a column Study/Travel in tafe data which holds True if any of Study or Travel column is True. This column is created to match Study/Travel column in dete data for easier comparisns further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Institute</th>\n",
       "      <th>WorkArea</th>\n",
       "      <th>Cessation_Year</th>\n",
       "      <th>Separation_Type</th>\n",
       "      <th>CareerMove_Public</th>\n",
       "      <th>CareerMove_Private</th>\n",
       "      <th>Self_Employment</th>\n",
       "      <th>Ill_Health</th>\n",
       "      <th>Maternity/Family</th>\n",
       "      <th>Dissatisfaction</th>\n",
       "      <th>Job_Dissatisfaction</th>\n",
       "      <th>Interpersonal_Conflict</th>\n",
       "      <th>Study</th>\n",
       "      <th>Travel</th>\n",
       "      <th>Other</th>\n",
       "      <th>None</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th>Position</th>\n",
       "      <th>Service_Length_Institute(years)</th>\n",
       "      <th>Service_Length_Role(years)</th>\n",
       "      <th>Dissatisfied</th>\n",
       "      <th>Study/Travel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.341330e+17</td>\n",
       "      <td>Southern Queensland Institute of TAFE</td>\n",
       "      <td>Non-Delivery (corporate)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Contract Expired</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>26-30</td>\n",
       "      <td>Temporary Full-time</td>\n",
       "      <td>Administration (AO)</td>\n",
       "      <td>1-2</td>\n",
       "      <td>1-2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.341337e+17</td>\n",
       "      <td>Mount Isa Institute of TAFE</td>\n",
       "      <td>Non-Delivery (corporate)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Retirement</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.341388e+17</td>\n",
       "      <td>Mount Isa Institute of TAFE</td>\n",
       "      <td>Delivery (teaching)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Retirement</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.341399e+17</td>\n",
       "      <td>Mount Isa Institute of TAFE</td>\n",
       "      <td>Non-Delivery (corporate)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Resignation</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.341466e+17</td>\n",
       "      <td>Southern Queensland Institute of TAFE</td>\n",
       "      <td>Delivery (teaching)</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Resignation</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Male</td>\n",
       "      <td>41-45</td>\n",
       "      <td>Permanent Full-time</td>\n",
       "      <td>Teacher (including LVT)</td>\n",
       "      <td>3-4</td>\n",
       "      <td>3-4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID                              Institute  \\\n",
       "0  6.341330e+17  Southern Queensland Institute of TAFE   \n",
       "1  6.341337e+17            Mount Isa Institute of TAFE   \n",
       "2  6.341388e+17            Mount Isa Institute of TAFE   \n",
       "3  6.341399e+17            Mount Isa Institute of TAFE   \n",
       "4  6.341466e+17  Southern Queensland Institute of TAFE   \n",
       "\n",
       "                   WorkArea  Cessation_Year   Separation_Type  \\\n",
       "0  Non-Delivery (corporate)          2010.0  Contract Expired   \n",
       "1  Non-Delivery (corporate)          2010.0        Retirement   \n",
       "2       Delivery (teaching)          2010.0        Retirement   \n",
       "3  Non-Delivery (corporate)          2010.0       Resignation   \n",
       "4       Delivery (teaching)          2010.0       Resignation   \n",
       "\n",
       "  CareerMove_Public CareerMove_Private Self_Employment Ill_Health  \\\n",
       "0               NaN                NaN             NaN        NaN   \n",
       "1             False              False           False          -   \n",
       "2             False              False           False          -   \n",
       "3             False              False           False          -   \n",
       "4             False               True           False          -   \n",
       "\n",
       "  Maternity/Family Dissatisfaction Job_Dissatisfaction Interpersonal_Conflict  \\\n",
       "0              NaN             NaN                 NaN                    NaN   \n",
       "1            False           False               False                  False   \n",
       "2            False           False               False                  False   \n",
       "3            False           False               False                  False   \n",
       "4            False           False               False                  False   \n",
       "\n",
       "   Study Travel Other  None  Gender    Age    Employment_Status  \\\n",
       "0    NaN    NaN   NaN   NaN  Female  26-30  Temporary Full-time   \n",
       "1  False   True     -     -     NaN    NaN                  NaN   \n",
       "2  False  False     -  NONE     NaN    NaN                  NaN   \n",
       "3  False   True     -     -     NaN    NaN                  NaN   \n",
       "4  False  False     -     -    Male  41-45  Permanent Full-time   \n",
       "\n",
       "                  Position Service_Length_Institute(years)  \\\n",
       "0      Administration (AO)                             1-2   \n",
       "1                      NaN                             NaN   \n",
       "2                      NaN                             NaN   \n",
       "3                      NaN                             NaN   \n",
       "4  Teacher (including LVT)                             3-4   \n",
       "\n",
       "  Service_Length_Role(years) Dissatisfied Study/Travel  \n",
       "0                        1-2          NaN          NaN  \n",
       "1                        NaN        False         True  \n",
       "2                        NaN        False        False  \n",
       "3                        NaN        False         True  \n",
       "4                        3-4        False        False  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def st(df):\n",
    "    if (pd.isna(df[\"Study\"]))&(pd.isna(df[\"Travel\"])):\n",
    "        return np.nan\n",
    "    elif (df[\"Study\"]|df[\"Travel\"]):\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "tafe[\"Study/Travel\"]=tafe.apply(st,axis=1)\n",
    "tafe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us extract only the resignations data because we need to answer the two questions regarding resignations, we will just focus on them for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(311, 37)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dete resignations\n",
    "dete_resignations=dete[(dete[\"Separation_Type\"]==\"Resignation-Other reasons\")|\n",
    "                      (dete[\"Separation_Type\"]==\"Resignation-Other employer\")|\n",
    "                      (dete[\"Separation_Type\"]==\"Resignation-Move overseas/interstate\")]\n",
    "tafe_resignations=tafe[tafe[\"Separation_Type\"]==\"Resignation\"]\n",
    "dete_resignations.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(340, 25)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tafe resignations\n",
    "tafe_resignations.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will add one more column **institute** to both of the data frames which stores the institute name for all of the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dete_resignations[\"institute\"]=\"DETE\"\n",
    "tafe_resignations[\"institute\"]=\"TAFE\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we need to combine both the dataframes for our analysis, we will concat them and store them in **combined** dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Separation_Type</th>\n",
       "      <th>Cessation_Year</th>\n",
       "      <th>DETE_Start_Year</th>\n",
       "      <th>Role_Start_Year</th>\n",
       "      <th>Position</th>\n",
       "      <th>Classification</th>\n",
       "      <th>Region</th>\n",
       "      <th>Business Unit</th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th>CareerMove_Public</th>\n",
       "      <th>CareerMove_Private</th>\n",
       "      <th>Interpersonal_Conflict</th>\n",
       "      <th>Job_Dissatisfaction</th>\n",
       "      <th>Department_Dissatisfaction</th>\n",
       "      <th>Physical work environment</th>\n",
       "      <th>Lack of recognition</th>\n",
       "      <th>Lack of job security</th>\n",
       "      <th>Work location</th>\n",
       "      <th>Employment conditions</th>\n",
       "      <th>Maternity/family</th>\n",
       "      <th>Relocation</th>\n",
       "      <th>Study/Travel</th>\n",
       "      <th>Ill_Health</th>\n",
       "      <th>Traumatic incident</th>\n",
       "      <th>Work life balance</th>\n",
       "      <th>Workload</th>\n",
       "      <th>None</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Disability</th>\n",
       "      <th>NESB</th>\n",
       "      <th>Service_Length_Institute(years)</th>\n",
       "      <th>Service_Length_Role(years)</th>\n",
       "      <th>Dissatisfied</th>\n",
       "      <th>Service_Length_Institute_yr_grp</th>\n",
       "      <th>Service_Length_Role_yr_grp</th>\n",
       "      <th>institute</th>\n",
       "      <th>Institute</th>\n",
       "      <th>WorkArea</th>\n",
       "      <th>Self_Employment</th>\n",
       "      <th>Maternity/Family</th>\n",
       "      <th>Dissatisfaction</th>\n",
       "      <th>Study</th>\n",
       "      <th>Travel</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>6.350660e+17</td>\n",
       "      <td>Resignation</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Operational (OO)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Temporary Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>Male</td>\n",
       "      <td>21-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5-6</td>\n",
       "      <td>5-6</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TAFE</td>\n",
       "      <td>Southern Queensland Institute of TAFE</td>\n",
       "      <td>Non-Delivery (corporate)</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>6.350668e+17</td>\n",
       "      <td>Resignation</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Teacher (including LVT)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Temporary Full-time</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>Male</td>\n",
       "      <td>51-55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1-2</td>\n",
       "      <td>1-2</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TAFE</td>\n",
       "      <td>Barrier Reef Institute of TAFE</td>\n",
       "      <td>Delivery (teaching)</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>6.350677e+17</td>\n",
       "      <td>Resignation</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TAFE</td>\n",
       "      <td>Southern Queensland Institute of TAFE</td>\n",
       "      <td>Non-Delivery (corporate)</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>6.350704e+17</td>\n",
       "      <td>Resignation</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Teacher (including LVT)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Permanent Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>Female</td>\n",
       "      <td>51-55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5-6</td>\n",
       "      <td>1-2</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TAFE</td>\n",
       "      <td>Tropical North Institute of TAFE</td>\n",
       "      <td>Delivery (teaching)</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>6.350730e+17</td>\n",
       "      <td>Resignation</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Administration (AO)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Contract/casual</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>Female</td>\n",
       "      <td>26-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3-4</td>\n",
       "      <td>1-2</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TAFE</td>\n",
       "      <td>Tropical North Institute of TAFE</td>\n",
       "      <td>Non-Delivery (corporate)</td>\n",
       "      <td>Career Move - Self-employment</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ID Separation_Type  Cessation_Year  DETE_Start_Year  \\\n",
       "696  6.350660e+17     Resignation          2013.0              NaN   \n",
       "697  6.350668e+17     Resignation          2013.0              NaN   \n",
       "698  6.350677e+17     Resignation          2013.0              NaN   \n",
       "699  6.350704e+17     Resignation          2013.0              NaN   \n",
       "701  6.350730e+17     Resignation          2013.0              NaN   \n",
       "\n",
       "     Role_Start_Year                 Position Classification Region  \\\n",
       "696              NaN         Operational (OO)            NaN    NaN   \n",
       "697              NaN  Teacher (including LVT)            NaN    NaN   \n",
       "698              NaN                      NaN            NaN    NaN   \n",
       "699              NaN  Teacher (including LVT)            NaN    NaN   \n",
       "701              NaN      Administration (AO)            NaN    NaN   \n",
       "\n",
       "    Business Unit    Employment_Status CareerMove_Public CareerMove_Private  \\\n",
       "696           NaN  Temporary Full-time             False               True   \n",
       "697           NaN  Temporary Full-time              True              False   \n",
       "698           NaN                  NaN              True              False   \n",
       "699           NaN  Permanent Full-time             False              False   \n",
       "701           NaN      Contract/casual             False              False   \n",
       "\n",
       "    Interpersonal_Conflict Job_Dissatisfaction Department_Dissatisfaction  \\\n",
       "696                  False               False                        NaN   \n",
       "697                  False               False                        NaN   \n",
       "698                  False               False                        NaN   \n",
       "699                  False               False                        NaN   \n",
       "701                  False               False                        NaN   \n",
       "\n",
       "    Physical work environment Lack of recognition Lack of job security  \\\n",
       "696                       NaN                 NaN                  NaN   \n",
       "697                       NaN                 NaN                  NaN   \n",
       "698                       NaN                 NaN                  NaN   \n",
       "699                       NaN                 NaN                  NaN   \n",
       "701                       NaN                 NaN                  NaN   \n",
       "\n",
       "    Work location Employment conditions Maternity/family Relocation  \\\n",
       "696           NaN                   NaN              NaN        NaN   \n",
       "697           NaN                   NaN              NaN        NaN   \n",
       "698           NaN                   NaN              NaN        NaN   \n",
       "699           NaN                   NaN              NaN        NaN   \n",
       "701           NaN                   NaN              NaN        NaN   \n",
       "\n",
       "    Study/Travel Ill_Health Traumatic incident Work life balance Workload  \\\n",
       "696        False          -                NaN               NaN      NaN   \n",
       "697        False          -                NaN               NaN      NaN   \n",
       "698        False          -                NaN               NaN      NaN   \n",
       "699        False          -                NaN               NaN      NaN   \n",
       "701         True          -                NaN               NaN      NaN   \n",
       "\n",
       "    None  Gender    Age Disability NESB Service_Length_Institute(years)  \\\n",
       "696    -    Male  21-25        NaN  NaN                             5-6   \n",
       "697    -    Male  51-55        NaN  NaN                             1-2   \n",
       "698    -     NaN    NaN        NaN  NaN                             NaN   \n",
       "699    -  Female  51-55        NaN  NaN                             5-6   \n",
       "701    -  Female  26-30        NaN  NaN                             3-4   \n",
       "\n",
       "    Service_Length_Role(years) Dissatisfied Service_Length_Institute_yr_grp  \\\n",
       "696                        5-6        False                             NaN   \n",
       "697                        1-2        False                             NaN   \n",
       "698                        NaN        False                             NaN   \n",
       "699                        1-2        False                             NaN   \n",
       "701                        1-2        False                             NaN   \n",
       "\n",
       "    Service_Length_Role_yr_grp institute  \\\n",
       "696                        NaN      TAFE   \n",
       "697                        NaN      TAFE   \n",
       "698                        NaN      TAFE   \n",
       "699                        NaN      TAFE   \n",
       "701                        NaN      TAFE   \n",
       "\n",
       "                                 Institute                  WorkArea  \\\n",
       "696  Southern Queensland Institute of TAFE  Non-Delivery (corporate)   \n",
       "697         Barrier Reef Institute of TAFE       Delivery (teaching)   \n",
       "698  Southern Queensland Institute of TAFE  Non-Delivery (corporate)   \n",
       "699       Tropical North Institute of TAFE       Delivery (teaching)   \n",
       "701       Tropical North Institute of TAFE  Non-Delivery (corporate)   \n",
       "\n",
       "                   Self_Employment Maternity/Family Dissatisfaction  Study  \\\n",
       "696                          False            False           False  False   \n",
       "697                          False            False           False  False   \n",
       "698                          False            False           False  False   \n",
       "699                          False            False           False  False   \n",
       "701  Career Move - Self-employment            False           False  False   \n",
       "\n",
       "    Travel  Other  \n",
       "696  False      -  \n",
       "697  False      -  \n",
       "698  False      -  \n",
       "699  False  Other  \n",
       "701   True      -  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined=pd.concat([dete_resignations,tafe_resignations])\n",
    "combined.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now exit (phew!) from Data Cleaning and move on to Analysis of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f64543203d0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "combined.groupby([\"Age\",\"Dissatisfied\"]).size().unstack(level=1).plot(kind=\"bar\",stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the combined resignations data, we see that dissatisfaction is most observed in the **age group 51-55** and the least is observed in **20 or younger** group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a=combined.groupby([\"Age\",\"Dissatisfied\",\"institute\"]).size().unstack(level=1).reset_index()\n",
    "for i in set(a[\"institute\"]):\n",
    "    a[a[\"institute\"]==i].plot(x=\"Age\",kind=\"bar\",stacked=True)\n",
    "    plt.title(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we look at the institutes individually, we can easily observe that for Dete resignations, **56 or older** is most dissatisfied and for Tafe resignations, **41-45,46-50 and 51-55** groups are most dissatisfied. \n",
    "\n",
    "Now let us observe the service length of these employees in their respective insititutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Service Length Institute (in years)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "a=combined.groupby([\"Service_Length_Institute(years)\",\"Dissatisfied\",\"institute\"]).size().unstack(level=1).reset_index()\n",
    "for i in set(a[\"institute\"]):\n",
    "    ax=a[a[\"institute\"]==i].plot(x=\"Service_Length_Institute(years)\",kind=\"bar\",stacked=True)\n",
    "    plt.title(i)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe, that for dete resignations data, more employees that have been with the institute for longer are dissatisfied.\n",
    "In tafe resignations data, more employees that are relatively new in the institute are dissatisfied."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined[\"Gender\"].value_counts().plot(kind=\"pie\",autopct=\"%1.1f%%\")\n",
    "plt.ylabel(\"\")\n",
    "plt.title(\"Total Resignations by Gender\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, a higher percentage (71.6%) of females resigned from the institutes as compared to males (28.4%).\n",
    "\n",
    "Let us see how many of them are dissatisfied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6453ee0e80>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.groupby([\"Gender\",\"Dissatisfied\"]).size().unstack(level=1).plot.bar(stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Larger portion of females who resigned are dissatisfied as compared to males.\n",
    "\n",
    "Now we will look at other possible reasons for resignations and whether those reasons are significant to different genders or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a=dete_resignations.groupby(\"Gender\")[[\"Lack of recognition\",\"Maternity/family\",\"CareerMove_Private\",\"CareerMove_Public\",\"Interpersonal_Conflict\",\"Lack of job security\",\"Work life balance\",\"Relocation\",\"Traumatic incident\",\"Employment conditions\",\"Workload\",\"Study/Travel\"]].sum()\n",
    "plt.figure(figsize=(8,8))\n",
    "x=np.arange(a.shape[1])\n",
    "plt.bar(x - 0.2,a.loc[\"Male\"] , 0.4, label = 'Male')\n",
    "plt.bar(x + 0.2, a.loc[\"Female\"], 0.4, label = 'Female')\n",
    "plt.legend()\n",
    "plt.xticks(x,a.columns,rotation=30,horizontalalignment='right')\n",
    "plt.title(\"Dete Resignations and Different Factors\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For every category, we observe that the number of females choosing the reason for resignation are higher than their male counterparts.\n",
    "But some factors are much more frequent for females like **Work Life Balance, Relocation, Maternity/Family and CareerMove_Private.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a=tafe_resignations.groupby(\"Gender\")[[\"CareerMove_Private\",\"CareerMove_Public\",\"Interpersonal_Conflict\" ,'Maternity/Family',\"Self_Employment\",\"Study/Travel\"]].sum()\n",
    "x=np.arange(a.shape[1])\n",
    "plt.bar(x - 0.2,a.loc[\"Male\"] , 0.4, label = 'Male')\n",
    "plt.bar(x + 0.2, a.loc[\"Female\"], 0.4, label = 'Female')\n",
    "plt.legend()\n",
    "plt.xticks(x,a.columns,rotation=30,horizontalalignment='right')\n",
    "plt.title(\"Tafe Resignations and Different Factors\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike dete data, the number of females are choosing a particular factor in the survey are not substantially higher than number of males.  The only significant difference is observed for the reason ,**CareerMove_Public**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3)  Other Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tafe_resignations[\"Employment_Status\"].value_counts().plot(kind=\"pie\",autopct='%1.1f%%')\n",
    "plt.ylabel(\"\")\n",
    "plt.title(\"Tafe Resignations by Employment Status\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dete_resignations[\"Employment_Status\"].value_counts().plot(kind=\"pie\",autopct='%1.1f%%')\n",
    "plt.ylabel(\"\")\n",
    "plt.title(\"Dete Resignations by Employment Status\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From Tafe institute, mostly, **Temporary Full-time** workers have resigned and from Dete institute, mostly, **Permanent Full-time** workers have resigned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dete_resignations[\"Cessation_Year\"].value_counts().plot(kind=\"pie\",autopct=\"%1.1f%%\")\n",
    "plt.ylabel(\"\")\n",
    "plt.title(\"Dete Resignations by Year\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tafe_resignations[\"Cessation_Year\"].value_counts().plot(kind=\"pie\",autopct=\"%1.1f%%\")\n",
    "plt.ylabel(\"\")\n",
    "plt.title(\"Tafe Resignations by Year\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From **Dete institute, most resignations were observed in the year 2013** and from **Tafe institute, most resignations were observed in the year 2011.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. For the combined resignations data, we see that dissatisfaction is most observed in the **age group 51-55** and the least is observed in **20 or younger** group. This means that older employees are much more dissatisfied with the institute or job they are working in, than the younger employees.\n",
    "\n",
    "2. We can observe, more dissatisfied employees that have resigned from DETE have been with the institute for **longer**.\n",
    "More dissatisfied employees that have resigned from TAFE are **relatively new** in the institute.\n",
    "\n",
    "3. Overall, a higher percentage **(71.6%)** of females resigned from the institutes as compared to males **(28.4%)**. Larger portion of females who resigned are dissatisfied as compared to males.\n",
    "\n",
    "4. From Tafe institute, mostly, **Temporary Full-time** workers have resigned and from Dete institute, mostly, **Permanent Full-time** workers have resigned.\n",
    "\n",
    "5. From **Dete institute, most resignations were observed in the year 2013** and from **Tafe institute, most resignations were observed in the year 2011.**\n",
    "\n",
    "6. In DETE survey, we observe that the number of females choosing the reason for resignation are higher than their male counterparts.\n",
    "But some factors are much more frequent for females like **Work Life Balance, Relocation, Maternity/Family and CareerMove_Private.**\n",
    "In TAFE institute, unlike dete, the number of females are choosing a particular factor in the survey are not substantially higher than number of males.  The only significant difference is observed for the reason ,**CareerMove_Public**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Steps that can be further taken by the Institutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Much more females resigned than males overall, due to some kind of dissatisfaction. Also, DETE institute needs to look into the provisions that they are providing to the employees, like ensuring work life balance and giving adequate maternity leaves/salary.\n",
    "2. Dissatisfaction is higher in older employees as compared to younger ones. The institutes should make the older employees feel more valued. More flexible schedules and additional training can be offered to them in order to retain them in the company.\n",
    "3. DETE institute's experienced employees are much more dissastisfied. The institute can look if their higher management is working properly, if their salaries are competent or if their are being promoted after years of working with the institute.\n",
    "4. TAFE institute's newer employees are much more dissatisfied. One can check if their onboarding process was smooth or if they are being given proper and appropriate training.\n",
    "5. More workers resigned in 2011 from TAFE and in 2013 from DETE. The institutes should observe if this happened due to any manjor changes in policies of the company."
   ]
  }
 ],
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