Clean and Analyze Employee Exit Surveys.ipynb

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    "# Clean and Analyze Employee Exit Surveys\n",
    "## Introduction\n",
    "- In this project, we'll clean and analyze exit surveys from employees of the [Department of Education, Training and Employment (DETE)](https://www.qld.gov.au/education) and the [Technical and Further Education (TAFE)](https://tafeqld.edu.au/) of Queensland in Australia.\n",
    "- Our goal is to identify the dissatisfaction of the employees that exit, the ones who worked for a short period and the ones that stayed longer. \n",
    "- Identifying this situation is very important for a workspace, [low job satisfaction can cause a lot of problems in the workspace](https://smallbusiness.chron.com/effects-low-job-satisfaction-10721.html), like employees with low productivity, more turnover cases and can affect customer experience. Besides that, with the exit of these employees, the replacement is more costful than maintaining them.\n",
    "- This project is directed to department heads and human resources."
   ]
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    "## Introducing the Data\n",
    "- We'll use the datasets published in the australian government site of data. We can find the DETE dataset [here](https://data.gov.au/dataset/ds-qld-fe96ff30-d157-4a81-851d-215f2a0fe26d/details?q=exit%20survey) and the TAFE dataset [here](https://data.gov.au/error?errorCode=404&recordType=Dataset&recordId=%22ds-qld-89970a3b-182b-41ea-aea2-6f9f17b5907e%22). "
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    "# importing the libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib.style as style\n",
    "import seaborn as sns"
   ]
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    "# importing the datasets\n",
    "pd.set_option('display.max_rows', 1000)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "\n",
    "dete = pd.read_csv('dete_survey.csv')\n",
    "tafe = pd.read_csv('tafe_survey.csv')"
   ]
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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",
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       "      <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",
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       "      <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",
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       "      <td>Permanent Full-time</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <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",
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       "      <td>N</td>\n",
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       "      <td>N</td>\n",
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       "      <td>Voluntary Early Retirement (VER)</td>\n",
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       "      <td>Public Servant</td>\n",
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       "      <td>Permanent Full-time</td>\n",
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       "      <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",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <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",
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       "      <th>3</th>\n",
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       "      <td>Resignation-Other reasons</td>\n",
       "      <td>05/2012</td>\n",
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       "      <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",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "      <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",
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       "    <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",
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      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Knowing the dataset\n",
    "dete.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae21b1db-7897-4e23-a3c7-dd64d6722af7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 822 entries, 0 to 821\n",
      "Data columns (total 56 columns):\n",
      " #   Column                               Non-Null Count  Dtype \n",
      "---  ------                               --------------  ----- \n",
      " 0   ID                                   822 non-null    int64 \n",
      " 1   SeparationType                       822 non-null    object\n",
      " 2   Cease Date                           822 non-null    object\n",
      " 3   DETE Start Date                      822 non-null    object\n",
      " 4   Role Start Date                      822 non-null    object\n",
      " 5   Position                             817 non-null    object\n",
      " 6   Classification                       455 non-null    object\n",
      " 7   Region                               822 non-null    object\n",
      " 8   Business Unit                        126 non-null    object\n",
      " 9   Employment Status                    817 non-null    object\n",
      " 10  Career move to public sector         822 non-null    bool  \n",
      " 11  Career move to private sector        822 non-null    bool  \n",
      " 12  Interpersonal conflicts              822 non-null    bool  \n",
      " 13  Job dissatisfaction                  822 non-null    bool  \n",
      " 14  Dissatisfaction with the department  822 non-null    bool  \n",
      " 15  Physical work environment            822 non-null    bool  \n",
      " 16  Lack of recognition                  822 non-null    bool  \n",
      " 17  Lack of job security                 822 non-null    bool  \n",
      " 18  Work location                        822 non-null    bool  \n",
      " 19  Employment conditions                822 non-null    bool  \n",
      " 20  Maternity/family                     822 non-null    bool  \n",
      " 21  Relocation                           822 non-null    bool  \n",
      " 22  Study/Travel                         822 non-null    bool  \n",
      " 23  Ill Health                           822 non-null    bool  \n",
      " 24  Traumatic incident                   822 non-null    bool  \n",
      " 25  Work life balance                    822 non-null    bool  \n",
      " 26  Workload                             822 non-null    bool  \n",
      " 27  None of the above                    822 non-null    bool  \n",
      " 28  Professional Development             808 non-null    object\n",
      " 29  Opportunities for promotion          735 non-null    object\n",
      " 30  Staff morale                         816 non-null    object\n",
      " 31  Workplace issue                      788 non-null    object\n",
      " 32  Physical environment                 817 non-null    object\n",
      " 33  Worklife balance                     815 non-null    object\n",
      " 34  Stress and pressure support          810 non-null    object\n",
      " 35  Performance of supervisor            813 non-null    object\n",
      " 36  Peer support                         812 non-null    object\n",
      " 37  Initiative                           813 non-null    object\n",
      " 38  Skills                               811 non-null    object\n",
      " 39  Coach                                767 non-null    object\n",
      " 40  Career Aspirations                   746 non-null    object\n",
      " 41  Feedback                             792 non-null    object\n",
      " 42  Further PD                           768 non-null    object\n",
      " 43  Communication                        814 non-null    object\n",
      " 44  My say                               812 non-null    object\n",
      " 45  Information                          816 non-null    object\n",
      " 46  Kept informed                        813 non-null    object\n",
      " 47  Wellness programs                    766 non-null    object\n",
      " 48  Health & Safety                      793 non-null    object\n",
      " 49  Gender                               798 non-null    object\n",
      " 50  Age                                  811 non-null    object\n",
      " 51  Aboriginal                           16 non-null     object\n",
      " 52  Torres Strait                        3 non-null      object\n",
      " 53  South Sea                            7 non-null      object\n",
      " 54  Disability                           23 non-null     object\n",
      " 55  NESB                                 32 non-null     object\n",
      "dtypes: bool(18), int64(1), object(37)\n",
      "memory usage: 258.6+ KB\n"
     ]
    }
   ],
   "source": [
    "dete.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aff91a4f-cfb0-4d1e-880d-22d8f70358cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tafe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5be7f43b-7054-49aa-a668-b12afe2fc3de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 702 entries, 0 to 701\n",
      "Data columns (total 72 columns):\n",
      " #   Column                                                                                                                                                         Non-Null Count  Dtype  \n",
      "---  ------                                                                                                                                                         --------------  -----  \n",
      " 0   Record ID                                                                                                                                                      702 non-null    float64\n",
      " 1   Institute                                                                                                                                                      702 non-null    object \n",
      " 2   WorkArea                                                                                                                                                       702 non-null    object \n",
      " 3   CESSATION YEAR                                                                                                                                                 695 non-null    float64\n",
      " 4   Reason for ceasing employment                                                                                                                                  701 non-null    object \n",
      " 5   Contributing Factors. Career Move - Public Sector                                                                                                              437 non-null    object \n",
      " 6   Contributing Factors. Career Move - Private Sector                                                                                                             437 non-null    object \n",
      " 7   Contributing Factors. Career Move - Self-employment                                                                                                            437 non-null    object \n",
      " 8   Contributing Factors. Ill Health                                                                                                                               437 non-null    object \n",
      " 9   Contributing Factors. Maternity/Family                                                                                                                         437 non-null    object \n",
      " 10  Contributing Factors. Dissatisfaction                                                                                                                          437 non-null    object \n",
      " 11  Contributing Factors. Job Dissatisfaction                                                                                                                      437 non-null    object \n",
      " 12  Contributing Factors. Interpersonal Conflict                                                                                                                   437 non-null    object \n",
      " 13  Contributing Factors. Study                                                                                                                                    437 non-null    object \n",
      " 14  Contributing Factors. Travel                                                                                                                                   437 non-null    object \n",
      " 15  Contributing Factors. Other                                                                                                                                    437 non-null    object \n",
      " 16  Contributing Factors. NONE                                                                                                                                     437 non-null    object \n",
      " 17  Main Factor. Which of these was the main factor for leaving?                                                                                                   113 non-null    object \n",
      " 18  InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction                                                                         608 non-null    object \n",
      " 19  InstituteViews. Topic:2. I was given access to skills training to help me do my job better                                                                     613 non-null    object \n",
      " 20  InstituteViews. Topic:3. I was given adequate opportunities for personal development                                                                           610 non-null    object \n",
      " 21  InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL%                                                            608 non-null    object \n",
      " 22  InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had                                                                615 non-null    object \n",
      " 23  InstituteViews. Topic:6. The organisation recognised when staff did good work                                                                                  607 non-null    object \n",
      " 24  InstituteViews. Topic:7. Management was generally supportive of me                                                                                             614 non-null    object \n",
      " 25  InstituteViews. Topic:8. Management was generally supportive of my team                                                                                        608 non-null    object \n",
      " 26  InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me                                                          610 non-null    object \n",
      " 27  InstituteViews. Topic:10. Staff morale was positive within the Institute                                                                                       602 non-null    object \n",
      " 28  InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly                                                                                 601 non-null    object \n",
      " 29  InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently                                                                             597 non-null    object \n",
      " 30  InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly                                                                              601 non-null    object \n",
      " 31  WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit                                                609 non-null    object \n",
      " 32  WorkUnitViews. Topic:15. I worked well with my colleagues                                                                                                      605 non-null    object \n",
      " 33  WorkUnitViews. Topic:16. My job was challenging and interesting                                                                                                607 non-null    object \n",
      " 34  WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work                                                                        610 non-null    object \n",
      " 35  WorkUnitViews. Topic:18. I had sufficient contact with other people in my job                                                                                  613 non-null    object \n",
      " 36  WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job                                                   609 non-null    object \n",
      " 37  WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job                                                                               609 non-null    object \n",
      " 38  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]  608 non-null    object \n",
      " 39  WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job                                                                            608 non-null    object \n",
      " 40  WorkUnitViews. Topic:23. My job provided sufficient variety                                                                                                    611 non-null    object \n",
      " 41  WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job                                                                    610 non-null    object \n",
      " 42  WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction                                                        611 non-null    object \n",
      " 43  WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance                                                    606 non-null    object \n",
      " 44  WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area                       610 non-null    object \n",
      " 45  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  609 non-null    object \n",
      " 46  WorkUnitViews. Topic:29. There was adequate communication between staff in my unit                                                                             603 non-null    object \n",
      " 47  WorkUnitViews. Topic:30. Staff morale was positive within my work unit                                                                                         606 non-null    object \n",
      " 48  Induction. Did you undertake Workplace Induction?                                                                                                              619 non-null    object \n",
      " 49  InductionInfo. Topic:Did you undertake a Corporate Induction?                                                                                                  432 non-null    object \n",
      " 50  InductionInfo. Topic:Did you undertake a Institute Induction?                                                                                                  483 non-null    object \n",
      " 51  InductionInfo. Topic: Did you undertake Team Induction?                                                                                                        440 non-null    object \n",
      " 52  InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted?                                                      555 non-null    object \n",
      " 53  InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted?                                                           555 non-null    object \n",
      " 54  InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction?                                                                                 555 non-null    object \n",
      " 55  InductionInfo. Face to Face Topic:Did you undertake a Institute Induction?                                                                                     530 non-null    object \n",
      " 56  InductionInfo. On-line Topic:Did you undertake a Institute Induction?                                                                                          555 non-null    object \n",
      " 57  InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction?                                                                                 553 non-null    object \n",
      " 58  InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category?                                                                                 555 non-null    object \n",
      " 59  InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.]                                                555 non-null    object \n",
      " 60  InductionInfo. Induction Manual Topic: Did you undertake Team Induction?                                                                                       555 non-null    object \n",
      " 61  Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)?                                                      608 non-null    object \n",
      " 62  Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination?                                                    594 non-null    object \n",
      " 63  Workplace. Topic:Does your workplace promote and practice the principles of employment equity?                                                                 587 non-null    object \n",
      " 64  Workplace. Topic:Does your workplace value the diversity of its employees?                                                                                     586 non-null    object \n",
      " 65  Workplace. Topic:Would you recommend the Institute as an employer to others?                                                                                   581 non-null    object \n",
      " 66  Gender. What is your Gender?                                                                                                                                   596 non-null    object \n",
      " 67  CurrentAge. Current Age                                                                                                                                        596 non-null    object \n",
      " 68  Employment Type. Employment Type                                                                                                                               596 non-null    object \n",
      " 69  Classification. Classification                                                                                                                                 596 non-null    object \n",
      " 70  LengthofServiceOverall. Overall Length of Service at Institute (in years)                                                                                      596 non-null    object \n",
      " 71  LengthofServiceCurrent. Length of Service at current workplace (in years)                                                                                      596 non-null    object \n",
      "dtypes: float64(2), object(70)\n",
      "memory usage: 395.0+ KB\n"
     ]
    }
   ],
   "source": [
    "tafe.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e34d2860-6d9b-45ae-89c8-03dce8f829c0",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Cleaning the data\n",
    "- Analysing the both datasets, we notice several differences about the column names, with the TAFE dataset including the questions of the survey and having 72 columns. The DETE dataset has 56 columns, with more short names;\n",
    "- Both datasets have a lot of missing data, expressed in different ways: \"Not Stated\", \"N\", \"NaN\", or just \"-\";\n",
    "- Both the DETE and TAFE datasets contains many columns that we don't need to complete our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2f9c288-c57d-4403-96d0-ed92fd98fdcd",
   "metadata": {},
   "source": [
    "### 1. Identifying missing values and dropping unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9bbaf4c7-bfdf-4b7a-9537-3cb09e96dbd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the pd.read_csv() function to specify values that should be represented as NaN:\n",
    "dete = pd.read_csv('dete_survey.csv', na_values='Not Stated')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d00459ff-e57b-4111-8a4f-9e804224bf12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping columns we don't need\n",
    "dete_updated = dete.drop(dete.columns[28:49], axis=1)\n",
    "tafe_updated = tafe.drop(tafe.columns[17:66], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "82bbe8b8-62f6-4db7-a1c1-2dad2ba5a57e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 822 entries, 0 to 821\n",
      "Data columns (total 35 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   ID                                   822 non-null    int64  \n",
      " 1   SeparationType                       822 non-null    object \n",
      " 2   Cease Date                           788 non-null    object \n",
      " 3   DETE Start Date                      749 non-null    float64\n",
      " 4   Role Start Date                      724 non-null    float64\n",
      " 5   Position                             817 non-null    object \n",
      " 6   Classification                       455 non-null    object \n",
      " 7   Region                               717 non-null    object \n",
      " 8   Business Unit                        126 non-null    object \n",
      " 9   Employment Status                    817 non-null    object \n",
      " 10  Career move to public sector         822 non-null    bool   \n",
      " 11  Career move to private sector        822 non-null    bool   \n",
      " 12  Interpersonal conflicts              822 non-null    bool   \n",
      " 13  Job dissatisfaction                  822 non-null    bool   \n",
      " 14  Dissatisfaction with the department  822 non-null    bool   \n",
      " 15  Physical work environment            822 non-null    bool   \n",
      " 16  Lack of recognition                  822 non-null    bool   \n",
      " 17  Lack of job security                 822 non-null    bool   \n",
      " 18  Work location                        822 non-null    bool   \n",
      " 19  Employment conditions                822 non-null    bool   \n",
      " 20  Maternity/family                     822 non-null    bool   \n",
      " 21  Relocation                           822 non-null    bool   \n",
      " 22  Study/Travel                         822 non-null    bool   \n",
      " 23  Ill Health                           822 non-null    bool   \n",
      " 24  Traumatic incident                   822 non-null    bool   \n",
      " 25  Work life balance                    822 non-null    bool   \n",
      " 26  Workload                             822 non-null    bool   \n",
      " 27  None of the above                    822 non-null    bool   \n",
      " 28  Gender                               798 non-null    object \n",
      " 29  Age                                  811 non-null    object \n",
      " 30  Aboriginal                           16 non-null     object \n",
      " 31  Torres Strait                        3 non-null      object \n",
      " 32  South Sea                            7 non-null      object \n",
      " 33  Disability                           23 non-null     object \n",
      " 34  NESB                                 32 non-null     object \n",
      "dtypes: bool(18), float64(2), int64(1), object(14)\n",
      "memory usage: 123.7+ KB\n"
     ]
    }
   ],
   "source": [
    "dete_updated.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c550dd78-9b2a-48a4-9cf0-fe1dec4f7773",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 702 entries, 0 to 701\n",
      "Data columns (total 23 columns):\n",
      " #   Column                                                                     Non-Null Count  Dtype  \n",
      "---  ------                                                                     --------------  -----  \n",
      " 0   Record ID                                                                  702 non-null    float64\n",
      " 1   Institute                                                                  702 non-null    object \n",
      " 2   WorkArea                                                                   702 non-null    object \n",
      " 3   CESSATION YEAR                                                             695 non-null    float64\n",
      " 4   Reason for ceasing employment                                              701 non-null    object \n",
      " 5   Contributing Factors. Career Move - Public Sector                          437 non-null    object \n",
      " 6   Contributing Factors. Career Move - Private Sector                         437 non-null    object \n",
      " 7   Contributing Factors. Career Move - Self-employment                        437 non-null    object \n",
      " 8   Contributing Factors. Ill Health                                           437 non-null    object \n",
      " 9   Contributing Factors. Maternity/Family                                     437 non-null    object \n",
      " 10  Contributing Factors. Dissatisfaction                                      437 non-null    object \n",
      " 11  Contributing Factors. Job Dissatisfaction                                  437 non-null    object \n",
      " 12  Contributing Factors. Interpersonal Conflict                               437 non-null    object \n",
      " 13  Contributing Factors. Study                                                437 non-null    object \n",
      " 14  Contributing Factors. Travel                                               437 non-null    object \n",
      " 15  Contributing Factors. Other                                                437 non-null    object \n",
      " 16  Contributing Factors. NONE                                                 437 non-null    object \n",
      " 17  Gender. What is your Gender?                                               596 non-null    object \n",
      " 18  CurrentAge. Current Age                                                    596 non-null    object \n",
      " 19  Employment Type. Employment Type                                           596 non-null    object \n",
      " 20  Classification. Classification                                             596 non-null    object \n",
      " 21  LengthofServiceOverall. Overall Length of Service at Institute (in years)  596 non-null    object \n",
      " 22  LengthofServiceCurrent. Length of Service at current workplace (in years)  596 non-null    object \n",
      "dtypes: float64(2), object(21)\n",
      "memory usage: 126.3+ KB\n"
     ]
    }
   ],
   "source": [
    "tafe_updated.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fde4a542-cc88-4368-8e97-f2d2a9475b77",
   "metadata": {},
   "source": [
    "### 2. Cleaning column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f0fc09bb-e29b-45bb-9231-c959911a0afc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 822 entries, 0 to 821\n",
      "Data columns (total 35 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   id                                   822 non-null    int64  \n",
      " 1   separationtype                       822 non-null    object \n",
      " 2   cease_date                           788 non-null    object \n",
      " 3   dete_start_date                      749 non-null    float64\n",
      " 4   role_start_date                      724 non-null    float64\n",
      " 5   position                             817 non-null    object \n",
      " 6   classification                       455 non-null    object \n",
      " 7   region                               717 non-null    object \n",
      " 8   business_unit                        126 non-null    object \n",
      " 9   employment_status                    817 non-null    object \n",
      " 10  career_move_to_public_sector         822 non-null    bool   \n",
      " 11  career_move_to_private_sector        822 non-null    bool   \n",
      " 12  interpersonal_conflicts              822 non-null    bool   \n",
      " 13  job_dissatisfaction                  822 non-null    bool   \n",
      " 14  dissatisfaction_with_the_department  822 non-null    bool   \n",
      " 15  physical_work_environment            822 non-null    bool   \n",
      " 16  lack_of_recognition                  822 non-null    bool   \n",
      " 17  lack_of_job_security                 822 non-null    bool   \n",
      " 18  work_location                        822 non-null    bool   \n",
      " 19  employment_conditions                822 non-null    bool   \n",
      " 20  maternity/family                     822 non-null    bool   \n",
      " 21  relocation                           822 non-null    bool   \n",
      " 22  study/travel                         822 non-null    bool   \n",
      " 23  ill_health                           822 non-null    bool   \n",
      " 24  traumatic_incident                   822 non-null    bool   \n",
      " 25  work_life_balance                    822 non-null    bool   \n",
      " 26  workload                             822 non-null    bool   \n",
      " 27  none_of_the_above                    822 non-null    bool   \n",
      " 28  gender                               798 non-null    object \n",
      " 29  age                                  811 non-null    object \n",
      " 30  aboriginal                           16 non-null     object \n",
      " 31  torres_strait                        3 non-null      object \n",
      " 32  south_sea                            7 non-null      object \n",
      " 33  disability                           23 non-null     object \n",
      " 34  nesb                                 32 non-null     object \n",
      "dtypes: bool(18), float64(2), int64(1), object(14)\n",
      "memory usage: 123.7+ KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 702 entries, 0 to 701\n",
      "Data columns (total 23 columns):\n",
      " #   Column                                               Non-Null Count  Dtype  \n",
      "---  ------                                               --------------  -----  \n",
      " 0   id                                                   702 non-null    float64\n",
      " 1   Institute                                            702 non-null    object \n",
      " 2   WorkArea                                             702 non-null    object \n",
      " 3   cease_date                                           695 non-null    float64\n",
      " 4   separationtype                                       701 non-null    object \n",
      " 5   Contributing Factors. Career Move - Public Sector    437 non-null    object \n",
      " 6   Contributing Factors. Career Move - Private Sector   437 non-null    object \n",
      " 7   Contributing Factors. Career Move - Self-employment  437 non-null    object \n",
      " 8   Contributing Factors. Ill Health                     437 non-null    object \n",
      " 9   Contributing Factors. Maternity/Family               437 non-null    object \n",
      " 10  Contributing Factors. Dissatisfaction                437 non-null    object \n",
      " 11  Contributing Factors. Job Dissatisfaction            437 non-null    object \n",
      " 12  Contributing Factors. Interpersonal Conflict         437 non-null    object \n",
      " 13  Contributing Factors. Study                          437 non-null    object \n",
      " 14  Contributing Factors. Travel                         437 non-null    object \n",
      " 15  Contributing Factors. Other                          437 non-null    object \n",
      " 16  Contributing Factors. NONE                           437 non-null    object \n",
      " 17  gender                                               596 non-null    object \n",
      " 18  age                                                  596 non-null    object \n",
      " 19  employment_status                                    596 non-null    object \n",
      " 20  position                                             596 non-null    object \n",
      " 21  institute_service                                    596 non-null    object \n",
      " 22  role_service                                         596 non-null    object \n",
      "dtypes: float64(2), object(21)\n",
      "memory usage: 126.3+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#Updating column names\n",
    "dete_updated.columns = dete_updated.columns.str.lower().str.replace(' ','_').str.strip()\n",
    "\n",
    "tafe_map = {'Record ID': 'id',\n",
    "'CESSATION YEAR': 'cease_date',\n",
    "'Reason for ceasing employment': 'separationtype',\n",
    "'Gender. What is your Gender?': 'gender',\n",
    "'CurrentAge. Current Age': 'age',\n",
    "'Employment Type. Employment Type': 'employment_status',\n",
    "'Classification. Classification': 'position',\n",
    "'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service',\n",
    "'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'}\n",
    "\n",
    "tafe_updated.rename(tafe_map, axis = 1, inplace = True)\n",
    "print(dete_updated.info())\n",
    "print(tafe_updated.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09a0ca02-d247-4251-8312-6e30a356ce27",
   "metadata": {},
   "source": [
    "- Each dataframe contains many of the same columns, but the column names are different. \n",
    "- Because we eventually want to combine them, we had to standardize the column names. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97680634-fd62-4bd1-870e-415e0bff5a35",
   "metadata": {},
   "source": [
    "### 3. Filtering the data\n",
    "- We want the informations about the employees that resignate, so, we are going to filter both datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b9c7ebd2-4813-4a0b-84d6-6dd9825e3197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age Retirement                          285\n",
       "Resignation-Other reasons               150\n",
       "Resignation-Other employer               91\n",
       "Resignation-Move overseas/interstate     70\n",
       "Voluntary Early Retirement (VER)         67\n",
       "Ill Health Retirement                    61\n",
       "Other                                    49\n",
       "Contract Expired                         34\n",
       "Termination                              15\n",
       "Name: separationtype, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the unique values\n",
    "dete_updated['separationtype'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b1cc9934-953a-4e2c-9d87-40b907422db9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 311 entries, 3 to 821\n",
      "Data columns (total 35 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   id                                   311 non-null    int64  \n",
      " 1   separationtype                       311 non-null    object \n",
      " 2   cease_date                           300 non-null    object \n",
      " 3   dete_start_date                      283 non-null    float64\n",
      " 4   role_start_date                      271 non-null    float64\n",
      " 5   position                             308 non-null    object \n",
      " 6   classification                       161 non-null    object \n",
      " 7   region                               265 non-null    object \n",
      " 8   business_unit                        32 non-null     object \n",
      " 9   employment_status                    307 non-null    object \n",
      " 10  career_move_to_public_sector         311 non-null    bool   \n",
      " 11  career_move_to_private_sector        311 non-null    bool   \n",
      " 12  interpersonal_conflicts              311 non-null    bool   \n",
      " 13  job_dissatisfaction                  311 non-null    bool   \n",
      " 14  dissatisfaction_with_the_department  311 non-null    bool   \n",
      " 15  physical_work_environment            311 non-null    bool   \n",
      " 16  lack_of_recognition                  311 non-null    bool   \n",
      " 17  lack_of_job_security                 311 non-null    bool   \n",
      " 18  work_location                        311 non-null    bool   \n",
      " 19  employment_conditions                311 non-null    bool   \n",
      " 20  maternity/family                     311 non-null    bool   \n",
      " 21  relocation                           311 non-null    bool   \n",
      " 22  study/travel                         311 non-null    bool   \n",
      " 23  ill_health                           311 non-null    bool   \n",
      " 24  traumatic_incident                   311 non-null    bool   \n",
      " 25  work_life_balance                    311 non-null    bool   \n",
      " 26  workload                             311 non-null    bool   \n",
      " 27  none_of_the_above                    311 non-null    bool   \n",
      " 28  gender                               302 non-null    object \n",
      " 29  age                                  306 non-null    object \n",
      " 30  aboriginal                           7 non-null      object \n",
      " 31  torres_strait                        0 non-null      object \n",
      " 32  south_sea                            3 non-null      object \n",
      " 33  disability                           8 non-null      object \n",
      " 34  nesb                                 9 non-null      object \n",
      "dtypes: bool(18), float64(2), int64(1), object(14)\n",
      "memory usage: 49.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Selecting resignations\n",
    "dete_resignations = dete_updated[dete_updated['separationtype'].str.contains('Resignation-')].copy()\n",
    "dete_resignations.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "16bf78fa-7266-4dcc-85ab-77f87952fbbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Resignation                 340\n",
       "Contract Expired            127\n",
       "Retrenchment/ Redundancy    104\n",
       "Retirement                   82\n",
       "Transfer                     25\n",
       "Termination                  23\n",
       "Name: separationtype, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the unique values\n",
    "tafe_updated['separationtype'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "471b1490-738a-4025-9b49-e9acad689204",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 340 entries, 3 to 701\n",
      "Data columns (total 23 columns):\n",
      " #   Column                                               Non-Null Count  Dtype  \n",
      "---  ------                                               --------------  -----  \n",
      " 0   id                                                   340 non-null    float64\n",
      " 1   Institute                                            340 non-null    object \n",
      " 2   WorkArea                                             340 non-null    object \n",
      " 3   cease_date                                           335 non-null    float64\n",
      " 4   separationtype                                       340 non-null    object \n",
      " 5   Contributing Factors. Career Move - Public Sector    332 non-null    object \n",
      " 6   Contributing Factors. Career Move - Private Sector   332 non-null    object \n",
      " 7   Contributing Factors. Career Move - Self-employment  332 non-null    object \n",
      " 8   Contributing Factors. Ill Health                     332 non-null    object \n",
      " 9   Contributing Factors. Maternity/Family               332 non-null    object \n",
      " 10  Contributing Factors. Dissatisfaction                332 non-null    object \n",
      " 11  Contributing Factors. Job Dissatisfaction            332 non-null    object \n",
      " 12  Contributing Factors. Interpersonal Conflict         332 non-null    object \n",
      " 13  Contributing Factors. Study                          332 non-null    object \n",
      " 14  Contributing Factors. Travel                         332 non-null    object \n",
      " 15  Contributing Factors. Other                          332 non-null    object \n",
      " 16  Contributing Factors. NONE                           332 non-null    object \n",
      " 17  gender                                               290 non-null    object \n",
      " 18  age                                                  290 non-null    object \n",
      " 19  employment_status                                    290 non-null    object \n",
      " 20  position                                             290 non-null    object \n",
      " 21  institute_service                                    290 non-null    object \n",
      " 22  role_service                                         290 non-null    object \n",
      "dtypes: float64(2), object(21)\n",
      "memory usage: 63.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Selecting resignations\n",
    "tafe_resignations = tafe_updated[tafe_updated['separationtype'].str.contains('Resignation', na=False)].copy()\n",
    "tafe_resignations.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "548908dd-932b-4354-b103-45cd69ae74e8",
   "metadata": {},
   "source": [
    "### 4. Verifying the data\n",
    "- Now, we'll verify the date columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "495652a7-c2ef-43a0-a349-96049b13ed3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012       126\n",
       "2013        74\n",
       "01/2014     22\n",
       "12/2013     17\n",
       "06/2013     14\n",
       "09/2013     11\n",
       "07/2013      9\n",
       "11/2013      9\n",
       "10/2013      6\n",
       "08/2013      4\n",
       "05/2012      2\n",
       "05/2013      2\n",
       "07/2012      1\n",
       "2010         1\n",
       "09/2010      1\n",
       "07/2006      1\n",
       "Name: cease_date, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values\n",
    "dete_resignations['cease_date'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a7b110a7-0d1c-4228-be3d-3c78140ecf27",
   "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>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>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>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Resignation-Other reasons</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2006.0</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>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>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Resignation-Other reasons</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Guidance 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>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>True</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>Female</td>\n",
       "      <td>41-45</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>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Resignation-Other reasons</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>Secondary</td>\n",
       "      <td>North 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>Female</td>\n",
       "      <td>31-35</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>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Resignation-Other employer</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Teacher Aide</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Permanent Part-time</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</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>Female</td>\n",
       "      <td>46-50</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>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Resignation-Move overseas/interstate</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>Secondary</td>\n",
       "      <td>Far North Queensland</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>True</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>Male</td>\n",
       "      <td>31-35</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",
       "3    4             Resignation-Other reasons      2012.0           2005.0   \n",
       "5    6             Resignation-Other reasons      2012.0           1994.0   \n",
       "8    9             Resignation-Other reasons      2012.0           2009.0   \n",
       "9   10            Resignation-Other employer      2012.0           1997.0   \n",
       "11  12  Resignation-Move overseas/interstate      2012.0           2009.0   \n",
       "\n",
       "    role_start_date          position classification                region  \\\n",
       "3            2006.0           Teacher        Primary    Central Queensland   \n",
       "5            1997.0  Guidance Officer            NaN        Central Office   \n",
       "8            2009.0           Teacher      Secondary      North Queensland   \n",
       "9            2008.0      Teacher Aide            NaN                   NaN   \n",
       "11           2009.0           Teacher      Secondary  Far North Queensland   \n",
       "\n",
       "           business_unit    employment_status  career_move_to_public_sector  \\\n",
       "3                    NaN  Permanent Full-time                         False   \n",
       "5   Education Queensland  Permanent Full-time                         False   \n",
       "8                    NaN  Permanent Full-time                         False   \n",
       "9                    NaN  Permanent Part-time                         False   \n",
       "11                   NaN  Permanent Full-time                         False   \n",
       "\n",
       "    career_move_to_private_sector  interpersonal_conflicts  \\\n",
       "3                            True                    False   \n",
       "5                            True                    False   \n",
       "8                            True                    False   \n",
       "9                           False                     True   \n",
       "11                          False                    False   \n",
       "\n",
       "    job_dissatisfaction  dissatisfaction_with_the_department  \\\n",
       "3                 False                                False   \n",
       "5                 False                                False   \n",
       "8                 False                                False   \n",
       "9                  True                                 True   \n",
       "11                False                                False   \n",
       "\n",
       "    physical_work_environment  lack_of_recognition  lack_of_job_security  \\\n",
       "3                       False                False                 False   \n",
       "5                       False                False                 False   \n",
       "8                       False                False                 False   \n",
       "9                       False                False                 False   \n",
       "11                      False                False                 False   \n",
       "\n",
       "    work_location  employment_conditions  maternity/family  relocation  \\\n",
       "3           False                  False             False       False   \n",
       "5           False                   True              True       False   \n",
       "8           False                  False             False       False   \n",
       "9           False                  False             False       False   \n",
       "11          False                  False              True        True   \n",
       "\n",
       "    study/travel  ill_health  traumatic_incident  work_life_balance  workload  \\\n",
       "3          False       False               False              False     False   \n",
       "5          False       False               False              False     False   \n",
       "8          False       False               False              False     False   \n",
       "9          False       False               False              False     False   \n",
       "11         False       False               False              False     False   \n",
       "\n",
       "    none_of_the_above  gender    age aboriginal torres_strait south_sea  \\\n",
       "3               False  Female  36-40        NaN           NaN       NaN   \n",
       "5               False  Female  41-45        NaN           NaN       NaN   \n",
       "8               False  Female  31-35        NaN           NaN       NaN   \n",
       "9               False  Female  46-50        NaN           NaN       NaN   \n",
       "11              False    Male  31-35        NaN           NaN       NaN   \n",
       "\n",
       "   disability nesb  \n",
       "3         NaN  NaN  \n",
       "5         NaN  NaN  \n",
       "8         NaN  NaN  \n",
       "9         NaN  NaN  \n",
       "11        NaN  NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting the year\n",
    "year_pattern = r\"([2][0][0-9][0-9])\"\n",
    "dete_resignations['cease_date'] = dete_resignations['cease_date'].str.extract(year_pattern).astype('float')\n",
    "dete_resignations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0c352f7c-9c80-4c6f-96fc-d1b229fd7fc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013.0    146\n",
       "2012.0    129\n",
       "2014.0     22\n",
       "2010.0      2\n",
       "2006.0      1\n",
       "Name: cease_date, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values\n",
    "dete_resignations['cease_date'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9af4cb10-7db4-4c14-88f1-8b2a9761d870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013.0    10\n",
       "2012.0    21\n",
       "2011.0    24\n",
       "2010.0    17\n",
       "2009.0    13\n",
       "2008.0    22\n",
       "2007.0    21\n",
       "2006.0    13\n",
       "2005.0    15\n",
       "2004.0    14\n",
       "2003.0     6\n",
       "2002.0     6\n",
       "2001.0     3\n",
       "2000.0     9\n",
       "1999.0     8\n",
       "1998.0     6\n",
       "1997.0     5\n",
       "1996.0     6\n",
       "1995.0     4\n",
       "1994.0     6\n",
       "1993.0     5\n",
       "1992.0     6\n",
       "1991.0     4\n",
       "1990.0     5\n",
       "1989.0     4\n",
       "1988.0     4\n",
       "1987.0     1\n",
       "1986.0     3\n",
       "1985.0     3\n",
       "1984.0     1\n",
       "1983.0     2\n",
       "1982.0     1\n",
       "1980.0     5\n",
       "1977.0     1\n",
       "1976.0     2\n",
       "1975.0     1\n",
       "1974.0     2\n",
       "1973.0     1\n",
       "1972.0     1\n",
       "1971.0     1\n",
       "1963.0     1\n",
       "Name: dete_start_date, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values of dete_start_date\n",
    "dete_resignations['dete_start_date'].value_counts().sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e41a18e1-84cc-428b-a304-20577a070c6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013.0     55\n",
       "2012.0     94\n",
       "2011.0    116\n",
       "2010.0     68\n",
       "2009.0      2\n",
       "Name: cease_date, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values \n",
    "tafe_resignations['cease_date'].value_counts().sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1ba8489-0ce5-405c-9bc3-b367708d6fd7",
   "metadata": {},
   "source": [
    "### 5. Creating a new column\n",
    "- In order to know how long the employee worked, we need to create a new column in dete dataset: 'institute_service'. The tafe dataset already have a column with this information, also called 'institute_service'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "34a01d36-f421-4176-ac31-db016b701cae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0     23\n",
       "1.0     22\n",
       "3.0     20\n",
       "0.0     20\n",
       "6.0     17\n",
       "4.0     16\n",
       "2.0     14\n",
       "9.0     14\n",
       "7.0     13\n",
       "13.0     8\n",
       "8.0      8\n",
       "20.0     7\n",
       "15.0     7\n",
       "17.0     6\n",
       "10.0     6\n",
       "22.0     6\n",
       "12.0     6\n",
       "14.0     6\n",
       "18.0     5\n",
       "16.0     5\n",
       "24.0     4\n",
       "23.0     4\n",
       "11.0     4\n",
       "32.0     3\n",
       "39.0     3\n",
       "21.0     3\n",
       "19.0     3\n",
       "25.0     2\n",
       "28.0     2\n",
       "26.0     2\n",
       "30.0     2\n",
       "36.0     2\n",
       "33.0     1\n",
       "35.0     1\n",
       "49.0     1\n",
       "38.0     1\n",
       "41.0     1\n",
       "27.0     1\n",
       "42.0     1\n",
       "29.0     1\n",
       "34.0     1\n",
       "31.0     1\n",
       "Name: institute_service, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']\n",
    "dete_resignations['institute_service'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ead8ae6-4c5a-4bb2-bd70-381e792e75a7",
   "metadata": {},
   "source": [
    "### 6. Identifying disatisfied employees\n",
    "- Now, we'll work on the 'Contributing Factors. Dissatisfaction' and 'Contributing Factors. Job Dissatisfaction' columns in the tafe dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "225127aa-ad81-4769-a172-1cfbac80a37e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-                                         277\n",
       "Contributing Factors. Dissatisfaction      55\n",
       "Name: Contributing Factors. Dissatisfaction, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values\n",
    "tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4d3f7520-005a-4f95-84ab-50437c9bfb0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-                      270\n",
       "Job Dissatisfaction     62\n",
       "Name: Contributing Factors. Job Dissatisfaction, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values\n",
    "tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6d998345-d94d-43aa-ab99-8bbcb6cd0236",
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_val(val):\n",
    "    \"\"\"Update the value to boolean or np.nan.\n",
    "\n",
    "    Args:\n",
    "      val: The value to be updated.\n",
    "\n",
    "    Returns:\n",
    "      bool or np.nan.\n",
    "    \"\"\"\n",
    "    if val == '-':\n",
    "        return False\n",
    "    elif pd.isnull(val):\n",
    "        return np.nan\n",
    "    else:\n",
    "        return True\n",
    "\n",
    "tafe_resignations['Contributing Factors. Dissatisfaction'] = tafe_resignations['Contributing Factors. Dissatisfaction'].apply(update_val)\n",
    "tafe_resignations['Contributing Factors. Job Dissatisfaction'] = tafe_resignations['Contributing Factors. Job Dissatisfaction'].apply(update_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a20c31fd-974e-40a2-817f-6deb4a07c931",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    277\n",
       "True      55\n",
       "Name: Contributing Factors. Dissatisfaction, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values\n",
    "tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0a0c9952-d884-4182-a040-e4b146421212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    270\n",
       "True      62\n",
       "Name: Contributing Factors. Job Dissatisfaction, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values\n",
    "tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b0cafa09-4987-4c86-ac19-2c3a173d3a59",
   "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>cease_date</th>\n",
       "      <th>separationtype</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>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>employment_status</th>\n",
       "      <th>position</th>\n",
       "      <th>institute_service</th>\n",
       "      <th>role_service</th>\n",
       "      <th>dissatisfied</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.341475e+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>-</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>Other</td>\n",
       "      <td>-</td>\n",
       "      <td>Female</td>\n",
       "      <td>56 or older</td>\n",
       "      <td>Contract/casual</td>\n",
       "      <td>Teacher (including LVT)</td>\n",
       "      <td>7-10</td>\n",
       "      <td>7-10</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.341520e+17</td>\n",
       "      <td>Barrier Reef Institute of TAFE</td>\n",
       "      <td>Non-Delivery (corporate)</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>Maternity/Family</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Other</td>\n",
       "      <td>-</td>\n",
       "      <td>Male</td>\n",
       "      <td>20 or younger</td>\n",
       "      <td>Temporary Full-time</td>\n",
       "      <td>Administration (AO)</td>\n",
       "      <td>3-4</td>\n",
       "      <td>3-4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.341537e+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>-</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>Other</td>\n",
       "      <td>-</td>\n",
       "      <td>Male</td>\n",
       "      <td>46  50</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",
       "3  6.341399e+17            Mount Isa Institute of TAFE   \n",
       "4  6.341466e+17  Southern Queensland Institute of TAFE   \n",
       "5  6.341475e+17  Southern Queensland Institute of TAFE   \n",
       "6  6.341520e+17         Barrier Reef Institute of TAFE   \n",
       "7  6.341537e+17  Southern Queensland Institute of TAFE   \n",
       "\n",
       "                   WorkArea  cease_date separationtype  \\\n",
       "3  Non-Delivery (corporate)      2010.0    Resignation   \n",
       "4       Delivery (teaching)      2010.0    Resignation   \n",
       "5       Delivery (teaching)      2010.0    Resignation   \n",
       "6  Non-Delivery (corporate)      2010.0    Resignation   \n",
       "7       Delivery (teaching)      2010.0    Resignation   \n",
       "\n",
       "  Contributing Factors. Career Move - Public Sector   \\\n",
       "3                                                  -   \n",
       "4                                                  -   \n",
       "5                                                  -   \n",
       "6                                                  -   \n",
       "7                                                  -   \n",
       "\n",
       "  Contributing Factors. Career Move - Private Sector   \\\n",
       "3                                                  -    \n",
       "4                       Career Move - Private Sector    \n",
       "5                                                  -    \n",
       "6                       Career Move - Private Sector    \n",
       "7                                                  -    \n",
       "\n",
       "  Contributing Factors. Career Move - Self-employment  \\\n",
       "3                                                  -    \n",
       "4                                                  -    \n",
       "5                                                  -    \n",
       "6                                                  -    \n",
       "7                                                  -    \n",
       "\n",
       "  Contributing Factors. Ill Health Contributing Factors. Maternity/Family  \\\n",
       "3                                -                                      -   \n",
       "4                                -                                      -   \n",
       "5                                -                                      -   \n",
       "6                                -                       Maternity/Family   \n",
       "7                                -                                      -   \n",
       "\n",
       "  Contributing Factors. Dissatisfaction  \\\n",
       "3                                 False   \n",
       "4                                 False   \n",
       "5                                 False   \n",
       "6                                 False   \n",
       "7                                 False   \n",
       "\n",
       "  Contributing Factors. Job Dissatisfaction  \\\n",
       "3                                     False   \n",
       "4                                     False   \n",
       "5                                     False   \n",
       "6                                     False   \n",
       "7                                     False   \n",
       "\n",
       "  Contributing Factors. Interpersonal Conflict Contributing Factors. Study  \\\n",
       "3                                            -                           -   \n",
       "4                                            -                           -   \n",
       "5                                            -                           -   \n",
       "6                                            -                           -   \n",
       "7                                            -                           -   \n",
       "\n",
       "  Contributing Factors. Travel Contributing Factors. Other  \\\n",
       "3                       Travel                           -   \n",
       "4                            -                           -   \n",
       "5                            -                       Other   \n",
       "6                            -                       Other   \n",
       "7                            -                       Other   \n",
       "\n",
       "  Contributing Factors. NONE  gender            age    employment_status  \\\n",
       "3                          -     NaN            NaN                  NaN   \n",
       "4                          -    Male         41  45  Permanent Full-time   \n",
       "5                          -  Female    56 or older      Contract/casual   \n",
       "6                          -    Male  20 or younger  Temporary Full-time   \n",
       "7                          -    Male         46  50  Permanent Full-time   \n",
       "\n",
       "                  position institute_service role_service  dissatisfied  \n",
       "3                      NaN               NaN          NaN         False  \n",
       "4  Teacher (including LVT)               3-4          3-4         False  \n",
       "5  Teacher (including LVT)              7-10         7-10         False  \n",
       "6      Administration (AO)               3-4          3-4         False  \n",
       "7  Teacher (including LVT)               3-4          3-4         False  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unifying the columns\n",
    "tafe_resignations['dissatisfied'] = tafe_resignations[['Contributing Factors. Dissatisfaction','Contributing Factors. Job Dissatisfaction']].any(axis = 1, skipna = False)\n",
    "tafe_resignations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5c39e9b6-b3e2-42e8-a900-d7c2dfef45a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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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>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>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",
       "      <th>institute_service</th>\n",
       "      <th>dissatisfied</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Resignation-Other reasons</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2006.0</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>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",
       "      <td>7.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Resignation-Other reasons</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Guidance 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>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>True</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>Female</td>\n",
       "      <td>41-45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Resignation-Other reasons</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>Secondary</td>\n",
       "      <td>North 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>Female</td>\n",
       "      <td>31-35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Resignation-Other employer</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Teacher Aide</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Permanent Part-time</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</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>Female</td>\n",
       "      <td>46-50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Resignation-Move overseas/interstate</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>Secondary</td>\n",
       "      <td>Far North Queensland</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>True</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>Male</td>\n",
       "      <td>31-35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id                        separationtype  cease_date  dete_start_date  \\\n",
       "3    4             Resignation-Other reasons      2012.0           2005.0   \n",
       "5    6             Resignation-Other reasons      2012.0           1994.0   \n",
       "8    9             Resignation-Other reasons      2012.0           2009.0   \n",
       "9   10            Resignation-Other employer      2012.0           1997.0   \n",
       "11  12  Resignation-Move overseas/interstate      2012.0           2009.0   \n",
       "\n",
       "    role_start_date          position classification                region  \\\n",
       "3            2006.0           Teacher        Primary    Central Queensland   \n",
       "5            1997.0  Guidance Officer            NaN        Central Office   \n",
       "8            2009.0           Teacher      Secondary      North Queensland   \n",
       "9            2008.0      Teacher Aide            NaN                   NaN   \n",
       "11           2009.0           Teacher      Secondary  Far North Queensland   \n",
       "\n",
       "           business_unit    employment_status  career_move_to_public_sector  \\\n",
       "3                    NaN  Permanent Full-time                         False   \n",
       "5   Education Queensland  Permanent Full-time                         False   \n",
       "8                    NaN  Permanent Full-time                         False   \n",
       "9                    NaN  Permanent Part-time                         False   \n",
       "11                   NaN  Permanent Full-time                         False   \n",
       "\n",
       "    career_move_to_private_sector  interpersonal_conflicts  \\\n",
       "3                            True                    False   \n",
       "5                            True                    False   \n",
       "8                            True                    False   \n",
       "9                           False                     True   \n",
       "11                          False                    False   \n",
       "\n",
       "    job_dissatisfaction  dissatisfaction_with_the_department  \\\n",
       "3                 False                                False   \n",
       "5                 False                                False   \n",
       "8                 False                                False   \n",
       "9                  True                                 True   \n",
       "11                False                                False   \n",
       "\n",
       "    physical_work_environment  lack_of_recognition  lack_of_job_security  \\\n",
       "3                       False                False                 False   \n",
       "5                       False                False                 False   \n",
       "8                       False                False                 False   \n",
       "9                       False                False                 False   \n",
       "11                      False                False                 False   \n",
       "\n",
       "    work_location  employment_conditions  maternity/family  relocation  \\\n",
       "3           False                  False             False       False   \n",
       "5           False                   True              True       False   \n",
       "8           False                  False             False       False   \n",
       "9           False                  False             False       False   \n",
       "11          False                  False              True        True   \n",
       "\n",
       "    study/travel  ill_health  traumatic_incident  work_life_balance  workload  \\\n",
       "3          False       False               False              False     False   \n",
       "5          False       False               False              False     False   \n",
       "8          False       False               False              False     False   \n",
       "9          False       False               False              False     False   \n",
       "11         False       False               False              False     False   \n",
       "\n",
       "    none_of_the_above  gender    age aboriginal torres_strait south_sea  \\\n",
       "3               False  Female  36-40        NaN           NaN       NaN   \n",
       "5               False  Female  41-45        NaN           NaN       NaN   \n",
       "8               False  Female  31-35        NaN           NaN       NaN   \n",
       "9               False  Female  46-50        NaN           NaN       NaN   \n",
       "11              False    Male  31-35        NaN           NaN       NaN   \n",
       "\n",
       "   disability nesb  institute_service  dissatisfied  \n",
       "3         NaN  NaN                7.0         False  \n",
       "5         NaN  NaN               18.0          True  \n",
       "8         NaN  NaN                3.0         False  \n",
       "9         NaN  NaN               15.0          True  \n",
       "11        NaN  NaN                3.0         False  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unifying the column\n",
    "job_dissatisfaction = ['job_dissatisfaction',\n",
    "'dissatisfaction_with_the_department',\n",
    "'physical_work_environment',\n",
    "'lack_of_recognition',\n",
    "'lack_of_job_security',\n",
    "'work_location',\n",
    "'employment_conditions',\n",
    "'work_life_balance',\n",
    "'workload']\n",
    "dete_resignations['dissatisfied'] = dete_resignations[job_dissatisfaction].any(axis = 1, skipna = False)\n",
    "dete_resignations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0664bb86-bd19-47dd-9a74-848e03ee2ae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Updating the changes\n",
    "dete_resignations_up = dete_resignations.copy()\n",
    "tafe_resignations_up = tafe_resignations.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6047cd95-c40f-4c4f-9489-baffdec7fa80",
   "metadata": {},
   "source": [
    "- We identifyed any employees who resigned because they were dissatisfied;\n",
    "- If the employee indicated any of the factors above caused them to resign, we marked them as dissatisfied in a new column in both datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b390764-127e-426f-b940-c9ddff74d004",
   "metadata": {},
   "source": [
    "### 7. Combining the data\n",
    "- On this topic, we'll combine the two datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f45bcfb0-6f88-4608-b7f2-4023dc54c69d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a 'institution' column\n",
    "dete_resignations_up['institute'] = 'DETE'\n",
    "tafe_resignations_up['institute'] = 'TAFE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8f090a85-d826-4841-983c-38b9fad72a63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torres_strait                                            0\n",
       "south_sea                                                3\n",
       "aboriginal                                               7\n",
       "disability                                               8\n",
       "nesb                                                     9\n",
       "business_unit                                           32\n",
       "classification                                         161\n",
       "region                                                 265\n",
       "role_start_date                                        271\n",
       "dete_start_date                                        283\n",
       "role_service                                           290\n",
       "none_of_the_above                                      311\n",
       "work_life_balance                                      311\n",
       "traumatic_incident                                     311\n",
       "ill_health                                             311\n",
       "study/travel                                           311\n",
       "relocation                                             311\n",
       "maternity/family                                       311\n",
       "employment_conditions                                  311\n",
       "workload                                               311\n",
       "lack_of_job_security                                   311\n",
       "career_move_to_public_sector                           311\n",
       "career_move_to_private_sector                          311\n",
       "interpersonal_conflicts                                311\n",
       "work_location                                          311\n",
       "dissatisfaction_with_the_department                    311\n",
       "physical_work_environment                              311\n",
       "lack_of_recognition                                    311\n",
       "job_dissatisfaction                                    311\n",
       "Contributing Factors. Job Dissatisfaction              332\n",
       "Contributing Factors. Travel                           332\n",
       "Contributing Factors. Maternity/Family                 332\n",
       "Contributing Factors. Ill Health                       332\n",
       "Contributing Factors. Career Move - Self-employment    332\n",
       "Contributing Factors. Career Move - Private Sector     332\n",
       "Contributing Factors. Career Move - Public Sector      332\n",
       "Contributing Factors. Dissatisfaction                  332\n",
       "Contributing Factors. Other                            332\n",
       "Contributing Factors. Interpersonal Conflict           332\n",
       "Contributing Factors. NONE                             332\n",
       "Contributing Factors. Study                            332\n",
       "Institute                                              340\n",
       "WorkArea                                               340\n",
       "institute_service                                      563\n",
       "gender                                                 592\n",
       "age                                                    596\n",
       "employment_status                                      597\n",
       "position                                               598\n",
       "cease_date                                             635\n",
       "dissatisfied                                           651\n",
       "separationtype                                         651\n",
       "institute                                              651\n",
       "id                                                     651\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the dataframes\n",
    "combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index=True)\n",
    "\n",
    "# Verify the number of non null values in each column\n",
    "combined.notnull().sum().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c4b88e08-6519-49f4-ba69-a774498e49fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 651 entries, 0 to 650\n",
      "Data columns (total 10 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 651 non-null    float64\n",
      " 1   separationtype     651 non-null    object \n",
      " 2   cease_date         635 non-null    float64\n",
      " 3   position           598 non-null    object \n",
      " 4   employment_status  597 non-null    object \n",
      " 5   gender             592 non-null    object \n",
      " 6   age                596 non-null    object \n",
      " 7   institute_service  563 non-null    object \n",
      " 8   dissatisfied       651 non-null    bool   \n",
      " 9   institute          651 non-null    object \n",
      "dtypes: bool(1), float64(2), object(7)\n",
      "memory usage: 46.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# Drop columns with less than 500 non null values\n",
    "combined_updated = combined.dropna(thresh = 500, axis =1).copy()\n",
    "combined_updated.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ef37d5bf-34da-4f66-95fe-5516c45ae2b1",
   "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>separationtype</th>\n",
       "      <th>cease_date</th>\n",
       "      <th>position</th>\n",
       "      <th>employment_status</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>institute_service</th>\n",
       "      <th>dissatisfied</th>\n",
       "      <th>institute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Resignation-Other reasons</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>Permanent Full-time</td>\n",
       "      <td>Female</td>\n",
       "      <td>36-40</td>\n",
       "      <td>7.0</td>\n",
       "      <td>False</td>\n",
       "      <td>DETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Resignation-Other reasons</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Guidance Officer</td>\n",
       "      <td>Permanent Full-time</td>\n",
       "      <td>Female</td>\n",
       "      <td>41-45</td>\n",
       "      <td>18.0</td>\n",
       "      <td>True</td>\n",
       "      <td>DETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>Resignation-Other reasons</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>Permanent Full-time</td>\n",
       "      <td>Female</td>\n",
       "      <td>31-35</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "      <td>DETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Resignation-Other employer</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Teacher Aide</td>\n",
       "      <td>Permanent Part-time</td>\n",
       "      <td>Female</td>\n",
       "      <td>46-50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>True</td>\n",
       "      <td>DETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.0</td>\n",
       "      <td>Resignation-Move overseas/interstate</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>Permanent Full-time</td>\n",
       "      <td>Male</td>\n",
       "      <td>31-35</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "      <td>DETE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                        separationtype  cease_date          position  \\\n",
       "0   4.0             Resignation-Other reasons      2012.0           Teacher   \n",
       "1   6.0             Resignation-Other reasons      2012.0  Guidance Officer   \n",
       "2   9.0             Resignation-Other reasons      2012.0           Teacher   \n",
       "3  10.0            Resignation-Other employer      2012.0      Teacher Aide   \n",
       "4  12.0  Resignation-Move overseas/interstate      2012.0           Teacher   \n",
       "\n",
       "     employment_status  gender    age institute_service  dissatisfied  \\\n",
       "0  Permanent Full-time  Female  36-40               7.0         False   \n",
       "1  Permanent Full-time  Female  41-45              18.0          True   \n",
       "2  Permanent Full-time  Female  31-35               3.0         False   \n",
       "3  Permanent Part-time  Female  46-50              15.0          True   \n",
       "4  Permanent Full-time    Male  31-35               3.0         False   \n",
       "\n",
       "  institute  \n",
       "0      DETE  \n",
       "1      DETE  \n",
       "2      DETE  \n",
       "3      DETE  \n",
       "4      DETE  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_updated.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f976bbe0-3a79-463e-ad12-92bd2d9d04fe",
   "metadata": {},
   "source": [
    "- At this point, we merged the data and drop columns that had less than 500 non null values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "288d5e25-13fc-4f08-bd40-3ea3e24be613",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 8. Cleaning the service column\n",
    "- With our main goal in mind (identify the main reasons of dissatisfaction of the employees that exit, the ones who worked for a short period and the ones that stayed longer), next, we'll clean the institute_service column and categorize employees according to the following definitions (We used [this article](https://www.businesswire.com/news/home/20171108006002/en/Age-Number-Engage-Employees-Career-Stage) to base our classification):\n",
    "  - New: Less than 3 years in the workplace;\n",
    "  - Experienced: 3-6 years in the workplace;\n",
    "  - Established: 7-10 years in the workplace;\n",
    "  - Veteran: 11 or more years in the workplace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "25922f05-36ed-42eb-b918-fd93fe65e7ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN                   88\n",
       "Less than 1 year      73\n",
       "1-2                   64\n",
       "3-4                   63\n",
       "5-6                   33\n",
       "11-20                 26\n",
       "5.0                   23\n",
       "1.0                   22\n",
       "7-10                  21\n",
       "3.0                   20\n",
       "0.0                   20\n",
       "6.0                   17\n",
       "4.0                   16\n",
       "9.0                   14\n",
       "2.0                   14\n",
       "7.0                   13\n",
       "More than 20 years    10\n",
       "13.0                   8\n",
       "8.0                    8\n",
       "15.0                   7\n",
       "20.0                   7\n",
       "10.0                   6\n",
       "14.0                   6\n",
       "12.0                   6\n",
       "17.0                   6\n",
       "22.0                   6\n",
       "18.0                   5\n",
       "16.0                   5\n",
       "11.0                   4\n",
       "23.0                   4\n",
       "24.0                   4\n",
       "32.0                   3\n",
       "39.0                   3\n",
       "19.0                   3\n",
       "21.0                   3\n",
       "36.0                   2\n",
       "25.0                   2\n",
       "30.0                   2\n",
       "26.0                   2\n",
       "28.0                   2\n",
       "49.0                   1\n",
       "41.0                   1\n",
       "27.0                   1\n",
       "42.0                   1\n",
       "29.0                   1\n",
       "34.0                   1\n",
       "31.0                   1\n",
       "33.0                   1\n",
       "35.0                   1\n",
       "38.0                   1\n",
       "Name: institute_service, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the unique values\n",
    "combined_updated['institute_service'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7019d912-3a1e-4c4d-a15a-9daec3f8768b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     159\n",
       "3.0      83\n",
       "5.0      56\n",
       "7.0      34\n",
       "11.0     30\n",
       "0.0      20\n",
       "20.0     17\n",
       "6.0      17\n",
       "4.0      16\n",
       "9.0      14\n",
       "2.0      14\n",
       "13.0      8\n",
       "8.0       8\n",
       "15.0      7\n",
       "17.0      6\n",
       "10.0      6\n",
       "22.0      6\n",
       "14.0      6\n",
       "12.0      6\n",
       "18.0      5\n",
       "16.0      5\n",
       "24.0      4\n",
       "23.0      4\n",
       "21.0      3\n",
       "19.0      3\n",
       "39.0      3\n",
       "32.0      3\n",
       "25.0      2\n",
       "28.0      2\n",
       "26.0      2\n",
       "36.0      2\n",
       "30.0      2\n",
       "34.0      1\n",
       "27.0      1\n",
       "29.0      1\n",
       "42.0      1\n",
       "33.0      1\n",
       "41.0      1\n",
       "35.0      1\n",
       "49.0      1\n",
       "38.0      1\n",
       "31.0      1\n",
       "Name: institute_service_up, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting the years of service and convert the type to float\n",
    "combined_updated['institute_service_up'] = combined_updated['institute_service'].astype('str').str.extract(r'(\\d+)')\n",
    "combined_updated['institute_service_up'] = combined_updated['institute_service_up'].astype('float')\n",
    "\n",
    "# Checking the years extracted are correct\n",
    "combined_updated['institute_service_up'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8557a50a-dacc-4cb4-8e40-b8bf06e6db1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New            193\n",
       "Experienced    172\n",
       "Veteran        136\n",
       "Established     62\n",
       "Name: service_cat, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert years of services in categories\n",
    "def transform_service(val):\n",
    "    \"\"\"Convert years of service to categories.\n",
    "\n",
    "    Args:\n",
    "      val: The value to be converted.\n",
    "\n",
    "    Returns:\n",
    "      object.\n",
    "    \"\"\"\n",
    "    if val >= 11:\n",
    "        return \"Veteran\"\n",
    "    elif 7 <= val < 11:\n",
    "        return \"Established\"\n",
    "    elif 3 <= val < 7:\n",
    "        return \"Experienced\"\n",
    "    elif pd.isnull(val):\n",
    "        return np.nan\n",
    "    else:\n",
    "        return \"New\"\n",
    "combined_updated['service_cat'] = combined_updated['institute_service_up'].apply(transform_service)\n",
    "\n",
    "# Checking the unique values\n",
    "combined_updated['service_cat'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1788744-ca9c-4888-990b-ab30517d7b6e",
   "metadata": {},
   "source": [
    "### 9. Checking other columns\n",
    "- In this topic, we'll check other columns that can be useful for our analysis: 'age', 'gender' and 'employment_status'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ba4fb542-396e-4410-81d2-e8344ce102d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51-55            71\n",
       "41-45            48\n",
       "41  45           45\n",
       "46-50            42\n",
       "36-40            41\n",
       "46  50           39\n",
       "26-30            35\n",
       "21  25           33\n",
       "36  40           32\n",
       "31  35           32\n",
       "26  30           32\n",
       "21-25            29\n",
       "56 or older      29\n",
       "31-35            29\n",
       "56-60            26\n",
       "61 or older      23\n",
       "20 or younger    10\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_updated['age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9cd900bc-f609-4b10-96bc-e46ff5415469",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaning the values\n",
    "age_map = {\n",
    "    '51-55':'51-55',\n",
    "    '41-45':'41-45',\n",
    "    '41  45':'41-45',\n",
    "    '46-50':'46-50',\n",
    "    '36-40':'36-40',\n",
    "    '46  50':'46-50',\n",
    "    '26-30':'26-30',\n",
    "    '21  25':'21-25',\n",
    "    '36  40':'36-40',\n",
    "    '31  35':'31-35',\n",
    "    '26  30':'26-30',\n",
    "    '21-25':'21-25',\n",
    "    '56 or older':'56+',\n",
    "    '31-35':'31-35',\n",
    "    '56-60':'56+',\n",
    "    '61 or older':'56+',\n",
    "    '20 or younger':'20-'\n",
    "    }\n",
    "combined_updated['age'] = combined_updated['age'].map(age_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3ee64695-05f8-4dc8-8ab2-43e8404d6b59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41-45    93\n",
       "46-50    81\n",
       "56+      78\n",
       "36-40    73\n",
       "51-55    71\n",
       "26-30    67\n",
       "21-25    62\n",
       "31-35    61\n",
       "NaN      55\n",
       "20-      10\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the unique values\n",
    "combined_updated['age'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1e8edf71-f80b-436f-94cc-f1a942d7a4ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    424\n",
       "Male      168\n",
       "NaN        59\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking gender values\n",
    "combined_updated['gender'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cc0e09dc-11b5-4384-ab21-998238f4ffbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Permanent Full-time    256\n",
       "Permanent Part-time    150\n",
       "Temporary Full-time    120\n",
       "NaN                     54\n",
       "Temporary Part-time     37\n",
       "Contract/casual         29\n",
       "Casual                   5\n",
       "Name: employment_status, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking employment_status values\n",
    "combined_updated['employment_status'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4f34bc4f-d16b-4fec-83a5-4872ddd3d803",
   "metadata": {},
   "outputs": [],
   "source": [
    "es_map = {\n",
    "    'Permanent Full-time':'Perm. FT',\n",
    "    'Permanent Part-time':'Perm. PT',\n",
    "    'Temporary Full-time':'Temp. FT',\n",
    "    'Temporary Part-time':'Temp. PT',\n",
    "    'Contract/casual':'Cont./Cas.',\n",
    "    'Casual':'Cont./Cas.'\n",
    "    }\n",
    "combined_updated['employment_status'] = combined_updated['employment_status'].map(es_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "938c6abb-e7be-4941-881c-70a681d8a7a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Perm. FT      256\n",
       "Perm. PT      150\n",
       "Temp. FT      120\n",
       "NaN            54\n",
       "Temp. PT       37\n",
       "Cont./Cas.     34\n",
       "Name: employment_status, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_updated['employment_status'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2322a8dd-8e7d-4592-b499-a2881ec1e4c7",
   "metadata": {},
   "source": [
    "### 10. Dealing with missing values\n",
    "- Last section, we identified some columns that can be useful for our analysis that have some missing values. The missing values represent less than 10% of the total for these columns, so we'll fill with the most commom value of each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "45094503-f39e-4a9e-8aba-4b8d19a50a75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace missing values with the most frequent value, '41-45'\n",
    "combined_updated['age'] = combined_updated['age'].fillna('41-45')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7819bad2-d1e6-46f9-a68c-e0dea80bfaae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41-45    148\n",
       "46-50     81\n",
       "56+       78\n",
       "36-40     73\n",
       "51-55     71\n",
       "26-30     67\n",
       "21-25     62\n",
       "31-35     61\n",
       "20-       10\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking new values\n",
    "combined_updated['age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "99167fb7-19db-4d6a-87ad-ed5a1ff2450a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace missing values with the most frequent value, 'Female'\n",
    "combined_updated['gender'] = combined_updated['gender'].fillna('Female')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b27e91be-ec02-405f-9d80-a9dc0b5179f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    483\n",
       "Male      168\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking new values\n",
    "combined_updated['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "34bbb90a-c757-4dce-83f9-b1824fa01409",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace missing values with the most frequent value, 'Permanent Full-time'\n",
    "combined_updated['employment_status'] = combined_updated['employment_status'].fillna('Perm. FT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "616698f1-da78-4d3a-96d7-baa3151e0feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Perm. FT      310\n",
       "Perm. PT      150\n",
       "Temp. FT      120\n",
       "Temp. PT       37\n",
       "Cont./Cas.     34\n",
       "Name: employment_status, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking new values\n",
    "combined_updated['employment_status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f130ef1-10f3-48dc-9c08-c4fcc08725bf",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)\n",
    "- After the data cleaning, we're ready for our analysis. As our goal is to identify the main reasons of dissatisfaction of the employees that exit (the ones who worked for a short period and the ones that stayed longer), we're going to base our exploration on the 'dissatisfaction' column and the 'service_cat' categories.  \n",
    "- First, we'll compare dissatisfaction between the 'service_cat' categories. After, we'll analyse each category with other columns: 'age', 'gender', and 'employment_status', so we can  characterize more the dissatisfaction.\n",
    "- Remembering that we are classifying the time of service in four categories:\n",
    "  - New: Less than 3 years in the workplace;\n",
    "  - Experienced: 3-6 years in the workplace;\n",
    "  - Established: 7-10 years in the workplace;\n",
    "  - Veteran: 11 or more years in the workplace.\n",
    "- Understanding the [differences of engagement and dissatisfaction in each category](https://www.businesswire.com/news/home/20171108006002/en/Age-Number-Engage-Employees-Career-Stage) is a key factor to find solutions to this problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e2e93d72-b1a0-472a-91fc-50d7c6eddb9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='service_cat'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n",
    "dis_sc = combined_updated.pivot_table(index='service_cat', values='dissatisfied')\n",
    "dis_sc.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "dis_sc = dis_sc['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "dis_sc.plot(kind='bar', rot=30, legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9a9d76f5-8d46-46bf-9047-d3a653bcd14d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n",
    "dis_age = combined_updated.pivot_table(index='age', values='dissatisfied')\n",
    "dis_age.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "dis_age = dis_age['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "dis_age.plot(kind='bar', rot=30, legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e3d1cbec-fa5f-47fb-bc88-f5b21b54a800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n",
    "dis_gen = combined_updated.pivot_table(index='gender', values='dissatisfied')\n",
    "dis_gen.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "dis_gen = dis_gen['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "dis_gen.plot(kind='bar', rot=30, legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0a98fec6-e6af-4344-a395-80be34be3353",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='employment_status'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aUP4AbCFpU0mrA58EbuqaYpmZWVs6vAceEW9IOgG4FegH/DAiHumykpmZ2Up1pgmFiLgFuKWLytJTek1zTi/guljOdbGc62K5Xl0XHT6JaWZmteVL6VeRJNW6DNa7SDpI0rBal6M3kLS1pCNrXY7eQNJm3X1xowN8FUg6HphQ63JY7yBpP0m/BT4GLK51eWpJ0lqSzgUuB16tdXlqqaourgPW7c7PcoC3g6T984b6PtJJW+vDJG0j6UbgKuC+iDgkIl6sdblqRdJngTnA3sDeEfGTGhepZvJO3uPA2IgYFRH3dufnOcBXIh8O3gD8FHg+IsZHxLNuRgFJfe67I2lNSZ8DzgPuAMYDm0gakuf3qe+FpHUkfQHYHfg08ASpa3GnOkeUSFKjpEuBDwNfAUJSQ57Xr7s+t89thO0hqb+k9wCfAWYAmwFbS9oKIPrwmV8lq0XE0lqXpSdJOg74NRDAfhFxPnAfsAg4toZF63GSNpd0GXAG8IeI2D8ibgfuAY6mm5sNehNJ75Z0BXAaMCUiPh4RPwQeAr6TF+u2bcUB3oykE4HbgK8B34uISyPiGeAXpC9snxbJUkkjJF0vaYdal6k7SXqnpPuB/YAJEfHdiHg5z14IXAvsImnLiIh6PzKRdBbpgr0HgNWBYyVVrlT8DunS8z27c6+zt8h1cQvw+4j4REQ8UDX7HNL3Yrv8veiW+qjrL9uqqNpQ9yAdGr8MfE3S2/IiXwJGSdozL99nDpebh1LeG/0J8MuI+ENry9WJdYHHgMsi4nFJ78m9TgZGxOvATKAJOA6gno9MJK0LDAfuiIgfAJ8n1c8WkC7uAy4FDgbeWaNi9oiqurgtIr6bp32w0pwWEY+RRmg9Iz9/szvKUY8bXEdVNtTpETEfOBHYDXg3QN5YvwP8T35e980olUBuIZSGAF8Frpc0XNJoSWtUlquzIH+EtJf1UUk/BH4GNETEvwEi4p/ADcC7JO1bs1L2gLyuFwLDJI3Kw0jPAxqqlrkWeAEYX2kDrkdVdbGhpOMk3Ura8ave074YGCzpUOie7aKeNrTOqmyoH5C0YUT8C7gX+FdlgTwwl+r97kOSBsDy4M53XrpM0kfzoeAi4GTSVWpn538n5GWnAB+qRbm7Q96rvI/UNW40sHNETGu22F9IJzXf0bOlq4mHSdvFlyVdDIwAfgsrnKz7HrAtsGZNSthzHia1dX8buDgijoyIhZWZOeR/DByan3f50ZmvxKwiaXNSEI0CNiR9UU+NiNcl9YuINyV9CNg9Ir5Sy7J2h7z+J5OG/L2Y9AP/XdLh8IWkAcsejoizJL0LeCaPifNF4LWIuEDS2yPiuRqtQreRtA/p7lPTIuKByvehav7qeY+07uWLU34EPB0R42pdnlrKdXE68P2IuLP590CSuvNo3XvgVSLicVJor08anOvk3HSyrA0rIu6sp/CutOVL+gbp5NRfSAG+Fukk1ZURsQewOWmv6mhJO0TEX0mH0qcDh7H85h5/79k16JzKXmNr5zSqpjcBfwUOzBvpCm2alY225HMjq3CI/zjwfeDfldfUWbPZqqzPk8DPgeMlrRkRr1V/B7q7qbWuKn1lVnFDvQ7YSFKrh4D18oXNZ8g3IAX0hyPivIj4ZUS8kC9OuTOfbd85IkaS2oC/nF9+MOnH7sMRcX3l/WqwGqus8n2o+mGOqnlv2QAj4u/A74GNSFdetqiU9W8u7ym26xA//1jdAYjU57muTt6uYl28AtwJ/Bv4Qp7WY9+Buu9wXznUbW1DrdpAK//+I/dGOZrUdeynLb1v6V/YHGAnka6gC2B4vkip8sNUqadBpBO8387PXyV1E3sfcG7lCKW0vuGV74Okj5M2vHtJvWruyD9qLR36PkhqOvgDdaJq+whJg0kn6hcAl0fEnJW89BlgKinE60In62IaNaiLPtMG3tKGmqe/ZUOVNAjYiXSRwks9XtgeIGkN4HhgNumHfA/g/0fEW24fJWk66Z6n25J6HVwSETOr5ndrO19XqOxVV8qZjzomAa8AVwI7k05STomIX7fn/Xr7Oq9M8x9cSe8AjgQG5knrkP6fZ5X247yqSq6LumgGqKas6vkGkqYBe5IO9xYBx0jaHVo+3ImIJRFxe0S8VHKb5spExKukL+g2pDbNzYH3VC8jaZCk7YBTgN8Bv42IYyrh3TwUe6tK2OY9q8pRp4B9SUMk3EXqOTEDOCS/Zg1Je0t6y30Iofevc2uq/s8qPYz2kPQQMBnYNiJOI524/l/gwMqyShduvbtGxe4W9VAXdRXg3lBb1vxHqOr5NNIX82+kppRPSNqxatGPkY5a/hUR10bEmdWv7+11U9XOXdnr/grwI0kH5Tbt/wH+My+zCHgeeDMfnWxNaj7qDTe07RJ577FSF+sqDfv6JeAI0sBcu0paKyL+l9RMNETSPvlc0G5At1yMUgv1Uhd1EeDeUFsmaW1JWzcP2kobL6nnyB2kOvguqV6ulHSqpNuBo4ALIuKF5q/vmTXomMpRWFU79/qSKj1pbga+rTQEwGXAgnySFtIVhWtHxKsR8WBEXN3b13VV5L3HNSSdA3wLeBcwNCL+GBE3kILqq3nxmaQ9zw8Cr0bE+ZF6HtWFuqmLiCj2j7R3rarn65Pacn8EHERqr92BdKXYr4Gz8nKfBa6qdfl7oH7eThrDZSRwAdDYbH4DcBawa9W0saSjk8NrXf4uWP935PWfCdwObJWnnwH8gNSE9FHgddIJuVuBD1a+W7Uufxes/2rNno8Gria1/QvYnvQj9sk8f0vSiIKVehoOrF7r9XBdtP5X9B54ZJLeIekXpA3wVODbEXE1aaP8b9KRxjnAKZKmkppUfgBl99ttiZZ3l1wt0gU1q5OulFtEOmFJ1fxXSHvdy25SERH3RsRVka82VAHdJavWWVreL/mzpAHJbgTGAS+RDo8hbbTvIn0PfkVq81wjIj4aqZmNyFttiSr1EMvbdisn44aQjrb+kddvHmlQqg9IekdEPArcDXwCICLmReEXJ9V7XfT6jbOaN9TWNW82IF1JCmlAnYWkI47Xq5qbKmfSpwBrSdq0pfeNXnTGvSWSTiKNCFj5vxyUZy0F3g/MjoinSN+FrSXtGBGLSU0pE4C1SQMwfUDSyB4ufpdS6j1V2bFZqjROzU3AeZK+mL/zl5DGbVkvUtPYA8AbwKfy2xwZ+VxHyfpKXRQT4N5QV67qaGRTpbvF3ChpfERcQmom+X7eE6m0C79d0sHAxqQrDNepXelXnaTdJN1Hapc8XtIHJN0JnCnphIi4iDQ42XvzS2aTjkQ+D5DnHxMRL0XEX0g9bRb18Gp0CUn9JH0TuFnSCUo3IlmbdLXkVGAiqd1/H9K4Lq+Q9yyBP+VpTTUoepfrc3VR6zactv5IZ3zvI434tjHwAdKVTxcAJ+RlrgWOyI8HkZpRrqp6j02qHh8KvLPW69VFdbNa1WPldfsVcADpLik3Afvn+Q+QxjJZHfgPUvv4fnleY0+Wu5PrPAy4BniUdAJ21/x//gtSu+bepJOz2+fHPwfenV87mtQLaaNK3VF4WzepDf850lHonnn9JpPO/RxDGubg18APgTXy9+Qw0pHZ1vVQB325LmpegJX8Z3hDbb1u3rIuwADS3sWCqmkn5S/w23Jo30oa6+ScWq9DJ9b946S7n/QDTiANJHQ6afjfT5Guljy5avkfkc5/9KfZiax6+CNdcLa06vm+pCPNT+dt5zrSMAjkOhhGal47Dtio1uV3XXTur9deial05eR7SZdwH0vqYQLwT1I79+dIl7iem5f/Eakr4Gmk/8Re3XbbEZLGkvYc7o2IVyXtRbrt21zSGfR/A+cDd0fE9yS9kxTqc4CLSOcDInpLF6hOkrQLqddRA+mq0itJ4b1YacD9QaSms/8EJsXy5qNedTVdZ0m6GlgSEZ/O35HPR8Q4STOAX5LaeiHdhGNBRBxXq7J2t75WF712LJSIuK7yWNIslm+op5A21F2bbajnkDbUiKobC9TDhqo0dOskUrPHlcAiSZuQLjw4HdgH+CYppC8HjpJ0fUT8TdJs0p7G4IiYl99vNXKzeU+vS1fIJ2sjIn4raXvSyIl3AX/L34mxwDdIP/A/Iv2ALVMP34lmjgGekvQysCOprRfSd+NTpAHIhgE3R8T/q0kJe06fqoteuwcOK443oXQ38LWAXUj3oPtGCxtq3VG68utq4P6oOiMu6WjS5b6fzV0hzyKNV3Itaa+7X563wrjV9aLy3VC6+XTlFnifIIX1VqQLkKZVLV8XP+atURqT/UzSD/WrzeZtBSyMiOdrUrge1pfqolf3QskbaKWf9i9JF17cCxwg6XJSc8GPq8O7hH7Lq2h74N+x/DL2yvoJ+IekjfKP3O9JJ3KfA64HnshdBis/gHXV373ywx6pB8lfgKGksW5OjYjGWN6PfYXxLupVREwijcX+QVg2WFll3tx6Caz26Et10WubUCqqN1RJfyEF2leAhyKNUwCscFhdbxvqUqClPtqPkEYH3Id0ouYF4I85tO+JiLurFy61uWRlqo7QfgG8Rjo3sDjPWzY0aE0L2bNOJXWbXb35nmcf1CfqotcHOPT5DfUZYI6kfSNiBvmanYj4jaRG4CBJ+5GaDT5Xj80lran6cf8HzcZt70v1UBER05VG3+xHOpFfr9tEm/pKXfTqNnBbdvXpRGAD4GuRBuOqXGl2NOkK1GGRryzty6rPmZj1BUW2F9dbe+7K5D3Jy0lHS9Mk7SDpINLFTO8m9by4C+qy/X+VOLytr/EeeCEkrU7qEz+ANCzqtyLfVcjM+iYHeGGquwVW9bDwf6JZH1TESUxbQV1dpGRmHec9cDOzQvXpk15mZiVzgJuZFcoBbmZWKAe4mVmhHOBmZoVygFuvJWlJrctQLd8Y99AOvG6IpDZvHNDe5cwqHOBm7TecdN/RVTWEdNuurlrODHCAWxeR9F+SHpA0S9IP8t3Bl0g6R9Ijkn4taUdJd0l6Mo+giKQjJN2Ypz8m6WstvLfy+zws6U+SDs7Tp0k6oGq5KyTtn9/zBkm3SZqndHfykyU9KOn3kt6Wl99M0i8lzZT0G0lb5uk/lnSBpPtyWcfljzgb+EBex8+3Ug/bVNXDQ5K2yK/bLE87R9IgSbdL+mNen/2r3r96uQ8q3Qqs8t4XSjoiPz5b0pz8GZM6839nBYtecGNO/5X9RxrK9mZgQH7+PeBw0s0k9srTrgd+RRrLZRQwK08/AngWWA9YE3gYaMzzluR/PwHcRrqR8YbA30g3rP4P4Ia8zGDgKdLVxUcAj5PuhzkUeBE4Ji83GTgpP74d2CI/3gm4Iz/+MemG2qsBWwOP5+kfBGa0URffBQ7Lj1fP6zQceLhqmf7AOvnx+rmsamG5FT4PuDCv23rAn1l+Id6QWn8H/FebP19Kb13hw8AY4A95eJY1gedJY7f/Mi/zJ+DViHhd0p9IYVVxW0QsBJB0Hem2eU1V83cBroo0BszfJd0N7BARN0n6nqShpJC/NiLeyGW4M9KY8YslvUj6gamUY2Qejndn4JqqwS3XqPrMGyINVTBH0oarUBe/A74iaRhwXUQ81sLgmQLOkrQraWiEjUk/TO31IvAKcFneQ5/RxvJWpxzg1hUETI2I01aYKH0xIipjNSwFXoV0ezNJ1d+95uM5rMr4DtOA/wI+SbppbUX1XViWVj1fSvrerwYsiojRrbxv9evbPXxxRFwp6X7SnZJuUbp36ZPNFjuMdGQwJv+gzSPdsLu5N1ixmbMhf8YbknYk/XCOA04AdmtvGa1+uA3cusLtwDhJGwBIepukd63C6z+SX7MmcADpvqfVfgMcnNvVhwK7Ag/keT8GTgKIiDm0U0S8RLp7+YG5zJI0qo2XLSY1y7RK0ruBJyPiAtLNNka28LrBwPM5vD8EVOqq+XJ/BbaWtIakIaTArtzMY3BE3AJ8ntQkZX2Q98Ct0yJijqSvAr/KN5V4HTh+Fd7iAeBaYBjwk4hoajb/euD9wGzS3vmXIt28mYj4u6S5wA0dKPphwPdz2QcA0/NntOYh4E1Js0k3057cwjIHAeMlvQ48B5wVES9IulfSw6TbAn4LuDk3JTUBj+Z1WVi9XEScIulq0nmBp4AH82esDdwoqYF0dHByB9bd6oBHI7Sayr0qGiPihA6+fiCpXXv7iHixK8tm1tu5CcWKJWl3YC7wXYe39UXeAzfrAEkfJTWFVHsqIj5Wi/JY3+QANzMrlJtQzMwK5QA3MyuUA9zMrFAOcDOzQjnAzcwK9X8aSZ0Y6Gs7VAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n",
    "dis_es = combined_updated.pivot_table(index='employment_status', values='dissatisfied')\n",
    "dis_es.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "dis_es = dis_es['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "dis_es.plot(kind='bar', rot=30, legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d263a457-9f45-4c14-b86b-7790bf1d3cb2",
   "metadata": {},
   "source": [
    "- Observing the general part of our analysis, we can highlight some categories with more dissatisfaction on our set: Establisheds and Veterans; Age rate: 56+, 51-55, 26-30; Gender: Male(with a little difference for Female); Employment Status: Permanent Full-time, followed by Permanent Part-time. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca252cd-54d8-4244-902d-b665677b4027",
   "metadata": {},
   "source": [
    "### 1. New\n",
    "- On this topic, we'll analyze the same columns as above, but for employees with less than 3 years in the workplace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "519edc13-a43c-4bcd-bb65-764bcda7daf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slicing the category\n",
    "new = combined_updated[combined_updated['service_cat'] == 'New']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "15084715-1e77-412f-a0aa-b421a5c658cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ETvOmAr+MiO2BX+ZpMzMrULcFPCJ+DTzdafbbgdn5+WzgHfWNZWZm3elpH/gWEbE4P/8bsMXqXihpiqQOSR1Lly7t4erMzKyzXh/EjIgAYg3LZ0ZEe0S0t7W19XZ1ZmaW9bSAPyVpKED+uaR+kczMrBY9LeA3Ayfm5ycCN9UnjpmZ1aqWYYTfA34D7ChpkaSTgPOAt0l6FDgoT5uZWYH6dfeCiDhuNYveWucsZma2FnwmpplZSbmAm5mVlAu4mVlJuYCbmZWUC7iZWUm5gJuZlZQLuJlZSbmAm5mVlAu4mVlJuYCbmZWUC7iZWUm5gJuZlZQLuJlZSbmAm5mVlAu4mVlJuYCbmZWUC7iZWUl1e0eeNZH0OLAceBFYFRHt9QhlZmbd61UBzw6IiL/X4e+YmdlacBeKmVlJ9baAB/BzSXMkTenqBZKmSOqQ1LF06dJers7MzCp6W8D3iYhxwKHAxyTt1/kFETEzItojor2tra2XqzMzs4peFfCIeDL/XALcAOxRj1BmZta9HhdwSRtKGlR5DhwMPFSvYGZmtma9GYWyBXCDpMrfuSoiflqXVGZm1q0eF/CI+CMwpo5ZzMxsLXgYoZlZSbmAm5mVlAu4mVlJuYCbmZWUC7iZWUm5gJuZlZQLuJlZSbmAm5mVlAu4mVlJuYCbmZWUC7iZWUm5gJuZlZQLuJlZSbmAm5mVlAu4mVlJuYCbmZWUC7iZWUn1qoBLmijpEUmPSZpar1BmZta93tzUuC8wAzgU2AU4TtIu9QpmZmZr1psW+B7AYxHxx4j4N3A18Pb6xDIzs+4oInr2i9IxwMSI+GCePh54c0Sc0ul1U4ApeXJH4JGex32VzYC/1+lv1Ysz1caZateKuZypNvXM9KaIaOs8s8d3pa9VRMwEZtb770rqiIj2ev/d3nCm2jhT7VoxlzPVpohMvelCeRLYump6WJ5nZmYF6E0Bvx/YXtIISesB7wZurk8sMzPrTo+7UCJilaRTgJ8BfYFLIuLhuiXrXt27ZerAmWrjTLVrxVzOVJuGZ+rxQUwzM2sun4lpZlZSLuBmZiXlAm4tRZKanaGzVsxkBi7gDSVpk2Zn6Eor5pL0JoBooYMyrZipWit+sEhquZrSitupXlpuY79eSJoIfE7SRs3OUq0Vc0k6BPiOpK2anaWiRTO9TdLJkk6F1vhgkbS/pEmSjs6ZXmp2wWyV7dR5OzTiw+11V8CbvfPkDIcBXwFujoh/dlrWtG3eirkkHQF8AfhqRLTEiWAtmmlf4FLSe/ZISddJ2r6Z+7uktwKzgVHARyTdLKlfRESzcrXSdqp8cEgakadfqvc6SlXAJW0taT1JG+bpfpXCU2kpNbtVImk46SqN10XE7ZLaJO0p6aCc76VmFMtWzCVp45xpXkTcIWmopGMlnSppYJFZWjlTth8wKyIujIiDgcXAfwIjoGkNl6OACyLi8znTi8BVkvrkIt6M+tJS20nSScD7Je2Qpy+ofFuph9IUcEmHAz8BvgVcKmnHiFiVC88E4DJJ2zY3JQABXAhsKOkE4Puki3l9UdIPoTGfxDVYBVwEDGyVXBHxLPA+YDtJXwKuBEYD7wBmStq0yDy58DwLvBfYthUyVbkf2EbSNgARcSrwL+D/5OlmNFzuBzaWtEHOcDTwBuCGPF3Y/lRVmO8Dtm6h7fRb4AXgIEnfJV3Q76a6/fWIaOkHINI1V34P7A9sAZxB+mQdlV/zAHBEk3OOr3q+HfAJ4HHglKr5dwPHFpxrOLAhMAAYDHyy2blIraGNgc3y9D7AU8DUqtfcDJxTYKZ9gEOAPnl6b+BvTc60IzAUeCOwKfDfwCRgUNVrfgGcWGCm6v38AOCXwIROr/kpcFCBmfaqqgU7AlcAxzRjOwH98k8BffPzrYB7gHnA2Mryeqyv4Vcj7K2ICEl/BX4DPAosiYhpkl4AfiKpHdgvIp6VpMhbp0h5tMKdku6LiP0j4jFJNwC/jfQ1vE+k1sh9wP8UmOtQUl/ug8A/gHOAa3Ou25uRK2c6l3xZYUlfiYi7JO0eEX+pynQbUEgLLh+wvAB4f143EXGPpDdHxJ+blGkicB7pw3VL4ATgeuCDabHuj4g/kd4XLxaUqbKf3xsRB0TEbZJ2AmYoXVZjbkSsAP5EajAUkekQUpfXuwEi4hFJNwEfAPoUuZ0kHQDsKen8iHi+an0fB1aS9p89JfWNiDl1WWlRn5I9/DTbDtgdGAJcA3y60/KzSQcsNqBOn2g9zLlxzvE7YE7VfFVyAe8C5gDbFZTpQNI3kwmkFspsYOPqbEXnAvYlFe49gB2AzwEfycv6Vr3uxJxp5wIyvZn0bW5int6IdI2gwZ22U5GZ9gUeyj+3AL4DbFm1bDbpBiozgL8UkSmvu3o//13V/MmkFu7/Bb4GLAR2KGg7LSS39nl1i3v/IrcT6c5kfwQO7jR/T+D6/HxLYDpwCrBeXdZbxH98DzfIEaSW4x2kfu+jSF/9z656zfC8czezeFfe4GeTuihmA3flwtmelx1DauWOLDDXWVU79pZ5R/92Lprj8vxJReYCTia1civTJ5EOqlam+5O6Mu4sMNP7gO/l9e4I/AC4DPgx8Jb8mn2AXxeYaXLVurckdS/NysVzNLB+Ll6nU1yDoKv9/J68n28D7EY6TvBlYKeCMn2G9K1kc2BbUhfTZbleVLrp9mnkdiI10t4A/D/gsDxvcM40JE/3q3r9jkBb3dZfxIbuwUbZG5gP7JanZwJfyjvzX4DPklrnk4EOYJMWyDwV+Fh+fhfpq/ZReXocsHXBeSpvuIG5KJ2Zd+bTcsEaAIwpMhfpqpXDqqZ3IrdO8vT6uYgPKTBTP9LX7Zmk69l/LOeaTPogGQK0FZmp0/aYkQvQ1jnTvcAWRWepytTVfn5kk7KsR+qOuxRYQGrZHkw6zjObqhZ5AVnOJzUGhpEO7s4GniAfHwD6N2S9zdoRutkYewOTq6bbgB/l5/8BXEJqTc4hH7xoYtZKoTwQ+BTpk/9RUovt983eljnbtlXPR5IO8jRkh+puO3Watz3QkZ+fSGrFFJar6v+uUsRPrFo2GLicqm6nJv3fVX/gbZQLw+Am5Gip/ZxXDjavRzrO8+GqZaNJrfF+Dc5wJPCJ/Pz9wBdJ33wr3YJTSN1zQxuWoegNX+OG6QtsVPV8GKnfbWie96b8ptu42VmrMm+cW0crgcPzvMuBEU3M1Kd6m+afx5JGDrTCt5Y3kb4Cf4CC+pdXt41IQ2r7d9pOd5NHyjQ4w2sKzRr+74rK1Hc185u2n3fOVLVd+uR6UL2dftHIDzpSS38uuc+bdKziF6TW95FVr7uUBnYpNXSD12lD9SN1A/wyT7+P1O+9frOz5TyVlslQUj/9Xs3O1ClX5eBXH1LLaQ4F9OXSdYu7kmlE1f/tn3OmXZqc6U1V8z6UGwy7FpDpAFK/8huq5lU+VIbnn/2BUwvMtC9p1Mt6XWRqyn7eTaYtq+adShp73bB9nNRD8BSwR54eQhpIsRPpG8l/AW8Bjid17TSsBd7yJ/JEOllnBfCEpK+Sxld/KyJWFplD0qGSju0iX0jam3SQ6Q8R8Zv8+kLO+Koh12xJ/0EqltuRugkeanCmw4FPVJ+5WBniKWkv4EZJu+RFvwFOiIh5Tc50s6RdJPUl3U38uGjwHabykMpZpJFLz1fmRzo5bW/gJkk7k04O2wx4TwGZDiOdvPQk6eSvynaqnDBX+H5eQ6bZkrbN/3fbAcc3eB9fRjo5Z6ikIcB1wI2k40s/Jx3YPJj0TeCdEbG4YUmK/BTt4aedSP1cC0kHMLdvQoYJ+T/sX6Q3dnW2oaRP/MNbMNeconORhn3+C3iM1Ac4sGrZ5sCPyEfr19VMVft0VyMXNs/TPy7y/y5nGkQ6wF3pFhhEOglsk7z89xR4wlyNmR4sMlNVtjGkYYOLSN/Y+uSfM3mlq7fhB1FLcSIP8G9JXwTuj4hHmxBjS9LXxn8A38sndlyZsy2WNCkiFjbhRKKtgMNIxenKLnK9q5ILCjuNeCBwNPB34OtAf0mXRcS/ImKJpI9EOmGnyEzr50xLSONwm5qpap/+N7CZpGGk08/nAQdIOhJ4e0S8UHCm5ZKW5J9bkwrnAtIH4BGkYbHPF7WfV2V6Gnh2NZl2r2Sq+p2Gi4gHlC56dmBEXJxnXyzpGFK9WAysaHSOli/gVWYXXBwrVzZ7Y0RcK2lwRDwj6YOk/6g+EXFFfmnjviJ1nWskqRVyH7AyIp7qLlejt52k7Uj9gL8lHZRbJmkq8NW0WFdEus7IsgIzHUkafnch6aDt05LOIp3h2KxM25FOi3+QdLLOCNK1Vy6JiAslfZh0zZ924K9F7POSdiUdhFtAagwcBPwTuCoivi3pDNJZhHuTzoQuItM+pNFTs4GlpIbK083M1Fmkbr+Xu/4kvZPU1bUoL298pqK/epThQfo6NBB4mLRTf6jT8reRvj4dAryTVKS6PGrfgGyHkt78N5GuzVE9zOzgZuTilZOubif1VY6qWrYn8CvSqc6nkIYwNnR4V9W2mEunM+Pysr2blKl6O83KGX9Opy44GjxyYQ3701WkbrkFedvtXPW6WaTGTKPzdH7vHUfqLnmY1IVTeKYaMos0kmoeBRxkftW6m/2Pb+UH8GnSyI3LeWW8Z2Wo0kjStQ7+SnFn6O0P/IFXjn7fwCtnW/bPP0cVmYvXnnT1bVJrEl4Z4bE16dvAE8DogjJVjxLYmDRkcRB5FEOeLjpT9Xb6DukKeYNJwzq/Rhpp0fCRC2vYn36Y9+s3kY43nU3qDnhfLvKbNzpTVbbKe+8q8vEd0jVWPk06vlN4pjVkVd6WhXzoVj/K1IXSDKtIpwnPBj4o6XzSQcOzSDvR06QC2tARFFWeAk6OiPskvZF0HQ9JmkQam3sJqbVSdK6vRcTv8vP/InXlvIG0rYJUwNcnnZXW0FEUWVejBFaS+iR/StpOw0inQB9UUCZ49Xb6LHBppG65E0hD5A4hnbXb2JELr+i8P7WTPkjuJ52SvitpZMUEUhFdUkCmisp7bxbw4Xyc4F5gZ1IRb29Cpi5FquK3N2vlfqz+k3Vb8uVESa2BlcC38/Q7gR2bmO0/gc/m55NJF/vamnR9k8JysfqTrtryvC1JY523LSpTXm9XowQ+QDoI9kZSi7iQ64h0s53eWNlO+eeGLbA/fZB03GB4ni78pK8u3nsvAF+sWj64Gdup1R6Vr7jWBUlbki7Ocw/pU/8K0pX0ro10cKVlSPoJ8KkortXdVYZ+pGus3BQRb5X0XtK3hLMj4l9NyLMLaZTAt6rm/RQ4Ixo8Fr6bXJ230/tI3Scfj4LPb1idvD99LiI6mjC6qqv33n+TRp78KNLB3qZcOrrVuAtlDSLir5KeIF3B72MR8UOla/4+1sxcnXfefPR7c1LXSdNExCpghaTKSVcHk64+WHjxznm6GiXQRhri2DSr2U6Tm1W817A/FTeaopPu3nsu3olb4N3IY083j3wBdr1ycf+my/3M7yNdfe3YZrYqcx6RTvuen3++NZozbv9Vcq73k+7kNCmK6/NeU55W3E6ttj+17HuvVbiA16gVv7JJ6k8a0rgwIh5pdp4KSZNJJ101tVBW5IL5FuBvEbGg2XkqWnA7ter+1HLvvVbhAm515zdcbbydrLdcwM3MSqrlr0ZoZmZdcwE3MyspF3Azs5JyATczKykXcDOzknIBt3WGpBslzZH0sKQped5Jkv4g6T5JF0v6Vp7fJukHku7PjwnNTW/2Wh5GaOsMSZtGuqnD+qQr7h1Cusv7OGA56RrhD0TEKZKuIl247C5J2wA/i4idmxberAu+FoqtS06TdHR+vjXp2tt3RMTTAJKuBXbIyw8CdtEr9+zdSNLASDfYNmsJLuC2TpC0P6ko7xUR/yPpdtKNE1bXqu4D7BkRzxUS0KwH3Adu64qNgX/k4r0T6VZvGwJvkbRJvsTrO6te/3Pg1MqEpLFFhjWrhQu4rSt+CvSTNJ90U+P/DzwJfIV0c+i7gceBZ/PrTwPaJT0oaR7w4cITm3XDBzFtnVbp184t8BtI9/O8odm5zGrhFrit686RNBd4iHTT3BubmsZsLbgFbmZWUm6Bm5mVlAu4mVlJuYCbmZWUC7iZWUm5gJuZlZQLuJlZSf0vD0MC9F2AzAQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n",
    "new_age = new.pivot_table(index='age', values='dissatisfied')\n",
    "new_age.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "new_age = new_age['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "new_age.plot(kind='bar', rot=45, legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "442bbd24-f864-43d6-bfd8-519b974cdc5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the percentage of employees who resigned due to dissatisfaction in each gender\n",
    "new_gen = new.pivot_table(index='gender', values='dissatisfied')\n",
    "new_gen.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "new_gen = new_gen['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "new_gen.plot(kind='bar', rot=30, legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "713a3e61-e269-4e7e-8ac6-b5e5bdedc733",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='employment_status'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n",
    "new_es = new.pivot_table(index='employment_status', values='dissatisfied')\n",
    "new_es.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "new_es = new_es['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "new_es.plot(kind='bar', rot='horizontal', legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9f9c738-e29c-4049-8af5-2650fe883105",
   "metadata": {},
   "source": [
    "- Analyzing only the employees that worked lass than three years, we can see some aspects in dissatisfaction: Age: 56+, followed by 46-50 and 36-40; Gender: Male, just like the general, but with more distance to Female); Employment Status: Permanent Full-Time, with a bigger diference from Permanent Part-Time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09d9fa06-502c-40e1-b771-7cc91cd5ec33",
   "metadata": {},
   "source": [
    "### 2. Experienced\n",
    "- On this topic, we'll analyze the same columns as above, but for employees with three to six years in the workplace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "64e0e799-a87e-460b-9f92-8de6d26bc4fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slicing the category\n",
    "experienced = combined_updated[combined_updated['service_cat'] == 'Experienced']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "11782d6a-7b7b-44b2-8e52-6a457122dd40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n",
    "exp_age = experienced.pivot_table(index='age', values='dissatisfied')\n",
    "exp_age.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "exp_age = exp_age['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "exp_age.plot(kind='bar', rot=30, legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "439b0202-7450-40cc-996d-b485ade0ac60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n",
    "exp_gen = experienced.pivot_table(index='gender', values='dissatisfied')\n",
    "exp_gen.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "exp_gen = exp_gen['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "exp_gen.plot(kind='bar', rot=30, legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d54bbbe8-a68d-48ef-a729-fdcddb6c4eb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='employment_status'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n",
    "exp_es = experienced.pivot_table(index='employment_status', values='dissatisfied')\n",
    "exp_es.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "exp_es = exp_es['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "exp_es.plot(kind='bar', rot=45, legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ba07c6b-0f5a-49f1-8308-7657f4d2cf3b",
   "metadata": {},
   "source": [
    "- Analyzing only the employees that worked from three to six years, we noticed some differences from the general plots in dissatisfaction: Age: 26-30 followed by 41-45; Gender: Female; Employment Status: Permanent Full-Time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76b60420-5f4f-48c0-996e-d01007e269c4",
   "metadata": {},
   "source": [
    "### 3. Established\n",
    "- On this topic, we'll analyze the same columns as above, but for employees with seven to ten years in the workplace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2617c988-edb2-436d-bebf-ab40ca5a4562",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slicing the category\n",
    "established = combined_updated[combined_updated['service_cat'] == 'Established']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "0d59b381-9d01-400a-b4cd-79eb090c8627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n",
    "est_age = established.pivot_table(index='age', values='dissatisfied')\n",
    "est_age.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "est_age = est_age['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "est_age.plot(kind='bar', rot=30, legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9fa08d18-4d42-497c-8a31-2e7fb92906ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n",
    "est_gen = established.pivot_table(index='gender', values='dissatisfied')\n",
    "est_gen.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "est_gen = est_gen['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "est_gen.plot(kind='bar', rot=30, legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "409a985a-2490-4498-ab0b-e196f3fb1f9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='employment_status'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n",
    "est_es = established.pivot_table(index='employment_status', values='dissatisfied')\n",
    "est_es.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "est_es = est_es['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "est_es.plot(kind='bar', rot=45, legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cd24556-89b9-4b54-8f4d-7d674a064e09",
   "metadata": {},
   "source": [
    "- Analyzing only the employees that worked from seven to ten years, the dissatisfaction occurs: Age:31-35 (differently of the others); Gender: Female; Employment Status: Permanent Part-Time, followed by Permanent Full-Time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "779a9a16-9565-46c5-b280-9d306125daf0",
   "metadata": {},
   "source": [
    "### 4. Veterans\n",
    "- On this topic, we'll analyze the same columns as above, but for employees with more than eleven years of service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "edac31b0-f4ee-45e3-be50-746afb9ce40c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slicing the category\n",
    "veteran = combined_updated[combined_updated['service_cat'] == 'Veteran']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6f0c08f6-655b-4263-9a16-cf18fa2a1869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n",
    "vet_age = veteran.pivot_table(index='age', values='dissatisfied')\n",
    "vet_age.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "vet_age = vet_age['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "vet_age.plot(kind='bar', rot=30, legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "31f1954f-5105-4524-800f-22c48d11a9ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n",
    "vet_gen = veteran.pivot_table(index='gender', values='dissatisfied')\n",
    "vet_gen.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "vet_gen = vet_gen['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "vet_gen.plot(kind='bar', rot=30, legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1b0ab7f6-c182-4a6b-907b-9d8c8d526235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='employment_status'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the percentage of employees who resigned due to dissatisfaction in each category\n",
    "vet_es = veteran.pivot_table(index='employment_status', values='dissatisfied')\n",
    "vet_es.sort_values('dissatisfied', ascending=True, inplace=True)\n",
    "vet_es = vet_es['dissatisfied'] * 100\n",
    "\n",
    "# Plot the results\n",
    "vet_es.plot(kind='bar', rot=45, legend='dissatisfied')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6fa68d7-20a9-4d92-9391-bffb3826356e",
   "metadata": {},
   "source": [
    "- Analyzing only the employees that worked more than eleven years, we can see some aspects in dissatisfaction: Age: 51-55, followed by 46-50; Gender: Male, with a little difference to Female; Employment Status: Permanent Part-Time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e66f20e-f3e8-43ef-a6f0-94f133c8a154",
   "metadata": {},
   "source": [
    "## Data Visualization\n",
    "- In this section, we'll make a presentation of the results founded in the Exploratory Data Analysis. Following the same structure of our analysis, we'll start with the general view and them each category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "80bc9b40-6b0e-45e8-b9e4-d109fb0ad311",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the style\n",
    "style.use(\"seaborn-dark\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "826dc657-5dca-4a5b-a7ed-835edcaee019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a graph with the general dissatisfaction\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(\n",
    "                                    ncols=2,\n",
    "                                    nrows=2,\n",
    "                                    figsize=(12, 12))\n",
    "\n",
    "ax1 = dis_sc.plot(kind='barh', ax=ax1, legend=None, color='#029386')\n",
    "ax1.set_title('Time of Service', weight=\"bold\")\n",
    "ax1.set_ylabel('')\n",
    "ax1.set_xticks([30,34,49,52])\n",
    "ax1.set_xticklabels([30,34,49,52])\n",
    "\n",
    "ax2 = dis_gen.plot(kind= 'barh', ax=ax2, legend=None, color='#A9561E')\n",
    "ax2.set_title('Gender', weight=\"bold\")\n",
    "ax2.set_ylabel('')\n",
    "ax2.set_xticks([37,40])\n",
    "ax2.set_xticklabels([37,40])\n",
    "\n",
    "ax3 = dis_age.plot(kind= 'barh', ax=ax3, legend=None, color='#C875C4')\n",
    "ax3.set_title('Age',weight=\"bold\")\n",
    "ax3.set_ylabel('')\n",
    "ax3.set_xticks([20,31,34,38,42])\n",
    "ax3.set_xticklabels([20,31,34,38,42])\n",
    "\n",
    "ax4 = dis_es.plot(kind= 'barh', ax=ax4, legend=None, color='#FFA500')\n",
    "ax4.set_title('Employment Status', weight=\"bold\") \n",
    "ax4.set_ylabel('')\n",
    "ax4.set_xticks([16,18,26,43,45])\n",
    "ax4.set_xticklabels([16,18,26,43,45])\n",
    "\n",
    "#Graph title\n",
    "fig.text(x=0.35,y=0.955,s=\"Dissatisfaction of ex employees\",size=21, weight=\"bold\")\n",
    "fig.text(x=0.15,y=0.935,s=\"The ones with 7 to 10 years of service, 26 to 30 or more than 50 years, Male\",size=19)\n",
    "fig.text(x=0.18,y=0.915, s=\" and worked on Permanent Full Time status are the most unhappy\",size=19)\n",
    "\n",
    "#Foot signature/source of graph:\n",
    "fig.text(x=0.12, y=0.088,s='©Ana Luiza Miranda' +' '*78 +\"Government of Queensland/Australia\", backgroundcolor=\"#4d4d4d\", size=10, color=\"#f0f0f0\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891c56df-16f3-4e35-84c6-1fa5e2f13079",
   "metadata": {},
   "source": [
    "### 1. New"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "14812261-bd11-40da-b268-f396a2ec3673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a graph with New employees values\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(\n",
    "                                    ncols=3,\n",
    "                                    nrows=1,\n",
    "                                    figsize=(14, 7))\n",
    "\n",
    "ax1 = new_age.plot(kind='barh', ax=ax1, legend=None, color='#029386')\n",
    "ax1.set_title('Age', weight=\"bold\")\n",
    "ax1.set_ylabel('')\n",
    "ax1.set_xticks([14,25,30,35,40])\n",
    "ax1.set_xticklabels([14,25,30,35,40])\n",
    "\n",
    "ax2 = new_gen.plot(kind= 'barh', ax=ax2, legend=None, color='#A9561E')\n",
    "ax2.set_title('Gender', weight=\"bold\")\n",
    "ax2.set_ylabel('')\n",
    "ax2.set_xticks([26,38])\n",
    "ax2.set_xticklabels([26,38])\n",
    "\n",
    "ax3 = new_es.plot(kind= 'barh', ax=ax3, legend=None, color='#C875C4')\n",
    "ax3.set_title('Employment Status',weight=\"bold\")\n",
    "ax3.set_ylabel('')\n",
    "ax3.set_xticks([17,21,28,47])\n",
    "ax3.set_xticklabels([17,21,28,47])\n",
    "\n",
    "#Graph title\n",
    "fig.text(x=0.38,y=1.02,s=\"Between the new employees\",size=20, weight=\"bold\")\n",
    "fig.text(x=0.25,y=0.98,s=\"The ones with 1 to 3 years of service: more than 56 years, Male\",size=18)\n",
    "fig.text(x=0.24,y=0.94, s=\" and worked on Permanent Full-Time status are the most unhappy\",size=18)\n",
    "\n",
    "#Foot signature/source of graph:\n",
    "fig.text(x=0.12, y=0.055,s='©Ana Luiza Miranda' +' '*78 +\"Government of Queensland/Australia\", backgroundcolor=\"#4d4d4d\", size=10, color=\"#f0f0f0\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b8e2822-3123-4a34-b040-728a1c576fae",
   "metadata": {},
   "source": [
    "### 2. Experienced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b534ad0b-1a9a-49f9-ba7c-f125cffa97a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a graph with Experienced employees values\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(\n",
    "                                    ncols=3,\n",
    "                                    nrows=1,\n",
    "                                    figsize=(14, 6))\n",
    "\n",
    "ax1 = exp_age.plot(kind='barh', ax=ax1, legend=None, color='#029386')\n",
    "ax1.set_title('Age', weight=\"bold\")\n",
    "ax1.set_ylabel('')\n",
    "ax1.set_xticks([25,28,30,33,38,41,44])\n",
    "ax1.set_xticklabels([25,28,30,33,38,41,44])\n",
    "\n",
    "ax2 = exp_gen.plot(kind= 'barh', ax=ax2, legend=None, color='#A9561E')\n",
    "ax2.set_title('Gender', weight=\"bold\")\n",
    "ax2.set_ylabel('')\n",
    "ax2.set_xticks([28,37])\n",
    "ax2.set_xticklabels([28,37])\n",
    "\n",
    "ax3 = exp_es.plot(kind= 'barh', ax=ax3, legend=None, color='#C875C4')\n",
    "ax3.set_title('Employment Status',weight=\"bold\")\n",
    "ax3.set_ylabel('')\n",
    "ax3.set_xticks([15,20,29,45])\n",
    "ax3.set_xticklabels([15,20,29,45])\n",
    "\n",
    "#Graph title\n",
    "fig.text(x=0.33,y=1.02,s=\"Between the experienced employees\",size=20, weight=\"bold\")\n",
    "fig.text(x=0.23,y=0.98,s=\"The ones with 3 to 6 years of service: 41-45 or 26-30 years, Female\",size=18)\n",
    "fig.text(x=0.24,y=0.94, s=\" and worked on Permanent Full-Time status are the most unhappy\",size=18)\n",
    "\n",
    "#Foot signature/source of graph:\n",
    "fig.text(x=0.12, y=0.055,s='©Ana Luiza Miranda' +' '*78 +\"Government of Queensland/Australia\", backgroundcolor=\"#4d4d4d\", size=10, color=\"#f0f0f0\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c20e2363-1df1-49f2-98f7-eb1b8100e4e8",
   "metadata": {},
   "source": [
    "### 3. Established"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c408d677-a36a-43de-a582-395eb0dcbdf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a graph with Established employees values\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(\n",
    "                                    ncols=3,\n",
    "                                    nrows=1,\n",
    "                                    figsize=(14, 6))\n",
    "\n",
    "ax1 = est_age.plot(kind='barh', ax=ax1, legend=None, color='#029386')\n",
    "ax1.set_title('Age', weight=\"bold\")\n",
    "ax1.set_ylabel('')\n",
    "ax1.set_xticks([36,40,50,55,67,75])\n",
    "ax1.set_xticklabels([36,40,50,55,67,75])\n",
    "\n",
    "ax2 = est_gen.plot(kind= 'barh', ax=ax2, legend=None, color='#A9561E')\n",
    "ax2.set_title('Gender', weight=\"bold\")\n",
    "ax2.set_ylabel('')\n",
    "ax2.set_xticks([44,55])\n",
    "ax2.set_xticklabels([44,55])\n",
    "\n",
    "ax3 = est_es.plot(kind= 'barh', ax=ax3, legend=None, color='#C875C4')\n",
    "ax3.set_title('Employment Status',weight=\"bold\")\n",
    "ax3.set_ylabel('')\n",
    "ax3.set_xticks([50,61])\n",
    "ax3.set_xticklabels([50,61])\n",
    "\n",
    "#Graph title\n",
    "fig.text(x=0.35,y=1.02,s=\"Between the established employees\",size=20, weight=\"bold\")\n",
    "fig.text(x=0.28,y=0.98,s=\"The ones with 7 to 10 years of service: 31-35 years, Female\",size=18)\n",
    "fig.text(x=0.25,y=0.94, s=\" and worked on Permanent Part-Time status are the most unhappy\",size=18)\n",
    "\n",
    "#Foot signature/source of graph:\n",
    "fig.text(x=0.12, y=0.055,s='©Ana Luiza Miranda' +' '*78 +\"Government of Queensland/Australia\", backgroundcolor=\"#4d4d4d\", size=10, color=\"#f0f0f0\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de468a7a-f955-4b7f-b7db-e2958fbe383e",
   "metadata": {},
   "source": [
    "### 4. Veteran"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b6ffc770-f4f4-43e7-b514-0c12c50c431f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a graph with Veteran employees values\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(\n",
    "                                    ncols=3,\n",
    "                                    nrows=1,\n",
    "                                    figsize=(14, 6))\n",
    "\n",
    "ax1 = vet_age.plot(kind='barh', ax=ax1, legend=None, color='#029386')\n",
    "ax1.set_title('Age', weight=\"bold\")\n",
    "ax1.set_ylabel('')\n",
    "ax1.set_xticks([29,40,48,55,60])\n",
    "ax1.set_xticklabels([29,40,48,55,60])\n",
    "\n",
    "ax2 = vet_gen.plot(kind= 'barh', ax=ax2, legend=None, color='#A9561E')\n",
    "ax2.set_title('Gender', weight=\"bold\")\n",
    "ax2.set_ylabel('')\n",
    "ax2.set_xticks([50])\n",
    "ax2.set_xticklabels([50])\n",
    "\n",
    "ax3 = vet_es.plot(kind= 'barh', ax=ax3, legend=None, color='#C875C4')\n",
    "ax3.set_title('Employment Status',weight=\"bold\")\n",
    "ax3.set_ylabel('')\n",
    "ax3.set_xticks([17,43,60])\n",
    "ax3.set_xticklabels([17,43,60])\n",
    "\n",
    "#Graph title\n",
    "fig.text(x=0.35,y=1.02,s=\"Between the veteran employees\",size=20, weight=\"bold\")\n",
    "fig.text(x=0.25,y=0.98,s=\"The ones with more than 10 years of service: 26-30 years, Male\",size=18)\n",
    "fig.text(x=0.235,y=0.94, s=\" and worked on Permanent Part-Time status are the most unhappy\",size=18)\n",
    "\n",
    "#Foot signature/source of graph:\n",
    "fig.text(x=0.12, y=0.055,s='©Ana Luiza Miranda' +' '*78 +\"Government of Queensland/Australia\", backgroundcolor=\"#4d4d4d\", size=10, color=\"#f0f0f0\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68cc06a3-728d-4571-af1b-de18c66b04f3",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "- In this project, we analyzed exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) of Queensland in Australia.\n",
    "- Our goal was identify the dissatisfaction of the employees that exit, the ones who worked for a short period and the ones that stayed longer. \n",
    "- In the Exploratory Data Analysis section, we analyse the percentages of dissatisfaction of ex employees according to the time of service, age, gender and employment status. Besides that, we also analyse the age, gender and employment status between the career stage category. We notice that the profiles of dissatisfied employees are different accordingly to the years of service, and the actions to minimize job dissatisfaction must be [suitable to each category](https://www.businesswire.com/news/home/20171108006002/en/Age-Number-Engage-Employees-Career-Stage).\n",
    "- In the Data Visualization section, we showed graphs with the results of our analysis. \n",
    "- For more informations about job dissatisfaction, we recommend [this article](https://www.bamboohr.com/hr-glossary/job-dissatisfaction/), with definition, possible causes and ways to identify the low motivation of employees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bac686f-e608-4064-b6d5-ad5c7b7e0026",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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