Basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Clean and Analyze Employee Exit Surveys\n",
    "In this project, we'll work with exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. You can find the TAFE exit survey and the survey for the DETE in the repository.\n",
    "\n",
    "We try to find answers to the following questions:\n",
    "- Are employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been there longer?\n",
    "- Are younger employees resigning due to some kind of dissatisfaction? What about older employees?\n",
    "- Are female employees resigning due to some kind of dissatisfaction? What about male employees?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Step 1*: Read the datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "dete_survey = pd.read_csv('dete_survey.csv')\n",
    "tafe_survey = pd.read_csv('tafe_survey.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
      "None \n",
      "\n",
      "   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  ...  Kept informed  Wellness programs  \\\n",
      "0  Permanent Full-time  ...              N                  N   \n",
      "1  Permanent Full-time  ...              N                  N   \n",
      "2  Permanent Full-time  ...              N                  N   \n",
      "3  Permanent Full-time  ...              A                  N   \n",
      "4  Permanent Full-time  ...              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  \n",
      "\n",
      "[5 rows x 56 columns] \n",
      "\n",
      "ID                                       0\n",
      "SeparationType                           0\n",
      "Cease Date                               0\n",
      "DETE Start Date                          0\n",
      "Role Start Date                          0\n",
      "Position                                 5\n",
      "Classification                         367\n",
      "Region                                   0\n",
      "Business Unit                          696\n",
      "Employment Status                        5\n",
      "Career move to public sector             0\n",
      "Career move to private sector            0\n",
      "Interpersonal conflicts                  0\n",
      "Job dissatisfaction                      0\n",
      "Dissatisfaction with the department      0\n",
      "Physical work environment                0\n",
      "Lack of recognition                      0\n",
      "Lack of job security                     0\n",
      "Work location                            0\n",
      "Employment conditions                    0\n",
      "Maternity/family                         0\n",
      "Relocation                               0\n",
      "Study/Travel                             0\n",
      "Ill Health                               0\n",
      "Traumatic incident                       0\n",
      "Work life balance                        0\n",
      "Workload                                 0\n",
      "None of the above                        0\n",
      "Professional Development                14\n",
      "Opportunities for promotion             87\n",
      "Staff morale                             6\n",
      "Workplace issue                         34\n",
      "Physical environment                     5\n",
      "Worklife balance                         7\n",
      "Stress and pressure support             12\n",
      "Performance of supervisor                9\n",
      "Peer support                            10\n",
      "Initiative                               9\n",
      "Skills                                  11\n",
      "Coach                                   55\n",
      "Career Aspirations                      76\n",
      "Feedback                                30\n",
      "Further PD                              54\n",
      "Communication                            8\n",
      "My say                                  10\n",
      "Information                              6\n",
      "Kept informed                            9\n",
      "Wellness programs                       56\n",
      "Health & Safety                         29\n",
      "Gender                                  24\n",
      "Age                                     11\n",
      "Aboriginal                             806\n",
      "Torres Strait                          819\n",
      "South Sea                              815\n",
      "Disability                             799\n",
      "NESB                                   790\n",
      "dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(dete_survey.info(), '\\n')\n",
    "print(dete_survey.head(), '\\n')\n",
    "print(dete_survey.isnull().sum(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
      "None \n",
      "\n",
      "      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",
      "   ...  \\\n",
      "0  ...   \n",
      "1  ...   \n",
      "2  ...   \n",
      "3  ...   \n",
      "4  ...   \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                         \n",
      "\n",
      "[5 rows x 72 columns] \n",
      "\n",
      "Record ID                                                                      0\n",
      "Institute                                                                      0\n",
      "WorkArea                                                                       0\n",
      "CESSATION YEAR                                                                 7\n",
      "Reason for ceasing employment                                                  1\n",
      "                                                                            ... \n",
      "CurrentAge. Current Age                                                      106\n",
      "Employment Type. Employment Type                                             106\n",
      "Classification. Classification                                               106\n",
      "LengthofServiceOverall. Overall Length of Service at Institute (in years)    106\n",
      "LengthofServiceCurrent. Length of Service at current workplace (in years)    106\n",
      "Length: 72, dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(tafe_survey.info(), '\\n')\n",
    "print(tafe_survey.head(), '\\n')\n",
    "print(tafe_survey.isnull().sum(), '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can first make the following observations:\n",
    "- The dete_survey dataframe contains 'Not Stated' values that indicate values are missing, but they aren't represented as NaN.\n",
    "- Both the dete_survey and tafe_survey dataframes contain many columns that we don't need to complete our analysis.\n",
    "- Each dataframe contains many of the same columns, but the column names are different.\n",
    "- There are multiple columns/answers that indicate an employee resigned because they were dissatisfied."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Step 2*: Data Cleaning\n",
    "### Identify Missing Values and Drop Unnecessary Columns\n",
    "We replace the Not Stated values with NaN, and drop some columns that we do not need for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dete_survey = pd.read_csv('dete_survey.csv', na_values='Not Stated')\n",
    "dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)\n",
    "tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ID', 'SeparationType', 'Cease Date', 'DETE Start Date',\n",
      "       'Role Start Date', 'Position', 'Classification', 'Region',\n",
      "       'Business Unit', 'Employment Status', 'Career move to public sector',\n",
      "       'Career move to private sector', 'Interpersonal conflicts',\n",
      "       'Job dissatisfaction', 'Dissatisfaction with the department',\n",
      "       'Physical work environment', 'Lack of recognition',\n",
      "       'Lack of job security', 'Work location', 'Employment conditions',\n",
      "       'Maternity/family', 'Relocation', 'Study/Travel', 'Ill Health',\n",
      "       'Traumatic incident', 'Work life balance', 'Workload',\n",
      "       'None of the above', 'Gender', 'Age', 'Aboriginal', 'Torres Strait',\n",
      "       'South Sea', 'Disability', 'NESB'],\n",
      "      dtype='object') \n",
      "\n",
      "Index(['Record ID', 'Institute', 'WorkArea', 'CESSATION YEAR',\n",
      "       'Reason for ceasing employment',\n",
      "       'Contributing Factors. Career Move - Public Sector ',\n",
      "       'Contributing Factors. Career Move - Private Sector ',\n",
      "       'Contributing Factors. Career Move - Self-employment',\n",
      "       'Contributing Factors. Ill Health',\n",
      "       'Contributing Factors. Maternity/Family',\n",
      "       'Contributing Factors. Dissatisfaction',\n",
      "       'Contributing Factors. Job Dissatisfaction',\n",
      "       'Contributing Factors. Interpersonal Conflict',\n",
      "       'Contributing Factors. Study', 'Contributing Factors. Travel',\n",
      "       'Contributing Factors. Other', 'Contributing Factors. NONE',\n",
      "       'Gender. What is your Gender?', 'CurrentAge. Current Age',\n",
      "       'Employment Type. Employment Type', 'Classification. Classification',\n",
      "       'LengthofServiceOverall. Overall Length of Service at Institute (in years)',\n",
      "       'LengthofServiceCurrent. Length of Service at current workplace (in years)'],\n",
      "      dtype='object') \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(dete_survey_updated.columns,'\\n')\n",
    "print(tafe_survey_updated.columns,'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rename Columns\n",
    "Each dataframe contains many of the same columns, but the column names are different. Because we eventually want to combine them, we will have to standardize the column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'separationtype', 'cease_date', 'dete_start_date',\n",
       "       'role_start_date', 'position', 'classification', 'region',\n",
       "       'business_unit', 'employment_status', 'career_move_to_public_sector',\n",
       "       'career_move_to_private_sector', 'interpersonal_conflicts',\n",
       "       'job_dissatisfaction', 'dissatisfaction_with_the_department',\n",
       "       'physical_work_environment', 'lack_of_recognition',\n",
       "       'lack_of_job_security', 'work_location', 'employment_conditions',\n",
       "       'maternity/family', 'relocation', 'study/travel', 'ill_health',\n",
       "       'traumatic_incident', 'work_life_balance', 'workload',\n",
       "       'none_of_the_above', 'gender', 'age', 'aboriginal', 'torres_strait',\n",
       "       'south_sea', 'disability', 'nesb'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dete_survey_updated.columns = (dete_survey_updated.columns\n",
    "                               .str.lower()\n",
    "                               .str.strip()\n",
    "                               .str.replace('\\s+', '_'))\n",
    "dete_survey_updated.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'Institute', 'WorkArea', 'cease_date', 'separationtype',\n",
       "       'Contributing Factors. Career Move - Public Sector ',\n",
       "       'Contributing Factors. Career Move - Private Sector ',\n",
       "       'Contributing Factors. Career Move - Self-employment',\n",
       "       'Contributing Factors. Ill Health',\n",
       "       'Contributing Factors. Maternity/Family',\n",
       "       'Contributing Factors. Dissatisfaction',\n",
       "       'Contributing Factors. Job Dissatisfaction',\n",
       "       'Contributing Factors. Interpersonal Conflict',\n",
       "       'Contributing Factors. Study', 'Contributing Factors. Travel',\n",
       "       'Contributing Factors. Other', 'Contributing Factors. NONE', 'gender',\n",
       "       'age', 'employment_status', 'position', 'institute_service',\n",
       "       'role_service'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping = {'Record ID': 'id', 'CESSATION YEAR': 'cease_date', 'Reason for ceasing employment': 'separationtype', 'Gender. What is your Gender?': 'gender', 'CurrentAge. Current Age': 'age', 'Employment Type. Employment Type': 'employment_status', 'Classification. Classification': 'position', 'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service', 'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'}\n",
    "tafe_survey_updated = tafe_survey_updated.rename(mapping, axis=1)\n",
    "tafe_survey_updated.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter the data\n",
    "Since we are interested in why employees are resigning, we would only focus on the separation type as 'Resignation'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dete_survey_updated['separationtype'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that dete_survey_updated dataframe contains multiple separation types with 'Resignation'. We have to take all of them in consideration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tafe_survey_updated['separationtype'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pattern = r'Resignation'\n",
    "resign_dete = dete_survey_updated['separationtype'].str.contains(pattern)\n",
    "dete_resignation = dete_survey_updated[resign_dete].copy()\n",
    "resign_tafe = tafe_survey_updated['separationtype'].str.contains(pattern, na=False)\n",
    "tafe_resignation = tafe_survey_updated[resign_tafe].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Verify the data\n",
    " Before we start cleaning and manipulating the rest of our data, let us verify that the data does not contain any major inconsistencies (to the best of our knowledge).\n",
    " \n",
    "In this step, we'll focus on verifying that the years in the cease_date and dete_start_date columns make sense. \n",
    "\n",
    "- Since the cease_date is the last year of the person's employment and the dete_start_date is the person's first year of employment, it wouldn't make sense to have years after the current date.\n",
    "- Given that most people in this field start working in their 20s, it's also unlikely that the dete_start_date was before the year 1940."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       "11/2013      9\n",
       "07/2013      9\n",
       "10/2013      6\n",
       "08/2013      4\n",
       "05/2012      2\n",
       "05/2013      2\n",
       "2010         1\n",
       "07/2012      1\n",
       "07/2006      1\n",
       "09/2010      1\n",
       "Name: cease_date, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dete_resignation['cease_date'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We extract the year from the given data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006.0      1\n",
       "2010.0      2\n",
       "2012.0    129\n",
       "2013.0    146\n",
       "2014.0     22\n",
       "Name: cease_date, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pattern = r'(?P<Years>[2][0][0-1][0-9])'\n",
    "year = dete_resignation['cease_date'].str.extract(pattern)\n",
    "dete_resignation['cease_date'] = year['Years'].astype(float)\n",
    "dete_resignation['cease_date'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1963.0     1\n",
       "1971.0     1\n",
       "1972.0     1\n",
       "1973.0     1\n",
       "1974.0     2\n",
       "1975.0     1\n",
       "1976.0     2\n",
       "1977.0     1\n",
       "1980.0     5\n",
       "1982.0     1\n",
       "1983.0     2\n",
       "1984.0     1\n",
       "1985.0     3\n",
       "1986.0     3\n",
       "1987.0     1\n",
       "1988.0     4\n",
       "1989.0     4\n",
       "1990.0     5\n",
       "1991.0     4\n",
       "1992.0     6\n",
       "1993.0     5\n",
       "1994.0     6\n",
       "1995.0     4\n",
       "1996.0     6\n",
       "1997.0     5\n",
       "1998.0     6\n",
       "1999.0     8\n",
       "2000.0     9\n",
       "2001.0     3\n",
       "2002.0     6\n",
       "2003.0     6\n",
       "2004.0    14\n",
       "2005.0    15\n",
       "2006.0    13\n",
       "2007.0    21\n",
       "2008.0    22\n",
       "2009.0    13\n",
       "2010.0    17\n",
       "2011.0    24\n",
       "2012.0    21\n",
       "2013.0    10\n",
       "Name: dete_start_date, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dete_resignation['dete_start_date'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2009.0      2\n",
       "2010.0     68\n",
       "2011.0    116\n",
       "2012.0     94\n",
       "2013.0     55\n",
       "Name: cease_date, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tafe_resignation['cease_date'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make a boxplot of cease_date values in both datasets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'dete_resignation')"
      ]
     },
     "execution_count": 15,
     "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": [
    "import seaborn as sns\n",
    "ax1 = sns.boxplot(y=dete_resignation['cease_date'])\n",
    "ax1.set_title('dete_resignation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'tafe_resignation')"
      ]
     },
     "execution_count": 16,
     "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": [
    "ax2 = sns.boxplot(y=tafe_resignation['cease_date'])\n",
    "ax2.set_title('tafe_resignation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that the years do not completely align. However, since we are not concerned with analyzing the results by year, we will leave them as it is."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create new column\n",
    "The tafe_resignations dataframe already contains a \"service\" column, which we renamed to institute_service. In order to analyze both surveys together, we'll have to create a corresponding institute_service column in dete_resignations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dete_resignation['institute_service'] = dete_resignation['cease_date'] - dete_resignation['dete_start_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3       7.0\n",
       "5      18.0\n",
       "8       3.0\n",
       "9      15.0\n",
       "11      3.0\n",
       "       ... \n",
       "808     3.0\n",
       "815     2.0\n",
       "816     2.0\n",
       "819     5.0\n",
       "821     NaN\n",
       "Name: institute_service, Length: 311, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dete_resignation['institute_service']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3       NaN\n",
       "4       3-4\n",
       "5      7-10\n",
       "6       3-4\n",
       "7       3-4\n",
       "       ... \n",
       "696     5-6\n",
       "697     1-2\n",
       "698     NaN\n",
       "699     5-6\n",
       "701     3-4\n",
       "Name: institute_service, Length: 340, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tafe_resignation['institute_service']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that the values are represted as a range. We modify these later after we combine the two datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify Dissatisfied Employees\n",
    "We identify any employees who resigned because they were dissatisfied.\n",
    "To do so, we consider these columns-\n",
    "1. tafe_survey_updated:\n",
    "    - Contributing Factors. Dissatisfaction\n",
    "    - Contributing Factors. Job Dissatisfaction\n",
    "2. dete_survey_updated:\n",
    "    - 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",
    "    \n",
    "If the employee indicated any of these factors caused them to resign, we will mark them as dissatisfied in a new column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-                                         277\n",
      "Contributing Factors. Dissatisfaction      55\n",
      "Name: Contributing Factors. Dissatisfaction, dtype: int64\n",
      "-                      270\n",
      "Job Dissatisfaction     62\n",
      "Name: Contributing Factors. Job Dissatisfaction, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "tafe_col = ['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']\n",
    "print(tafe_resignation['Contributing Factors. Dissatisfaction'].value_counts())\n",
    "print(tafe_resignation['Contributing Factors. Job Dissatisfaction'].value_counts())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We must first map these values to True/False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    241\n",
       "True      91\n",
       "True       8\n",
       "Name: dissatisfied, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def update_vals(x):\n",
    "    if pd.isnull(x):\n",
    "        return np.nan\n",
    "    elif x == '-':\n",
    "        return False\n",
    "    else:\n",
    "        return True\n",
    "\n",
    "tafe_resignation['dissatisfied'] = tafe_resignation[tafe_col].applymap(update_vals).any(axis=1, skipna=False)\n",
    "tafe_resignation['dissatisfied'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    270\n",
      "True      41\n",
      "Name: job_dissatisfaction, dtype: int64 \n",
      "\n",
      "False    282\n",
      "True      29\n",
      "Name: dissatisfaction_with_the_department, dtype: int64 \n",
      "\n",
      "False    305\n",
      "True       6\n",
      "Name: physical_work_environment, dtype: int64 \n",
      "\n",
      "False    278\n",
      "True      33\n",
      "Name: lack_of_recognition, dtype: int64 \n",
      "\n",
      "False    297\n",
      "True      14\n",
      "Name: lack_of_job_security, dtype: int64 \n",
      "\n",
      "False    293\n",
      "True      18\n",
      "Name: work_location, dtype: int64 \n",
      "\n",
      "False    288\n",
      "True      23\n",
      "Name: employment_conditions, dtype: int64 \n",
      "\n",
      "False    243\n",
      "True      68\n",
      "Name: work_life_balance, dtype: int64 \n",
      "\n",
      "False    284\n",
      "True      27\n",
      "Name: workload, dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "dete_col = ['job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions', 'work_life_balance', 'workload']\n",
    "for col in dete_col:\n",
    "    print(dete_resignation[col].value_counts(),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dete_resignation['dissatisfied'] = dete_resignation[dete_col].any(axis=1, skipna=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tafe_resignation_up = tafe_resignation.copy()\n",
    "dete_resignation_up = dete_resignation.copy()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine the datasets\n",
    "Our end goal is to aggregate the data according to the institute_service column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dete_resignation_up['institute'] = 'DETE'\n",
    "tafe_resignation_up['institute'] = 'TAFE'\n",
    "combined = pd.concat([dete_resignation_up,tafe_resignation_up],ignore_index=True)\n",
    "combined_updated = combined.dropna(thresh=500, axis=1).copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean the Service column\n",
    "As observed earlier this column contains values in different forms. We must clean the data so a uniform type is followed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
       "0.0                   20\n",
       "3.0                   20\n",
       "6.0                   17\n",
       "4.0                   16\n",
       "2.0                   14\n",
       "9.0                   14\n",
       "7.0                   13\n",
       "More than 20 years    10\n",
       "8.0                    8\n",
       "13.0                   8\n",
       "15.0                   7\n",
       "20.0                   7\n",
       "14.0                   6\n",
       "10.0                   6\n",
       "12.0                   6\n",
       "17.0                   6\n",
       "22.0                   6\n",
       "16.0                   5\n",
       "18.0                   5\n",
       "24.0                   4\n",
       "11.0                   4\n",
       "23.0                   4\n",
       "19.0                   3\n",
       "39.0                   3\n",
       "21.0                   3\n",
       "32.0                   3\n",
       "25.0                   2\n",
       "26.0                   2\n",
       "36.0                   2\n",
       "28.0                   2\n",
       "30.0                   2\n",
       "33.0                   1\n",
       "38.0                   1\n",
       "35.0                   1\n",
       "34.0                   1\n",
       "31.0                   1\n",
       "49.0                   1\n",
       "29.0                   1\n",
       "27.0                   1\n",
       "42.0                   1\n",
       "41.0                   1\n",
       "Name: institute_service, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_updated['institute_service'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     159\n",
       "NaN      88\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",
       "22.0      6\n",
       "10.0      6\n",
       "17.0      6\n",
       "14.0      6\n",
       "12.0      6\n",
       "16.0      5\n",
       "18.0      5\n",
       "24.0      4\n",
       "23.0      4\n",
       "21.0      3\n",
       "39.0      3\n",
       "32.0      3\n",
       "19.0      3\n",
       "36.0      2\n",
       "30.0      2\n",
       "25.0      2\n",
       "26.0      2\n",
       "28.0      2\n",
       "42.0      1\n",
       "29.0      1\n",
       "35.0      1\n",
       "27.0      1\n",
       "41.0      1\n",
       "49.0      1\n",
       "38.0      1\n",
       "34.0      1\n",
       "33.0      1\n",
       "31.0      1\n",
       "Name: institute_service, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_updated['institute_service'] = (combined_updated['institute_service']\n",
    "                                         .astype(str)\n",
    "                                         .str.extract(r'(\\d+)')\n",
    "                                         .astype(float))\n",
    "combined_updated['institute_service'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we map the service to the following definitions:\n",
    "- New: Less than 3 years at a company\n",
    "- Experienced: 3-6 years at a company\n",
    "- Established: 7-10 years at a company\n",
    "- Veteran: 11 or more years at a company\n",
    "\n",
    "These are saved in a new column named service_cat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_values(x):\n",
    "    if pd.isnull(x):\n",
    "        return np.nan\n",
    "    elif x<3:\n",
    "        return 'New'\n",
    "    elif x<7:\n",
    "        return 'Experienced'\n",
    "    elif x<11:\n",
    "        return 'Established'\n",
    "    else:\n",
    "        return 'Veteran'\n",
    "\n",
    "combined_updated['service_cat'] = combined_updated['institute_service'].apply(map_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New            193\n",
       "Experienced    172\n",
       "Veteran        136\n",
       "Established     62\n",
       "Name: service_cat, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_updated['service_cat'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean the Age column\n",
    "We check the age column to identify if it needs to be cleaned. We need this for the second part of our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51-55            71\n",
       "NaN              55\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",
       "31  35           32\n",
       "36  40           32\n",
       "26  30           32\n",
       "31-35            29\n",
       "56 or older      29\n",
       "21-25            29\n",
       "56-60            26\n",
       "61 or older      23\n",
       "20 or younger    10\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_updated['age'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ages are represented as ranges. We extract digits from these and club them in the following categories:\n",
    "- 25 or younger\n",
    "- 26 - 35\n",
    "- 36 - 45\n",
    "- 46 - 55\n",
    "- 56 or older\n",
    "\n",
    "These are saved in a new column age_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41.0    93\n",
       "46.0    81\n",
       "36.0    73\n",
       "51.0    71\n",
       "26.0    67\n",
       "21.0    62\n",
       "31.0    61\n",
       "NaN     55\n",
       "56.0    55\n",
       "61.0    23\n",
       "20.0    10\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_updated['age'] = (combined_updated['age']\n",
    "                                         .astype(str)\n",
    "                                         .str.extract(r'(\\d+)')\n",
    "                                         .astype(float))\n",
    "combined_updated['age'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_values(x):\n",
    "    if pd.isnull(x):\n",
    "        return np.nan\n",
    "    elif x<26:\n",
    "        return '25 or younger'\n",
    "    elif x<36:\n",
    "        return '26-35'\n",
    "    elif x<46:\n",
    "        return '36-45'\n",
    "    elif x<56:\n",
    "        return '46-55'\n",
    "    else:\n",
    "        return '56 or older'\n",
    "    \n",
    "combined_updated['age_cat'] = combined_updated['age'].apply(map_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36-45            166\n",
       "46-55            152\n",
       "26-35            128\n",
       "56 or older       78\n",
       "25 or younger     72\n",
       "Name: age_cat, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_updated['age_cat'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean the Gender column\n",
    "We check the gender column to identify if it needs to be cleaned. We need this for the third part of our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    424\n",
       "Male      168\n",
       "NaN        59\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_updated['gender'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will ignore the NaN values in our analysis as there is no way to fill those. We do not need to classify these into categories (obviously)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Step 3*: Analysis\n",
    "### Based on Service\n",
    "We want to identify dissatisfied resignations based on Experience and Age.\n",
    "Let's start with Experience (or service)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    403\n",
       "True     240\n",
       "True       8\n",
       "Name: dissatisfied, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_updated['dissatisfied'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_updated['dissatisfied'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We replace the null values with the most frequent value, False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "service_pvt = combined_updated.pivot_table(index='service_cat', values='dissatisfied')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "%matplotlib inline\n",
    "ax1 = service_pvt.plot(kind='bar', rot=30)\n",
    "ax1.tick_params(bottom=False, left=False)\n",
    "for key,spine in ax1.spines.items():\n",
    "    spine.set_visible(False)\n",
    "ax1.set_title('Dissatisfaction of Employees Based on Experience')\n",
    "ax1.get_legend().remove()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the analysis above, we can assume that employees with 7 or more years of experience are more likely to resign due to some kind of dissatisfaction with the job."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Based on Age\n",
    "Now, we perform the same analysis for age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "age_pvt = combined_updated.pivot_table(index='age_cat', values='dissatisfied')\n",
    "ax2 = age_pvt.plot(kind='bar', rot=30)\n",
    "ax2.tick_params(bottom=False, left=False)\n",
    "for key,spine in ax2.spines.items():\n",
    "    spine.set_visible(False)\n",
    "ax2.set_title('Dissatisfaction of Employees Based on Age')\n",
    "ax2.get_legend().remove()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no obvious pattern here. We can only draw that employees aged '25 or younger' are less likely to resign based on dissatisfaction. This might be because they are at the beginning of their career. Similarly those nearing their retirement age are more likely to resign."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Based on Gender\n",
    "We perform the same analysis for gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "gender_pvt = combined_updated.pivot_table(index='gender', values='dissatisfied')\n",
    "ax3 = gender_pvt.plot(kind='bar', rot=30)\n",
    "ax3.tick_params(bottom=False, left=False)\n",
    "for key,spine in ax3.spines.items():\n",
    "    spine.set_visible(False)\n",
    "ax3.set_title('Dissatisfaction of Employees Based on Gender')\n",
    "ax3.get_legend().remove()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out that men are more dissatisfied with their jobs in comparision to women."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}