{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "

Analyzing Employee exit surveys
\n", "

Analyzing dissatisafaction amongst leaving employees of DETE and TAFE Institutes

\n", "\n", "The goal of the project is to analyze the Exit Surveys collected from the Employees of Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia.
\n", "\n", "The Department of Employment, Education and Training was an Australian government department that existed between July 1987 and March 1996. At its creation, the Department was responsible for the following:\n", "\n", " * Education, other than migrant adult education\n", " * Youth Affairs\n", " * Employment and training\n", " * Commonwealth Employment Service\n", " * Labour market programs\n", " * Co-ordination of research policy\n", " * Research grants and fellowships\n", "\n", "In Australia, technical and further education or TAFE institutions provide a wide range of predominantly vocational courses, mostly qualifying courses under the National Training System/Australian Qualifications Framework/Australian Quality Training Framework. Fields covered include business, finance, hospitality, tourism, construction, engineering, visual arts, information technology and community work.\n", "\n", "The purpose of the analysis is to answer the following questions -
\n", "\n", " 1. Are employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been there longer?\n", "\n", " 2. Are younger employees resigning due to some kind of dissatisfaction? What about older employees?\n", "\n", "The datasets from both the institutes are surveys collected from the out going employees. They have large number of columns, predominantly columns that are questions asked to the employees and the answer either boolean or on the `Likert` scale. A few of the columns, enough to get started, from both the datasets are described below:- \n", "\n", "dete_survey.csv 's :\n", "\n", " ID: An id used to identify the participant of the survey\n", " SeparationType: The reason why the person's employment ended\n", " Cease Date: The year or month the person's employment ended\n", " DETE Start Date: The year the person began employment with the DETE\n", "tafe_survey.csv 's :\n", "\n", " Record ID: An id used to identify the participant of the survey\n", " Reason for ceasing employment: The reason why the person's employment ended\n", " LengthofServiceOverall. Overall Length of Service at Institute (in years): The length of the person's employment (in years)\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from pywaffle import Waffle" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df_dete = pd.read_csv('dete_survey.csv')\n", "df_tafe = pd.read_csv('tafe_survey.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
IDSeparationTypeCease DateDETE Start DateRole Start DatePositionClassificationRegionBusiness UnitEmployment Status...Kept informedWellness programsHealth & SafetyGenderAgeAboriginalTorres StraitSouth SeaDisabilityNESB
01Ill Health Retirement08/201219842004Public ServantA01-A04Central OfficeCorporate Strategy and PeformancePermanent Full-time...NNNMale56-60NaNNaNNaNNaNYes
12Voluntary Early Retirement (VER)08/2012Not StatedNot StatedPublic ServantAO5-AO7Central OfficeCorporate Strategy and PeformancePermanent Full-time...NNNMale56-60NaNNaNNaNNaNNaN
23Voluntary Early Retirement (VER)05/201220112011Schools OfficerNaNCentral OfficeEducation QueenslandPermanent Full-time...NNNMale61 or olderNaNNaNNaNNaNNaN
34Resignation-Other reasons05/201220052006TeacherPrimaryCentral QueenslandNaNPermanent Full-time...ANAFemale36-40NaNNaNNaNNaNNaN
45Age Retirement05/201219701989Head of Curriculum/Head of Special EducationNaNSouth EastNaNPermanent Full-time...NAMFemale61 or olderNaNNaNNaNNaNNaN
\n", "

5 rows × 56 columns

\n", "
" ], "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 ... 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]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_dete.head(5)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Record IDInstituteWorkAreaCESSATION YEARReason for ceasing employmentContributing Factors. Career Move - Public SectorContributing Factors. Career Move - Private SectorContributing Factors. Career Move - Self-employmentContributing Factors. Ill HealthContributing Factors. Maternity/Family...Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination?Workplace. Topic:Does your workplace promote and practice the principles of employment equity?Workplace. Topic:Does your workplace value the diversity of its employees?Workplace. Topic:Would you recommend the Institute as an employer to others?Gender. What is your Gender?CurrentAge. Current AgeEmployment Type. Employment TypeClassification. ClassificationLengthofServiceOverall. Overall Length of Service at Institute (in years)LengthofServiceCurrent. Length of Service at current workplace (in years)
06.341330e+17Southern Queensland Institute of TAFENon-Delivery (corporate)2010.0Contract ExpiredNaNNaNNaNNaNNaN...YesYesYesYesFemale26 30Temporary Full-timeAdministration (AO)1-21-2
16.341337e+17Mount Isa Institute of TAFENon-Delivery (corporate)2010.0Retirement-----...YesYesYesYesNaNNaNNaNNaNNaNNaN
26.341388e+17Mount Isa Institute of TAFEDelivery (teaching)2010.0Retirement-----...YesYesYesYesNaNNaNNaNNaNNaNNaN
36.341399e+17Mount Isa Institute of TAFENon-Delivery (corporate)2010.0Resignation-----...YesYesYesYesNaNNaNNaNNaNNaNNaN
46.341466e+17Southern Queensland Institute of TAFEDelivery (teaching)2010.0Resignation-Career Move - Private Sector---...YesYesYesYesMale41 45Permanent Full-timeTeacher (including LVT)3-43-4
\n", "

5 rows × 72 columns

\n", "
" ], "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", " ... \\\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]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_tafe.head(5)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\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": [ "df_dete.info()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\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": [ "df_tafe.info()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "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" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_dete.isna().sum()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "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" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_tafe.isna().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data in Dete Survey contains several missing values,but instead given as - 'Not Stated', This infact is NaN. Thus to rectify this, the dataset is read into `pandas` again with the `na_values` parameter set to 'Not Stated'. This will convert every occurence of 'Not Stated' to NaN value." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "df_dete = pd.read_csv('dete_survey.csv',na_values='Not Stated')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After analyzing the column names in the Dete Survey dataset the columns *Professional Development* to *Health & Safety* (28:49) are not required for the analysis. This data is general employee survey data regarding the employee's engagement with the company on the Likert scale. Since the purpose is to find dissatisfied employees and which employee is likely to report dissatisafaction, the engagement of employee with the institute is not relevant for now. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "df_dete.iloc[:,28:49].head(5)\n", "df_dete.drop(columns=df_dete.columns[28:49],axis=1,inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On similar lines, the Tafe Survey dataset contains the columns *Main Factor. Which of these was the main factor for leaving?* to *Workplace. Topic:Would you recommend the Institute as an employer to others?* (17:66) are irrelevant to the analysis, as this data is of employee's engagement with the institute on the Likert scale." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "df_tafe.iloc[:,17:66].head(5)\n", "df_tafe.drop(columns=df_tafe.columns[17:66],axis=1,inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the ease of analysis and eventually combining both datasets for inference later, the columns names are cleaned made uniform across both the datasets (the columns common between the two)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "columns = df_dete.columns.str.replace(\" \",\"_\").str.lower().str.strip()\n", "df_dete.columns = columns" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "new_name = {\n", " '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", "\n", "df_tafe.rename(new_name,inplace=True,axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The *separationtype* column in both the datasets holds the reason why an employee left the institute. For the given analysis, the label 'Resignation' only is relevant, since we are interested in employees who resigned due to dissatisfaction. The datasets are reduced to only employees who have resigned." ] }, { "cell_type": "code", "execution_count": 14, "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": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_dete.separationtype.value_counts()" ] }, { "cell_type": "code", "execution_count": 15, "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": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_tafe.separationtype.value_counts()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "df_dete = df_dete[df_dete.separationtype.str.contains('Resignation')].copy()\n", "df_tafe = df_tafe[df_tafe.separationtype == 'Resignation'].copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The *cease_date* column indicates the last date the employee worked for or basically resignation date. These columns are in both the sets and hence have to be made uniform.
\n", "All NaN values are removed and only the year is maintained rather than the exact date or month." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2011.0 116\n", "2012.0 94\n", "2010.0 68\n", "2013.0 55\n", "2009.0 2\n", "Name: cease_date, dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_tafe.cease_date.value_counts()" ] }, { "cell_type": "code", "execution_count": 18, "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", "NaN 11\n", "11/2013 9\n", "07/2013 9\n", "10/2013 6\n", "08/2013 4\n", "05/2013 2\n", "05/2012 2\n", "2010 1\n", "07/2012 1\n", "09/2010 1\n", "07/2006 1\n", "Name: cease_date, dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_dete.cease_date.value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "df_dete = df_dete[~df_dete.cease_date.isna()]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def clean_date(row):\n", " if '/' in row:\n", " return row.split('/')[1]\n", " else:\n", " return row\n", "\n", "df_dete.cease_date = df_dete.cease_date.apply(clean_date).astype(float)" ] }, { "cell_type": "code", "execution_count": 21, "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": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_dete.cease_date.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The *end_date* and *start date* columns give the period the employee has worked for the insitute. The assumption can be made - The start dates shouldn't be greater than current date and the start date shouldn't be previous to 1970 given that people are usually employed in their 20s.
\n", "Future dates are obviously out of question and for dates previous to 1970 means that given the person was employed in 20s the current age of the person would be 70+, which is usually the retirement age. For these reasons, the lower limit has been set to 1970.
\n", "\n", "A box plot is a good way to catch outiers if any and to view general distribution of the data." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,8))\n", "sns.set_style('white')\n", "sns.boxplot(df_dete.dete_start_date)\n", "plt.xlabel('Start Year')\n", "plt.title(\"Start Year Distribution\")\n", "plt.gca().spines['left'].set_visible(False)\n", "plt.gca().spines['top'].set_visible(False)\n", "plt.gca().spines['right'].set_visible(False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The distribution contains one entry with the year 1963 which is previous to the stipulated lower bound. Hence the removal of the outlier." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "df_dete = df_dete[~(df_dete.dete_start_date == 1963.0)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Tafe Survey dataset's column *institute_service* describes the service years of employees. This column does not exist for the Dete Survery dataset.
\n", "To deduce this column, 'cease_date' and 'dete_start_date' can be used. The subtraction of the two columns results in the length of service of an employee. The rows with null values are dropped for convenience." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Less than 1 year 73\n", "1-2 64\n", "3-4 63\n", "NaN 50\n", "5-6 33\n", "11-20 26\n", "7-10 21\n", "More than 20 years 10\n", "Name: institute_service, dtype: int64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_tafe.institute_service.value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": true }, "outputs": [], "source": [ "df_dete = df_dete[~df_dete.dete_start_date.isna()]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "df_dete['institute_service'] = abs(df_dete.cease_date - df_dete.dete_start_date)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The purpose of the project is to understand which employees are dissatisfied. For this we have the following column relevant to us :\n", "\n", "In the Tafe Survey dataset - \n", " \n", " Contributing Factors. Dissatisfaction\n", " Contributing Factors. Job Dissatisfaction\n", "\n", "In the Dete Survey dataset - \n", " \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", "From the above columns, we can infer that each column talks about dissatisafaction of an employee. Any one of them being true indicates the employee has resigned (already filtered) due to dissatisfaction of some sorts. A new column is created in both the datasets indicating afore-mentioned employee." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Contributing Factors. DissatisfactionContributing Factors. Job Dissatisfaction
3--
4--
5--
6--
7--
.........
696--
697--
698--
699--
701--
\n", "

340 rows × 2 columns

\n", "
" ], "text/plain": [ " Contributing Factors. Dissatisfaction \\\n", "3 - \n", "4 - \n", "5 - \n", "6 - \n", "7 - \n", ".. ... \n", "696 - \n", "697 - \n", "698 - \n", "699 - \n", "701 - \n", "\n", " Contributing Factors. Job Dissatisfaction \n", "3 - \n", "4 - \n", "5 - \n", "6 - \n", "7 - \n", ".. ... \n", "696 - \n", "697 - \n", "698 - \n", "699 - \n", "701 - \n", "\n", "[340 rows x 2 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_tafe[['Contributing Factors. Dissatisfaction'\n", " ,'Contributing Factors. Job Dissatisfaction']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The '-' value in these columns simply stands for not answered which can be concluded as `False`. Similarly, if the question was answered it indicates that there was dissatisfaction related to the employement. Hence for ease of compiling the data and making the afore-mentioned column, these columns will be cleaned and converted to boolean." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
job_dissatisfactiondissatisfaction_with_the_departmentphysical_work_environmentlack_of_recognitionlack_of_job_securitywork_locationemployment_conditionswork_life_balanceworkload
3FalseFalseFalseFalseFalseFalseFalseFalseFalse
5FalseFalseFalseFalseFalseFalseTrueFalseFalse
8FalseFalseFalseFalseFalseFalseFalseFalseFalse
9TrueTrueFalseFalseFalseFalseFalseFalseFalse
11FalseFalseFalseFalseFalseFalseFalseFalseFalse
..............................
807FalseTrueFalseFalseFalseFalseFalseTrueFalse
808FalseFalseFalseFalseFalseFalseFalseFalseFalse
815FalseFalseFalseFalseFalseFalseFalseFalseFalse
816FalseFalseFalseFalseFalseFalseFalseFalseFalse
819FalseFalseFalseFalseFalseFalseFalseTrueFalse
\n", "

272 rows × 9 columns

\n", "
" ], "text/plain": [ " 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", "807 False True \n", "808 False False \n", "815 False False \n", "816 False False \n", "819 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", "807 False False False \n", "808 False False False \n", "815 False False False \n", "816 False False False \n", "819 False False False \n", "\n", " work_location employment_conditions work_life_balance workload \n", "3 False False False False \n", "5 False True False False \n", "8 False False False False \n", "9 False False False False \n", "11 False False False False \n", ".. ... ... ... ... \n", "807 False False True False \n", "808 False False False False \n", "815 False False False False \n", "816 False False False False \n", "819 False False True False \n", "\n", "[272 rows x 9 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_dete[[\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", "]]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "- 277\n", "Contributing Factors. Dissatisfaction 55\n", "NaN 8\n", "Name: Contributing Factors. Dissatisfaction, dtype: int64\n" ] } ], "source": [ "print(df_tafe['Contributing Factors. Dissatisfaction'].value_counts(dropna=False))\n", "\n", "def clean_factors(row):\n", " if row == '-':\n", " return False\n", " elif pd.isnull(row):\n", " return np.NaN\n", " else:\n", " return True\n", "\n", "df_tafe['Contributing Factors. Dissatisfaction'] = df_tafe['Contributing Factors. Dissatisfaction'].apply(clean_factors)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "- 270\n", "Job Dissatisfaction 62\n", "NaN 8\n", "Name: Contributing Factors. Job Dissatisfaction, dtype: int64\n" ] } ], "source": [ "print(df_tafe['Contributing Factors. Job Dissatisfaction'].value_counts(dropna=False))\n", "df_tafe['Contributing Factors. Job Dissatisfaction'] = df_tafe['Contributing Factors. Job Dissatisfaction'].apply(clean_factors)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the `DataFrame.any()` function, the columns are compiled along the rows i.e. if any value along a row for these columns is `True`, then the *dissatisfied* column takes a `True` value." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "df_dete['dissatisfied'] = df_dete[[\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", "]].any(axis=1,skipna=False)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "df_tafe['dissatisfied'] = df_tafe[[\n", " 'Contributing Factors. Dissatisfaction',\n", " 'Contributing Factors. Job Dissatisfaction'\n", "]].any(axis=1,skipna=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cleaning and identification of dissatisfied employees is concluded for both the datasets individually. For further analysis, the datasets need to be combined, to find a generalized trend in terms of dissatisfaction.
\n", "To differentiate between the rows of the two datasets - *df_dete* and *df_tafe*, a new column is created, identifying the institute." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "df_dete['institute'] = 'DETE'\n", "df_tafe['institute'] = 'TAFE'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The *institute_service* columns in both the datasets do not match. In the DETE dataset, these values are on the interval scale, where as for the TAFE column these are on an ordinal scale. For uniformity, the DETE dataset column is converted to an ordinal scale with the labels :\n", "\n", " * Less than 1 year\n", " * 1-2\n", " * 3-4\n", " * 5-6\n", " * 7-10\n", " * 11-20\n", " * More than 20 years\n", "\n", "These labels are derived from the TAFE dataset." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "bins = pd.IntervalIndex.from_tuples([\n", " (-1,0),(1,2),(3,4),(5,6),(7,10),(11,20),(21,100)\n", "],\n", " closed='both'\n", ")\n", "\n", "tmp = pd.cut(\n", " x=df_dete.institute_service,\n", " bins=bins\n", ")" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "def assign_labels(row):\n", " if row == pd.Interval(0,1,closed='both'):\n", " return 'Less than 1 year'\n", " elif row == pd.Interval(1,2,closed='both'):\n", " return '1-2'\n", " elif row == pd.Interval(3,4,closed='both'):\n", " return '3-4'\n", " elif row == pd.Interval(5,6,closed='both'):\n", " return '5-6'\n", " elif row == pd.Interval(7,10,closed='both'):\n", " return '7-10'\n", " elif row == pd.Interval(11,20,closed='both'):\n", " return '11-20'\n", " else:\n", " return 'More than 20 years'\n", "\n", "df_dete.institute_service = tmp.apply(assign_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The datasets are finally uniform in terms of the common column. Since the relevant columns for the analysis have been cleaned or derived, all other columns are irrelevant now and hence are removed before joining the datasets.
\n", "\n", "The relevant columns are:-\n", " * institute_service\n", " * gender\n", " * age\n", " * employment_status\n", " * position\n", " * cease_date\n", " * dissatisfied\n", " * id\n", " * separationtype\n", " * institute" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "relevant_cols = ['institute_service','gender','age','employment_status',\n", " 'position','cease_date','dissatisfied','id',\n", " 'separationtype','institute']" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "df_tafe = df_tafe[relevant_cols]\n", "df_dete = df_dete[relevant_cols]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "df = pd.concat([df_dete,df_tafe])" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
institute_servicegenderageemployment_statuspositioncease_datedissatisfiedidseparationtypeinstitute
37-10Female36-40Permanent Full-timeTeacher2012.0False4.0Resignation-Other reasonsDETE
511-20Female41-45Permanent Full-timeGuidance Officer2012.0True6.0Resignation-Other reasonsDETE
83-4Female31-35Permanent Full-timeTeacher2012.0False9.0Resignation-Other reasonsDETE
911-20Female46-50Permanent Part-timeTeacher Aide2012.0True10.0Resignation-Other employerDETE
113-4Male31-35Permanent Full-timeTeacher2012.0False12.0Resignation-Move overseas/interstateDETE
\n", "
" ], "text/plain": [ " institute_service gender age employment_status position \\\n", "3 7-10 Female 36-40 Permanent Full-time Teacher \n", "5 11-20 Female 41-45 Permanent Full-time Guidance Officer \n", "8 3-4 Female 31-35 Permanent Full-time Teacher \n", "9 11-20 Female 46-50 Permanent Part-time Teacher Aide \n", "11 3-4 Male 31-35 Permanent Full-time Teacher \n", "\n", " cease_date dissatisfied id separationtype \\\n", "3 2012.0 False 4.0 Resignation-Other reasons \n", "5 2012.0 True 6.0 Resignation-Other reasons \n", "8 2012.0 False 9.0 Resignation-Other reasons \n", "9 2012.0 True 10.0 Resignation-Other employer \n", "11 2012.0 False 12.0 Resignation-Move overseas/interstate \n", "\n", " institute \n", "3 DETE \n", "5 DETE \n", "8 DETE \n", "9 DETE \n", "11 DETE " ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The datasets are concatenated to form one dataset with the columns afore mentioned. The *institute_service* column is uniform, but not exactly intuitive, hence the column is further binned and categorized into the following :\n", " \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 categories are intuitive and easy to understand. The previous labels did show the limits and hence conveyed more data but in terms of analysis, that is very granular and a technical jargon.
\n", "\n", "NOTE : The service length (category) of an employee does not infer the age of the employee. The employee can be New (recently joined the institute) and yet be quite old in terms of age." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "def service_catgs(row):\n", " if row in ['Less than 1 year','1-2']:\n", " return 'New'\n", " elif row in ['3-4','5-6']:\n", " return 'Experienced'\n", " elif row == '7-10':\n", " return 'Established'\n", " elif pd.isnull(row):\n", " return np.NaN\n", " else:\n", " return 'Veteran'\n", " \n", "df['service_catg'] = df.institute_service.apply(service_catgs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The questions to be answered via this analysis were:- \n", " 1. Are employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been there longer?\n", "\n", " 2. Are younger employees resigning due to some kind of dissatisfaction? What about older employees?\n", " \n", "For the analysis we have coagulated the service lengths for each employee into a column *service_catg*. This column is now compared with the previously derived column *dissatisfied*. The end goal is to understand which serive category in general shows dissapointment in the employement and hence resigned." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False 376\n", "True 228\n", "NaN 8\n", "Name: dissatisfied, dtype: int64\n" ] } ], "source": [ "print(df.dissatisfied.value_counts(dropna=False))\n", "df.dissatisfied.fillna(False,inplace=True)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
service_catgdissatisfied
2New0.265896
1Experienced0.343023
0Established0.516129
3Veteran0.496774
\n", "
" ], "text/plain": [ " service_catg dissatisfied\n", "2 New 0.265896\n", "1 Experienced 0.343023\n", "0 Established 0.516129\n", "3 Veteran 0.496774" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "catg_percent= df.pivot_table(\n", " values='dissatisfied',\n", " index='service_catg'\n", ")\n", "catg_percent.reset_index(inplace=True)\n", "catg_percent = catg_percent.iloc[[2,1,0,3]]\n", "catg_percent" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `pivot_table` function is used with the default `numpy.meam` as the aggregate function. This grouped the data by *service_catg* and aggregated the *disstatisfied* column. Since the *dissatisfied* is boolean, the mean is nothing but the proportion of `True` values for that group." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,7))\n", "sns.set_style('white')\n", "splt = sns.barplot(\n", " x='service_catg',\n", " y='dissatisfied',\n", " data=catg_percent,\n", " color='skyblue'\n", ")\n", "plt.yticks([])\n", "plt.xlabel(\"Service Categories\")\n", "plt.ylabel(\"Dissatisfaction percentage\")\n", "plt.title(\"Dissatisfaction in Service Categories\")\n", "for loc in ['left','right','top']:\n", " plt.gca().spines[loc].set_visible(False)\n", "for p in splt.patches:\n", " splt.annotate(format(p.get_height(),'.3f'),\n", " (p.get_x()+p.get_width()/2,p.get_height()+0.01),\n", " ha='center', \n", " va='center'\n", " )\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting the resulting proportions obtained, the following conclusion can be drawn:- \n", "* The employees of service categories - 'Established' and 'Veteran' in general show dissatisfaction than the others.\n", "* The employees just starting their new job i.e. 'New' category in general have lower dissatisfaction rates. Maybe since they have just joined." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The conclusion made above categorizes which employee is more likely to be dissatisfied and resign. The service lenghts are just one aspect of it. There are various aspects to an employee. One such aspect is the age, analogous to service categories. Using the age in a similar way as service category, the aim is to find categories that more likely to be dissatisfied and resign.
\n", "\n", "The *age* column is on the ordinal scale but the labels are intervals and less inuitive. To make the comparision easier, the *age* is converted to the labels given below :\n", "\n", " Young: Aged 20 or younger to 30\n", " Middle: Aged 31 to 45\n", " Senior: Aged 46 to 55\n", " Elder: Aged 56 or older\n", "\n", "These catgories are intuitive and are easier to compare than the previous labels \n", " " ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "51-55 69\n", "NaN 52\n", "41 45 45\n", "41-45 44\n", "46 50 39\n", "36-40 36\n", "46-50 34\n", "21 25 33\n", "36 40 32\n", "26 30 32\n", "31 35 32\n", "26-30 31\n", "56 or older 29\n", "31-35 29\n", "21-25 26\n", "56-60 22\n", "61 or older 17\n", "20 or younger 10\n", "Name: age, dtype: int64" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.age.value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "df.age = df.age.str.replace(\" \",\"-\")\n", "df.age = df.age.str.replace(\"56 or older\",\"56-60\")\n", "\n", "def age_catg(row):\n", " if row in ['20 or younger','21-25','26-30']:\n", " return 'Young'\n", " elif row in ['31-35','36-40','41-45']:\n", " return 'Middle'\n", " elif row in ['46-50','51-55']:\n", " return 'Senior'\n", " elif pd.isna(row):\n", " return np.NaN\n", " else:\n", " return 'Elder'\n", " \n", "df['age_catg'] = df.age.apply(age_catg)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Middle 218\n", "Senior 142\n", "Young 132\n", "Elder 68\n", "NaN 52\n", "Name: age_catg, dtype: int64\n" ] } ], "source": [ "print(df.age_catg.value_counts(dropna=False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The *age* still contains NaN values. These values cannot be imputed from any available data.
\n", "\n", "Similar to *service_catg*, the `pivot_table` function is used on the *age* and *dissatisfied* columns to retrieve the proportions of dissatisfied reignations amongst employees for each age category." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
age_catgdissatisfied
3Young0.340909
1Middle0.376147
2Senior0.408451
0Elder0.426471
\n", "
" ], "text/plain": [ " age_catg dissatisfied\n", "3 Young 0.340909\n", "1 Middle 0.376147\n", "2 Senior 0.408451\n", "0 Elder 0.426471" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "age_catg_percent = df.pivot_table(index='age_catg',values='dissatisfied')\n", "age_catg_percent.reset_index(inplace=True)\n", "age_catg_percent = age_catg_percent.iloc[[3,1,2,0]]\n", "age_catg_percent" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,7))\n", "sns.set_style('white')\n", "splt = sns.barplot(\n", " x='age_catg',\n", " y='dissatisfied',\n", " data=age_catg_percent,\n", " color='skyblue'\n", ")\n", "plt.yticks([])\n", "plt.xlabel(\"Age Categories\")\n", "plt.ylabel(\"Dissatisfaction percentage\")\n", "plt.title(\"Dissatisfaction in Age Categories\")\n", "for loc in ['left','right','top']:\n", " plt.gca().spines[loc].set_visible(False)\n", "for p in splt.patches:\n", " splt.annotate(format(p.get_height(),'.3f'),\n", " (p.get_x()+p.get_width()/2,p.get_height()+0.01),\n", " ha='center', \n", " va='center'\n", " )\n", "#splt.annote(\"bar text\",(loc_x,loc_y),ha(horizontal allign),va(vertical allign))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following conclusions can be made from the resulting plot:-\n", "\n", "* The employees of the 'Senior' and 'Elder' age category are likely to be dissatisfied and resign than the others.\n", "* The 'Young' employees are less likely to be dissatisfied. Maybe since its the start of their career." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uptil now the analysis has focused on the mainly the temporal aspects of the employee. Since there are two institutes under analysis, the comparision between the two institutes in terms of having dissatiesfied employees can give a peak at how the institute engages with its employees.
\n", "\n", "Between the two institutions - DETE and TAFE, using `pivot_table` function, proportion of dissatisfied employees is found." ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "TAFE 340\n", "DETE 272\n", "Name: institute, dtype: int64" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.institute.value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
institutedissatisfied
0DETE0.503676
1TAFE0.267647
\n", "
" ], "text/plain": [ " institute dissatisfied\n", "0 DETE 0.503676\n", "1 TAFE 0.267647" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "institute_catg = df.pivot_table(index='institute',values='dissatisfied')\n", "institute_catg.reset_index(inplace=True)\n", "institute_catg" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,7))\n", "sns.set_style('white')\n", "splt = sns.barplot(\n", " x='institute',\n", " y='dissatisfied',\n", " data=institute_catg,\n", " color='skyblue'\n", ")\n", "plt.yticks([])\n", "plt.xlabel(\"Institute\")\n", "plt.ylabel(\"Dissatisfaction percentage\")\n", "plt.title(\"Dissatisfaction in Institutions\")\n", "for loc in ['left','right','top']:\n", " plt.gca().spines[loc].set_visible(False)\n", "for p in splt.patches:\n", " splt.annotate(format(p.get_height(),'.3f'),\n", " (p.get_x()+p.get_width()/2,p.get_height()+0.01),\n", " ha='center', \n", " va='center'\n", " )\n", "#splt.annote(\"bar text\",(loc_x,loc_y),ha(horizontal allign),va(vertical allign))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the results, the plot concludes :-\n", "\n", "* The DETE institute has about 50% employees resign due to dissatisfaction. This is a high proportion and speaks for the institute.\n", "* The TAFE institute comparitively boasts only about 26% employees resigning due to dissatisfaction." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The *employee_status* which describes the kind of employment can be analyzed for the dissatisafaction the categories are made simpler as -\n", "\n", " Permanent: Permanent Full-time / Part-time\n", " Temporary: Temporary Full-time / Part-time\n", " Casual: Contract / Casual\n", " \n", "The results of this analysis would convey, which of these category employees are likely to resign due to dissatisfaction." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Permanent Full-time 244\n", "Permanent Part-time 130\n", "Temporary Full-time 120\n", "NaN 50\n", "Temporary Part-time 35\n", "Contract/casual 29\n", "Casual 4\n", "Name: employment_status, dtype: int64" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.employment_status.value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "def emp_status(row):\n", " if row in ['Permanent Full-time','Permanent Part-time']:\n", " return 'Permanent'\n", " elif row in ['Temporary Full-time','Temporary Part-time']:\n", " return 'Temporary'\n", " elif pd.isna(row):\n", " return np.NaN\n", " else:\n", " return \"Casual\"\n", "\n", "df['employment_catg'] = df.employment_status.apply(emp_status)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
employment_catgdissatisfied
0Casual0.181818
1Permanent0.459893
2Temporary0.232258
\n", "
" ], "text/plain": [ " employment_catg dissatisfied\n", "0 Casual 0.181818\n", "1 Permanent 0.459893\n", "2 Temporary 0.232258" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "emp_status_catg = df.pivot_table(\n", " index='employment_catg',\n", " values='dissatisfied'\n", ")\n", "\n", "emp_status_catg.reset_index(inplace=True)\n", "emp_status_catg" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,7))\n", "sns.set_style('white')\n", "splt = sns.barplot(\n", " x='employment_catg',\n", " y='dissatisfied',\n", " data=emp_status_catg,\n", " color='skyblue'\n", ")\n", "plt.yticks([])\n", "plt.xticks(rotation=0)\n", "plt.xlabel(\"Employment Type\")\n", "plt.ylabel(\"Dissatisfaction percentage\")\n", "plt.title(\"Dissatisfaction vs Employment Type\")\n", "for loc in ['left','right','top']:\n", " plt.gca().spines[loc].set_visible(False)\n", "for p in splt.patches:\n", " splt.annotate(format(p.get_height(),'.3f'),\n", " (p.get_x()+p.get_width()/2,p.get_height()+0.01),\n", " ha='center', \n", " va='center'\n", " )\n", "#splt.annote(\"bar text\",(loc_x,loc_y),ha(horizontal allign),va(vertical allign))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The conclusion from the resulting plot:-\n", "\n", "* Employees of 'Permanent' status are likely to resign due to dissatisfaction.\n", "* Employees of the 'Casual' and 'Temporary' status have lesser likelihood as compared to 'Permanent'." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next step in analysis, is only to get an idea of sorts whether dissatisfaction is the driving criterion for attrition in these institutes." ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
dissatisfied
index
Not Dissatisfied384
Dissatisfied228
\n", "
" ], "text/plain": [ " dissatisfied\n", "index \n", "Not Dissatisfied 384\n", "Dissatisfied 228" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dissatisfied_employees = df.dissatisfied.value_counts().reset_index()\n", "dissatisfied_employees['index'] = pd.Series(['Not Dissatisfied','Dissatisfied'])\n", "dissatisfied_employees.set_index('index',inplace=True)\n", "dissatisfied_employees" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(\n", " figsize=(12,6),\n", " FigureClass=Waffle,\n", " rows=1,\n", " columns=5,\n", " values=dissatisfied_employees.to_dict()['dissatisfied'],\n", " legend={'loc': 'upper left', 'bbox_to_anchor': (1.1, 1)},\n", " icons='child',\n", " font_size=50,\n", " title={'label': 'Resignation due to dissatisfaction per 5 employees', 'loc': 'center'}\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The waffle plot shows the number of resignations due to dissatisfaction per 5 people in the survey. 2 out of 5 people resign due to dissatisfaction. Conclusions drawn are :- \n", "\n", "* Dissatisfaction is not the only reason for resignation\n", "* Other reasons for resignation collectively out weigh dissatisfaction.\n", "* 2 out of 5 people exiting the insitutes resign due to dissatisfaction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The analysis comes to an end here. The final conclusions drawn from the project are :-\n", "\n", "* Resignation due to dissatisfaction makes up for only about 37% of the total employees who resigned from these institutes collectively.\n", "* An employee of the following type has more likely resigned due to dissatisfaction:-\n", " * Established or Veteran service category
\n", " Employee has worked for more than 7 years.\n", " * Senior or Elder of age
\n", " Employee aged 46 or older.\n", " * Permanent status
\n", " Employee working full time.\n", "* Young or New employees mostly do not resign due to dissatisfaction.\n", "* Department of Education, Training and Employment (DETE) have higher resignations due to dissatisfaction than the Technical and Further Education (TAFE) institute." ] } ], "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 2 }