{ "cells": [ { "cell_type": "markdown", "id": "1f118884-0a85-422c-86d0-0fa85ca37f25", "metadata": {}, "source": [ "# Clean and Analyze Employee Exit Surveys\n", "## Introduction\n", "- In this project, we'll clean and analyze exit surveys from employees of the [Department of Education, Training and Employment (DETE)](https://www.qld.gov.au/education) and the [Technical and Further Education (TAFE)](https://tafeqld.edu.au/) of Queensland in Australia.\n", "- Our goal is to identify the dissatisfaction of the employees that exit, the ones who worked for a short period and the ones that stayed longer. \n", "- Identifying this situation is very important for a workspace, [low job satisfaction can cause a lot of problems in the workspace](https://smallbusiness.chron.com/effects-low-job-satisfaction-10721.html), like employees with low productivity, more turnover cases and can affect customer experience. Besides that, with the exit of these employees, the replacement is more costful than maintaining them.\n", "- This project is directed to department heads and human resources." ] }, { "cell_type": "markdown", "id": "62cd9d6e-c30b-4230-9599-8ee61ea0cea1", "metadata": {}, "source": [ "## Introducing the Data\n", "- We'll use the datasets published in the australian government site of data. We can find the DETE dataset [here](https://data.gov.au/dataset/ds-qld-fe96ff30-d157-4a81-851d-215f2a0fe26d/details?q=exit%20survey) and the TAFE dataset [here](https://data.gov.au/error?errorCode=404&recordType=Dataset&recordId=%22ds-qld-89970a3b-182b-41ea-aea2-6f9f17b5907e%22). " ] }, { "cell_type": "code", "execution_count": 1, "id": "6cb0a136-8cdb-4856-979b-50e353c6726f", "metadata": {}, "outputs": [], "source": [ "# importing the libraries\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import matplotlib.style as style\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "id": "fd758014-7bc7-41d2-bc76-2047d4ccb75e", "metadata": {}, "outputs": [], "source": [ "# importing the datasets\n", "pd.set_option('display.max_rows', 1000)\n", "pd.set_option('display.max_columns', 100)\n", "\n", "dete = pd.read_csv('dete_survey.csv')\n", "tafe = pd.read_csv('tafe_survey.csv')" ] }, { "cell_type": "code", "execution_count": 3, "id": "6f16645d-2d4a-46b2-bf28-601d86511b44", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IDSeparationTypeCease DateDETE Start DateRole Start DatePositionClassificationRegionBusiness UnitEmployment StatusCareer move to public sectorCareer move to private sectorInterpersonal conflictsJob dissatisfactionDissatisfaction with the departmentPhysical work environmentLack of recognitionLack of job securityWork locationEmployment conditionsMaternity/familyRelocationStudy/TravelIll HealthTraumatic incidentWork life balanceWorkloadNone of the aboveProfessional DevelopmentOpportunities for promotionStaff moraleWorkplace issuePhysical environmentWorklife balanceStress and pressure supportPerformance of supervisorPeer supportInitiativeSkillsCoachCareer AspirationsFeedbackFurther PDCommunicationMy sayInformationKept informedWellness programsHealth & SafetyGenderAgeAboriginalTorres StraitSouth SeaDisabilityNESB
01Ill Health Retirement08/201219842004Public ServantA01-A04Central OfficeCorporate Strategy and PeformancePermanent Full-timeTrueFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueAANNNAAAANNNAAANAANNNMale56-60NaNNaNNaNNaNYes
12Voluntary Early Retirement (VER)08/2012Not StatedNot StatedPublic ServantAO5-AO7Central OfficeCorporate Strategy and PeformancePermanent Full-timeFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseAANNNNAAANNNAAANAANNNMale56-60NaNNaNNaNNaNNaN
23Voluntary Early Retirement (VER)05/201220112011Schools OfficerNaNCentral OfficeEducation QueenslandPermanent Full-timeFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueNNNNNNNNNNNNNNNAANNNNMale61 or olderNaNNaNNaNNaNNaN
34Resignation-Other reasons05/201220052006TeacherPrimaryCentral QueenslandNaNPermanent Full-timeFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseANNNAANNAAAAAAAAAAANAFemale36-40NaNNaNNaNNaNNaN
45Age Retirement05/201219701989Head of Curriculum/Head of Special EducationNaNSouth EastNaNPermanent Full-timeFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseAANNDDNAAAAAASASADDANAMFemale61 or olderNaNNaNNaNNaNNaN
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" ], "text/plain": [ " ID SeparationType Cease Date DETE Start Date \\\n", "0 1 Ill Health Retirement 08/2012 1984 \n", "1 2 Voluntary Early Retirement (VER) 08/2012 Not Stated \n", "2 3 Voluntary Early Retirement (VER) 05/2012 2011 \n", "3 4 Resignation-Other reasons 05/2012 2005 \n", "4 5 Age Retirement 05/2012 1970 \n", "\n", " Role Start Date Position \\\n", "0 2004 Public Servant \n", "1 Not Stated Public Servant \n", "2 2011 Schools Officer \n", "3 2006 Teacher \n", "4 1989 Head of Curriculum/Head of Special Education \n", "\n", " Classification Region Business Unit \\\n", "0 A01-A04 Central Office Corporate Strategy and Peformance \n", "1 AO5-AO7 Central Office Corporate Strategy and Peformance \n", "2 NaN Central Office Education Queensland \n", "3 Primary Central Queensland NaN \n", "4 NaN South East NaN \n", "\n", " Employment Status Career move to public sector \\\n", "0 Permanent Full-time True \n", "1 Permanent Full-time False \n", "2 Permanent Full-time False \n", "3 Permanent Full-time False \n", "4 Permanent Full-time False \n", "\n", " Career move to private sector Interpersonal conflicts \\\n", "0 False False \n", "1 False False \n", "2 False False \n", "3 True False \n", "4 False False \n", "\n", " Job dissatisfaction Dissatisfaction with the department \\\n", "0 True False \n", "1 False False \n", "2 False False \n", "3 False False \n", "4 False False \n", "\n", " Physical work environment Lack of recognition Lack of job security \\\n", "0 False True False \n", "1 False False False \n", "2 False False False \n", "3 False False False \n", "4 False False False \n", "\n", " Work location Employment conditions Maternity/family Relocation \\\n", "0 False False False False \n", "1 False False False False \n", "2 False False False False \n", "3 False False False False \n", "4 False False False False \n", "\n", " Study/Travel Ill Health Traumatic incident Work life balance Workload \\\n", "0 False False False False False \n", "1 False False False False False \n", "2 False False False False False \n", "3 False False False False False \n", "4 False False False True False \n", "\n", " None of the above Professional Development Opportunities for promotion \\\n", "0 True A A \n", "1 False A A \n", "2 True N N \n", "3 False A N \n", "4 False A A \n", "\n", " Staff morale Workplace issue Physical environment Worklife balance \\\n", "0 N N N A \n", "1 N N N N \n", "2 N N N N \n", "3 N N A A \n", "4 N N D D \n", "\n", " Stress and pressure support Performance of supervisor Peer support \\\n", "0 A A A \n", "1 A A A \n", "2 N N N \n", "3 N N A \n", "4 N A A \n", "\n", " Initiative Skills Coach Career Aspirations Feedback Further PD \\\n", "0 N N N A A A \n", "1 N N N A A A \n", "2 N N N N N N \n", "3 A A A A A A \n", "4 A A A A SA SA \n", "\n", " Communication My say Information Kept informed Wellness programs \\\n", "0 N A A N N \n", "1 N A A N N \n", "2 A A N N N \n", "3 A A A A N \n", "4 D D A N A \n", "\n", " Health & Safety Gender Age Aboriginal Torres Strait South Sea \\\n", "0 N Male 56-60 NaN NaN NaN \n", "1 N Male 56-60 NaN NaN NaN \n", "2 N Male 61 or older NaN NaN NaN \n", "3 A Female 36-40 NaN NaN NaN \n", "4 M Female 61 or older NaN NaN NaN \n", "\n", " Disability NESB \n", "0 NaN Yes \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Knowing the dataset\n", "dete.head()" ] }, { "cell_type": "code", "execution_count": 4, "id": "ae21b1db-7897-4e23-a3c7-dd64d6722af7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 822 entries, 0 to 821\n", "Data columns (total 56 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 ID 822 non-null int64 \n", " 1 SeparationType 822 non-null object\n", " 2 Cease Date 822 non-null object\n", " 3 DETE Start Date 822 non-null object\n", " 4 Role Start Date 822 non-null object\n", " 5 Position 817 non-null object\n", " 6 Classification 455 non-null object\n", " 7 Region 822 non-null object\n", " 8 Business Unit 126 non-null object\n", " 9 Employment Status 817 non-null object\n", " 10 Career move to public sector 822 non-null bool \n", " 11 Career move to private sector 822 non-null bool \n", " 12 Interpersonal conflicts 822 non-null bool \n", " 13 Job dissatisfaction 822 non-null bool \n", " 14 Dissatisfaction with the department 822 non-null bool \n", " 15 Physical work environment 822 non-null bool \n", " 16 Lack of recognition 822 non-null bool \n", " 17 Lack of job security 822 non-null bool \n", " 18 Work location 822 non-null bool \n", " 19 Employment conditions 822 non-null bool \n", " 20 Maternity/family 822 non-null bool \n", " 21 Relocation 822 non-null bool \n", " 22 Study/Travel 822 non-null bool \n", " 23 Ill Health 822 non-null bool \n", " 24 Traumatic incident 822 non-null bool \n", " 25 Work life balance 822 non-null bool \n", " 26 Workload 822 non-null bool \n", " 27 None of the above 822 non-null bool \n", " 28 Professional Development 808 non-null object\n", " 29 Opportunities for promotion 735 non-null object\n", " 30 Staff morale 816 non-null object\n", " 31 Workplace issue 788 non-null object\n", " 32 Physical environment 817 non-null object\n", " 33 Worklife balance 815 non-null object\n", " 34 Stress and pressure support 810 non-null object\n", " 35 Performance of supervisor 813 non-null object\n", " 36 Peer support 812 non-null object\n", " 37 Initiative 813 non-null object\n", " 38 Skills 811 non-null object\n", " 39 Coach 767 non-null object\n", " 40 Career Aspirations 746 non-null object\n", " 41 Feedback 792 non-null object\n", " 42 Further PD 768 non-null object\n", " 43 Communication 814 non-null object\n", " 44 My say 812 non-null object\n", " 45 Information 816 non-null object\n", " 46 Kept informed 813 non-null object\n", " 47 Wellness programs 766 non-null object\n", " 48 Health & Safety 793 non-null object\n", " 49 Gender 798 non-null object\n", " 50 Age 811 non-null object\n", " 51 Aboriginal 16 non-null object\n", " 52 Torres Strait 3 non-null object\n", " 53 South Sea 7 non-null object\n", " 54 Disability 23 non-null object\n", " 55 NESB 32 non-null object\n", "dtypes: bool(18), int64(1), object(37)\n", "memory usage: 258.6+ KB\n" ] } ], "source": [ "dete.info()" ] }, { "cell_type": "code", "execution_count": 5, "id": "aff91a4f-cfb0-4d1e-880d-22d8f70358cb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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/FamilyContributing Factors. DissatisfactionContributing Factors. Job DissatisfactionContributing Factors. Interpersonal ConflictContributing Factors. StudyContributing Factors. TravelContributing Factors. OtherContributing Factors. NONEMain Factor. Which of these was the main factor for leaving?InstituteViews. Topic:1. I feel the senior leadership had a clear vision and directionInstituteViews. Topic:2. I was given access to skills training to help me do my job betterInstituteViews. Topic:3. I was given adequate opportunities for personal developmentInstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL%InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I hadInstituteViews. Topic:6. The organisation recognised when staff did good workInstituteViews. Topic:7. Management was generally supportive of meInstituteViews. Topic:8. Management was generally supportive of my teamInstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect meInstituteViews. Topic:10. Staff morale was positive within the InstituteInstituteViews. Topic:11. If I had a workplace issue it was dealt with quicklyInstituteViews. Topic:12. If I had a workplace issue it was dealt with efficientlyInstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetlyWorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unitWorkUnitViews. Topic:15. I worked well with my colleaguesWorkUnitViews. Topic:16. My job was challenging and interestingWorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my workWorkUnitViews. Topic:18. I had sufficient contact with other people in my jobWorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my jobWorkUnitViews. Topic:20. I was able to use the full range of my skills in my jobWorkUnitViews. 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]WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my jobWorkUnitViews. Topic:23. My job provided sufficient varietyWorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my jobWorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfactionWorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performanceWorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working areaWorkUnitViews. 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 dateWorkUnitViews. Topic:29. There was adequate communication between staff in my unitWorkUnitViews. Topic:30. Staff morale was positive within my work unitInduction. Did you undertake Workplace Induction?InductionInfo. Topic:Did you undertake a Corporate Induction?InductionInfo. Topic:Did you undertake a Institute Induction?InductionInfo. Topic: Did you undertake Team Induction?InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted?InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted?InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction?InductionInfo. Face to Face Topic:Did you undertake a Institute Induction?InductionInfo. On-line Topic:Did you undertake a Institute Induction?InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction?InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category?InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.]InductionInfo. Induction Manual Topic: Did you undertake Team Induction?Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)?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 ExpiredNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAgreeAgreeAgreeNeutralAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeStrongly AgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeNeutralAgreeAgreeYesYesYesYesFace to Face--Face to Face--Face to Face--YesYesYesYesYesFemale26 30Temporary Full-timeAdministration (AO)1-21-2
16.341337e+17Mount Isa Institute of TAFENon-Delivery (corporate)2010.0Retirement---------Travel--NaNAgreeAgreeAgreeAgreeAgreeStrongly AgreeStrongly AgreeAgreeStrongly AgreeAgreeAgreeAgreeDisagreeStrongly AgreeStrongly AgreeStrongly AgreeAgreeAgreeAgreeStrongly AgreeAgreeAgreeAgreeStrongly AgreeAgreeStrongly AgreeStrongly AgreeAgreeAgreeStrongly AgreeNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNYesYesYesYesYesNaNNaNNaNNaNNaNNaN
26.341388e+17Mount Isa Institute of TAFEDelivery (teaching)2010.0Retirement-----------NONENaNAgreeAgreeAgreeAgreeAgreeAgreeStrongly AgreeAgreeAgreeAgreeAgreeNeutralNeutralStrongly AgreeStrongly AgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNYesYesYesYesYesNaNNaNNaNNaNNaNNaN
36.341399e+17Mount Isa Institute of TAFENon-Delivery (corporate)2010.0Resignation---------Travel--NaNAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeYesNoYesYes---NaN-----YesYesYesYesYesNaNNaNNaNNaNNaNNaN
46.341466e+17Southern Queensland Institute of TAFEDelivery (teaching)2010.0Resignation-Career Move - Private Sector----------NaNAgreeAgreeStrongly AgreeAgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeAgreeStrongly AgreeStrongly AgreeAgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeStrongly AgreeYesYesYesYes--Induction ManualFace to Face--Face to Face--YesYesYesYesYesMale41 45Permanent Full-timeTeacher (including LVT)3-43-4
\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", " Contributing Factors. Dissatisfaction \\\n", "0 NaN \n", "1 - \n", "2 - \n", "3 - \n", "4 - \n", "\n", " Contributing Factors. Job Dissatisfaction \\\n", "0 NaN \n", "1 - \n", "2 - \n", "3 - \n", "4 - \n", "\n", " Contributing Factors. Interpersonal Conflict Contributing Factors. Study \\\n", "0 NaN NaN \n", "1 - - \n", "2 - - \n", "3 - - \n", "4 - - \n", "\n", " Contributing Factors. Travel Contributing Factors. Other \\\n", "0 NaN NaN \n", "1 Travel - \n", "2 - - \n", "3 Travel - \n", "4 - - \n", "\n", " Contributing Factors. NONE \\\n", "0 NaN \n", "1 - \n", "2 NONE \n", "3 - \n", "4 - \n", "\n", " Main Factor. Which of these was the main factor for leaving? \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction \\\n", "0 Agree \n", "1 Agree \n", "2 Agree \n", "3 Agree \n", "4 Agree \n", "\n", " InstituteViews. Topic:2. I was given access to skills training to help me do my job better \\\n", "0 Agree \n", "1 Agree \n", "2 Agree \n", "3 Agree \n", "4 Agree \n", "\n", " InstituteViews. Topic:3. I was given adequate opportunities for personal development \\\n", "0 Agree \n", "1 Agree \n", "2 Agree \n", "3 Agree \n", "4 Strongly Agree \n", "\n", " InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% \\\n", "0 Neutral \n", "1 Agree \n", "2 Agree \n", "3 Agree \n", "4 Agree \n", "\n", " InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had \\\n", "0 Agree \n", "1 Agree \n", "2 Agree \n", "3 Agree \n", "4 Strongly Agree \n", "\n", " InstituteViews. Topic:6. The organisation recognised when staff did good work \\\n", "0 Agree \n", "1 Strongly Agree \n", "2 Agree \n", "3 Agree \n", "4 Strongly Agree \n", "\n", " InstituteViews. Topic:7. Management was generally supportive of me \\\n", "0 Agree \n", "1 Strongly Agree \n", "2 Strongly Agree \n", "3 Agree \n", "4 Strongly Agree \n", "\n", " InstituteViews. Topic:8. Management was generally supportive of my team \\\n", "0 Agree \n", "1 Agree \n", "2 Agree \n", "3 Agree \n", "4 Strongly Agree \n", "\n", " InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me \\\n", "0 Agree \n", "1 Strongly Agree \n", "2 Agree \n", "3 Agree \n", "4 Agree \n", "\n", " InstituteViews. Topic:10. Staff morale was positive within the Institute \\\n", "0 Agree \n", "1 Agree \n", "2 Agree \n", "3 Agree \n", "4 Strongly Agree \n", "\n", " InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly \\\n", "0 Agree \n", "1 Agree \n", "2 Agree \n", "3 Agree \n", "4 Strongly Agree \n", "\n", " InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently \\\n", "0 Agree \n", "1 Agree \n", "2 Neutral \n", "3 Agree \n", "4 Agree \n", "\n", " InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly \\\n", "0 Agree \n", "1 Disagree \n", "2 Neutral \n", "3 Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit \\\n", "0 Agree \n", "1 Strongly Agree \n", "2 Strongly Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:15. I worked well with my colleagues \\\n", "0 Agree \n", "1 Strongly Agree \n", "2 Strongly Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:16. My job was challenging and interesting \\\n", "0 Agree \n", "1 Strongly Agree \n", "2 Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work \\\n", "0 Strongly Agree \n", "1 Agree \n", "2 Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:18. I had sufficient contact with other people in my job \\\n", "0 Agree \n", "1 Agree \n", "2 Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job \\\n", "0 Agree \n", "1 Agree \n", "2 Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job \\\n", "0 Agree \n", "1 Strongly Agree \n", "2 Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] \\\n", "0 Agree \n", "1 Agree \n", "2 Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job \\\n", "0 Agree \n", "1 Agree \n", "2 Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:23. My job provided sufficient variety \\\n", "0 Agree \n", "1 Agree \n", "2 Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job \\\n", "0 Agree \n", "1 Strongly Agree \n", "2 Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction \\\n", "0 Agree \n", "1 Agree \n", "2 Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance \\\n", "0 Agree \n", "1 Strongly Agree \n", "2 Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area \\\n", "0 Agree \n", "1 Strongly Agree \n", "2 Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date \\\n", "0 Neutral \n", "1 Agree \n", "2 Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:29. There was adequate communication between staff in my unit \\\n", "0 Agree \n", "1 Agree \n", "2 Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " WorkUnitViews. Topic:30. Staff morale was positive within my work unit \\\n", "0 Agree \n", "1 Strongly Agree \n", "2 Agree \n", "3 Strongly Agree \n", "4 Strongly Agree \n", "\n", " Induction. Did you undertake Workplace Induction? \\\n", "0 Yes \n", "1 No \n", "2 No \n", "3 Yes \n", "4 Yes \n", "\n", " InductionInfo. Topic:Did you undertake a Corporate Induction? \\\n", "0 Yes \n", "1 NaN \n", "2 NaN \n", "3 No \n", "4 Yes \n", "\n", " InductionInfo. Topic:Did you undertake a Institute Induction? \\\n", "0 Yes \n", "1 NaN \n", "2 NaN \n", "3 Yes \n", "4 Yes \n", "\n", " InductionInfo. Topic: Did you undertake Team Induction? \\\n", "0 Yes \n", "1 NaN \n", "2 NaN \n", "3 Yes \n", "4 Yes \n", "\n", " InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? \\\n", "0 Face to Face \n", "1 NaN \n", "2 NaN \n", "3 - \n", "4 - \n", "\n", " InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? \\\n", "0 - \n", "1 NaN \n", "2 NaN \n", "3 - \n", "4 - \n", "\n", " InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? \\\n", "0 - \n", "1 NaN \n", "2 NaN \n", "3 - \n", "4 Induction Manual \n", "\n", " InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? \\\n", "0 Face to Face \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 Face to Face \n", "\n", " InductionInfo. On-line Topic:Did you undertake a Institute Induction? \\\n", "0 - \n", "1 NaN \n", "2 NaN \n", "3 - \n", "4 - \n", "\n", " InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? \\\n", "0 - \n", "1 NaN \n", "2 NaN \n", "3 - \n", "4 - \n", "\n", " InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? \\\n", "0 Face to Face \n", "1 NaN \n", "2 NaN \n", "3 - \n", "4 Face to Face \n", "\n", " InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] \\\n", "0 - \n", "1 NaN \n", "2 NaN \n", "3 - \n", "4 - \n", "\n", " InductionInfo. Induction Manual Topic: Did you undertake Team Induction? \\\n", "0 - \n", "1 NaN \n", "2 NaN \n", "3 - \n", "4 - \n", "\n", " Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? \\\n", "0 Yes \n", "1 Yes \n", "2 Yes \n", "3 Yes \n", "4 Yes \n", "\n", " Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? \\\n", "0 Yes \n", "1 Yes \n", "2 Yes \n", "3 Yes \n", "4 Yes \n", "\n", " Workplace. Topic:Does your workplace promote and practice the principles of employment equity? \\\n", "0 Yes \n", "1 Yes \n", "2 Yes \n", "3 Yes \n", "4 Yes \n", "\n", " Workplace. Topic:Does your workplace value the diversity of its employees? \\\n", "0 Yes \n", "1 Yes \n", "2 Yes \n", "3 Yes \n", "4 Yes \n", "\n", " Workplace. Topic:Would you recommend the Institute as an employer to others? \\\n", "0 Yes \n", "1 Yes \n", "2 Yes \n", "3 Yes \n", "4 Yes \n", "\n", " Gender. What is your Gender? CurrentAge. Current Age \\\n", "0 Female 26 30 \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 Male 41 45 \n", "\n", " Employment Type. Employment Type Classification. Classification \\\n", "0 Temporary Full-time Administration (AO) \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 Permanent Full-time Teacher (including LVT) \n", "\n", " LengthofServiceOverall. Overall Length of Service at Institute (in years) \\\n", "0 1-2 \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 3-4 \n", "\n", " LengthofServiceCurrent. Length of Service at current workplace (in years) \n", "0 1-2 \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 3-4 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tafe.head()" ] }, { "cell_type": "code", "execution_count": 6, "id": "5be7f43b-7054-49aa-a668-b12afe2fc3de", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 702 entries, 0 to 701\n", "Data columns (total 72 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Record ID 702 non-null float64\n", " 1 Institute 702 non-null object \n", " 2 WorkArea 702 non-null object \n", " 3 CESSATION YEAR 695 non-null float64\n", " 4 Reason for ceasing employment 701 non-null object \n", " 5 Contributing Factors. Career Move - Public Sector 437 non-null object \n", " 6 Contributing Factors. Career Move - Private Sector 437 non-null object \n", " 7 Contributing Factors. Career Move - Self-employment 437 non-null object \n", " 8 Contributing Factors. Ill Health 437 non-null object \n", " 9 Contributing Factors. Maternity/Family 437 non-null object \n", " 10 Contributing Factors. Dissatisfaction 437 non-null object \n", " 11 Contributing Factors. Job Dissatisfaction 437 non-null object \n", " 12 Contributing Factors. Interpersonal Conflict 437 non-null object \n", " 13 Contributing Factors. Study 437 non-null object \n", " 14 Contributing Factors. Travel 437 non-null object \n", " 15 Contributing Factors. Other 437 non-null object \n", " 16 Contributing Factors. NONE 437 non-null object \n", " 17 Main Factor. Which of these was the main factor for leaving? 113 non-null object \n", " 18 InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object \n", " 19 InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object \n", " 20 InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object \n", " 21 InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object \n", " 22 InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object \n", " 23 InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object \n", " 24 InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object \n", " 25 InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object \n", " 26 InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object \n", " 27 InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object \n", " 28 InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object \n", " 29 InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object \n", " 30 InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object \n", " 31 WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object \n", " 32 WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object \n", " 33 WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object \n", " 34 WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object \n", " 35 WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object \n", " 36 WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 609 non-null object \n", " 37 WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object \n", " 38 WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] 608 non-null object \n", " 39 WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object \n", " 40 WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object \n", " 41 WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object \n", " 42 WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object \n", " 43 WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object \n", " 44 WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object \n", " 45 WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 609 non-null object \n", " 46 WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object \n", " 47 WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object \n", " 48 Induction. Did you undertake Workplace Induction? 619 non-null object \n", " 49 InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object \n", " 50 InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object \n", " 51 InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object \n", " 52 InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object \n", " 53 InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object \n", " 54 InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object \n", " 55 InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object \n", " 56 InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object \n", " 57 InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object \n", " 58 InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object \n", " 59 InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object \n", " 60 InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object \n", " 61 Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object \n", " 62 Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object \n", " 63 Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object \n", " 64 Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object \n", " 65 Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object \n", " 66 Gender. What is your Gender? 596 non-null object \n", " 67 CurrentAge. Current Age 596 non-null object \n", " 68 Employment Type. Employment Type 596 non-null object \n", " 69 Classification. Classification 596 non-null object \n", " 70 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object \n", " 71 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object \n", "dtypes: float64(2), object(70)\n", "memory usage: 395.0+ KB\n" ] } ], "source": [ "tafe.info()" ] }, { "cell_type": "markdown", "id": "e34d2860-6d9b-45ae-89c8-03dce8f829c0", "metadata": { "tags": [] }, "source": [ "## Cleaning the data\n", "- Analysing the both datasets, we notice several differences about the column names, with the TAFE dataset including the questions of the survey and having 72 columns. The DETE dataset has 56 columns, with more short names;\n", "- Both datasets have a lot of missing data, expressed in different ways: \"Not Stated\", \"N\", \"NaN\", or just \"-\";\n", "- Both the DETE and TAFE datasets contains many columns that we don't need to complete our analysis." ] }, { "cell_type": "markdown", "id": "d2f9c288-c57d-4403-96d0-ed92fd98fdcd", "metadata": {}, "source": [ "### 1. Identifying missing values and dropping unnecessary columns" ] }, { "cell_type": "code", "execution_count": 7, "id": "9bbaf4c7-bfdf-4b7a-9537-3cb09e96dbd5", "metadata": {}, "outputs": [], "source": [ "# Use the pd.read_csv() function to specify values that should be represented as NaN:\n", "dete = pd.read_csv('dete_survey.csv', na_values='Not Stated')" ] }, { "cell_type": "code", "execution_count": 8, "id": "d00459ff-e57b-4111-8a4f-9e804224bf12", "metadata": {}, "outputs": [], "source": [ "# Dropping columns we don't need\n", "dete_updated = dete.drop(dete.columns[28:49], axis=1)\n", "tafe_updated = tafe.drop(tafe.columns[17:66], axis=1)" ] }, { "cell_type": "code", "execution_count": 9, "id": "82bbe8b8-62f6-4db7-a1c1-2dad2ba5a57e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 822 entries, 0 to 821\n", "Data columns (total 35 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 ID 822 non-null int64 \n", " 1 SeparationType 822 non-null object \n", " 2 Cease Date 788 non-null object \n", " 3 DETE Start Date 749 non-null float64\n", " 4 Role Start Date 724 non-null float64\n", " 5 Position 817 non-null object \n", " 6 Classification 455 non-null object \n", " 7 Region 717 non-null object \n", " 8 Business Unit 126 non-null object \n", " 9 Employment Status 817 non-null object \n", " 10 Career move to public sector 822 non-null bool \n", " 11 Career move to private sector 822 non-null bool \n", " 12 Interpersonal conflicts 822 non-null bool \n", " 13 Job dissatisfaction 822 non-null bool \n", " 14 Dissatisfaction with the department 822 non-null bool \n", " 15 Physical work environment 822 non-null bool \n", " 16 Lack of recognition 822 non-null bool \n", " 17 Lack of job security 822 non-null bool \n", " 18 Work location 822 non-null bool \n", " 19 Employment conditions 822 non-null bool \n", " 20 Maternity/family 822 non-null bool \n", " 21 Relocation 822 non-null bool \n", " 22 Study/Travel 822 non-null bool \n", " 23 Ill Health 822 non-null bool \n", " 24 Traumatic incident 822 non-null bool \n", " 25 Work life balance 822 non-null bool \n", " 26 Workload 822 non-null bool \n", " 27 None of the above 822 non-null bool \n", " 28 Gender 798 non-null object \n", " 29 Age 811 non-null object \n", " 30 Aboriginal 16 non-null object \n", " 31 Torres Strait 3 non-null object \n", " 32 South Sea 7 non-null object \n", " 33 Disability 23 non-null object \n", " 34 NESB 32 non-null object \n", "dtypes: bool(18), float64(2), int64(1), object(14)\n", "memory usage: 123.7+ KB\n" ] } ], "source": [ "dete_updated.info()" ] }, { "cell_type": "code", "execution_count": 10, "id": "c550dd78-9b2a-48a4-9cf0-fe1dec4f7773", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 702 entries, 0 to 701\n", "Data columns (total 23 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Record ID 702 non-null float64\n", " 1 Institute 702 non-null object \n", " 2 WorkArea 702 non-null object \n", " 3 CESSATION YEAR 695 non-null float64\n", " 4 Reason for ceasing employment 701 non-null object \n", " 5 Contributing Factors. Career Move - Public Sector 437 non-null object \n", " 6 Contributing Factors. Career Move - Private Sector 437 non-null object \n", " 7 Contributing Factors. Career Move - Self-employment 437 non-null object \n", " 8 Contributing Factors. Ill Health 437 non-null object \n", " 9 Contributing Factors. Maternity/Family 437 non-null object \n", " 10 Contributing Factors. Dissatisfaction 437 non-null object \n", " 11 Contributing Factors. Job Dissatisfaction 437 non-null object \n", " 12 Contributing Factors. Interpersonal Conflict 437 non-null object \n", " 13 Contributing Factors. Study 437 non-null object \n", " 14 Contributing Factors. Travel 437 non-null object \n", " 15 Contributing Factors. Other 437 non-null object \n", " 16 Contributing Factors. NONE 437 non-null object \n", " 17 Gender. What is your Gender? 596 non-null object \n", " 18 CurrentAge. Current Age 596 non-null object \n", " 19 Employment Type. Employment Type 596 non-null object \n", " 20 Classification. Classification 596 non-null object \n", " 21 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object \n", " 22 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object \n", "dtypes: float64(2), object(21)\n", "memory usage: 126.3+ KB\n" ] } ], "source": [ "tafe_updated.info()" ] }, { "cell_type": "markdown", "id": "fde4a542-cc88-4368-8e97-f2d2a9475b77", "metadata": {}, "source": [ "### 2. Cleaning column names" ] }, { "cell_type": "code", "execution_count": 11, "id": "f0fc09bb-e29b-45bb-9231-c959911a0afc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 822 entries, 0 to 821\n", "Data columns (total 35 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 id 822 non-null int64 \n", " 1 separationtype 822 non-null object \n", " 2 cease_date 788 non-null object \n", " 3 dete_start_date 749 non-null float64\n", " 4 role_start_date 724 non-null float64\n", " 5 position 817 non-null object \n", " 6 classification 455 non-null object \n", " 7 region 717 non-null object \n", " 8 business_unit 126 non-null object \n", " 9 employment_status 817 non-null object \n", " 10 career_move_to_public_sector 822 non-null bool \n", " 11 career_move_to_private_sector 822 non-null bool \n", " 12 interpersonal_conflicts 822 non-null bool \n", " 13 job_dissatisfaction 822 non-null bool \n", " 14 dissatisfaction_with_the_department 822 non-null bool \n", " 15 physical_work_environment 822 non-null bool \n", " 16 lack_of_recognition 822 non-null bool \n", " 17 lack_of_job_security 822 non-null bool \n", " 18 work_location 822 non-null bool \n", " 19 employment_conditions 822 non-null bool \n", " 20 maternity/family 822 non-null bool \n", " 21 relocation 822 non-null bool \n", " 22 study/travel 822 non-null bool \n", " 23 ill_health 822 non-null bool \n", " 24 traumatic_incident 822 non-null bool \n", " 25 work_life_balance 822 non-null bool \n", " 26 workload 822 non-null bool \n", " 27 none_of_the_above 822 non-null bool \n", " 28 gender 798 non-null object \n", " 29 age 811 non-null object \n", " 30 aboriginal 16 non-null object \n", " 31 torres_strait 3 non-null object \n", " 32 south_sea 7 non-null object \n", " 33 disability 23 non-null object \n", " 34 nesb 32 non-null object \n", "dtypes: bool(18), float64(2), int64(1), object(14)\n", "memory usage: 123.7+ KB\n", "None\n", "\n", "RangeIndex: 702 entries, 0 to 701\n", "Data columns (total 23 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 id 702 non-null float64\n", " 1 Institute 702 non-null object \n", " 2 WorkArea 702 non-null object \n", " 3 cease_date 695 non-null float64\n", " 4 separationtype 701 non-null object \n", " 5 Contributing Factors. Career Move - Public Sector 437 non-null object \n", " 6 Contributing Factors. Career Move - Private Sector 437 non-null object \n", " 7 Contributing Factors. Career Move - Self-employment 437 non-null object \n", " 8 Contributing Factors. Ill Health 437 non-null object \n", " 9 Contributing Factors. Maternity/Family 437 non-null object \n", " 10 Contributing Factors. Dissatisfaction 437 non-null object \n", " 11 Contributing Factors. Job Dissatisfaction 437 non-null object \n", " 12 Contributing Factors. Interpersonal Conflict 437 non-null object \n", " 13 Contributing Factors. Study 437 non-null object \n", " 14 Contributing Factors. Travel 437 non-null object \n", " 15 Contributing Factors. Other 437 non-null object \n", " 16 Contributing Factors. NONE 437 non-null object \n", " 17 gender 596 non-null object \n", " 18 age 596 non-null object \n", " 19 employment_status 596 non-null object \n", " 20 position 596 non-null object \n", " 21 institute_service 596 non-null object \n", " 22 role_service 596 non-null object \n", "dtypes: float64(2), object(21)\n", "memory usage: 126.3+ KB\n", "None\n" ] } ], "source": [ "#Updating column names\n", "dete_updated.columns = dete_updated.columns.str.lower().str.replace(' ','_').str.strip()\n", "\n", "tafe_map = {'Record ID': 'id',\n", "'CESSATION YEAR': 'cease_date',\n", "'Reason for ceasing employment': 'separationtype',\n", "'Gender. What is your Gender?': 'gender',\n", "'CurrentAge. Current Age': 'age',\n", "'Employment Type. Employment Type': 'employment_status',\n", "'Classification. Classification': 'position',\n", "'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service',\n", "'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'}\n", "\n", "tafe_updated.rename(tafe_map, axis = 1, inplace = True)\n", "print(dete_updated.info())\n", "print(tafe_updated.info())" ] }, { "cell_type": "markdown", "id": "09a0ca02-d247-4251-8312-6e30a356ce27", "metadata": {}, "source": [ "- Each dataframe contains many of the same columns, but the column names are different. \n", "- Because we eventually want to combine them, we had to standardize the column names. " ] }, { "cell_type": "markdown", "id": "97680634-fd62-4bd1-870e-415e0bff5a35", "metadata": {}, "source": [ "### 3. Filtering the data\n", "- We want the informations about the employees that resignate, so, we are going to filter both datasets." ] }, { "cell_type": "code", "execution_count": 12, "id": "b9c7ebd2-4813-4a0b-84d6-6dd9825e3197", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Age Retirement 285\n", "Resignation-Other reasons 150\n", "Resignation-Other employer 91\n", "Resignation-Move overseas/interstate 70\n", "Voluntary Early Retirement (VER) 67\n", "Ill Health Retirement 61\n", "Other 49\n", "Contract Expired 34\n", "Termination 15\n", "Name: separationtype, dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking the unique values\n", "dete_updated['separationtype'].value_counts()" ] }, { "cell_type": "code", "execution_count": 13, "id": "b1cc9934-953a-4e2c-9d87-40b907422db9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 311 entries, 3 to 821\n", "Data columns (total 35 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 id 311 non-null int64 \n", " 1 separationtype 311 non-null object \n", " 2 cease_date 300 non-null object \n", " 3 dete_start_date 283 non-null float64\n", " 4 role_start_date 271 non-null float64\n", " 5 position 308 non-null object \n", " 6 classification 161 non-null object \n", " 7 region 265 non-null object \n", " 8 business_unit 32 non-null object \n", " 9 employment_status 307 non-null object \n", " 10 career_move_to_public_sector 311 non-null bool \n", " 11 career_move_to_private_sector 311 non-null bool \n", " 12 interpersonal_conflicts 311 non-null bool \n", " 13 job_dissatisfaction 311 non-null bool \n", " 14 dissatisfaction_with_the_department 311 non-null bool \n", " 15 physical_work_environment 311 non-null bool \n", " 16 lack_of_recognition 311 non-null bool \n", " 17 lack_of_job_security 311 non-null bool \n", " 18 work_location 311 non-null bool \n", " 19 employment_conditions 311 non-null bool \n", " 20 maternity/family 311 non-null bool \n", " 21 relocation 311 non-null bool \n", " 22 study/travel 311 non-null bool \n", " 23 ill_health 311 non-null bool \n", " 24 traumatic_incident 311 non-null bool \n", " 25 work_life_balance 311 non-null bool \n", " 26 workload 311 non-null bool \n", " 27 none_of_the_above 311 non-null bool \n", " 28 gender 302 non-null object \n", " 29 age 306 non-null object \n", " 30 aboriginal 7 non-null object \n", " 31 torres_strait 0 non-null object \n", " 32 south_sea 3 non-null object \n", " 33 disability 8 non-null object \n", " 34 nesb 9 non-null object \n", "dtypes: bool(18), float64(2), int64(1), object(14)\n", "memory usage: 49.2+ KB\n" ] } ], "source": [ "# Selecting resignations\n", "dete_resignations = dete_updated[dete_updated['separationtype'].str.contains('Resignation-')].copy()\n", "dete_resignations.info()" ] }, { "cell_type": "code", "execution_count": 14, "id": "16bf78fa-7266-4dcc-85ab-77f87952fbbe", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Resignation 340\n", "Contract Expired 127\n", "Retrenchment/ Redundancy 104\n", "Retirement 82\n", "Transfer 25\n", "Termination 23\n", "Name: separationtype, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking the unique values\n", "tafe_updated['separationtype'].value_counts()" ] }, { "cell_type": "code", "execution_count": 15, "id": "471b1490-738a-4025-9b49-e9acad689204", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 340 entries, 3 to 701\n", "Data columns (total 23 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 id 340 non-null float64\n", " 1 Institute 340 non-null object \n", " 2 WorkArea 340 non-null object \n", " 3 cease_date 335 non-null float64\n", " 4 separationtype 340 non-null object \n", " 5 Contributing Factors. Career Move - Public Sector 332 non-null object \n", " 6 Contributing Factors. Career Move - Private Sector 332 non-null object \n", " 7 Contributing Factors. Career Move - Self-employment 332 non-null object \n", " 8 Contributing Factors. Ill Health 332 non-null object \n", " 9 Contributing Factors. Maternity/Family 332 non-null object \n", " 10 Contributing Factors. Dissatisfaction 332 non-null object \n", " 11 Contributing Factors. Job Dissatisfaction 332 non-null object \n", " 12 Contributing Factors. Interpersonal Conflict 332 non-null object \n", " 13 Contributing Factors. Study 332 non-null object \n", " 14 Contributing Factors. Travel 332 non-null object \n", " 15 Contributing Factors. Other 332 non-null object \n", " 16 Contributing Factors. NONE 332 non-null object \n", " 17 gender 290 non-null object \n", " 18 age 290 non-null object \n", " 19 employment_status 290 non-null object \n", " 20 position 290 non-null object \n", " 21 institute_service 290 non-null object \n", " 22 role_service 290 non-null object \n", "dtypes: float64(2), object(21)\n", "memory usage: 63.8+ KB\n" ] } ], "source": [ "# Selecting resignations\n", "tafe_resignations = tafe_updated[tafe_updated['separationtype'].str.contains('Resignation', na=False)].copy()\n", "tafe_resignations.info()" ] }, { "cell_type": "markdown", "id": "548908dd-932b-4354-b103-45cd69ae74e8", "metadata": {}, "source": [ "### 4. Verifying the data\n", "- Now, we'll verify the date columns" ] }, { "cell_type": "code", "execution_count": 16, "id": "495652a7-c2ef-43a0-a349-96049b13ed3e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2012 126\n", "2013 74\n", "01/2014 22\n", "12/2013 17\n", "06/2013 14\n", "09/2013 11\n", "07/2013 9\n", "11/2013 9\n", "10/2013 6\n", "08/2013 4\n", "05/2012 2\n", "05/2013 2\n", "07/2012 1\n", "2010 1\n", "09/2010 1\n", "07/2006 1\n", "Name: cease_date, dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking unique values\n", "dete_resignations['cease_date'].value_counts()" ] }, { "cell_type": "code", "execution_count": 17, "id": "a7b110a7-0d1c-4228-be3d-3c78140ecf27", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idseparationtypecease_datedete_start_daterole_start_datepositionclassificationregionbusiness_unitemployment_statuscareer_move_to_public_sectorcareer_move_to_private_sectorinterpersonal_conflictsjob_dissatisfactiondissatisfaction_with_the_departmentphysical_work_environmentlack_of_recognitionlack_of_job_securitywork_locationemployment_conditionsmaternity/familyrelocationstudy/travelill_healthtraumatic_incidentwork_life_balanceworkloadnone_of_the_abovegenderageaboriginaltorres_straitsouth_seadisabilitynesb
34Resignation-Other reasons2012.02005.02006.0TeacherPrimaryCentral QueenslandNaNPermanent Full-timeFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFemale36-40NaNNaNNaNNaNNaN
56Resignation-Other reasons2012.01994.01997.0Guidance OfficerNaNCentral OfficeEducation QueenslandPermanent Full-timeFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseFalseFalseFalseFalseFalseFalseFemale41-45NaNNaNNaNNaNNaN
89Resignation-Other reasons2012.02009.02009.0TeacherSecondaryNorth QueenslandNaNPermanent Full-timeFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFemale31-35NaNNaNNaNNaNNaN
910Resignation-Other employer2012.01997.02008.0Teacher AideNaNNaNNaNPermanent Part-timeFalseFalseTrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFemale46-50NaNNaNNaNNaNNaN
1112Resignation-Move overseas/interstate2012.02009.02009.0TeacherSecondaryFar North QueenslandNaNPermanent Full-timeFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseFalseFalseFalseFalseFalseMale31-35NaNNaNNaNNaNNaN
\n", "
" ], "text/plain": [ " id separationtype cease_date dete_start_date \\\n", "3 4 Resignation-Other reasons 2012.0 2005.0 \n", "5 6 Resignation-Other reasons 2012.0 1994.0 \n", "8 9 Resignation-Other reasons 2012.0 2009.0 \n", "9 10 Resignation-Other employer 2012.0 1997.0 \n", "11 12 Resignation-Move overseas/interstate 2012.0 2009.0 \n", "\n", " role_start_date position classification region \\\n", "3 2006.0 Teacher Primary Central Queensland \n", "5 1997.0 Guidance Officer NaN Central Office \n", "8 2009.0 Teacher Secondary North Queensland \n", "9 2008.0 Teacher Aide NaN NaN \n", "11 2009.0 Teacher Secondary Far North Queensland \n", "\n", " business_unit employment_status career_move_to_public_sector \\\n", "3 NaN Permanent Full-time False \n", "5 Education Queensland Permanent Full-time False \n", "8 NaN Permanent Full-time False \n", "9 NaN Permanent Part-time False \n", "11 NaN Permanent Full-time False \n", "\n", " career_move_to_private_sector interpersonal_conflicts \\\n", "3 True False \n", "5 True False \n", "8 True False \n", "9 False True \n", "11 False False \n", "\n", " job_dissatisfaction dissatisfaction_with_the_department \\\n", "3 False False \n", "5 False False \n", "8 False False \n", "9 True True \n", "11 False False \n", "\n", " physical_work_environment lack_of_recognition lack_of_job_security \\\n", "3 False False False \n", "5 False False False \n", "8 False False False \n", "9 False False False \n", "11 False False False \n", "\n", " work_location employment_conditions maternity/family relocation \\\n", "3 False False False False \n", "5 False True True False \n", "8 False False False False \n", "9 False False False False \n", "11 False False True True \n", "\n", " study/travel ill_health traumatic_incident work_life_balance workload \\\n", "3 False False False False False \n", "5 False False False False False \n", "8 False False False False False \n", "9 False False False False False \n", "11 False False False False False \n", "\n", " none_of_the_above gender age aboriginal torres_strait south_sea \\\n", "3 False Female 36-40 NaN NaN NaN \n", "5 False Female 41-45 NaN NaN NaN \n", "8 False Female 31-35 NaN NaN NaN \n", "9 False Female 46-50 NaN NaN NaN \n", "11 False Male 31-35 NaN NaN NaN \n", "\n", " disability nesb \n", "3 NaN NaN \n", "5 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "11 NaN NaN " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Extracting the year\n", "year_pattern = r\"([2][0][0-9][0-9])\"\n", "dete_resignations['cease_date'] = dete_resignations['cease_date'].str.extract(year_pattern).astype('float')\n", "dete_resignations.head()" ] }, { "cell_type": "code", "execution_count": 18, "id": "0c352f7c-9c80-4c6f-96fc-d1b229fd7fc3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2013.0 146\n", "2012.0 129\n", "2014.0 22\n", "2010.0 2\n", "2006.0 1\n", "Name: cease_date, dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking unique values\n", "dete_resignations['cease_date'].value_counts()" ] }, { "cell_type": "code", "execution_count": 19, "id": "9af4cb10-7db4-4c14-88f1-8b2a9761d870", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2013.0 10\n", "2012.0 21\n", "2011.0 24\n", "2010.0 17\n", "2009.0 13\n", "2008.0 22\n", "2007.0 21\n", "2006.0 13\n", "2005.0 15\n", "2004.0 14\n", "2003.0 6\n", "2002.0 6\n", "2001.0 3\n", "2000.0 9\n", "1999.0 8\n", "1998.0 6\n", "1997.0 5\n", "1996.0 6\n", "1995.0 4\n", "1994.0 6\n", "1993.0 5\n", "1992.0 6\n", "1991.0 4\n", "1990.0 5\n", "1989.0 4\n", "1988.0 4\n", "1987.0 1\n", "1986.0 3\n", "1985.0 3\n", "1984.0 1\n", "1983.0 2\n", "1982.0 1\n", "1980.0 5\n", "1977.0 1\n", "1976.0 2\n", "1975.0 1\n", "1974.0 2\n", "1973.0 1\n", "1972.0 1\n", "1971.0 1\n", "1963.0 1\n", "Name: dete_start_date, dtype: int64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking unique values of dete_start_date\n", "dete_resignations['dete_start_date'].value_counts().sort_index(ascending=False)" ] }, { "cell_type": "code", "execution_count": 20, "id": "e41a18e1-84cc-428b-a304-20577a070c6a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2013.0 55\n", "2012.0 94\n", "2011.0 116\n", "2010.0 68\n", "2009.0 2\n", "Name: cease_date, dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking unique values \n", "tafe_resignations['cease_date'].value_counts().sort_index(ascending=False)" ] }, { "cell_type": "markdown", "id": "e1ba8489-0ce5-405c-9bc3-b367708d6fd7", "metadata": {}, "source": [ "### 5. Creating a new column\n", "- In order to know how long the employee worked, we need to create a new column in dete dataset: 'institute_service'. The tafe dataset already have a column with this information, also called 'institute_service'." ] }, { "cell_type": "code", "execution_count": 21, "id": "34a01d36-f421-4176-ac31-db016b701cae", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.0 23\n", "1.0 22\n", "3.0 20\n", "0.0 20\n", "6.0 17\n", "4.0 16\n", "2.0 14\n", "9.0 14\n", "7.0 13\n", "13.0 8\n", "8.0 8\n", "20.0 7\n", "15.0 7\n", "17.0 6\n", "10.0 6\n", "22.0 6\n", "12.0 6\n", "14.0 6\n", "18.0 5\n", "16.0 5\n", "24.0 4\n", "23.0 4\n", "11.0 4\n", "32.0 3\n", "39.0 3\n", "21.0 3\n", "19.0 3\n", "25.0 2\n", "28.0 2\n", "26.0 2\n", "30.0 2\n", "36.0 2\n", "33.0 1\n", "35.0 1\n", "49.0 1\n", "38.0 1\n", "41.0 1\n", "27.0 1\n", "42.0 1\n", "29.0 1\n", "34.0 1\n", "31.0 1\n", "Name: institute_service, dtype: int64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']\n", "dete_resignations['institute_service'].value_counts()" ] }, { "cell_type": "markdown", "id": "7ead8ae6-4c5a-4bb2-bd70-381e792e75a7", "metadata": {}, "source": [ "### 6. Identifying disatisfied employees\n", "- Now, we'll work on the 'Contributing Factors. Dissatisfaction' and 'Contributing Factors. Job Dissatisfaction' columns in the tafe dataset." ] }, { "cell_type": "code", "execution_count": 22, "id": "225127aa-ad81-4769-a172-1cfbac80a37e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- 277\n", "Contributing Factors. Dissatisfaction 55\n", "Name: Contributing Factors. Dissatisfaction, dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking unique values\n", "tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()" ] }, { "cell_type": "code", "execution_count": 23, "id": "4d3f7520-005a-4f95-84ab-50437c9bfb0a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- 270\n", "Job Dissatisfaction 62\n", "Name: Contributing Factors. Job Dissatisfaction, dtype: int64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking unique values\n", "tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()" ] }, { "cell_type": "code", "execution_count": 24, "id": "6d998345-d94d-43aa-ab99-8bbcb6cd0236", "metadata": {}, "outputs": [], "source": [ "def update_val(val):\n", " \"\"\"Update the value to boolean or np.nan.\n", "\n", " Args:\n", " val: The value to be updated.\n", "\n", " Returns:\n", " bool or np.nan.\n", " \"\"\"\n", " if val == '-':\n", " return False\n", " elif pd.isnull(val):\n", " return np.nan\n", " else:\n", " return True\n", "\n", "tafe_resignations['Contributing Factors. Dissatisfaction'] = tafe_resignations['Contributing Factors. Dissatisfaction'].apply(update_val)\n", "tafe_resignations['Contributing Factors. Job Dissatisfaction'] = tafe_resignations['Contributing Factors. Job Dissatisfaction'].apply(update_val)" ] }, { "cell_type": "code", "execution_count": 25, "id": "a20c31fd-974e-40a2-817f-6deb4a07c931", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False 277\n", "True 55\n", "Name: Contributing Factors. Dissatisfaction, dtype: int64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking unique values\n", "tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()" ] }, { "cell_type": "code", "execution_count": 26, "id": "0a0c9952-d884-4182-a040-e4b146421212", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False 270\n", "True 62\n", "Name: Contributing Factors. Job Dissatisfaction, dtype: int64" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking unique values\n", "tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()" ] }, { "cell_type": "code", "execution_count": 27, "id": "b0cafa09-4987-4c86-ac19-2c3a173d3a59", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idInstituteWorkAreacease_dateseparationtypeContributing Factors. Career Move - Public SectorContributing Factors. Career Move - Private SectorContributing Factors. Career Move - Self-employmentContributing Factors. Ill HealthContributing Factors. Maternity/FamilyContributing Factors. DissatisfactionContributing Factors. Job DissatisfactionContributing Factors. Interpersonal ConflictContributing Factors. StudyContributing Factors. TravelContributing Factors. OtherContributing Factors. NONEgenderageemployment_statuspositioninstitute_servicerole_servicedissatisfied
36.341399e+17Mount Isa Institute of TAFENon-Delivery (corporate)2010.0Resignation-----FalseFalse--Travel--NaNNaNNaNNaNNaNNaNFalse
46.341466e+17Southern Queensland Institute of TAFEDelivery (teaching)2010.0Resignation-Career Move - Private Sector---FalseFalse-----Male41 45Permanent Full-timeTeacher (including LVT)3-43-4False
56.341475e+17Southern Queensland Institute of TAFEDelivery (teaching)2010.0Resignation-----FalseFalse---Other-Female56 or olderContract/casualTeacher (including LVT)7-107-10False
66.341520e+17Barrier Reef Institute of TAFENon-Delivery (corporate)2010.0Resignation-Career Move - Private Sector--Maternity/FamilyFalseFalse---Other-Male20 or youngerTemporary Full-timeAdministration (AO)3-43-4False
76.341537e+17Southern Queensland Institute of TAFEDelivery (teaching)2010.0Resignation-----FalseFalse---Other-Male46 50Permanent Full-timeTeacher (including LVT)3-43-4False
\n", "
" ], "text/plain": [ " id Institute \\\n", "3 6.341399e+17 Mount Isa Institute of TAFE \n", "4 6.341466e+17 Southern Queensland Institute of TAFE \n", "5 6.341475e+17 Southern Queensland Institute of TAFE \n", "6 6.341520e+17 Barrier Reef Institute of TAFE \n", "7 6.341537e+17 Southern Queensland Institute of TAFE \n", "\n", " WorkArea cease_date separationtype \\\n", "3 Non-Delivery (corporate) 2010.0 Resignation \n", "4 Delivery (teaching) 2010.0 Resignation \n", "5 Delivery (teaching) 2010.0 Resignation \n", "6 Non-Delivery (corporate) 2010.0 Resignation \n", "7 Delivery (teaching) 2010.0 Resignation \n", "\n", " Contributing Factors. Career Move - Public Sector \\\n", "3 - \n", "4 - \n", "5 - \n", "6 - \n", "7 - \n", "\n", " Contributing Factors. Career Move - Private Sector \\\n", "3 - \n", "4 Career Move - Private Sector \n", "5 - \n", "6 Career Move - Private Sector \n", "7 - \n", "\n", " Contributing Factors. Career Move - Self-employment \\\n", "3 - \n", "4 - \n", "5 - \n", "6 - \n", "7 - \n", "\n", " Contributing Factors. Ill Health Contributing Factors. Maternity/Family \\\n", "3 - - \n", "4 - - \n", "5 - - \n", "6 - Maternity/Family \n", "7 - - \n", "\n", " Contributing Factors. Dissatisfaction \\\n", "3 False \n", "4 False \n", "5 False \n", "6 False \n", "7 False \n", "\n", " Contributing Factors. Job Dissatisfaction \\\n", "3 False \n", "4 False \n", "5 False \n", "6 False \n", "7 False \n", "\n", " Contributing Factors. Interpersonal Conflict Contributing Factors. Study \\\n", "3 - - \n", "4 - - \n", "5 - - \n", "6 - - \n", "7 - - \n", "\n", " Contributing Factors. Travel Contributing Factors. Other \\\n", "3 Travel - \n", "4 - - \n", "5 - Other \n", "6 - Other \n", "7 - Other \n", "\n", " Contributing Factors. NONE gender age employment_status \\\n", "3 - NaN NaN NaN \n", "4 - Male 41 45 Permanent Full-time \n", "5 - Female 56 or older Contract/casual \n", "6 - Male 20 or younger Temporary Full-time \n", "7 - Male 46 50 Permanent Full-time \n", "\n", " position institute_service role_service dissatisfied \n", "3 NaN NaN NaN False \n", "4 Teacher (including LVT) 3-4 3-4 False \n", "5 Teacher (including LVT) 7-10 7-10 False \n", "6 Administration (AO) 3-4 3-4 False \n", "7 Teacher (including LVT) 3-4 3-4 False " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Unifying the columns\n", "tafe_resignations['dissatisfied'] = tafe_resignations[['Contributing Factors. Dissatisfaction','Contributing Factors. Job Dissatisfaction']].any(axis = 1, skipna = False)\n", "tafe_resignations.head()" ] }, { "cell_type": "code", "execution_count": 28, "id": "5c39e9b6-b3e2-42e8-a900-d7c2dfef45a7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idseparationtypecease_datedete_start_daterole_start_datepositionclassificationregionbusiness_unitemployment_statuscareer_move_to_public_sectorcareer_move_to_private_sectorinterpersonal_conflictsjob_dissatisfactiondissatisfaction_with_the_departmentphysical_work_environmentlack_of_recognitionlack_of_job_securitywork_locationemployment_conditionsmaternity/familyrelocationstudy/travelill_healthtraumatic_incidentwork_life_balanceworkloadnone_of_the_abovegenderageaboriginaltorres_straitsouth_seadisabilitynesbinstitute_servicedissatisfied
34Resignation-Other reasons2012.02005.02006.0TeacherPrimaryCentral QueenslandNaNPermanent Full-timeFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFemale36-40NaNNaNNaNNaNNaN7.0False
56Resignation-Other reasons2012.01994.01997.0Guidance OfficerNaNCentral OfficeEducation QueenslandPermanent Full-timeFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseFalseFalseFalseFalseFalseFalseFemale41-45NaNNaNNaNNaNNaN18.0True
89Resignation-Other reasons2012.02009.02009.0TeacherSecondaryNorth QueenslandNaNPermanent Full-timeFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFemale31-35NaNNaNNaNNaNNaN3.0False
910Resignation-Other employer2012.01997.02008.0Teacher AideNaNNaNNaNPermanent Part-timeFalseFalseTrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFemale46-50NaNNaNNaNNaNNaN15.0True
1112Resignation-Move overseas/interstate2012.02009.02009.0TeacherSecondaryFar North QueenslandNaNPermanent Full-timeFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseFalseFalseFalseFalseFalseMale31-35NaNNaNNaNNaNNaN3.0False
\n", "
" ], "text/plain": [ " id separationtype cease_date dete_start_date \\\n", "3 4 Resignation-Other reasons 2012.0 2005.0 \n", "5 6 Resignation-Other reasons 2012.0 1994.0 \n", "8 9 Resignation-Other reasons 2012.0 2009.0 \n", "9 10 Resignation-Other employer 2012.0 1997.0 \n", "11 12 Resignation-Move overseas/interstate 2012.0 2009.0 \n", "\n", " role_start_date position classification region \\\n", "3 2006.0 Teacher Primary Central Queensland \n", "5 1997.0 Guidance Officer NaN Central Office \n", "8 2009.0 Teacher Secondary North Queensland \n", "9 2008.0 Teacher Aide NaN NaN \n", "11 2009.0 Teacher Secondary Far North Queensland \n", "\n", " business_unit employment_status career_move_to_public_sector \\\n", "3 NaN Permanent Full-time False \n", "5 Education Queensland Permanent Full-time False \n", "8 NaN Permanent Full-time False \n", "9 NaN Permanent Part-time False \n", "11 NaN Permanent Full-time False \n", "\n", " career_move_to_private_sector interpersonal_conflicts \\\n", "3 True False \n", "5 True False \n", "8 True False \n", "9 False True \n", "11 False False \n", "\n", " job_dissatisfaction dissatisfaction_with_the_department \\\n", "3 False False \n", "5 False False \n", "8 False False \n", "9 True True \n", "11 False False \n", "\n", " physical_work_environment lack_of_recognition lack_of_job_security \\\n", "3 False False False \n", "5 False False False \n", "8 False False False \n", "9 False False False \n", "11 False False False \n", "\n", " work_location employment_conditions maternity/family relocation \\\n", "3 False False False False \n", "5 False True True False \n", "8 False False False False \n", "9 False False False False \n", "11 False False True True \n", "\n", " study/travel ill_health traumatic_incident work_life_balance workload \\\n", "3 False False False False False \n", "5 False False False False False \n", "8 False False False False False \n", "9 False False False False False \n", "11 False False False False False \n", "\n", " none_of_the_above gender age aboriginal torres_strait south_sea \\\n", "3 False Female 36-40 NaN NaN NaN \n", "5 False Female 41-45 NaN NaN NaN \n", "8 False Female 31-35 NaN NaN NaN \n", "9 False Female 46-50 NaN NaN NaN \n", "11 False Male 31-35 NaN NaN NaN \n", "\n", " disability nesb institute_service dissatisfied \n", "3 NaN NaN 7.0 False \n", "5 NaN NaN 18.0 True \n", "8 NaN NaN 3.0 False \n", "9 NaN NaN 15.0 True \n", "11 NaN NaN 3.0 False " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Unifying the column\n", "job_dissatisfaction = ['job_dissatisfaction',\n", "'dissatisfaction_with_the_department',\n", "'physical_work_environment',\n", "'lack_of_recognition',\n", "'lack_of_job_security',\n", "'work_location',\n", "'employment_conditions',\n", "'work_life_balance',\n", "'workload']\n", "dete_resignations['dissatisfied'] = dete_resignations[job_dissatisfaction].any(axis = 1, skipna = False)\n", "dete_resignations.head()" ] }, { "cell_type": "code", "execution_count": 29, "id": "0664bb86-bd19-47dd-9a74-848e03ee2ae8", "metadata": {}, "outputs": [], "source": [ "# Updating the changes\n", "dete_resignations_up = dete_resignations.copy()\n", "tafe_resignations_up = tafe_resignations.copy()" ] }, { "cell_type": "markdown", "id": "6047cd95-c40f-4c4f-9489-baffdec7fa80", "metadata": {}, "source": [ "- We identifyed any employees who resigned because they were dissatisfied;\n", "- If the employee indicated any of the factors above caused them to resign, we marked them as dissatisfied in a new column in both datasets." ] }, { "cell_type": "markdown", "id": "3b390764-127e-426f-b940-c9ddff74d004", "metadata": {}, "source": [ "### 7. Combining the data\n", "- On this topic, we'll combine the two datasets." ] }, { "cell_type": "code", "execution_count": 30, "id": "f45bcfb0-6f88-4608-b7f2-4023dc54c69d", "metadata": {}, "outputs": [], "source": [ "# Creating a 'institution' column\n", "dete_resignations_up['institute'] = 'DETE'\n", "tafe_resignations_up['institute'] = 'TAFE'" ] }, { "cell_type": "code", "execution_count": 31, "id": "8f090a85-d826-4841-983c-38b9fad72a63", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "torres_strait 0\n", "south_sea 3\n", "aboriginal 7\n", "disability 8\n", "nesb 9\n", "business_unit 32\n", "classification 161\n", "region 265\n", "role_start_date 271\n", "dete_start_date 283\n", "role_service 290\n", "none_of_the_above 311\n", "work_life_balance 311\n", "traumatic_incident 311\n", "ill_health 311\n", "study/travel 311\n", "relocation 311\n", "maternity/family 311\n", "employment_conditions 311\n", "workload 311\n", "lack_of_job_security 311\n", "career_move_to_public_sector 311\n", "career_move_to_private_sector 311\n", "interpersonal_conflicts 311\n", "work_location 311\n", "dissatisfaction_with_the_department 311\n", "physical_work_environment 311\n", "lack_of_recognition 311\n", "job_dissatisfaction 311\n", "Contributing Factors. Job Dissatisfaction 332\n", "Contributing Factors. Travel 332\n", "Contributing Factors. Maternity/Family 332\n", "Contributing Factors. Ill Health 332\n", "Contributing Factors. Career Move - Self-employment 332\n", "Contributing Factors. Career Move - Private Sector 332\n", "Contributing Factors. Career Move - Public Sector 332\n", "Contributing Factors. Dissatisfaction 332\n", "Contributing Factors. Other 332\n", "Contributing Factors. Interpersonal Conflict 332\n", "Contributing Factors. NONE 332\n", "Contributing Factors. Study 332\n", "Institute 340\n", "WorkArea 340\n", "institute_service 563\n", "gender 592\n", "age 596\n", "employment_status 597\n", "position 598\n", "cease_date 635\n", "dissatisfied 651\n", "separationtype 651\n", "institute 651\n", "id 651\n", "dtype: int64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Combine the dataframes\n", "combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index=True)\n", "\n", "# Verify the number of non null values in each column\n", "combined.notnull().sum().sort_values()" ] }, { "cell_type": "code", "execution_count": 32, "id": "c4b88e08-6519-49f4-ba69-a774498e49fa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 651 entries, 0 to 650\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 id 651 non-null float64\n", " 1 separationtype 651 non-null object \n", " 2 cease_date 635 non-null float64\n", " 3 position 598 non-null object \n", " 4 employment_status 597 non-null object \n", " 5 gender 592 non-null object \n", " 6 age 596 non-null object \n", " 7 institute_service 563 non-null object \n", " 8 dissatisfied 651 non-null bool \n", " 9 institute 651 non-null object \n", "dtypes: bool(1), float64(2), object(7)\n", "memory usage: 46.5+ KB\n" ] } ], "source": [ "# Drop columns with less than 500 non null values\n", "combined_updated = combined.dropna(thresh = 500, axis =1).copy()\n", "combined_updated.info()" ] }, { "cell_type": "code", "execution_count": 33, "id": "ef37d5bf-34da-4f66-95fe-5516c45ae2b1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idseparationtypecease_datepositionemployment_statusgenderageinstitute_servicedissatisfiedinstitute
04.0Resignation-Other reasons2012.0TeacherPermanent Full-timeFemale36-407.0FalseDETE
16.0Resignation-Other reasons2012.0Guidance OfficerPermanent Full-timeFemale41-4518.0TrueDETE
29.0Resignation-Other reasons2012.0TeacherPermanent Full-timeFemale31-353.0FalseDETE
310.0Resignation-Other employer2012.0Teacher AidePermanent Part-timeFemale46-5015.0TrueDETE
412.0Resignation-Move overseas/interstate2012.0TeacherPermanent Full-timeMale31-353.0FalseDETE
\n", "
" ], "text/plain": [ " id separationtype cease_date position \\\n", "0 4.0 Resignation-Other reasons 2012.0 Teacher \n", "1 6.0 Resignation-Other reasons 2012.0 Guidance Officer \n", "2 9.0 Resignation-Other reasons 2012.0 Teacher \n", "3 10.0 Resignation-Other employer 2012.0 Teacher Aide \n", "4 12.0 Resignation-Move overseas/interstate 2012.0 Teacher \n", "\n", " employment_status gender age institute_service dissatisfied \\\n", "0 Permanent Full-time Female 36-40 7.0 False \n", "1 Permanent Full-time Female 41-45 18.0 True \n", "2 Permanent Full-time Female 31-35 3.0 False \n", "3 Permanent Part-time Female 46-50 15.0 True \n", "4 Permanent Full-time Male 31-35 3.0 False \n", "\n", " institute \n", "0 DETE \n", "1 DETE \n", "2 DETE \n", "3 DETE \n", "4 DETE " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined_updated.head()" ] }, { "cell_type": "markdown", "id": "f976bbe0-3a79-463e-ad12-92bd2d9d04fe", "metadata": {}, "source": [ "- At this point, we merged the data and drop columns that had less than 500 non null values." ] }, { "cell_type": "markdown", "id": "288d5e25-13fc-4f08-bd40-3ea3e24be613", "metadata": { "tags": [] }, "source": [ "### 8. Cleaning the service column\n", "- With our main goal in mind (identify the main reasons of dissatisfaction of the employees that exit, the ones who worked for a short period and the ones that stayed longer), next, we'll clean the institute_service column and categorize employees according to the following definitions (We used [this article](https://www.businesswire.com/news/home/20171108006002/en/Age-Number-Engage-Employees-Career-Stage) to base our classification):\n", " - New: Less than 3 years in the workplace;\n", " - Experienced: 3-6 years in the workplace;\n", " - Established: 7-10 years in the workplace;\n", " - Veteran: 11 or more years in the workplace." ] }, { "cell_type": "code", "execution_count": 34, "id": "25922f05-36ed-42eb-b918-fd93fe65e7ea", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "NaN 88\n", "Less than 1 year 73\n", "1-2 64\n", "3-4 63\n", "5-6 33\n", "11-20 26\n", "5.0 23\n", "1.0 22\n", "7-10 21\n", "3.0 20\n", "0.0 20\n", "6.0 17\n", "4.0 16\n", "9.0 14\n", "2.0 14\n", "7.0 13\n", "More than 20 years 10\n", "13.0 8\n", "8.0 8\n", "15.0 7\n", "20.0 7\n", "10.0 6\n", "14.0 6\n", "12.0 6\n", "17.0 6\n", "22.0 6\n", "18.0 5\n", "16.0 5\n", "11.0 4\n", "23.0 4\n", "24.0 4\n", "32.0 3\n", "39.0 3\n", "19.0 3\n", "21.0 3\n", "36.0 2\n", "25.0 2\n", "30.0 2\n", "26.0 2\n", "28.0 2\n", "49.0 1\n", "41.0 1\n", "27.0 1\n", "42.0 1\n", "29.0 1\n", "34.0 1\n", "31.0 1\n", "33.0 1\n", "35.0 1\n", "38.0 1\n", "Name: institute_service, dtype: int64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking the unique values\n", "combined_updated['institute_service'].value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 35, "id": "7019d912-3a1e-4c4d-a15a-9daec3f8768b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0 159\n", "3.0 83\n", "5.0 56\n", "7.0 34\n", "11.0 30\n", "0.0 20\n", "20.0 17\n", "6.0 17\n", "4.0 16\n", "9.0 14\n", "2.0 14\n", "13.0 8\n", "8.0 8\n", "15.0 7\n", "17.0 6\n", "10.0 6\n", "22.0 6\n", "14.0 6\n", "12.0 6\n", "18.0 5\n", "16.0 5\n", "24.0 4\n", "23.0 4\n", "21.0 3\n", "19.0 3\n", "39.0 3\n", "32.0 3\n", "25.0 2\n", "28.0 2\n", "26.0 2\n", "36.0 2\n", "30.0 2\n", "34.0 1\n", "27.0 1\n", "29.0 1\n", "42.0 1\n", "33.0 1\n", "41.0 1\n", "35.0 1\n", "49.0 1\n", "38.0 1\n", "31.0 1\n", "Name: institute_service_up, dtype: int64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Extracting the years of service and convert the type to float\n", "combined_updated['institute_service_up'] = combined_updated['institute_service'].astype('str').str.extract(r'(\\d+)')\n", "combined_updated['institute_service_up'] = combined_updated['institute_service_up'].astype('float')\n", "\n", "# Checking the years extracted are correct\n", "combined_updated['institute_service_up'].value_counts()" ] }, { "cell_type": "code", "execution_count": 36, "id": "8557a50a-dacc-4cb4-8e40-b8bf06e6db1f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "New 193\n", "Experienced 172\n", "Veteran 136\n", "Established 62\n", "Name: service_cat, dtype: int64" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#convert years of services in categories\n", "def transform_service(val):\n", " \"\"\"Convert years of service to categories.\n", "\n", " Args:\n", " val: The value to be converted.\n", "\n", " Returns:\n", " object.\n", " \"\"\"\n", " if val >= 11:\n", " return \"Veteran\"\n", " elif 7 <= val < 11:\n", " return \"Established\"\n", " elif 3 <= val < 7:\n", " return \"Experienced\"\n", " elif pd.isnull(val):\n", " return np.nan\n", " else:\n", " return \"New\"\n", "combined_updated['service_cat'] = combined_updated['institute_service_up'].apply(transform_service)\n", "\n", "# Checking the unique values\n", "combined_updated['service_cat'].value_counts()" ] }, { "cell_type": "markdown", "id": "e1788744-ca9c-4888-990b-ab30517d7b6e", "metadata": {}, "source": [ "### 9. Checking other columns\n", "- In this topic, we'll check other columns that can be useful for our analysis: 'age', 'gender' and 'employment_status'." ] }, { "cell_type": "code", "execution_count": 37, "id": "ba4fb542-396e-4410-81d2-e8344ce102d3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "51-55 71\n", "41-45 48\n", "41 45 45\n", "46-50 42\n", "36-40 41\n", "46 50 39\n", "26-30 35\n", "21 25 33\n", "36 40 32\n", "31 35 32\n", "26 30 32\n", "21-25 29\n", "56 or older 29\n", "31-35 29\n", "56-60 26\n", "61 or older 23\n", "20 or younger 10\n", "Name: age, dtype: int64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined_updated['age'].value_counts()" ] }, { "cell_type": "code", "execution_count": 38, "id": "9cd900bc-f609-4b10-96bc-e46ff5415469", "metadata": {}, "outputs": [], "source": [ "# Cleaning the values\n", "age_map = {\n", " '51-55':'51-55',\n", " '41-45':'41-45',\n", " '41 45':'41-45',\n", " '46-50':'46-50',\n", " '36-40':'36-40',\n", " '46 50':'46-50',\n", " '26-30':'26-30',\n", " '21 25':'21-25',\n", " '36 40':'36-40',\n", " '31 35':'31-35',\n", " '26 30':'26-30',\n", " '21-25':'21-25',\n", " '56 or older':'56+',\n", " '31-35':'31-35',\n", " '56-60':'56+',\n", " '61 or older':'56+',\n", " '20 or younger':'20-'\n", " }\n", "combined_updated['age'] = combined_updated['age'].map(age_map)" ] }, { "cell_type": "code", "execution_count": 39, "id": "3ee64695-05f8-4dc8-8ab2-43e8404d6b59", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "41-45 93\n", "46-50 81\n", "56+ 78\n", "36-40 73\n", "51-55 71\n", "26-30 67\n", "21-25 62\n", "31-35 61\n", "NaN 55\n", "20- 10\n", "Name: age, dtype: int64" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking the unique values\n", "combined_updated['age'].value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 40, "id": "1e8edf71-f80b-436f-94cc-f1a942d7a4ac", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Female 424\n", "Male 168\n", "NaN 59\n", "Name: gender, dtype: int64" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking gender values\n", "combined_updated['gender'].value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 41, "id": "cc0e09dc-11b5-4384-ab21-998238f4ffbe", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Permanent Full-time 256\n", "Permanent Part-time 150\n", "Temporary Full-time 120\n", "NaN 54\n", "Temporary Part-time 37\n", "Contract/casual 29\n", "Casual 5\n", "Name: employment_status, dtype: int64" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking employment_status values\n", "combined_updated['employment_status'].value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 42, "id": "4f34bc4f-d16b-4fec-83a5-4872ddd3d803", "metadata": {}, "outputs": [], "source": [ "es_map = {\n", " 'Permanent Full-time':'Perm. FT',\n", " 'Permanent Part-time':'Perm. PT',\n", " 'Temporary Full-time':'Temp. FT',\n", " 'Temporary Part-time':'Temp. PT',\n", " 'Contract/casual':'Cont./Cas.',\n", " 'Casual':'Cont./Cas.'\n", " }\n", "combined_updated['employment_status'] = combined_updated['employment_status'].map(es_map)" ] }, { "cell_type": "code", "execution_count": 43, "id": "938c6abb-e7be-4941-881c-70a681d8a7a1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Perm. FT 256\n", "Perm. PT 150\n", "Temp. FT 120\n", "NaN 54\n", "Temp. PT 37\n", "Cont./Cas. 34\n", "Name: employment_status, dtype: int64" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined_updated['employment_status'].value_counts(dropna=False)" ] }, { "cell_type": "markdown", "id": "2322a8dd-8e7d-4592-b499-a2881ec1e4c7", "metadata": {}, "source": [ "### 10. Dealing with missing values\n", "- Last section, we identified some columns that can be useful for our analysis that have some missing values. The missing values represent less than 10% of the total for these columns, so we'll fill with the most commom value of each." ] }, { "cell_type": "code", "execution_count": 44, "id": "45094503-f39e-4a9e-8aba-4b8d19a50a75", "metadata": {}, "outputs": [], "source": [ "# Replace missing values with the most frequent value, '41-45'\n", "combined_updated['age'] = combined_updated['age'].fillna('41-45')" ] }, { "cell_type": "code", "execution_count": 45, "id": "7819bad2-d1e6-46f9-a68c-e0dea80bfaae", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "41-45 148\n", "46-50 81\n", "56+ 78\n", "36-40 73\n", "51-55 71\n", "26-30 67\n", "21-25 62\n", "31-35 61\n", "20- 10\n", "Name: age, dtype: int64" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking new values\n", "combined_updated['age'].value_counts()" ] }, { "cell_type": "code", "execution_count": 46, "id": "99167fb7-19db-4d6a-87ad-ed5a1ff2450a", "metadata": {}, "outputs": [], "source": [ "# Replace missing values with the most frequent value, 'Female'\n", "combined_updated['gender'] = combined_updated['gender'].fillna('Female')" ] }, { "cell_type": "code", "execution_count": 47, "id": "b27e91be-ec02-405f-9d80-a9dc0b5179f1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Female 483\n", "Male 168\n", "Name: gender, dtype: int64" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking new values\n", "combined_updated['gender'].value_counts()" ] }, { "cell_type": "code", "execution_count": 48, "id": "34bbb90a-c757-4dce-83f9-b1824fa01409", "metadata": {}, "outputs": [], "source": [ "# Replace missing values with the most frequent value, 'Permanent Full-time'\n", "combined_updated['employment_status'] = combined_updated['employment_status'].fillna('Perm. FT')" ] }, { "cell_type": "code", "execution_count": 49, "id": "616698f1-da78-4d3a-96d7-baa3151e0feb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Perm. FT 310\n", "Perm. PT 150\n", "Temp. FT 120\n", "Temp. PT 37\n", "Cont./Cas. 34\n", "Name: employment_status, dtype: int64" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking new values\n", "combined_updated['employment_status'].value_counts()" ] }, { "cell_type": "markdown", "id": "1f130ef1-10f3-48dc-9c08-c4fcc08725bf", "metadata": {}, "source": [ "## Exploratory Data Analysis (EDA)\n", "- After the data cleaning, we're ready for our analysis. As our goal is to identify the main reasons of dissatisfaction of the employees that exit (the ones who worked for a short period and the ones that stayed longer), we're going to base our exploration on the 'dissatisfaction' column and the 'service_cat' categories. \n", "- First, we'll compare dissatisfaction between the 'service_cat' categories. After, we'll analyse each category with other columns: 'age', 'gender', and 'employment_status', so we can characterize more the dissatisfaction.\n", "- Remembering that we are classifying the time of service in four categories:\n", " - New: Less than 3 years in the workplace;\n", " - Experienced: 3-6 years in the workplace;\n", " - Established: 7-10 years in the workplace;\n", " - Veteran: 11 or more years in the workplace.\n", "- Understanding the [differences of engagement and dissatisfaction in each category](https://www.businesswire.com/news/home/20171108006002/en/Age-Number-Engage-Employees-Career-Stage) is a key factor to find solutions to this problem." ] }, { "cell_type": "code", "execution_count": 50, "id": "e2e93d72-b1a0-472a-91fc-50d7c6eddb9e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n", "dis_sc = combined_updated.pivot_table(index='service_cat', values='dissatisfied')\n", "dis_sc.sort_values('dissatisfied', ascending=True, inplace=True)\n", "dis_sc = dis_sc['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "dis_sc.plot(kind='bar', rot=30, legend='dissatisfied')" ] }, { "cell_type": "code", "execution_count": 51, "id": "9a9d76f5-8d46-46bf-9047-d3a653bcd14d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n", "dis_age = combined_updated.pivot_table(index='age', values='dissatisfied')\n", "dis_age.sort_values('dissatisfied', ascending=True, inplace=True)\n", "dis_age = dis_age['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "dis_age.plot(kind='bar', rot=30, legend='dissatisfied')" ] }, { "cell_type": "code", "execution_count": 52, "id": "e3d1cbec-fa5f-47fb-bc88-f5b21b54a800", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n", "dis_gen = combined_updated.pivot_table(index='gender', values='dissatisfied')\n", "dis_gen.sort_values('dissatisfied', ascending=True, inplace=True)\n", "dis_gen = dis_gen['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "dis_gen.plot(kind='bar', rot=30, legend='dissatisfied')" ] }, { "cell_type": "code", "execution_count": 53, "id": "0a98fec6-e6af-4344-a395-80be34be3353", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n", "dis_es = combined_updated.pivot_table(index='employment_status', values='dissatisfied')\n", "dis_es.sort_values('dissatisfied', ascending=True, inplace=True)\n", "dis_es = dis_es['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "dis_es.plot(kind='bar', rot=30, legend='dissatisfied')" ] }, { "cell_type": "markdown", "id": "d263a457-9f45-4c14-b86b-7790bf1d3cb2", "metadata": {}, "source": [ "- Observing the general part of our analysis, we can highlight some categories with more dissatisfaction on our set: Establisheds and Veterans; Age rate: 56+, 51-55, 26-30; Gender: Male(with a little difference for Female); Employment Status: Permanent Full-time, followed by Permanent Part-time. " ] }, { "cell_type": "markdown", "id": "0ca252cd-54d8-4244-902d-b665677b4027", "metadata": {}, "source": [ "### 1. New\n", "- On this topic, we'll analyze the same columns as above, but for employees with less than 3 years in the workplace." ] }, { "cell_type": "code", "execution_count": 54, "id": "519edc13-a43c-4bcd-bb65-764bcda7daf7", "metadata": {}, "outputs": [], "source": [ "# Slicing the category\n", "new = combined_updated[combined_updated['service_cat'] == 'New']" ] }, { "cell_type": "code", "execution_count": 55, "id": "15084715-1e77-412f-a0aa-b421a5c658cc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n", "new_age = new.pivot_table(index='age', values='dissatisfied')\n", "new_age.sort_values('dissatisfied', ascending=True, inplace=True)\n", "new_age = new_age['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "new_age.plot(kind='bar', rot=45, legend='dissatisfied')" ] }, { "cell_type": "code", "execution_count": 56, "id": "442bbd24-f864-43d6-bfd8-519b974cdc5a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each gender\n", "new_gen = new.pivot_table(index='gender', values='dissatisfied')\n", "new_gen.sort_values('dissatisfied', ascending=True, inplace=True)\n", "new_gen = new_gen['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "new_gen.plot(kind='bar', rot=30, legend='dissatisfied')" ] }, { "cell_type": "code", "execution_count": 57, "id": "713a3e61-e269-4e7e-8ac6-b5e5bdedc733", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n", "new_es = new.pivot_table(index='employment_status', values='dissatisfied')\n", "new_es.sort_values('dissatisfied', ascending=True, inplace=True)\n", "new_es = new_es['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "new_es.plot(kind='bar', rot='horizontal', legend='dissatisfied')" ] }, { "cell_type": "markdown", "id": "b9f9c738-e29c-4049-8af5-2650fe883105", "metadata": {}, "source": [ "- Analyzing only the employees that worked lass than three years, we can see some aspects in dissatisfaction: Age: 56+, followed by 46-50 and 36-40; Gender: Male, just like the general, but with more distance to Female); Employment Status: Permanent Full-Time, with a bigger diference from Permanent Part-Time." ] }, { "cell_type": "markdown", "id": "09d9fa06-502c-40e1-b771-7cc91cd5ec33", "metadata": {}, "source": [ "### 2. Experienced\n", "- On this topic, we'll analyze the same columns as above, but for employees with three to six years in the workplace." ] }, { "cell_type": "code", "execution_count": 58, "id": "64e0e799-a87e-460b-9f92-8de6d26bc4fa", "metadata": {}, "outputs": [], "source": [ "# Slicing the category\n", "experienced = combined_updated[combined_updated['service_cat'] == 'Experienced']" ] }, { "cell_type": "code", "execution_count": 59, "id": "11782d6a-7b7b-44b2-8e52-6a457122dd40", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n", "exp_age = experienced.pivot_table(index='age', values='dissatisfied')\n", "exp_age.sort_values('dissatisfied', ascending=True, inplace=True)\n", "exp_age = exp_age['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "exp_age.plot(kind='bar', rot=30, legend='dissatisfied')" ] }, { "cell_type": "code", "execution_count": 60, "id": "439b0202-7450-40cc-996d-b485ade0ac60", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n", "exp_gen = experienced.pivot_table(index='gender', values='dissatisfied')\n", "exp_gen.sort_values('dissatisfied', ascending=True, inplace=True)\n", "exp_gen = exp_gen['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "exp_gen.plot(kind='bar', rot=30, legend='dissatisfied')" ] }, { "cell_type": "code", "execution_count": 61, "id": "d54bbbe8-a68d-48ef-a729-fdcddb6c4eb6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n", "exp_es = experienced.pivot_table(index='employment_status', values='dissatisfied')\n", "exp_es.sort_values('dissatisfied', ascending=True, inplace=True)\n", "exp_es = exp_es['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "exp_es.plot(kind='bar', rot=45, legend='dissatisfied')" ] }, { "cell_type": "markdown", "id": "4ba07c6b-0f5a-49f1-8308-7657f4d2cf3b", "metadata": {}, "source": [ "- Analyzing only the employees that worked from three to six years, we noticed some differences from the general plots in dissatisfaction: Age: 26-30 followed by 41-45; Gender: Female; Employment Status: Permanent Full-Time." ] }, { "cell_type": "markdown", "id": "76b60420-5f4f-48c0-996e-d01007e269c4", "metadata": {}, "source": [ "### 3. Established\n", "- On this topic, we'll analyze the same columns as above, but for employees with seven to ten years in the workplace." ] }, { "cell_type": "code", "execution_count": 62, "id": "2617c988-edb2-436d-bebf-ab40ca5a4562", "metadata": {}, "outputs": [], "source": [ "# Slicing the category\n", "established = combined_updated[combined_updated['service_cat'] == 'Established']" ] }, { "cell_type": "code", "execution_count": 63, "id": "0d59b381-9d01-400a-b4cd-79eb090c8627", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n", "est_age = established.pivot_table(index='age', values='dissatisfied')\n", "est_age.sort_values('dissatisfied', ascending=True, inplace=True)\n", "est_age = est_age['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "est_age.plot(kind='bar', rot=30, legend='dissatisfied')" ] }, { "cell_type": "code", "execution_count": 64, "id": "9fa08d18-4d42-497c-8a31-2e7fb92906ed", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n", "est_gen = established.pivot_table(index='gender', values='dissatisfied')\n", "est_gen.sort_values('dissatisfied', ascending=True, inplace=True)\n", "est_gen = est_gen['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "est_gen.plot(kind='bar', rot=30, legend='dissatisfied')" ] }, { "cell_type": "code", "execution_count": 65, "id": "409a985a-2490-4498-ab0b-e196f3fb1f9d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n", "est_es = established.pivot_table(index='employment_status', values='dissatisfied')\n", "est_es.sort_values('dissatisfied', ascending=True, inplace=True)\n", "est_es = est_es['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "est_es.plot(kind='bar', rot=45, legend='dissatisfied')" ] }, { "cell_type": "markdown", "id": "5cd24556-89b9-4b54-8f4d-7d674a064e09", "metadata": {}, "source": [ "- Analyzing only the employees that worked from seven to ten years, the dissatisfaction occurs: Age:31-35 (differently of the others); Gender: Female; Employment Status: Permanent Part-Time, followed by Permanent Full-Time." ] }, { "cell_type": "markdown", "id": "779a9a16-9565-46c5-b280-9d306125daf0", "metadata": {}, "source": [ "### 4. Veterans\n", "- On this topic, we'll analyze the same columns as above, but for employees with more than eleven years of service." ] }, { "cell_type": "code", "execution_count": 66, "id": "edac31b0-f4ee-45e3-be50-746afb9ce40c", "metadata": {}, "outputs": [], "source": [ "# Slicing the category\n", "veteran = combined_updated[combined_updated['service_cat'] == 'Veteran']" ] }, { "cell_type": "code", "execution_count": 67, "id": "6f0c08f6-655b-4263-9a16-cf18fa2a1869", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n", "vet_age = veteran.pivot_table(index='age', values='dissatisfied')\n", "vet_age.sort_values('dissatisfied', ascending=True, inplace=True)\n", "vet_age = vet_age['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "vet_age.plot(kind='bar', rot=30, legend='dissatisfied')" ] }, { "cell_type": "code", "execution_count": 68, "id": "31f1954f-5105-4524-800f-22c48d11a9ed", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n", "vet_gen = veteran.pivot_table(index='gender', values='dissatisfied')\n", "vet_gen.sort_values('dissatisfied', ascending=True, inplace=True)\n", "vet_gen = vet_gen['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "vet_gen.plot(kind='bar', rot=30, legend='dissatisfied')" ] }, { "cell_type": "code", "execution_count": 69, "id": "1b0ab7f6-c182-4a6b-907b-9d8c8d526235", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculate the percentage of employees who resigned due to dissatisfaction in each category\n", "vet_es = veteran.pivot_table(index='employment_status', values='dissatisfied')\n", "vet_es.sort_values('dissatisfied', ascending=True, inplace=True)\n", "vet_es = vet_es['dissatisfied'] * 100\n", "\n", "# Plot the results\n", "vet_es.plot(kind='bar', rot=45, legend='dissatisfied')" ] }, { "cell_type": "markdown", "id": "b6fa68d7-20a9-4d92-9391-bffb3826356e", "metadata": {}, "source": [ "- Analyzing only the employees that worked more than eleven years, we can see some aspects in dissatisfaction: Age: 51-55, followed by 46-50; Gender: Male, with a little difference to Female; Employment Status: Permanent Part-Time." ] }, { "cell_type": "markdown", "id": "3e66f20e-f3e8-43ef-a6f0-94f133c8a154", "metadata": {}, "source": [ "## Data Visualization\n", "- In this section, we'll make a presentation of the results founded in the Exploratory Data Analysis. Following the same structure of our analysis, we'll start with the general view and them each category." ] }, { "cell_type": "code", "execution_count": 70, "id": "80bc9b40-6b0e-45e8-b9e4-d109fb0ad311", "metadata": {}, "outputs": [], "source": [ "#Setting the style\n", "style.use(\"seaborn-dark\")" ] }, { "cell_type": "code", "execution_count": 71, "id": "826dc657-5dca-4a5b-a7ed-835edcaee019", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plotting a graph with the general dissatisfaction\n", "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(\n", " ncols=2,\n", " nrows=2,\n", " figsize=(12, 12))\n", "\n", "ax1 = dis_sc.plot(kind='barh', ax=ax1, legend=None, color='#029386')\n", "ax1.set_title('Time of Service', weight=\"bold\")\n", "ax1.set_ylabel('')\n", "ax1.set_xticks([30,34,49,52])\n", "ax1.set_xticklabels([30,34,49,52])\n", "\n", "ax2 = dis_gen.plot(kind= 'barh', ax=ax2, legend=None, color='#A9561E')\n", "ax2.set_title('Gender', weight=\"bold\")\n", "ax2.set_ylabel('')\n", "ax2.set_xticks([37,40])\n", "ax2.set_xticklabels([37,40])\n", "\n", "ax3 = dis_age.plot(kind= 'barh', ax=ax3, legend=None, color='#C875C4')\n", "ax3.set_title('Age',weight=\"bold\")\n", "ax3.set_ylabel('')\n", "ax3.set_xticks([20,31,34,38,42])\n", "ax3.set_xticklabels([20,31,34,38,42])\n", "\n", "ax4 = dis_es.plot(kind= 'barh', ax=ax4, legend=None, color='#FFA500')\n", "ax4.set_title('Employment Status', weight=\"bold\") \n", "ax4.set_ylabel('')\n", "ax4.set_xticks([16,18,26,43,45])\n", "ax4.set_xticklabels([16,18,26,43,45])\n", "\n", "#Graph title\n", "fig.text(x=0.35,y=0.955,s=\"Dissatisfaction of ex employees\",size=21, weight=\"bold\")\n", "fig.text(x=0.15,y=0.935,s=\"The ones with 7 to 10 years of service, 26 to 30 or more than 50 years, Male\",size=19)\n", "fig.text(x=0.18,y=0.915, s=\" and worked on Permanent Full Time status are the most unhappy\",size=19)\n", "\n", "#Foot signature/source of graph:\n", "fig.text(x=0.12, y=0.088,s='©Ana Luiza Miranda' +' '*78 +\"Government of Queensland/Australia\", backgroundcolor=\"#4d4d4d\", size=10, color=\"#f0f0f0\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "891c56df-16f3-4e35-84c6-1fa5e2f13079", "metadata": {}, "source": [ "### 1. New" ] }, { "cell_type": "code", "execution_count": 72, "id": "14812261-bd11-40da-b268-f396a2ec3673", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plotting a graph with New employees values\n", "fig, (ax1, ax2, ax3) = plt.subplots(\n", " ncols=3,\n", " nrows=1,\n", " figsize=(14, 7))\n", "\n", "ax1 = new_age.plot(kind='barh', ax=ax1, legend=None, color='#029386')\n", "ax1.set_title('Age', weight=\"bold\")\n", "ax1.set_ylabel('')\n", "ax1.set_xticks([14,25,30,35,40])\n", "ax1.set_xticklabels([14,25,30,35,40])\n", "\n", "ax2 = new_gen.plot(kind= 'barh', ax=ax2, legend=None, color='#A9561E')\n", "ax2.set_title('Gender', weight=\"bold\")\n", "ax2.set_ylabel('')\n", "ax2.set_xticks([26,38])\n", "ax2.set_xticklabels([26,38])\n", "\n", "ax3 = new_es.plot(kind= 'barh', ax=ax3, legend=None, color='#C875C4')\n", "ax3.set_title('Employment Status',weight=\"bold\")\n", "ax3.set_ylabel('')\n", "ax3.set_xticks([17,21,28,47])\n", "ax3.set_xticklabels([17,21,28,47])\n", "\n", "#Graph title\n", "fig.text(x=0.38,y=1.02,s=\"Between the new employees\",size=20, weight=\"bold\")\n", "fig.text(x=0.25,y=0.98,s=\"The ones with 1 to 3 years of service: more than 56 years, Male\",size=18)\n", "fig.text(x=0.24,y=0.94, s=\" and worked on Permanent Full-Time status are the most unhappy\",size=18)\n", "\n", "#Foot signature/source of graph:\n", "fig.text(x=0.12, y=0.055,s='©Ana Luiza Miranda' +' '*78 +\"Government of Queensland/Australia\", backgroundcolor=\"#4d4d4d\", size=10, color=\"#f0f0f0\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "1b8e2822-3123-4a34-b040-728a1c576fae", "metadata": {}, "source": [ "### 2. Experienced" ] }, { "cell_type": "code", "execution_count": 73, "id": "b534ad0b-1a9a-49f9-ba7c-f125cffa97a7", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plotting a graph with Experienced employees values\n", "fig, (ax1, ax2, ax3) = plt.subplots(\n", " ncols=3,\n", " nrows=1,\n", " figsize=(14, 6))\n", "\n", "ax1 = exp_age.plot(kind='barh', ax=ax1, legend=None, color='#029386')\n", "ax1.set_title('Age', weight=\"bold\")\n", "ax1.set_ylabel('')\n", "ax1.set_xticks([25,28,30,33,38,41,44])\n", "ax1.set_xticklabels([25,28,30,33,38,41,44])\n", "\n", "ax2 = exp_gen.plot(kind= 'barh', ax=ax2, legend=None, color='#A9561E')\n", "ax2.set_title('Gender', weight=\"bold\")\n", "ax2.set_ylabel('')\n", "ax2.set_xticks([28,37])\n", "ax2.set_xticklabels([28,37])\n", "\n", "ax3 = exp_es.plot(kind= 'barh', ax=ax3, legend=None, color='#C875C4')\n", "ax3.set_title('Employment Status',weight=\"bold\")\n", "ax3.set_ylabel('')\n", "ax3.set_xticks([15,20,29,45])\n", "ax3.set_xticklabels([15,20,29,45])\n", "\n", "#Graph title\n", "fig.text(x=0.33,y=1.02,s=\"Between the experienced employees\",size=20, weight=\"bold\")\n", "fig.text(x=0.23,y=0.98,s=\"The ones with 3 to 6 years of service: 41-45 or 26-30 years, Female\",size=18)\n", "fig.text(x=0.24,y=0.94, s=\" and worked on Permanent Full-Time status are the most unhappy\",size=18)\n", "\n", "#Foot signature/source of graph:\n", "fig.text(x=0.12, y=0.055,s='©Ana Luiza Miranda' +' '*78 +\"Government of Queensland/Australia\", backgroundcolor=\"#4d4d4d\", size=10, color=\"#f0f0f0\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "c20e2363-1df1-49f2-98f7-eb1b8100e4e8", "metadata": {}, "source": [ "### 3. Established" ] }, { "cell_type": "code", "execution_count": 74, "id": "c408d677-a36a-43de-a582-395eb0dcbdf5", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plotting a graph with Established employees values\n", "fig, (ax1, ax2, ax3) = plt.subplots(\n", " ncols=3,\n", " nrows=1,\n", " figsize=(14, 6))\n", "\n", "ax1 = est_age.plot(kind='barh', ax=ax1, legend=None, color='#029386')\n", "ax1.set_title('Age', weight=\"bold\")\n", "ax1.set_ylabel('')\n", "ax1.set_xticks([36,40,50,55,67,75])\n", "ax1.set_xticklabels([36,40,50,55,67,75])\n", "\n", "ax2 = est_gen.plot(kind= 'barh', ax=ax2, legend=None, color='#A9561E')\n", "ax2.set_title('Gender', weight=\"bold\")\n", "ax2.set_ylabel('')\n", "ax2.set_xticks([44,55])\n", "ax2.set_xticklabels([44,55])\n", "\n", "ax3 = est_es.plot(kind= 'barh', ax=ax3, legend=None, color='#C875C4')\n", "ax3.set_title('Employment Status',weight=\"bold\")\n", "ax3.set_ylabel('')\n", "ax3.set_xticks([50,61])\n", "ax3.set_xticklabels([50,61])\n", "\n", "#Graph title\n", "fig.text(x=0.35,y=1.02,s=\"Between the established employees\",size=20, weight=\"bold\")\n", "fig.text(x=0.28,y=0.98,s=\"The ones with 7 to 10 years of service: 31-35 years, Female\",size=18)\n", "fig.text(x=0.25,y=0.94, s=\" and worked on Permanent Part-Time status are the most unhappy\",size=18)\n", "\n", "#Foot signature/source of graph:\n", "fig.text(x=0.12, y=0.055,s='©Ana Luiza Miranda' +' '*78 +\"Government of Queensland/Australia\", backgroundcolor=\"#4d4d4d\", size=10, color=\"#f0f0f0\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "de468a7a-f955-4b7f-b7db-e2958fbe383e", "metadata": {}, "source": [ "### 4. Veteran" ] }, { "cell_type": "code", "execution_count": 75, "id": "b6ffc770-f4f4-43e7-b514-0c12c50c431f", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plotting a graph with Veteran employees values\n", "fig, (ax1, ax2, ax3) = plt.subplots(\n", " ncols=3,\n", " nrows=1,\n", " figsize=(14, 6))\n", "\n", "ax1 = vet_age.plot(kind='barh', ax=ax1, legend=None, color='#029386')\n", "ax1.set_title('Age', weight=\"bold\")\n", "ax1.set_ylabel('')\n", "ax1.set_xticks([29,40,48,55,60])\n", "ax1.set_xticklabels([29,40,48,55,60])\n", "\n", "ax2 = vet_gen.plot(kind= 'barh', ax=ax2, legend=None, color='#A9561E')\n", "ax2.set_title('Gender', weight=\"bold\")\n", "ax2.set_ylabel('')\n", "ax2.set_xticks([50])\n", "ax2.set_xticklabels([50])\n", "\n", "ax3 = vet_es.plot(kind= 'barh', ax=ax3, legend=None, color='#C875C4')\n", "ax3.set_title('Employment Status',weight=\"bold\")\n", "ax3.set_ylabel('')\n", "ax3.set_xticks([17,43,60])\n", "ax3.set_xticklabels([17,43,60])\n", "\n", "#Graph title\n", "fig.text(x=0.35,y=1.02,s=\"Between the veteran employees\",size=20, weight=\"bold\")\n", "fig.text(x=0.25,y=0.98,s=\"The ones with more than 10 years of service: 26-30 years, Male\",size=18)\n", "fig.text(x=0.235,y=0.94, s=\" and worked on Permanent Part-Time status are the most unhappy\",size=18)\n", "\n", "#Foot signature/source of graph:\n", "fig.text(x=0.12, y=0.055,s='©Ana Luiza Miranda' +' '*78 +\"Government of Queensland/Australia\", backgroundcolor=\"#4d4d4d\", size=10, color=\"#f0f0f0\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "68cc06a3-728d-4571-af1b-de18c66b04f3", "metadata": {}, "source": [ "## Conclusion\n", "- In this project, we analyzed exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) of Queensland in Australia.\n", "- Our goal was identify the dissatisfaction of the employees that exit, the ones who worked for a short period and the ones that stayed longer. \n", "- In the Exploratory Data Analysis section, we analyse the percentages of dissatisfaction of ex employees according to the time of service, age, gender and employment status. Besides that, we also analyse the age, gender and employment status between the career stage category. We notice that the profiles of dissatisfied employees are different accordingly to the years of service, and the actions to minimize job dissatisfaction must be [suitable to each category](https://www.businesswire.com/news/home/20171108006002/en/Age-Number-Engage-Employees-Career-Stage).\n", "- In the Data Visualization section, we showed graphs with the results of our analysis. \n", "- For more informations about job dissatisfaction, we recommend [this article](https://www.bamboohr.com/hr-glossary/job-dissatisfaction/), with definition, possible causes and ways to identify the low motivation of employees." ] }, { "cell_type": "code", "execution_count": null, "id": "5bac686f-e608-4064-b6d5-ad5c7b7e0026", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.7" }, "toc-autonumbering": false }, "nbformat": 4, "nbformat_minor": 5 }