{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Guided Project: Clean and Analyze Employee Exit Surveys\n", "\n", "**In this guided project**, we'll work with exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. You can find the DETE exit survey data here here. The original TAFE exit survey data is no longer available. We've made some slight modifications to the original datasets to make them easier to work with, including changing the encoding to UTF-8 (the original ones are encoded using cp1252.) \n", "\n", "**We'll play the role of data analyst and pretend our stakeholders want to know the following**:\n", "\n", "They want us to combine the results for both surveys to answer these questions. However, although both used the same survey template, one of them customized some of the answers. In the guided steps, we'll aim to do most of the data cleaning and get you started analyzing the first question.\n", "\n", "A data dictionary wasn't provided with the dataset. In a job setting, we'd make sure to meet with a manager to confirm the definitions of the data. For this project, we'll use our general knowledge to define the columns.\n", "\n", "Below is a preview of a couple columns we'll work with from the dete_survey.csv:\n", "\n", "\n", "\n", "Below is a preview of a couple columns we'll work with from the tafe_survey.csv:\n", "\n", "\n", "The length of the person's employment (in years)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#importing necessary datasets\n", "\n", "import pandas as pd\n", "import numpy as np\n", "#we can use the pd.read_csv() function to specify values that should be represented as NaN. \n", "#We'll use this function to fix the missing values first.\n", "dete_survey = pd.read_csv('dete_survey.csv', na_values='Not Stated')\n", "\n", "tafe_survey = pd.read_csv('tafe_survey.csv')" ] }, { "cell_type": "code", "execution_count": 2, "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 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 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), float64(2), int64(1), object(35)\n", "memory usage: 258.6+ KB\n" ] } ], "source": [ "dete_survey.info()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "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_survey.info()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IDSeparationTypeCease DateDETE Start DateRole Start DatePositionClassificationRegionBusiness UnitEmployment Status...Kept informedWellness programsHealth & SafetyGenderAgeAboriginalTorres StraitSouth SeaDisabilityNESB
01Ill Health Retirement08/20121984.02004.0Public ServantA01-A04Central OfficeCorporate Strategy and PeformancePermanent Full-time...NNNMale56-60NaNNaNNaNNaNYes
12Voluntary Early Retirement (VER)08/2012NaNNaNPublic ServantAO5-AO7Central OfficeCorporate Strategy and PeformancePermanent Full-time...NNNMale56-60NaNNaNNaNNaNNaN
23Voluntary Early Retirement (VER)05/20122011.02011.0Schools OfficerNaNCentral OfficeEducation QueenslandPermanent Full-time...NNNMale61 or olderNaNNaNNaNNaNNaN
34Resignation-Other reasons05/20122005.02006.0TeacherPrimaryCentral QueenslandNaNPermanent Full-time...ANAFemale36-40NaNNaNNaNNaNNaN
45Age Retirement05/20121970.01989.0Head of Curriculum/Head of Special EducationNaNSouth EastNaNPermanent Full-time...NAMFemale61 or olderNaNNaNNaNNaNNaN
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" ], "text/plain": [ " ID SeparationType Cease Date DETE Start Date \\\n", "0 1 Ill Health Retirement 08/2012 1984.0 \n", "1 2 Voluntary Early Retirement (VER) 08/2012 NaN \n", "2 3 Voluntary Early Retirement (VER) 05/2012 2011.0 \n", "3 4 Resignation-Other reasons 05/2012 2005.0 \n", "4 5 Age Retirement 05/2012 1970.0 \n", "\n", " Role Start Date Position \\\n", "0 2004.0 Public Servant \n", "1 NaN Public Servant \n", "2 2011.0 Schools Officer \n", "3 2006.0 Teacher \n", "4 1989.0 Head of Curriculum/Head of Special Education \n", "\n", " Classification Region Business Unit \\\n", "0 A01-A04 Central Office Corporate Strategy and Peformance \n", "1 AO5-AO7 Central Office Corporate Strategy and Peformance \n", "2 NaN Central Office Education Queensland \n", "3 Primary Central Queensland NaN \n", "4 NaN South East NaN \n", "\n", " Employment Status ... Kept informed Wellness programs \\\n", "0 Permanent Full-time ... N N \n", "1 Permanent Full-time ... N N \n", "2 Permanent Full-time ... N N \n", "3 Permanent Full-time ... A N \n", "4 Permanent Full-time ... N A \n", "\n", " Health & Safety Gender Age Aboriginal Torres Strait South Sea \\\n", "0 N Male 56-60 NaN NaN NaN \n", "1 N Male 56-60 NaN NaN NaN \n", "2 N Male 61 or older NaN NaN NaN \n", "3 A Female 36-40 NaN NaN NaN \n", "4 M Female 61 or older NaN NaN NaN \n", "\n", " Disability NESB \n", "0 NaN Yes \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", "[5 rows x 56 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_survey.head()" ] }, { "cell_type": "code", "execution_count": 5, "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/Family...Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination?Workplace. Topic:Does your workplace promote and practice the principles of employment equity?Workplace. Topic:Does your workplace value the diversity of its employees?Workplace. Topic:Would you recommend the Institute as an employer to others?Gender. What is your Gender?CurrentAge. Current AgeEmployment Type. Employment TypeClassification. ClassificationLengthofServiceOverall. Overall Length of Service at Institute (in years)LengthofServiceCurrent. Length of Service at current workplace (in years)
06.341330e+17Southern Queensland Institute of TAFENon-Delivery (corporate)2010.0Contract ExpiredNaNNaNNaNNaNNaN...YesYesYesYesFemale26 30Temporary Full-timeAdministration (AO)1-21-2
16.341337e+17Mount Isa Institute of TAFENon-Delivery (corporate)2010.0Retirement-----...YesYesYesYesNaNNaNNaNNaNNaNNaN
26.341388e+17Mount Isa Institute of TAFEDelivery (teaching)2010.0Retirement-----...YesYesYesYesNaNNaNNaNNaNNaNNaN
36.341399e+17Mount Isa Institute of TAFENon-Delivery (corporate)2010.0Resignation-----...YesYesYesYesNaNNaNNaNNaNNaNNaN
46.341466e+17Southern Queensland Institute of TAFEDelivery (teaching)2010.0Resignation-Career Move - Private Sector---...YesYesYesYesMale41 45Permanent Full-timeTeacher (including LVT)3-43-4
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5 rows × 72 columns

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" ], "text/plain": [ " Record ID Institute \\\n", "0 6.341330e+17 Southern Queensland Institute of TAFE \n", "1 6.341337e+17 Mount Isa Institute of TAFE \n", "2 6.341388e+17 Mount Isa Institute of TAFE \n", "3 6.341399e+17 Mount Isa Institute of TAFE \n", "4 6.341466e+17 Southern Queensland Institute of TAFE \n", "\n", " WorkArea CESSATION YEAR Reason for ceasing employment \\\n", "0 Non-Delivery (corporate) 2010.0 Contract Expired \n", "1 Non-Delivery (corporate) 2010.0 Retirement \n", "2 Delivery (teaching) 2010.0 Retirement \n", "3 Non-Delivery (corporate) 2010.0 Resignation \n", "4 Delivery (teaching) 2010.0 Resignation \n", "\n", " Contributing Factors. Career Move - Public Sector \\\n", "0 NaN \n", "1 - \n", "2 - \n", "3 - \n", "4 - \n", "\n", " Contributing Factors. Career Move - Private Sector \\\n", "0 NaN \n", "1 - \n", "2 - \n", "3 - \n", "4 Career Move - Private Sector \n", "\n", " Contributing Factors. Career Move - Self-employment \\\n", "0 NaN \n", "1 - \n", "2 - \n", "3 - \n", "4 - \n", "\n", " Contributing Factors. Ill Health Contributing Factors. Maternity/Family \\\n", "0 NaN NaN \n", "1 - - \n", "2 - - \n", "3 - - \n", "4 - - \n", "\n", " ... \\\n", "0 ... \n", "1 ... \n", "2 ... \n", "3 ... \n", "4 ... \n", "\n", " Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? \\\n", "0 Yes \n", "1 Yes \n", "2 Yes \n", "3 Yes \n", "4 Yes \n", "\n", " Workplace. Topic:Does your workplace promote and practice the principles of employment equity? \\\n", "0 Yes \n", "1 Yes \n", "2 Yes \n", "3 Yes \n", "4 Yes \n", "\n", " Workplace. Topic:Does your workplace value the diversity of its employees? \\\n", "0 Yes \n", "1 Yes \n", "2 Yes \n", "3 Yes \n", "4 Yes \n", "\n", " Workplace. Topic:Would you recommend the Institute as an employer to others? \\\n", "0 Yes \n", "1 Yes \n", "2 Yes \n", "3 Yes \n", "4 Yes \n", "\n", " Gender. What is your Gender? CurrentAge. Current Age \\\n", "0 Female 26 30 \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 Male 41 45 \n", "\n", " Employment Type. Employment Type Classification. Classification \\\n", "0 Temporary Full-time Administration (AO) \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 Permanent Full-time Teacher (including LVT) \n", "\n", " LengthofServiceOverall. Overall Length of Service at Institute (in years) \\\n", "0 1-2 \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 3-4 \n", "\n", " LengthofServiceCurrent. Length of Service at current workplace (in years) \n", "0 1-2 \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 3-4 \n", "\n", "[5 rows x 72 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tafe_survey.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IDSeparationTypeCease DateDETE Start DateRole Start DatePositionClassificationRegionBusiness UnitEmployment Status...Kept informedWellness programsHealth & SafetyGenderAgeAboriginalTorres StraitSouth SeaDisabilityNESB
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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/Family...Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination?Workplace. Topic:Does your workplace promote and practice the principles of employment equity?Workplace. Topic:Does your workplace value the diversity of its employees?Workplace. Topic:Would you recommend the Institute as an employer to others?Gender. What is your Gender?CurrentAge. Current AgeEmployment Type. Employment TypeClassification. ClassificationLengthofServiceOverall. Overall Length of Service at Institute (in years)LengthofServiceCurrent. Length of Service at current workplace (in years)
0FalseFalseFalseFalseFalseTrueTrueTrueTrueTrue...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
1FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse...FalseFalseFalseFalseTrueTrueTrueTrueTrueTrue
2FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse...FalseFalseFalseFalseTrueTrueTrueTrueTrueTrue
3FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse...FalseFalseFalseFalseTrueTrueTrueTrueTrueTrue
4FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
..................................................................
697FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
698FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse...TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue
699FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
700FalseFalseFalseFalseFalseTrueTrueTrueTrueTrue...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
701FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
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702 rows × 72 columns

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" ], "text/plain": [ " Record ID Institute WorkArea CESSATION YEAR \\\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", "697 False False False False \n", "698 False False False False \n", "699 False False False False \n", "700 False False False False \n", "701 False False False False \n", "\n", " Reason for ceasing employment \\\n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", ".. ... \n", "697 False \n", "698 False \n", "699 False \n", "700 False \n", "701 False \n", "\n", " Contributing Factors. Career Move - Public Sector \\\n", "0 True \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", ".. ... \n", "697 False \n", "698 False \n", "699 False \n", "700 True \n", "701 False \n", "\n", " Contributing Factors. Career Move - Private Sector \\\n", "0 True \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", ".. ... \n", "697 False \n", "698 False \n", "699 False \n", "700 True \n", "701 False \n", "\n", " Contributing Factors. Career Move - Self-employment \\\n", "0 True \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", ".. ... \n", "697 False \n", "698 False \n", "699 False \n", "700 True \n", "701 False \n", "\n", " Contributing Factors. Ill Health Contributing Factors. Maternity/Family \\\n", "0 True True \n", "1 False False \n", "2 False False \n", "3 False False \n", "4 False False \n", ".. ... ... \n", "697 False False \n", "698 False False \n", "699 False False \n", "700 True True \n", "701 False False \n", "\n", " ... \\\n", "0 ... \n", "1 ... \n", "2 ... \n", "3 ... \n", "4 ... \n", ".. ... \n", "697 ... \n", "698 ... \n", "699 ... \n", "700 ... \n", "701 ... \n", "\n", " Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? \\\n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", ".. ... \n", "697 False \n", "698 True \n", "699 False \n", "700 False \n", "701 False \n", "\n", " Workplace. Topic:Does your workplace promote and practice the principles of employment equity? \\\n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", ".. ... \n", "697 False \n", "698 True \n", "699 False \n", "700 False \n", "701 False \n", "\n", " Workplace. Topic:Does your workplace value the diversity of its employees? \\\n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", ".. ... \n", "697 False \n", "698 True \n", "699 False \n", "700 False \n", "701 False \n", "\n", " Workplace. Topic:Would you recommend the Institute as an employer to others? \\\n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", ".. ... \n", "697 False \n", "698 True \n", "699 False \n", "700 False \n", "701 False \n", "\n", " Gender. What is your Gender? CurrentAge. Current Age \\\n", "0 False False \n", "1 True True \n", "2 True True \n", "3 True True \n", "4 False False \n", ".. ... ... \n", "697 False False \n", "698 True True \n", "699 False False \n", "700 False False \n", "701 False False \n", "\n", " Employment Type. Employment Type Classification. Classification \\\n", "0 False False \n", "1 True True \n", "2 True True \n", "3 True True \n", "4 False False \n", ".. ... ... \n", "697 False False \n", "698 True True \n", "699 False False \n", "700 False False \n", "701 False False \n", "\n", " LengthofServiceOverall. Overall Length of Service at Institute (in years) \\\n", "0 False \n", "1 True \n", "2 True \n", "3 True \n", "4 False \n", ".. ... \n", "697 False \n", "698 True \n", "699 False \n", "700 False \n", "701 False \n", "\n", " LengthofServiceCurrent. Length of Service at current workplace (in years) \n", "0 False \n", "1 True \n", "2 True \n", "3 True \n", "4 False \n", ".. ... \n", "697 False \n", "698 True \n", "699 False \n", "700 False \n", "701 False \n", "\n", "[702 rows x 72 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tafe_survey.isnull()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Identify Missing Values and Drop Unnecessary Columns\n", "\n", "we can first make the following observations:\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To start, we'll handle the first two issues. we use the pd.read_csv() function to specify values that should be represented as NaN. We'll use this function to fix the missing values first. Then, we'll drop columns we know we don't need for our analysis." ] }, { "cell_type": "code", "execution_count": 8, "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": [ " #let's drop some columns from each dataframe that we won't use in our analysis to make the dataframes easier to work with.\n", "dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)\n", "\n", "dete_survey_updated.info()" ] }, { "cell_type": "code", "execution_count": 9, "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_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis=1)\n", "\n", "tafe_survey_updated.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Clean Column Names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's turn our attention to the column names. Each dataframe contains many of the same columns, but the column names are different. Below are some of the columns we'd like to use for our final analysis:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['Record ID', 'Institute', 'WorkArea', 'CESSATION YEAR',\n", " 'Reason for ceasing employment',\n", " 'Contributing Factors. Career Move - Public Sector ',\n", " 'Contributing Factors. Career Move - Private Sector ',\n", " 'Contributing Factors. Career Move - Self-employment',\n", " 'Contributing Factors. Ill Health',\n", " 'Contributing Factors. Maternity/Family',\n", " 'Contributing Factors. Dissatisfaction',\n", " 'Contributing Factors. Job Dissatisfaction',\n", " 'Contributing Factors. Interpersonal Conflict',\n", " 'Contributing Factors. Study', 'Contributing Factors. Travel',\n", " 'Contributing Factors. Other', 'Contributing Factors. NONE',\n", " 'Gender. What is your Gender?', 'CurrentAge. Current Age',\n", " 'Employment Type. Employment Type', 'Classification. Classification',\n", " 'LengthofServiceOverall. Overall Length of Service at Institute (in years)',\n", " 'LengthofServiceCurrent. Length of Service at current workplace (in years)'],\n", " dtype='object')\n", "Index(['ID', 'SeparationType', 'Cease Date', 'DETE Start Date',\n", " 'Role Start Date', 'Position', 'Classification', 'Region',\n", " 'Business Unit', 'Employment Status', 'Career move to public sector',\n", " 'Career move to private sector', 'Interpersonal conflicts',\n", " 'Job dissatisfaction', 'Dissatisfaction with the department',\n", " 'Physical work environment', 'Lack of recognition',\n", " 'Lack of job security', 'Work location', 'Employment conditions',\n", " 'Maternity/family', 'Relocation', 'Study/Travel', 'Ill Health',\n", " 'Traumatic incident', 'Work life balance', 'Workload',\n", " 'None of the above', 'Gender', 'Age', 'Aboriginal', 'Torres Strait',\n", " 'South Sea', 'Disability', 'NESB'],\n", " dtype='object')\n" ] } ], "source": [ "print(tafe_survey_updated.columns)\n", "\n", "print(dete_survey_updated.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because we eventually want to combine them, we'll have to standardize the column names. we will use the DataFrame.columns attribute along with vectorized string methods to update all of the columns at once. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "#Rename the remaining columns in the dete_survey_updated dataframe\n", "dete_survey_updated.columns = dete_survey_updated.columns.str.replace(' ', '_').str.strip().str.lower()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['id', 'separationtype', 'cease_date', 'dete_start_date',\n", " 'role_start_date', 'position', 'classification', 'region',\n", " 'business_unit', 'employment_status', 'career_move_to_public_sector',\n", " 'career_move_to_private_sector', 'interpersonal_conflicts',\n", " 'job_dissatisfaction', 'dissatisfaction_with_the_department',\n", " 'physical_work_environment', 'lack_of_recognition',\n", " 'lack_of_job_security', 'work_location', 'employment_conditions',\n", " 'maternity/family', 'relocation', 'study/travel', 'ill_health',\n", " 'traumatic_incident', 'work_life_balance', 'workload',\n", " 'none_of_the_above', 'gender', 'age', 'aboriginal', 'torres_strait',\n", " 'south_sea', 'disability', 'nesb'],\n", " dtype='object')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_survey_updated.columns" ] }, { "cell_type": "code", "execution_count": 13, "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/Family...Contributing Factors. StudyContributing Factors. TravelContributing Factors. OtherContributing Factors. NONEgenderageemployment_statuspositioninstitute_servicerole_service
06.341330e+17Southern Queensland Institute of TAFENon-Delivery (corporate)2010.0Contract ExpiredNaNNaNNaNNaNNaN...NaNNaNNaNNaNFemale26 30Temporary Full-timeAdministration (AO)1-21-2
16.341337e+17Mount Isa Institute of TAFENon-Delivery (corporate)2010.0Retirement-----...-Travel--NaNNaNNaNNaNNaNNaN
26.341388e+17Mount Isa Institute of TAFEDelivery (teaching)2010.0Retirement-----...---NONENaNNaNNaNNaNNaNNaN
36.341399e+17Mount Isa Institute of TAFENon-Delivery (corporate)2010.0Resignation-----...-Travel--NaNNaNNaNNaNNaNNaN
46.341466e+17Southern Queensland Institute of TAFEDelivery (teaching)2010.0Resignation-Career Move - Private Sector---...----Male41 45Permanent Full-timeTeacher (including LVT)3-43-4
\n", "

5 rows × 23 columns

\n", "
" ], "text/plain": [ " id Institute \\\n", "0 6.341330e+17 Southern Queensland Institute of TAFE \n", "1 6.341337e+17 Mount Isa Institute of TAFE \n", "2 6.341388e+17 Mount Isa Institute of TAFE \n", "3 6.341399e+17 Mount Isa Institute of TAFE \n", "4 6.341466e+17 Southern Queensland Institute of TAFE \n", "\n", " WorkArea cease_date separationtype \\\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. Study Contributing Factors. Travel \\\n", "0 ... NaN NaN \n", "1 ... - Travel \n", "2 ... - - \n", "3 ... - Travel \n", "4 ... - - \n", "\n", " Contributing Factors. Other Contributing Factors. NONE gender age \\\n", "0 NaN NaN Female 26 30 \n", "1 - - NaN NaN \n", "2 - NONE NaN NaN \n", "3 - - NaN NaN \n", "4 - - Male 41 45 \n", "\n", " employment_status position institute_service role_service \n", "0 Temporary Full-time Administration (AO) 1-2 1-2 \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 Permanent Full-time Teacher (including LVT) 3-4 3-4 \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapping = {'Record ID': 'id', 'CESSATION YEAR': 'cease_date', 'Reason for ceasing employment': 'separationtype', 'Gender. What is your Gender?': 'gender', 'CurrentAge. Current Age': 'age',\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", "tafe_survey_updated = tafe_survey_updated.rename(mapping, axis = 1)\n", "\n", "tafe_survey_updated.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idseparationtypecease_datedete_start_daterole_start_datepositionclassificationregionbusiness_unitemployment_status...work_life_balanceworkloadnone_of_the_abovegenderageaboriginaltorres_straitsouth_seadisabilitynesb
01Ill Health Retirement08/20121984.02004.0Public ServantA01-A04Central OfficeCorporate Strategy and PeformancePermanent Full-time...FalseFalseTrueMale56-60NaNNaNNaNNaNYes
12Voluntary Early Retirement (VER)08/2012NaNNaNPublic ServantAO5-AO7Central OfficeCorporate Strategy and PeformancePermanent Full-time...FalseFalseFalseMale56-60NaNNaNNaNNaNNaN
23Voluntary Early Retirement (VER)05/20122011.02011.0Schools OfficerNaNCentral OfficeEducation QueenslandPermanent Full-time...FalseFalseTrueMale61 or olderNaNNaNNaNNaNNaN
34Resignation-Other reasons05/20122005.02006.0TeacherPrimaryCentral QueenslandNaNPermanent Full-time...FalseFalseFalseFemale36-40NaNNaNNaNNaNNaN
45Age Retirement05/20121970.01989.0Head of Curriculum/Head of Special EducationNaNSouth EastNaNPermanent Full-time...TrueFalseFalseFemale61 or olderNaNNaNNaNNaNNaN
\n", "

5 rows × 35 columns

\n", "
" ], "text/plain": [ " id separationtype cease_date dete_start_date \\\n", "0 1 Ill Health Retirement 08/2012 1984.0 \n", "1 2 Voluntary Early Retirement (VER) 08/2012 NaN \n", "2 3 Voluntary Early Retirement (VER) 05/2012 2011.0 \n", "3 4 Resignation-Other reasons 05/2012 2005.0 \n", "4 5 Age Retirement 05/2012 1970.0 \n", "\n", " role_start_date position \\\n", "0 2004.0 Public Servant \n", "1 NaN Public Servant \n", "2 2011.0 Schools Officer \n", "3 2006.0 Teacher \n", "4 1989.0 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 ... work_life_balance workload none_of_the_above \\\n", "0 Permanent Full-time ... False False True \n", "1 Permanent Full-time ... False False False \n", "2 Permanent Full-time ... False False True \n", "3 Permanent Full-time ... False False False \n", "4 Permanent Full-time ... True False False \n", "\n", " gender age aboriginal torres_strait south_sea disability nesb \n", "0 Male 56-60 NaN NaN NaN NaN Yes \n", "1 Male 56-60 NaN NaN NaN NaN NaN \n", "2 Male 61 or older NaN NaN NaN NaN NaN \n", "3 Female 36-40 NaN NaN NaN NaN NaN \n", "4 Female 61 or older NaN NaN NaN NaN NaN \n", "\n", "[5 rows x 35 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_survey_updated.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we renamed the columns that we'll use in our analysis for better understanding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Filtering the data\n", "\n", "Filtering out the data we don't need.\n", "\n", "Our end goal is to answer the following question:\n", "\n", "
    \n", "
  • Are employees who have only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been at the job longer?
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the unique values in the separationtype columns in each dataframe, we'll see that each contains a couple of different separation types. For this project, we'll only analyze survey respondents who resigned, so their separation type contains the string 'Resignation'." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that dete_survey_updated dataframe contains multiple separation types with the string 'Resignation':\n", "\n", "
    \n", "
  • Resignation-Other reasons
    • \n", "
  • Resignation-Other employer
    • \n", "
  • Resignation-Move overseas/interstate
    • \n", "
\n", "We'll have to account for each of these variations so we don't unintentionally drop data!" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Age Retirement 285\n", "Resignation-Other reasons 150\n", "Resignation-Other employer 91\n", "Resignation-Move overseas/interstate 70\n", "Voluntary Early Retirement (VER) 67\n", "Ill Health Retirement 61\n", "Other 49\n", "Contract Expired 34\n", "Termination 15\n", "Name: separationtype, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#review the unique values in the separationtype column in both dete_survey_updated and tafe_survey_updated.\n", "\n", "dete_survey_updated['separationtype'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Age is the major reason people exit the company at Department of Education, Training and Employment (DETE)" ] }, { "cell_type": "code", "execution_count": 16, "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": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tafe_survey_updated['separationtype'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Resignation is the means by which employes exited the Technical and Further Education (TAFE) institute in Queensland, Australia" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Resignation 311\n", "Age Retirement 285\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": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Update all separation types containing the word \"resignation\" to 'Resignation'\n", "dete_survey_updated['separationtype'] = dete_survey_updated['separationtype'].str.split('-').str[0]\n", "\n", "# Check the values in the separationtype column were updated correctly\n", "dete_survey_updated['separationtype'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Upon updating all separation types containing the word \"Resignation\" to \"resignation\" because 'dete_survey_updated' dataframe contains three Resignation separation types, so resignation toppled the reason why people left DEFE. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Select only the resignation separation types from each dataframe\n", "dete_resignations = dete_survey_updated[dete_survey_updated['separationtype'] == 'Resignation'].copy()\n", "tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " We will verify to know if the data doesn't contain any major inconsistencies (to the best of our knowledge)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this step, we'll focus on verifying that the years in the cease_date and dete_start_date columns make sense. \n", "\n", "
    \n", "
  • Since the cease_date is the last year of the person's employment and the dete_start_date is the person's first year of employment, it wouldn't make sense to have years after the current date.
    • \n", "
  • Given that most people in this field start working in their 20s, it's also unlikely that the dete_start_date was before the year 1940
    • \n", "
\n", "\n", "If we have many years higher than the current date or lower than 1940, we wouldn't want to continue with our analysis, because it could mean there's something very wrong with the data. If there are a small amount of values that are unrealistically high or low, we can remove them." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check the years in each dataframe for logical inconsistencies." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2012 126\n", "2013 74\n", "01/2014 22\n", "12/2013 17\n", "06/2013 14\n", "09/2013 11\n", "11/2013 9\n", "07/2013 9\n", "10/2013 6\n", "08/2013 4\n", "05/2012 2\n", "05/2013 2\n", "07/2006 1\n", "2010 1\n", "09/2010 1\n", "07/2012 1\n", "Name: cease_date, dtype: int64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_resignations['cease_date'].value_counts()" ] }, { "cell_type": "code", "execution_count": 20, "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": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Extract the years and convert them to a float type\n", "dete_resignations['cease_date'] = dete_resignations['cease_date'].str.split('/').str[-1]\n", "dete_resignations['cease_date'] = dete_resignations['cease_date'].astype(\"float\")\n", "\n", "# Checking the values again and look for outliers\n", "dete_resignations['cease_date'].value_counts()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2011.0 24\n", "2008.0 22\n", "2007.0 21\n", "2012.0 21\n", "2010.0 17\n", "2005.0 15\n", "2004.0 14\n", "2009.0 13\n", "2006.0 13\n", "2013.0 10\n", "2000.0 9\n", "1999.0 8\n", "1996.0 6\n", "2002.0 6\n", "1992.0 6\n", "1998.0 6\n", "2003.0 6\n", "1994.0 6\n", "1993.0 5\n", "1990.0 5\n", "1980.0 5\n", "1997.0 5\n", "1991.0 4\n", "1989.0 4\n", "1988.0 4\n", "1995.0 4\n", "2001.0 3\n", "1985.0 3\n", "1986.0 3\n", "1983.0 2\n", "1976.0 2\n", "1974.0 2\n", "1971.0 1\n", "1972.0 1\n", "1984.0 1\n", "1982.0 1\n", "1987.0 1\n", "1975.0 1\n", "1973.0 1\n", "1977.0 1\n", "1963.0 1\n", "Name: dete_start_date, dtype: int64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_resignations['dete_start_date'].value_counts()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2011.0 116\n", "2012.0 94\n", "2010.0 68\n", "2013.0 55\n", "2009.0 2\n", "Name: cease_date, dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tafe_resignations['cease_date'].value_counts()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2009.0 2\n", "2013.0 55\n", "2010.0 68\n", "2012.0 94\n", "2011.0 116\n", "Name: cease_date, dtype: int64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check the unique values\n", "tafe_resignations['cease_date'].value_counts().sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Creat a New column" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've verified the years in the dete_resignations dataframe, we'll use them to create a new column, In the Human Resources field, the length of time an employee spent in a workplace is referred to as their years of service." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may have noticed that the tafe_resignations dataframe already contains a \"service\" column, which we renamed to institute_service. In order to analyze both surveys together, we'll have to create a corresponding institute_service column in dete_resignations" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3 7.0\n", "5 18.0\n", "8 3.0\n", "9 15.0\n", "11 3.0\n", "Name: institute_service, dtype: float64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_resignations['institute_service'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we created a new institute_service column that we'll use to analyze survey respondents according to their length of employment.Next, we'll identify any employees who resigned because they were dissatisfied" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Identify Dissatisfied Employees" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below are the columns we'll use to categorize employees as \"dissatisfied\" from each dataframe.\n", "\n", "
    \n", "
      tafe_survey_updated:
    \n", "
      \n", "
    • Contributing Factors. Dissatisfaction
      • \n", "
    • Contributing Factors. Job Dissatisfaction
      • \n", "
    \n", "
      dete_survey_updated:
    \n", "
      \n", "
    • job_dissatisfaction
      • \n", "
    • dissatisfaction_with_the_department
      • \n", "
    • physical_work_environment
      • \n", "
    • lack_of_recognition
      • \n", "
    • lack_of_job_security
      • \n", "
    • work_location
      • \n", "
    • employment_conditions
      • \n", "
    • work_life_balance
      • \n", "
    • workload
      • \n", "
    \n", "
\n", "\n", "If the employee indicated any of the factors above caused them to resign, we'll mark them as dissatisfied in a new column." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- 277\n", "Contributing Factors. Dissatisfaction 55\n", "Name: Contributing Factors. Dissatisfaction, dtype: int64" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check the unique values\n", "tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- 270\n", "Job Dissatisfaction 62\n", "Name: Contributing Factors. Job Dissatisfaction, dtype: int64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check the unique values\n", "tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False 241\n", "True 91\n", "NaN 8\n", "Name: dissatisfied, dtype: int64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Updating the values in the contributing factors columns to be either True, False, or NaN\n", "def update_vals(x):\n", " if x == '-':\n", " return False\n", " elif pd.isnull(x):\n", " return np.nan\n", " else:\n", " return True\n", "tafe_resignations['dissatisfied'] = tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']].applymap(update_vals).any(1, skipna=False)\n", "tafe_resignations_up = tafe_resignations.copy()\n", "\n", "# Check the unique values after the updates\n", "tafe_resignations_up['dissatisfied'].value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False 162\n", "True 149\n", "Name: dissatisfied, dtype: int64" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Update the values in columns related to dissatisfaction to be either True, False, or NaN\n", "dete_resignations['dissatisfied'] = dete_resignations[['job_dissatisfaction',\n", " 'dissatisfaction_with_the_department', 'physical_work_environment',\n", " 'lack_of_recognition', 'lack_of_job_security', 'work_location',\n", " 'employment_conditions', 'work_life_balance',\n", " 'workload']].any(1, skipna=False)\n", "dete_resignations_up = dete_resignations.copy()\n", "dete_resignations_up['dissatisfied'].value_counts(dropna=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we're finally ready to combine our datasets! Our end goal is to aggregate the data according to the institute_service column, so when you combine the data, think about how to get the data into a form that's easy to aggregate." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Combinining the Data" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "# Add an institute column\n", "dete_resignations_up['institute'] = 'DETE'\n", "tafe_resignations_up['institute'] = 'TAFE'" ] }, { "cell_type": "code", "execution_count": 31, "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 643\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, "metadata": {}, "outputs": [], "source": [ "# Drop columns with less than 500 non null values\n", "combined_updated = combined.dropna(thresh = 500, axis =1).copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Clean the Service Column\n", "\n", "Now that we've combined our dataframes, we're almost at a place where we can perform some kind of analysis! First, though, we'll have to clean up the institute_service column. This column is tricky to clean because it currently contains values in a couple different forms:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To analyze the data, we'll convert these numbers into categories. We'll base our analysis on this article, which makes the argument that understanding employee's needs according to career stage instead of age is more effective.\n", "\n", "We'll use the slightly modified definitions below:\n", "\n", "
    \n", "
  • New: Less than 3 years at a company
    • \n", "
  • Experienced: 3-6 years at a company
    • \n", "
  • Established: 7-10 years at a company
    • \n", "
  • Veteran: 11 or more years at a company
    • \n", "
\n", "Let's categorize the values in the institute_service column using the definitions above." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "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", "20.0 7\n", "15.0 7\n", "14.0 6\n", "17.0 6\n", "12.0 6\n", "10.0 6\n", "22.0 6\n", "18.0 5\n", "16.0 5\n", "24.0 4\n", "23.0 4\n", "11.0 4\n", "39.0 3\n", "19.0 3\n", "21.0 3\n", "32.0 3\n", "36.0 2\n", "25.0 2\n", "26.0 2\n", "28.0 2\n", "30.0 2\n", "42.0 1\n", "35.0 1\n", "49.0 1\n", "34.0 1\n", "38.0 1\n", "33.0 1\n", "29.0 1\n", "27.0 1\n", "41.0 1\n", "31.0 1\n", "Name: institute_service, dtype: int64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined_updated['institute_service'].value_counts()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "# Extract 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')" ] }, { "cell_type": "code", "execution_count": 35, "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", "12.0 6\n", "14.0 6\n", "22.0 6\n", "16.0 5\n", "18.0 5\n", "24.0 4\n", "23.0 4\n", "39.0 3\n", "19.0 3\n", "21.0 3\n", "32.0 3\n", "28.0 2\n", "36.0 2\n", "25.0 2\n", "30.0 2\n", "26.0 2\n", "29.0 1\n", "38.0 1\n", "42.0 1\n", "27.0 1\n", "41.0 1\n", "35.0 1\n", "49.0 1\n", "34.0 1\n", "33.0 1\n", "31.0 1\n", "Name: institute_service_up, dtype: int64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check the years extracted are correct\n", "combined_updated['institute_service_up'].value_counts()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "#Next, we'll map each value to one of the career stage definitions.\n", "\n", "def transform_service(val):\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\"" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "combined_updated['service_cat'] = combined_updated['institute_service_up'].apply(transform_service)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Established\n", "1 Veteran\n", "2 Experienced\n", "3 Veteran\n", "4 Experienced\n", "Name: service_cat, dtype: object" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined_updated['service_cat'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we created a service_cat column, that categorizes employees according to the amount of years spent in their workplace:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Perform Initial Analysis\n", "\n", "Now, let's finally do our first piece of analysis! We'll help you fill in missing values in the dissatisfied column and then aggregate the data to get you started." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**dissatisfied** column consists of Boolean values, meaning they're either True or False. Methods such as the df.pivot_table() method actually treat Boolean values as integers, so a True value is considered to be 1 and a False value is considered to be 0. That means that we can aggregate the dissatisfied column and calculate the number of people in each group, the percentage of people in each group, etc." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "False 403\n", "True 240\n", "Name: dissatisfied, dtype: int64" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined_updated['dissatisfied'].value_counts().dropna(inplace=False)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "# Replace missing values with the most frequent value, False\n", "combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dissatisfied
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" ], "text/plain": [ " dissatisfied\n", "service_cat \n", "Established 0.516129\n", "Experienced 0.343023\n", "New 0.295337\n", "Veteran 0.485294" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dissatisfied_resignations = combined_updated.pivot_table(values='dissatisfied', index = 'service_cat')\n", "dissatisfied_resignations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Doing a bivariate analysis between service category and dissatisfied employees, I obsreved that 51.6% of the resignations were from employees with more than 7 years in the institute (51.6% from established employees and 48.5% from veteran employees). With this data we can infer that people that work in the DETE and TAFE institute become unmotivated with their jobs probably because of the few challenges they are facing." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Calculating the percentage of employees who resigned due to dissatisfaction in each category\n", "dis_pct = combined_updated.pivot_table(index='service_cat', values='dissatisfied')\n", "\n", "# Plot the results\n", "%matplotlib inline\n", "dis_pct.plot(kind='bar', rot=30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the initial analysis above, we can tentatively conclude that employees with 7 or more years of service are more likely to resign due to some kind of dissatisfaction with the job than employees with less than 7 years of service. However, we need to handle the rest of the missing data to finalize our analysis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clean the age column. \n", "How many people in each age group resgined due to some kind of dissatisfaction?\n", "Instead of analyzing the survey results together, analyze each survey separately.
\n", "\n", "Did more employees in the DETE survey or TAFE survey end their employment because they were dissatisfied in some way?" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "20 or younger 10\n", "21 25 33\n", "21-25 29\n", "26 30 32\n", "26-30 35\n", "31 35 32\n", "31-35 29\n", "36 40 32\n", "36-40 41\n", "41 45 45\n", "41-45 48\n", "46 50 39\n", "46-50 42\n", "51-55 71\n", "56 or older 29\n", "56-60 26\n", "61 or older 23\n", "Name: age, dtype: int64" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined_updated['age'].value_counts().sort_index()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "20 or younger 10\n", "21-25 62\n", "26-30 67\n", "31-35 61\n", "36-40 73\n", "41-45 93\n", "46-50 81\n", "51-55 71\n", "56 or older 78\n", "NaN 55\n", "Name: age, dtype: int64" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined_updated['age'] = combined_updated['age'].str.replace(\" \",\"-\")\n", "\n", "def age_cleanup(element):\n", " if element == \"61 or older\": return \"56 or older\"\n", " elif element == \"56-60\": return \"56 or older\"\n", " else: return element\n", "\n", "combined_updated['age'] = combined_updated['age'].map(age_cleanup)\n", "\n", "combined_updated['age'].value_counts(dropna=False).sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ages are neatly organized now, it is time to see whether age has an influence on likelyhood of an resignee to be dissatisfied. Below the amount of dissatisfied resignees will be summed for each category." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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TotalDissatisfiedOther reasonsDissatisfied %Other reasons %
Age
20 or younger10280.200.80
21-256219430.310.69
26-306728390.420.58
31-356123380.380.62
36-407325480.340.66
41-459335580.380.62
46-508131500.380.62
51-557130410.420.58
56 or older7833450.420.58
\n", "
" ], "text/plain": [ " Total Dissatisfied Other reasons Dissatisfied % \\\n", "Age \n", "20 or younger 10 2 8 0.20 \n", "21-25 62 19 43 0.31 \n", "26-30 67 28 39 0.42 \n", "31-35 61 23 38 0.38 \n", "36-40 73 25 48 0.34 \n", "41-45 93 35 58 0.38 \n", "46-50 81 31 50 0.38 \n", "51-55 71 30 41 0.42 \n", "56 or older 78 33 45 0.42 \n", "\n", " Other reasons % \n", "Age \n", "20 or younger 0.80 \n", "21-25 0.69 \n", "26-30 0.58 \n", "31-35 0.62 \n", "36-40 0.66 \n", "41-45 0.62 \n", "46-50 0.62 \n", "51-55 0.58 \n", "56 or older 0.58 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "combined_up_dis = combined_updated.loc[combined_updated['dissatisfied']==True,]\n", "\n", "df_Age = combined_updated['age'].value_counts().sort_index().to_frame(name='Total')\n", "df_Age['Dissatisfied'] = combined_up_dis['age'].value_counts().sort_index()\n", "df_Age['Other reasons'] = df_Age['Total'] - df_Age['Dissatisfied']\n", "df_Age['Dissatisfied %'] = round(df_Age['Dissatisfied'] / df_Age['Total'],2)\n", "df_Age['Other reasons %'] = round(df_Age['Other reasons'] / df_Age['Total'],2)\n", "df_Age.index.name = 'Age' \n", "display(df_Age)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_Age['Dissatisfied %'].plot(kind='bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dissatisfaction distribution by age also seems to suggest that older people tend to renounce due to dissatisfaction more than younger people. The exception in the 26-30 group of people who also have high percentage of dissatisfied employees. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 2 }