In this project, we'll clean and analyze exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) body of the Queensland government in Australia. The TAFE exit survey is no longer available and the survey for the DETE can be found here.
We'll pretend our stakeholders want us to combine the results for both surveys to answer the following question:
First, we'll read in the datasets and do some initial exporation.
import pandas as pd
import numpy as np
dete_survey = pd.read_csv('dete-exit-survey-january-2014.csv')
pd.options.display.max_columns = 150
dete_survey.head()
| ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | Career move to public sector | Career move to private sector | Interpersonal conflicts | Job dissatisfaction | Dissatisfaction with the department | Physical work environment | Lack of recognition | Lack of job security | Work location | Employment conditions | Maternity/family | Relocation | Study/Travel | Ill Health | Traumatic incident | Work life balance | Workload | None of the above | Professional Development | Opportunities for promotion | Staff morale | Workplace issue | Physical environment | Worklife balance | Stress and pressure support | Performance of supervisor | Peer support | Initiative | Skills | Coach | Career Aspirations | Feedback | Further PD | Communication | My say | Information | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Ill Health Retirement | 08/2012 | 1984 | 2004 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | True | False | False | True | False | False | True | False | False | False | False | False | False | False | False | False | False | True | A | A | N | N | N | A | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
| 1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | Not Stated | Not Stated | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | A | N | N | N | N | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
| 2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011 | 2011 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | N | N | N | N | N | N | N | N | N | N | N | N | N | N | N | A | A | N | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
| 3 | 4 | Resignation-Other reasons | 05/2012 | 2005 | 2006 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | N | N | N | A | A | N | N | A | A | A | A | A | A | A | A | A | A | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
| 4 | 5 | Age Retirement | 05/2012 | 1970 | 1989 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | A | A | N | N | D | D | N | A | A | A | A | A | A | SA | SA | D | D | A | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
dete_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 822 non-null int64 1 SeparationType 822 non-null object 2 Cease Date 822 non-null object 3 DETE Start Date 822 non-null object 4 Role Start Date 822 non-null object 5 Position 817 non-null object 6 Classification 455 non-null object 7 Region 822 non-null object 8 Business Unit 126 non-null object 9 Employment Status 817 non-null object 10 Career move to public sector 822 non-null bool 11 Career move to private sector 822 non-null bool 12 Interpersonal conflicts 822 non-null bool 13 Job dissatisfaction 822 non-null bool 14 Dissatisfaction with the department 822 non-null bool 15 Physical work environment 822 non-null bool 16 Lack of recognition 822 non-null bool 17 Lack of job security 822 non-null bool 18 Work location 822 non-null bool 19 Employment conditions 822 non-null bool 20 Maternity/family 822 non-null bool 21 Relocation 822 non-null bool 22 Study/Travel 822 non-null bool 23 Ill Health 822 non-null bool 24 Traumatic incident 822 non-null bool 25 Work life balance 822 non-null bool 26 Workload 822 non-null bool 27 None of the above 822 non-null bool 28 Professional Development 808 non-null object 29 Opportunities for promotion 735 non-null object 30 Staff morale 816 non-null object 31 Workplace issue 788 non-null object 32 Physical environment 817 non-null object 33 Worklife balance 815 non-null object 34 Stress and pressure support 810 non-null object 35 Performance of supervisor 813 non-null object 36 Peer support 812 non-null object 37 Initiative 813 non-null object 38 Skills 811 non-null object 39 Coach 767 non-null object 40 Career Aspirations 746 non-null object 41 Feedback 792 non-null object 42 Further PD 768 non-null object 43 Communication 814 non-null object 44 My say 812 non-null object 45 Information 816 non-null object 46 Kept informed 813 non-null object 47 Wellness programs 766 non-null object 48 Health & Safety 793 non-null object 49 Gender 798 non-null object 50 Age 811 non-null object 51 Aboriginal 16 non-null object 52 Torres Strait 3 non-null object 53 South Sea 7 non-null object 54 Disability 23 non-null object 55 NESB 32 non-null object dtypes: bool(18), int64(1), object(37) memory usage: 258.6+ KB
#Read in the data
tafe_survey = pd.read_csv("tafe_survey.csv")
#Quick exploration of the data
tafe_survey.head()
| Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | Contributing Factors. Dissatisfaction | Contributing Factors. Job Dissatisfaction | Contributing Factors. Interpersonal Conflict | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | Main Factor. Which of these was the main factor for leaving? | InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction | InstituteViews. Topic:2. I was given access to skills training to help me do my job better | InstituteViews. Topic:3. I was given adequate opportunities for personal development | InstituteViews. 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 had | InstituteViews. Topic:6. The organisation recognised when staff did good work | InstituteViews. Topic:7. Management was generally supportive of me | InstituteViews. Topic:8. Management was generally supportive of my team | InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me | InstituteViews. Topic:10. Staff morale was positive within the Institute | InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly | InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently | InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly | WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit | WorkUnitViews. Topic:15. I worked well with my colleagues | WorkUnitViews. Topic:16. My job was challenging and interesting | WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work | WorkUnitViews. Topic:18. I had sufficient contact with other people in my job | WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job | WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job | 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] | WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job | WorkUnitViews. Topic:23. My job provided sufficient variety | WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job | WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction | WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance | WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area | 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 | WorkUnitViews. Topic:29. There was adequate communication between staff in my unit | WorkUnitViews. Topic:30. Staff morale was positive within my work unit | Induction. 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 Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Agree | Agree | Agree | Neutral | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Neutral | Agree | Agree | Yes | Yes | Yes | Yes | Face to Face | - | - | Face to Face | - | - | Face to Face | - | - | Yes | Yes | Yes | Yes | Yes | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
| 1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Agree | Agree | Agree | Disagree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Agree | Agree | Strongly Agree | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | - | - | - | - | - | - | NONE | NaN | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Agree | Neutral | Neutral | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Yes | No | Yes | Yes | - | - | - | NaN | - | - | - | - | - | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | - | - | - | - | - | - | - | NaN | Agree | Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Yes | Yes | Yes | Yes | - | - | Induction Manual | Face to Face | - | - | Face to Face | - | - | Yes | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
tafe_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 72 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Record ID 702 non-null float64 1 Institute 702 non-null object 2 WorkArea 702 non-null object 3 CESSATION YEAR 695 non-null float64 4 Reason for ceasing employment 701 non-null object 5 Contributing Factors. Career Move - Public Sector 437 non-null object 6 Contributing Factors. Career Move - Private Sector 437 non-null object 7 Contributing Factors. Career Move - Self-employment 437 non-null object 8 Contributing Factors. Ill Health 437 non-null object 9 Contributing Factors. Maternity/Family 437 non-null object 10 Contributing Factors. Dissatisfaction 437 non-null object 11 Contributing Factors. Job Dissatisfaction 437 non-null object 12 Contributing Factors. Interpersonal Conflict 437 non-null object 13 Contributing Factors. Study 437 non-null object 14 Contributing Factors. Travel 437 non-null object 15 Contributing Factors. Other 437 non-null object 16 Contributing Factors. NONE 437 non-null object 17 Main Factor. Which of these was the main factor for leaving? 113 non-null object 18 InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object 19 InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object 20 InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object 21 InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object 22 InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object 23 InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object 24 InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object 25 InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object 26 InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object 27 InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object 28 InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object 29 InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object 30 InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object 31 WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object 32 WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object 33 WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object 34 WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object 35 WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object 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 37 WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object 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 39 WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object 40 WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object 41 WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object 42 WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object 43 WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object 44 WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object 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 46 WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object 47 WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object 48 Induction. Did you undertake Workplace Induction? 619 non-null object 49 InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object 50 InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object 51 InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object 52 InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 53 InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 54 InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object 55 InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object 56 InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object 57 InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object 58 InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object 59 InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object 60 InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object 61 Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object 62 Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object 63 Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object 64 Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object 65 Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object 66 Gender. What is your Gender? 596 non-null object 67 CurrentAge. Current Age 596 non-null object 68 Employment Type. Employment Type 596 non-null object 69 Classification. Classification 596 non-null object 70 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object 71 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(70) memory usage: 395.0+ KB
We can make the following observations based on the work above:
dete_survey dataframe contains Not Stated values that indicate values are missing, but they aren't represented as NaN.dete_survey and tafe_survey contain many columns that we don't need to complete our analysis.First, we'll correct the Not Stated values and drop some of the columns we don't need for our analysis.
# Read in the data again, but this time read `Not Stated` values as `NaN`
dete_survey = pd.read_csv('dete-exit-survey-january-2014.csv', na_values='Not Stated')
# Quick exploration of the data
dete_survey.head()
| ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | Career move to public sector | Career move to private sector | Interpersonal conflicts | Job dissatisfaction | Dissatisfaction with the department | Physical work environment | Lack of recognition | Lack of job security | Work location | Employment conditions | Maternity/family | Relocation | Study/Travel | Ill Health | Traumatic incident | Work life balance | Workload | None of the above | Professional Development | Opportunities for promotion | Staff morale | Workplace issue | Physical environment | Worklife balance | Stress and pressure support | Performance of supervisor | Peer support | Initiative | Skills | Coach | Career Aspirations | Feedback | Further PD | Communication | My say | Information | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | True | False | False | True | False | False | True | False | False | False | False | False | False | False | False | False | False | True | A | A | N | N | N | A | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
| 1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | A | N | N | N | N | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
| 2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | N | N | N | N | N | N | N | N | N | N | N | N | N | N | N | A | A | N | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
| 3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | N | N | N | A | A | N | N | A | A | A | A | A | A | A | A | A | A | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
| 4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | A | A | N | N | D | D | N | A | A | A | A | A | A | SA | SA | D | D | A | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
# Remove columns we don't need for our analysis
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis=1)
# Check that the columns were dropped
print(dete_survey_updated.columns)
print(tafe_survey_updated.columns)
Index(['ID', 'SeparationType', 'Cease Date', 'DETE Start Date',
'Role Start Date', 'Position', 'Classification', 'Region',
'Business Unit', 'Employment Status', 'Career move to public sector',
'Career move to private sector', 'Interpersonal conflicts',
'Job dissatisfaction', 'Dissatisfaction with the department',
'Physical work environment', 'Lack of recognition',
'Lack of job security', 'Work location', 'Employment conditions',
'Maternity/family', 'Relocation', 'Study/Travel', 'Ill Health',
'Traumatic incident', 'Work life balance', 'Workload',
'None of the above', 'Gender', 'Age', 'Aboriginal', 'Torres Strait',
'South Sea', 'Disability', 'NESB'],
dtype='object')
Index(['Record ID', 'Institute', 'WorkArea', 'CESSATION YEAR',
'Reason for ceasing employment',
'Contributing Factors. Career Move - Public Sector ',
'Contributing Factors. Career Move - Private Sector ',
'Contributing Factors. Career Move - Self-employment',
'Contributing Factors. Ill Health',
'Contributing Factors. Maternity/Family',
'Contributing Factors. Dissatisfaction',
'Contributing Factors. Job Dissatisfaction',
'Contributing Factors. Interpersonal Conflict',
'Contributing Factors. Study', 'Contributing Factors. Travel',
'Contributing Factors. Other', 'Contributing Factors. NONE',
'Gender. What is your Gender?', 'CurrentAge. Current Age',
'Employment Type. Employment Type', 'Classification. Classification',
'LengthofServiceOverall. Overall Length of Service at Institute (in years)',
'LengthofServiceCurrent. Length of Service at current workplace (in years)'],
dtype='object')
Next, we'll standardize the names of the columns we want to work with, because we eventually want to combine the dataframes.
# Clean the column names
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(' ', '_')
# Check that the column names were updated correctly
dete_survey_updated.columns
Index(['id', 'separationtype', 'cease_date', 'dete_start_date',
'role_start_date', 'position', 'classification', 'region',
'business_unit', 'employment_status', 'career_move_to_public_sector',
'career_move_to_private_sector', 'interpersonal_conflicts',
'job_dissatisfaction', 'dissatisfaction_with_the_department',
'physical_work_environment', 'lack_of_recognition',
'lack_of_job_security', 'work_location', 'employment_conditions',
'maternity/family', 'relocation', 'study/travel', 'ill_health',
'traumatic_incident', 'work_life_balance', 'workload',
'none_of_the_above', 'gender', 'age', 'aboriginal', 'torres_strait',
'south_sea', 'disability', 'nesb'],
dtype='object')
# Update column names to match the names in dete_survey_updated
mapping = {'Record ID': 'id', 'CESSATION YEAR': 'cease_date', 'Reason for ceasing employment': 'separationtype', 'Gender. What is your Gender?': 'gender', 'CurrentAge. Current Age': 'age',
'Employment Type. Employment Type': 'employment_status',
'Classification. Classification': 'position',
'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service',
'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'}
tafe_survey_updated = tafe_survey_updated.rename(mapping, axis = 1)
# Check that the specified column names were updated correctly
tafe_survey_updated.columns
Index(['id', 'Institute', 'WorkArea', 'cease_date', 'separationtype',
'Contributing Factors. Career Move - Public Sector ',
'Contributing Factors. Career Move - Private Sector ',
'Contributing Factors. Career Move - Self-employment',
'Contributing Factors. Ill Health',
'Contributing Factors. Maternity/Family',
'Contributing Factors. Dissatisfaction',
'Contributing Factors. Job Dissatisfaction',
'Contributing Factors. Interpersonal Conflict',
'Contributing Factors. Study', 'Contributing Factors. Travel',
'Contributing Factors. Other', 'Contributing Factors. NONE', 'gender',
'age', 'employment_status', 'position', 'institute_service',
'role_service'],
dtype='object')
For this project, we'll only analyze survey respondents who resigned, so we'll only select separation types containing the string 'Resignation'.
# Check the unique values for the separationtype column
dete_survey_updated['separationtype'].value_counts()
Age Retirement 285 Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
# Check the unique values for the separationtype column
tafe_survey_updated['separationtype'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
# Update all separation types containing the word "resignation" to 'Resignation'
dete_survey_updated['separationtype'] = dete_survey_updated['separationtype'].str.split('-').str[0]
# Check the values in the separationtype column were updated correctly
dete_survey_updated['separationtype'].value_counts()
Resignation 311 Age Retirement 285 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
# Select only the resignation separation types from each dataframe
dete_resignations = dete_survey_updated[dete_survey_updated['separationtype'] == 'Resignation'].copy()
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
Below, we clean and explore the cease_date and dete_start_date columns to make sure all of the years make sense. We'll use the following criteria:
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.dete_start_date was before the year 1940.# Check the unique values
dete_resignations['cease_date'].value_counts()
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 07/2013 9 11/2013 9 10/2013 6 08/2013 4 05/2012 2 05/2013 2 07/2012 1 2010 1 09/2010 1 07/2006 1 Name: cease_date, dtype: int64
# Extract the years and convert them to a float type
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.split('/').str[-1]
dete_resignations['cease_date'] = dete_resignations['cease_date'].astype(float)
# Check the values again and look for outliers
dete_resignations['cease_date'].value_counts()
2013.0 146 2012.0 129 2014.0 22 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
# Check the unique values and look for outliers
dete_resignations['dete_start_date'].value_counts().sort_values()
1987.0 1 1975.0 1 1984.0 1 1971.0 1 1973.0 1 1972.0 1 1963.0 1 1977.0 1 1982.0 1 1974.0 2 1983.0 2 1976.0 2 1985.0 3 2001.0 3 1986.0 3 1995.0 4 1988.0 4 1991.0 4 1989.0 4 1993.0 5 1980.0 5 1990.0 5 1997.0 5 2002.0 6 1998.0 6 1996.0 6 1992.0 6 2003.0 6 1994.0 6 1999.0 8 2000.0 9 2013.0 10 2006.0 13 2009.0 13 2004.0 14 2005.0 15 2010.0 17 2007.0 21 2012.0 21 2008.0 22 2011.0 24 Name: dete_start_date, dtype: int64
# Check the unique values
tafe_resignations['cease_date'].value_counts().sort_values()
2009.0 2 2013.0 55 2010.0 68 2012.0 94 2011.0 116 Name: cease_date, dtype: int64
Below are our findings:
tafe_survey_updated dataframe contains some cease dates in 2009, but the dete_survey_updated dataframe does not. The tafe_survey_updated dataframe also contains many more cease dates in 2010 than the dete_survey_updaed dataframe. Since we aren't concerned with analyzing the results by year, we'll leave them as is.Since our end goal is to answer the question below, we need a column containing the length of time an employee spent in their workplace, or years of service, in both dataframes.
The tafe_resignations dataframe already contains a "service" column, which we renamed to institute_service.
Below, we calculate the years of service in the dete_survey_updated dataframe by subtracting the dete_start_date from the cease_date and create a new column named institute_service.
# Calculate the length of time an employee spent in their respective workplace and create a new column
dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']
# Quick check of the result
dete_resignations['institute_service'].head()
3 7.0 5 18.0 8 3.0 9 15.0 11 3.0 Name: institute_service, dtype: float64
Next, we'll identify any employees who resigned because they were dissatisfied. Below are the columns we'll use to categorize employees as "dissatisfied" from each dataframe:
If the employee indicated any of the factors above caused them to resign, we'll mark them as dissatisfied in a new column. After our changes, the new dissatisfied column will contain just the following values:
# Check the unique values
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
# Check the unique values
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()
- 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
# Update the values in the contributing factors columns to be either True, False, or NaN
def update_vals(x):
if x == '-':
return False
elif pd.isnull(x):
return np.nan
else:
return True
tafe_resignations['dissatisfied'] = (tafe_resignations[['Contributing Factors. Dissatisfaction',
'Contributing Factors. Job Dissatisfaction']]
.applymap(update_vals).any(axis=1, skipna=False))
tafe_resignations_up = tafe_resignations.copy()
# Check the unique values after the updates
tafe_resignations_up['dissatisfied'].value_counts(dropna=False)
False 241 True 99 Name: dissatisfied, dtype: int64
# Update the values in columns related to dissatisfaction to be either True, False, or NaN
dete_resignations['dissatisfied'] = dete_resignations[['job_dissatisfaction',
'dissatisfaction_with_the_department', 'physical_work_environment',
'lack_of_recognition', 'lack_of_job_security', 'work_location',
'employment_conditions', 'work_life_balance',
'workload']].any(axis=1, skipna=False)
dete_resignations_up = dete_resignations.copy()
dete_resignations_up['dissatisfied'].value_counts(dropna=False)
False 162 True 149 Name: dissatisfied, dtype: int64
Below, we'll add an institute column so that we can differentiate the data from each survey after we combine them. Then, we'll combine the dataframes and drop any remaining columns we don't need.
# Add an institute column
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
# Combine the dataframes
combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index=True)
# Verify the number of non null values in each column
combined.notnull().sum().sort_values()
torres_strait 0 south_sea 3 aboriginal 7 disability 8 nesb 9 business_unit 32 classification 161 region 265 role_start_date 271 dete_start_date 283 role_service 290 none_of_the_above 311 work_life_balance 311 traumatic_incident 311 ill_health 311 study/travel 311 relocation 311 maternity/family 311 employment_conditions 311 workload 311 lack_of_job_security 311 career_move_to_public_sector 311 career_move_to_private_sector 311 interpersonal_conflicts 311 work_location 311 dissatisfaction_with_the_department 311 physical_work_environment 311 lack_of_recognition 311 job_dissatisfaction 311 Contributing Factors. Job Dissatisfaction 332 Contributing Factors. Travel 332 Contributing Factors. Maternity/Family 332 Contributing Factors. Ill Health 332 Contributing Factors. Career Move - Self-employment 332 Contributing Factors. Career Move - Private Sector 332 Contributing Factors. Career Move - Public Sector 332 Contributing Factors. Dissatisfaction 332 Contributing Factors. Other 332 Contributing Factors. Interpersonal Conflict 332 Contributing Factors. NONE 332 Contributing Factors. Study 332 Institute 340 WorkArea 340 institute_service 563 gender 592 age 596 employment_status 597 position 598 cease_date 635 dissatisfied 651 separationtype 651 institute 651 id 651 dtype: int64
# Drop columns with less than 500 non null values
combined_updated = combined.dropna(thresh = 500, axis =1).copy()
combined_updated.notnull().sum().sort_values()
institute_service 563 gender 592 age 596 employment_status 597 position 598 cease_date 635 id 651 separationtype 651 dissatisfied 651 institute 651 dtype: int64
Next, we'll clean the institute_service column and categorize employees according to the following definitions:
Our analysis is based on this article, which makes the argument that understanding employee's needs according to career stage instead of age is more effective.
# Check the unique values
combined_updated['institute_service'].value_counts(dropna=False)
NaN 88 Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 5.0 23 1.0 22 7-10 21 3.0 20 0.0 20 6.0 17 4.0 16 9.0 14 2.0 14 7.0 13 More than 20 years 10 13.0 8 8.0 8 15.0 7 20.0 7 10.0 6 14.0 6 12.0 6 17.0 6 22.0 6 18.0 5 16.0 5 11.0 4 23.0 4 24.0 4 32.0 3 39.0 3 19.0 3 21.0 3 36.0 2 25.0 2 30.0 2 26.0 2 28.0 2 49.0 1 41.0 1 27.0 1 42.0 1 29.0 1 34.0 1 31.0 1 33.0 1 35.0 1 38.0 1 Name: institute_service, dtype: int64
# Extract the years of service and convert the type to float
# For 11-20, we count as 11
combined_updated['institute_service_up'] = combined_updated['institute_service'].astype('str').str.extract(r'(\d+)')
combined_updated['institute_service_up'] = combined_updated['institute_service_up'].astype('float')
# Check the years extracted are correct
combined_updated['institute_service_up'].value_counts()
1.0 159 3.0 83 5.0 56 7.0 34 11.0 30 0.0 20 20.0 17 6.0 17 4.0 16 9.0 14 2.0 14 13.0 8 8.0 8 15.0 7 17.0 6 10.0 6 22.0 6 14.0 6 12.0 6 18.0 5 16.0 5 24.0 4 23.0 4 21.0 3 19.0 3 39.0 3 32.0 3 25.0 2 28.0 2 26.0 2 36.0 2 30.0 2 34.0 1 27.0 1 29.0 1 42.0 1 33.0 1 41.0 1 35.0 1 49.0 1 38.0 1 31.0 1 Name: institute_service_up, dtype: int64
# Convert years of service to categories
def transform_service(val):
if val >= 11:
return "Veteran"
elif 7 <= val < 11:
return "Established"
elif 3 <= val < 7:
return "Experienced"
elif pd.isnull(val):
return np.nan
else:
return "New"
combined_updated['service_cat'] = combined_updated['institute_service_up'].apply(transform_service)
# Quick check of the update
combined_updated['service_cat'].value_counts()
New 193 Experienced 172 Veteran 136 Established 62 Name: service_cat, dtype: int64
Finally, we'll replace the missing values in the dissatisfied column with the most frequent value, False. Then, we'll calculate the percentage of employees who resigned due to dissatisfaction in each service_cat group and plot the results.
Note that since we still have additional missing values left to deal with, this is meant to be an initial introduction to the analysis, not the final analysis.
# Verify the unique values
combined_updated['dissatisfied'].value_counts(dropna=False)
False 403 True 248 Name: dissatisfied, dtype: int64
# Replace missing values with the most frequent value, False
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
# Calculate the percentage of employees who resigned due to dissatisfaction in each category
dis_pct = combined_updated.pivot_table(index='service_cat', values='dissatisfied')
# Plot the results
%matplotlib inline
dis_pct.plot(kind='bar', rot=30)
<Axes: xlabel='service_cat'>
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.