The collected data are exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. In this project, as a data analyst our company stakeholders want to know the following:
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.
Below is a preview of a couple columns we'll work with from the dete_survey.csv:
Below is a preview of a couple columns we'll work with from the tafe_survey.csv:
Analyzing both institute's data together we could see that people in the Veteran and Established service categories are resigning due to some kind of dissatisfaction. These service categories include people in the age ranges from 40s to over 60s.
Analyzing the institute's data separately, we have seen that DETE institute employees follow the same dissatisfaction percentages in terms of category and age range.
On the other hand, in the TAFE institute, we have seen that New employees service category resignation numbers increased in the last years and seem that the motive was dissatisfaction.
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
dete_survey = pd.read_csv("dete_survey.csv")
tafe_survey = pd.read_csv("tafe_survey.csv")
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
dete_survey.head(5)
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | 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 | ... | 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 | ... | 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 | ... | 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 | ... | 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 | ... | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 56 columns
dete_survey.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', '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'], dtype='object')
dete_survey.shape
(822, 56)
dete_survey.isnull().sum()
ID 0 SeparationType 0 Cease Date 0 DETE Start Date 0 Role Start Date 0 Position 5 Classification 367 Region 0 Business Unit 696 Employment Status 5 Career move to public sector 0 Career move to private sector 0 Interpersonal conflicts 0 Job dissatisfaction 0 Dissatisfaction with the department 0 Physical work environment 0 Lack of recognition 0 Lack of job security 0 Work location 0 Employment conditions 0 Maternity/family 0 Relocation 0 Study/Travel 0 Ill Health 0 Traumatic incident 0 Work life balance 0 Workload 0 None of the above 0 Professional Development 14 Opportunities for promotion 87 Staff morale 6 Workplace issue 34 Physical environment 5 Worklife balance 7 Stress and pressure support 12 Performance of supervisor 9 Peer support 10 Initiative 9 Skills 11 Coach 55 Career Aspirations 76 Feedback 30 Further PD 54 Communication 8 My say 10 Information 6 Kept informed 9 Wellness programs 56 Health & Safety 29 Gender 24 Age 11 Aboriginal 806 Torres Strait 819 South Sea 815 Disability 799 NESB 790 dtype: int64
dete_survey["Age"].value_counts()
61 or older 222 56-60 174 51-55 103 46-50 63 41-45 61 26-30 57 36-40 51 21-25 40 31-35 39 20 or younger 1 Name: Age, dtype: int64
dete_survey["Cease Date"].value_counts()
2012 344 2013 200 01/2014 43 12/2013 40 Not Stated 34 09/2013 34 06/2013 27 07/2013 22 10/2013 20 11/2013 16 08/2013 12 05/2013 7 05/2012 6 04/2014 2 08/2012 2 07/2014 2 04/2013 2 02/2014 2 11/2012 1 2014 1 07/2006 1 09/2014 1 2010 1 09/2010 1 07/2012 1 Name: Cease Date, dtype: int64
dete_survey["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
From the previous analysis, we can extract the following conclusions:
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
tafe_survey.head(5)
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 | ... | 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 | ... | 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 | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | 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 | - | - | - | - | - | ... | 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 | - | - | - | ... | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 72 columns
tafe_survey.columns
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', '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)'], dtype='object')
tafe_survey.shape
(702, 72)
tafe_survey.isnull().sum().sort_values(ascending=False)
Main Factor. Which of these was the main factor for leaving? 589 InductionInfo. Topic:Did you undertake a Corporate Induction? 270 Contributing Factors. Ill Health 265 Contributing Factors. Maternity/Family 265 Contributing Factors. Career Move - Public Sector 265 ... CESSATION YEAR 7 Reason for ceasing employment 1 WorkArea 0 Institute 0 Record ID 0 Length: 72, dtype: int64
tafe_survey["CurrentAge. Current Age"].value_counts()
56 or older 162 51-55 82 41 45 80 46 50 59 31 35 52 36 40 51 26 30 50 21 25 44 20 or younger 16 Name: CurrentAge. Current Age, dtype: int64
tafe_survey["CESSATION YEAR"].value_counts()
2011.0 268 2012.0 235 2010.0 103 2013.0 85 2009.0 4 Name: CESSATION YEAR, dtype: int64
tafe_survey["Reason for ceasing employment"].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: Reason for ceasing employment, dtype: int64
From the previous analysis, we can extract the following conclusions:
dete_survey = pd.read_csv("dete_survey.csv", na_values="Not Stated")
dete_survey.head(5)
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | 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 | ... | 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 | ... | 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 | ... | 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 | ... | 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 | ... | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 56 columns
In the next step, we are going to remove all the columns that don't give information to answer the questions described in the introduction.
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)
dete_survey_updated.head(5)
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Work life balance | Workload | None of the above | 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 | ... | False | False | True | 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 | 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 | True | 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 | False | False | 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 | ... | True | False | False | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 35 columns
dete_survey_updated.isnull().sum()
ID 0 SeparationType 0 Cease Date 34 DETE Start Date 73 Role Start Date 98 Position 5 Classification 367 Region 105 Business Unit 696 Employment Status 5 Career move to public sector 0 Career move to private sector 0 Interpersonal conflicts 0 Job dissatisfaction 0 Dissatisfaction with the department 0 Physical work environment 0 Lack of recognition 0 Lack of job security 0 Work location 0 Employment conditions 0 Maternity/family 0 Relocation 0 Study/Travel 0 Ill Health 0 Traumatic incident 0 Work life balance 0 Workload 0 None of the above 0 Gender 24 Age 11 Aboriginal 806 Torres Strait 819 South Sea 815 Disability 799 NESB 790 dtype: int64
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis=1)
tafe_survey_updated.head(5)
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. 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) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | 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 | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | - | - | - | NONE | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | - | - | - | - | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 23 columns
tafe_survey_updated.isnull().sum()
Record ID 0 Institute 0 WorkArea 0 CESSATION YEAR 7 Reason for ceasing employment 1 Contributing Factors. Career Move - Public Sector 265 Contributing Factors. Career Move - Private Sector 265 Contributing Factors. Career Move - Self-employment 265 Contributing Factors. Ill Health 265 Contributing Factors. Maternity/Family 265 Contributing Factors. Dissatisfaction 265 Contributing Factors. Job Dissatisfaction 265 Contributing Factors. Interpersonal Conflict 265 Contributing Factors. Study 265 Contributing Factors. Travel 265 Contributing Factors. Other 265 Contributing Factors. NONE 265 Gender. What is your Gender? 106 CurrentAge. Current Age 106 Employment Type. Employment Type 106 Classification. Classification 106 LengthofServiceOverall. Overall Length of Service at Institute (in years) 106 LengthofServiceCurrent. Length of Service at current workplace (in years) 106 dtype: int64
From the steps above, the original datasets were processed and any unnecessary info for our analysis which happened to be a combination of non-grievance or personal reasoning as well as duplicate information columns were removed.
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')
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(' ', '_')
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')
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)
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')
In the cells above, we have prepared the columns of each dataset to have similar or equal names to make easier the datasets concatenation.
Recall that our end goal is to answer the following question:
If we look at the unique values in the separationtype columns in each dataframe, we'll see that each contains a couple of different separation types. We'll only analyze survey respondents who resigned, so their separation type contains the string Resignation. Note that dete_survey_updated dataframe contains multiple separation types with the string Resignation:
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
tafe_survey_updated["separationtype"].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
dete_survey_updated["separationtype"] = dete_survey_updated["separationtype"].str.split("-").str.get(0)
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
dete_resignations = dete_survey_updated[dete_survey_updated["separationtype"] == "Resignation"].copy()
dete_resignations.head(5)
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | work_life_balance | workload | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 4 | Resignation | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
5 | 6 | Resignation | 05/2012 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | False | False | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
8 | 9 | Resignation | 07/2012 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
9 | 10 | Resignation | 2012 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | ... | False | False | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
11 | 12 | Resignation | 2012 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | ... | False | False | False | Male | 31-35 | NaN | NaN | NaN | NaN | NaN |
5 rows × 35 columns
dete_resignations.shape
(311, 35)
tafe_resignations = tafe_survey_updated[tafe_survey_updated["separationtype"] == "Resignation"].copy()
tafe_resignations.head(5)
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. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | gender | age | employment_status | position | institute_service | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | - | - | - | - | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 | 6.341475e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | ... | - | - | Other | - | Female | 56 or older | Contract/casual | Teacher (including LVT) | 7-10 | 7-10 |
6 | 6.341520e+17 | Barrier Reef Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | Maternity/Family | ... | - | - | Other | - | Male | 20 or younger | Temporary Full-time | Administration (AO) | 3-4 | 3-4 |
7 | 6.341537e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | ... | - | - | Other | - | Male | 46 50 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 23 columns
tafe_resignations.shape
(340, 23)
In the previous steps, we selected only the cases where former employees left due to a resignation. In other words, we excluded al the cases where the separation type was different from a resignation.
Previously in the dete_survey_updated dataframe, we removed all the Resignation subtypes of separation type creating a general one with the name: Resignation.
The shape method helps us to verify that the filter has worked properly comparing the shape result with the value_counts for Resignation cases.
In this step, we'll focus on verifying that the years in the cease_date and dete_start_date columns make sense.
If there are a small amount of values that are unrealistically high or low, we can remove them.
dete_resignations['cease_date'].value_counts()
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 11/2013 9 07/2013 9 10/2013 6 08/2013 4 05/2012 2 05/2013 2 09/2010 1 2010 1 07/2006 1 07/2012 1 Name: cease_date, dtype: int64
The cease_date column includes the year in two formats: yyyy and mm/yyyy. Before analyzing the consistency of the cease date and start date, we must change this format and count values correctly.
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.strip().str[-4:].astype(float)
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
plt.figure(figsize=(20, 10))
ax = sns.boxplot(dete_resignations['cease_date'])
ax.set_title("DETE Cease year groups")
ax.set_xlabel("Cease Year")
sns.despine(left = True)
From the boxplot above we can conclude that the majority of the ceases were between 2012 and 2013 but there are some outliers in 2006 or 2010.
dete_resignations["dete_start_date"].value_counts().sort_index(ascending=False)
2013.0 10 2012.0 21 2011.0 24 2010.0 17 2009.0 13 2008.0 22 2007.0 21 2006.0 13 2005.0 15 2004.0 14 2003.0 6 2002.0 6 2001.0 3 2000.0 9 1999.0 8 1998.0 6 1997.0 5 1996.0 6 1995.0 4 1994.0 6 1993.0 5 1992.0 6 1991.0 4 1990.0 5 1989.0 4 1988.0 4 1987.0 1 1986.0 3 1985.0 3 1984.0 1 1983.0 2 1982.0 1 1980.0 5 1977.0 1 1976.0 2 1975.0 1 1974.0 2 1973.0 1 1972.0 1 1971.0 1 1963.0 1 Name: dete_start_date, dtype: int64
plt.figure(figsize=(20, 10))
ax = sns.boxplot(dete_resignations['dete_start_date'])
ax.set_title("DETE Start year groups")
ax.set_xlabel("Start Year")
sns.despine(left = True)
From the DETE Start year groups chart we can observe that the majority of the employees start working at the end of the _90's__ or between 2000 and 2010. Then comparing both boxplots the majority of employees have been working minimum of 12/13 years.
tafe_resignations["cease_date"].value_counts()
2011.0 116 2012.0 94 2010.0 68 2013.0 55 2009.0 2 Name: cease_date, dtype: int64
plt.figure(figsize=(20, 10))
ax = sns.boxplot(tafe_resignations['cease_date'])
ax.set_title("TAFE Cease year groups")
ax.set_xlabel("Cease Year")
sns.despine(left = True)
From the TAFE Cease year groups we can see that the majority of the ceases are between 2011 and 2012 one year before than the DATE Cease year groups majority.
After verifying the date columns, we can summarize the following conclusions:
In the Human Resources field, the length of time an employee spent in a workplace is referred to as their years of service.
The tafe_resignations dataframe already contains a service column, which we renamed previously to institute_service. In order to analyze both surveys together, we'll have to create a corresponding institute_service column in dete_resignations.
After analysing the dete_resingations dataframe we know that the institute_service column can be computed by subtracting the dete_start_date from the cease_date columns.
dete_resignations["institute_service"] = dete_resignations["cease_date"] - dete_resignations["dete_start_date"]
dete_resignations["institute_service"].value_counts()
5.0 23 1.0 22 3.0 20 0.0 20 6.0 17 4.0 16 9.0 14 2.0 14 7.0 13 13.0 8 8.0 8 20.0 7 15.0 7 10.0 6 22.0 6 14.0 6 17.0 6 12.0 6 16.0 5 18.0 5 23.0 4 11.0 4 24.0 4 39.0 3 19.0 3 21.0 3 32.0 3 28.0 2 26.0 2 25.0 2 30.0 2 36.0 2 29.0 1 33.0 1 42.0 1 27.0 1 41.0 1 35.0 1 38.0 1 34.0 1 49.0 1 31.0 1 Name: institute_service, dtype: int64
plt.figure(figsize=(20, 10))
sns.set_style("darkgrid")
ax = sns.distplot(dete_resignations["institute_service"])
ax.set_yticks([])
ax.set_title("DETE years of service distribution")
ax.set_xlabel("Institue Service")
ax.tick_params(bottom=True, top=False, left=False, right=False, labelbottom=True)
sns.despine(left=True)
tafe_resignations['institute_service'].value_counts()
Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 7-10 21 More than 20 years 10 Name: institute_service, dtype: int64
From the previous anaylisis steps we obtained the years of service in the date_resignations dataframe. We could see that the majority of the cases have between 1 and 10 years of service.
We observed that before working with the tafe_resignations years of service and se their distibutions, we must change the date_resignations column values into range groups as is shown in the cell above.
Below are the columns we'll use to categorize employees as dissatisfied from each dataframe.
tafe_resignations["Contributing Factors. Dissatisfaction"].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
tafe_resignations["Contributing Factors. Job Dissatisfaction"].value_counts()
- 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
As we have seen in the previous cells the colums Contributing Factors. Dissatisfaction and Contributing Factors. Job Dissatisfaction contain the character - or a string. We will change the content of this columns to a bool or a NaN value with the next function:
def update_vals(val):
if pd.isnull(val):
return np.nan
elif val == "-":
return False
else:
return True
columns_to_change = ["Contributing Factors. Dissatisfaction", "Contributing Factors. Job Dissatisfaction"]
tafe_resignations['dissatisfied'] = tafe_resignations[columns_to_change].applymap(update_vals).any(axis = 1, skipna = False)
tafe_resignations['dissatisfied'].value_counts(dropna = False)
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
columns_to_change = ["job_dissatisfaction",
"dissatisfaction_with_the_department",
"physical_work_environment",
"lack_of_recognition",
"lack_of_job_security",
"work_location",
"employment_conditions",
"work_life_balance",
"workload"]
dete_resignations['dissatisfied'] = dete_resignations[columns_to_change].any(axis = 1, skipna = False)
dete_resignations['dissatisfied'].value_counts(dropna = False)
False 162 True 149 Name: dissatisfied, dtype: int64
dete_resignations_up = dete_resignations.copy()
tafe_resignations_up = tafe_resignations.copy()
In the cells above we created a column for both datasets that indicates if employees resigned because they were dissatisfied in some way, where we will insert:
We can extract the following conclusions comparing both dataframes values counts for the dissatisfied column:
First of all we start adding a new column to distinguish between the two institutes.
dete_resignations_up["institute"] = "DETE"
tafe_resignations_up["institute"] = "TAFE"
combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index=True)
combined.head(5)
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | 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 | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 4.0 | Resignation | 2012.0 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
1 | 6.0 | Resignation | 2012.0 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 9.0 | Resignation | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 10.0 | Resignation | 2012.0 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 12.0 | Resignation | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 53 columns
combined.isnull().sum()
id 0 separationtype 0 cease_date 16 dete_start_date 368 role_start_date 380 position 53 classification 490 region 386 business_unit 619 employment_status 54 career_move_to_public_sector 340 career_move_to_private_sector 340 interpersonal_conflicts 340 job_dissatisfaction 340 dissatisfaction_with_the_department 340 physical_work_environment 340 lack_of_recognition 340 lack_of_job_security 340 work_location 340 employment_conditions 340 maternity/family 340 relocation 340 study/travel 340 ill_health 340 traumatic_incident 340 work_life_balance 340 workload 340 none_of_the_above 340 gender 59 age 55 aboriginal 644 torres_strait 651 south_sea 648 disability 643 nesb 642 institute_service 88 dissatisfied 8 institute 0 Institute 311 WorkArea 311 Contributing Factors. Career Move - Public Sector 319 Contributing Factors. Career Move - Private Sector 319 Contributing Factors. Career Move - Self-employment 319 Contributing Factors. Ill Health 319 Contributing Factors. Maternity/Family 319 Contributing Factors. Dissatisfaction 319 Contributing Factors. Job Dissatisfaction 319 Contributing Factors. Interpersonal Conflict 319 Contributing Factors. Study 319 Contributing Factors. Travel 319 Contributing Factors. Other 319 Contributing Factors. NONE 319 role_service 361 dtype: int64
combined_updated = combined.dropna(thresh=500, axis=1).copy()
combined_updated.isnull().sum()
id 0 separationtype 0 cease_date 16 position 53 employment_status 54 gender 59 age 55 institute_service 88 dissatisfied 8 institute 0 dtype: int64
combined_updated.describe(include = 'all')
id | separationtype | cease_date | position | employment_status | gender | age | institute_service | dissatisfied | institute | |
---|---|---|---|---|---|---|---|---|---|---|
count | 6.510000e+02 | 651 | 635.000000 | 598 | 597 | 592 | 596 | 563 | 643 | 651 |
unique | NaN | 1 | NaN | 21 | 6 | 2 | 17 | 49 | 2 | 2 |
top | NaN | Resignation | NaN | Administration (AO) | Permanent Full-time | Female | 51-55 | Less than 1 year | False | TAFE |
freq | NaN | 651 | NaN | 148 | 256 | 424 | 71 | 73 | 403 | 340 |
mean | 3.314265e+17 | NaN | 2011.963780 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
std | 3.172210e+17 | NaN | 1.079028 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
min | 4.000000e+00 | NaN | 2006.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
25% | 4.525000e+02 | NaN | 2011.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
50% | 6.341820e+17 | NaN | 2012.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
75% | 6.345770e+17 | NaN | 2013.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
max | 6.350730e+17 | NaN | 2014.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
In this process, we've gone ahead and concatenate the two datasets into one through the institute column that delineates DETE and TAFE. Furthermore, while we still had some columns within both dataset that are not necessary for the analysis, we dropped them in cleaning the dataframe. Specifically, we've dropped columns with less than 500 non-null values.
As we said in the Compute employees years of service part, the institute_service column is is tricky to clean because it currently contains values in a couple different forms.
To analyze the data, we'll convert these numbers into categories. We'll base our anlaysis on this article: New Data Shows Career Stage More Effective Indicator of Engagement Than Employee Age Alone, which makes the argument that understanding employee's needs according to career stage instead of age is more effective.
We'll use the slightly modified definitions below:
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 0.0 20 3.0 20 6.0 17 4.0 16 9.0 14 2.0 14 7.0 13 More than 20 years 10 8.0 8 13.0 8 15.0 7 20.0 7 10.0 6 12.0 6 14.0 6 22.0 6 17.0 6 18.0 5 16.0 5 11.0 4 23.0 4 24.0 4 19.0 3 39.0 3 32.0 3 21.0 3 26.0 2 28.0 2 30.0 2 36.0 2 25.0 2 27.0 1 29.0 1 31.0 1 49.0 1 33.0 1 34.0 1 35.0 1 38.0 1 41.0 1 42.0 1 Name: institute_service, dtype: int64
With this pattern r'(\d+)' we will extract the first number of each entry in the institute_service column.
combined_updated["institute_service"] = combined_updated["institute_service"].astype(str).str.extract(r'(\d+)')
combined_updated["institute_service"] = combined_updated["institute_service"].astype(float)
combined_updated["institute_service"].value_counts(dropna=False)
1.0 159 NaN 88 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 22.0 6 10.0 6 17.0 6 14.0 6 12.0 6 16.0 5 18.0 5 24.0 4 23.0 4 21.0 3 39.0 3 32.0 3 19.0 3 36.0 2 30.0 2 25.0 2 26.0 2 28.0 2 42.0 1 29.0 1 35.0 1 27.0 1 41.0 1 49.0 1 38.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service, dtype: int64
def add_service_category(val):
if pd.isnull(val):
return np.nan
elif val < 3:
return "New"
elif val >= 3 and val <= 6:
return "Experienced"
elif val >= 7 and val <= 10:
return "Established"
elif val >= 11:
return "Veteran"
combined_updated["service_cat"] = combined_updated["institute_service"].apply(add_service_category)
combined_updated["service_cat"].value_counts(dropna=False)
New 193 Experienced 172 Veteran 136 NaN 88 Established 62 Name: service_cat, dtype: int64
plt.figure(figsize=(20, 10))
sns.set_style("darkgrid")
ax = sns.distplot(combined_updated["institute_service"])
ax.set_yticks([])
ax.set_title("Years of service distribution")
ax.set_xlabel("Institue Service")
ax.tick_params(bottom=True, top=False, left=False, right=False, labelbottom=True)
sns.despine(left=True)
plt.figure(figsize=(20, 10))
sns.set_style("darkgrid")
ax = sns.barplot(x =combined_updated["service_cat"].value_counts().index,
y=combined_updated["service_cat"].value_counts(),
palette=sns.color_palette("GnBu_d"))
ax.set_yticks([])
ax.set_title("Service Categories Count Comparation")
ax.set_xlabel("Service Categories")
ax.set_ylabel("Count")
ax.tick_params(bottom=True, top=False, left=False, right=False, labelbottom=True)
sns.despine(left=True)
After combining both data frames we notice that the institute_service column is tricky to clean because it currently contains values in a couple of different forms, to do it we defined new groups depending on the years of service of the employees.
After seeing the Years of service distribution we can conclude that the majority of employees that resigned have worked between 0-2 years.
Comparing it with the Service Categories Count Comparation we can see that the majority of the employees' category is New, a fact that agrees with the previous conclusions knowing that the New category includes all the employees that worked less than 3 years at a company.
combined_updated['dissatisfied'].value_counts(dropna = False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
As is shown in the cell above the predominant value in the dissatisifed column is False. In the next step we will aggregate data by changing the NaN values to False.
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
combined_updated['dissatisfied'].value_counts(dropna = False)
False 411 True 240 Name: dissatisfied, dtype: int64
pv_dissatisfied =combined_updated.pivot_table(values="dissatisfied", index="service_cat")
pv_dissatisfied
dissatisfied | |
---|---|
service_cat | |
Established | 0.516129 |
Experienced | 0.343023 |
New | 0.295337 |
Veteran | 0.485294 |
plt.figure(figsize=(20, 10))
sns.set_style("darkgrid")
ax = sns.barplot(x=pv_dissatisfied.index,
y=pv_dissatisfied["dissatisfied"],
palette=sns.color_palette("GnBu_d"))
ax.set_yticks([])
ax.set_title("Dissatisfied Percentage by Service Categories")
ax.set_xlabel("Service Categories")
ax.set_ylabel("Percentage")
ax.tick_params(bottom=True, top=False, left=False, right=False, labelbottom=True)
sns.despine(left=True)
Previously in the Service Categories Count Comparation chart we have seen that the New category is the one with most personal. But after seeing the Dissatisfied Percentage by Service Categories we can conclude that employees that have been working at either DETE or TAFE institute for a long period (Established and Veteran categories) tend to have been dissatisfied compared to the newer ex-employees.
combined_updated.isnull().sum()
id 0 separationtype 0 cease_date 16 position 53 employment_status 54 gender 59 age 55 institute_service 88 dissatisfied 0 institute 0 service_cat 88 dtype: int64
combined_updated["age"].value_counts(dropna=False)
51-55 71 NaN 55 41-45 48 41 45 45 46-50 42 36-40 41 46 50 39 26-30 35 21 25 33 26 30 32 36 40 32 31 35 32 56 or older 29 31-35 29 21-25 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
From the previous list we observed that 55 of the ex-emplyoees didn't include the age, meanwhile the other cases are grouped in intervals of 5 years. In the next part we will extract the age from the first number in the age column interval.
pattern = r"([0-9]+)"
combined_updated["age_estimation"] = combined_updated["age"].astype(str).str.extract(pattern, expand=True).astype(float)
combined_updated["age_estimation"].value_counts(dropna=False)
41.0 93 46.0 81 36.0 73 51.0 71 26.0 67 21.0 62 31.0 61 NaN 55 56.0 55 61.0 23 20.0 10 Name: age_estimation, dtype: int64
Next let's compare the age_estimation column mean, median and mode.
combined_updated["age_estimation"].mean()
39.27181208053691
combined_updated["age_estimation"].median()
41.0
combined_updated["age_estimation"].mode()
0 41.0 dtype: float64
In the value counts a preview of the mode was shown, the most repeated value is 41.0, after the comparison we have seen that the value of the median and mode are the same. We choose to replace the NaN values with the 41 instead of the 39 (rounded down) value of the mean result.
Now let's add a new column differencing by age groups:
def add_age_category_value(val):
if pd.isnull(val):
return np.nan
elif val == 20:
return "16 - 20s"
elif (val == 21) or (val == 26):
return "20s"
elif (val == 31) or (val == 36):
return '30s'
elif (val == 41) or (val == 46):
return '40s'
elif (val == 51) or (val == 56):
return '50s'
elif val == 61:
return '60s or greater'
We considered that the age to start working legally is 16.
combined_updated["age_cat"] = combined_updated["age_estimation"].apply(add_age_category_value)
combined_updated["age_cat"].fillna("40s")
combined_updated["age_cat"].value_counts(dropna=False)
40s 174 30s 134 20s 129 50s 126 NaN 55 60s or greater 23 16 - 20s 10 Name: age_cat, dtype: int64
Previously we have seen the Disatisfied Percentage by Service Categories now let's see the results comparing the different age intervals.
pv_dissatisfied_age = combined_updated.pivot_table(values = "dissatisfied", index = "age_cat")
pv_dissatisfied_age
dissatisfied | |
---|---|
age_cat | |
16 - 20s | 0.200000 |
20s | 0.364341 |
30s | 0.358209 |
40s | 0.379310 |
50s | 0.404762 |
60s or greater | 0.521739 |
plt.figure(figsize=(20, 10))
sns.set_style("darkgrid")
ax = sns.barplot(x=pv_dissatisfied_age.index,
y=pv_dissatisfied_age["dissatisfied"],
palette=sns.color_palette("GnBu_d"))
ax.set_yticks([])
ax.set_title("Dissatisfied Percentage by Age Categories")
ax.set_xlabel("Age Categories")
ax.set_ylabel("Percentage")
ax.tick_params(bottom=True, top=False, left=False, right=False, labelbottom=True)
sns.despine(left=True)
It seems that the isn't a significant difference observed amongst young ex-employees (i.e. 20s-30s) as it pertains to dissatisfaction. However, this measure of dissatisfaction is noticeably increased amongst those in their 50s and 60s+. In the next charts, we see the comparison between service category and age category and as was expected Established and Veteran employees which are in the age categories 50s and 60s or greater then to have a higher level of dissatisfaction.
fig, axes = plt.subplots(1, 2, figsize=(20, 10))
sns.set_style("darkgrid")
ax = sns.barplot(x=pv_dissatisfied.index,
y=pv_dissatisfied["dissatisfied"],
palette=sns.color_palette("GnBu_d"),
ax=axes[0])
ax.set_yticks([])
ax.set_title("Dissatisfied Percentage by Service Categories")
ax.set_xlabel("Service Categories")
ax.set_ylabel("Percentage")
ax.tick_params(bottom=True, top=False, left=False, right=False, labelbottom=True)
sns.despine(left=True)
plt.figure(figsize=(20, 10))
sns.set_style("darkgrid")
ax = sns.barplot(x=pv_dissatisfied_age.index,
y=pv_dissatisfied_age["dissatisfied"],
palette=sns.color_palette("GnBu_d"),
ax=axes[1])
ax.set_yticks([])
ax.set_title("Dissatisfied Percentage by Age Categories")
ax.set_xlabel("Age Categories")
ax.set_ylabel("Percentage")
ax.tick_params(bottom=True, top=False, left=False, right=False, labelbottom=True)
sns.despine(left=True)
<Figure size 1440x720 with 0 Axes>
combined_dissatisfied = combined_updated[combined_updated["dissatisfied"] == True]
combined_dissatisfied.groupby("age_cat")["dissatisfied"].count()
age_cat 16 - 20s 2 20s 47 30s 48 40s 66 50s 51 60s or greater 12 Name: dissatisfied, dtype: int64
combined_dissatisfied.groupby("service_cat")["dissatisfied"].count()
service_cat Established 32 Experienced 59 New 57 Veteran 66 Name: dissatisfied, dtype: int64
From the previous list we can conclude that in terms of age, workers at their 40s are more used to resing due to a dissatisfaction reason.
In terms of years of service veteran employees category is where most employees left due to a dissatisfaction reason.
As a continuation let's analyze the institute's data separately.
dete_df = combined_updated[combined_updated["institute"] == "DETE"]
dete_df
id | separationtype | cease_date | position | employment_status | gender | age | institute_service | dissatisfied | institute | service_cat | age_estimation | age_cat | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 4.0 | Resignation | 2012.0 | Teacher | Permanent Full-time | Female | 36-40 | 7.0 | False | DETE | Established | 36.0 | 30s |
1 | 6.0 | Resignation | 2012.0 | Guidance Officer | Permanent Full-time | Female | 41-45 | 18.0 | True | DETE | Veteran | 41.0 | 40s |
2 | 9.0 | Resignation | 2012.0 | Teacher | Permanent Full-time | Female | 31-35 | 3.0 | False | DETE | Experienced | 31.0 | 30s |
3 | 10.0 | Resignation | 2012.0 | Teacher Aide | Permanent Part-time | Female | 46-50 | 15.0 | True | DETE | Veteran | 46.0 | 40s |
4 | 12.0 | Resignation | 2012.0 | Teacher | Permanent Full-time | Male | 31-35 | 3.0 | False | DETE | Experienced | 31.0 | 30s |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
306 | 810.0 | Resignation | 2013.0 | Teacher Aide | Permanent Part-time | Female | 26-30 | 3.0 | False | DETE | Experienced | 26.0 | 20s |
307 | 817.0 | Resignation | 2014.0 | Teacher | Permanent Full-time | Male | 21-25 | 2.0 | False | DETE | New | 21.0 | 20s |
308 | 818.0 | Resignation | 2014.0 | Teacher | Permanent Full-time | Female | 21-25 | 2.0 | False | DETE | New | 21.0 | 20s |
309 | 821.0 | Resignation | 2014.0 | Public Servant | Permanent Full-time | Female | 31-35 | 5.0 | True | DETE | Experienced | 31.0 | 30s |
310 | 823.0 | Resignation | 2013.0 | Teacher Aide | NaN | NaN | NaN | NaN | False | DETE | NaN | NaN | NaN |
311 rows × 13 columns
dete_df["dissatisfied"].value_counts(dropna=False)
False 162 True 149 Name: dissatisfied, dtype: int64
From the DETE instiute data, 149 cases of resignation where due a dissatisfaction reason and 162 weren't.
dete_pv_dissatisfied =dete_df.pivot_table(values="dissatisfied", index="service_cat").sort_values(by="dissatisfied")
dete_pv_dissatisfied
dissatisfied | |
---|---|
service_cat | |
New | 0.375000 |
Experienced | 0.460526 |
Veteran | 0.560000 |
Established | 0.609756 |
plt.figure(figsize=(20, 10))
sns.set_style("darkgrid")
ax = sns.barplot(x=dete_pv_dissatisfied.index,
y=dete_pv_dissatisfied["dissatisfied"],
palette=sns.color_palette("GnBu_d"))
ax.set_yticks([])
ax.set_title("DETE Institute. Dissatisfied Percentage by Service Categories")
ax.set_xlabel("Service Categories")
ax.set_ylabel("Percentage")
ax.tick_params(bottom=True, top=False, left=False, right=False, labelbottom=True)
sns.despine(left=True)
In the previous chart, is shown that Established and Veteran categories are the ones with most dissatisfied employees in the DETE institute.
tafe_df = combined_updated[combined_updated["institute"] == "TAFE"]
tafe_df
id | separationtype | cease_date | position | employment_status | gender | age | institute_service | dissatisfied | institute | service_cat | age_estimation | age_cat | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
311 | 6.341399e+17 | Resignation | 2010.0 | NaN | NaN | NaN | NaN | NaN | False | TAFE | NaN | NaN | NaN |
312 | 6.341466e+17 | Resignation | 2010.0 | Teacher (including LVT) | Permanent Full-time | Male | 41 45 | 3.0 | False | TAFE | Experienced | 41.0 | 40s |
313 | 6.341475e+17 | Resignation | 2010.0 | Teacher (including LVT) | Contract/casual | Female | 56 or older | 7.0 | False | TAFE | Established | 56.0 | 50s |
314 | 6.341520e+17 | Resignation | 2010.0 | Administration (AO) | Temporary Full-time | Male | 20 or younger | 3.0 | False | TAFE | Experienced | 20.0 | 16 - 20s |
315 | 6.341537e+17 | Resignation | 2010.0 | Teacher (including LVT) | Permanent Full-time | Male | 46 50 | 3.0 | False | TAFE | Experienced | 46.0 | 40s |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
646 | 6.350660e+17 | Resignation | 2013.0 | Operational (OO) | Temporary Full-time | Male | 21 25 | 5.0 | False | TAFE | Experienced | 21.0 | 20s |
647 | 6.350668e+17 | Resignation | 2013.0 | Teacher (including LVT) | Temporary Full-time | Male | 51-55 | 1.0 | False | TAFE | New | 51.0 | 50s |
648 | 6.350677e+17 | Resignation | 2013.0 | NaN | NaN | NaN | NaN | NaN | False | TAFE | NaN | NaN | NaN |
649 | 6.350704e+17 | Resignation | 2013.0 | Teacher (including LVT) | Permanent Full-time | Female | 51-55 | 5.0 | False | TAFE | Experienced | 51.0 | 50s |
650 | 6.350730e+17 | Resignation | 2013.0 | Administration (AO) | Contract/casual | Female | 26 30 | 3.0 | False | TAFE | Experienced | 26.0 | 20s |
340 rows × 13 columns
tafe_df["dissatisfied"].value_counts(dropna=False)
False 249 True 91 Name: dissatisfied, dtype: int64
From the TAFE instiute data, only 91 cases of resignation where due a dissatisfaction reason and 249 weren't.
tafe_pv_dissatisfied = tafe_df.pivot_table(values="dissatisfied", index="service_cat").sort_values(by="dissatisfied")
tafe_pv_dissatisfied
dissatisfied | |
---|---|
service_cat | |
Experienced | 0.250000 |
New | 0.262774 |
Veteran | 0.277778 |
Established | 0.333333 |
plt.figure(figsize=(20, 10))
sns.set_style("darkgrid")
ax = sns.barplot(x=tafe_pv_dissatisfied.index,
y=tafe_pv_dissatisfied["dissatisfied"],
palette=sns.color_palette("GnBu_d"))
ax.set_yticks([])
ax.set_title("TAFE Institute. Dissatisfied Percentage by Service Categories")
ax.set_xlabel("Service Categories")
ax.set_ylabel("Percentage")
ax.tick_params(bottom=True, top=False, left=False, right=False, labelbottom=True)
sns.despine(left=True)
In the previous chart, comparing the TAFE institute data we observed that the Established category is the one with most dissatisfied employees but unlike the DETE institute data, the dissatisfaction percentage by category for each category are nearby.
In the TAFE institute data, New employees aren't the ones with the lowest percentage of dissatisfaction, this fact could mean that something changed recently in the TAFE institute and make New employees resing.
In the following part, we will analyze this fact.
tafe_df[(tafe_df["service_cat"] == "New") & (tafe_df["dissatisfied"] == True)]["cease_date"].value_counts(dropna=False).sort_index()
2010.0 8 2011.0 13 2012.0 10 2013.0 5 Name: cease_date, dtype: int64
From the previous we can conclude that something happend between 2010 and 2012 maybe the laboral conditions of the New emploes that's why dissatified New employees category increased cosiderably compared with 2013 in the TAFE institute.
Regarding to the intital questions:
Analyzing both institute's data together we could see that people in the Veteran and Established service categories are resigning due to some kind of dissatisfaction. These service categories include people in the age ranges from 40s to over 60s.
Analyzing the institute's data separately, we have seen that DETE institute employees follow the same dissatisfaction percentages in terms of category and age range.
On the other hand, in the TAFE institute, we have seen that New employees service category resignation numbers increased in the last years and seem that the motive was dissatisfaction.