This project aims to clean and analyze the employee exit surveys from DETE (Department of Education, Training and Employment) and TAFE institute in Queensland, Australia.
DETE dataset is here. The original TAFE data is no longer available.
This project aims to answer the following questions:
#Reading Datasets
import pandas as pd
import numpy as np
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
#Explore DETE data
dete_survey.info()
dete_survey.head(20)
<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
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 | 6 | Resignation-Other reasons | 05/2012 | 1994 | 1997 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | D | D | NaN | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
6 | 7 | Age Retirement | 05/2012 | 1972 | 2007 | Teacher | Secondary | Darling Downs South West | NaN | Permanent Part-time | ... | D | D | SD | Female | 56-60 | NaN | NaN | NaN | NaN | NaN |
7 | 8 | Age Retirement | 05/2012 | 1988 | 1990 | Teacher Aide | NaN | North Coast | NaN | Permanent Part-time | ... | SA | NaN | SA | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
8 | 9 | Resignation-Other reasons | 07/2012 | 2009 | 2009 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | A | D | N | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
9 | 10 | Resignation-Other employer | 2012 | 1997 | 2008 | Teacher Aide | NaN | Not Stated | NaN | Permanent Part-time | ... | SD | SD | SD | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
10 | 11 | Age Retirement | 2012 | 1999 | 1999 | Teacher | Primary | Central Office | Education Queensland | Permanent Full-time | ... | A | NaN | A | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
11 | 12 | Resignation-Move overseas/interstate | 2012 | 2009 | 2009 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | ... | N | N | N | Male | 31-35 | NaN | NaN | NaN | NaN | NaN |
12 | 13 | Resignation-Other reasons | 2012 | 1998 | 1998 | Teacher | Primary | Far North Queensland | NaN | Permanent Full-time | ... | SA | A | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
13 | 14 | Age Retirement | 2012 | 1967 | 2000 | Teacher | Primary | Metropolitan | NaN | Permanent Part-time | ... | A | D | A | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
14 | 15 | Resignation-Other employer | 2012 | 2007 | 2010 | Teacher | Secondary | Central Queensland | NaN | Permanent Full-time | ... | SA | N | SA | Male | 31-35 | NaN | NaN | NaN | NaN | NaN |
15 | 16 | Voluntary Early Retirement (VER) | 2012 | 1995 | 2004 | Teacher | Secondary | Central Queensland | NaN | Permanent Full-time | ... | A | N | A | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
16 | 17 | Resignation-Other reasons | 2012 | Not Stated | Not Stated | Teacher Aide | NaN | South East | NaN | Permanent Part-time | ... | M | M | M | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
17 | 18 | Age Retirement | 2012 | 1996 | 1996 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | A | A | Female | 56-60 | NaN | NaN | NaN | NaN | NaN |
18 | 19 | Age Retirement | 2012 | 2006 | 2006 | Cleaner | NaN | Central Office | Education Queensland | Permanent Full-time | ... | A | A | A | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
19 | 20 | Age Retirement | 2012 | 1989 | 1989 | Cleaner | NaN | Central Office | Education Queensland | Permanent Full-time | ... | A | A | A | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
20 rows × 56 columns
#Explore DETE data Continued
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
# Explore TAFE
tafe_survey.info()
tafe_survey.head(20)
<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
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 | 6.341475e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | ... | NaN | NaN | NaN | NaN | 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 | ... | Yes | Yes | Yes | Yes | 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 | - | - | - | - | - | ... | Yes | Yes | Yes | No | Male | 46 50 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
8 | 6.341579e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2009.0 | Resignation | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | Female | 36 40 | Temporary Full-time | Tutor | 3-4 | 3-4 |
9 | 6.341588e+17 | Barrier Reef Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | Career Move - Public Sector | - | - | - | - | ... | Yes | Yes | Yes | Yes | Female | 21 25 | Permanent Full-time | Administration (AO) | 1-2 | 1-2 |
10 | 6.341588e+17 | Barrier Reef Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | Career Move - Public Sector | - | - | - | - | ... | Yes | Yes | Yes | Yes | Female | 41 45 | Temporary Part-time | Administration (AO) | Less than 1 year | Less than 1 year |
11 | 6.341719e+17 | Barrier Reef Institute of TAFE | Delivery (teaching) | 2010.0 | Retrenchment/ Redundancy | NaN | NaN | NaN | NaN | NaN | ... | No | No | Yes | Yes | Male | 56 or older | Permanent Part-time | Tutor | 11-20 | 11-20 |
12 | 6.341719e+17 | Barrier Reef Institute of TAFE | Delivery (teaching) | 2010.0 | Retrenchment/ Redundancy | NaN | NaN | NaN | NaN | NaN | ... | Yes | Yes | Yes | Yes | Male | 51-55 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
13 | 6.341725e+17 | Barrier Reef Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | Yes | Yes | Yes | Yes | Female | 31 35 | Temporary Full-time | Administration (AO) | 11-20 | Less than 1 year |
14 | 6.341726e+17 | Central Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | Yes | Yes | Yes | No | Female | 31 35 | Permanent Part-time | Teacher (including LVT) | 7-10 | 7-10 |
15 | 6.341761e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | Yes | Yes | Yes | No | Female | 46 50 | Permanent Part-time | Technical Officer (TO) | 11-20 | 11-20 |
16 | 6.341770e+17 | Brisbane North Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
17 | 6.341771e+17 | Brisbane North Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | Career Move - Public Sector | - | - | - | - | ... | Yes | Yes | Yes | No | Female | 31 35 | Permanent Full-time | Administration (AO) | 7-10 | 1-2 |
18 | 6.341779e+17 | Brisbane North Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
19 | 6.341820e+17 | Southbank Institute of Technology | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
20 rows × 72 columns
#Explore TAFE continued
tafe_survey.isnull().sum()
Record ID 0 Institute 0 WorkArea 0 CESSATION YEAR 7 Reason for ceasing employment 1 ... 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 Length: 72, dtype: int64
The DETE survey has 822 rows and 56 columns. 37 columns are strings, 18 are bools, and one is int. Columns with large numbers of missing values include:
Apart from NaNs, the DETE dataset also uses the string 'Not Stated' to represent missing values.
The TAFE survey has 702 rows and 72 columns. Two are float numbers, and the other 70 columns are strings. Most of the column names are too long to properly display columns with missing values and will need renaming.
First we will re-read the DETE survey and read the 'Not Stated' values as NaNs, and drop unncessary columns from the datasets.
# Re-read DETE with 'Not Stated' as NaN
dete_survey = pd.read_csv('dete_survey.csv', na_values = 'Not Stated')
# Drop Columns that are not used in 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)
Columns in both datasets will be renamed and standardized to prepare the datasets for combination
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')
#Rename DETE columns
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')
#Rename TAFE columns
tafe_cols_new = {
'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(columns = tafe_cols_new)
tafe_survey_updated.head()
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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
The project aims at answering the following question: Are employees who have only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been at the job longer? Therefore, only response from those who resigned will be used for analysis.
We will use series.value_counts() method on separationtype column to find out how resignation data is represented first.
Upon inspection, the resignation data in DETE survey are represented by three separate categories:
* Resignation-Other reasons
* Resignation-Other employer
* Resignation-Move overseas/interstate
In this case we will select the three categories separately and concatenate back into one.
#separationtype in DETE dataset
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
#separationtype in TAFE dataset
tafe_survey_updated['separationtype'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
#Resignation data from TAFE dataset
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
#Resignation data from DETE dataset
dete_resignation_other_reason = dete_survey_updated[dete_survey_updated['separationtype'] == 'Resignation-Other reasons'].copy()
dete_resignation_other_employer = dete_survey_updated[dete_survey_updated['separationtype'] == 'Resignation-Other employer'].copy()
dete_resignation_move = dete_survey_updated[dete_survey_updated['separationtype'] == 'Resignation-Move overseas/interstate'].copy()
dete_resignations = pd.concat([dete_resignation_other_reason, dete_resignation_other_employer, dete_resignation_move], ignore_index = True)
dete_resignations['separationtype'].value_counts()
Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Name: separationtype, dtype: int64
In DETE survey, cease_date is last year of the person's employment and the dete_start_date is the person's first year of employment. Therefore, both columns should not contain years after the current year. Also, given that most people in this field start working in their 20s, it's also unlikely that the dete_start_date was before the year 1940.
We will verify the values in those columns to determine if we can proceed with our analysis and if there are anomalies to remove.
# Explore unique values in cease_date column
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 07/2006 1 09/2010 1 Name: cease_date, dtype: int64
#Extract the years and convert to float
cease_year = dete_resignations['cease_date'].str[-4:]
dete_resignations['cease_date'] = cease_year.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
#Explore dete_start_date
dete_resignations['dete_start_date'].value_counts().sort_index(ascending = True)
1963.0 1 1971.0 1 1972.0 1 1973.0 1 1974.0 2 1975.0 1 1976.0 2 1977.0 1 1980.0 5 1982.0 1 1983.0 2 1984.0 1 1985.0 3 1986.0 3 1987.0 1 1988.0 4 1989.0 4 1990.0 5 1991.0 4 1992.0 6 1993.0 5 1994.0 6 1995.0 4 1996.0 6 1997.0 5 1998.0 6 1999.0 8 2000.0 9 2001.0 3 2002.0 6 2003.0 6 2004.0 14 2005.0 15 2006.0 13 2007.0 21 2008.0 22 2009.0 13 2010.0 17 2011.0 24 2012.0 21 2013.0 10 Name: dete_start_date, dtype: int64
#Explore cease_date in tafe_resignations
tafe_resignations['cease_date'].value_counts().sort_index(ascending = True)
2009.0 2 2010.0 68 2011.0 116 2012.0 94 2013.0 55 Name: cease_date, dtype: int64
After inspecting the cleaned data in the cease_date and dete_start_date columns in dete_resignations and the cease_date in tafe_resignation, there are no rows containing years after the current year. There are also no dete_start_date before the year 1940. The data does not seem to contain major consistency and can be used in further analysis.
To answer the question if employees who have worked for the institutes for a short/long period of time resigning due to some kind of dissatisfaction, we need to calculate the length of time an employee spent in a workplace, also known as the years of service.
In the tafe dataset, the years of service are represented by the column institute_service. In the dete dataset the start and end year of employment are represented in the dete_start_date and cease_date respectively, which can be used to calculate a new column for the years of service
# Calculating the years of service in DETE survey
dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']
To identify employees who resigned out of dissatisfacation, we will mark anyone indicating any of the following factors to be their cause of resignation as 'dissatisfied':
TAFE:
DETE:
All values in these columns will be first converted to one of the three following values: True, False and NaN (if not already in this format). If any of the columns contain a True value, a True value will be added to a new column named 'dissafisfied'.
#Explore TAFE resignations caused by dissatisfaction
print(tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts(dropna = False))
print(tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts(dropna = False))
- 277 Contributing Factors. Dissatisfaction 55 NaN 8 Name: Contributing Factors. Dissatisfaction, dtype: int64 - 270 Job Dissatisfaction 62 NaN 8 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
# Convert resignations caused by dissatisfaction in TAFE dataset
def update_vals(value):
if pd.isnull(value) == True:
return np.nan
elif value == '-':
return False
else:
return True
dissatification_cols = ['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']
tafe_resignations_dissatisfacation = tafe_resignations[dissatification_cols].applymap(update_vals)
tafe_resignations['Contributing Factors. Dissatisfaction'] = tafe_resignations_dissatisfacation['Contributing Factors. Dissatisfaction']
tafe_resignations['Contributing Factors. Job Dissatisfaction'] = tafe_resignations_dissatisfacation['Contributing Factors. Job Dissatisfaction']
print(tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts(dropna = False))
print(tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts(dropna = False))
False 277 True 55 NaN 8 Name: Contributing Factors. Dissatisfaction, dtype: int64 False 270 True 62 NaN 8 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
#Explore DETE dissatisfication columns
dete_resignations['job_dissatisfaction'].value_counts(dropna = False)
False 270 True 41 Name: job_dissatisfaction, dtype: int64
# Create New dissatisfied column in TAFE
tafe_dissatisfication_cols = dissatification_cols = ['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']
tafe_resignations['dissatisfied'] = tafe_resignations[tafe_dissatisfication_cols].any(axis = 1, skipna = False)
#Create New dissatisfied column in DETE
dete_dissatisfication_cols = ['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[dete_dissatisfication_cols].any(axis =1, skipna = False)
dete_resignations['dissatisfied'].value_counts()
False 162 True 149 Name: dissatisfied, dtype: int64
#Create new copies of datasets
dete_resignations_up = dete_resignations.copy()
tafe_resignations_up = tafe_resignations.copy()
The datasets are combined vertically for aggregation on institute_service column. Before combining the datasets, another column indicating the institute is added so the two datasets can still be distinguished. After combining the datasets, columns with less than 500 non-null values are deleted.
# Add institute column to both datasets
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
# Combine datasets
combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index = True, axis = 0)
# Drop columns with less than 500 non-null values
combined_updated = combined.dropna(thresh = 500, axis = 1)
combined_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 651 entries, 0 to 650 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 651 non-null float64 1 separationtype 651 non-null object 2 cease_date 635 non-null float64 3 position 598 non-null object 4 employment_status 597 non-null object 5 gender 592 non-null object 6 age 596 non-null object 7 institute_service 563 non-null object 8 dissatisfied 643 non-null object 9 institute 651 non-null object dtypes: float64(2), object(8) memory usage: 51.0+ KB
The years of service in institute_service column will be cleaned and converted to categories for analysis.
The years of services in different format will be converted to strings, extracted and convert back to numbers. We will then use the extracted year to map each value to one of the four career stages below:
# Convert years of service to string
combined_updated['institute_service'] = combined['institute_service'].astype('str')
combined_updated['institute_service'].value_counts()
<ipython-input-30-b2af5fcdd98b>:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy combined_updated['institute_service'] = combined['institute_service'].astype('str')
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 20.0 7 15.0 7 17.0 6 22.0 6 10.0 6 14.0 6 12.0 6 18.0 5 16.0 5 11.0 4 23.0 4 24.0 4 21.0 3 19.0 3 32.0 3 39.0 3 36.0 2 26.0 2 28.0 2 25.0 2 30.0 2 49.0 1 33.0 1 31.0 1 42.0 1 29.0 1 34.0 1 38.0 1 41.0 1 27.0 1 35.0 1 Name: institute_service, dtype: int64
# Extract years of service
combined_updated['institute_service'] = combined_updated['institute_service'].str.replace("Less than 1 year", "0.0", regex = False)
combined_updated['institute_service'] = combined_updated['institute_service'].str.replace("More than 20 years", "20.0", regex = False)
combined_updated['institute_service'] = combined_updated['institute_service'].str.split(pat = '-', expand = True)
combined_updated['institute_service'] = combined_updated['institute_service'].astype('float')
combined_updated['institute_service'].value_counts(dropna = False)
<ipython-input-31-2eb472a9a79b>:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy combined_updated['institute_service'] = combined_updated['institute_service'].str.replace("Less than 1 year", "0.0", regex = False) <ipython-input-31-2eb472a9a79b>:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy combined_updated['institute_service'] = combined_updated['institute_service'].str.replace("More than 20 years", "20.0", regex = False) <ipython-input-31-2eb472a9a79b>:4: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy combined_updated['institute_service'] = combined_updated['institute_service'].str.split(pat = '-', expand = True) <ipython-input-31-2eb472a9a79b>:5: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy combined_updated['institute_service'] = combined_updated['institute_service'].astype('float')
0.0 93 NaN 88 1.0 86 3.0 83 5.0 56 7.0 34 11.0 30 20.0 17 6.0 17 4.0 16 9.0 14 2.0 14 8.0 8 13.0 8 15.0 7 22.0 6 10.0 6 14.0 6 12.0 6 17.0 6 18.0 5 16.0 5 23.0 4 24.0 4 32.0 3 39.0 3 19.0 3 21.0 3 30.0 2 36.0 2 25.0 2 28.0 2 26.0 2 33.0 1 38.0 1 34.0 1 31.0 1 41.0 1 27.0 1 35.0 1 29.0 1 49.0 1 42.0 1 Name: institute_service, dtype: int64
# Map years of service to career stage
def year_to_stage(year_val):
if pd.isnull(year_val) == True:
return np.nan
elif year_val < 3:
return 'New'
elif year_val <= 6:
return 'Experienced'
elif year_val <= 10:
return 'Established'
else:
return 'Veteran'
combined_updated['service_cat'] = combined_updated['institute_service'].apply(year_to_stage)
combined_updated['service_cat'].value_counts(dropna = False)
<ipython-input-32-cb30e5cb18b6>:14: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy combined_updated['service_cat'] = combined_updated['institute_service'].apply(year_to_stage)
New 193 Experienced 172 Veteran 136 NaN 88 Established 62 Name: service_cat, dtype: int64
# Explore dissatisfied column
combined_updated['dissatisfied'].value_counts(dropna = False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
# Fill missing values
combined_updated = combined_updated.fillna({'dissatisfied': False})
combined_updated['dissatisfied'].value_counts(dropna = False)
False 411 True 240 Name: dissatisfied, dtype: int64
# Create Pivot table
dissatisfied_pivot = pd.pivot_table(combined_updated, values = 'dissatisfied', index = 'service_cat')
dissatisfied_pivot
dissatisfied | |
---|---|
service_cat | |
Established | 0.516129 |
Experienced | 0.343023 |
New | 0.295337 |
Veteran | 0.485294 |
#Plot Pivot Table
%matplotlib inline
dissatisfied_pivot.plot(kind = 'bar')
<matplotlib.axes._subplots.AxesSubplot at 0x7fcdeffc0550>
The percentage of employees quitting out of dissatification increases from around 30% to over 50% as the years of service increases from new to established. Meanwhile, the ratio decreased slightly from established to veteran employees. It seems that employees with 7 or more years of service are more likely to resign due to dissatisfaction.