In this project I will work with exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia.
The goal of this project is to answer the following questions:
To do this I will follow three steps:
# Import modules
import pandas as pd
import numpy as np
pd.set_option('display.max_columns', None)
# Read datasets into pandas
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
Let's have a look at the data that we will be working with.
# Check first 5 rows in dete survey
dete_survey.head(5)
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | Career move to public sector | Career move to private sector | Interpersonal conflicts | Job dissatisfaction | Dissatisfaction with the department | Physical work environment | Lack of recognition | Lack of job security | Work location | Employment conditions | Maternity/family | Relocation | Study/Travel | Ill Health | Traumatic incident | Work life balance | Workload | None of the above | Professional Development | Opportunities for promotion | Staff morale | Workplace issue | Physical environment | Worklife balance | Stress and pressure support | Performance of supervisor | Peer support | Initiative | Skills | Coach | Career Aspirations | Feedback | Further PD | Communication | My say | Information | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984 | 2004 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | True | False | False | True | False | False | True | False | False | False | False | False | False | False | False | False | False | True | A | A | N | N | N | A | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | Not Stated | Not Stated | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | A | N | N | N | N | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011 | 2011 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | N | N | N | N | N | N | N | N | N | N | N | N | N | N | N | A | A | N | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005 | 2006 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | N | N | N | A | A | N | N | A | A | A | A | A | A | A | A | A | A | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970 | 1989 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | A | A | N | N | D | D | N | A | A | A | A | A | A | SA | SA | D | D | A | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
# Check information about dete survey
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: 139.7+ KB
# Check first 5 rows in tafe survey
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 | Contributing Factors. Dissatisfaction | Contributing Factors. Job Dissatisfaction | Contributing Factors. Interpersonal Conflict | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | Main Factor. Which of these was the main factor for leaving? | InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction | InstituteViews. Topic:2. I was given access to skills training to help me do my job better | InstituteViews. Topic:3. I was given adequate opportunities for personal development | InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% | InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had | InstituteViews. Topic:6. The organisation recognised when staff did good work | InstituteViews. Topic:7. Management was generally supportive of me | InstituteViews. Topic:8. Management was generally supportive of my team | InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me | InstituteViews. Topic:10. Staff morale was positive within the Institute | InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly | InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently | InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly | WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit | WorkUnitViews. Topic:15. I worked well with my colleagues | WorkUnitViews. Topic:16. My job was challenging and interesting | WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work | WorkUnitViews. Topic:18. I had sufficient contact with other people in my job | WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job | WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job | WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] | WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job | WorkUnitViews. Topic:23. My job provided sufficient variety | WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job | WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction | WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance | WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area | WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date | WorkUnitViews. Topic:29. There was adequate communication between staff in my unit | WorkUnitViews. Topic:30. Staff morale was positive within my work unit | Induction. Did you undertake Workplace Induction? | InductionInfo. Topic:Did you undertake a Corporate Induction? | InductionInfo. Topic:Did you undertake a Institute Induction? | InductionInfo. Topic: Did you undertake Team Induction? | InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? | InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? | InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? | InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? | InductionInfo. On-line Topic:Did you undertake a Institute Induction? | InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? | InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? | InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] | InductionInfo. Induction Manual Topic: Did you undertake Team Induction? | Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Agree | Agree | Agree | Neutral | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Neutral | Agree | Agree | Yes | Yes | Yes | Yes | Face to Face | - | - | Face to Face | - | - | Face to Face | - | - | Yes | Yes | Yes | Yes | Yes | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Agree | Agree | Agree | Disagree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Agree | Agree | Strongly Agree | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | - | - | - | - | - | - | NONE | NaN | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Agree | Neutral | Neutral | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Yes | No | Yes | Yes | - | - | - | NaN | - | - | - | - | - | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | - | - | - | - | - | - | - | NaN | Agree | Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Yes | Yes | Yes | Yes | - | - | Induction Manual | Face to Face | - | - | Face to Face | - | - | Yes | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
# Check information in tafe survey
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: 203.0+ KB
After a look at both datasets we have some cleaning to do:
let's begin by removing replacing 'not stated' with na values.
# Replace 'not stated' values with na values.
dete_survey = pd.read_csv('dete_survey.csv', na_values='Not Stated')
Next on the agenda is removing those columns that we don't need.
# Remove redundent columns from dete survey data
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)
# Remove redundent columns from dete survey data
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis=1)
Now, lets ensure that the column names are the same in each dataset.
# Remove upper case, replace spaces with underscore.
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(" ", "_")
# Map old column headings in tafe survey with the new ones.
replace_dict = {
'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(mapper=replace_dict, axis=1)
#Show updated column names
tafe_survey_updated
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 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 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
697 | 6.350668e+17 | Barrier Reef Institute of TAFE | Delivery (teaching) | 2013.0 | Resignation | Career Move - Public Sector | - | - | - | - | - | - | - | - | - | - | - | Male | 51-55 | Temporary Full-time | Teacher (including LVT) | 1-2 | 1-2 |
698 | 6.350677e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2013.0 | Resignation | Career Move - Public Sector | - | - | - | - | - | - | - | - | - | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
699 | 6.350704e+17 | Tropical North Institute of TAFE | Delivery (teaching) | 2013.0 | Resignation | - | - | - | - | - | - | - | - | - | - | Other | - | Female | 51-55 | Permanent Full-time | Teacher (including LVT) | 5-6 | 1-2 |
700 | 6.350712e+17 | Southbank Institute of Technology | Non-Delivery (corporate) | 2013.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Female | 41 45 | Temporary Full-time | Professional Officer (PO) | 1-2 | 1-2 |
701 | 6.350730e+17 | Tropical North Institute of TAFE | Non-Delivery (corporate) | 2013.0 | Resignation | - | - | Career Move - Self-employment | - | - | - | - | - | - | Travel | - | - | Female | 26 30 | Contract/casual | Administration (AO) | 3-4 | 1-2 |
702 rows × 23 columns
Let's have a look at what seperation types we are dealing with.
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
We have lot's of different seperation types. In the dete survey we will keep all rows that have the three resignation values and in tafe survey it is easier and we will just keep the rows that have resignation in the seperation type column.
# Only include ex employees that put resignation as their seperation type.
tafe_resignations = tafe_survey_updated[tafe_survey_updated["separationtype"]
== "Resignation"]
# Only include ex employees that put a resignation type as their seperation type.
dete_resignations = dete_survey_updated[(dete_survey_updated["separationtype"] == "Resignation-Other reasons") |
(dete_survey_updated["separationtype"] == "Resignation-Other employer") |
(dete_survey_updated["separationtype"] == "Resignation-Move overseas/interstate")]
Now our first question refers to the length that employees have been with the company. This is simple with the tafe survey because there is a length of service column. However, we do not have one for the dete survey. In order to make one we need to substract the start date from the end date for each employee. Let's begin by isolating the year that each employee left in the 'cease date' column.
# Isolate the year in the cease date column.
dete_resignation_copy = dete_resignations.copy()
dete_resignation_copy["cease_date"] = dete_resignation_copy["cease_date"].str.extract("\d?\d?/?(\d{4})")
dete_resignations = dete_resignation_copy
dete_resignations["cease_date"] = dete_resignations["cease_date"].astype(float)
# Check cease dates.
dete_resignations["cease_date"].value_counts().sort_index(ascending=False)
2014.0 22 2013.0 146 2012.0 129 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
# Check end dates.
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
# check end dates.
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
All of these dates seem reasonable so we can proceed.
I will now create a new column titled 'institute_service' for how long they worked for the institution.
# Create new column
dete_resignations["institute_service"] = dete_resignations["cease_date"] - dete_resignations["dete_start_date"]
Each of our datasets contain multiple columns which identify dissatisfaction.
The Tafe survey has 2 columns :
The Dete survey has lot's:
To decide which employees were dissatsified when they resigned we will define anyone who replied to one of these questions with true.
# Check values in tafe survey data for job dissatisfaction.
tafe_resignations["Contributing Factors. Job Dissatisfaction"].value_counts()
- 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
# Check values in dete survey data for job dissatisfaction.
dete_resignations['job_dissatisfaction'].value_counts()
False 270 True 41 Name: job_dissatisfaction, dtype: int64
Currently the tafe survey data regarding dissatsfaction is not in the boolean true/false format that we need (The dete survey data is fine). Let's change '-' to False and 'Job Dissatisfaction' to True.
# Create function to clean dissatisfaction column
tafe_resignations_copied = tafe_resignations.copy()
def update_vals(element):
import numpy as np
if element == np.nan:
return np.nan
elif element == "-":
return False
else:
return True
# Apply function to column
tafe_resignations_copied[["Contributing Factors. Dissatisfaction", "Contributing Factors. Job Dissatisfaction"]] = tafe_resignations_copied[["Contributing Factors. Dissatisfaction", "Contributing Factors. Job Dissatisfaction"]].applymap(update_vals)
# Check the column has been cleaned
tafe_resignations_copied["Contributing Factors. Dissatisfaction"].value_counts()
tafe_resignations_copied["Contributing Factors. Job Dissatisfaction"].value_counts()
False 270 True 70 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
There we go. Our tafe survey dissatisfaction columns are now in the correct format.
Next I am going to add a new column "dissatisfied". This column will give a True value if any of the dissatisfaction columns have an answer of True and will have a value of False if none of the dissatisfaction columns have a value of True.
# Create new dissatisfied column
tafe_resignations = tafe_resignations_copied
any_dataframe = tafe_resignations[["Contributing Factors. Dissatisfaction", 'Contributing Factors. Job Dissatisfaction']]
tafe_resignations["dissatisfied"] = any_dataframe.any(axis=1, skipna=False)
# Check new column
tafe_resignations["dissatisfied"].value_counts(dropna=False)
False 241 True 99 Name: dissatisfied, dtype: int64
As you can see above we now have a new column in the tafe survey data titled dissatisfied. From the value counts we can see that 99 people are categorized as unsatisfied.
Now let's do the same with the dete survey data.
# Create dissatisfied column
any_dataframe_1 = dete_resignations[["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", "work_life_balance",
"workload"]]
dete_resignations["dissatisfied"] = any_dataframe_1.any(axis=1, skipna=False)
# Check new column
dete_resignations["dissatisfied"].value_counts()
True 204 False 107 Name: dissatisfied, dtype: int64
Excellent we have a dissatisfied column in the dete survey and this one has 204 dissatisfied former employees.
Now we are going to merge the two datasets together.
Before doing that let's just add a column to each dataset so that we can identify which dataset each row came from.
# Add column to identify each dataset
tafe_resignations["institute"] = 'TAFE'
dete_resignations['institute'] = 'DETE'
Great, now we are ready to combine the 2 datasets.
I will also remove the rows that have less than 500 no null values. After our cleaning we only need these columns.
# Combine datasets
combined = pd.concat([dete_resignations, tafe_resignations], ignore_index=True, sort=False)
#Remove columns with less than 500 non null values
combined_updated = combined.dropna(thresh=500, axis=1).copy()
# Check combined dataset
combined_updated.head(5)
id | separationtype | cease_date | position | employment_status | gender | age | institute_service | dissatisfied | institute | |
---|---|---|---|---|---|---|---|---|---|---|
0 | 4.0 | Resignation-Other reasons | 2012.0 | Teacher | Permanent Full-time | Female | 36-40 | 7 | True | DETE |
1 | 6.0 | Resignation-Other reasons | 2012.0 | Guidance Officer | Permanent Full-time | Female | 41-45 | 18 | True | DETE |
2 | 9.0 | Resignation-Other reasons | 2012.0 | Teacher | Permanent Full-time | Female | 31-35 | 3 | True | DETE |
3 | 10.0 | Resignation-Other employer | 2012.0 | Teacher Aide | Permanent Part-time | Female | 46-50 | 15 | True | DETE |
4 | 12.0 | Resignation-Move overseas/interstate | 2012.0 | Teacher | Permanent Full-time | Male | 31-35 | 3 | False | DETE |
Excellent, we now have all our data in one easy to visualize dataset. We still have a little bit more cleaning to do in order to answer our questions as shown below:
# Show values in institute service column.
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 2.0 14 9.0 14 7.0 13 More than 20 years 10 13.0 8 8.0 8 15.0 7 20.0 7 14.0 6 12.0 6 17.0 6 22.0 6 10.0 6 16.0 5 18.0 5 11.0 4 23.0 4 24.0 4 19.0 3 32.0 3 39.0 3 21.0 3 28.0 2 30.0 2 26.0 2 36.0 2 25.0 2 29.0 1 31.0 1 27.0 1 34.0 1 35.0 1 38.0 1 41.0 1 42.0 1 49.0 1 33.0 1 Name: institute_service, dtype: int64
We need to make sure that all of these values are in the same format. Let's rename those values that aren't single number floats. We will later be grouping thse values into 4 categories so we can just take the first number in rages like 11-20 and it will not affect our data.
# Convert all data types to floats
combined_updated['institute_service_up'] = combined_updated['institute_service'].astype('str').str.extract(r'(\d+)')
combined_updated['institute_service_up'] = combined_updated['institute_service_up'].astype('float')
# Check new values
combined_updated["institute_service_up"].value_counts()
1.0 159 3.0 83 5.0 56 7.0 34 11.0 30 0.0 20 20.0 17 6.0 17 4.0 16 9.0 14 2.0 14 13.0 8 8.0 8 15.0 7 17.0 6 10.0 6 12.0 6 14.0 6 22.0 6 16.0 5 18.0 5 24.0 4 23.0 4 39.0 3 19.0 3 21.0 3 32.0 3 28.0 2 36.0 2 25.0 2 30.0 2 26.0 2 29.0 1 38.0 1 42.0 1 27.0 1 41.0 1 35.0 1 49.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service_up, dtype: int64
As mentioned before we are going to group our values into four categories. The categories we will group them into are as follows:
We will add a new column titled 'service_cat' for this categorized data.
# Create function to categorize length of service
import numpy as np
def career(val):
if val < 3:
return "New"
elif val < 6:
return "Experienced"
elif val < 10:
return "Established"
elif pd.isnull(val):
return np.nan
else:
return "Veteran"
# Add new column with categorized data
combined_updated["service_cat"] = combined_updated["institute_service_up"].apply(career)
# Check new Column
combined_updated["service_cat"].value_counts()
New 193 Experienced 155 Veteran 142 Established 73 Name: service_cat, dtype: int64
Everything is now set to answer question number 1.
We also need to do the same to our age column in order to answer question number 2. In this case we will group the ages into roughly 5 year blocks and create a new column titles 'age brackets'.
# Check values in age column
combined_updated["age"].value_counts()
51-55 71 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 21-25 29 31-35 29 56 or older 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
# replace white space with dash
combined_updated['age'] = combined_updated["age"].astype(str).str.replace(" ", "-")
# Create function to clean age column
def ages(val):
if val == '56-60' or val == "61 or older":
return "56 or older"
elif val == 'nan':
return np.nan
else:
return val
# Create new column with cleaned age values
combined_updated["age_brackets"] = combined_updated["age"].apply(ages)
# Drop na values from new column
combined_updated["age_brackets"].dropna
<bound method Series.dropna of 0 36-40 1 41-45 2 31-35 3 46-50 4 31-35 ... 646 21-25 647 51-55 648 NaN 649 51-55 650 26-30 Name: age_brackets, Length: 651, dtype: object>
# Check values in age brackets column
combined_updated["age_brackets"].value_counts()
41-45 93 46-50 81 56 or older 78 36-40 73 51-55 71 26-30 67 21-25 62 31-35 61 20 or younger 10 Name: age_brackets, dtype: int64
We are now set to answer both questions.
I will start by creating a pivto table to show the dissatisfaction rate for resignations of our service categories.
# Create pivot table
combined_pivot = combined_updated.pivot_table(values="dissatisfied", index="service_cat")
combined_pivot
dissatisfied | |
---|---|
service_cat | |
Established | 0.698630 |
Experienced | 0.354839 |
New | 0.357513 |
Veteran | 0.605634 |
I order to make this easier to visualize we will add a bar chart below:
# Create bar chart
%matplotlib inline
ax = combined_pivot.plot(kind='bar', legend=False)
ax.set_ylabel("Share of resignations dissatisfied")
ax.set_xlabel("Seniority")
ax.set_title("Does tenure affect dissatisfaction?")
Text(0.5, 1.0, 'Does tenure affect dissatisfaction?')
As we can see from the bar chart the established and veteran employeed are almost twice likely to resign due to dissatisfaction as the new and experienced employees.
Now let's make a pivot chart and bar chart to answer question number 2:
# Create pivot table
age_pivot = combined_updated.pivot_table(values='dissatisfied', index='age_brackets')
age_pivot
dissatisfied | |
---|---|
age_brackets | |
20 or younger | 0.200000 |
21-25 | 0.435484 |
26-30 | 0.492537 |
31-35 | 0.426230 |
36-40 | 0.506849 |
41-45 | 0.483871 |
46-50 | 0.506173 |
51-55 | 0.478873 |
56 or older | 0.448718 |
# Create bar chart
ax = age_pivot.plot(kind='bar', legend=False)
ax.set_title("Does age affect dissatsifaction?")
ax.set_xlabel("Age")
ax.set_ylabel("Share of employees dissatisfied")
Text(0, 0.5, 'Share of employees dissatisfied')
All ages have roughly the same dissatisfaction rate except for the 20 or younger employees which is much lower.
A reminder that the two questions we are answering:
1- Are employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been there longer?
2- Are younger employees resigning due to some kind of dissatisfaction? What about older employees?
In answer to question number 1 there is a definite split between the long serving members of staff and the less experienced staff. The 'New' and 'Experienced' groups both have a dissatisfaction rate between 30% and 40%. Meanwhile, the 'Established' and 'Veteran' groups both have a much higher dissatisfaction rate between 60% and 70%. We can therefore conclude that the longer employees stay at the TAFE and DETE institues the more likely they are to resign due to resignation.
For question 2 there is only one outlier in the different ages. Resignations of people who were 20 or below had a roughly 20% dissatisfaction rate whilst all other age groups were between 40% and 50%. This could be realted to question number 1 because the 20 and below employees have much less time to grow dissatisfied. However, we only have a sample size of 10 for employees who were less than 20 so it is dangerous to read too much into this outlier. On the whole age has little affect on the likelihood of resigning due to dissatisfaction.