This project aims to first clean exit survey data from the Department of Education, Training, and Employement (DETE) as well as the Technical and Further Education (TAFE) institute. From there, the data will be combined and factors affecting job dissatisfaction will be assessed in order to draw conclusions on what the most important factors were according to the exit surveys.
import pandas as pd
import numpy as np
dete_survey = pd.read_csv('dete_survey.csv', na_values = 'Not Stated') ## this reads 'Not Stated'
## values in as NaN
tafe_survey = pd.read_csv('tafe_survey.csv')
pd.options.display.max_columns = 150
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 788 non-null object 3 DETE Start Date 749 non-null float64 4 Role Start Date 724 non-null float64 5 Position 817 non-null object 6 Classification 455 non-null object 7 Region 717 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), float64(2), int64(1), object(35) memory usage: 258.6+ KB
dete_survey.head()
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | Career move to public sector | Career move to private sector | Interpersonal conflicts | Job dissatisfaction | Dissatisfaction with the department | Physical work environment | Lack of recognition | Lack of job security | Work location | Employment conditions | Maternity/family | Relocation | Study/Travel | Ill Health | Traumatic incident | Work life balance | Workload | None of the above | Professional Development | Opportunities for promotion | Staff morale | Workplace issue | Physical environment | Worklife balance | Stress and pressure support | Performance of supervisor | Peer support | Initiative | Skills | Coach | Career Aspirations | Feedback | Further PD | Communication | My say | Information | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | True | False | False | True | False | False | True | False | False | False | False | False | False | False | False | False | False | True | A | A | N | N | N | A | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | A | N | N | N | N | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | N | N | N | N | N | N | N | N | N | N | N | N | N | N | N | A | A | N | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | N | N | N | A | A | N | N | A | A | A | A | A | A | A | A | A | A | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | A | A | N | N | D | D | N | A | A | A | A | A | A | SA | SA | D | D | A | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
print(dete_survey['SeparationType'].value_counts())
## ↓↓↓↓ Other columns of interest:
# print(dete_survey['DETE Start Date'].value_counts())
# print('\n')
# print(dete_survey['Role Start Date'].value_counts())
# print('\n')
# print(dete_survey['Age'].value_counts())
# print('\n')
# print(dete_survey['Cease Date'].value_counts())
# print('\n')
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
print(tafe_survey.info())
## ↓↓↓↓ Other columns of interest:
# print(tafe_survey['CurrentAge. Current Age'].value_counts())
# print('\n')
# print(tafe_survey['CESSATION YEAR'].value_counts())
# print('\n')
# print(tafe_survey['LengthofServiceCurrent. Length of Service at current workplace (in years)'].value_counts())
# print('\n')
# print(tafe_survey['LengthofServiceOverall. Overall Length of Service at Institute (in years)'].value_counts())
# print('\n')
# print(tafe_survey['Reason for ceasing employment'].value_counts())
# print('\n')
# tafe_survey.isnull()
<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 None
tafe_survey.head()
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | Contributing Factors. Dissatisfaction | Contributing Factors. Job Dissatisfaction | Contributing Factors. Interpersonal Conflict | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | Main Factor. Which of these was the main factor for leaving? | InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction | InstituteViews. Topic:2. I was given access to skills training to help me do my job better | InstituteViews. Topic:3. I was given adequate opportunities for personal development | InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% | InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had | InstituteViews. Topic:6. The organisation recognised when staff did good work | InstituteViews. Topic:7. Management was generally supportive of me | InstituteViews. Topic:8. Management was generally supportive of my team | InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me | InstituteViews. Topic:10. Staff morale was positive within the Institute | InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly | InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently | InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly | WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit | WorkUnitViews. Topic:15. I worked well with my colleagues | WorkUnitViews. Topic:16. My job was challenging and interesting | WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work | WorkUnitViews. Topic:18. I had sufficient contact with other people in my job | WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job | WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job | WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] | WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job | WorkUnitViews. Topic:23. My job provided sufficient variety | WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job | WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction | WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance | WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area | WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date | WorkUnitViews. Topic:29. There was adequate communication between staff in my unit | WorkUnitViews. Topic:30. Staff morale was positive within my work unit | Induction. Did you undertake Workplace Induction? | InductionInfo. Topic:Did you undertake a Corporate Induction? | InductionInfo. Topic:Did you undertake a Institute Induction? | InductionInfo. Topic: Did you undertake Team Induction? | InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? | InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? | InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? | InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? | InductionInfo. On-line Topic:Did you undertake a Institute Induction? | InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? | InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? | InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] | InductionInfo. Induction Manual Topic: Did you undertake Team Induction? | Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Agree | Agree | Agree | Neutral | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Neutral | Agree | Agree | Yes | Yes | Yes | Yes | Face to Face | - | - | Face to Face | - | - | Face to Face | - | - | Yes | Yes | Yes | Yes | Yes | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Agree | Agree | Agree | Disagree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Agree | Agree | Strongly Agree | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | - | - | - | - | - | - | NONE | NaN | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Agree | Neutral | Neutral | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Yes | No | Yes | Yes | - | - | - | NaN | - | - | - | - | - | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | - | - | - | - | - | - | - | NaN | Agree | Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Yes | Yes | Yes | Yes | - | - | Induction Manual | Face to Face | - | - | Face to Face | - | - | Yes | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
dete_drop = dete_survey.iloc[:,28:49]
dete_survey_updated = dete_survey.drop(dete_drop,axis=1)
dete_survey_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 35 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 822 non-null int64 1 SeparationType 822 non-null object 2 Cease Date 788 non-null object 3 DETE Start Date 749 non-null float64 4 Role Start Date 724 non-null float64 5 Position 817 non-null object 6 Classification 455 non-null object 7 Region 717 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 Gender 798 non-null object 29 Age 811 non-null object 30 Aboriginal 16 non-null object 31 Torres Strait 3 non-null object 32 South Sea 7 non-null object 33 Disability 23 non-null object 34 NESB 32 non-null object dtypes: bool(18), float64(2), int64(1), object(14) memory usage: 123.7+ KB
tafe_drop = tafe_survey.iloc[:,17:66]
tafe_survey_updated = tafe_survey.drop(tafe_drop,axis=1)
tafe_drop.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 49 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Main Factor. Which of these was the main factor for leaving? 113 non-null object 1 InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object 2 InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object 3 InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object 4 InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object 5 InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object 6 InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object 7 InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object 8 InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object 9 InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object 10 InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object 11 InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object 12 InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object 13 InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object 14 WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object 15 WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object 16 WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object 17 WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object 18 WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object 19 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 20 WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object 21 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 22 WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object 23 WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object 24 WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object 25 WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object 26 WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object 27 WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object 28 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 29 WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object 30 WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object 31 Induction. Did you undertake Workplace Induction? 619 non-null object 32 InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object 33 InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object 34 InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object 35 InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 36 InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 37 InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object 38 InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object 39 InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object 40 InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object 41 InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object 42 InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object 43 InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object 44 Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object 45 Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object 46 Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object 47 Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object 48 Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object dtypes: object(49) memory usage: 268.9+ KB
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')
tafe_survey_updated.head()
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | Contributing Factors. Dissatisfaction | Contributing Factors. Job Dissatisfaction | Contributing Factors. Interpersonal Conflict | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | 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 | 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 |
dete_survey_updated.columns = dete_survey_updated.columns.str.replace('/','_').str.replace(' ','_').str.lower()
dete_survey_updated.head()
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | career_move_to_public_sector | career_move_to_private_sector | interpersonal_conflicts | job_dissatisfaction | dissatisfaction_with_the_department | physical_work_environment | lack_of_recognition | lack_of_job_security | work_location | employment_conditions | maternity_family | relocation | study_travel | ill_health | traumatic_incident | work_life_balance | workload | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | True | False | False | True | False | False | True | False | False | False | False | False | False | False | False | False | False | True | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
mapping_info = {'Record ID': 'id',
'CESSATION YEAR': 'cease_date',
'Reason for ceasing employment': 'separation_type',
'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_info,axis=1)
tafe_survey_updated.head()
id | Institute | WorkArea | cease_date | separation_type | 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 |
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['separation_type'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separation_type, dtype: int64
dete_survey_updated['separationtype'] = dete_survey_updated['separationtype'].str.split('-').str[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_resignation = dete_survey_updated[dete_survey_updated['separationtype'] == 'Resignation'].copy()
tafe_resignation = tafe_survey_updated[tafe_survey_updated['separation_type']=='Resignation'].copy
tafe_survey_updated = tafe_survey_updated.copy()[tafe_survey_updated['separation_type'] == 'Resignation']
tafe_resignations = tafe_survey_updated
For the separation type category (or column) in both surveys, only Resignation can be analyzed as a direct reason for dissatisfaction. Therefore, new data frames are created using only Resignation as the separation type. This should provide a clearer view of job dissatisfaction moving forward.
dete_resignation['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 05/2013 2 05/2012 2 2010 1 07/2012 1 07/2006 1 09/2010 1 Name: cease_date, dtype: int64
dete_resignation['cease_date'] = dete_resignation['cease_date'].str.split('/').str[-1]
dete_resignation['cease_date'].value_counts()
2013 146 2012 129 2014 22 2010 2 2006 1 Name: cease_date, dtype: int64
value_counts_dete = dete_resignation["cease_date"].astype(float).value_counts()
dete_resignation['cease_date'] = dete_resignation['cease_date'].astype(float)
print(value_counts_dete)
2013.0 146 2012.0 129 2014.0 22 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
value_counts_tafe = tafe_resignations["cease_date"].value_counts()
print(value_counts_tafe)
2011.0 116 2012.0 94 2010.0 68 2013.0 55 2009.0 2 Name: cease_date, dtype: int64
value_counts_dete.sort_index(ascending = False)
2014.0 22 2013.0 146 2012.0 129 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
value_counts_tafe.sort_index(ascending = False)
2013.0 55 2012.0 94 2011.0 116 2010.0 68 2009.0 2 Name: cease_date, dtype: int64
dete_resignation.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 311 entries, 3 to 821 Data columns (total 35 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 311 non-null int64 1 separationtype 311 non-null object 2 cease_date 300 non-null float64 3 dete_start_date 283 non-null float64 4 role_start_date 271 non-null float64 5 position 308 non-null object 6 classification 161 non-null object 7 region 265 non-null object 8 business_unit 32 non-null object 9 employment_status 307 non-null object 10 career_move_to_public_sector 311 non-null bool 11 career_move_to_private_sector 311 non-null bool 12 interpersonal_conflicts 311 non-null bool 13 job_dissatisfaction 311 non-null bool 14 dissatisfaction_with_the_department 311 non-null bool 15 physical_work_environment 311 non-null bool 16 lack_of_recognition 311 non-null bool 17 lack_of_job_security 311 non-null bool 18 work_location 311 non-null bool 19 employment_conditions 311 non-null bool 20 maternity_family 311 non-null bool 21 relocation 311 non-null bool 22 study_travel 311 non-null bool 23 ill_health 311 non-null bool 24 traumatic_incident 311 non-null bool 25 work_life_balance 311 non-null bool 26 workload 311 non-null bool 27 none_of_the_above 311 non-null bool 28 gender 302 non-null object 29 age 306 non-null object 30 aboriginal 7 non-null object 31 torres_strait 0 non-null object 32 south_sea 3 non-null object 33 disability 8 non-null object 34 nesb 9 non-null object dtypes: bool(18), float64(3), int64(1), object(13) memory usage: 49.2+ KB
%matplotlib inline
dete_resignation.boxplot(column = ['dete_start_date','role_start_date','cease_date'])
<matplotlib.axes._subplots.AxesSubplot at 0x7fba5673d3a0>
dete_resignation = dete_resignation[dete_resignation['role_start_date'] != 200.0]
dete_resignation['role_start_date'].value_counts()
dete_resignation.boxplot(column = ['dete_start_date','role_start_date','cease_date'])
<matplotlib.axes._subplots.AxesSubplot at 0x7fba54923f70>
dete_resignation = dete_resignation[(dete_resignation['cease_date'] != 2006.0) & (dete_resignation['cease_date'] != 2010.0)]
tafe_resignations = tafe_resignations[tafe_resignations['cease_date'] != 2009.0]
dete_resignation['cease_date'].value_counts()
2013.0 145 2012.0 129 2014.0 22 Name: cease_date, dtype: int64
tafe_resignations['cease_date'].value_counts()
2011.0 116 2012.0 94 2010.0 68 2013.0 55 Name: cease_date, dtype: int64
dete_resignation['institute_service'] = dete_resignation['cease_date'] - dete_resignation['dete_start_date']
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()
- 275 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()
- 268 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
tafe_resignations.head()
id | Institute | WorkArea | cease_date | separation_type | 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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
dete_resignation.head()
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | career_move_to_public_sector | career_move_to_private_sector | interpersonal_conflicts | job_dissatisfaction | dissatisfaction_with_the_department | physical_work_environment | lack_of_recognition | lack_of_job_security | work_location | employment_conditions | maternity_family | relocation | study_travel | ill_health | traumatic_incident | work_life_balance | workload | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | institute_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 4 | Resignation | 2012.0 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN | 7.0 |
5 | 6 | Resignation | 2012.0 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | True | False | False | False | False | False | False | False | True | True | False | False | False | False | False | False | False | Female | 41-45 | NaN | NaN | NaN | NaN | NaN | 18.0 |
8 | 9 | Resignation | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | Female | 31-35 | NaN | NaN | NaN | NaN | NaN | 3.0 |
9 | 10 | Resignation | 2012.0 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | False | False | True | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN | 15.0 |
11 | 12 | Resignation | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | True | True | False | False | False | False | False | False | Male | 31-35 | NaN | NaN | NaN | NaN | NaN | 3.0 |
def update_vals(val):
if pd.isnull(val):
return np.nan
elif val == '-':
return False
else:
return True
tafe_resignations.copy()['dissatisfied'] = tafe_resignations[['Contributing Factors. Dissatisfaction','Contributing Factors. Job Dissatisfaction']].applymap(update_vals).any(axis=1, skipna=False)
tafe_resignations_up = tafe_resignations
tafe_resignations['dissatisfied'].value_counts(dropna = False)
False 239 True 91 NaN 8 Name: dissatisfied, dtype: int64
dete_resignation['dissatisfied'] = dete_resignation[['job_dissatisfaction', 'dissatisfaction_with_the_department','lack_of_recognition','lack_of_job_security','work_location','employment_conditions','work_life_balance','workload']].any(axis=1,skipna=False)
dete_resignation['dissatisfied'] = dete_resignation[['job_dissatisfaction', 'dissatisfaction_with_the_department','lack_of_recognition','lack_of_job_security','work_location','employment_conditions','work_life_balance','workload']]
dete_resignation['dissatisfied'].isnull().sum() ###applymap is not necessary in this circumstance
0
dete_resignations_up = dete_resignation.copy()
dete_resignations_up['dissatisfied'].value_counts(dropna=False)
False 266 True 41 Name: dissatisfied, dtype: int64
job_dissatisfaction
,dissatisfaction_with_the_department
,lack_of_recognition
,lack_of_job_security
,work_location
,
employment_conditions
,work_life_balance
, and workload
. For the TAFE survey, those columns are: Contributing Factors. Dissatisfaction
and Contributing Factors. Job Dissatisfaction
dete_resignations_up.copy()['institute'] = 'DETE'
tafe_resignations_up.copy()['institute'] = 'TAFE'
combined = pd.concat([tafe_resignations_up,dete_resignations_up], ignore_index=True)
combined_updated = combined.dropna(axis=1,thresh=500)
combined_updated.head()
id | cease_date | gender | age | employment_status | position | institute_service | dissatisfied | |
---|---|---|---|---|---|---|---|---|
0 | 6.341399e+17 | 2010.0 | NaN | NaN | NaN | NaN | NaN | False |
1 | 6.341466e+17 | 2010.0 | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | False |
2 | 6.341475e+17 | 2010.0 | Female | 56 or older | Contract/casual | Teacher (including LVT) | 7-10 | False |
3 | 6.341520e+17 | 2010.0 | Male | 20 or younger | Temporary Full-time | Administration (AO) | 3-4 | False |
4 | 6.341537e+17 | 2010.0 | Male | 46 50 | Permanent Full-time | Teacher (including LVT) | 3-4 | False |
combined_updated['institute_service'].value_counts(dropna=False)
NaN 88 Less than 1 year 73 1-2 64 3-4 61 5-6 33 11-20 26 5.0 23 1.0 22 7-10 21 0.0 19 3.0 19 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 7 15.0 7 20.0 7 10.0 6 12.0 6 14.0 6 17.0 6 22.0 6 18.0 5 16.0 5 24.0 4 23.0 4 19.0 3 39.0 3 11.0 3 21.0 3 32.0 3 25.0 2 28.0 2 30.0 2 26.0 2 36.0 2 34.0 1 49.0 1 42.0 1 41.0 1 33.0 1 38.0 1 35.0 1 29.0 1 27.0 1 31.0 1 Name: institute_service, dtype: int64
combined_updated.copy()['institute_service_up'] = combined_updated['institute_service'].astype('str').str.extract(r"(\d+)")
combined_updated.copy()['institute_service_up'] = combined_updated['institute_service_up'].astype('float')
combined_updated.copy()['institute_service_up'].value_counts()
1.0 159 3.0 80 5.0 56 7.0 34 11.0 29 0.0 19 6.0 17 20.0 17 4.0 16 9.0 14 2.0 14 8.0 8 13.0 7 15.0 7 22.0 6 14.0 6 17.0 6 12.0 6 10.0 6 18.0 5 16.0 5 23.0 4 24.0 4 21.0 3 19.0 3 32.0 3 39.0 3 30.0 2 26.0 2 36.0 2 28.0 2 25.0 2 35.0 1 38.0 1 34.0 1 33.0 1 49.0 1 41.0 1 27.0 1 42.0 1 29.0 1 31.0 1 Name: institute_service_up, dtype: int64
After looking at the frequency for the amount of years using value_counts, the numbers needed to be extracted—either from strings or from ranges. Once those values were extracted, value_counts was used again to evaluate frequencies. While the ranges aren't averaged and the phrases before extraction don't give exact values, it doesn't necessarily mean the data will be misrepresented. For instance, "5-6" years changes to 5 years, however since 5-6 years fits into the 3-6 years category which will be created below, it makes no difference if the code written counts more values towards 5 years than 6 years. Likewise, with an expression like 'More than 20 years', if the threshold for "Veteran" is anyone who's served at the company longer than 11 years, than the code extracting "More than 20 years" as just 20 years, will ultimately be categorized correctly as more than 11 years.
def exp_range(val):
## if pd.isnull(val):
## return 'NaN'
if val < 3:
return "New: Less than 3 years"
elif 3 < val < 6:
return "Experienced: 3-6 years"
elif 7 < val < 10:
return "Established: 7-10 years"
else:
return "Veteran 11 or more years"
combined_updated.copy()['service_cat'] = combined_updated['institute_service_up'].apply(exp_range)
combined_updated.dropna(axis=0,subset=['service_cat'])
id | cease_date | gender | age | employment_status | position | institute_service | dissatisfied | institute_service_up | service_cat | |
---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341399e+17 | 2010.0 | NaN | NaN | NaN | NaN | NaN | False | NaN | Veteran 11 or more years |
1 | 6.341466e+17 | 2010.0 | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | False | 3.0 | Veteran 11 or more years |
2 | 6.341475e+17 | 2010.0 | Female | 56 or older | Contract/casual | Teacher (including LVT) | 7-10 | False | 7.0 | Veteran 11 or more years |
3 | 6.341520e+17 | 2010.0 | Male | 20 or younger | Temporary Full-time | Administration (AO) | 3-4 | False | 3.0 | Veteran 11 or more years |
4 | 6.341537e+17 | 2010.0 | Male | 46 50 | Permanent Full-time | Teacher (including LVT) | 3-4 | False | 3.0 | Veteran 11 or more years |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
640 | 8.100000e+02 | 2013.0 | Female | 26-30 | Permanent Part-time | Teacher Aide | 3 | False | 3.0 | Veteran 11 or more years |
641 | 8.170000e+02 | 2014.0 | Male | 21-25 | Permanent Full-time | Teacher | 2 | False | 2.0 | New: Less than 3 years |
642 | 8.180000e+02 | 2014.0 | Female | 21-25 | Permanent Full-time | Teacher | 2 | False | 2.0 | New: Less than 3 years |
643 | 8.210000e+02 | 2014.0 | Female | 31-35 | Permanent Full-time | Public Servant | 5 | False | 5.0 | Experienced: 3-6 years |
644 | 8.230000e+02 | 2013.0 | NaN | NaN | NaN | Teacher Aide | NaN | False | NaN | Veteran 11 or more years |
645 rows × 10 columns
combined_updated.copy()['dissatisfied'].value_counts(dropna = False)
False 505 True 132 NaN 8 Name: dissatisfied, dtype: int64
combined_updated.copy()['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
combined_updated.copy()['dissatisfied'].value_counts()
combined_updated.copy()['dissatisfied'] = combined_updated['dissatisfied'].astype(float)
service_cat_pv = combined_updated.pivot_table(values = 'dissatisfied',index = 'service_cat',margins=False)
print(service_cat_pv)
dissatisfied service_cat Established: 7-10 years 0.227273 Experienced: 3-6 years 0.180556 New: Less than 3 years 0.208333 Veteran 11 or more years 0.210826
import matplotlib.pyplot as plt
%matplotlib inline
service_cat_pv.plot(kind = 'bar', title = 'Average Dissatisfaction Based on time with company', xlim=(0,1), legend = False)
plt.ylabel('percentage dissatisfied')
Text(0, 0.5, 'percentage dissatisfied')
The following bar plot indicates all experience levels are dissatisfied within the range of about 18-23%. The most dissatisfied group—based on service time is the Established group (about 23%). The Veteran group (followed very closely by the New group) are second most dissatisfied (~21%). The least dissatisfied was the Experienced group (about 18%). This is perhaps because those who are established are looking to expand their skillset and didn't think they could do that at their current company.