This project aims cleaning and analyzing exit surveys to learn how different factors affect employee resignations.
To reach this goal, we wil try to reply for the following questions:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
dete_survey=pd.read_csv("dete_survey.csv")
tafe_survey=pd.read_csv("tafe_survey.csv")
pd.set_option('display.max_columns', None)
dete_survey
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 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
817 | 819 | Age Retirement | 02/2014 | 1977 | 1999 | Teacher | Primary | Central Queensland | NaN | Permanent Part-time | False | False | False | False | False | False | False | False | False | False | True | False | False | True | False | False | True | False | SA | N | D | D | A | N | N | D | A | N | A | A | N | SA | SA | N | D | A | A | A | SA | Female | 56-60 | NaN | NaN | NaN | NaN | NaN |
818 | 820 | Age Retirement | 01/2014 | 1980 | 1980 | Teacher | Secondary | North Coast | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | A | SA | D | D | D | A | A | N | A | N | A | A | N | A | N | N | A | A | N | N | N | Male | 51-55 | NaN | NaN | NaN | NaN | NaN |
819 | 821 | Resignation-Move overseas/interstate | 01/2014 | 2009 | 2009 | Public Servant | A01-A04 | Central Office | Education Queensland | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | True | True | False | False | False | True | False | False | A | A | A | A | A | D | N | A | A | A | A | A | A | A | A | A | A | A | A | N | A | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
820 | 822 | Ill Health Retirement | 12/2013 | 2001 | 2009 | Teacher | Secondary | Darling Downs South West | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | A | D | D | A | SD | SD | SD | A | D | SD | SD | D | A | A | N | N | N | SD | A | N | A | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
821 | 823 | Resignation-Move overseas/interstate | 12/2013 | Not Stated | Not Stated | Teacher Aide | NaN | Metropolitan | NaN | NaN | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
822 rows × 56 columns
dete_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 822 non-null int64 1 SeparationType 822 non-null object 2 Cease Date 822 non-null object 3 DETE Start Date 822 non-null object 4 Role Start Date 822 non-null object 5 Position 817 non-null object 6 Classification 455 non-null object 7 Region 822 non-null object 8 Business Unit 126 non-null object 9 Employment Status 817 non-null object 10 Career move to public sector 822 non-null bool 11 Career move to private sector 822 non-null bool 12 Interpersonal conflicts 822 non-null bool 13 Job dissatisfaction 822 non-null bool 14 Dissatisfaction with the department 822 non-null bool 15 Physical work environment 822 non-null bool 16 Lack of recognition 822 non-null bool 17 Lack of job security 822 non-null bool 18 Work location 822 non-null bool 19 Employment conditions 822 non-null bool 20 Maternity/family 822 non-null bool 21 Relocation 822 non-null bool 22 Study/Travel 822 non-null bool 23 Ill Health 822 non-null bool 24 Traumatic incident 822 non-null bool 25 Work life balance 822 non-null bool 26 Workload 822 non-null bool 27 None of the above 822 non-null bool 28 Professional Development 808 non-null object 29 Opportunities for promotion 735 non-null object 30 Staff morale 816 non-null object 31 Workplace issue 788 non-null object 32 Physical environment 817 non-null object 33 Worklife balance 815 non-null object 34 Stress and pressure support 810 non-null object 35 Performance of supervisor 813 non-null object 36 Peer support 812 non-null object 37 Initiative 813 non-null object 38 Skills 811 non-null object 39 Coach 767 non-null object 40 Career Aspirations 746 non-null object 41 Feedback 792 non-null object 42 Further PD 768 non-null object 43 Communication 814 non-null object 44 My say 812 non-null object 45 Information 816 non-null object 46 Kept informed 813 non-null object 47 Wellness programs 766 non-null object 48 Health & Safety 793 non-null object 49 Gender 798 non-null object 50 Age 811 non-null object 51 Aboriginal 16 non-null object 52 Torres Strait 3 non-null object 53 South Sea 7 non-null object 54 Disability 23 non-null object 55 NESB 32 non-null object dtypes: bool(18), int64(1), object(37) memory usage: 258.6+ KB
dete_survey.isnull().sum()
ID 0 SeparationType 0 Cease Date 0 DETE Start Date 0 Role Start Date 0 Position 5 Classification 367 Region 0 Business Unit 696 Employment Status 5 Career move to public sector 0 Career move to private sector 0 Interpersonal conflicts 0 Job dissatisfaction 0 Dissatisfaction with the department 0 Physical work environment 0 Lack of recognition 0 Lack of job security 0 Work location 0 Employment conditions 0 Maternity/family 0 Relocation 0 Study/Travel 0 Ill Health 0 Traumatic incident 0 Work life balance 0 Workload 0 None of the above 0 Professional Development 14 Opportunities for promotion 87 Staff morale 6 Workplace issue 34 Physical environment 5 Worklife balance 7 Stress and pressure support 12 Performance of supervisor 9 Peer support 10 Initiative 9 Skills 11 Coach 55 Career Aspirations 76 Feedback 30 Further PD 54 Communication 8 My say 10 Information 6 Kept informed 9 Wellness programs 56 Health & Safety 29 Gender 24 Age 11 Aboriginal 806 Torres Strait 819 South Sea 815 Disability 799 NESB 790 dtype: int64
tafe_survey
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 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
697 | 6.350668e+17 | Barrier Reef Institute of TAFE | Delivery (teaching) | 2013.0 | Resignation | Career Move - Public Sector | - | - | - | - | - | - | - | - | - | - | - | NaN | Neutral | Agree | Agree | Neutral | Disagree | Neutral | Agree | Agree | Agree | Disagree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Neutral | Neutral | Neutral | Agree | Agree | Neutral | Neutral | Agree | Neutral | Neutral | Neutral | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Yes | Yes | Yes | Yes | Yes | 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 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
699 | 6.350704e+17 | Tropical North Institute of TAFE | Delivery (teaching) | 2013.0 | Resignation | - | - | - | - | - | - | - | - | - | - | Other | - | NaN | Agree | Strongly Agree | Strongly Agree | Neutral | Agree | Agree | Agree | Agree | Agree | Neutral | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Yes | No | Yes | Yes | - | - | - | - | - | Induction Manual | Face to Face | - | - | Yes | Yes | Yes | Yes | Yes | 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 | NaN | Strongly Agree | Strongly Disagree | Strongly Disagree | Strongly Disagree | Disagree | Agree | Neutral | Neutral | Strongly Agree | Agree | Strongly Disagree | Strongly Disagree | Strongly Disagree | Strongly Disagree | Agree | Strongly Agree | Neutral | Agree | Disagree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Disagree | Strongly Disagree | Disagree | Agree | Strongly Agree | Agree | Neutral | Yes | No | Yes | Yes | - | On-line | - | Face to Face | - | - | Face to Face | - | - | No | No | No | Yes | No | 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 | - | - | Career Move - Self-employment | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Neutral | Strongly Agree | Strongly Agree | Agree | Disagree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Neutral | Strongly Agree | Strongly Agree | Yes | Yes | Yes | Yes | - | - | - | - | - | - | - | - | - | Yes | Yes | Yes | Yes | Yes | Female | 26 30 | Contract/casual | Administration (AO) | 3-4 | 1-2 |
702 rows × 72 columns
tafe_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 72 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Record ID 702 non-null float64 1 Institute 702 non-null object 2 WorkArea 702 non-null object 3 CESSATION YEAR 695 non-null float64 4 Reason for ceasing employment 701 non-null object 5 Contributing Factors. Career Move - Public Sector 437 non-null object 6 Contributing Factors. Career Move - Private Sector 437 non-null object 7 Contributing Factors. Career Move - Self-employment 437 non-null object 8 Contributing Factors. Ill Health 437 non-null object 9 Contributing Factors. Maternity/Family 437 non-null object 10 Contributing Factors. Dissatisfaction 437 non-null object 11 Contributing Factors. Job Dissatisfaction 437 non-null object 12 Contributing Factors. Interpersonal Conflict 437 non-null object 13 Contributing Factors. Study 437 non-null object 14 Contributing Factors. Travel 437 non-null object 15 Contributing Factors. Other 437 non-null object 16 Contributing Factors. NONE 437 non-null object 17 Main Factor. Which of these was the main factor for leaving? 113 non-null object 18 InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object 19 InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object 20 InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object 21 InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object 22 InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object 23 InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object 24 InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object 25 InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object 26 InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object 27 InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object 28 InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object 29 InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object 30 InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object 31 WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object 32 WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object 33 WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object 34 WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object 35 WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object 36 WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 609 non-null object 37 WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object 38 WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] 608 non-null object 39 WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object 40 WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object 41 WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object 42 WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object 43 WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object 44 WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object 45 WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 609 non-null object 46 WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object 47 WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object 48 Induction. Did you undertake Workplace Induction? 619 non-null object 49 InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object 50 InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object 51 InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object 52 InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 53 InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 54 InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object 55 InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object 56 InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object 57 InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object 58 InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object 59 InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object 60 InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object 61 Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object 62 Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object 63 Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object 64 Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object 65 Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object 66 Gender. What is your Gender? 596 non-null object 67 CurrentAge. Current Age 596 non-null object 68 Employment Type. Employment Type 596 non-null object 69 Classification. Classification 596 non-null object 70 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object 71 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(70) memory usage: 395.0+ KB
tafe_survey.isnull().sum().sort_values()
Record ID 0 Institute 0 WorkArea 0 Reason for ceasing employment 1 CESSATION YEAR 7 ... Contributing Factors. Career Move - Public Sector 265 Contributing Factors. Maternity/Family 265 Contributing Factors. Ill Health 265 InductionInfo. Topic:Did you undertake a Corporate Induction? 270 Main Factor. Which of these was the main factor for leaving? 589 Length: 72, dtype: int64
dataset dimension: 822 rows × 56 columns
columns with many missing values which we will need mandatory explore and try to understand the reason and if we have any solution:
Business Unit 696
Aboriginal 806
Torres Strait 819
South Sea 815
Disability 799
NESB 790
Then we have following column still with many missing values, although less than above ones:
Classification 367
Dates columns Cease Date
andDETE Start Date
contains value "Not Stated" - this kind of information must be replace by NaN in order to be list as null field.
dataset dimension: 702 rows × 72 columns
the columns with more missing values are the following:
Contributing Factors. Career Move - Public Sector 265
Contributing Factors. Maternity/Family 265
Contributing Factors. Ill Health 265
InductionInfo. Topic:Did you undertake a Corporate Induction? 270
Main Factor. Which of these was the main factor for leaving? 589
From the information collected, the dictionary for the main columns should be according to the following:
dete_survey:
ID
: An id used to identify the participant of the surveySeparationType
: Tason why the person's employment endedCease Date
: The year or month the person's employment endedDETE Start Date
: The year the person began employment with the DETEtafe_survey:
Record ID
: An id used to identify the participant of the surveyReason for ceasing employment:
The reason why the person's employment endedLengthofServiceOverall
. Overall Length of Service at Institute (in years)
: The length of the person's employment (in years)dete_survey=pd.read_csv("dete_survey.csv", na_values= "Not Stated")
dete_survey.head(5)
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | 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 |
Attending our main goal for this project, we can drop some columns which even contains interesting information, that information is not mandatory to answer to the questions of our project's goal.
Regarding DETE survey we will drop following columns because we consider all of them are referring to dissatisfaction reason. So, we will not see to them now.
dete_survey_updated= dete_survey.drop(dete_survey.columns[28:49], axis=1)
The new columns in our dete dataset are the following
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
Regarding tafe_survey we decided to drop following columns, which we considered that are detailed reason of dissatisfaction:
tafe_survey_updated= tafe_survey.drop(tafe_survey.columns[17:66], axis=1)
tafe_survey_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 23 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 Gender. What is your Gender? 596 non-null object 18 CurrentAge. Current Age 596 non-null object 19 Employment Type. Employment Type 596 non-null object 20 Classification. Classification 596 non-null object 21 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object 22 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(21) memory usage: 126.3+ KB
IMPORTANT:
Following dropping above columns we have smaller dataset, with what will be much more easier to check the common columns and to give to those columns the same name. New datasets dimension:
Regarding dete_survey_updated, we will use the following criteria to update the column names:
dete_survey_updated.columns=dete_survey_updated.columns.str.lower().str.strip().str.replace("\s+","_", regex=True)
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')
Regarding tafe_survey_updated, we will use the same criteria as before to update the column names, and additionally we will check columns one by one and check if we we can immediately to correspond it to the same name that we we have already in dete_survey_updated.
tafe_survey_updated.columns
Index(['Record ID', 'Institute', 'WorkArea', 'CESSATION YEAR', 'Reason for ceasing employment', 'Contributing Factors. Career Move - Public Sector ', 'Contributing Factors. Career Move - Private Sector ', 'Contributing Factors. Career Move - Self-employment', 'Contributing Factors. Ill Health', 'Contributing Factors. Maternity/Family', 'Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Interpersonal Conflict', 'Contributing Factors. Study', 'Contributing Factors. Travel', 'Contributing Factors. Other', 'Contributing Factors. NONE', 'Gender. What is your Gender?', 'CurrentAge. Current Age', 'Employment Type. Employment Type', 'Classification. Classification', 'LengthofServiceOverall. Overall Length of Service at Institute (in years)', 'LengthofServiceCurrent. Length of Service at current workplace (in years)'], dtype='object')
tafe_columns | common_column_with_dete | not_common_column | |
---|---|---|---|
'Record ID', | id | ||
'Institute', | business_unit | ||
'WorkArea', | workarea | ||
'CESSATION YEAR', | cease_date | ||
'Reason for ceasing employment', | separationtype | ||
'Contributing Factors. Career Move - Public Sector ', | career_move_to_public_sector' | ||
'Contributing Factors. Career Move - Private Sector ', | career_move_to_private_sector | ||
'Contributing Factors. Career Move - Self-employment', | self_employment | ||
'Contributing Factors. Ill Health', | ill_health | ||
'Contributing Factors. Maternity/Family', | maternity/family | ||
'Contributing Factors. Dissatisfaction', | dissatisfaction | ||
'Contributing Factors. Job Dissatisfaction', | job_dissatisfaction | ||
'Contributing Factors. Interpersonal Conflict', | interpersonal_conflict | ||
'Contributing Factors. Study', | ~study/travel | ||
'Contributing Factors. Travel', | ~study/travel | ||
'Contributing Factors. Other', | other | ||
'Contributing Factors. NONE', | none_of_the_above | ||
'Gender. What is your Gender?', | gender | ||
'CurrentAge. Current Age', | age | ||
'Employment Type. Employment Type' | employment_status | ||
'Classification. 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 |
columns_rename= {'Record ID':"id",
'Institute': "business_unit",
'WorkArea':"workarea",
'CESSATION YEAR':"cease_date",
'Reason for ceasing employment':"separationtype",
'Contributing Factors. Career Move - Public Sector ':"career_move_to_public_sector",
'Contributing Factors. Career Move - Private Sector ':"career_move_to_private_sector",
'Contributing Factors. Career Move - Self-employment':"self_employment",
'Contributing Factors. Ill Health':"ill_health",
'Contributing Factors. Maternity/Family':"maternity/family",
'Contributing Factors. Dissatisfaction':"dissatisfaction",
'Contributing Factors. Job Dissatisfaction':"job_dissatisfaction",
'Contributing Factors. Interpersonal Conflict':"interpersonal_conflict",
'Contributing Factors. Study':"study",
'Contributing Factors. Travel':"travel",
'Contributing Factors. Other':"other",
'Contributing Factors. NONE':"none_of_the_above",
'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.rename(columns_rename, axis=1, inplace=True)
tafe_survey_updated.columns
Index(['id', 'business_unit', 'workarea', 'cease_date', 'separationtype', 'career_move_to_public_sector', 'career_move_to_private_sector', 'self_employment', 'ill_health', 'maternity/family', 'dissatisfaction', 'job_dissatisfaction', 'interpersonal_conflict', 'study', 'travel', 'other', 'none_of_the_above', 'gender', 'age', 'employment_status', 'position', 'institute_service', 'role_service'], dtype='object')
In separationtype
we have other kind of reasons to leave from the job that were not by employees resignation. Attending our project goal, we will drop those reasons:
# checking the unique values in this column
dete_survey_updated["separationtype"].value_counts()
Age Retirement 285 Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
tafe_survey_updated["separationtype"].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
plt.figure(figsize=(30,12))
plt.subplot(1,2,1)
dete_survey_updated["separationtype"].value_counts().plot(kind='pie',
autopct='%1.0f%%',
startangle=55,
cmap='tab10',
explode=(0,0.2,0.2,0.2,0,0,0,0,0),
textprops={'fontsize': 24,'color':"w", "weight":"bold"},
shadow=True,
radius=0.9,
pctdistance=0.8,
title="DETE Separation type",
legend=True
)
plt.title('DETE Separation type', fontsize=40)
plt.legend(loc="upper left", fontsize= 24, bbox_to_anchor=(-0.5, 0, 1.5, 0))
plt.subplot(1,2,2)
tafe_survey_updated["separationtype"].value_counts().plot(kind='pie',
autopct='%1.0f%%',
startangle=55,
cmap='tab20b',
explode=(0.1,0,0,0,0,0),
legend=True,
shadow=True,
radius=0.9,
pctdistance=0.8,
label=False,
title="TAFE Separation type",
textprops={'fontsize': 24, "weight":"bold",'color':"w" },
)
plt.title('TAFE Separation type', fontsize=40)
plt.legend(loc="upper left", fontsize= 24, bbox_to_anchor=(0.5, 0, 1.5, 0))
plt.show()
Based on this results, we can see that the resignation has a big percentage among the reasons for employee exit.
# identifying columns which we want to keep for our analyze
DETE dataset:
dete_survey_updated["resignation"]=dete_survey_updated["separationtype"].str.contains(r'[Rr]esignation', regex=True)
dete_survey_updated["resignation"]
0 False 1 False 2 False 3 True 4 False ... 817 False 818 False 819 True 820 False 821 True Name: resignation, Length: 822, dtype: bool
tafe_survey_updated["resignation"]=tafe_survey_updated["separationtype"].str.contains(r'[Rr]esignation', regex=True)
tafe_survey_updated["resignation"]
0 False 1 False 2 False 3 True 4 True ... 697 True 698 True 699 True 700 False 701 True Name: resignation, Length: 702, dtype: object
# dropping rows with what we will not work
dete_resignation=dete_survey_updated.copy().set_index("resignation").drop(False)
dete_resignation
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
resignation | |||||||||||||||||||||||||||||||||||
True | 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 |
True | 6 | Resignation-Other reasons | 05/2012 | 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 |
True | 9 | Resignation-Other reasons | 07/2012 | 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 |
True | 10 | Resignation-Other employer | 2012 | 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 |
True | 12 | Resignation-Move overseas/interstate | 2012 | 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 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
True | 810 | Resignation-Other reasons | 12/2013 | 2010.0 | 2010.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | Female | 26-30 | NaN | NaN | NaN | NaN | NaN |
True | 817 | Resignation-Other employer | 01/2014 | 2012.0 | 2012.0 | Teacher | Primary | Far North Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | Male | 21-25 | NaN | NaN | NaN | NaN | NaN |
True | 818 | Resignation-Move overseas/interstate | 01/2014 | 2012.0 | 2012.0 | Teacher | Secondary | North Coast | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | Female | 21-25 | NaN | NaN | NaN | NaN | NaN |
True | 821 | Resignation-Move overseas/interstate | 01/2014 | 2009.0 | 2009.0 | Public Servant | A01-A04 | Central Office | Education Queensland | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | True | True | False | False | False | True | False | False | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
True | 823 | Resignation-Move overseas/interstate | 12/2013 | NaN | NaN | Teacher Aide | NaN | Metropolitan | NaN | NaN | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
311 rows × 35 columns
TAFE dataset:
tafe_resignation=tafe_survey_updated.copy().set_index("resignation").drop(False)
tafe_resignation
id | business_unit | workarea | cease_date | separationtype | career_move_to_public_sector | career_move_to_private_sector | self_employment | ill_health | maternity/family | dissatisfaction | job_dissatisfaction | interpersonal_conflict | study | travel | other | none_of_the_above | gender | age | employment_status | position | institute_service | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
resignation | |||||||||||||||||||||||
True | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
True | 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 |
True | 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 |
True | 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 |
True | 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 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
True | 6.350660e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2013.0 | Resignation | - | Career Move - Private Sector | - | - | - | - | - | - | - | - | - | - | Male | 21 25 | Temporary Full-time | Operational (OO) | 5-6 | 5-6 |
True | 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 |
True | 6.350677e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2013.0 | Resignation | Career Move - Public Sector | - | - | - | - | - | - | - | - | - | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
True | 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 |
True | 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 |
341 rows × 23 columns
IMPORTANT:
Following dropping above rows we have smaller datasets, with what will be much more easier to work: New datasets dimension:
Now we are in conditions to analyze the following 2 information in order we can reply to the two questions in this project objectives:
Tafe dataset:
Let's us check the tafe dataset. Our date columns are:
institute_service
- The length of the person's employment in this institute (in years) - split per years rangesrole_service
- The length of the person's employment in the current workplace (in years)cease_date
- The year or month the person's employment endedtafe_resignation["cease_date"].value_counts()
2011.0 117 2012.0 94 2010.0 68 2013.0 55 2009.0 2 Name: cease_date, dtype: int64
tafe_resignation["institute_service"].value_counts()
Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 7-10 21 More than 20 years 10 Name: institute_service, dtype: int64
tafe_resignation["role_service"].value_counts()
Less than 1 year 92 1-2 74 3-4 54 5-6 22 11-20 21 7-10 19 More than 20 years 8 Name: role_service, dtype: int64
DETE dataset:
In the dete we have:
cease_date
: The year or month the person's employment endeddete_start_date
: The year the person began employment with the DETErole_start_dat
: the year the person began the employment in the current workplaceFirst, if we only have the year that person began. We will disregard the month and put both columns at same format.
Then, we will create one column with the difference between these values, then present the values between years ranges which will correspond to the institute_service
and the role_service
of tafe dataset
# 1 - we will extract from the cease_date the year information:
dete_resignation["cease_date"].value_counts()
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 07/2013 9 11/2013 9 10/2013 6 08/2013 4 05/2012 2 05/2013 2 09/2010 1 07/2012 1 2010 1 07/2006 1 Name: cease_date, dtype: int64
dete_resignation["cease_date"]=dete_resignation["cease_date"].astype(str)
dete_resignation["cease_date"].dtype
dtype('O')
dete_resignation["cease_date"]=dete_resignation["cease_date"].str.extract(r"([1-2]{1}[0-9]{3})")
dete_resignation["cease_date"]
resignation True 2012 True 2012 True 2012 True 2012 True 2012 ... True 2013 True 2014 True 2014 True 2014 True 2013 Name: cease_date, Length: 311, dtype: object
dete_resignation["cease_date"].value_counts()
2013 146 2012 129 2014 22 2010 2 2006 1 Name: cease_date, dtype: int64
dete_resignation["cease_date"]=dete_resignation["cease_date"].astype("float")
dete_resignation["cease_date"].dtype
dtype('float64')
# 2 - we will check if dete_start_date at same format
dete_resignation["dete_start_date"].dtype
dtype('float64')
dete_resignation["dete_start_date"].value_counts()
2011.0 24 2008.0 22 2007.0 21 2012.0 21 2010.0 17 2005.0 15 2004.0 14 2006.0 13 2009.0 13 2013.0 10 2000.0 9 1999.0 8 2003.0 6 1998.0 6 1992.0 6 1994.0 6 1996.0 6 2002.0 6 1980.0 5 1997.0 5 1993.0 5 1990.0 5 1995.0 4 1989.0 4 1988.0 4 1991.0 4 1986.0 3 1985.0 3 2001.0 3 1976.0 2 1983.0 2 1974.0 2 1973.0 1 1971.0 1 1982.0 1 1987.0 1 1977.0 1 1963.0 1 1972.0 1 1984.0 1 1975.0 1 Name: dete_start_date, dtype: int64
dete_resignation["role_start_date"]
resignation True 2006.0 True 1997.0 True 2009.0 True 2008.0 True 2009.0 ... True 2010.0 True 2012.0 True 2012.0 True 2009.0 True NaN Name: role_start_date, Length: 311, dtype: float64
# 3 - we will calculate the period during what employee work at the institute and at current workplace:
dete_resignation["institute_service_exact_time"]=dete_resignation["cease_date"]-dete_resignation["dete_start_date"]
dete_resignation["institute_service_exact_time"].value_counts().sort_index()
0.0 20 1.0 22 2.0 14 3.0 20 4.0 16 5.0 23 6.0 17 7.0 13 8.0 8 9.0 14 10.0 6 11.0 4 12.0 6 13.0 8 14.0 6 15.0 7 16.0 5 17.0 6 18.0 5 19.0 3 20.0 7 21.0 3 22.0 6 23.0 4 24.0 4 25.0 2 26.0 2 27.0 1 28.0 2 29.0 1 30.0 2 31.0 1 32.0 3 33.0 1 34.0 1 35.0 1 36.0 2 38.0 1 39.0 3 41.0 1 42.0 1 49.0 1 Name: institute_service_exact_time, dtype: int64
dete_resignation.boxplot("institute_service_exact_time")
<AxesSubplot:>
dete_resignation["role_service_exact_time"]=dete_resignation["cease_date"]-dete_resignation["role_start_date"]
dete_resignation["role_service_exact_time"].value_counts().sort_index()
-1.0 1 0.0 34 1.0 38 2.0 28 3.0 27 4.0 18 5.0 21 6.0 14 7.0 9 8.0 7 9.0 10 10.0 5 11.0 7 12.0 1 13.0 1 14.0 7 15.0 4 16.0 4 17.0 2 18.0 2 19.0 1 20.0 4 21.0 2 22.0 2 23.0 3 24.0 4 25.0 1 26.0 1 27.0 1 30.0 1 32.0 1 36.0 2 1813.0 1 Name: role_service_exact_time, dtype: int64
dete_resignation.boxplot("role_service_exact_time")
<AxesSubplot:>
CONCLUSION: based ont this data reffering the role service in the dete, we should disregard "-1" and "1813".
# 4 - we will present the information in ranges of year
#if we just want split the data by random bins:
dete_resignation["institute_service_exact_time"].value_counts(bins=7).sort_index()
(-0.05, 7.0] 145 (7.0, 14.0] 52 (14.0, 21.0] 36 (21.0, 28.0] 21 (28.0, 35.0] 10 (35.0, 42.0] 8 (42.0, 49.0] 1 Name: institute_service_exact_time, dtype: int64
#if we just want split the data exactly in a specific bins:
bins=[(-1, 1), (1, 2), (2, 4), (4, 6), (6, 10), (10, 20), (20,49)]
index=pd.IntervalIndex.from_tuples(bins)
intervals=index.values
labels=["Less than 1 year", "1-2", "3-4", "5-6", "7-10", "11-20", "More than 20 years"]
to_name={interval:label for interval, label in zip(intervals,labels)}
dete_resignation["institute_service"]=pd.CategoricalIndex(pd.cut(dete_resignation["institute_service_exact_time"], bins=index)).rename_categories(to_name)
dete_resignation["institute_service"].value_counts().sort_index()
Less than 1 year 42 1-2 14 3-4 36 5-6 40 7-10 41 11-20 57 More than 20 years 43 Name: institute_service, dtype: int64
bins=[(-1, 1), (1, 2), (2, 4), (4, 6), (6, 10), (10, 20), (20,49)]
index=pd.IntervalIndex.from_tuples(bins)
intervals=index.values
labels=["Less than 1 year", "1-2", "3-4", "5-6", "7-10", "11-20", "More than 20 years"]
to_name={interval:label for interval, label in zip(intervals,labels)}
#dete_resignation["role_service"]=pd.cut(dete_resignation["role_service_exact_time"], bins=bins, labels=labels, ordered=True)
dete_resignation["role_service"]=pd.CategoricalIndex(pd.cut(dete_resignation["role_service_exact_time"], bins=index)).rename_categories(to_name)
dete_resignation["role_service"].value_counts().sort_index()
Less than 1 year 72 1-2 28 3-4 45 5-6 35 7-10 31 11-20 33 More than 20 years 18 Name: role_service, dtype: int64
The following columns are the columns that identified if employees resigned becauase dissatisfaction or not. This columns must only contains boolean (True, False, NaN). If the employee indicated any of these factors caused them to resign, we'll mark them as dissatisfied in a new column.
Tafe survey: The columns reffering to dissatisfaction are the following and are not with boolean description, yet:
Dete survey: The columns reffering to dissatisfaction are the following and are already with boolean description:
#rechecking content of each column:
tafe_resignation["dissatisfaction"].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: dissatisfaction, dtype: int64
tafe_resignation["job_dissatisfaction"].value_counts()
- 270 Job Dissatisfaction 62 Name: job_dissatisfaction, dtype: int64
#create a function to change the column content:
def label(element, x):
if element == x:
return True
elif element== "-":
return False
else:
return None
# convert the column content in boolean value:
tafe_resignation["dissatisfaction_bol"]=tafe_resignation["dissatisfaction"].apply(label, x="Contributing Factors. Dissatisfaction ")
tafe_resignation["dissatisfaction_bol"].value_counts(dropna=False)
False 277 True 55 NaN 9 Name: dissatisfaction_bol, dtype: int64
tafe_resignation["job_dissatisfaction_bol"]=tafe_resignation["job_dissatisfaction"].apply(label, x="Job Dissatisfaction")
tafe_resignation["job_dissatisfaction_bol"].value_counts(dropna=False)
False 270 True 62 NaN 9 Name: job_dissatisfaction_bol, dtype: int64
tafe survey:
# create a new column if with final classification:
tafe_resignation["dissatisfied"]=tafe_resignation[["dissatisfaction_bol","job_dissatisfaction_bol"]].any(axis=1, skipna=False)
tafe_resignation["dissatisfied"]. value_counts()
False 241 True 91 Name: dissatisfied, dtype: int64
dete survey:
dete_resignation["dissatisfied"]=dete_resignation[["job_dissatisfaction",
"dissatisfaction_with_the_department",
"physical_work_environment",
"lack_of_recognition",
"lack_of_job_security",
"work_location",
"employment_conditions",
"work_life_balance",
"workload"]].any(axis=1, skipna=False)
dete_resignation["dissatisfied"]. value_counts()
False 162 True 149 Name: dissatisfied, dtype: int64
First, let's add a column to each dataframe that will allow us to easily distinguish between the two.
dete_resignation["institute"]="DETE"
tafe_resignation["institute"]="TAFE"
combined=pd.concat([dete_resignation,tafe_resignation])
combined
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_exact_time | role_service_exact_time | institute_service | role_service | dissatisfied | institute | workarea | self_employment | dissatisfaction | interpersonal_conflict | study | travel | other | dissatisfaction_bol | job_dissatisfaction_bol | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
resignation | ||||||||||||||||||||||||||||||||||||||||||||||||||
True | 4.000000e+00 | Resignation-Other reasons | 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 | 6.0 | 7-10 | 5-6 | False | DETE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
True | 6.000000e+00 | Resignation-Other reasons | 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 | 15.0 | 11-20 | 11-20 | True | DETE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
True | 9.000000e+00 | Resignation-Other reasons | 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 | 3.0 | 3-4 | 3-4 | False | DETE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
True | 1.000000e+01 | Resignation-Other employer | 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 | 4.0 | 11-20 | 3-4 | True | DETE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
True | 1.200000e+01 | Resignation-Move overseas/interstate | 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 | 3.0 | 3-4 | 3-4 | False | DETE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
True | 6.350660e+17 | Resignation | 2013.0 | NaN | NaN | Operational (OO) | NaN | NaN | Southern Queensland Institute of TAFE | Temporary Full-time | - | Career Move - Private Sector | NaN | - | NaN | NaN | NaN | NaN | NaN | NaN | - | NaN | NaN | - | NaN | NaN | NaN | - | Male | 21 25 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5-6 | 5-6 | False | TAFE | Non-Delivery (corporate) | - | - | - | - | - | - | False | False |
True | 6.350668e+17 | Resignation | 2013.0 | NaN | NaN | Teacher (including LVT) | NaN | NaN | Barrier Reef Institute of TAFE | Temporary Full-time | Career Move - Public Sector | - | NaN | - | NaN | NaN | NaN | NaN | NaN | NaN | - | NaN | NaN | - | NaN | NaN | NaN | - | Male | 51-55 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1-2 | 1-2 | False | TAFE | Delivery (teaching) | - | - | - | - | - | - | False | False |
True | 6.350677e+17 | Resignation | 2013.0 | NaN | NaN | NaN | NaN | NaN | Southern Queensland Institute of TAFE | NaN | Career Move - Public Sector | - | NaN | - | NaN | NaN | NaN | NaN | NaN | NaN | - | NaN | NaN | - | NaN | NaN | NaN | - | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | TAFE | Non-Delivery (corporate) | - | - | - | - | - | - | False | False |
True | 6.350704e+17 | Resignation | 2013.0 | NaN | NaN | Teacher (including LVT) | NaN | NaN | Tropical North Institute of TAFE | Permanent Full-time | - | - | NaN | - | NaN | NaN | NaN | NaN | NaN | NaN | - | NaN | NaN | - | NaN | NaN | NaN | - | Female | 51-55 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5-6 | 1-2 | False | TAFE | Delivery (teaching) | - | - | - | - | - | Other | False | False |
True | 6.350730e+17 | Resignation | 2013.0 | NaN | NaN | Administration (AO) | NaN | NaN | Tropical North Institute of TAFE | Contract/casual | - | - | NaN | - | NaN | NaN | NaN | NaN | NaN | NaN | - | NaN | NaN | - | NaN | NaN | NaN | - | Female | 26 30 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3-4 | 1-2 | False | TAFE | Non-Delivery (corporate) | Career Move - Self-employment | - | - | - | Travel | - | False | False |
652 rows × 50 columns
combined.notnull().sum().sort_values()
torres_strait 0 south_sea 3 aboriginal 7 disability 8 nesb 9 classification 161 role_service_exact_time 264 region 265 role_start_date 271 institute_service_exact_time 273 dete_start_date 283 work_life_balance 311 employment_conditions 311 study/travel 311 relocation 311 workload 311 work_location 311 traumatic_incident 311 lack_of_recognition 311 physical_work_environment 311 interpersonal_conflicts 311 lack_of_job_security 311 dissatisfaction_with_the_department 311 self_employment 332 dissatisfaction 332 interpersonal_conflict 332 study 332 travel 332 other 332 job_dissatisfaction_bol 332 dissatisfaction_bol 332 workarea 341 business_unit 373 role_service 552 institute_service 563 gender 592 age 596 employment_status 597 position 598 cease_date 636 career_move_to_public_sector 643 dissatisfied 643 career_move_to_private_sector 643 job_dissatisfaction 643 maternity/family 643 ill_health 643 none_of_the_above 643 separationtype 651 institute 652 id 652 dtype: int64
combined_updated=combined.copy().dropna(axis=1, thresh=500)
combined_updated
id | separationtype | cease_date | position | employment_status | career_move_to_public_sector | career_move_to_private_sector | job_dissatisfaction | maternity/family | ill_health | none_of_the_above | gender | age | institute_service | role_service | dissatisfied | institute | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
resignation | |||||||||||||||||
True | 4.000000e+00 | Resignation-Other reasons | 2012.0 | Teacher | Permanent Full-time | False | True | False | False | False | False | Female | 36-40 | 7-10 | 5-6 | False | DETE |
True | 6.000000e+00 | Resignation-Other reasons | 2012.0 | Guidance Officer | Permanent Full-time | False | True | False | True | False | False | Female | 41-45 | 11-20 | 11-20 | True | DETE |
True | 9.000000e+00 | Resignation-Other reasons | 2012.0 | Teacher | Permanent Full-time | False | True | False | False | False | False | Female | 31-35 | 3-4 | 3-4 | False | DETE |
True | 1.000000e+01 | Resignation-Other employer | 2012.0 | Teacher Aide | Permanent Part-time | False | False | True | False | False | False | Female | 46-50 | 11-20 | 3-4 | True | DETE |
True | 1.200000e+01 | Resignation-Move overseas/interstate | 2012.0 | Teacher | Permanent Full-time | False | False | False | True | False | False | Male | 31-35 | 3-4 | 3-4 | False | DETE |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
True | 6.350660e+17 | Resignation | 2013.0 | Operational (OO) | Temporary Full-time | - | Career Move - Private Sector | - | - | - | - | Male | 21 25 | 5-6 | 5-6 | False | TAFE |
True | 6.350668e+17 | Resignation | 2013.0 | Teacher (including LVT) | Temporary Full-time | Career Move - Public Sector | - | - | - | - | - | Male | 51-55 | 1-2 | 1-2 | False | TAFE |
True | 6.350677e+17 | Resignation | 2013.0 | NaN | NaN | Career Move - Public Sector | - | - | - | - | - | NaN | NaN | NaN | NaN | False | TAFE |
True | 6.350704e+17 | Resignation | 2013.0 | Teacher (including LVT) | Permanent Full-time | - | - | - | - | - | - | Female | 51-55 | 5-6 | 1-2 | False | TAFE |
True | 6.350730e+17 | Resignation | 2013.0 | Administration (AO) | Contract/casual | - | - | - | - | - | - | Female | 26 30 | 3-4 | 1-2 | False | TAFE |
652 rows × 17 columns
Since this moment we will work only with columns with at least 500 non null values (after combined both dataset), this means that are the common columns between dataset with less missing values.
combined_updated["institute_service"].value_counts()
Less than 1 year 115 3-4 99 11-20 83 1-2 78 5-6 73 7-10 62 More than 20 years 53 Name: institute_service, dtype: int64
As we saw before, we have 7 ranges of years for institute_service
and role_service
.
However in order to simplify, we decided to reduce it to the following 4 ranges of years and classification:
New: Less than 3 years at a company
Experienced: 3-6 years at a company
Established: 7-10 years at a company
Veteran: 11 or more years at a company
def label_service_time(element):
if element == "Less than 1 year" or element == "1-2":
return "New"
elif element == "3-4" or element == "5-6":
return "Experienced"
elif element == "7-10":
return "Established"
elif element == "11-20" or element == "More than 20 years":
return "Veteran"
else:
return None
combined_updated["service_cat"]=combined_updated["institute_service"].apply(label_service_time)
service_cat_dist=combined_updated["service_cat"].value_counts()
service_cat_dist
New 193 Experienced 172 Veteran 136 Established 62 Name: service_cat, dtype: int64
service_cat_dist.plot(kind='pie', subplots=True, wedgeprops=dict(width=.5),
startangle=45, autopct="%1.1f%%", pctdistance=0.8
)
plt.title("Experience level of resigned employee")
plt.show()
IMPORTANT Based on this results we can conclude that the majority of employers that resigned are the newer employees
combined_updated["dissatisfied"].value_counts(dropna=False)
False 403 True 240 NaN 9 Name: dissatisfied, dtype: int64
print("IMPORTANT:"
"\n"
"Rechecking the results about the dissatisfaction frequency, we can conclude: \n"
f"* only {240/(403+240+9)*100:,.2f}% of employees confirmed that dissastifaction were the motivation for their resignation.\n"
f"* {403/(403+240+9)*100:,.2f}% of employees are not dissastified\n"
f"* and the remaining {9/(403+240+9)*100:,.2f}% of employees didn't reply to this question\n"
"\n"
"--> As the percentage of NULL values are too small, we will replace this values, for the most common value in this column.\n"
"So we will replace NaN values by FALSE" )
IMPORTANT: Rechecking the results about the dissatisfaction frequency, we can conclude: * only 36.81% of employees confirmed that dissastifaction were the motivation for their resignation. * 61.81% of employees are not dissastified * and the remaining 1.38% of employees didn't reply to this question --> As the percentage of NULL values are too small, we will replace this values, for the most common value in this column. So we will replace NaN values by FALSE
#replacing NaN values by FALSE
combined_updated["dissatisfied"]=combined_updated["dissatisfied"].fillna(False)
combined_updated["dissatisfied"].value_counts(dropna=False)
False 412 True 240 Name: dissatisfied, dtype: int64
#creating pivot table comparing dissastified and service_cat
dissatisfaction_pvt=combined_updated.pivot_table(values="dissatisfied", index="service_cat")
dissatisfaction_pvt
dissatisfied | |
---|---|
service_cat | |
Established | 0.516129 |
Experienced | 0.343023 |
New | 0.295337 |
Veteran | 0.485294 |
# creating plot of the results
dissatisfaction_pvt.plot(kind="bar", rot=30, legend =False, colormap='Paired')
plt.title("Dissatisfied percentage distrubution attending institute service time")
plt.ylabel("dissatisfation")
plt.show()
IMPORTANT Based on this results, the most dissatisfied employees that resigned are the ones that worked for the institutes for a long periods
We checked that there are different kind of exits from their job. We focus our analyze in the resignation type, which represent a big part of employees exits from this institutes:
After check the information regarding dissatisfaction and institute service times, we are in conditions to reply to our initial questions:
Based on data: the majority of employees that resigned worked for the institutes for a short period (less than 6 years)
The most dissatisfied employees that resigned are the ones that worked for the institutes for a long periods (more than 7 years)
So we can conclude that employees with 7 or more years of service in institute are more likely to resign due to some kind of dissatisfaction with the job than employees with less than 7 years of service.
Employees_category | TT resigned employees | % dissatisfied employees |
---|---|---|
New: Less than 3 years at a company | 193 | 29.53% |
Experienced: 3-6 years at a company | 172 | 34.30% |
Established: 7-10 years at a company | 62 | 51.61% |
Veteran: 11 or more years at a company | 136 | 48.53% |