The project aim to answers the following questions
Are employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been there longer?
Are younger employees resigning due to some kind of dissatisfaction? What about older employees?
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
dete_survey.info()
tafe_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 <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
dete_survey.head()
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984 | 2004 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | Not Stated | Not Stated | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011 | 2011 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005 | 2006 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970 | 1989 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 56 columns
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 | ... | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | ... | Yes | Yes | Yes | Yes | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 72 columns
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
#shape of the dete_survey
dete_survey.shape
(822, 56)
Above is the number of missing values among the columns in dete_survey. Fortunately, the SeparationType, Cease Date, DETE Start Date, do not have any missing values. these columns will be concerned to answer our final goals. There are several columns at the end of dataset has a lot of missing value. it makes of about 90% of the rows. For example, Torres Strait has 815 missing values while the dataset has 822 rows. It comprises 99% of all of entries.
print(tafe_survey.isnull().sum())
Record ID 0 Institute 0 WorkArea 0 CESSATION YEAR 7 Reason for ceasing employment 1 ... CurrentAge. Current Age 106 Employment Type. Employment Type 106 Classification. Classification 106 LengthofServiceOverall. Overall Length of Service at Institute (in years) 106 LengthofServiceCurrent. Length of Service at current workplace (in years) 106 Length: 72, dtype: int64
#shape of tafe_survey
tafe_survey.shape
(702, 72)
CESSATION YEAR and Reason for ceasing employment which are needed to answer our final question has just a few missing values comparing to the total rows
#explore the reasons for employee resigning based on dete_survey
dete_survey['SeparationType'].value_counts()
Age Retirement 285 Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: SeparationType, dtype: int64
# explore the reasons for employee resigning based on tafe_survey
tafe_survey['Reason for ceasing employment'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: Reason for ceasing employment, dtype: int64
dete_survey['DETE Start Date'].value_counts()
Not Stated 73 2011 40 2007 34 2008 31 2012 27 2010 27 2009 24 2006 23 1970 21 2013 21 1975 21 2005 20 1990 20 1999 19 1996 19 2004 18 2000 18 1991 18 1992 18 1989 17 1978 15 1976 15 2003 15 1988 15 2002 15 1980 14 1974 14 1979 14 1995 14 1997 14 1998 14 1993 13 1972 12 1986 12 1977 11 1969 10 1994 10 1984 10 2001 10 1971 10 1983 9 1981 9 1973 8 1985 8 1987 7 1982 4 1963 4 1968 3 1967 2 1965 1 1966 1 Name: DETE Start Date, dtype: int64
There are 73 "Not Stated" values which indicate as missing values. thus we need to replace it as NaN
dete_survey = pd.read_csv('dete_survey.csv', na_values = 'Not Stated')
dete_survey.columns[28:49]
Index(['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'], dtype='object')
# remove some columns not needed for the goal
dete_survey_updated = dete_survey.drop(labels = ['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'], 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_survey.columns[17:66]
Index(['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?'], dtype='object')
tafe_survey_updated = tafe_survey.drop(labels = ['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?'], axis = 1)
I have removed several columns in the two datasets because these columns are unnecessary for the final goals
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(' ','_')
dete_survey_updated.columns
Index(['id', 'separationtype', 'cease_date', 'dete_start_date', 'role_start_date', 'position', 'classification', 'region', 'business_unit', 'employment_status', 'career_move_to_public_sector', 'career_move_to_private_sector', 'interpersonal_conflicts', 'job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions', 'maternity/family', 'relocation', 'study/travel', 'ill_health', 'traumatic_incident', 'work_life_balance', 'workload', 'none_of_the_above', 'gender', 'age', 'aboriginal', 'torres_strait', 'south_sea', 'disability', 'nesb'], dtype='object')
tafe_survey_updated.columns = tafe_survey_updated.columns.str.lower().str.strip().str.replace(' ','_')
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')
I have changed the columns name following the pattern: lowercase for all character, remove the whitespace after the end of string, and replace all whitespace with underscores
# rename columns label in tafe_survey_updated
mapper = {'record_id':'id', 'cessation_year':'cease_date','gender._what_is_your_gender?':'gender',
'currentage._current_age':'age','employment_type._employment_type':'employment_status','classification._classification':'position',
'lengthofserviceoverall._overall_length_of_service_at_institute_(in_years)':'institute_service',
'lengthofservicecurrent._length_of_service_at_current_workplace_(in_years)':'role_service'}
tafe_survey_updated = tafe_survey_updated.rename(mapper = mapper, axis =1)
dete_survey_updated.head()
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | work_life_balance | workload | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | False | False | True | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | False | False | False | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | False | True | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | True | False | False | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 35 columns
tafe_survey_updated.head()
id | institute | workarea | cease_date | reason_for_ceasing_employment | contributing_factors._career_move_-_public_sector | contributing_factors._career_move_-_private_sector | contributing_factors._career_move_-_self-employment | contributing_factors._ill_health | contributing_factors._maternity/family | ... | contributing_factors._study | contributing_factors._travel | contributing_factors._other | contributing_factors._none | gender | age | employment_status | position | institute_service | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | - | - | - | NONE | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | - | - | - | - | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 23 columns
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['reason_for_ceasing_employment'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: reason_for_ceasing_employment, dtype: int64
dete_resignations = dete_survey_updated.loc[(dete_survey_updated['separationtype']=='Resignation-Other reasons')|
(dete_survey_updated['separationtype']=='Resignation-Other employer')|
(dete_survey_updated['separationtype']=='Resignation-Move overseas/interstate'),:]
dete_resignations['separationtype'].value_counts()
Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Name: separationtype, dtype: int64
tafe_resignations = tafe_survey_updated.loc[tafe_survey_updated['reason_for_ceasing_employment']=='Resignation',:]
dete_resignations['cease_date'].value_counts(dropna = False)
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 NaN 11 07/2013 9 11/2013 9 10/2013 6 08/2013 4 05/2013 2 05/2012 2 07/2006 1 2010 1 07/2012 1 09/2010 1 Name: cease_date, dtype: int64
dete_resignations_copy = dete_resignations.copy()
dete_resignations_copy=dete_resignations_copy.loc[dete_resignations_copy['cease_date'].notna(),:]
dete_cease_date_pattern = r"(?P<cease_year>[2][0][0-1][0-9])"
dete_cease_year = dete_resignations_copy['cease_date'].str.extractall(dete_cease_date_pattern).astype(float)
dete_cease_year['cease_year'].index
MultiIndex([( 3, 0), ( 5, 0), ( 8, 0), ( 9, 0), ( 11, 0), ( 12, 0), ( 14, 0), ( 16, 0), ( 20, 0), ( 21, 0), ... (802, 0), (803, 0), (804, 0), (806, 0), (807, 0), (808, 0), (815, 0), (816, 0), (819, 0), (821, 0)], names=[None, 'match'], length=300)
# dete_cease_year as i see, it is multiindex, so i want to drop one of the index
# because i want to replace this dete_cease_year with the dete_year column in the
# dataframe dete_resignation_copy. If i don't remove this index, it will have the out of index
# when i try to replace it with the column in dataframe
dete_cease_year = dete_cease_year.reset_index(level = 1)
# now the index i have leveled above become a new column named 'match'
# i will drop this column
dete_cease_year
dete_cease_year=dete_cease_year.drop(columns = 'match')
# now i can replace the dete_cease_year as a new format(with contain only year)
# with the cease_date column in the df 'dete_resignation_copy'
dete_resignations_copy['cease_date'] =dete_cease_year
dete_resignations_copy
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | work_life_balance | workload | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 4 | Resignation-Other reasons | 2012.0 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
5 | 6 | Resignation-Other reasons | 2012.0 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | False | False | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
8 | 9 | Resignation-Other reasons | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
9 | 10 | Resignation-Other employer | 2012.0 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | ... | False | False | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
11 | 12 | Resignation-Move overseas/interstate | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | ... | False | False | False | Male | 31-35 | NaN | NaN | NaN | NaN | NaN |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
808 | 810 | Resignation-Other reasons | 2013.0 | 2010.0 | 2010.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | ... | False | False | False | Female | 26-30 | NaN | NaN | NaN | NaN | NaN |
815 | 817 | Resignation-Other employer | 2014.0 | 2012.0 | 2012.0 | Teacher | Primary | Far North Queensland | NaN | Permanent Full-time | ... | False | False | False | Male | 21-25 | NaN | NaN | NaN | NaN | NaN |
816 | 818 | Resignation-Move overseas/interstate | 2014.0 | 2012.0 | 2012.0 | Teacher | Secondary | North Coast | NaN | Permanent Full-time | ... | False | False | False | Female | 21-25 | NaN | NaN | NaN | NaN | NaN |
819 | 821 | Resignation-Move overseas/interstate | 2014.0 | 2009.0 | 2009.0 | Public Servant | A01-A04 | Central Office | Education Queensland | Permanent Full-time | ... | True | False | False | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
821 | 823 | Resignation-Move overseas/interstate | 2013.0 | NaN | NaN | Teacher Aide | NaN | Metropolitan | NaN | NaN | ... | False | False | False | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
300 rows × 35 columns
#check values in cease_date and dete_start_date columns in dete_resignation
print('Here is cease_date in dete_resignation:',dete_resignations_copy['cease_date'].value_counts().sort_index(
ascending=True),sep = '\n')
print('Here is dete_start_date in dete_resignation:',dete_resignations_copy['dete_start_date'].value_counts().sort_index(
ascending = True),sep = '\n')
Here is cease_date in dete_resignation: 2006.0 1 2010.0 2 2012.0 129 2013.0 146 2014.0 22 Name: cease_date, dtype: int64 Here is dete_start_date in dete_resignation: 1963.0 1 1971.0 1 1972.0 1 1973.0 1 1974.0 2 1975.0 1 1976.0 2 1977.0 1 1980.0 5 1982.0 1 1983.0 2 1985.0 3 1986.0 3 1988.0 4 1989.0 4 1990.0 4 1991.0 4 1992.0 6 1993.0 5 1994.0 6 1995.0 3 1996.0 6 1997.0 5 1998.0 6 1999.0 8 2000.0 8 2001.0 3 2002.0 6 2003.0 6 2004.0 14 2005.0 14 2006.0 12 2007.0 20 2008.0 22 2009.0 13 2010.0 17 2011.0 23 2012.0 20 2013.0 10 Name: dete_start_date, dtype: int64
#display the value in cease_date column in tafe_resignation
tafe_resignations['cease_date'].value_counts()
2011.0 116 2012.0 94 2010.0 68 2013.0 55 2009.0 2 Name: cease_date, dtype: int64
dete_ceasedate_boxplot = dete_resignations_copy.boxplot(column = 'cease_date')
dete_ceasedate_boxplot
plt.show()
dete_startdate_boxplot = dete_resignations_copy.boxplot(column ='dete_start_date')
dete_startdate_boxplot
plt.show()
tafe_boxplot = tafe_resignations.boxplot(column = 'cease_date')
tafe_boxplot
plt.show()
dete_resignations_copy['institute_service'] = dete_resignations_copy['cease_date'] - dete_resignations_copy['dete_start_date']
Above, I added the new column named 'institute_service' which is calculating the length of time employees resigning work at the company
dete_resignations_copy['institute_service'].value_counts(dropna = False)
NaN 27 5.0 23 1.0 22 3.0 20 0.0 20 6.0 17 4.0 16 9.0 14 2.0 14 7.0 13 13.0 8 8.0 8 20.0 7 15.0 7 12.0 6 22.0 6 17.0 6 10.0 6 14.0 6 16.0 5 18.0 5 24.0 4 23.0 4 11.0 4 39.0 3 32.0 3 19.0 3 21.0 3 36.0 2 30.0 2 25.0 2 28.0 2 26.0 2 29.0 1 42.0 1 38.0 1 27.0 1 41.0 1 35.0 1 49.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service, dtype: int64
dete_resignations_copy['separationtype'].value_counts()
Resignation-Other reasons 145 Resignation-Other employer 87 Resignation-Move overseas/interstate 68 Name: separationtype, dtype: int64
tafe_resignations.columns
Index(['id', 'institute', 'workarea', 'cease_date', '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', 'age', 'employment_status', 'position', 'institute_service', 'role_service'], dtype='object')
tafe_resignations['contributing_factors._dissatisfaction'].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: contributing_factors._dissatisfaction, dtype: int64
tafe_resignations['contributing_factors._job_dissatisfaction'].value_counts()
- 270 Job Dissatisfaction 62 Name: contributing_factors._job_dissatisfaction, dtype: int64
def update_vals(val):
if val=='-':
return False
elif pd.isnull(val):
return np.nan
else:
return True
tafe_resignations_copy = tafe_resignations.copy()
tafe_resignations[['contributing_factors._job_dissatisfaction','contributing_factors._dissatisfaction']].applymap(update_vals)
contributing_factors._job_dissatisfaction | contributing_factors._dissatisfaction | |
---|---|---|
3 | False | False |
4 | False | False |
5 | False | False |
6 | False | False |
7 | False | False |
... | ... | ... |
696 | False | False |
697 | False | False |
698 | False | False |
699 | False | False |
701 | False | False |
340 rows × 2 columns
tafe_resignations_copy[['contributing_factors._job_dissatisfaction','contributing_factors._dissatisfaction']]=tafe_resignations[['contributing_factors._job_dissatisfaction','contributing_factors._dissatisfaction']].applymap(update_vals)
tafe_resignations_copy['dissatisfied'] = tafe_resignations_copy[['contributing_factors._job_dissatisfaction','contributing_factors._dissatisfaction']].any(axis = 1, skipna = False)
dissatisfaction_list = ['job_dissatisfaction','dissatisfaction_with_the_department','physical_work_environment',
'lack_of_recognition','lack_of_job_security','work_location','employment_conditions',
'work_life_balance','workload']
dete_resignations_copy['dissatisfied'] = dete_resignations[dissatisfaction_list].any(axis = 1, skipna = False)
tafe_resignations_up = tafe_resignations_copy.copy()
dete_resignations_up = dete_resignations_copy.copy()
tafe_resignations_up['institute'] = 'TAFE'
dete_resignations_up['institute'] = 'DETE'
combined = pd.concat([tafe_resignations_up, dete_resignations_up])
combined_updated = combined.dropna(axis = 1, thresh = 500)
combined_updated['institute_service'].value_counts()
Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 5.0 23 1.0 22 7-10 21 3.0 20 0.0 20 6.0 17 4.0 16 2.0 14 9.0 14 7.0 13 More than 20 years 10 8.0 8 13.0 8 15.0 7 20.0 7 22.0 6 12.0 6 14.0 6 10.0 6 17.0 6 18.0 5 16.0 5 24.0 4 11.0 4 23.0 4 19.0 3 21.0 3 32.0 3 39.0 3 30.0 2 36.0 2 28.0 2 26.0 2 25.0 2 35.0 1 29.0 1 38.0 1 41.0 1 42.0 1 31.0 1 33.0 1 49.0 1 27.0 1 34.0 1 Name: institute_service, dtype: int64
def trans(values):
if (values == 'Less than 1 year') or (values == '1-2') or values == 'New':
return '1'
elif values == 'More than 20 years':
return '21'
elif values == '3-4' or values == '5-6':
return '5'
elif values == '11-20':
return '11'
elif values == '7-10':
return '8'
else:
return values
combined_updated1 = combined_updated.copy()
combined_updated1['institute_service'] = combined_updated['institute_service'].apply(trans).astype('float')
combined_updated1['institute_service']
3 NaN 4 5.0 5 8.0 6 5.0 7 5.0 ... 808 3.0 815 2.0 816 2.0 819 5.0 821 NaN Name: institute_service, Length: 640, dtype: float64
def trans1(values):
if values < 3:
return 'New'
elif values >= 3 and values <=6:
return 'Experience'
elif values >= 7 and values <= 10:
return 'Established'
elif values >= 11:
return 'Veteran'
else:
return np.nan
service_cat = combined_updated1['institute_service'].apply(trans1)
combined_updated1['institute_service'] = service_cat
combined_updated1['dissatisfied'].value_counts(dropna = False)
False 395 True 237 NaN 8 Name: dissatisfied, dtype: int64
combined_updated1['dissatisfied']=combined_updated1['dissatisfied'].fillna(False)
pivot_table = pd.pivot_table(combined_updated1,values = 'dissatisfied', index = 'institute_service' )
print(pivot_table)
dissatisfied institute_service Established 0.516129 Experience 0.343023 New 0.295337 Veteran 0.485294
pivot_table.plot(kind = 'bar')
plt.show()
from the bar I can conclude that 51.61% of established group resign due to some kinds of dissatifition, followed by Veteran, 48.53%. The Newbie and Experience are 29.53% and 34.3% respectively. According to the article, the factor of the work engagement is the need of the stage carrer. At each stage in one's career, they have differrent demand. For example, group of Established want to contribute to the company's goal, and seek the public recognition, promotion and ownership over the project. When they feel that company does not provide their need, they will resign.