We will be working with two datasets, one from the Department of Education,Training and Employment(DETE) and the other from the Technical and Further Education(TAFE) institute in Queensland, Australia. The goal of the analysis is to answer certain questions on the reasons why workers are resigning,whether it is due to some disatisfaction, etc. We will also try to figure out the reason those who have worked for a short period are resigning and the reason for those who have worked for longer. We will aslo figure out if younger employees are resigning because of a particular type of disatisfaction and we will do the same for the older employees.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
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
dete_survey.tail()
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
817 | 819 | Age Retirement | 02/2014 | 1977 | 1999 | Teacher | Primary | Central Queensland | NaN | Permanent Part-time | ... | 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 | ... | 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 | ... | 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 | ... | 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 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 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
dete_survey['Business Unit'].value_counts(dropna=False)
NaN 696 Education Queensland 54 Information and Technologies 26 Training and Tertiary Education Queensland 12 Other 11 Human Resources 6 Corporate Strategy and Peformance 5 Early Childhood Education and Care 3 Infrastructure 2 Policy, Research, Legislation 2 Finance 1 Calliope State School 1 Pacific Pines SHS 1 Indigenous Education and Training Futures 1 Corporate Procurement 1 Name: Business Unit, dtype: int64
dete_survey['Classification'].value_counts(dropna=False)
NaN 367 Primary 161 Secondary 124 A01-A04 66 AO5-AO7 46 Special Education 33 AO8 and Above 14 PO1-PO4 8 Middle 3 Name: Classification, dtype: int64
There are a lof of missing values in the Aboriginal and disabliity column but that is to be expected as they are probably minorities whichh means a lot of people who took the survey are neither disabled or Aboriginal. There are also columns in our dataframe tha contains 'Not Stated'
which are mssing values that are not represented as NaN
. We are going to read the datset again and pass in an argument to read 'Not Stated' as NaN
. The columns not needed for the analysis are going to be dropped.
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
tafe_survey.tail()
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) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
697 | 6.350668e+17 | Barrier Reef Institute of TAFE | Delivery (teaching) | 2013.0 | Resignation | Career Move - Public Sector | - | - | - | - | ... | 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 |
699 | 6.350704e+17 | Tropical North Institute of TAFE | Delivery (teaching) | 2013.0 | Resignation | - | - | - | - | - | ... | 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 | ... | 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 | - | - | ... | Yes | Yes | Yes | Yes | Female | 26 30 | Contract/casual | Administration (AO) | 3-4 | 1-2 |
5 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()
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
tafe_survey['Contributing Factors. Career Move - Public Sector '].value_counts(dropna=False)
- 375 NaN 265 Career Move - Public Sector 62 Name: Contributing Factors. Career Move - Public Sector , dtype: int64
The data set above contains a number of missing values in several columns. Columns that aren't needed for the analysis are going to be dropped.
dete_survey = pd.read_csv('dete_survey.csv', na_values = 'Not Stated')
dete_survey.tail()
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
817 | 819 | Age Retirement | 02/2014 | 1977.0 | 1999.0 | Teacher | Primary | Central Queensland | NaN | Permanent Part-time | ... | A | A | SA | Female | 56-60 | NaN | NaN | NaN | NaN | NaN |
818 | 820 | Age Retirement | 01/2014 | 1980.0 | 1980.0 | Teacher | Secondary | North Coast | NaN | Permanent Full-time | ... | N | N | N | Male | 51-55 | NaN | NaN | NaN | NaN | NaN |
819 | 821 | Resignation-Move overseas/interstate | 01/2014 | 2009.0 | 2009.0 | Public Servant | A01-A04 | Central Office | Education Queensland | Permanent Full-time | ... | A | N | A | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
820 | 822 | Ill Health Retirement | 12/2013 | 2001.0 | 2009.0 | Teacher | Secondary | Darling Downs South West | NaN | Permanent Full-time | ... | A | N | A | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
821 | 823 | Resignation-Move overseas/interstate | 12/2013 | NaN | NaN | Teacher Aide | NaN | Metropolitan | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 56 columns
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis=1)
The columns that have been dropped from both of the datasets are not necessary for the analysis needed to be done, and having them dropped will also make it easier to combine both datasets.
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')
dete_survey_updated.columns = dete_survey_updated.columns.str.replace(' ', '_').str.lower().str.strip()
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
new_column_names = {'Record ID': 'id',
'CESSATION YEAR': 'cease_date',
'Reason for ceasing employment': 'separationtype',
'Gender. What is your Gender?': 'gender',
'CurrentAge. Current Age': 'age',
'Employment Type. Employment Type': 'employment_status',
'Classification. Classification': 'position',
'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service',
'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'}
tafe_survey_updated.rename(new_column_names, axis=1, inplace=True)
tafe_survey_updated.head()
id | Institute | WorkArea | cease_date | separationtype | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | gender | age | employment_status | position | institute_service | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | - | - | - | NONE | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | - | - | - | - | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 23 columns
Some changes have been made in the names of the columns in both dataframes. This is helpful in preventing erros or missing values when we cobine the two dataframes together.
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
#selects values that contains the string resignation
dete_resignations = dete_survey_updated[
dete_survey_updated['separationtype'].str.contains('Resignation')].copy()
tafe_resignations = tafe_survey_updated[
tafe_survey_updated['separationtype'] == 'Resignation'].copy()
dete_resignations.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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
5 | 6 | Resignation-Other reasons | 05/2012 | 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 | 07/2012 | 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 | 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 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | ... | False | False | False | Male | 31-35 | NaN | NaN | NaN | NaN | NaN |
5 rows × 35 columns
tafe_resignations.head()
id | Institute | WorkArea | cease_date | separationtype | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | gender | age | employment_status | position | institute_service | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | - | - | - | - | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 | 6.341475e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | ... | - | - | Other | - | Female | 56 or older | Contract/casual | Teacher (including LVT) | 7-10 | 7-10 |
6 | 6.341520e+17 | Barrier Reef Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | Maternity/Family | ... | - | - | Other | - | Male | 20 or younger | Temporary Full-time | Administration (AO) | 3-4 | 3-4 |
7 | 6.341537e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | ... | - | - | Other | - | Male | 46 50 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 23 columns
Because we are concerned with the people who left due to some sort of dissatisfaction, both dataframes have been filtered to contain only response from those who resigned.
dete_resignations['cease_date'].value_counts().sort_index()
01/2014 22 05/2012 2 05/2013 2 06/2013 14 07/2006 1 07/2012 1 07/2013 9 08/2013 4 09/2010 1 09/2013 11 10/2013 6 11/2013 9 12/2013 17 2010 1 2012 126 2013 74 Name: cease_date, dtype: int64
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.split('/').str[-1].astype(float)
dete_resignations['dete_start_date'].value_counts().sort_index()
1963.0 1 1971.0 1 1972.0 1 1973.0 1 1974.0 2 1975.0 1 1976.0 2 1977.0 1 1980.0 5 1982.0 1 1983.0 2 1984.0 1 1985.0 3 1986.0 3 1987.0 1 1988.0 4 1989.0 4 1990.0 5 1991.0 4 1992.0 6 1993.0 5 1994.0 6 1995.0 4 1996.0 6 1997.0 5 1998.0 6 1999.0 8 2000.0 9 2001.0 3 2002.0 6 2003.0 6 2004.0 14 2005.0 15 2006.0 13 2007.0 21 2008.0 22 2009.0 13 2010.0 17 2011.0 24 2012.0 21 2013.0 10 Name: dete_start_date, dtype: int64
tafe_resignations['cease_date'].value_counts().sort_index()
2009.0 2 2010.0 68 2011.0 116 2012.0 94 2013.0 55 Name: cease_date, dtype: int64
dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']
Unlike the tafe_resignations dataset, the dete_resignations dataset doesn't have any column that tells us how many years an employee has worked before resignation. So a new column was created for it and we got the values from subtracting the values in the 'dete_start_date'
column from those in the 'cease_date'
column.
print(tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts())
print('\n')
print(tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts())
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64 - 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
#modifies the values in our dataframe to return True, False or Nan
def update_vals(val):
NaN = pd.isnull(val)
if NaN:
return np.nan
elif val == '-':
return False
else:
return True
tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']] = tafe_resignations[['Contributing Factors. Dissatisfaction',
'Contributing Factors. Job Dissatisfaction']].applymap(update_vals)
print(tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts())
print('\n')
print( tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts())
False 277 True 55 Name: Contributing Factors. Dissatisfaction, dtype: int64 False 270 True 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
tafe_resignations['dissatisfied'] = tafe_resignations[['Contributing Factors. Dissatisfaction',
'Contributing Factors. Job Dissatisfaction']].any(axis=1, skipna=False)
tafe_resignations['dissatisfied'].value_counts(dropna=False)
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
dete_resignations['dissatisfied'] = dete_resignations[['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_resignations['dissatisfied'].value_counts(dropna=False)
False 162 True 149 Name: dissatisfied, dtype: int64
dete_resignations_up = dete_resignations.copy()
tafe_resignations_up = tafe_resignations.copy()
Creating a new dissatisfaction column makes it easier to identify the employees who were dissatisfied and those who weren't.
#creates a new institute column
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index=True)
combined.head()
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | 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 | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 4.0 | Resignation-Other reasons | 2012.0 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
1 | 6.0 | Resignation-Other reasons | 2012.0 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 9.0 | Resignation-Other reasons | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 10.0 | Resignation-Other employer | 2012.0 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 12.0 | Resignation-Move overseas/interstate | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 53 columns
combined.notnull().sum()
id 651 separationtype 651 cease_date 635 dete_start_date 283 role_start_date 271 position 598 classification 161 region 265 business_unit 32 employment_status 597 career_move_to_public_sector 311 career_move_to_private_sector 311 interpersonal_conflicts 311 job_dissatisfaction 311 dissatisfaction_with_the_department 311 physical_work_environment 311 lack_of_recognition 311 lack_of_job_security 311 work_location 311 employment_conditions 311 maternity/family 311 relocation 311 study/travel 311 ill_health 311 traumatic_incident 311 work_life_balance 311 workload 311 none_of_the_above 311 gender 592 age 596 aboriginal 7 torres_strait 0 south_sea 3 disability 8 nesb 9 institute_service 563 dissatisfied 643 institute 651 Institute 340 WorkArea 340 Contributing Factors. Career Move - Public Sector 332 Contributing Factors. Career Move - Private Sector 332 Contributing Factors. Career Move - Self-employment 332 Contributing Factors. Ill Health 332 Contributing Factors. Maternity/Family 332 Contributing Factors. Dissatisfaction 332 Contributing Factors. Job Dissatisfaction 332 Contributing Factors. Interpersonal Conflict 332 Contributing Factors. Study 332 Contributing Factors. Travel 332 Contributing Factors. Other 332 Contributing Factors. NONE 332 role_service 290 dtype: int64
#drops columns with less than 500 non-null values
combined_updated = combined.dropna(axis=1,thresh=500).copy()
combined_updated.head()
id | separationtype | cease_date | position | employment_status | gender | age | institute_service | dissatisfied | institute | |
---|---|---|---|---|---|---|---|---|---|---|
0 | 4.0 | Resignation-Other reasons | 2012.0 | Teacher | Permanent Full-time | Female | 36-40 | 7 | False | DETE |
1 | 6.0 | Resignation-Other reasons | 2012.0 | Guidance Officer | Permanent Full-time | Female | 41-45 | 18 | True | DETE |
2 | 9.0 | Resignation-Other reasons | 2012.0 | Teacher | Permanent Full-time | Female | 31-35 | 3 | False | DETE |
3 | 10.0 | Resignation-Other employer | 2012.0 | Teacher Aide | Permanent Part-time | Female | 46-50 | 15 | True | DETE |
4 | 12.0 | Resignation-Move overseas/interstate | 2012.0 | Teacher | Permanent Full-time | Male | 31-35 | 3 | False | DETE |
Both dataframes have been combined into one so they can both be easily analysed. The columns with less than 500 non-null values have also been dropped for this analysis.
'institute_service'
Column¶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 9.0 14 2.0 14 7.0 13 More than 20 years 10 13.0 8 8.0 8 20.0 7 15.0 7 14.0 6 17.0 6 12.0 6 10.0 6 22.0 6 18.0 5 16.0 5 24.0 4 23.0 4 11.0 4 39.0 3 19.0 3 21.0 3 32.0 3 36.0 2 25.0 2 26.0 2 28.0 2 30.0 2 42.0 1 35.0 1 49.0 1 34.0 1 38.0 1 33.0 1 29.0 1 27.0 1 41.0 1 31.0 1 Name: institute_service, dtype: int64
#converts the column to an str dtype
combined_updated['institute_service'] = combined_updated['institute_service'].astype(str)
#extract strings that match with figures[0-9]
combined_updated['institute_service'] = combined_updated['institute_service'].str.extract(r'(\d+)')
#converts column to a float dtype
combined_updated['institute_service'] = combined_updated['institute_service'].astype(float)
combined_updated['institute_service'].value_counts(dropna=False)
1.0 159 NaN 88 3.0 83 5.0 56 7.0 34 11.0 30 0.0 20 20.0 17 6.0 17 4.0 16 9.0 14 2.0 14 13.0 8 8.0 8 15.0 7 22.0 6 10.0 6 17.0 6 14.0 6 12.0 6 16.0 5 18.0 5 24.0 4 23.0 4 21.0 3 39.0 3 32.0 3 19.0 3 36.0 2 30.0 2 25.0 2 26.0 2 28.0 2 42.0 1 29.0 1 35.0 1 27.0 1 41.0 1 49.0 1 38.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service, dtype: int64
#assigns a category to a specific range of service years
def category(val):
NaN = pd.isnull(val)
if NaN:
return np.nan
elif val < 3:
return 'New'
elif val >= 3 and val <= 6:
return 'Experienced'
elif val > 6 and val <= 10:
return 'Established'
elif val > 10:
return 'Veteran'
combined_updated['service_cat'] = combined_updated['institute_service'].apply(category)
combined_updated['service_cat'].value_counts()
New 193 Experienced 172 Veteran 136 Established 62 Name: service_cat, dtype: int64
To make it easier to analyse the type of employees that are dissatisfied, we group the employees into either of New, Experienced, Established and Veteran. Employees with less than 3 years are classed as New, w
combined_updated['dissatisfied'].value_counts(dropna=False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
Since false is the most common result in the dissatisfied column, the missing data in the coolumn will be replaced with false values
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
###calculates the percentage of dissatisfied employees since a True value =1 and False=0
dis_pvt =combined_updated.pivot_table(index='service_cat', values='dissatisfied' )
%matplotlib inline
dis_pvt.plot(kind='bar', rot=30)
plt.xlabel('Employee Category')
plt.show()
From the chart above, it shows that establised and veteran employees are more likely to resign due to some kind of dissatisfaction than the new and experienced ones.
combined_updated['age'].value_counts()
51-55 71 41-45 48 41 45 45 46-50 42 36-40 41 46 50 39 26-30 35 21 25 33 26 30 32 36 40 32 31 35 32 21-25 29 31-35 29 56 or older 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
#converts the column to an str dtype
combined_updated['age'] = combined_updated['age'].astype(str)
#extract strings that match with figures[0-9]
combined_updated['age'] = combined_updated['age'].str.extract(r'(\d+)')
#converts column to a float dtype
combined_updated['age'] = combined_updated['age'].astype(float)
combined_updated['age'].value_counts(dropna=False)
41.0 93 46.0 81 36.0 73 51.0 71 26.0 67 21.0 62 31.0 61 NaN 55 56.0 55 61.0 23 20.0 10 Name: age, dtype: int64
#assigns a category to a specific range of age
def age_category(val):
NaN = pd.isnull(val)
if NaN:
return np.nan
elif val < 30:
return 'Twenties'
elif val >= 30 and val < 40:
return 'Thirties'
elif val >= 40 and val < 50:
return 'Forties'
elif val >= 50 and val < 60:
return 'Fifties'
else:
return 'Sixties'
combined_updated['age_cat'] = combined_updated['age'].apply(age_category)
combined_updated['age_cat'].value_counts(dropna=False)
Forties 174 Twenties 139 Thirties 134 Fifties 126 NaN 55 Sixties 23 Name: age_cat, dtype: int64
The ages have been classified into categories to make it easier to understand what age group of people are dissatisfied.
#gets the percentage of dissatisfied employees for a particular age group
dis_age_pvt = combined_updated.pivot_table(index='age_cat',
values='dissatisfied')
dis_age_pvt.plot(kind='bar', rot=30)
plt.xlabel('Age Category')
plt.show()
From the chart above, we can deduce that employees in their 60's were more likely to leave due to some kind of dissatisfaction than others in other age range.
combined_updated['institute'].value_counts()
TAFE 340 DETE 311 Name: institute, dtype: int64
institute_pvt = combined_updated.pivot_table(index='institute',
values='dissatisfied')
institute_pvt.plot(kind='bar', rot=45)
plt.xlabel('Institute')
plt.show()
At the start of our analysis, we wanted to find out what type of employees were resigning due to some sort of dissatisfaction. After analysing our data, we found out the following: