In this project, I work with exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia.
This project provides answers to the following:
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?
Below is a preview of a couple columns I'll work with from the dete_survey.csv
:
ID
: An id used to identify the participant of the surveySeparationType
: The reason 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 DETEBelow is a preview of a couple columns I'll work with from the tafe_survey.csv
:
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)# import pandas as pd
# import numpy as np
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from importlib import reload
reload(plt)
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
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.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
print(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.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.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
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
The following were observed based on the exploration work above:
dete_survey
and tafe_survey
contain many columns that we don't need to complete our analysis.Missing Values were identified and unneccessary columns dropped.
First, the Not Stated
values are corrected and some of the columns that are not needed for the anaysis are dropped
# Reading the data again make `Not Stated` a `NaN`
dete_survey = pd.read_csv('dete_survey.csv', na_values= 'Not Stated')
# Exploring the dete_survey
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 | 1984.0 | 2004.0 | 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 | NaN | NaN | 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 | 2011.0 | 2011.0 | 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 | 2005.0 | 2006.0 | 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 | 1970.0 | 1989.0 | 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
# Drop unneccessary columns from `dete_survey`
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49],axis=1)
#Check that the columns were dropped
print(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')
# Drop unneccessary columns from `dete_survey`
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66],axis=1)
#Check that the columns were dropped
print(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')
Since the two dataframes will be combined, there is a need to standardise their columns name.
# Clean the column names
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(' ', '_')
# Check that column names were updated correctly
print(dete_survey_updated.columns)
dete_survey_updated.head()
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')
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
# Update column names to match the names in dete_survey_updated
mapping = {'Record ID': 'id', 'CESSATION YEAR': 'cease_date', 'Reason for ceasing employment': 'separationtype', 'Gender. What is your Gender?': 'gender', 'CurrentAge. Current Age': 'age',
'Employment Type. Employment Type': 'employment_status',
'Classification. Classification': 'position',
'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service',
'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'}
tafe_survey_updated = tafe_survey_updated.rename(mapping, axis = 1)
# Check to enure columns names are updated correctly
print(tafe_survey_updated.columns)
tafe_survey_updated.head()
Index(['id', 'Institute', 'WorkArea', 'cease_date', 'separationtype', 'Contributing Factors. Career Move - Public Sector ', 'Contributing Factors. Career Move - Private Sector ', 'Contributing Factors. Career Move - Self-employment', 'Contributing Factors. Ill Health', 'Contributing Factors. Maternity/Family', 'Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Interpersonal Conflict', 'Contributing Factors. Study', 'Contributing Factors. Travel', 'Contributing Factors. Other', 'Contributing Factors. NONE', 'gender', 'age', 'employment_status', 'position', 'institute_service', 'role_service'], dtype='object')
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
Recall that our end goal is to answer about employees resignation.
If we look at the unique values in the separationtype
columns in each dataframe, we'll see that each contains a couple of different separation types. For this project, I'll only analyze survey respondents who resigned, so their separation type contains the string Resignation
.
# Confirm the unique values for the separationtype 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
# Confirm the unique values for the separationtype column
tafe_survey_updated['separationtype'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
# dete_survey_update has "Resignation" in three compound words seperated by "-". These words are combined into one word (Resignation)
dete_survey_updated['separationtype'] = dete_survey_updated['separationtype'].str.split('-').str[0]
# confirm that the values has been updated successfully
dete_survey_updated['separationtype'].value_counts()
Resignation 311 Age Retirement 285 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
# Since the interest of this project on resignation separation type, value with resignationsignation will be separate from other separation.
dete_resignation = dete_survey_updated[dete_survey_updated['separationtype'] == 'Resignation'].copy()
tafe_resignation = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
#confirming the shape for the two dataframe
print (dete_resignation.shape)
print (tafe_resignation.shape)
(311, 35) (340, 23)
Dataframe.copy method was used to copy seperately the findings from the resignation type in the separationtype column. This is done to prevent SettingwithCopy warning.
As a reminder, this project is interested among other things in the period of time an employee worked for the instition.
In this step, I'll focus on verifying that the years in the cease_date
and dete_start_date
columns make sense and are free from logical inconsistencies
# Exploring for inconsistency check in the cease date column
dete_resignation['cease_date'].value_counts()
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 11/2013 9 07/2013 9 10/2013 6 08/2013 4 05/2012 2 05/2013 2 2010 1 07/2012 1 09/2010 1 07/2006 1 Name: cease_date, dtype: int64
From the exploration above, the column contains some irregularities with date. Hence, the need to clean up the column data.
# Years are extracted from the column and converted to float
dete_resignation['cease_date'] = dete_resignation['cease_date'].str.split('/').str[-1]
dete_resignation['cease_date'] = dete_resignation['cease_date'].astype('float')
dete_resignation['cease_date'].value_counts()
2013.0 146 2012.0 129 2014.0 22 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
# Exploring for inconsistency check in the dete_start date column
dete_resignation['dete_start_date'].value_counts().sort_index(ascending = True)
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
Analysed data shows that employees starts of employment ranges from 1963 to 2013
# Sort unique values
dete_resignation['cease_date'].value_counts().sort_index(ascending = True)
2006.0 1 2010.0 2 2012.0 129 2013.0 146 2014.0 22 Name: cease_date, dtype: int64
# Exploring for inconsistency check in the tafe_start date column
tafe_resignation['cease_date'].value_counts().sort_index(ascending = True)
2009.0 2 2010.0 68 2011.0 116 2012.0 94 2013.0 55 Name: cease_date, dtype: int64
TAFE resignation happens between year 2009 and 2013. Highest resignation was recorded in year 2011
Boxplot is plotted to identify any values that look wrong.
# Boxplot for dete start date on dete survey
ax = dete_resignation.boxplot(column = ['dete_start_date'])
plt.title("dete start date")
ax.set_ylim(1963,2013)
plt.ylabel("year")
plt.show()
ax = tafe_resignation.boxplot(column=['cease_date'])
plt.title("tafe employee cease date")
ax.set_ylim(2009,2013)
plt.ylabel("year")
plt.show()
After careful look of the plot shown above, it seems there is no major issues with the values.
tafe_resignations
dataframe already contains a "service" column, which I renamed to institute_service. In order to analyze both surveys together, I'll have to create a corresponding institute_service
column in dete_resignations
.
# Calculate the service years (institute_service)
dete_resignation['institute_service'] = dete_resignation['cease_date'] - dete_resignation['dete_start_date']
# confirm the result
dete_resignation['institute_service'].head()
3 7.0 5 18.0 8 3.0 9 15.0 11 3.0 Name: institute_service, dtype: float64
Some employees resigned because they were dissatisfied. Here are the columns that categorise employee as "dissatisfied" from the two dataframes:
tafe_survey_updated:
dafe_survey_updated:
An employee is marked as dissatisfied
in a new column if any of the factors aboe caused them to resign. The values for the new column is shon below:
True:
indicates a person resigned because they were dissatisfied in some wayFalse:
indicates a person resigned because of a reason other than dissatisfaction with the jobNaN:
indicates the value is missing# Confirm unique values
tafe_resignation['Contributing Factors. Dissatisfaction'].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
# Confirm unique values
tafe_resignation['Contributing Factors. Job Dissatisfaction'].value_counts()
- 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
# contributing factors columns is updated as to be either True, False, or NaN
def update_values (val):
if val == '-':
return False
elif pd.isnull(val):
return np.nan
else:
return True
tafe_resignation['dissatisfied'] = tafe_resignation[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']].applymap(update_values).any(1, skipna = False)
tafe_resignation_update = tafe_resignation.copy()
# veify unique values of dissatisfied columns after update
tafe_resignation_update['dissatisfied'].value_counts(dropna = False)
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
# Columns related to dissatisfaction dete survey is updated as to be either True, False, or NaN
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(1, skipna=False)
dete_resignation_update = dete_resignation.copy()
dete_resignation_update['dissatisfied'].value_counts(dropna = False)
False 162 True 149 Name: dissatisfied, dtype: int64
A column institute
is added to each dataframe to allow us to easily distinguish between the two.
A column named institute
is addede to dete_resignations_update. Each row should contain the value DETE.
A column named institute
is added to tafe_resignations_update. Each row should contain the value TAFE.
# institute colum is added to both dataframe
dete_resignation_update['institute'] = 'DETE'
tafe_resignation_update['institute'] = 'TAFE'
# Combine the dataframes
combined = pd.concat([dete_resignation_update, tafe_resignation_update], ignore_index=True)
# Check the number of non null
combined.notnull().sum().sort_values()
torres_strait 0 south_sea 3 aboriginal 7 disability 8 nesb 9 business_unit 32 classification 161 region 265 role_start_date 271 dete_start_date 283 role_service 290 none_of_the_above 311 work_life_balance 311 traumatic_incident 311 ill_health 311 study/travel 311 relocation 311 maternity/family 311 employment_conditions 311 workload 311 lack_of_job_security 311 career_move_to_public_sector 311 career_move_to_private_sector 311 interpersonal_conflicts 311 work_location 311 dissatisfaction_with_the_department 311 physical_work_environment 311 lack_of_recognition 311 job_dissatisfaction 311 Contributing Factors. Job Dissatisfaction 332 Contributing Factors. Travel 332 Contributing Factors. Maternity/Family 332 Contributing Factors. Ill Health 332 Contributing Factors. Career Move - Self-employment 332 Contributing Factors. Career Move - Private Sector 332 Contributing Factors. Career Move - Public Sector 332 Contributing Factors. Dissatisfaction 332 Contributing Factors. Other 332 Contributing Factors. Interpersonal Conflict 332 Contributing Factors. NONE 332 Contributing Factors. Study 332 Institute 340 WorkArea 340 institute_service 563 gender 592 age 596 employment_status 597 position 598 cease_date 635 dissatisfied 643 separationtype 651 institute 651 id 651 dtype: int64
# Columns with less than 500 non null values is dropped
combined_updated = combined.dropna(thresh = 500, axis = 1).copy()
The institute_service
column needed to be cleaned. This column currently contains values in a couple different forms.
The column is modified according to career stage using the following definations:
# verify the unique values
combined_updated['institute_service'].value_counts(dropna=False)
NaN 88 Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 5.0 23 1.0 22 7-10 21 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 10.0 6 12.0 6 14.0 6 17.0 6 22.0 6 18.0 5 16.0 5 24.0 4 11.0 4 23.0 4 21.0 3 32.0 3 19.0 3 39.0 3 26.0 2 28.0 2 30.0 2 25.0 2 36.0 2 38.0 1 49.0 1 42.0 1 41.0 1 33.0 1 35.0 1 34.0 1 29.0 1 27.0 1 31.0 1 Name: institute_service, dtype: int64
# Extract the years of service and convert the type to float
combined_updated['institute_service_up'] = combined_updated['institute_service'].astype('str').str.extract(r'(\d+)')
combined_updated['institute_service_up'] = combined_updated['institute_service_up'].astype('float')
# verify the years extracted are correct
combined_updated['institute_service_up'].value_counts()
1.0 159 3.0 83 5.0 56 7.0 34 11.0 30 0.0 20 20.0 17 6.0 17 4.0 16 9.0 14 2.0 14 13.0 8 8.0 8 15.0 7 17.0 6 10.0 6 12.0 6 14.0 6 22.0 6 16.0 5 18.0 5 24.0 4 23.0 4 39.0 3 19.0 3 21.0 3 32.0 3 28.0 2 36.0 2 25.0 2 30.0 2 26.0 2 29.0 1 38.0 1 42.0 1 27.0 1 41.0 1 35.0 1 49.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service_up, dtype: int64
# Service year is converted to categories
def transform_service(val):
if val >= 11:
return "Veteran"
elif 7 <= val < 11:
return "Established"
elif 3 <= val < 7:
return "Experienced"
elif pd.isnull(val):
return np.nan
else:
return "New"
combined_updated['service_cat'] = combined_updated['institute_service_up'].apply(transform_service)
# Check for update
combined_updated['service_cat'].value_counts()
New 193 Experienced 172 Veteran 136 Established 62 Name: service_cat, dtype: int64
Missing values in the dissatisfied column is filled up with the most frequent value False
. Then, the percentage of employees who resigned due to dissatisfaction in each service_cat group is calculated and the results ploted.
# Check the unique values
combined_updated['dissatisfied'].value_counts(dropna = False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
# Missing values is replaced with the most frequent value(False)
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
# Percentage of employees who resigned due to dissatisfaction in each service category is calculated
dissatisfaction_perc = combined_updated.pivot_table(index='service_cat', values='dissatisfied')
# Check dissatisfaction percentage
dissatisfaction_perc
dissatisfied | |
---|---|
service_cat | |
Established | 0.516129 |
Experienced | 0.343023 |
New | 0.295337 |
Veteran | 0.485294 |
# Result is Plotted using bar chart
dissatisfaction_perc.plot(kind='bar', rot=30, legend = False, title='Dissatisfaction Plot of Employee in different Service Categories' )
plt.xlabel ('Employee Service Categories')
plt.ylabel ('Percentage Dissatisfied')
plt.show
<function matplotlib.pyplot.show(*args, **kw)>
New employees of about 29.5% resigned due to dissastifaction, while Established of about 7-10 years of experience has the highest resigning rate with about 51.6% while veterans with about 48% rate resigned and Experienced at about 34% resigned
# check age of employee
combined_updated['age']
0 36-40 1 41-45 2 31-35 3 46-50 4 31-35 ... 646 21 25 647 51-55 648 NaN 649 51-55 650 26 30 Name: age, Length: 651, dtype: object
# Check for unique values using value_counts
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 31-35 29 56 or older 29 21-25 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
The age
column needed to be cleaned. From the age column, there are irregularities in the different rows, some contain "-" while others dont. i will be made uniform by converting those without "-" to contain "-", and i will replace 56 or older to 56-60.
# Clean the age column
combined_updated['age_updated'] = combined_updated['age'].str.replace(' ', '-').str.replace('56 or older', '56-60').str.replace('61 or older', '61 or >').str.replace('20 or younger', '21 or <')
# Check for unique values
combined_updated['age_updated'].value_counts(dropna = False)
41-45 93 46-50 81 36-40 73 51-55 71 26-30 67 21-25 62 31-35 61 56-60 55 NaN 55 61 or > 23 21 or < 10 Name: age_updated, dtype: int64
The age_updated
column used in analysing the dissastifaction rate of each service catgory
# Percentage of employees who resigned due to dissatisfaction in different age range is calculated
age_dissatisfaction_perc = combined_updated.pivot_table(index = 'age_updated', values = 'dissatisfied' )
age_dissatisfaction_perc
dissatisfied | |
---|---|
age_updated | |
21 or < | 0.200000 |
21-25 | 0.306452 |
26-30 | 0.417910 |
31-35 | 0.377049 |
36-40 | 0.342466 |
41-45 | 0.376344 |
46-50 | 0.382716 |
51-55 | 0.422535 |
56-60 | 0.381818 |
61 or > | 0.521739 |
age_dissatisfaction_perc.plot(kind = 'bar', rot = 90, title = 'Dissatisfaction Plot of Employee in different Service Categorie', legend = False)
plt.xlabel('Age')
plt.ylabel('Dissatisfied Percent')
plt.show()
Employees 20 years younger are the least to resign at the rate of 20%, while those with more than 61 years of age resign the most at the rate of 52%
In order to determine the institute with the highest resignation rate, the resignation rate of both institute will be compared.
# Check the data in each institute survey
combined_updated['institute'].value_counts()
TAFE 340 DETE 311 Name: institute, dtype: int64
TAFE seems to record more survey than DETE
# Check the effect on the resignation rate
institute_pivot_table = combined_updated.pivot_table(index= 'institute', values = 'dissatisfied')
# plot to comare the two institute
institute_pivot_table.plot(kind = 'bar', rot = 30, legend = False, title = 'Comparing Dissatisfaction Resignation of both institute')
plt.xlabel('institute')
plt.ylabel('Dissatisfaction Percentage')
plt.show
<function matplotlib.pyplot.show(*args, **kw)>
This plot shows that DETE has more resignation rate than TAFE. DETE has about 48% resignation rate while TAFE recorded about 28%.
# Check dissatisfaction of DETE and TAFE on different service categories.
institute_service_pivot = combined_updated.pivot_table(index = 'service_cat', columns = 'institute', values = 'dissatisfied')
institute_service_pivot
institute | DETE | TAFE |
---|---|---|
service_cat | ||
Established | 0.609756 | 0.333333 |
Experienced | 0.460526 | 0.250000 |
New | 0.375000 | 0.262774 |
Veteran | 0.560000 | 0.277778 |
institute_service_pivot.plot(kind = 'bar', rot = 30, title = 'Institute Service Categories Dissatisfaction plot')
plt.xlabel = 'Service Categories'
plt.ylabel = 'Dissatisfied Percentage'
plt.legend(loc='upper center', fontsize='small')
plt.show
<function matplotlib.pyplot.show(*args, **kw)>
from the plot shown above, DETE institute had the highest resigning rate from the dete survey data. DETE had about 60% resignations due to dissatisfaction on the Established
service category. This is followed by the Veteran
.
In TAFE, it seems that resignation is relatively constant across the levels but, Established
had the highest resigning rate.
Analysis of exit surveys from employees of the Department of Education , Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia was carried out in order to answer 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?
From the analysis carried out and result presented in above, it has been found that younger employees with fewer years of experience are less likely to resign due to some sort of dissatisfaction than older employees with long years of experience.