Data Science Project: Employee Exit Surveys
Introduction
In this project, we’ll clean and analyze exit surveys from employees of the [Department of Education, Training and Employment (DETE)](https://en.wikipedia.org/wiki/Department_of_Education_and_Training_(Queensland) and the Technical and Further Education (TAFE) body of the Queensland government in Australia. The TAFE exit survey can be found here and the survey for the DETE can be found here.
We’ll pretend our stakeholders want us to combine the results for both surveys to answer the following question:
- 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?
Introduction
First, we’ll read in the datasets and do some initial exporation. Below is a preview of a couple columns we’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 DETE
Below is a preview of a couple columns we’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)
#Read in the data
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
dete_survey = pd.read_csv('dete_survey.csv')
#Quick exploration of the data
pd.options.display.max_columns = 150 #to avoid truncated output
dete_survey.head()
| ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | Career move to public sector | Career move to private sector | Interpersonal conflicts | Job dissatisfaction | Dissatisfaction with the department | Physical work environment | Lack of recognition | Lack of job security | Work location | Employment conditions | Maternity/family | Relocation | Study/Travel | Ill Health | Traumatic incident | Work life balance | Workload | None of the above | Professional Development | Opportunities for promotion | Staff morale | Workplace issue | Physical environment | Worklife balance | Stress and pressure support | Performance of supervisor | Peer support | Initiative | Skills | Coach | Career Aspirations | Feedback | Further PD | Communication | My say | Information | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Ill Health Retirement | 08/2012 | 1984 | 2004 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | True | False | False | True | False | False | True | False | False | False | False | False | False | False | False | False | False | True | A | A | N | N | N | A | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
| 1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | Not Stated | Not Stated | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | A | N | N | N | N | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
| 2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011 | 2011 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | N | N | N | N | N | N | N | N | N | N | N | N | N | N | N | A | A | N | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
| 3 | 4 | Resignation-Other reasons | 05/2012 | 2005 | 2006 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | N | N | N | A | A | N | N | A | A | A | A | A | A | A | A | A | A | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
| 4 | 5 | Age Retirement | 05/2012 | 1970 | 1989 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | A | A | N | N | D | D | N | A | A | A | A | A | A | SA | SA | D | D | A | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
dete_survey.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 822 entries, 0 to 821
Data columns (total 56 columns):
ID 822 non-null int64
SeparationType 822 non-null object
Cease Date 822 non-null object
DETE Start Date 822 non-null object
Role Start Date 822 non-null object
Position 817 non-null object
Classification 455 non-null object
Region 822 non-null object
Business Unit 126 non-null object
Employment Status 817 non-null object
Career move to public sector 822 non-null bool
Career move to private sector 822 non-null bool
Interpersonal conflicts 822 non-null bool
Job dissatisfaction 822 non-null bool
Dissatisfaction with the department 822 non-null bool
Physical work environment 822 non-null bool
Lack of recognition 822 non-null bool
Lack of job security 822 non-null bool
Work location 822 non-null bool
Employment conditions 822 non-null bool
Maternity/family 822 non-null bool
Relocation 822 non-null bool
Study/Travel 822 non-null bool
Ill Health 822 non-null bool
Traumatic incident 822 non-null bool
Work life balance 822 non-null bool
Workload 822 non-null bool
None of the above 822 non-null bool
Professional Development 808 non-null object
Opportunities for promotion 735 non-null object
Staff morale 816 non-null object
Workplace issue 788 non-null object
Physical environment 817 non-null object
Worklife balance 815 non-null object
Stress and pressure support 810 non-null object
Performance of supervisor 813 non-null object
Peer support 812 non-null object
Initiative 813 non-null object
Skills 811 non-null object
Coach 767 non-null object
Career Aspirations 746 non-null object
Feedback 792 non-null object
Further PD 768 non-null object
Communication 814 non-null object
My say 812 non-null object
Information 816 non-null object
Kept informed 813 non-null object
Wellness programs 766 non-null object
Health & Safety 793 non-null object
Gender 798 non-null object
Age 811 non-null object
Aboriginal 16 non-null object
Torres Strait 3 non-null object
South Sea 7 non-null object
Disability 23 non-null object
NESB 32 non-null object
dtypes: bool(18), int64(1), object(37)
memory usage: 258.6+ KB
#Read in the data
tafe_survey = pd.read_csv('tafe_survey.csv')
#Quick exploration of the data
tafe_survey.head()
| Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | Contributing Factors. Dissatisfaction | Contributing Factors. Job Dissatisfaction | Contributing Factors. Interpersonal Conflict | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | Main Factor. Which of these was the main factor for leaving? | InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction | InstituteViews. Topic:2. I was given access to skills training to help me do my job better | InstituteViews. Topic:3. I was given adequate opportunities for personal development | InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% | InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had | InstituteViews. Topic:6. The organisation recognised when staff did good work | InstituteViews. Topic:7. Management was generally supportive of me | InstituteViews. Topic:8. Management was generally supportive of my team | InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me | InstituteViews. Topic:10. Staff morale was positive within the Institute | InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly | InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently | InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly | WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit | WorkUnitViews. Topic:15. I worked well with my colleagues | WorkUnitViews. Topic:16. My job was challenging and interesting | WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work | WorkUnitViews. Topic:18. I had sufficient contact with other people in my job | WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job | WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job | WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] | WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job | WorkUnitViews. Topic:23. My job provided sufficient variety | WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job | WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction | WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance | WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area | WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date | WorkUnitViews. Topic:29. There was adequate communication between staff in my unit | WorkUnitViews. Topic:30. Staff morale was positive within my work unit | Induction. Did you undertake Workplace Induction? | InductionInfo. Topic:Did you undertake a Corporate Induction? | InductionInfo. Topic:Did you undertake a Institute Induction? | InductionInfo. Topic: Did you undertake Team Induction? | InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? | InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? | InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? | InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? | InductionInfo. On-line Topic:Did you undertake a Institute Induction? | InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? | InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? | InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] | InductionInfo. Induction Manual Topic: Did you undertake Team Induction? | Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Agree | Agree | Agree | Neutral | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Neutral | Agree | Agree | Yes | Yes | Yes | Yes | Face to Face | - | - | Face to Face | - | - | Face to Face | - | - | Yes | Yes | Yes | Yes | Yes | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
| 1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Agree | Agree | Agree | Disagree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Agree | Agree | Strongly Agree | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | - | - | - | - | - | - | NONE | NaN | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Agree | Neutral | Neutral | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Yes | No | Yes | Yes | - | - | - | NaN | - | - | - | - | - | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | - | - | - | - | - | - | - | NaN | Agree | Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Yes | Yes | Yes | Yes | - | - | Induction Manual | Face to Face | - | - | Face to Face | - | - | Yes | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
tafe_survey.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 702 entries, 0 to 701
Data columns (total 72 columns):
Record ID 702 non-null float64
Institute 702 non-null object
WorkArea 702 non-null object
CESSATION YEAR 695 non-null float64
Reason for ceasing employment 701 non-null object
Contributing Factors. Career Move - Public Sector 437 non-null object
Contributing Factors. Career Move - Private Sector 437 non-null object
Contributing Factors. Career Move - Self-employment 437 non-null object
Contributing Factors. Ill Health 437 non-null object
Contributing Factors. Maternity/Family 437 non-null object
Contributing Factors. Dissatisfaction 437 non-null object
Contributing Factors. Job Dissatisfaction 437 non-null object
Contributing Factors. Interpersonal Conflict 437 non-null object
Contributing Factors. Study 437 non-null object
Contributing Factors. Travel 437 non-null object
Contributing Factors. Other 437 non-null object
Contributing Factors. NONE 437 non-null object
Main Factor. Which of these was the main factor for leaving? 113 non-null object
InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object
InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object
InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object
InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object
InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object
InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object
InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object
InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object
InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object
InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object
InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object
InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object
InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object
WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object
WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object
WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object
WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object
WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object
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
WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object
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
WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object
WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object
WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object
WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object
WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object
WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object
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
WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object
WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object
Induction. Did you undertake Workplace Induction? 619 non-null object
InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object
InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object
InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object
InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object
InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object
InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object
InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object
InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object
InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object
InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object
InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object
InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object
Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object
Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object
Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object
Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object
Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object
Gender. What is your Gender? 596 non-null object
CurrentAge. Current Age 596 non-null object
Employment Type. Employment Type 596 non-null object
Classification. Classification 596 non-null object
LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object
LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object
dtypes: float64(2), object(70)
memory usage: 395.0+ KB
We can make the following observations based on the work above:
- The
dete_surveydataframe contains'Not Stated'values that indicate values are missing, but they aren’t represented as NaN. - Both the
dete_surveyandtafe_surveycontain many columns that we don’t need to complete our analysis. - Each dataframe contains many of the same columns, but the column names are different.
- There are multiple columns/answers that indicate an employee resigned because they were dissatisfied.
Identify Missing Values and Drop Unneccessary Columns
First, we’ll correct the Not Stated values and drop some of the columns we don’t need for our analysis.
#Read in the data again, but this time read `not stated` values as `NaN`
dete_survey = pd.read_csv('dete_survey.csv', na_values = 'Not Stated')
#Quick exploration of the data
dete_survey.head()
| ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | Career move to public sector | Career move to private sector | Interpersonal conflicts | Job dissatisfaction | Dissatisfaction with the department | Physical work environment | Lack of recognition | Lack of job security | Work location | Employment conditions | Maternity/family | Relocation | Study/Travel | Ill Health | Traumatic incident | Work life balance | Workload | None of the above | Professional Development | Opportunities for promotion | Staff morale | Workplace issue | Physical environment | Worklife balance | Stress and pressure support | Performance of supervisor | Peer support | Initiative | Skills | Coach | Career Aspirations | Feedback | Further PD | Communication | My say | Information | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | True | False | False | True | False | False | True | False | False | False | False | False | False | False | False | False | False | True | A | A | N | N | N | A | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
| 1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | A | N | N | N | N | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
| 2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | N | N | N | N | N | N | N | N | N | N | N | N | N | N | N | A | A | N | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
| 3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | N | N | N | A | A | N | N | A | A | A | A | A | A | A | A | A | A | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
| 4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | A | A | N | N | D | D | N | A | A | A | A | A | A | SA | SA | D | D | A | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
#Remove columns we don't need for our analysis
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)
#check that the columns were dropped
print(dete_survey_updated.columns)
print(tafe_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')
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')
Rename Columns
Next, we’ll standardize the names of the columns we want to work with, because we eventually want to combine the dataframes.
#Clean the column names for dete_survey
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(' ','_')
#Check that column names are updated correctly
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')
#Update column names to match the names in tafe_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 that the specified column names were updated correctly
tafe_survey_updated.columns
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')
Filter the Data
For this project, we’ll only analyze survey respondents who resigned, so we’ll only select separation types containing the string 'Resignation'.
# Check the unique values for the separationtype column in tafe_survey_updated
tafe_survey_updated['separationtype'].value_counts()
Resignation 340
Contract Expired 127
Retrenchment/ Redundancy 104
Retirement 82
Transfer 25
Termination 23
Name: separationtype, dtype: int64
# Check the unique values for the separationtype column in dete_survey_updated
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
# Update all separations types containing 'resignation- ....' to one single column "Resignation"
dete_survey_updated['separationtype'] = dete_survey_updated['separationtype'].str.split('-').str[0]
# Check the values in the separationtype column were updated correctly
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
# Select only the resignation separation types from each dataframe
dete_resignation = dete_survey_updated[dete_survey_updated['separationtype'] == 'Resignation'].copy()
tafe_resignation = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
Verify the Data
Below, we clean and explore the cease_date and dete_start_date columns to make sure all of the years make sense. We’ll use the following criteria:
- Since the cease_date is the last year of the person’s employment and the dete_start_date is the person’s first year of employment, it wouldn’t make sense to have years after the current date.
- Given that most people in this field start working in their 20s, it’s also unlikely that the dete_start_date was before the year 1940.
#Check the unique values
dete_resignation['cease_date'].value_counts()
2012 126
2013 74
01/2014 22
12/2013 17
06/2013 14
09/2013 11
11/2013 9
07/2013 9
10/2013 6
08/2013 4
05/2013 2
05/2012 2
09/2010 1
07/2006 1
07/2012 1
2010 1
Name: cease_date, dtype: int64
#Extract the years and convert them to a float type
dete_resignation['cease_date'] = dete_resignation['cease_date'].str.split('/').str[-1]
dete_resignation['cease_date'] = dete_resignation['cease_date'].astype('float')
#Check the values again and look for outliers
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
#Check the unique values and look for outliers
dete_resignation['dete_start_date'].value_counts().sort_values()
1963.0 1
1971.0 1
1972.0 1
1984.0 1
1977.0 1
1987.0 1
1975.0 1
1973.0 1
1982.0 1
1974.0 2
1983.0 2
1976.0 2
1986.0 3
1985.0 3
2001.0 3
1995.0 4
1988.0 4
1989.0 4
1991.0 4
1997.0 5
1980.0 5
1993.0 5
1990.0 5
1994.0 6
2003.0 6
1998.0 6
1992.0 6
2002.0 6
1996.0 6
1999.0 8
2000.0 9
2013.0 10
2009.0 13
2006.0 13
2004.0 14
2005.0 15
2010.0 17
2012.0 21
2007.0 21
2008.0 22
2011.0 24
Name: dete_start_date, dtype: int64
#Check the unique values from tafe
tafe_resignation['cease_date'].value_counts().sort_values()
2009.0 2
2013.0 55
2010.0 68
2012.0 94
2011.0 116
Name: cease_date, dtype: int64
Below are our findings:
- The years in both dataframes don’t completely align. The
tafe_survey_updateddataframe contains some cease dates in 2009, but thedete_survey_updateddataframe does not. Thetafe_survey_updateddataframe also contains many more cease dates in 2010 than thedete_survey_updateddataframe. Since we aren’t concerned with analyzing the results by year, we’ll leave them as is.
Create a New Column
Since our end goal is to answer the question below, we need a column containing the length of time an employee spent in their workplace, or years of service, in both dataframes.
- End goal: Are employees who have only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been at the job longer?
The tafe_resignations dataframe already contains a “service” column, which we renamed to institute_service.
Below, we calculate the years of service in the dete_survey_updated dataframe by subtracting the dete_start_date from the cease_date and create a new column named institute_service.
# Calculate the length of time an employee spent in their respective workplace and create a new column
dete_resignation['institute_service'] = dete_resignation['cease_date'] - dete_resignation['dete_start_date']
#Quick check of 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
Identify Dissatisfied Employees
Next, we’ll identify any employees who resigned because they were dissatisfied. Below are the columns we’ll use to categorize employees as “dissatisfied” from each dataframe:
- tafe_survey_updated:
- Contributing Factors. Dissatisfaction
- Contributing Factors. Job Dissatisfaction
- dafe_survey_updated:
- job_dissatisfaction
- dissatisfaction_with_the_department
- physical_work_environment
- lack_of_recognition
- lack_of_job_security
- work_location
- employment_conditions
- work_life_balance
- workload
If the employee indicated any of the factors above caused them to resign, we’ll mark them as dissatisfied in a new column. After our changes, the new dissatisfied column will contain just the following values:
- True: indicates a person resigned because they were dissatisfied in some way
- False: indicates a person resigned because of a reason other than dissatisfaction with the job
- NaN: indicates the value is missing
#Check the unique values
tafe_resignation['Contributing Factors. Dissatisfaction'].value_counts()
- 277
Contributing Factors. Dissatisfaction 55
Name: Contributing Factors. Dissatisfaction, dtype: int64
tafe_resignation['Contributing Factors. Job Dissatisfaction'].value_counts()
- 270
Job Dissatisfaction 62
Name: Contributing Factors. Job Dissatisfaction, dtype: int64
# Update the values in the contributing factors columns to be either True, False, or NaN
def update_val(x):
if x == '-':
return False
elif pd.isnull(x):
return np.nan
else:
return True
tafe_resignation['dissatisfied'] = tafe_resignation[['Contributing Factors. Dissatisfaction',
'Contributing Factors. Job Dissatisfaction']].applymap(update_val).any(1, skipna = False)
tafe_resignation_up = tafe_resignation.copy()
#Check the unique values after the update
tafe_resignation_up['dissatisfied'].value_counts(dropna = False)
False 241
True 91
NaN 8
Name: dissatisfied, dtype: int64
# Update the values in columns related to dissatisfaction 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_up = dete_resignation.copy()
dete_resignation_up['dissatisfied'].value_counts(dropna = False)
False 162
True 149
Name: dissatisfied, dtype: int64
So far, we’ve accomplished the following:
- Renamed our columns
- Dropped any data not needed for our analysis
- Verified the quality of our data
- Created a new institute_service column
- Cleaned the Contributing Factors columns
- Created a new column indicating if an employee resigned because they were dissatisfied in some way
Now, we’re finally ready to combine our datasets! Our end goal is to aggregate the data according to the institute_service column.
Combining the Data
Below, we’ll add an institute column so that we can differentiate the data from each survey after we combine them. Then, we’ll combine the dataframes and drop any remaining columns we don’t need.
# Add an institue column
dete_resignation_up['institute'] = 'DETE'
tafe_resignation_up['institute'] = 'TAFE'
# Combine the dataframes
combined = pd.concat([dete_resignation_up, tafe_resignation_up], ignore_index = True)
# Verify the number of non null values in each column
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
career_move_to_public_sector 311
employment_conditions 311
work_location 311
lack_of_job_security 311
job_dissatisfaction 311
dissatisfaction_with_the_department 311
workload 311
lack_of_recognition 311
interpersonal_conflicts 311
maternity/family 311
none_of_the_above 311
physical_work_environment 311
relocation 311
study/travel 311
traumatic_incident 311
work_life_balance 311
career_move_to_private_sector 311
ill_health 311
Contributing Factors. Career Move - Private Sector 332
Contributing Factors. Other 332
Contributing Factors. Career Move - Public Sector 332
Contributing Factors. Career Move - Self-employment 332
Contributing Factors. Travel 332
Contributing Factors. Study 332
Contributing Factors. Dissatisfaction 332
Contributing Factors. Ill Health 332
Contributing Factors. NONE 332
Contributing Factors. Maternity/Family 332
Contributing Factors. Job Dissatisfaction 332
Contributing Factors. Interpersonal Conflict 332
WorkArea 340
Institute 340
institute_service 563
gender 592
age 596
employment_status 597
position 598
cease_date 635
dissatisfied 643
id 651
separationtype 651
institute 651
dtype: int64
# Drop columns with less than 500 non null values
combined_updated = combined.dropna(thresh = 500, axis = 1).copy()
Clean the Service Column
Next, we’ll clean the institute_service column and categorize employees according to the following definitions:
- New: Less than 3 years in the workplace
- Experienced: 3-6 years in the workplace
- Established: 7-10 years in the workplace
- Veteran: 11 or more years in the workplace
Our analysis is based on this article, which makes the argument that understanding employee’s needs according to career stage instead of age is more effective.
# Check 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
0.0 20
3.0 20
6.0 17
4.0 16
9.0 14
2.0 14
7.0 13
More than 20 years 10
8.0 8
13.0 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
11.0 4
23.0 4
24.0 4
19.0 3
32.0 3
39.0 3
21.0 3
28.0 2
30.0 2
26.0 2
36.0 2
25.0 2
29.0 1
31.0 1
27.0 1
34.0 1
35.0 1
38.0 1
41.0 1
42.0 1
49.0 1
33.0 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')
# Check the years extracted are correct
combined_updated['institute_service_up'].value_counts()
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:2: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame)
from ipykernel import kernelapp as app
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
# Convert years of service 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)
# Quick check of the update
combined_updated['service_cat'].value_counts()
New 193
Experienced 172
veteran 136
Established 62
Name: service_cat, dtype: int64
Perform Some Initial Analysis
Finally, we’ll replace the missing values in the dissatisfied column with the most frequent value, False. Then, we’ll calculate the percentage of employees who resigned due to dissatisfaction in each service_cat group and plot the results.
Note that since we still have additional missing values left to deal with, this is meant to be an initial introduction to the analysis, not the final analysis.
# Verify the unique values
combined_updated['dissatisfied'].value_counts(dropna = False)
False 403
True 240
NaN 8
Name: dissatisfied, dtype: int64
# Replace missing values with the most frequent value, False
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
# Calculate the percentage of employees who resigned due to dissatisfaction in each category
dis_pct = combined_updated.pivot_table(index='service_cat', values = 'dissatisfied')
# Plot the results
dis_pct.plot(kind='bar', rot = 30)
<matplotlib.axes._subplots.AxesSubplot at 0x7fe6183c7128>
From the initial analysis above, we can tentatively conclude that employees with 7 or more years of service are more likely to resign due to some kind of dissatisfaction with the job than employees with less than 7 years of service. However, we need to handle the rest of the missing data to finalize our analysis.