Data Science Project: Employee Exit Surveys

29 minute read

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 survey
  • SeparationType: The reason why the person’s employment ended
  • Cease Date: The year or month the person’s employment ended
  • DETE 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 survey
  • Reason for ceasing employment: The reason why the person’s employment ended
  • LengthofServiceOverall. 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_survey dataframe contains 'Not Stated' values that indicate values are missing, but they aren’t represented as NaN.
  • Both the dete_survey and tafe_survey contain 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_updated dataframe contains some cease dates in 2009, but the dete_survey_updated dataframe does not. The tafe_survey_updated dataframe also contains many more cease dates in 2010 than the dete_survey_updated dataframe. 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:

  1. tafe_survey_updated:
    • Contributing Factors. Dissatisfaction
    • Contributing Factors. Job Dissatisfaction
  2. 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.