Clean and Analyze Employee Exit Surveys

In this project, we'll clean and analyze exit surveys from employees of the Department of Education, Training and Employment (DETE)}) 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?

Introduction

First, we'll read in the datasets and do some initial exporation.

In [1]:
#Read in the data
import pandas as pd
import numpy as np
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()
Out[1]:
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
In [2]:
dete_survey.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 822 entries, 0 to 821
Data columns (total 56 columns):
 #   Column                               Non-Null Count  Dtype 
---  ------                               --------------  ----- 
 0   ID                                   822 non-null    int64 
 1   SeparationType                       822 non-null    object
 2   Cease Date                           822 non-null    object
 3   DETE Start Date                      822 non-null    object
 4   Role Start Date                      822 non-null    object
 5   Position                             817 non-null    object
 6   Classification                       455 non-null    object
 7   Region                               822 non-null    object
 8   Business Unit                        126 non-null    object
 9   Employment Status                    817 non-null    object
 10  Career move to public sector         822 non-null    bool  
 11  Career move to private sector        822 non-null    bool  
 12  Interpersonal conflicts              822 non-null    bool  
 13  Job dissatisfaction                  822 non-null    bool  
 14  Dissatisfaction with the department  822 non-null    bool  
 15  Physical work environment            822 non-null    bool  
 16  Lack of recognition                  822 non-null    bool  
 17  Lack of job security                 822 non-null    bool  
 18  Work location                        822 non-null    bool  
 19  Employment conditions                822 non-null    bool  
 20  Maternity/family                     822 non-null    bool  
 21  Relocation                           822 non-null    bool  
 22  Study/Travel                         822 non-null    bool  
 23  Ill Health                           822 non-null    bool  
 24  Traumatic incident                   822 non-null    bool  
 25  Work life balance                    822 non-null    bool  
 26  Workload                             822 non-null    bool  
 27  None of the above                    822 non-null    bool  
 28  Professional Development             808 non-null    object
 29  Opportunities for promotion          735 non-null    object
 30  Staff morale                         816 non-null    object
 31  Workplace issue                      788 non-null    object
 32  Physical environment                 817 non-null    object
 33  Worklife balance                     815 non-null    object
 34  Stress and pressure support          810 non-null    object
 35  Performance of supervisor            813 non-null    object
 36  Peer support                         812 non-null    object
 37  Initiative                           813 non-null    object
 38  Skills                               811 non-null    object
 39  Coach                                767 non-null    object
 40  Career Aspirations                   746 non-null    object
 41  Feedback                             792 non-null    object
 42  Further PD                           768 non-null    object
 43  Communication                        814 non-null    object
 44  My say                               812 non-null    object
 45  Information                          816 non-null    object
 46  Kept informed                        813 non-null    object
 47  Wellness programs                    766 non-null    object
 48  Health & Safety                      793 non-null    object
 49  Gender                               798 non-null    object
 50  Age                                  811 non-null    object
 51  Aboriginal                           16 non-null     object
 52  Torres Strait                        3 non-null      object
 53  South Sea                            7 non-null      object
 54  Disability                           23 non-null     object
 55  NESB                                 32 non-null     object
dtypes: bool(18), int64(1), object(37)
memory usage: 258.6+ KB
In [3]:
#Read in the data
tafe_survey = pd.read_csv("tafe_survey.csv")

#Quick exploration of the data
tafe_survey.head()
Out[3]:
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
In [4]:
tafe_survey.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 702 entries, 0 to 701
Data columns (total 72 columns):
 #   Column                                                                                                                                                         Non-Null Count  Dtype  
---  ------                                                                                                                                                         --------------  -----  
 0   Record ID                                                                                                                                                      702 non-null    float64
 1   Institute                                                                                                                                                      702 non-null    object 
 2   WorkArea                                                                                                                                                       702 non-null    object 
 3   CESSATION YEAR                                                                                                                                                 695 non-null    float64
 4   Reason for ceasing employment                                                                                                                                  701 non-null    object 
 5   Contributing Factors. Career Move - Public Sector                                                                                                              437 non-null    object 
 6   Contributing Factors. Career Move - Private Sector                                                                                                             437 non-null    object 
 7   Contributing Factors. Career Move - Self-employment                                                                                                            437 non-null    object 
 8   Contributing Factors. Ill Health                                                                                                                               437 non-null    object 
 9   Contributing Factors. Maternity/Family                                                                                                                         437 non-null    object 
 10  Contributing Factors. Dissatisfaction                                                                                                                          437 non-null    object 
 11  Contributing Factors. Job Dissatisfaction                                                                                                                      437 non-null    object 
 12  Contributing Factors. Interpersonal Conflict                                                                                                                   437 non-null    object 
 13  Contributing Factors. Study                                                                                                                                    437 non-null    object 
 14  Contributing Factors. Travel                                                                                                                                   437 non-null    object 
 15  Contributing Factors. Other                                                                                                                                    437 non-null    object 
 16  Contributing Factors. NONE                                                                                                                                     437 non-null    object 
 17  Main Factor. Which of these was the main factor for leaving?                                                                                                   113 non-null    object 
 18  InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction                                                                         608 non-null    object 
 19  InstituteViews. Topic:2. I was given access to skills training to help me do my job better                                                                     613 non-null    object 
 20  InstituteViews. Topic:3. I was given adequate opportunities for personal development                                                                           610 non-null    object 
 21  InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL%                                                            608 non-null    object 
 22  InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had                                                                615 non-null    object 
 23  InstituteViews. Topic:6. The organisation recognised when staff did good work                                                                                  607 non-null    object 
 24  InstituteViews. Topic:7. Management was generally supportive of me                                                                                             614 non-null    object 
 25  InstituteViews. Topic:8. Management was generally supportive of my team                                                                                        608 non-null    object 
 26  InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me                                                          610 non-null    object 
 27  InstituteViews. Topic:10. Staff morale was positive within the Institute                                                                                       602 non-null    object 
 28  InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly                                                                                 601 non-null    object 
 29  InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently                                                                             597 non-null    object 
 30  InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly                                                                              601 non-null    object 
 31  WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit                                                609 non-null    object 
 32  WorkUnitViews. Topic:15. I worked well with my colleagues                                                                                                      605 non-null    object 
 33  WorkUnitViews. Topic:16. My job was challenging and interesting                                                                                                607 non-null    object 
 34  WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work                                                                        610 non-null    object 
 35  WorkUnitViews. Topic:18. I had sufficient contact with other people in my job                                                                                  613 non-null    object 
 36  WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job                                                   609 non-null    object 
 37  WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job                                                                               609 non-null    object 
 38  WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT]  608 non-null    object 
 39  WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job                                                                            608 non-null    object 
 40  WorkUnitViews. Topic:23. My job provided sufficient variety                                                                                                    611 non-null    object 
 41  WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job                                                                    610 non-null    object 
 42  WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction                                                        611 non-null    object 
 43  WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance                                                    606 non-null    object 
 44  WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area                       610 non-null    object 
 45  WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date  609 non-null    object 
 46  WorkUnitViews. Topic:29. There was adequate communication between staff in my unit                                                                             603 non-null    object 
 47  WorkUnitViews. Topic:30. Staff morale was positive within my work unit                                                                                         606 non-null    object 
 48  Induction. Did you undertake Workplace Induction?                                                                                                              619 non-null    object 
 49  InductionInfo. Topic:Did you undertake a Corporate Induction?                                                                                                  432 non-null    object 
 50  InductionInfo. Topic:Did you undertake a Institute Induction?                                                                                                  483 non-null    object 
 51  InductionInfo. Topic: Did you undertake Team Induction?                                                                                                        440 non-null    object 
 52  InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted?                                                      555 non-null    object 
 53  InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted?                                                           555 non-null    object 
 54  InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction?                                                                                 555 non-null    object 
 55  InductionInfo. Face to Face Topic:Did you undertake a Institute Induction?                                                                                     530 non-null    object 
 56  InductionInfo. On-line Topic:Did you undertake a Institute Induction?                                                                                          555 non-null    object 
 57  InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction?                                                                                 553 non-null    object 
 58  InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category?                                                                                 555 non-null    object 
 59  InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.]                                                555 non-null    object 
 60  InductionInfo. Induction Manual Topic: Did you undertake Team Induction?                                                                                       555 non-null    object 
 61  Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)?                                                      608 non-null    object 
 62  Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination?                                                    594 non-null    object 
 63  Workplace. Topic:Does your workplace promote and practice the principles of employment equity?                                                                 587 non-null    object 
 64  Workplace. Topic:Does your workplace value the diversity of its employees?                                                                                     586 non-null    object 
 65  Workplace. Topic:Would you recommend the Institute as an employer to others?                                                                                   581 non-null    object 
 66  Gender. What is your Gender?                                                                                                                                   596 non-null    object 
 67  CurrentAge. Current Age                                                                                                                                        596 non-null    object 
 68  Employment Type. Employment Type                                                                                                                               596 non-null    object 
 69  Classification. Classification                                                                                                                                 596 non-null    object 
 70  LengthofServiceOverall. Overall Length of Service at Institute (in years)                                                                                      596 non-null    object 
 71  LengthofServiceCurrent. Length of Service at current workplace (in years)                                                                                      596 non-null    object 
dtypes: float64(2), object(70)
memory usage: 395.0+ KB

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.

In [5]:
# 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()
Out[5]:
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
In [6]:
# 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.

In [7]:
# Clean the column names
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(' ', '_')

# Check that the column names were updated correctly
dete_survey_updated.columns
Out[7]:
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')
In [8]:
# Update column names to match the names in dete_survey_updated
mapping = {'Record ID': 'id', 'CESSATION YEAR': 'cease_date', 'Reason for ceasing employment': 'separationtype', 'Gender. What is your Gender?': 'gender', 'CurrentAge. Current Age': 'age',
       'Employment Type. Employment Type': 'employment_status',
       'Classification. Classification': 'position',
       'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service',
       'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'}
tafe_survey_updated = tafe_survey_updated.rename(mapping, axis = 1)

# Check that the specified column names were updated correctly
tafe_survey_updated.columns
Out[8]:
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'.

In [9]:
# Check the unique values for the separationtype column
tafe_survey_updated['separationtype'].value_counts()
Out[9]:
Resignation                 340
Contract Expired            127
Retrenchment/ Redundancy    104
Retirement                   82
Transfer                     25
Termination                  23
Name: separationtype, dtype: int64
In [10]:
# Check the unique values for the separationtype column
dete_survey_updated['separationtype'].value_counts()
Out[10]:
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
In [11]:
# Update all separation types containing the word "resignation" to '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()
Out[11]:
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
In [12]:
# Select only the resignation separation types from each dataframe
dete_resignations = dete_survey_updated[dete_survey_updated['separationtype'] == 'Resignation'].copy()
tafe_resignations = 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.
In [13]:
# Check the unique values
dete_resignations['cease_date'].value_counts()
Out[13]:
2012       126
2013        74
01/2014     22
12/2013     17
06/2013     14
09/2013     11
07/2013      9
11/2013      9
10/2013      6
08/2013      4
05/2013      2
05/2012      2
2010         1
07/2006      1
09/2010      1
07/2012      1
Name: cease_date, dtype: int64
In [14]:
# Extract the years and convert them to a float type
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.split('/').str[-1]
dete_resignations['cease_date'] = dete_resignations['cease_date'].astype("float")

# Check the values again and look for outliers
dete_resignations['cease_date'].value_counts()
Out[14]:
2013.0    146
2012.0    129
2014.0     22
2010.0      2
2006.0      1
Name: cease_date, dtype: int64
In [15]:
# Check the unique values and look for outliers
dete_resignations['dete_start_date'].value_counts().sort_values()
Out[15]:
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
In [16]:
# Check the unique values
tafe_resignations['cease_date'].value_counts().sort_values()
Out[16]:
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_updaed 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.

In [17]:
# Calculate the length of time an employee spent in their respective workplace and create a new column
dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']

# Quick check of the result
dete_resignations['institute_service'].head()
Out[17]:
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
  1. 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
In [18]:
# Check the unique values
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()
Out[18]:
-                                         277
Contributing Factors. Dissatisfaction      55
Name: Contributing Factors. Dissatisfaction, dtype: int64
In [19]:
# Check the unique values
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()
Out[19]:
-                      270
Job Dissatisfaction     62
Name: Contributing Factors. Job Dissatisfaction, dtype: int64
In [20]:
# Update the values in the contributing factors columns to be either True, False, or NaN
def update_vals(x):
    if x == '-':
        return False
    elif pd.isnull(x):
        return np.nan
    else:
        return True
tafe_resignations['dissatisfied'] = tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']].applymap(update_vals).any(1, skipna=False)
tafe_resignations_up = tafe_resignations.copy()

# Check the unique values after the updates
tafe_resignations_up['dissatisfied'].value_counts(dropna=False)
Out[20]:
False    241
True      91
NaN        8
Name: dissatisfied, dtype: int64
In [21]:
# Update the values in columns related to dissatisfaction to be either True, False, or NaN
dete_resignations['dissatisfied'] = dete_resignations[['job_dissatisfaction',
       'dissatisfaction_with_the_department', 'physical_work_environment',
       'lack_of_recognition', 'lack_of_job_security', 'work_location',
       'employment_conditions', 'work_life_balance',
       'workload']].any(1, skipna=False)
dete_resignations_up = dete_resignations.copy()
dete_resignations_up['dissatisfied'].value_counts(dropna=False)
Out[21]:
False    162
True     149
Name: dissatisfied, dtype: int64

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.

In [22]:
# Add an institute column
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
In [23]:
# Combine the dataframes
combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index=True)

# Verify the number of non null values in each column
combined.notnull().sum().sort_values()
Out[23]:
torres_strait                                            0
south_sea                                                3
aboriginal                                               7
disability                                               8
nesb                                                     9
business_unit                                           32
classification                                         161
region                                                 265
role_start_date                                        271
dete_start_date                                        283
role_service                                           290
none_of_the_above                                      311
work_life_balance                                      311
traumatic_incident                                     311
ill_health                                             311
study/travel                                           311
relocation                                             311
maternity/family                                       311
employment_conditions                                  311
workload                                               311
lack_of_job_security                                   311
career_move_to_public_sector                           311
career_move_to_private_sector                          311
interpersonal_conflicts                                311
work_location                                          311
dissatisfaction_with_the_department                    311
physical_work_environment                              311
lack_of_recognition                                    311
job_dissatisfaction                                    311
Contributing Factors. Job Dissatisfaction              332
Contributing Factors. Travel                           332
Contributing Factors. Maternity/Family                 332
Contributing Factors. Ill Health                       332
Contributing Factors. Career Move - Self-employment    332
Contributing Factors. Career Move - Private Sector     332
Contributing Factors. Career Move - Public Sector      332
Contributing Factors. Dissatisfaction                  332
Contributing Factors. Other                            332
Contributing Factors. Interpersonal Conflict           332
Contributing Factors. NONE                             332
Contributing Factors. Study                            332
Institute                                              340
WorkArea                                               340
institute_service                                      563
gender                                                 592
age                                                    596
employment_status                                      597
position                                               598
cease_date                                             635
dissatisfied                                           643
separationtype                                         651
institute                                              651
id                                                     651
dtype: int64
In [24]:
# 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.
In [25]:
# Check the unique values
combined_updated['institute_service'].value_counts(dropna=False)
Out[25]:
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
22.0                   6
17.0                   6
18.0                   5
16.0                   5
11.0                   4
23.0                   4
24.0                   4
19.0                   3
39.0                   3
32.0                   3
21.0                   3
26.0                   2
28.0                   2
30.0                   2
36.0                   2
25.0                   2
27.0                   1
29.0                   1
31.0                   1
49.0                   1
33.0                   1
34.0                   1
35.0                   1
38.0                   1
41.0                   1
42.0                   1
Name: institute_service, dtype: int64
In [26]:
# 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()
Out[26]:
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
In [27]:
# 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()
Out[27]:
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.

In [28]:
# Verify the unique values
combined_updated['dissatisfied'].value_counts(dropna=False)
Out[28]:
False    403
True     240
NaN        8
Name: dissatisfied, dtype: int64
In [29]:
# Replace missing values with the most frequent value, False
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
In [30]:
# 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
%matplotlib inline
dis_pct.plot(kind='bar', rot=30)
Out[30]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f3e66e2a280>

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.