DETE and TAFE employee satisfaction survey study

Why are employees resigning?

In this guided project, we'll work with exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. we will work to solve the following prompts, provided by our stakeholders.

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?

Let's start by reading the datasets into pandas and exploring them.

In [769]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

DETE survey

In [770]:
dete_survey = pd.read_csv('dete_survey.csv')

dete_survey.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 822 entries, 0 to 821
Data columns (total 56 columns):
 #   Column                               Non-Null Count  Dtype 
---  ------                               --------------  ----- 
 0   ID                                   822 non-null    int64 
 1   SeparationType                       822 non-null    object
 2   Cease Date                           822 non-null    object
 3   DETE Start Date                      822 non-null    object
 4   Role Start Date                      822 non-null    object
 5   Position                             817 non-null    object
 6   Classification                       455 non-null    object
 7   Region                               822 non-null    object
 8   Business Unit                        126 non-null    object
 9   Employment Status                    817 non-null    object
 10  Career move to public sector         822 non-null    bool  
 11  Career move to private sector        822 non-null    bool  
 12  Interpersonal conflicts              822 non-null    bool  
 13  Job dissatisfaction                  822 non-null    bool  
 14  Dissatisfaction with the department  822 non-null    bool  
 15  Physical work environment            822 non-null    bool  
 16  Lack of recognition                  822 non-null    bool  
 17  Lack of job security                 822 non-null    bool  
 18  Work location                        822 non-null    bool  
 19  Employment conditions                822 non-null    bool  
 20  Maternity/family                     822 non-null    bool  
 21  Relocation                           822 non-null    bool  
 22  Study/Travel                         822 non-null    bool  
 23  Ill Health                           822 non-null    bool  
 24  Traumatic incident                   822 non-null    bool  
 25  Work life balance                    822 non-null    bool  
 26  Workload                             822 non-null    bool  
 27  None of the above                    822 non-null    bool  
 28  Professional Development             808 non-null    object
 29  Opportunities for promotion          735 non-null    object
 30  Staff morale                         816 non-null    object
 31  Workplace issue                      788 non-null    object
 32  Physical environment                 817 non-null    object
 33  Worklife balance                     815 non-null    object
 34  Stress and pressure support          810 non-null    object
 35  Performance of supervisor            813 non-null    object
 36  Peer support                         812 non-null    object
 37  Initiative                           813 non-null    object
 38  Skills                               811 non-null    object
 39  Coach                                767 non-null    object
 40  Career Aspirations                   746 non-null    object
 41  Feedback                             792 non-null    object
 42  Further PD                           768 non-null    object
 43  Communication                        814 non-null    object
 44  My say                               812 non-null    object
 45  Information                          816 non-null    object
 46  Kept informed                        813 non-null    object
 47  Wellness programs                    766 non-null    object
 48  Health & Safety                      793 non-null    object
 49  Gender                               798 non-null    object
 50  Age                                  811 non-null    object
 51  Aboriginal                           16 non-null     object
 52  Torres Strait                        3 non-null      object
 53  South Sea                            7 non-null      object
 54  Disability                           23 non-null     object
 55  NESB                                 32 non-null     object
dtypes: bool(18), int64(1), object(37)
memory usage: 258.6+ KB
In [771]:
dete_survey.isnull().sum()
Out[771]:
ID                                       0
SeparationType                           0
Cease Date                               0
DETE Start Date                          0
Role Start Date                          0
Position                                 5
Classification                         367
Region                                   0
Business Unit                          696
Employment Status                        5
Career move to public sector             0
Career move to private sector            0
Interpersonal conflicts                  0
Job dissatisfaction                      0
Dissatisfaction with the department      0
Physical work environment                0
Lack of recognition                      0
Lack of job security                     0
Work location                            0
Employment conditions                    0
Maternity/family                         0
Relocation                               0
Study/Travel                             0
Ill Health                               0
Traumatic incident                       0
Work life balance                        0
Workload                                 0
None of the above                        0
Professional Development                14
Opportunities for promotion             87
Staff morale                             6
Workplace issue                         34
Physical environment                     5
Worklife balance                         7
Stress and pressure support             12
Performance of supervisor                9
Peer support                            10
Initiative                               9
Skills                                  11
Coach                                   55
Career Aspirations                      76
Feedback                                30
Further PD                              54
Communication                            8
My say                                  10
Information                              6
Kept informed                            9
Wellness programs                       56
Health & Safety                         29
Gender                                  24
Age                                     11
Aboriginal                             806
Torres Strait                          819
South Sea                              815
Disability                             799
NESB                                   790
dtype: int64
In [772]:
dete_survey.head(5)
Out[772]:
ID SeparationType Cease Date DETE Start Date Role Start Date Position Classification Region Business Unit Employment Status ... Kept informed Wellness programs Health & Safety Gender Age Aboriginal Torres Strait South Sea Disability NESB
0 1 Ill Health Retirement 08/2012 1984 2004 Public Servant A01-A04 Central Office Corporate Strategy and Peformance Permanent Full-time ... N N N Male 56-60 NaN NaN NaN NaN Yes
1 2 Voluntary Early Retirement (VER) 08/2012 Not Stated Not Stated Public Servant AO5-AO7 Central Office Corporate Strategy and Peformance Permanent Full-time ... N N N Male 56-60 NaN NaN NaN NaN NaN
2 3 Voluntary Early Retirement (VER) 05/2012 2011 2011 Schools Officer NaN Central Office Education Queensland Permanent Full-time ... N N N Male 61 or older NaN NaN NaN NaN NaN
3 4 Resignation-Other reasons 05/2012 2005 2006 Teacher Primary Central Queensland NaN Permanent Full-time ... A N A Female 36-40 NaN NaN NaN NaN NaN
4 5 Age Retirement 05/2012 1970 1989 Head of Curriculum/Head of Special Education NaN South East NaN Permanent Full-time ... N A M Female 61 or older NaN NaN NaN NaN NaN

5 rows × 56 columns

TAFE survey

In [773]:
tafe_survey = pd.read_csv('tafe_survey.csv')

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
In [774]:
tafe_survey.isnull().sum()
Out[774]:
Record ID                                                                      0
Institute                                                                      0
WorkArea                                                                       0
CESSATION YEAR                                                                 7
Reason for ceasing employment                                                  1
                                                                            ... 
CurrentAge. Current Age                                                      106
Employment Type. Employment Type                                             106
Classification. Classification                                               106
LengthofServiceOverall. Overall Length of Service at Institute (in years)    106
LengthofServiceCurrent. Length of Service at current workplace (in years)    106
Length: 72, dtype: int64
In [775]:
tafe_survey.head(5)
Out[775]:
Record ID Institute WorkArea CESSATION YEAR Reason for ceasing employment Contributing Factors. Career Move - Public Sector Contributing Factors. Career Move - Private Sector Contributing Factors. Career Move - Self-employment Contributing Factors. Ill Health Contributing Factors. Maternity/Family ... Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? Workplace. Topic:Does your workplace promote and practice the principles of employment equity? Workplace. Topic:Does your workplace value the diversity of its employees? Workplace. Topic:Would you recommend the Institute as an employer to others? Gender. What is your Gender? CurrentAge. Current Age Employment Type. Employment Type Classification. Classification LengthofServiceOverall. Overall Length of Service at Institute (in years) LengthofServiceCurrent. Length of Service at current workplace (in years)
0 6.341330e+17 Southern Queensland Institute of TAFE Non-Delivery (corporate) 2010.0 Contract Expired NaN NaN NaN NaN NaN ... Yes Yes Yes Yes Female 26 30 Temporary Full-time Administration (AO) 1-2 1-2
1 6.341337e+17 Mount Isa Institute of TAFE Non-Delivery (corporate) 2010.0 Retirement - - - - - ... Yes Yes Yes Yes NaN NaN NaN NaN NaN NaN
2 6.341388e+17 Mount Isa Institute of TAFE Delivery (teaching) 2010.0 Retirement - - - - - ... Yes Yes Yes Yes NaN NaN NaN NaN NaN NaN
3 6.341399e+17 Mount Isa Institute of TAFE Non-Delivery (corporate) 2010.0 Resignation - - - - - ... Yes Yes Yes Yes NaN NaN NaN NaN NaN NaN
4 6.341466e+17 Southern Queensland Institute of TAFE Delivery (teaching) 2010.0 Resignation - Career Move - Private Sector - - - ... Yes Yes Yes Yes Male 41 45 Permanent Full-time Teacher (including LVT) 3-4 3-4

5 rows × 72 columns

After careful observation we can see that:

1) There are many columns with information that is not relevant to the questions we are tyring to answer.

2) Many of the columns have different names in the two data sets but provide almost identical data.

3) A few columns contain such a small amount of data, I'm not sure if it will be important to keep that information in such a large dataset. I can;t dismisss this information, but it might be a lot of work extracting information that doesn't affectthe outcome of our answers.

4) There are multiple columns that indicate an employee resigned because they were dissatisfied.

5) The dete_survey dataframe contains 'Not Stated' values that indicate values are missing, but they aren't represented as NaN.

We'll start by taking a closer look at issue #5 and #1.

Identifying Missing values and Drop Columns

Let's begin by making some changes to the DETE survey, starting with unifying the missing data. As we said earlier, some of the missing values in this data set are recorded as 'Not Stated', which doesn't accurately represent the data in that column (there is nothing there). We'll udpate those data points to read 'NaN', so they may be counted correctly as missing data. After that we will delete some of the columns that won't be helpful is answering our stakeholders questions.

In [776]:
dete_survey = pd.read_csv('dete_survey.csv', na_values = 'Not Stated')

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) 
In [777]:
dete_survey_updated.head(5)
Out[777]:
ID SeparationType Cease Date DETE Start Date Role Start Date Position Classification Region Business Unit Employment Status ... Work life balance Workload None of the above Gender Age Aboriginal Torres Strait South Sea Disability NESB
0 1 Ill Health Retirement 08/2012 1984.0 2004.0 Public Servant A01-A04 Central Office Corporate Strategy and Peformance Permanent Full-time ... False False True Male 56-60 NaN NaN NaN NaN Yes
1 2 Voluntary Early Retirement (VER) 08/2012 NaN NaN Public Servant AO5-AO7 Central Office Corporate Strategy and Peformance Permanent Full-time ... False False False Male 56-60 NaN NaN NaN NaN NaN
2 3 Voluntary Early Retirement (VER) 05/2012 2011.0 2011.0 Schools Officer NaN Central Office Education Queensland Permanent Full-time ... False False True Male 61 or older NaN NaN NaN NaN NaN
3 4 Resignation-Other reasons 05/2012 2005.0 2006.0 Teacher Primary Central Queensland NaN Permanent Full-time ... False False False Female 36-40 NaN NaN NaN NaN NaN
4 5 Age Retirement 05/2012 1970.0 1989.0 Head of Curriculum/Head of Special Education NaN South East NaN Permanent Full-time ... True False False Female 61 or older NaN NaN NaN NaN NaN

5 rows × 35 columns

In [778]:
tafe_survey_updated.head(5)
Out[778]:
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. 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)
0 6.341330e+17 Southern Queensland Institute of TAFE Non-Delivery (corporate) 2010.0 Contract Expired NaN NaN NaN NaN NaN ... NaN NaN NaN NaN Female 26 30 Temporary Full-time Administration (AO) 1-2 1-2
1 6.341337e+17 Mount Isa Institute of TAFE Non-Delivery (corporate) 2010.0 Retirement - - - - - ... - Travel - - NaN NaN NaN NaN NaN NaN
2 6.341388e+17 Mount Isa Institute of TAFE Delivery (teaching) 2010.0 Retirement - - - - - ... - - - NONE NaN NaN NaN NaN NaN NaN
3 6.341399e+17 Mount Isa Institute of TAFE Non-Delivery (corporate) 2010.0 Resignation - - - - - ... - Travel - - NaN NaN NaN NaN NaN NaN
4 6.341466e+17 Southern Queensland Institute of TAFE Delivery (teaching) 2010.0 Resignation - Career Move - Private Sector - - - ... - - - - Male 41 45 Permanent Full-time Teacher (including LVT) 3-4 3-4

5 rows × 23 columns

Clean Column Names

Next, let's turn our attention to the column names. Each dataframe contains many of the same columns, but the column names are different. Because we eventually want to combine them, we'll have to standardize the column names. For the DETE survey we will clean the existing names and for the TAFE survey we will change the column names.

In [779]:
dete_survey_updated.columns = dete_survey_updated.columns.str.replace(' ', '_').str.strip().str.lower()

dete_survey_updated.head(5)
Out[779]:
id separationtype cease_date dete_start_date role_start_date position classification region business_unit employment_status ... work_life_balance workload none_of_the_above gender age aboriginal torres_strait south_sea disability nesb
0 1 Ill Health Retirement 08/2012 1984.0 2004.0 Public Servant A01-A04 Central Office Corporate Strategy and Peformance Permanent Full-time ... False False True Male 56-60 NaN NaN NaN NaN Yes
1 2 Voluntary Early Retirement (VER) 08/2012 NaN NaN Public Servant AO5-AO7 Central Office Corporate Strategy and Peformance Permanent Full-time ... False False False Male 56-60 NaN NaN NaN NaN NaN
2 3 Voluntary Early Retirement (VER) 05/2012 2011.0 2011.0 Schools Officer NaN Central Office Education Queensland Permanent Full-time ... False False True Male 61 or older NaN NaN NaN NaN NaN
3 4 Resignation-Other reasons 05/2012 2005.0 2006.0 Teacher Primary Central Queensland NaN Permanent Full-time ... False False False Female 36-40 NaN NaN NaN NaN NaN
4 5 Age Retirement 05/2012 1970.0 1989.0 Head of Curriculum/Head of Special Education NaN South East NaN Permanent Full-time ... True False False Female 61 or older NaN NaN NaN NaN NaN

5 rows × 35 columns

In [780]:
rename = {'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(rename, axis = 1)

tafe_survey_updated.head(5)
Out[780]:
id Institute WorkArea cease_date separationtype Contributing Factors. Career Move - Public Sector Contributing Factors. Career Move - Private Sector Contributing Factors. Career Move - Self-employment Contributing Factors. Ill Health Contributing Factors. Maternity/Family ... Contributing Factors. Study Contributing Factors. Travel Contributing Factors. Other Contributing Factors. NONE gender age employment_status position institute_service role_service
0 6.341330e+17 Southern Queensland Institute of TAFE Non-Delivery (corporate) 2010.0 Contract Expired NaN NaN NaN NaN NaN ... NaN NaN NaN NaN Female 26 30 Temporary Full-time Administration (AO) 1-2 1-2
1 6.341337e+17 Mount Isa Institute of TAFE Non-Delivery (corporate) 2010.0 Retirement - - - - - ... - Travel - - NaN NaN NaN NaN NaN NaN
2 6.341388e+17 Mount Isa Institute of TAFE Delivery (teaching) 2010.0 Retirement - - - - - ... - - - NONE NaN NaN NaN NaN NaN NaN
3 6.341399e+17 Mount Isa Institute of TAFE Non-Delivery (corporate) 2010.0 Resignation - - - - - ... - Travel - - NaN NaN NaN NaN NaN NaN
4 6.341466e+17 Southern Queensland Institute of TAFE Delivery (teaching) 2010.0 Resignation - Career Move - Private Sector - - - ... - - - - Male 41 45 Permanent Full-time Teacher (including LVT) 3-4 3-4

5 rows × 23 columns

Filter the Data

Next, we will remove more data that we don't need. In particular, we will begin cleaning the 'separationtype' column to extract all of the data for empoyees that resigned.

In [781]:
print(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
In [782]:
print(tafe_survey_updated['separationtype'].value_counts())
Resignation                 340
Contract Expired            127
Retrenchment/ Redundancy    104
Retirement                   82
Transfer                     25
Termination                  23
Name: separationtype, dtype: int64
In [783]:
tafe_resignations = tafe_survey_updated.copy()[tafe_survey_updated['separationtype'].str.contains(r'[Rr]esignation', na = False)]


print('\n')
print(tafe_resignations.info())
print('\n')
print(tafe_resignations.head())
<class 'pandas.core.frame.DataFrame'>
Int64Index: 340 entries, 3 to 701
Data columns (total 23 columns):
 #   Column                                               Non-Null Count  Dtype  
---  ------                                               --------------  -----  
 0   id                                                   340 non-null    float64
 1   Institute                                            340 non-null    object 
 2   WorkArea                                             340 non-null    object 
 3   cease_date                                           335 non-null    float64
 4   separationtype                                       340 non-null    object 
 5   Contributing Factors. Career Move - Public Sector    332 non-null    object 
 6   Contributing Factors. Career Move - Private Sector   332 non-null    object 
 7   Contributing Factors. Career Move - Self-employment  332 non-null    object 
 8   Contributing Factors. Ill Health                     332 non-null    object 
 9   Contributing Factors. Maternity/Family               332 non-null    object 
 10  Contributing Factors. Dissatisfaction                332 non-null    object 
 11  Contributing Factors. Job Dissatisfaction            332 non-null    object 
 12  Contributing Factors. Interpersonal Conflict         332 non-null    object 
 13  Contributing Factors. Study                          332 non-null    object 
 14  Contributing Factors. Travel                         332 non-null    object 
 15  Contributing Factors. Other                          332 non-null    object 
 16  Contributing Factors. NONE                           332 non-null    object 
 17  gender                                               290 non-null    object 
 18  age                                                  290 non-null    object 
 19  employment_status                                    290 non-null    object 
 20  position                                             290 non-null    object 
 21  institute_service                                    290 non-null    object 
 22  role_service                                         290 non-null    object 
dtypes: float64(2), object(21)
memory usage: 63.8+ KB
None


             id                              Institute  \
3  6.341399e+17            Mount Isa Institute of TAFE   
4  6.341466e+17  Southern Queensland Institute of TAFE   
5  6.341475e+17  Southern Queensland Institute of TAFE   
6  6.341520e+17         Barrier Reef Institute of TAFE   
7  6.341537e+17  Southern Queensland Institute of TAFE   

                   WorkArea  cease_date separationtype  \
3  Non-Delivery (corporate)      2010.0    Resignation   
4       Delivery (teaching)      2010.0    Resignation   
5       Delivery (teaching)      2010.0    Resignation   
6  Non-Delivery (corporate)      2010.0    Resignation   
7       Delivery (teaching)      2010.0    Resignation   

  Contributing Factors. Career Move - Public Sector   \
3                                                  -   
4                                                  -   
5                                                  -   
6                                                  -   
7                                                  -   

  Contributing Factors. Career Move - Private Sector   \
3                                                  -    
4                       Career Move - Private Sector    
5                                                  -    
6                       Career Move - Private Sector    
7                                                  -    

  Contributing Factors. Career Move - Self-employment  \
3                                                  -    
4                                                  -    
5                                                  -    
6                                                  -    
7                                                  -    

  Contributing Factors. Ill Health Contributing Factors. Maternity/Family  \
3                                -                                      -   
4                                -                                      -   
5                                -                                      -   
6                                -                       Maternity/Family   
7                                -                                      -   

   ... Contributing Factors. Study Contributing Factors. Travel  \
3  ...                           -                       Travel   
4  ...                           -                            -   
5  ...                           -                            -   
6  ...                           -                            -   
7  ...                           -                            -   

  Contributing Factors. Other Contributing Factors. NONE  gender  \
3                           -                          -     NaN   
4                           -                          -    Male   
5                       Other                          -  Female   
6                       Other                          -    Male   
7                       Other                          -    Male   

             age    employment_status                 position  \
3            NaN                  NaN                      NaN   
4         41  45  Permanent Full-time  Teacher (including LVT)   
5    56 or older      Contract/casual  Teacher (including LVT)   
6  20 or younger  Temporary Full-time      Administration (AO)   
7         46  50  Permanent Full-time  Teacher (including LVT)   

  institute_service role_service  
3               NaN          NaN  
4               3-4          3-4  
5              7-10         7-10  
6               3-4          3-4  
7               3-4          3-4  

[5 rows x 23 columns]
In [784]:
dete_resiginations = dete_survey_updated.copy()[dete_survey_updated['separationtype'].str.contains(r'[Rr]esignation')]

print('\n')
print(dete_resiginations.info())
print('\n')
print(dete_resiginations.head())
<class 'pandas.core.frame.DataFrame'>
Int64Index: 311 entries, 3 to 821
Data columns (total 35 columns):
 #   Column                               Non-Null Count  Dtype  
---  ------                               --------------  -----  
 0   id                                   311 non-null    int64  
 1   separationtype                       311 non-null    object 
 2   cease_date                           300 non-null    object 
 3   dete_start_date                      283 non-null    float64
 4   role_start_date                      271 non-null    float64
 5   position                             308 non-null    object 
 6   classification                       161 non-null    object 
 7   region                               265 non-null    object 
 8   business_unit                        32 non-null     object 
 9   employment_status                    307 non-null    object 
 10  career_move_to_public_sector         311 non-null    bool   
 11  career_move_to_private_sector        311 non-null    bool   
 12  interpersonal_conflicts              311 non-null    bool   
 13  job_dissatisfaction                  311 non-null    bool   
 14  dissatisfaction_with_the_department  311 non-null    bool   
 15  physical_work_environment            311 non-null    bool   
 16  lack_of_recognition                  311 non-null    bool   
 17  lack_of_job_security                 311 non-null    bool   
 18  work_location                        311 non-null    bool   
 19  employment_conditions                311 non-null    bool   
 20  maternity/family                     311 non-null    bool   
 21  relocation                           311 non-null    bool   
 22  study/travel                         311 non-null    bool   
 23  ill_health                           311 non-null    bool   
 24  traumatic_incident                   311 non-null    bool   
 25  work_life_balance                    311 non-null    bool   
 26  workload                             311 non-null    bool   
 27  none_of_the_above                    311 non-null    bool   
 28  gender                               302 non-null    object 
 29  age                                  306 non-null    object 
 30  aboriginal                           7 non-null      object 
 31  torres_strait                        0 non-null      object 
 32  south_sea                            3 non-null      object 
 33  disability                           8 non-null      object 
 34  nesb                                 9 non-null      object 
dtypes: bool(18), float64(2), int64(1), object(14)
memory usage: 49.2+ KB
None


    id                        separationtype cease_date  dete_start_date  \
3    4             Resignation-Other reasons    05/2012           2005.0   
5    6             Resignation-Other reasons    05/2012           1994.0   
8    9             Resignation-Other reasons    07/2012           2009.0   
9   10            Resignation-Other employer       2012           1997.0   
11  12  Resignation-Move overseas/interstate       2012           2009.0   

    role_start_date          position classification                region  \
3            2006.0           Teacher        Primary    Central Queensland   
5            1997.0  Guidance Officer            NaN        Central Office   
8            2009.0           Teacher      Secondary      North Queensland   
9            2008.0      Teacher Aide            NaN                   NaN   
11           2009.0           Teacher      Secondary  Far North Queensland   

           business_unit    employment_status  ...  work_life_balance  \
3                    NaN  Permanent Full-time  ...              False   
5   Education Queensland  Permanent Full-time  ...              False   
8                    NaN  Permanent Full-time  ...              False   
9                    NaN  Permanent Part-time  ...              False   
11                   NaN  Permanent Full-time  ...              False   

    workload  none_of_the_above  gender    age  aboriginal  torres_strait  \
3      False              False  Female  36-40         NaN            NaN   
5      False              False  Female  41-45         NaN            NaN   
8      False              False  Female  31-35         NaN            NaN   
9      False              False  Female  46-50         NaN            NaN   
11     False              False    Male  31-35         NaN            NaN   

    south_sea  disability  nesb  
3         NaN         NaN   NaN  
5         NaN         NaN   NaN  
8         NaN         NaN   NaN  
9         NaN         NaN   NaN  
11        NaN         NaN   NaN  

[5 rows x 35 columns]

Verify the Data

Before we start cleaning and manipulating the rest of our data, let's verify that the data doesn't contain any major inconsistencies. In this step, we'll focus on verifying that the years in the cease_date and dete_start_date columns make sense.

In [785]:
dete_resiginations['cease_date'].value_counts()
Out[785]:
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
07/2012      1
07/2006      1
2010         1
Name: cease_date, dtype: int64
In [786]:
dete_resiginations['cease_date'] = dete_resiginations['cease_date'].str.split('/').str.get(-1).astype(float)


dete_resiginations['cease_date'].value_counts()
Out[786]:
2013.0    146
2012.0    129
2014.0     22
2010.0      2
2006.0      1
Name: cease_date, dtype: int64
In [787]:
dete_resiginations['dete_start_date'].value_counts()
Out[787]:
2011.0    24
2008.0    22
2007.0    21
2012.0    21
2010.0    17
2005.0    15
2004.0    14
2009.0    13
2006.0    13
2013.0    10
2000.0     9
1999.0     8
1996.0     6
2002.0     6
1992.0     6
1998.0     6
2003.0     6
1994.0     6
1993.0     5
1990.0     5
1980.0     5
1997.0     5
1991.0     4
1989.0     4
1988.0     4
1995.0     4
2001.0     3
1985.0     3
1986.0     3
1983.0     2
1976.0     2
1974.0     2
1971.0     1
1972.0     1
1984.0     1
1982.0     1
1987.0     1
1975.0     1
1973.0     1
1977.0     1
1963.0     1
Name: dete_start_date, dtype: int64
In [788]:
tafe_resignations['cease_date'].value_counts()
Out[788]:
2011.0    116
2012.0     94
2010.0     68
2013.0     55
2009.0      2
Name: cease_date, dtype: int64
In [789]:
tafe_resignations['cease_date'].sort_index(ascending = True)
Out[789]:
3      2010.0
4      2010.0
5      2010.0
6      2010.0
7      2010.0
        ...  
696    2013.0
697    2013.0
698    2013.0
699    2013.0
701    2013.0
Name: cease_date, Length: 340, dtype: float64
In [790]:
dete_resiginations['dete_start_date'].sort_index(ascending = True)
Out[790]:
3      2005.0
5      1994.0
8      2009.0
9      1997.0
11     2009.0
        ...  
808    2010.0
815    2012.0
816    2012.0
819    2009.0
821       NaN
Name: dete_start_date, Length: 311, dtype: float64
In [791]:
dete_resiginations['cease_date'].sort_index(ascending = True)
Out[791]:
3      2012.0
5      2012.0
8      2012.0
9      2012.0
11     2012.0
        ...  
808    2013.0
815    2014.0
816    2014.0
819    2014.0
821    2013.0
Name: cease_date, Length: 311, dtype: float64
In [792]:
dete_resiginations[['dete_start_date', 'cease_date']].plot(kind='box')
Out[792]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fa3c587a700>

The value counts above show that there are not any majot outliers on start and cease dates. No one would have come to work for this company before 1950 and no one would have been fired after the data was relesased. Using those paramentes this data looks depndable.

Create a New Column

Now that we've verified the years in the dete_resignations dataframe, we'll use them to create a new column.n the Human Resources field, the length of time an employee spent in a workplace is referred to as their years of service.

You may have noticed that the tafe_resignations dataframe already contains a "service" column, which we renamed to institute_service. In order to analyze both surveys together, we'll have to create a corresponding institute_service column in dete_resignations.

In [793]:
dete_resiginations['institute_service'] = dete_resiginations['cease_date'] - dete_resiginations['dete_start_date']

print(dete_resiginations['institute_service'].value_counts().sort_index(ascending = False))
49.0     1
42.0     1
41.0     1
39.0     3
38.0     1
36.0     2
35.0     1
34.0     1
33.0     1
32.0     3
31.0     1
30.0     2
29.0     1
28.0     2
27.0     1
26.0     2
25.0     2
24.0     4
23.0     4
22.0     6
21.0     3
20.0     7
19.0     3
18.0     5
17.0     6
16.0     5
15.0     7
14.0     6
13.0     8
12.0     6
11.0     4
10.0     6
9.0     14
8.0      8
7.0     13
6.0     17
5.0     23
4.0     16
3.0     20
2.0     14
1.0     22
0.0     20
Name: institute_service, dtype: int64

Idenftify Dissatisfied Employees

Next, we'll identify any employees who resigned because they were dissatisfied. If the employee indicated any of the factors above caused them to resign, we'll mark them as dissatisfied in a new column.

In [794]:
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()
Out[794]:
-                                         277
Contributing Factors. Dissatisfaction      55
Name: Contributing Factors. Dissatisfaction, dtype: int64
In [795]:
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()
Out[795]:
-                      270
Job Dissatisfaction     62
Name: Contributing Factors. Job Dissatisfaction, dtype: int64
In [796]:
def update_vals(val):
    if pd.isnull(val):
        return np.nan
    elif val == '-':
        return False
    else:
        return True
In [797]:
tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']] = tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']].applymap(update_vals)
print(tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']])
    Contributing Factors. Dissatisfaction  \
3                                   False   
4                                   False   
5                                   False   
6                                   False   
7                                   False   
..                                    ...   
696                                 False   
697                                 False   
698                                 False   
699                                 False   
701                                 False   

    Contributing Factors. Job Dissatisfaction  
3                                       False  
4                                       False  
5                                       False  
6                                       False  
7                                       False  
..                                        ...  
696                                     False  
697                                     False  
698                                     False  
699                                     False  
701                                     False  

[340 rows x 2 columns]
In [798]:
tafe_resignations['dissatisfied'] = tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']].any(axis = 1, skipna = False)
tafe_resignations_up = tafe_resignations.copy()
tafe_resignations['dissatisfied'].value_counts()
Out[798]:
False    241
True      91
Name: dissatisfied, dtype: int64
In [799]:
diss_numbers = ['job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions', 'work_life_balance', 'workload']
dete_resiginations['dissatisfied'] = dete_resiginations[diss_numbers].any(axis = 1, skipna = False)
dete_resiginations_up = dete_resiginations.copy()
dete_resiginations['dissatisfied'].value_counts()
Out[799]:
False    162
True     149
Name: dissatisfied, dtype: int64

We now have boolean statements for each of the columns that could correalte to any employee quitting their job.

Combine the Data

Now, we're finally ready to combine our datasets. Our end goal is to aggregate the data according to the institute_service column, so when combining the data, we will get the data into a form that's easy to aggregate. First, let's add a column to each dataframe that will allow us to easily distinguish between the two.

In [800]:
dete_resiginations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'

combined = pd.concat([dete_resiginations_up, tafe_resignations_up], ignore_index = True)

combined_updated = combined.dropna(axis = 1, thresh = 500)

print(combined_updated.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 651 entries, 0 to 650
Data columns (total 10 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   id                 651 non-null    float64
 1   separationtype     651 non-null    object 
 2   cease_date         635 non-null    float64
 3   position           598 non-null    object 
 4   employment_status  597 non-null    object 
 5   gender             592 non-null    object 
 6   age                596 non-null    object 
 7   institute_service  563 non-null    object 
 8   dissatisfied       643 non-null    object 
 9   institute          651 non-null    object 
dtypes: float64(2), object(8)
memory usage: 51.0+ KB
None

Clean the Service Column

Next, we will clean up the institute_service column.To analyze the data, we'll convert these numbers into categories.

New: Less than 3 years at a company

Experienced: 3-6 years at a company

Established: 7-10 years at a company

Veteran: 11 or more years at a company

First, we'll extract the years of service from each value in the institute_service column.

In [801]:
combined_updated = combined_updated.copy()
combined_updated['institute_service'] = combined_updated['institute_service'].astype(str)
combined_updated['institute_service'] = combined_updated['institute_service'].str.extract(r'(\d+)').astype(float)
print(combined_updated['institute_service'].value_counts())
1.0     159
3.0      83
5.0      56
7.0      34
11.0     30
0.0      20
20.0     17
6.0      17
4.0      16
9.0      14
2.0      14
13.0      8
8.0       8
15.0      7
17.0      6
10.0      6
12.0      6
14.0      6
22.0      6
16.0      5
18.0      5
24.0      4
23.0      4
39.0      3
19.0      3
21.0      3
32.0      3
28.0      2
36.0      2
25.0      2
30.0      2
26.0      2
29.0      1
38.0      1
42.0      1
27.0      1
41.0      1
35.0      1
49.0      1
34.0      1
33.0      1
31.0      1
Name: institute_service, dtype: int64
In [802]:
def vetrook(val):
    if pd.isnull(val):
        return np.nan
    elif val < 3:
        return 'New'
    elif 3 <= val <= 6:
        return 'Experienced'
    elif 7 <= val <= 10:
        return 'Established'
    elif 11 <= val:
        return 'Veteran'
    
In [803]:
combined_updated['service_cat'] = combined_updated['institute_service'].apply(vetrook)
combined_updated['service_cat'].value_counts()
Out[803]:
New            193
Experienced    172
Veteran        136
Established     62
Name: service_cat, dtype: int64

Analysis

In [804]:
combined_updated['dissatisfied'].value_counts(dropna = False)
Out[804]:
False    403
True     240
NaN        8
Name: dissatisfied, dtype: int64
In [805]:
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(True)
In [806]:
dissatisfaction_per_cat = combined_updated.pivot_table(values = 'dissatisfied', index = 'service_cat')
print(dissatisfaction_per_cat)
             dissatisfied
service_cat              
Established      0.516129
Experienced      0.343023
New              0.295337
Veteran          0.485294
In [807]:
dissatisfaction_per_cat.plot(kind = 'bar')
Out[807]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fa3c580de80>

Now lets examine the age column to see if there is a correlation between a persons age and thier tendency to resign due to dissatisfcation.

In [808]:
combined_updated['age'].value_counts()
Out[808]:
51-55            71
41-45            48
41  45           45
46-50            42
36-40            41
46  50           39
26-30            35
21  25           33
26  30           32
31  35           32
36  40           32
31-35            29
56 or older      29
21-25            29
56-60            26
61 or older      23
20 or younger    10
Name: age, dtype: int64
In [809]:
combined_updated = combined_updated.copy()
combined_updated['age'] = combined_updated['age'].astype(str)
combined_updated['age'] = combined_updated['age'].str.extract(r'(\d+)').astype(float)
print(combined_updated['age'].value_counts().sort_index)
<bound method Series.sort_index of 41.0    93
46.0    81
36.0    73
51.0    71
26.0    67
21.0    62
31.0    61
56.0    55
61.0    23
20.0    10
Name: age, dtype: int64>
In [810]:
diss_per_age = combined_updated.pivot_table(values = 'dissatisfied', index = 'age')
print(diss_per_age)
      dissatisfied
age               
20.0      0.200000
21.0      0.306452
26.0      0.417910
31.0      0.377049
36.0      0.342466
41.0      0.376344
46.0      0.382716
51.0      0.422535
56.0      0.381818
61.0      0.521739
In [811]:
diss_per_age.plot(kind = 'bar')
Out[811]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fa3c5b2fa60>

Conclusions

Our objecetive was to answer these two questions.

Are employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been there longer?

Are younger employees resigning due to some kind of dissatisfaction? What about older employees?

We can see that employees that work for a short period of time are not resigning due to dissatisfaction, compared to their more tenured colleagues. New employees (defined as less than 3 years of service) actually had the least level of dissatisfaction upon their departure.

Age has a moderate correlation to dissatisfaction. Employees 41 or older show more dissatisfaction at the time of departure, especially employeees 61 or over, perhaps they are so close to retirement.