In this project, we will 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. The objective of this project is to be able to answer the following questions:
- Are employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been there longer?
- Are younger employees resigning due to some kind of dissatisfaction? What about older employees?
We will combine the data from both surveys to answer these questions. However, although both used the same survey template, one of them customized some of the answers. A data dictionary wasn’t provided with the dataset. For this project, we’ll use our intuition and common sense to define the columns.
Below is a preview of a couple of columns we will work with from DETE survey:
- ID: An id used to identify the participant of the survey
- SeparationType: The reason why the person’s employment ended
- Cease Date: The year or month the person’s employment ended
- DETE Start Date: The year the person began employment with the DETE
Below is a preview of a couple of columns we’ll work with from the TAFE survey:
- Record ID: An id used to identify the participant of the survey
- Reason for ceasing employment: The reason why the person’s employment ended
- LengthofServiceOverall. Overall Length of Service at Institute (in years): The length of the person’s employment (in years)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
print(dete_survey.info())
print(dete_survey.head())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 822 entries, 0 to 821
Data columns (total 56 columns):
ID 822 non-null int64
SeparationType 822 non-null object
Cease Date 822 non-null object
DETE Start Date 822 non-null object
Role Start Date 822 non-null object
Position 817 non-null object
Classification 455 non-null object
Region 822 non-null object
Business Unit 126 non-null object
Employment Status 817 non-null object
Career move to public sector 822 non-null bool
Career move to private sector 822 non-null bool
Interpersonal conflicts 822 non-null bool
Job dissatisfaction 822 non-null bool
Dissatisfaction with the department 822 non-null bool
Physical work environment 822 non-null bool
Lack of recognition 822 non-null bool
Lack of job security 822 non-null bool
Work location 822 non-null bool
Employment conditions 822 non-null bool
Maternity/family 822 non-null bool
Relocation 822 non-null bool
Study/Travel 822 non-null bool
Ill Health 822 non-null bool
Traumatic incident 822 non-null bool
Work life balance 822 non-null bool
Workload 822 non-null bool
None of the above 822 non-null bool
Professional Development 808 non-null object
Opportunities for promotion 735 non-null object
Staff morale 816 non-null object
Workplace issue 788 non-null object
Physical environment 817 non-null object
Worklife balance 815 non-null object
Stress and pressure support 810 non-null object
Performance of supervisor 813 non-null object
Peer support 812 non-null object
Initiative 813 non-null object
Skills 811 non-null object
Coach 767 non-null object
Career Aspirations 746 non-null object
Feedback 792 non-null object
Further PD 768 non-null object
Communication 814 non-null object
My say 812 non-null object
Information 816 non-null object
Kept informed 813 non-null object
Wellness programs 766 non-null object
Health & Safety 793 non-null object
Gender 798 non-null object
Age 811 non-null object
Aboriginal 16 non-null object
Torres Strait 3 non-null object
South Sea 7 non-null object
Disability 23 non-null object
NESB 32 non-null object
dtypes: bool(18), int64(1), object(37)
memory usage: 258.6+ KB
None
ID SeparationType Cease Date DETE Start Date \
0 1 Ill Health Retirement 08/2012 1984
1 2 Voluntary Early Retirement (VER) 08/2012 Not Stated
2 3 Voluntary Early Retirement (VER) 05/2012 2011
3 4 Resignation-Other reasons 05/2012 2005
4 5 Age Retirement 05/2012 1970
Role Start Date Position \
0 2004 Public Servant
1 Not Stated Public Servant
2 2011 Schools Officer
3 2006 Teacher
4 1989 Head of Curriculum/Head of Special Education
Classification Region Business Unit \
0 A01-A04 Central Office Corporate Strategy and Peformance
1 AO5-AO7 Central Office Corporate Strategy and Peformance
2 NaN Central Office Education Queensland
3 Primary Central Queensland NaN
4 NaN South East NaN
Employment Status ... Kept informed Wellness programs \
0 Permanent Full-time ... N N
1 Permanent Full-time ... N N
2 Permanent Full-time ... N N
3 Permanent Full-time ... A N
4 Permanent Full-time ... N A
Health & Safety Gender Age Aboriginal Torres Strait South Sea \
0 N Male 56-60 NaN NaN NaN
1 N Male 56-60 NaN NaN NaN
2 N Male 61 or older NaN NaN NaN
3 A Female 36-40 NaN NaN NaN
4 M Female 61 or older NaN NaN NaN
Disability NESB
0 NaN Yes
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
[5 rows x 56 columns]
print(tafe_survey.info())
print(tafe_survey.head())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 702 entries, 0 to 701
Data columns (total 72 columns):
Record ID 702 non-null float64
Institute 702 non-null object
WorkArea 702 non-null object
CESSATION YEAR 695 non-null float64
Reason for ceasing employment 701 non-null object
Contributing Factors. Career Move - Public Sector 437 non-null object
Contributing Factors. Career Move - Private Sector 437 non-null object
Contributing Factors. Career Move - Self-employment 437 non-null object
Contributing Factors. Ill Health 437 non-null object
Contributing Factors. Maternity/Family 437 non-null object
Contributing Factors. Dissatisfaction 437 non-null object
Contributing Factors. Job Dissatisfaction 437 non-null object
Contributing Factors. Interpersonal Conflict 437 non-null object
Contributing Factors. Study 437 non-null object
Contributing Factors. Travel 437 non-null object
Contributing Factors. Other 437 non-null object
Contributing Factors. NONE 437 non-null object
Main Factor. Which of these was the main factor for leaving? 113 non-null object
InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object
InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object
InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object
InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object
InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object
InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object
InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object
InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object
InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object
InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object
InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object
InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object
InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object
WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object
WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object
WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object
WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object
WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object
WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 609 non-null object
WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object
WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] 608 non-null object
WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object
WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object
WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object
WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object
WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object
WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object
WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 609 non-null object
WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object
WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object
Induction. Did you undertake Workplace Induction? 619 non-null object
InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object
InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object
InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object
InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object
InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object
InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object
InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object
InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object
InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object
InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object
InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object
InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object
Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object
Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object
Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object
Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object
Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object
Gender. What is your Gender? 596 non-null object
CurrentAge. Current Age 596 non-null object
Employment Type. Employment Type 596 non-null object
Classification. Classification 596 non-null object
LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object
LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object
dtypes: float64(2), object(70)
memory usage: 395.0+ KB
None
Record ID Institute \
0 6.341330e+17 Southern Queensland Institute of TAFE
1 6.341337e+17 Mount Isa Institute of TAFE
2 6.341388e+17 Mount Isa Institute of TAFE
3 6.341399e+17 Mount Isa Institute of TAFE
4 6.341466e+17 Southern Queensland Institute of TAFE
WorkArea CESSATION YEAR Reason for ceasing employment \
0 Non-Delivery (corporate) 2010.0 Contract Expired
1 Non-Delivery (corporate) 2010.0 Retirement
2 Delivery (teaching) 2010.0 Retirement
3 Non-Delivery (corporate) 2010.0 Resignation
4 Delivery (teaching) 2010.0 Resignation
Contributing Factors. Career Move - Public Sector \
0 NaN
1 -
2 -
3 -
4 -
Contributing Factors. Career Move - Private Sector \
0 NaN
1 -
2 -
3 -
4 Career Move - Private Sector
Contributing Factors. Career Move - Self-employment \
0 NaN
1 -
2 -
3 -
4 -
Contributing Factors. Ill Health Contributing Factors. Maternity/Family \
0 NaN NaN
1 - -
2 - -
3 - -
4 - -
... \
0 ...
1 ...
2 ...
3 ...
4 ...
Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? \
0 Yes
1 Yes
2 Yes
3 Yes
4 Yes
Workplace. Topic:Does your workplace promote and practice the principles of employment equity? \
0 Yes
1 Yes
2 Yes
3 Yes
4 Yes
Workplace. Topic:Does your workplace value the diversity of its employees? \
0 Yes
1 Yes
2 Yes
3 Yes
4 Yes
Workplace. Topic:Would you recommend the Institute as an employer to others? \
0 Yes
1 Yes
2 Yes
3 Yes
4 Yes
Gender. What is your Gender? CurrentAge. Current Age \
0 Female 26 30
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 Male 41 45
Employment Type. Employment Type Classification. Classification \
0 Temporary Full-time Administration (AO)
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 Permanent Full-time Teacher (including LVT)
LengthofServiceOverall. Overall Length of Service at Institute (in years) \
0 1-2
1 NaN
2 NaN
3 NaN
4 3-4
LengthofServiceCurrent. Length of Service at current workplace (in years)
0 1-2
1 NaN
2 NaN
3 NaN
4 3-4
[5 rows x 72 columns]
print(dete_survey.isnull().sum())
print(tafe_survey.isnull().sum())
ID 0
SeparationType 0
Cease Date 0
DETE Start Date 0
Role Start Date 0
Position 5
Classification 367
Region 0
Business Unit 696
Employment Status 5
Career move to public sector 0
Career move to private sector 0
Interpersonal conflicts 0
Job dissatisfaction 0
Dissatisfaction with the department 0
Physical work environment 0
Lack of recognition 0
Lack of job security 0
Work location 0
Employment conditions 0
Maternity/family 0
Relocation 0
Study/Travel 0
Ill Health 0
Traumatic incident 0
Work life balance 0
Workload 0
None of the above 0
Professional Development 14
Opportunities for promotion 87
Staff morale 6
Workplace issue 34
Physical environment 5
Worklife balance 7
Stress and pressure support 12
Performance of supervisor 9
Peer support 10
Initiative 9
Skills 11
Coach 55
Career Aspirations 76
Feedback 30
Further PD 54
Communication 8
My say 10
Information 6
Kept informed 9
Wellness programs 56
Health & Safety 29
Gender 24
Age 11
Aboriginal 806
Torres Strait 819
South Sea 815
Disability 799
NESB 790
dtype: int64
Record ID 0
Institute 0
WorkArea 0
CESSATION YEAR 7
Reason for ceasing employment 1
Contributing Factors. Career Move - Public Sector 265
Contributing Factors. Career Move - Private Sector 265
Contributing Factors. Career Move - Self-employment 265
Contributing Factors. Ill Health 265
Contributing Factors. Maternity/Family 265
Contributing Factors. Dissatisfaction 265
Contributing Factors. Job Dissatisfaction 265
Contributing Factors. Interpersonal Conflict 265
Contributing Factors. Study 265
Contributing Factors. Travel 265
Contributing Factors. Other 265
Contributing Factors. NONE 265
Main Factor. Which of these was the main factor for leaving? 589
InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 94
InstituteViews. Topic:2. I was given access to skills training to help me do my job better 89
InstituteViews. Topic:3. I was given adequate opportunities for personal development 92
InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 94
InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 87
InstituteViews. Topic:6. The organisation recognised when staff did good work 95
InstituteViews. Topic:7. Management was generally supportive of me 88
InstituteViews. Topic:8. Management was generally supportive of my team 94
InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 92
InstituteViews. Topic:10. Staff morale was positive within the Institute 100
InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 101
InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 105
...
WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 91
WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 96
WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 92
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 93
WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 99
WorkUnitViews. Topic:30. Staff morale was positive within my work unit 96
Induction. Did you undertake Workplace Induction? 83
InductionInfo. Topic:Did you undertake a Corporate Induction? 270
InductionInfo. Topic:Did you undertake a Institute Induction? 219
InductionInfo. Topic: Did you undertake Team Induction? 262
InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 147
InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 147
InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 147
InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 172
InductionInfo. On-line Topic:Did you undertake a Institute Induction? 147
InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 149
InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 147
InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 147
InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 147
Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 94
Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 108
Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 115
Workplace. Topic:Does your workplace value the diversity of its employees? 116
Workplace. Topic:Would you recommend the Institute as an employer to others? 121
Gender. What is your Gender? 106
CurrentAge. Current Age 106
Employment Type. Employment Type 106
Classification. Classification 106
LengthofServiceOverall. Overall Length of Service at Institute (in years) 106
LengthofServiceCurrent. Length of Service at current workplace (in years) 106
Length: 72, dtype: int64
The survey from DETE has 822 with 56 columns while the TAFE survey has 702 entries with 72 columns. Torres Strait, South Sea, Disability and NESB are missing over 70% of their observations. Ther are a couple of observations in the date column that are neither NANs or a proper date. Except for ID and Institute, almost all the columns under TAFE survey have missing values. 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.
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)
DETE dataframe has ‘Not Stated’ present in some of its variables instead of NAN. We are utilizing pandas read_csv function to replace ‘Not Stated’ with NAN and dropping the columns which we do not need or will not add information to our analysis in both the dataframes.
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.replace('\s+', ' ').str.replace(' ', '_')
new_names = {'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.rename(columns = new_names, inplace = True)
dete_survey_updated.head()
| 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
tafe_survey_updated.head()
| 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
The following changes are made to the columns in DETE dataframe: - All capitalizations are changed to lowercase - Any trailing whitespace at the end of the strings are removed - Replacing any spaces with underscores (’_‘)
The columns in TAFE dataframe are also renamed which can be seen when displaying the first few rows of the dataframe. This is done because each dataframe contains many of the same columns, but the column names are different
dete_survey_updated.loc[:,'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
tafe_survey_updated.loc[:,'separationtype'].value_counts()
Resignation 340
Contract Expired 127
Retrenchment/ Redundancy 104
Retirement 82
Transfer 25
Termination 23
Name: separationtype, dtype: int64
We will analyze only the survey respondents who resigned, therefore their separation type contains the string ‘Resignation’. Note that dete_survey_updated dataframe contains multiple separation types with the string ‘Resignation’:
- Resignation-Other reasons
- Resignation-Other employer
- Resignation-Move overseas/interstate
We will have to account for each of these variations so we don’t unintentionally drop data!
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
resign = ['Resignation-Other reasons', 'Resignation-Other employer', 'Resignation-Move overseas/interstate']
dete_resignations = dete_survey_updated[dete_survey_updated.separationtype.isin(resign)].copy()
dete_resignations['cease_date'].value_counts()
2012 126
2013 74
01/2014 22
12/2013 17
06/2013 14
09/2013 11
11/2013 9
07/2013 9
10/2013 6
08/2013 4
05/2012 2
05/2013 2
07/2006 1
2010 1
09/2010 1
07/2012 1
Name: cease_date, dtype: int64
dete_resignations['cease_date'] = pd.to_datetime(dete_resignations['cease_date'])
dete_resignations['cease_date'] = dete_resignations['cease_date'].dt.year
dete_resignations['cease_date'].value_counts()
2013.0 146
2012.0 129
2014.0 22
2010.0 2
2006.0 1
Name: cease_date, dtype: int64
dete_resignations['dete_start_date'].value_counts().sort_index(ascending = False)
2013.0 10
2012.0 21
2011.0 24
2010.0 17
2009.0 13
2008.0 22
2007.0 21
2006.0 13
2005.0 15
2004.0 14
2003.0 6
2002.0 6
2001.0 3
2000.0 9
1999.0 8
1998.0 6
1997.0 5
1996.0 6
1995.0 4
1994.0 6
1993.0 5
1992.0 6
1991.0 4
1990.0 5
1989.0 4
1988.0 4
1987.0 1
1986.0 3
1985.0 3
1984.0 1
1983.0 2
1982.0 1
1980.0 5
1977.0 1
1976.0 2
1975.0 1
1974.0 2
1973.0 1
1972.0 1
1971.0 1
1963.0 1
Name: dete_start_date, dtype: int64
tafe_resignations['cease_date'].value_counts().sort_index(ascending = False)
2013.0 55
2012.0 94
2011.0 116
2010.0 68
2009.0 2
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.
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.
dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']
dete_resignations['institute_service'].head()
3 7.0
5 18.0
8 3.0
9 15.0
11 3.0
Name: institute_service, dtype: float64
Next, we’ll identify any employees who resigned because they were dissatisfied. Below are the columns we’ll use to categorize employees as “dissatisfied” from each dataframe:
- tafe_survey_updated:
- Contributing Factors. Dissatisfaction
- Contributing Factors. Job Dissatisfaction
- dafe_survey_updated:
- job_dissatisfaction
- dissatisfaction_with_the_department
- physical_work_environment
- lack_of_recognition
- lack_of_job_security
- work_location
- employment_conditions
- work_life_balance
- workload
If the employee indicated any of the factors above caused them to resign, we’ll mark them as dissatisfied in a new column. After our changes, the new dissatisfied column will contain just the following values:
- True: indicates a person resigned because they were dissatisfied in some way
- False: indicates a person resigned because of a reason other than dissatisfaction with the job
- NaN: indicates the value is missing
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()
- 277
Contributing Factors. Dissatisfaction 55
Name: Contributing Factors. Dissatisfaction, dtype: int64
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()
- 270
Job Dissatisfaction 62
Name: Contributing Factors. Job Dissatisfaction, dtype: int64
def update_vals(col):
if pd.isnull(col):
return np.nan
elif col == '-':
return False
else:
return True
tafe_resignations['dissatisfied'] = tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']].applymap(update_vals).any(axis = 1, skipna = False)
dete_resignations['dissatisfied'] = dete_resignations[['dissatisfaction_with_the_department','work_life_balance', 'workload','job_dissatisfaction', 'physical_work_environment', 'lack_of_recognition',
'lack_of_job_security', 'work_location', 'employment_conditions']].any(axis = 1, skipna = False)
dete_resignations_up = dete_resignations.copy()
tafe_resignations_up = tafe_resignations.copy()
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.
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index=True)
combined_updated = combined.dropna(thresh = 500, axis = 1).copy()
combined_updated.head()
| age | cease_date | dissatisfied | employment_status | gender | id | institute | institute_service | position | separationtype | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 36-40 | 2012.0 | False | Permanent Full-time | Female | 4.0 | DETE | 7 | Teacher | Resignation-Other reasons |
| 1 | 41-45 | 2012.0 | True | Permanent Full-time | Female | 6.0 | DETE | 18 | Guidance Officer | Resignation-Other reasons |
| 2 | 31-35 | 2012.0 | False | Permanent Full-time | Female | 9.0 | DETE | 3 | Teacher | Resignation-Other reasons |
| 3 | 46-50 | 2012.0 | True | Permanent Part-time | Female | 10.0 | DETE | 15 | Teacher Aide | Resignation-Other employer |
| 4 | 31-35 | 2012.0 | False | Permanent Full-time | Male | 12.0 | DETE | 3 | Teacher | Resignation-Move overseas/interstate |
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
combined_updated['institute_service'].value_counts(dropna=False)
NaN 88
Less than 1 year 73
1-2 64
3-4 63
5-6 33
11-20 26
5.0 23
1.0 22
7-10 21
0.0 20
3.0 20
6.0 17
4.0 16
2.0 14
9.0 14
7.0 13
More than 20 years 10
13.0 8
8.0 8
15.0 7
20.0 7
10.0 6
12.0 6
14.0 6
17.0 6
22.0 6
18.0 5
16.0 5
24.0 4
11.0 4
23.0 4
21.0 3
32.0 3
19.0 3
39.0 3
26.0 2
28.0 2
30.0 2
25.0 2
36.0 2
38.0 1
49.0 1
42.0 1
41.0 1
33.0 1
35.0 1
34.0 1
29.0 1
27.0 1
31.0 1
Name: institute_service, dtype: int64
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()
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
def change_cat(val):
if pd.isnull(val):
return np.nan
elif val >= 11:
return 'Veteran'
elif 7 <= val < 11:
return 'Established'
elif 3 <= val < 7:
return 'Experienced'
else:
return 'New'
combined_updated['service_cat'] = combined_updated['institute_service_up'].apply(change_cat)
combined_updated['service_cat'].value_counts()
New 193
Experienced 172
Veteran 136
Established 62
Name: service_cat, dtype: int64
combined_updated['dissatisfied'].value_counts(dropna = False)
False 403
True 240
NaN 8
Name: dissatisfied, dtype: int64
combined_updated['dissatisfied'].fillna(False, inplace=True)
combined_updated['dissatisfied'].value_counts(dropna = False)
False 411
True 240
Name: dissatisfied, dtype: int64
# 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)
<matplotlib.axes._subplots.AxesSubplot at 0x7fb1e3c9a080>
Conclusion
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.
