This project aims to clean and analyse data from the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia.
Once the data has been cleaned, the following questions will be asked:
The data sets will be analysed both separately and combined in order to gain an understanding of the reasons for employee resignations.
#import libraries
import pandas as pd
import numpy as np
#read in csv files
dete_survey = pd.read_csv('dete_survey.csv', na_values='Not Stated')
tafe_survey = pd.read_csv('tafe_survey.csv')
We will first explore the data in order to gain an an understanding of the data that we are working with. From this, we will assess which columns are required for our analysis.
#data exploration
dete_survey.info()
dete_survey.head()
<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 788 non-null object 3 DETE Start Date 749 non-null float64 4 Role Start Date 724 non-null float64 5 Position 817 non-null object 6 Classification 455 non-null object 7 Region 717 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), float64(2), int64(1), object(35) memory usage: 258.6+ KB
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 | 1984.0 | 2004.0 | 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 | NaN | NaN | 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 | 2011.0 | 2011.0 | 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 | 2005.0 | 2006.0 | 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 | 1970.0 | 1989.0 | 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
dete_survey.isnull().sum()
ID 0 SeparationType 0 Cease Date 34 DETE Start Date 73 Role Start Date 98 Position 5 Classification 367 Region 105 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 the DETE survey, we see that there are 56 columns and the columns that are relating to reasons for leaving [job dissatisfaction:workload] have 0 null values, which is great for the analysis.
tafe_survey.info()
tafe_survey.head()
<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
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
tafe_survey.isnull().sum()
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
The TAFE data has 72 columns and unlike the DETE surveys, contains more null values in important columns such as 'Job dissatisfaction' and 'Dissatisfaction'. This will have to be addressed in the cleaning process.
Step 1 Dropping columns
The first thing that we will do to clean the data is to drop the columns that we can immediately see are not necessary for our analysis. These include all columns where the values do not directly answer our questions about whether the employee left due to job dissatisfaction. Columns 28-49 will be dropped from DETE and 17-66 will be dropped from TAFE. This will also leave us with data that is more readable and easier to work with.
# drop unnecessary columns
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis=1)
# check that columns were dropped
print('DETE survey shape:', dete_survey_updated.shape)
print('TAFE survey shape:', tafe_survey_updated.shape)
DETE survey shape: (822, 35) TAFE survey shape: (702, 23)
Step 2 Renaming columns
As the datasets will later be combined, we will rename the columns to standardise them, since the same columns in both datasets currently have different names.
We will also rename them so that they all follow the same criteria, such as being in lower case, snake case, removing whitespace, etc.
# rename columns
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(' ', '_')
tafe_columns = {'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(tafe_columns, axis=1)
#check column names
print('DETE columns:', dete_survey_updated.columns)
print('TAFE columns:', tafe_survey_updated.columns)
DETE columns: Index(['id', 'separationtype', 'cease_date', 'dete_start_date', 'role_start_date', 'position', 'classification', 'region', 'business_unit', 'employment_status', 'career_move_to_public_sector', 'career_move_to_private_sector', 'interpersonal_conflicts', 'job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions', 'maternity/family', 'relocation', 'study/travel', 'ill_health', 'traumatic_incident', 'work_life_balance', 'workload', 'none_of_the_above', 'gender', 'age', 'aboriginal', 'torres_strait', 'south_sea', 'disability', 'nesb'], dtype='object') TAFE columns: Index(['id', 'Institute', 'WorkArea', 'cease_date', 'separationtype', 'Contributing Factors. Career Move - Public Sector ', 'Contributing Factors. Career Move - Private Sector ', 'Contributing Factors. Career Move - Self-employment', 'Contributing Factors. Ill Health', 'Contributing Factors. Maternity/Family', 'Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Interpersonal Conflict', 'Contributing Factors. Study', 'Contributing Factors. Travel', 'Contributing Factors. Other', 'Contributing Factors. NONE', 'gender', 'age', 'employment_status', 'position', 'institute_service', 'role_service'], dtype='object')
# check overall changes
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
Step 3 Isolating specific data
Since the goal of this project is to analyse those employees that resigned from their job, it is only necessary to use the data where the 'separationtype' is related to 'resignation'. Therefore, we will isolate from both datasets those employees that resigned from their job and use this information going forward.
# isolate values that are related to resignation
print(dete_survey_updated['separationtype'].value_counts())
print('\n')
print(tafe_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 Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
dete_resignation = dete_survey_updated[dete_survey_updated['separationtype'].str.contains('Resignation')].copy()
tafe_resignation = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
print('DETE:', dete_resignation['separationtype'].value_counts())
print('TAFE:', tafe_resignation['separationtype'].value_counts())
DETE: Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Name: separationtype, dtype: int64 TAFE: Resignation 340 Name: separationtype, dtype: int64
Step 4 Cleaning date columns
Since the start and cease dates are important for our analysis (we need to know how long the employees worked at the institute for), we will clean the dates to ensure that they are as type 'float', and that they are in the same format.
# clean date columns
dete_resignation['cease_date'].value_counts()
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 11/2013 9 07/2013 9 10/2013 6 08/2013 4 05/2012 2 05/2013 2 07/2006 1 2010 1 09/2010 1 07/2012 1 Name: cease_date, dtype: int64
dete_resignation['cease_date'] = dete_resignation['cease_date'].str[-4:].astype(float)
dete_resignation['cease_date'].value_counts(dropna=False).sort_index()
2006.0 1 2010.0 2 2012.0 129 2013.0 146 2014.0 22 NaN 11 Name: cease_date, dtype: int64
dete_resignation['dete_start_date'].value_counts(dropna=False).sort_index()
1963.0 1 1971.0 1 1972.0 1 1973.0 1 1974.0 2 1975.0 1 1976.0 2 1977.0 1 1980.0 5 1982.0 1 1983.0 2 1984.0 1 1985.0 3 1986.0 3 1987.0 1 1988.0 4 1989.0 4 1990.0 5 1991.0 4 1992.0 6 1993.0 5 1994.0 6 1995.0 4 1996.0 6 1997.0 5 1998.0 6 1999.0 8 2000.0 9 2001.0 3 2002.0 6 2003.0 6 2004.0 14 2005.0 15 2006.0 13 2007.0 21 2008.0 22 2009.0 13 2010.0 17 2011.0 24 2012.0 21 2013.0 10 NaN 28 Name: dete_start_date, dtype: int64
tafe_resignation['cease_date'].value_counts(dropna=False).sort_index()
2009.0 2 2010.0 68 2011.0 116 2012.0 94 2013.0 55 NaN 5 Name: cease_date, dtype: int64
Step 5 Create new columns
We will create a new column called 'institute_service' in the DETE survey, which is the difference between the cease date and the start date, to give us the length of service. This is not necessary for the TAFE data since the column is already available in this dataset.
# create new column in dete survey for length of service
dete_resignation['institute_service'] = dete_resignation['cease_date'] - dete_resignation['dete_start_date']
dete_resignation['institute_service'].head()
3 7.0 5 18.0 8 3.0 9 15.0 11 3.0 Name: institute_service, dtype: float64
Step 6 updating values
The TAFE survey currently contains three answers for the columns relating to dissatisfaction: '-', 'Dissatisfaction' or NaN. We will replace these values with False for '-' and True for anything else. NaN values will remain null for now...
We will then add a new column called 'dissatisfied', where if any of the contributing columns are True (i.e. dissatisfaction was the cause of resignation), the new column will be assigned True. Otherwise, it will be assigned False or null.
# create new column to indicate if employees left due to dissatisfaction
tafe_dissatisfied = ['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']
def update_vals(val):
if pd.isnull(val):
return np.nan
if val == '-':
return False
else:
return True
tafe_resignation['dissatisfied'] = tafe_resignation[tafe_dissatisfied].applymap(update_vals).any(axis=1, skipna=False)
tafe_resignation['dissatisfied'].value_counts(dropna=False)
False 241 True 91 True 8 Name: dissatisfied, dtype: int64
Although when we do value_counts()
above, it reads as True, we see below that the null values are still counted and so this is a glitch in the output, but the null values are still respresented correctly in the dataframe...
# check that null values remain
tafe_resignation['dissatisfied'].isnull().sum()
8
dete_dissatisfied = ['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_resignation['dissatisfied'] = dete_resignation[dete_dissatisfied].any(axis=1, skipna=False)
dete_resignation['dissatisfied'].value_counts(dropna=False)
dete_resignation.head()
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | institute_service | dissatisfied | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 4 | Resignation-Other reasons | 2012.0 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN | 7.0 | False |
5 | 6 | Resignation-Other reasons | 2012.0 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | Female | 41-45 | NaN | NaN | NaN | NaN | NaN | 18.0 | True |
8 | 9 | Resignation-Other reasons | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | False | Female | 31-35 | NaN | NaN | NaN | NaN | NaN | 3.0 | False |
9 | 10 | Resignation-Other employer | 2012.0 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | ... | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN | 15.0 | True |
11 | 12 | Resignation-Move overseas/interstate | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | ... | False | Male | 31-35 | NaN | NaN | NaN | NaN | NaN | 3.0 | False |
5 rows × 37 columns
# create copy of results
dete_resignation_up = dete_resignation.copy()
tafe_resignation_up = tafe_resignation.copy()
Step 7 concatenating the dataframes
As we will concatenate the two dataframes, we need to add another column 'institute', which will indicate which survey the information comes from. This will allow us to analyse the institutes separately later on.
# add a column to specify the institute
dete_resignation_up['institute'] = 'DETE'
tafe_resignation_up['institute'] = 'TAFE'
#check column had been added correctly
print(dete_resignation_up.head())
print(tafe_resignation_up.head())
id separationtype cease_date dete_start_date \ 3 4 Resignation-Other reasons 2012.0 2005.0 5 6 Resignation-Other reasons 2012.0 1994.0 8 9 Resignation-Other reasons 2012.0 2009.0 9 10 Resignation-Other employer 2012.0 1997.0 11 12 Resignation-Move overseas/interstate 2012.0 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 ... gender age aboriginal \ 3 NaN Permanent Full-time ... Female 36-40 NaN 5 Education Queensland Permanent Full-time ... Female 41-45 NaN 8 NaN Permanent Full-time ... Female 31-35 NaN 9 NaN Permanent Part-time ... Female 46-50 NaN 11 NaN Permanent Full-time ... Male 31-35 NaN torres_strait south_sea disability nesb institute_service \ 3 NaN NaN NaN NaN 7.0 5 NaN NaN NaN NaN 18.0 8 NaN NaN NaN NaN 3.0 9 NaN NaN NaN NaN 15.0 11 NaN NaN NaN NaN 3.0 dissatisfied institute 3 False DETE 5 True DETE 8 False DETE 9 True DETE 11 False DETE [5 rows x 38 columns] 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. 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 dissatisfied institute 3 NaN NaN False TAFE 4 3-4 3-4 False TAFE 5 7-10 7-10 False TAFE 6 3-4 3-4 False TAFE 7 3-4 3-4 False TAFE [5 rows x 25 columns]
# combine dataframes
combined = pd.concat([dete_resignation_up, tafe_resignation_up], axis=0, ignore_index=True)
combined.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 651 entries, 0 to 650 Data columns (total 53 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 dete_start_date 283 non-null float64 4 role_start_date 271 non-null float64 5 position 598 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 597 non-null object 10 career_move_to_public_sector 311 non-null object 11 career_move_to_private_sector 311 non-null object 12 interpersonal_conflicts 311 non-null object 13 job_dissatisfaction 311 non-null object 14 dissatisfaction_with_the_department 311 non-null object 15 physical_work_environment 311 non-null object 16 lack_of_recognition 311 non-null object 17 lack_of_job_security 311 non-null object 18 work_location 311 non-null object 19 employment_conditions 311 non-null object 20 maternity/family 311 non-null object 21 relocation 311 non-null object 22 study/travel 311 non-null object 23 ill_health 311 non-null object 24 traumatic_incident 311 non-null object 25 work_life_balance 311 non-null object 26 workload 311 non-null object 27 none_of_the_above 311 non-null object 28 gender 592 non-null object 29 age 596 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 35 institute_service 563 non-null object 36 dissatisfied 643 non-null object 37 institute 651 non-null object 38 Institute 340 non-null object 39 WorkArea 340 non-null object 40 Contributing Factors. Career Move - Public Sector 332 non-null object 41 Contributing Factors. Career Move - Private Sector 332 non-null object 42 Contributing Factors. Career Move - Self-employment 332 non-null object 43 Contributing Factors. Ill Health 332 non-null object 44 Contributing Factors. Maternity/Family 332 non-null object 45 Contributing Factors. Dissatisfaction 332 non-null object 46 Contributing Factors. Job Dissatisfaction 332 non-null object 47 Contributing Factors. Interpersonal Conflict 332 non-null object 48 Contributing Factors. Study 332 non-null object 49 Contributing Factors. Travel 332 non-null object 50 Contributing Factors. Other 332 non-null object 51 Contributing Factors. NONE 332 non-null object 52 role_service 290 non-null object dtypes: float64(4), object(49) memory usage: 269.7+ KB
Step 8 further cleaning of the combined dataframe
Once the two dataframes are combined, we see that there are many columns that have a high number of null values, as the columns belonged to one of the original dataframes, but not the other. Since we will not be needing these columns, we will drop any with less than 500 non null values.
# drop columns with less that 500 non-null values
combined_update = combined.dropna(axis=1, thresh=500).copy()
combined_update.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
Step 9 updating values for 'institute_service'
As we are wanting to find out whether the length of service is related to job dissatisfaction, we need to understand the values in this column.
We can see that there are many different values and data types, including a range of years, floats and strings. In order to standardise these, we will put the service time into categories:
We will need to extract the numbers as a string from the 'institute_service' column before assigning these updated categories.
# check values for institute service
combined_update['institute_service'].value_counts(dropna=False)
NaN 88 Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 5.0 23 1.0 22 7-10 21 0.0 20 3.0 20 6.0 17 4.0 16 9.0 14 2.0 14 7.0 13 More than 20 years 10 8.0 8 13.0 8 15.0 7 20.0 7 10.0 6 12.0 6 14.0 6 17.0 6 22.0 6 18.0 5 16.0 5 11.0 4 23.0 4 24.0 4 19.0 3 32.0 3 39.0 3 21.0 3 28.0 2 30.0 2 26.0 2 36.0 2 25.0 2 29.0 1 31.0 1 27.0 1 34.0 1 35.0 1 38.0 1 41.0 1 42.0 1 49.0 1 33.0 1 Name: institute_service, dtype: int64
# standardize institute service by categories
combined_update['institute_service'] = combined_update['institute_service'].astype(str)
combined_update['institute_service'].value_counts(dropna=False)
nan 88 Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 5.0 23 1.0 22 7-10 21 0.0 20 3.0 20 6.0 17 4.0 16 9.0 14 2.0 14 7.0 13 More than 20 years 10 13.0 8 8.0 8 15.0 7 20.0 7 22.0 6 12.0 6 10.0 6 17.0 6 14.0 6 16.0 5 18.0 5 11.0 4 23.0 4 24.0 4 39.0 3 32.0 3 19.0 3 21.0 3 25.0 2 36.0 2 26.0 2 28.0 2 30.0 2 31.0 1 27.0 1 38.0 1 42.0 1 29.0 1 41.0 1 34.0 1 35.0 1 49.0 1 33.0 1 Name: institute_service, dtype: int64
combined_update['institute_service'] = combined_update['institute_service'].str.extract(r'([0-9]?[0-9])', expand=False).astype(float)
combined_update['institute_service'].value_counts(dropna=False)
1.0 159 NaN 88 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 22.0 6 10.0 6 17.0 6 14.0 6 12.0 6 16.0 5 18.0 5 24.0 4 23.0 4 21.0 3 39.0 3 32.0 3 19.0 3 36.0 2 30.0 2 25.0 2 26.0 2 28.0 2 42.0 1 29.0 1 35.0 1 27.0 1 41.0 1 49.0 1 38.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service, dtype: int64
# define a new function to update service categories
def new_cat(val):
if val < 3:
return 'new'
elif 3 <= val <= 6:
return 'experienced'
elif 7 <= val <= 10:
return 'established'
elif val >= 11:
return 'veteran'
elif pd.isnull(val):
return 'unknown'
combined_update['service_cat'] = combined_update['institute_service'].apply(new_cat)
combined_update['service_cat'].value_counts(dropna=False)
new 193 experienced 172 veteran 136 unknown 88 established 62 Name: service_cat, dtype: int64
We will fill in the missing values in the 'dissatisfied' column with the most frequent answer, which is False
# fill in missing values
combined_update['dissatisfied'].value_counts(dropna=False)
False 403 True 240 True 8 Name: dissatisfied, dtype: int64
combined_update['dissatisfied'] = combined_update['dissatisfied'].fillna(False)
combined_update['dissatisfied'].value_counts(dropna=False)
False 411 True 240 Name: dissatisfied, dtype: int64
Step 1 aggregate data
Once our data is ready, we will aggregate it using a pivot table. We will get the percentage of employees that were dissatisfied in each length of service category.
# aggregate data
combined_pivot = combined_update.pivot_table(values='dissatisfied', index='service_cat')
combined_pivot
dissatisfied | |
---|---|
service_cat | |
established | 0.516129 |
experienced | 0.343023 |
new | 0.295337 |
unknown | 0.295455 |
veteran | 0.485294 |
# import matplotlib for data visualisation
import matplotlib.pyplot as plt
%matplotlib inline
# generate bar plot to visualise the results
combined_pivot.plot(kind='bar', rot=30, legend=False, title='Length of service of employees vs percentage of dissatisfied employees')
plt.xlabel('Length of service')
plt.ylabel('Percentage dissatisfied')
Text(0, 0.5, 'Percentage dissatisfied')
The bar plot above shows us that a higher percentage of employees that were established or veterans (i.e had worked at the institute for 7 years or longer) resigned due to dissatisfaction (~50%) compared to newer employees, where only ~30% resigned due to dissatisfaction.
Now, let's analyse the institutes separately.
# analyse institutes separately
dete_employees = combined_update[combined_updated['institute']=='DETE']
dete_pivot = dete_employees.pivot_table(values='dissatisfied', index='service_cat')
dete_pivot
dissatisfied | |
---|---|
service_cat | |
established | 0.609756 |
experienced | 0.460526 |
new | 0.375000 |
unknown | 0.315789 |
veteran | 0.560000 |
tafe_employees = combined_update[combined_updated['institute']=='TAFE']
tafe_pivot = tafe_employees.pivot_table(values='dissatisfied', index='service_cat')
tafe_pivot
dissatisfied | |
---|---|
service_cat | |
established | 0.333333 |
experienced | 0.250000 |
new | 0.262774 |
unknown | 0.280000 |
veteran | 0.277778 |
pivots = pd.merge(dete_pivot, tafe_pivot, on='service_cat', suffixes=('_DETE', '_TAFE'))
pivots
dissatisfied_DETE | dissatisfied_TAFE | |
---|---|---|
service_cat | ||
established | 0.609756 | 0.333333 |
experienced | 0.460526 | 0.250000 |
new | 0.375000 | 0.262774 |
unknown | 0.315789 | 0.280000 |
veteran | 0.560000 | 0.277778 |
pivots.plot(kind='bar', rot=30)
plt.title('Percentage of employees in different services categories resigning due to dissatisfaction')
plt.xlabel('Service category')
plt.ylabel('Percent dissatisfied')
plt.tick_params(axis='both', length=0)
When we analyse the institutes separately, we see that in all service categories, a higher percentage of employees at DETE who resigned, resigned due to dissatisfaction, compared to those at TAFE. We still see that at both institutes, a higher number of employees that worked for a longer time period were dissatisfied with their jobs, and thus, resigned.
Now that we have analysed the service categories and resignations, we will analyse how many people in each age group resigned due to job dissatisfaction. Lets first take a look at the age columns.
combined_update['age'].value_counts(dropna=False)
51-55 71 NaN 55 41-45 48 41 45 45 46-50 42 36-40 41 46 50 39 26-30 35 21 25 33 31 35 32 26 30 32 36 40 32 21-25 29 56 or older 29 31-35 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
#extract numeric values
combined_update['age'] = combined_update['age'].astype(str).str.extract(r'(\d+)', expand=False).astype(float)
combined_update['age'].value_counts(dropna=False)
41.0 93 46.0 81 36.0 73 51.0 71 26.0 67 21.0 62 31.0 61 NaN 55 56.0 55 61.0 23 20.0 10 Name: age, dtype: int64
mean_age = combined_update['age'].mean()
print(mean_age)
39.27181208053691
# fill null values with mean age
combined_update['age'] = combined_update['age'].fillna(mean_age)
# define function to replace age ranges
def update_ages(val):
if 20 <= val <= 30:
return '20-30'
elif 30 < val <= 40:
return '31-40'
elif 40 < val <= 50:
return '41-50'
else:
return '50+'
combined_update['age'] = combined_update['age'].apply(update_ages)
combined_update['age'].value_counts()
31-40 189 41-50 174 50+ 149 20-30 139 Name: age, dtype: int64
# aggregate data
age_pivot = combined_update.pivot_table(values='dissatisfied', index='age')
age_pivot
dissatisfied | |
---|---|
age | |
20-30 | 0.352518 |
31-40 | 0.328042 |
41-50 | 0.379310 |
50+ | 0.422819 |
# generate bar plot to visualise results
age_pivot.plot(kind='bar', rot=30, legend=False, title='Age of employees vs percentage of dissatisfied employees')
plt.xlabel('Age of employees')
plt.ylabel('Percentage dissatisfied')
plt.tick_params(axis='both', length=0)
When analysing the age of dissatisfied employees that resigned, we see that there is very little difference in the age ranges. Employees over 50 years old had the highest percent of dissatisfied employees, while the age group with the lowest percent of dissatisfied employees was the 31-40 age group. However, we can see that in general, age is not a factor in employee dissatisfaction.
Now, lets see if we can see any difference between the institutes...
# isolate institutes to analyse separately
dete_age = combined_update[combined_update['institute']=='DETE']
dete_age['institute'].value_counts()
DETE 311 Name: institute, dtype: int64
dete_age_pivot = dete_age.pivot_table(values='dissatisfied', index='age')
dete_age_pivot
dissatisfied | |
---|---|
age | |
20-30 | 0.446154 |
31-40 | 0.426667 |
41-50 | 0.466667 |
50+ | 0.567901 |
tafe_age = combined_update[combined_update['institute']=='TAFE']
tafe_age['institute'].value_counts()
TAFE 340 Name: institute, dtype: int64
tafe_age_pivot = tafe_age.pivot_table(values='dissatisfied', index='age')
tafe_age_pivot
dissatisfied | |
---|---|
age | |
20-30 | 0.270270 |
31-40 | 0.263158 |
41-50 | 0.285714 |
50+ | 0.250000 |
age_pivots = pd.merge(dete_age_pivot, tafe_age_pivot, on='age', suffixes=('_DETE', '_TAFE'))
age_pivots
dissatisfied_DETE | dissatisfied_TAFE | |
---|---|---|
age | ||
20-30 | 0.446154 | 0.270270 |
31-40 | 0.426667 | 0.263158 |
41-50 | 0.466667 | 0.285714 |
50+ | 0.567901 | 0.250000 |
age_pivots.plot(kind='bar', rot=30)
plt.title('Percentage of employees in different age groups resigning due to dissatisfaction')
plt.xlabel('Age')
plt.ylabel('Percent dissatisfied')
plt.tick_params(axis='both', length=0)
When we analyse the institutes separately, we once again see that age does not contribute greatly to employee dissatisfaction, although we do see that employee satisfaction decreases as age increases at DETE, while at TAFE it remains stable across all age groups.
In this project, we cleaned and analysed the data from employee exit surveys in order to determine whether the length of service and age contribute to job dissatisfaction and resulting resignation.
We found that employees that were either 'established' or 'veterans' (i.e. had worked at the institutes for 7 years of longer), resigned due to dissatisfaction more than those that had worked at the institute for a shorter period.
Age does not seem to contribute to job dissatisfaction, and the percentage of employees that resigned due to dissatisfaction was relatively uniform across all age ranges, although the percentage of job dissatisfaction did increase slightly in the older age groups.
We also found the employees resigned due to job dissatisfaction at DETE at a higher rate than at TAFE.