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. The DETE exit survey data can be found here. The original TAFE exit survey data is no longer available.
Some slight modifications were made to the original datasets to make them easier to work with, including changing the encoding to UTF-8 (the original ones are encoded using cp1252.)
In this project, we'll play the role of data analyst and pretend our stakeholders want to know the following:
We'll start by importing the libraries and data, followed by a brief inspection of it.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.set_option('display.max_columns', None)
dete_survey = pd.read_csv('dete_survey.csv', na_values='Not Stated')
tafe_survey = pd.read_csv('tafe_survey.csv')
display('DETE survey info:', dete_survey.info())
display('DETE survey first rows:', dete_survey.head())
display('DETE survey null values:', dete_survey.isnull().sum())
display('TAFE survey info:', tafe_survey.info())
display('TAFE survey first rows:', tafe_survey.head())
display('TAFE survey null values:', tafe_survey.isnull().sum())
<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
'DETE survey info:'
None
'DETE survey first rows:'
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | Career move to public sector | Career move to private sector | Interpersonal conflicts | Job dissatisfaction | Dissatisfaction with the department | Physical work environment | Lack of recognition | Lack of job security | Work location | Employment conditions | Maternity/family | Relocation | Study/Travel | Ill Health | Traumatic incident | Work life balance | Workload | None of the above | Professional Development | Opportunities for promotion | Staff morale | Workplace issue | Physical environment | Worklife balance | Stress and pressure support | Performance of supervisor | Peer support | Initiative | Skills | Coach | Career Aspirations | Feedback | Further PD | Communication | My say | Information | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | True | False | False | True | False | False | True | False | False | False | False | False | False | False | False | False | False | True | A | A | N | N | N | A | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | A | N | N | N | N | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | N | N | N | N | N | N | N | N | N | N | N | N | N | N | N | A | A | N | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | N | N | N | A | A | N | N | A | A | A | A | A | A | A | A | A | A | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | A | A | N | N | D | D | N | A | A | A | A | A | A | SA | SA | D | D | A | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
'DETE survey null values:'
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
<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
'TAFE survey info:'
None
'TAFE survey first rows:'
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | Contributing Factors. Dissatisfaction | Contributing Factors. Job Dissatisfaction | Contributing Factors. Interpersonal Conflict | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | Main Factor. Which of these was the main factor for leaving? | InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction | InstituteViews. Topic:2. I was given access to skills training to help me do my job better | InstituteViews. Topic:3. I was given adequate opportunities for personal development | InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% | InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had | InstituteViews. Topic:6. The organisation recognised when staff did good work | InstituteViews. Topic:7. Management was generally supportive of me | InstituteViews. Topic:8. Management was generally supportive of my team | InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me | InstituteViews. Topic:10. Staff morale was positive within the Institute | InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly | InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently | InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly | WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit | WorkUnitViews. Topic:15. I worked well with my colleagues | WorkUnitViews. Topic:16. My job was challenging and interesting | WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work | WorkUnitViews. Topic:18. I had sufficient contact with other people in my job | WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job | WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job | WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] | WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job | WorkUnitViews. Topic:23. My job provided sufficient variety | WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job | WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction | WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance | WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area | WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date | WorkUnitViews. Topic:29. There was adequate communication between staff in my unit | WorkUnitViews. Topic:30. Staff morale was positive within my work unit | Induction. Did you undertake Workplace Induction? | InductionInfo. Topic:Did you undertake a Corporate Induction? | InductionInfo. Topic:Did you undertake a Institute Induction? | InductionInfo. Topic: Did you undertake Team Induction? | InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? | InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? | InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? | InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? | InductionInfo. On-line Topic:Did you undertake a Institute Induction? | InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? | InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? | InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] | InductionInfo. Induction Manual Topic: Did you undertake Team Induction? | Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Agree | Agree | Agree | Neutral | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Neutral | Agree | Agree | Yes | Yes | Yes | Yes | Face to Face | - | - | Face to Face | - | - | Face to Face | - | - | Yes | Yes | Yes | Yes | Yes | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Agree | Agree | Agree | Disagree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Agree | Agree | Strongly Agree | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | - | - | - | - | - | - | NONE | NaN | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Agree | Neutral | Neutral | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Yes | No | Yes | Yes | - | - | - | NaN | - | - | - | - | - | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | - | - | - | - | - | - | - | NaN | Agree | Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Yes | Yes | Yes | Yes | - | - | Induction Manual | Face to Face | - | - | Face to Face | - | - | Yes | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
'TAFE survey null values:'
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 information above shows that the DETE survey data contains 822 rows x 56 columns where the columns are object and boolean; while the TAFE survey data contains 702 rows x 72 columns where most of them are object type. Several columns contains null data.
Let's drop some columns from each dataframe that we won't use in our analysis to make the dataframes easier to work with.
dete_survey_updated = dete_survey.drop(dete_survey.iloc[:, 28:49], axis=1)
tafe_survey_updated = tafe_survey.drop(tafe_survey.iloc[:, 17:66], axis=1)
Next, let's turn our attention to the column names. Each dataframe contains many of the same columns, but the column names are different.
We'll use the following criteria to update the column names:
Then, we'll update the columns below in tafe_survey_updated:
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(' ','_')
tafe_rename_dict = {'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(tafe_rename_dict, axis=1, inplace=True)
display('DETE survey first rows:', dete_survey_updated.head())
display('TAFE survey first rows:', tafe_survey_updated.head())
'DETE survey first rows:'
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | True | False | False | True | False | False | True | False | False | False | False | False | False | False | False | False | False | True | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
'TAFE survey first rows:'
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 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 |
We've just renamed the columns that we'll use in our analysis. Next, let's remove more of the data we don't need.
If we look at the unique values in the 'separationtype' columns in each dataframe, we'll see that each contains a couple of different separation types. For this project, we'll only analyze survey respondents who resigned, so their separation type contains the string 'Resignation'.
display('DETE survey value counts:', dete_survey_updated['separationtype'].value_counts())
display('TAFE survey value counts:', tafe_survey_updated['separationtype'].value_counts())
'DETE survey 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 value counts:'
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
Note that dete_survey_updated dataframe contains multiple separation types with the string 'Resignation':
We'll keep all of these and the only 'Resignation' separation type in TAFE data:
dete_resignations = dete_survey_updated[dete_survey_updated['separationtype'].str.contains(r"Resignation*")].copy()
display('DETE survey value counts:', dete_resignations['separationtype'].value_counts())
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'].str.contains(r"Resignation", na=False)].copy()
display('TAFE survey value counts:', tafe_resignations['separationtype'].value_counts())
'DETE survey value counts:'
Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Name: separationtype, dtype: int64
'TAFE survey value counts:'
Resignation 340 Name: separationtype, dtype: int64
Now, before we start cleaning and manipulating the rest of our data, let's verify that the data doesn't contain any major inconsistencies (to the best of our knowledge).
In this step, we'll focus on verifying that the years in the cease_date and dete_start_date columns make sense. However, we encourage you to check the data for other issues as well!
If we have many years higher than the current date or lower than 1940, we wouldn't want to continue with our analysis, because it could mean there's something very wrong with the data. If there are a small amount of values that are unrealistically high or low, we can remove them.
dete_resignations['cease_year'] = dete_resignations['cease_date'].str.extract(r"([1-2][0-9][0-9][0-9])").astype(float)
display('DETE start date values:', dete_resignations['dete_start_date'].value_counts().sort_index())
display('DETE cease year values:', dete_resignations['cease_year'].value_counts().sort_index())
display('TAFE cease year values:', tafe_resignations['cease_date'].value_counts().sort_index())
'DETE start date values:'
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 Name: dete_start_date, dtype: int64
'DETE cease year values:'
2006.0 1 2010.0 2 2012.0 129 2013.0 146 2014.0 22 Name: cease_year, dtype: int64
'TAFE cease year values:'
2009.0 2 2010.0 68 2011.0 116 2012.0 94 2013.0 55 Name: cease_date, dtype: int64
From the information above, we conclude that there's nothing that seems wrong in start and cease dates in our 'resignations' dataframes.
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'.
dete_resignations['institute_service'] = dete_resignations['cease_year'] - dete_resignations['dete_start_date']
display(dete_resignations['institute_service'].value_counts().sort_index())
0.0 20 1.0 22 2.0 14 3.0 20 4.0 16 5.0 23 6.0 17 7.0 13 8.0 8 9.0 14 10.0 6 11.0 4 12.0 6 13.0 8 14.0 6 15.0 7 16.0 5 17.0 6 18.0 5 19.0 3 20.0 7 21.0 3 22.0 6 23.0 4 24.0 4 25.0 2 26.0 2 27.0 1 28.0 2 29.0 1 30.0 2 31.0 1 32.0 3 33.0 1 34.0 1 35.0 1 36.0 2 38.0 1 39.0 3 41.0 1 42.0 1 49.0 1 Name: institute_service, dtype: int64
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:
If the employee indicated any of the factors above caused them to resign, we'll mark them as dissatisfied in a new column.
First, we'll update the values in the 'Contributing Factors. Dissatisfaction' and 'Contributing Factors. Job Dissatisfaction' in the tafe_resignations dataframe so that each contains only True, False, or NaN values.
dete_columns = ['job_dissatisfaction', 'dissatisfaction_with_the_department',
'physical_work_environment', 'lack_of_recognition',
'lack_of_job_security', 'work_location',
'employment_conditions', 'work_life_balance', 'workload']
tafe_columns = ['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']
def update_vals(value):
if pd.isnull(value):
return np.nan
elif value == '-':
return False
else:
return True
tafe_resignations[tafe_columns] = tafe_resignations[tafe_columns].applymap(update_vals)
Next, we'll create a 'dissatisfied' column in BOTH the tafe_resignations and dete_resignations dataframes.
tafe_resignations['dissatisfied'] = tafe_resignations[tafe_columns].any(axis=1)
dete_resignations['dissatisfied'] = dete_resignations[dete_columns].any(axis=1)
dete_resignations_up = dete_resignations.copy()
tafe_resignations_up = tafe_resignations.copy()
display(dete_resignations_up['dissatisfied'].head())
display(tafe_resignations_up['dissatisfied'].head())
3 False 5 True 8 False 9 True 11 False Name: dissatisfied, dtype: bool
3 False 4 False 5 False 6 False 7 False Name: dissatisfied, dtype: bool
Therefore, we now have a column 'dissatisfied' which tells us if the employees were dissatisfied at all with any of the included factors, so we can separate dissatisfied employees from satisfied employees.
To recap, we've accomplished the following:
Now, we're finally ready to combine our datasets! Our end goal is to aggregate the data according to the institute_service column.
#First, let's add a column to each dataframe that will allow us to easily distinguish between the two.
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
#Combine the dataframes
combined = pd.concat([dete_resignations_up, tafe_resignations_up])
#Drop any columns with less than 500 non null values
combined_updated1 = combined.dropna(axis=1, thresh=500)
We'll have to clean up the 'institute_service' column. This column is tricky to clean because it currently contains values in a couple different forms:
To analyze the data, we'll convert these numbers into categories. We'll base our analysis on this article, which makes the argument that understanding employee's needs according to career stage instead of age is more effective.
We'll use the slightly modified definitions below:
combined_updated2 = combined_updated1.copy()
#Reduce the values of 'institute_service' to either numbers "12", periods "1-2" or "nan"
combined_updated2['institute_service'] = (combined_updated2['institute_service']
.astype('str')
.str.replace('Less than 1 year', '1', regex=False)
.str.replace('More than 20 years', '20', regex=False)
.str.replace('.0', '', regex=False))
#Drop 'nan' values, as they are not of our interest
combined_updated2 = combined_updated2[combined_updated2['institute_service'] != 'nan']
#Create two columns to average in case of periods
combined_updated2['years1'] = (combined_updated2['institute_service']
.str.split('-', expand=True)[0])
combined_updated2['years2'] = (combined_updated2['institute_service']
.str.split('-', expand=True)[1])
#Fill the missing values in 'years2' with values from 'years1' for average calculation
combined_updated2.years2.fillna(value=combined_updated2['years1'], inplace=True)
#Convert strings to integers
combined_updated2['years1'] = combined_updated2['years1'].astype('int')
combined_updated2['years2'] = combined_updated2['years2'].astype('int')
#Calculate average years
combined_updated2['years_avg'] = (combined_updated2['years1'] + combined_updated2['years2']) / 2
#Show some rows to see how this code worked
display(combined_updated2.tail())
id | separationtype | cease_date | position | employment_status | gender | age | institute_service | dissatisfied | institute | years1 | years2 | years_avg | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
693 | 6.350599e+17 | Resignation | 2013 | Administration (AO) | Temporary Full-time | Female | 26 30 | 1-2 | False | TAFE | 1 | 2 | 1.5 |
696 | 6.350660e+17 | Resignation | 2013 | Operational (OO) | Temporary Full-time | Male | 21 25 | 5-6 | False | TAFE | 5 | 6 | 5.5 |
697 | 6.350668e+17 | Resignation | 2013 | Teacher (including LVT) | Temporary Full-time | Male | 51-55 | 1-2 | False | TAFE | 1 | 2 | 1.5 |
699 | 6.350704e+17 | Resignation | 2013 | Teacher (including LVT) | Permanent Full-time | Female | 51-55 | 5-6 | False | TAFE | 5 | 6 | 5.5 |
701 | 6.350730e+17 | Resignation | 2013 | Administration (AO) | Contract/casual | Female | 26 30 | 3-4 | False | TAFE | 3 | 4 | 3.5 |
Next, we'll map each value to one of the career stage definitions above.
def range(value):
if value < 3:
return 'New'
elif value < 7:
return 'Experienced'
elif value < 11:
return 'Established'
else:
return 'Veteran'
combined_updated2['service_cat'] = combined_updated2['years_avg'].apply(range)
display(combined_updated2.tail())
id | separationtype | cease_date | position | employment_status | gender | age | institute_service | dissatisfied | institute | years1 | years2 | years_avg | service_cat | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
693 | 6.350599e+17 | Resignation | 2013 | Administration (AO) | Temporary Full-time | Female | 26 30 | 1-2 | False | TAFE | 1 | 2 | 1.5 | New |
696 | 6.350660e+17 | Resignation | 2013 | Operational (OO) | Temporary Full-time | Male | 21 25 | 5-6 | False | TAFE | 5 | 6 | 5.5 | Experienced |
697 | 6.350668e+17 | Resignation | 2013 | Teacher (including LVT) | Temporary Full-time | Male | 51-55 | 1-2 | False | TAFE | 1 | 2 | 1.5 | New |
699 | 6.350704e+17 | Resignation | 2013 | Teacher (including LVT) | Permanent Full-time | Female | 51-55 | 5-6 | False | TAFE | 5 | 6 | 5.5 | Experienced |
701 | 6.350730e+17 | Resignation | 2013 | Administration (AO) | Contract/casual | Female | 26 30 | 3-4 | False | TAFE | 3 | 4 | 3.5 | Experienced |
pivot_data = combined_updated2.pivot_table(values='dissatisfied', index='service_cat')
%matplotlib inline
pivot_data.plot(kind='bar', title='Percentage of dissatisfied ceases per service years category', legend=False)
plt.show()
From the analysis above, we can conclude that work dissatisfaction affects more experienced employees compared to less experienced employees.
This is, less experienced employees may have other reasons to leave current work (e.g., a better offer, trying different jobs, etc.), while more experienced employees will mainly leave their current job if they are not satisfied with it.
Piece of advice: Take good care of your experienced teams!!