In the following project, we will analyze the exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia.
We will pursue the following questions in search for any insight as to why employees leave:
Our first task will be combining both surveys in order to answer any of our questions. Below, we will create two Data Frames for the data from each survey, named dete_survey and tafe_survey.
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')
Now, we will look into each survey and see what we will need to adjust in order to be able to combine the two data frames into one.
dete_survey.info()
<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
dete_survey.head(5)
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984 | 2004 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | Not Stated | Not Stated | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011 | 2011 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005 | 2006 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970 | 1989 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 56 columns
tafe_survey.info()
<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
tafe_survey.head(5)
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
dete_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
tafe_survey.isnull().sum()
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
As we observe above, we notice a few areas that we will need to fix.
For example, the 'ID' column and the 'Record ID' columns in the DETE and TAFE surveys, respectively, have values that do not match. In the TAFE survey, many of the columns are named for the individual questions from the survey. In fact, the two data frames are of different sizes, with the TAFE survey having more columns than the DETE survey.
Additionally, both surveys have columns with very high quantities of null values. Additionally, the DETE survey has some values listed as "Not Stated", which we should consider as null, but is not represented as NaN. We will now begin to identify exactly what we will need to get rid of, rename, or adjust in order to be able to combine the two surveys.
Our first step will be to resolve the issue with Not Stated in the DETE survey not being seen as NaN. To do this, we will include this phrase as a parameter for NA values, shown below.
dete_survey = pd.read_csv('dete_survey.csv',na_values='Not Stated')
Next, we will drop some of the columns that we will not need in our analysis.
In the DETE survey, we have many columns that are not of interest to us. Because we are interested in the age profile and the length of time that the employee has been at the company, many of the other characteristics are not of interest to us. This means we will drop the majority of the columns that reference other characteristics of the employees.
In the TAFE survey, we have many columns that are for individual questions in the survey, which we don't need. Our concern is if the employee was dissatisfied, so we will focus on the quesitons that are answered in regards to this point.
dete_survey_updated = dete_survey.drop(columns = dete_survey.columns[28:49],axis=1)
tafe_survey_updated = tafe_survey.drop(columns = tafe_survey.columns[17:66],axis=1)
Now we will move on to identifying the columns that need to be adjusted so they match between the two surveys. For example, ID and Record ID both refer to the employee taking the survey, but are different names.
dete_survey_updated.columns = dete_survey_updated.columns.str.replace('.', ' ').str.replace('\s+', ' ').str.replace(' ','_').str.strip().str.lower()
dete_survey_updated.columns
Index(['id', 'separationtype', 'cease_date', 'dete_start_date', 'role_start_date', 'position', 'classification', 'region', 'business_unit', 'employment_status', 'career_move_to_public_sector', 'career_move_to_private_sector', 'interpersonal_conflicts', 'job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions', 'maternity/family', 'relocation', 'study/travel', 'ill_health', 'traumatic_incident', 'work_life_balance', 'workload', 'none_of_the_above', 'gender', 'age', 'aboriginal', 'torres_strait', 'south_sea', 'disability', 'nesb'], dtype='object')
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
col_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 = tafe_survey_updated.rename(columns = col_names)
tafe_survey_updated.columns = tafe_survey_updated.columns.str.replace('.', ' ').str.replace('\s+', ' ').str.replace(' ','_').str.strip().str.lower()
tafe_survey_updated.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')
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
Our last few steps were to remove many of the inconsistencies, such as replacing whitespace with underscores and converting all letters to lower case. We still have a few more changes to make, but now many of the columns match up, including the columns that store the same types of info in both surveys.
dete_survey_updated['separationtype'].value_counts()
Age Retirement 285 Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
tafe_survey_updated['separationtype'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | 6.341475e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | ... | - | - | Other | - | Female | 56 or older | Contract/casual | Teacher (including LVT) | 7-10 | 7-10 |
6 | 6.341520e+17 | Barrier Reef Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | Maternity/Family | ... | - | - | Other | - | Male | 20 or younger | Temporary Full-time | Administration (AO) | 3-4 | 3-4 |
7 | 6.341537e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | ... | - | - | Other | - | Male | 46 50 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 23 columns
dete_resignations = dete_survey_updated[dete_survey_updated['separationtype'].str.contains('Resignation')].copy()
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
5 | 6 | Resignation-Other reasons | 05/2012 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | False | False | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
8 | 9 | Resignation-Other reasons | 07/2012 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
9 | 10 | Resignation-Other employer | 2012 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | ... | False | False | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
11 | 12 | Resignation-Move overseas/interstate | 2012 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | ... | False | False | False | Male | 31-35 | NaN | NaN | NaN | NaN | NaN |
5 rows × 35 columns
In the above steps, we identified all the values that corresponded to resignations. In the DETE survey, there are 3 different categories for resignation, so we created a copy of update data frame that contained only these entries. For the TAFE survey, we were able to just copy over the rows that corresponed to Resignation, as there is only one type.
Now, our next task is to look at any data that may have been corrupted. We start with the "cease_date" and "dete_start_date" columns. Since these all correspond to dates, we will see if there are any inconsistencies, then remove those entries from our data frame.
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 2010 1 07/2006 1 07/2012 1 09/2010 1 Name: cease_date, dtype: int64
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.split('/').str.get(-1).astype('float')
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
tafe_resignations['cease_date'].value_counts()
2011.0 116 2012.0 94 2010.0 68 2013.0 55 2009.0 2 Name: cease_date, dtype: int64
tafe_resignations['cease_date'].value_counts().sort_index(ascending=True)
2009.0 2 2010.0 68 2011.0 116 2012.0 94 2013.0 55 Name: cease_date, dtype: int64
tafe_box = tafe_resignations.boxplot(column=['cease_date'])
tafe_box.set_ylim(2005,2020)
tafe_box
<matplotlib.axes._subplots.AxesSubplot at 0x7f00f7f46c50>
dete_box = dete_resignations.boxplot(column=['cease_date'])
dete_box.set_ylim(2005,2020)
dete_box
<matplotlib.axes._subplots.AxesSubplot at 0x7f00f8177978>
We included boxplots to see the values more clearly, and the majority of the resignations happened in the same frame of time. Moreover, there are no glaring oddities in the data, so we can continue to clean the data to display the information that we are interested.
Because we are concerned with the amount of time that each employee worked, we need to create a service column that stores this information.
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
In the above cell, we created a new column in dete_resignations called 'institute_service', which holds the info for how long an employee worked before resigning. The info is given in the number of years between the start date and the cease date.
tafe_resignations['contributing_factors_job_dissatisfaction'].value_counts()
- 270 Job Dissatisfaction 62 Name: contributing_factors_job_dissatisfaction, dtype: int64
tafe_resignations['contributing_factors_dissatisfaction'].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: contributing_factors_dissatisfaction, dtype: int64
def update_values(num):
if pd.isnull(num):
return
elif num == '-':
return False
else:
return True
tafe_resignations['dissatisfied'] = tafe_resignations[['contributing_factors_dissatisfaction','contributing_factors_job_dissatisfaction']].applymap(update_values).any(axis=1,skipna = False)
tafe_resignations['dissatisfied'].value_counts()
False 241 True 91 Name: dissatisfied, dtype: int64
dete_resignations['job_dissatisfaction'].value_counts()
False 270 True 41 Name: job_dissatisfaction, dtype: int64
dete_resignations['dissatisfaction_with_the_department'].value_counts()
False 282 True 29 Name: dissatisfaction_with_the_department, dtype: int64
dete_resignations[['job_dissatisfaction','dissatisfaction_with_the_department']]
job_dissatisfaction | dissatisfaction_with_the_department | |
---|---|---|
3 | False | False |
5 | False | False |
8 | False | False |
9 | True | True |
11 | False | False |
12 | False | False |
14 | True | True |
16 | False | False |
20 | False | False |
21 | False | False |
22 | False | True |
23 | True | False |
25 | False | False |
27 | False | False |
33 | False | False |
34 | False | False |
37 | False | False |
39 | True | False |
40 | False | False |
41 | False | False |
42 | False | False |
43 | True | False |
48 | False | False |
50 | False | False |
51 | False | False |
55 | False | False |
57 | False | False |
61 | False | False |
69 | True | False |
71 | False | False |
... | ... | ... |
747 | False | False |
751 | True | False |
752 | False | False |
753 | True | False |
755 | False | False |
762 | False | False |
766 | False | False |
769 | False | False |
770 | False | False |
771 | False | False |
774 | False | False |
784 | True | False |
786 | True | False |
788 | False | False |
789 | False | False |
790 | False | False |
791 | False | False |
794 | False | False |
797 | False | False |
798 | False | False |
802 | False | False |
803 | False | False |
804 | False | False |
806 | False | False |
807 | False | True |
808 | False | False |
815 | False | False |
816 | False | False |
819 | False | False |
821 | False | False |
311 rows × 2 columns
dete_resignations['dissatisfied'] = dete_resignations[['job_dissatisfaction','dissatisfaction_with_the_department','physical_work_environment','lack_of_recognition','lack_of_job_security','work_location','employment_conditions','work_life_balance','workload']].any(axis=1,skipna = False)
dete_resignations['dissatisfied'].value_counts()
False 162 True 149 Name: dissatisfied, dtype: int64
dete_resignations_up = dete_resignations.copy()
tafe_resignations_up = tafe_resignations.copy()
In the above cells, we combined the information from the columns for dissatisfaction in each survey into on column, called 'dissatisfied'. We stored the information as True, False, or NaN, depending on the answers that were given. This meant that we had to create a function to convert the answers from the TAFE survey into boolean values, rather than the string values that the columns held. In the DETE survey, we already had the format in True/False, so we simply had to combine the two columns that referred to employee dissatisfaction as the cause for resignation.
Once we completed this task, we made a copy of the updated data frames.
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
combined = pd.concat([dete_resignations_up,tafe_resignations_up])
combined_updated = combined.dropna(thresh=500, axis=1)
combined_updated.columns
Index(['age', 'cease_date', 'dissatisfied', 'employment_status', 'gender', 'id', 'institute', 'institute_service', 'position', 'separationtype'], dtype='object')
We now have a data frame called 'combined_updated' that has all the columns that we desire for our analysis. We created this by combining the two dataframes using a simple concatenation. We then dropped all the columns with at least 500 null values.
combined_updated['institute_service'].value_counts()
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 8.0 8 13.0 8 20.0 7 15.0 7 10.0 6 12.0 6 14.0 6 17.0 6 22.0 6 16.0 5 18.0 5 11.0 4 24.0 4 23.0 4 19.0 3 21.0 3 39.0 3 32.0 3 25.0 2 26.0 2 28.0 2 30.0 2 36.0 2 27.0 1 29.0 1 33.0 1 35.0 1 38.0 1 31.0 1 41.0 1 42.0 1 49.0 1 34.0 1 Name: institute_service, dtype: int64
combined_updated.loc[:,'institute_service'] = combined_updated['institute_service'].astype('str').str.extract(r'(\d+)')
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:1: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame) /dataquest/system/env/python3/lib/python3.4/site-packages/pandas/core/indexing.py:537: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
combined_updated['institute_service'].value_counts()
1 159 3 83 5 56 7 34 11 30 0 20 6 17 20 17 4 16 9 14 2 14 13 8 8 8 15 7 22 6 17 6 10 6 12 6 14 6 16 5 18 5 24 4 23 4 32 3 39 3 19 3 21 3 36 2 30 2 25 2 26 2 28 2 29 1 34 1 41 1 31 1 49 1 42 1 38 1 33 1 27 1 35 1 Name: institute_service, dtype: int64
When we do a quick check between the two value counts, we see that the data in the form of 'A-B' with A and B being digits, was converted to equal A. This results in all values that are in those ranges being counted by the first digit. This works, though, for our analysis, since we are considering less than 3, 3-6, 7-10, and 11+ as our categories. These ranges happen to preserve the change when each range was mapped to the lower bound of the range.
Now, we will make a function to convert our numeric info into the categories that we desire.
def career_stage(num):
if pd.isnull(num):
return np.nan
elif (num < 3):
return 'New'
elif (num < 6):
return 'Experienced'
elif (num < 10):
return 'Established'
else:
return 'Veteran'
combined_updated['service_cat'] = combined_updated['institute_service'].astype('float').apply(career_stage)
combined_updated['service_cat'].value_counts()
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:13: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
New 193 Experienced 155 Veteran 142 Established 73 Name: service_cat, dtype: int64
We have just combined the institute time of service information into 4 categories: New (0-2 years), Experienced (3-6 years), Established (7-10 years), and Veteran (11+ years). This was done by converting all the info into strings, then parsing out the numbers to get values we could work with, then applying a map from the values as floats to the tags for the year ranges that they correspond to.
combined_updated['dissatisfied'].value_counts(dropna=False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
combined_updated['dissatisfied'].value_counts(dropna=False)
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
False 411 True 240 Name: dissatisfied, dtype: int64
pv_combined_updated = combined_updated.pivot_table(index = 'service_cat', values = 'dissatisfied')
pv_combined_updated
dissatisfied | |
---|---|
service_cat | |
Established | 0.561644 |
Experienced | 0.316129 |
New | 0.295337 |
Veteran | 0.471831 |
pv_combined_updated.plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x7f00f80989b0>
Our first analysis covers the relationship between the time in the company and whether or not the employee sited dissastisfaction as the reason for resigning.
** We find that 56% of Established (7-10 years in the job) employees who resigned did so due to some form of dissatisfaction.**
We also find nearly half of all resignations among Veterans (11+ years on the job) were due to dissatisfaction as well.
For all employees in their positions for less than 7 years, we see roughly 30% left because they were dissastified.
combined_updated['age'].value_counts()
51-55 71 41-45 48 41 45 45 46-50 42 36-40 41 46 50 39 26-30 35 21 25 33 36 40 32 31 35 32 26 30 32 56 or older 29 21-25 29 31-35 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
Looking at our Age data, we recognize that we are going to have to take similar steps as before to create workable analysis. We will recategorize the data into four categories:
def age_stage(num):
if pd.isnull(num):
return np.nan
elif (num < 26):
return 'Entry Level'
elif (num < 41):
return 'Early Professional'
elif (num < 56):
return 'Late Professional'
else:
return 'Senior Level'
combined_updated.loc[:,'age'] = combined_updated['age'].astype('str').str.extract(r'(\d+)')
combined_updated['age_cat'] = combined_updated['age'].astype('float').apply(age_stage)
combined_updated['age_cat'].value_counts(dropna=False)
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:13: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame) /dataquest/system/env/python3/lib/python3.4/site-packages/pandas/core/indexing.py:537: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy /dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:14: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
Late Professional 245 Early Professional 201 Senior Level 78 Entry Level 72 NaN 55 Name: age_cat, dtype: int64
We recognize that we have 55 surveys that do not have an age associated, so we will replace these values with an average age of the entire population.
age_mean = combined_updated['age'].astype(float).mean()
age_mean_cat = age_stage(age_mean)
combined_updated['age_cat'] = combined_updated['age_cat'].fillna(age_mean_cat)
combined_updated['age_cat'].value_counts(dropna=False)
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
Early Professional 256 Late Professional 245 Senior Level 78 Entry Level 72 Name: age_cat, dtype: int64
Now, we will do some analysis in regards to age and whether the employee was dissatisfied.
pv_age = combined_updated.pivot_table(index = 'age_cat', values= 'dissatisfied')
pv_age.plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x7f00f7e8e668>
We see in the above bar plot the following information:
Our analysis of the data clearly shows a relationship between job dissatisfaction and the employees age or experience level. What seems to be true is that older or more experienced employees will only resign if they are dissatisfied, while younger or less experienced employees may resign for other reasons.
My recommendation, as a result, would be to survey employees who've been at the company for over 7 years regularly, giving them an opportunity to voice any problems they may have. With more information, the company will likely be able to stave off some of the dissatisfaction that arises among these populations and could result in employee retention, especially of those with great experience.