This project aims to clean and analyze employee exit surveys from department of Education , Training(DETE) and Employment and Technical & Further Education (TAFE) Institute in Queensland , Australia. Both data sets can be found through following links: https://data.gov.au/dataset/ds-qld-fe96ff30-d157-4a81-851d-215f2a0fe26d/details?q = dete exit survey, and https://data.gov.au/dataset/ds-qld-89970a3b-182b-41ea-aea2-6f9f17b5907e/details?q= fate exit. It's project strategy is to know if employees exit job due to dissatisfaction. Thus, I will start by identifying and cleaning our datasets, drop data not needed for analysis, verify quality of data, create new columns and perform initial anaysis.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
dete_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 822 non-null int64 1 SeparationType 822 non-null object 2 Cease Date 822 non-null object 3 DETE Start Date 822 non-null object 4 Role Start Date 822 non-null object 5 Position 817 non-null object 6 Classification 455 non-null object 7 Region 822 non-null object 8 Business Unit 126 non-null object 9 Employment Status 817 non-null object 10 Career move to public sector 822 non-null bool 11 Career move to private sector 822 non-null bool 12 Interpersonal conflicts 822 non-null bool 13 Job dissatisfaction 822 non-null bool 14 Dissatisfaction with the department 822 non-null bool 15 Physical work environment 822 non-null bool 16 Lack of recognition 822 non-null bool 17 Lack of job security 822 non-null bool 18 Work location 822 non-null bool 19 Employment conditions 822 non-null bool 20 Maternity/family 822 non-null bool 21 Relocation 822 non-null bool 22 Study/Travel 822 non-null bool 23 Ill Health 822 non-null bool 24 Traumatic incident 822 non-null bool 25 Work life balance 822 non-null bool 26 Workload 822 non-null bool 27 None of the above 822 non-null bool 28 Professional Development 808 non-null object 29 Opportunities for promotion 735 non-null object 30 Staff morale 816 non-null object 31 Workplace issue 788 non-null object 32 Physical environment 817 non-null object 33 Worklife balance 815 non-null object 34 Stress and pressure support 810 non-null object 35 Performance of supervisor 813 non-null object 36 Peer support 812 non-null object 37 Initiative 813 non-null object 38 Skills 811 non-null object 39 Coach 767 non-null object 40 Career Aspirations 746 non-null object 41 Feedback 792 non-null object 42 Further PD 768 non-null object 43 Communication 814 non-null object 44 My say 812 non-null object 45 Information 816 non-null object 46 Kept informed 813 non-null object 47 Wellness programs 766 non-null object 48 Health & Safety 793 non-null object 49 Gender 798 non-null object 50 Age 811 non-null object 51 Aboriginal 16 non-null object 52 Torres Strait 3 non-null object 53 South Sea 7 non-null object 54 Disability 23 non-null object 55 NESB 32 non-null object dtypes: bool(18), int64(1), object(37) memory usage: 258.6+ KB
tafe_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 72 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Record ID 702 non-null float64 1 Institute 702 non-null object 2 WorkArea 702 non-null object 3 CESSATION YEAR 695 non-null float64 4 Reason for ceasing employment 701 non-null object 5 Contributing Factors. Career Move - Public Sector 437 non-null object 6 Contributing Factors. Career Move - Private Sector 437 non-null object 7 Contributing Factors. Career Move - Self-employment 437 non-null object 8 Contributing Factors. Ill Health 437 non-null object 9 Contributing Factors. Maternity/Family 437 non-null object 10 Contributing Factors. Dissatisfaction 437 non-null object 11 Contributing Factors. Job Dissatisfaction 437 non-null object 12 Contributing Factors. Interpersonal Conflict 437 non-null object 13 Contributing Factors. Study 437 non-null object 14 Contributing Factors. Travel 437 non-null object 15 Contributing Factors. Other 437 non-null object 16 Contributing Factors. NONE 437 non-null object 17 Main Factor. Which of these was the main factor for leaving? 113 non-null object 18 InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object 19 InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object 20 InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object 21 InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object 22 InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object 23 InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object 24 InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object 25 InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object 26 InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object 27 InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object 28 InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object 29 InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object 30 InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object 31 WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object 32 WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object 33 WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object 34 WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object 35 WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object 36 WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 609 non-null object 37 WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object 38 WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] 608 non-null object 39 WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object 40 WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object 41 WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object 42 WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object 43 WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object 44 WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object 45 WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 609 non-null object 46 WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object 47 WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object 48 Induction. Did you undertake Workplace Induction? 619 non-null object 49 InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object 50 InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object 51 InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object 52 InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 53 InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 54 InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object 55 InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object 56 InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object 57 InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object 58 InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object 59 InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object 60 InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object 61 Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object 62 Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object 63 Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object 64 Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object 65 Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object 66 Gender. What is your Gender? 596 non-null object 67 CurrentAge. Current Age 596 non-null object 68 Employment Type. Employment Type 596 non-null object 69 Classification. Classification 596 non-null object 70 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object 71 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(70) memory usage: 395.0+ KB
dete_survey.head(10)
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 | 6 | Resignation-Other reasons | 05/2012 | 1994 | 1997 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | D | D | NaN | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
6 | 7 | Age Retirement | 05/2012 | 1972 | 2007 | Teacher | Secondary | Darling Downs South West | NaN | Permanent Part-time | ... | D | D | SD | Female | 56-60 | NaN | NaN | NaN | NaN | NaN |
7 | 8 | Age Retirement | 05/2012 | 1988 | 1990 | Teacher Aide | NaN | North Coast | NaN | Permanent Part-time | ... | SA | NaN | SA | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
8 | 9 | Resignation-Other reasons | 07/2012 | 2009 | 2009 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | A | D | N | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
9 | 10 | Resignation-Other employer | 2012 | 1997 | 2008 | Teacher Aide | NaN | Not Stated | NaN | Permanent Part-time | ... | SD | SD | SD | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
10 rows × 56 columns
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 ... 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
dete_survey = pd.read_csv('dete_survey.csv', na_values = 'Not Stated')# re-read to change Not Stated to NaN
dete_survey.head(10)
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 | 6 | Resignation-Other reasons | 05/2012 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | D | D | NaN | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
6 | 7 | Age Retirement | 05/2012 | 1972.0 | 2007.0 | Teacher | Secondary | Darling Downs South West | NaN | Permanent Part-time | ... | D | D | SD | Female | 56-60 | NaN | NaN | NaN | NaN | NaN |
7 | 8 | Age Retirement | 05/2012 | 1988.0 | 1990.0 | Teacher Aide | NaN | North Coast | NaN | Permanent Part-time | ... | SA | NaN | SA | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
8 | 9 | Resignation-Other reasons | 07/2012 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | A | D | N | 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 | ... | SD | SD | SD | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
10 rows × 56 columns
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis = 1) # To drop any column not important for analysis
dete_survey_updated.head(5)
print(dete_survey_updated.columns) # To check columns are droped
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_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis = 1)
tafe_survey_updated.head(5)
print(tafe_survey_updated.columns) # To check columns are droped
Index(['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', '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)'], dtype='object')
Before I proceed with identification of missing values, in region column, there are missing values indicated as 'Not Stated', which are not represented as NaN, because pd.read_csv() function only specifies missing values as NaN. There fore, I re-read the dete_survey data again by setting na_values parameter to 'Not Stated'. Besides that, some of the columns are not necessary for my analysis, therefore I used DataFrame.drop() method to drop some columns from both data sets
In order to update, Dataframe.columns attribute along with vectorized string methods was used on dete_survey data. While, DataFrame.rename() method with a dictionary as argument to the columns parameters is used on tafe_survey because it works well with any number of columns. Its important to rename columns for both data sets to look uniform because it will be easier to combined the data sets for further analysis later.
# Clean the column names
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(' ', '_')
# Check that the column names were updated correctly
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')
# Rename column, show column names for tafe_survey
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(columns, axis = 1)
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. What is your Gender?', 'age', 'Employment Type. Employment Type', 'position', 'institute_service', 'role_service'], dtype='object')
In order to filter the data set, series.value_counts() method is used to accounts for columns of employees who were resigning . Many reasons cause employees to exit work but in this project, I would like to focus on those employees who resigned and those who quit for other reasons e.g. age versus role services(number of years in job before quiting job)
#Check the unique values for the separationtype column for tafe_survey_updated
tafe_survey_updated['separationtype'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
# Check the unique values for the separationtype column for dete_survey_updated
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
# Update all separation types containing the word "resignation" to 'Resignation'
dete_survey_updated['separationtype'] = dete_survey_updated['separationtype'].str.split('-').str[0]
# Check the values in the separationtype column were updated correctly
dete_survey_updated['separationtype'].value_counts()
Resignation 311 Age Retirement 285 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
# Select only the resignation separation types from each dataframe
dete_resignations = dete_survey_updated[dete_survey_updated['separationtype'] == 'Resignation'].copy()
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
tafe_survey_updated['role_service'].value_counts() # To check the number of years of service by employees
Less than 1 year 177 1-2 113 3-4 86 11-20 82 More than 20 years 54 7-10 44 5-6 40 Name: role_service, dtype: int64
Before cleaning and Manipulating our data sets, i will try to verify if there is any inconsistancies in the columns. In this mission, i would focus on verifying the years in cease_date and dete_start_date columns. Ideally, most employees start working at their 20's, therefore it won't make sense to have dete_start_date before 1940.
# Check the unique values
dete_resignations['cease_date'].value_counts()
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 07/2013 9 11/2013 9 10/2013 6 08/2013 4 05/2013 2 05/2012 2 07/2006 1 2010 1 07/2012 1 09/2010 1 Name: cease_date, dtype: int64
#Extract the years and convert them to a float type
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.split('/').str[-1]
dete_resignations['cease_date'] = dete_resignations['cease_date'].astype("float")
# Check the values again and look for outliers
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
# Check the unique values and look for outliers
dete_resignations['dete_start_date'].value_counts().sort_values(ascending = True)
1963.0 1 1971.0 1 1972.0 1 1984.0 1 1977.0 1 1987.0 1 1975.0 1 1973.0 1 1982.0 1 1974.0 2 1983.0 2 1976.0 2 1986.0 3 1985.0 3 2001.0 3 1995.0 4 1988.0 4 1989.0 4 1991.0 4 1997.0 5 1980.0 5 1993.0 5 1990.0 5 1994.0 6 2003.0 6 1998.0 6 1992.0 6 2002.0 6 1996.0 6 1999.0 8 2000.0 9 2013.0 10 2009.0 13 2006.0 13 2004.0 14 2005.0 15 2010.0 17 2012.0 21 2007.0 21 2008.0 22 2011.0 24 Name: dete_start_date, dtype: int64
Based on my findings, there aren't any inconsistencies in cease_date and dete_start_date columns.
Since there is already an institute_service in tafe_resignations dataframe, I wil create a similar column in dete_resignations by subtracting dete_start_date from cease_date as seen below. I have made these changes to maneveur a way to have common columns names to join them later for further anaylsis
# Calculate the length of time an employee spent in their respective workplace and create a new column
dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']
# display results
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 order to idnetify dissatisfied employees, Contributing Factors. Dissatisfaction,Contributing Factors. Job Dissatisfaction columns in tafe_survey_updated data set is used. While in dete_survey_updated, job_dissatisfaction, dissatisfaction_with_the_department,physical_work_environment, lack_of_recognition, lack_of_job_security, work_location, employment_conditions, work_life_balance, workload columns are used to identify dissatistified employees.
# checking for employees who resigned due dissatifaction
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()
- 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
# function to check for NaN and '_' characters in tafe_resignations dataframe
def update_vals(value):
if value == '-':
return False
elif pd.isnull(value):
return np.nan
else:
return True
tafe_resignations['dissatisfied'] = tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']].applymap(update_vals).any(1, skipna=False)
tafe_resignations_up = tafe_resignations.copy()
# Check the unique values after the updates
tafe_resignations_up['dissatisfied'].value_counts(dropna=False)
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
tafe_resignations.applymap(update_vals)
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. Travel | Contributing Factors. Other | Contributing Factors. NONE | Gender. What is your Gender? | age | Employment Type. Employment Type | position | institute_service | role_service | dissatisfied | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | True | True | True | True | True | False | False | False | False | False | ... | True | False | False | NaN | NaN | NaN | NaN | NaN | NaN | True |
4 | True | True | True | True | True | False | True | False | False | False | ... | False | False | False | True | True | True | True | True | True | True |
5 | True | True | True | True | True | False | False | False | False | False | ... | False | True | False | True | True | True | True | True | True | True |
6 | True | True | True | True | True | False | True | False | False | True | ... | False | True | False | True | True | True | True | True | True | True |
7 | True | True | True | True | True | False | False | False | False | False | ... | False | True | False | True | True | True | True | True | True | True |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
696 | True | True | True | True | True | False | True | False | False | False | ... | False | False | False | True | True | True | True | True | True | True |
697 | True | True | True | True | True | True | False | False | False | False | ... | False | False | False | True | True | True | True | True | True | True |
698 | True | True | True | True | True | True | False | False | False | False | ... | False | False | False | NaN | NaN | NaN | NaN | NaN | NaN | True |
699 | True | True | True | True | True | False | False | False | False | False | ... | False | True | False | True | True | True | True | True | True | True |
701 | True | True | True | True | True | False | False | True | False | False | ... | True | False | False | True | True | True | True | True | True | True |
340 rows × 24 columns
# checking for updates in dete_resignations
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(1, skipna=False)
dete_resignations_up = dete_resignations.copy() # To create a copy of data and avoid SettingWithCopy warning
# Check the unique values after the update in dete_resignations
dete_resignations_up['dissatisfied'].value_counts(dropna=False)
False 162 True 149 Name: dissatisfied, dtype: int64
dete_resignations.applymap(update_vals)
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 | True | True | True | True | True | True | True | True | NaN | True | ... | True | True | True | NaN | NaN | NaN | NaN | NaN | True | True |
5 | True | True | True | True | True | True | NaN | True | True | True | ... | True | True | True | NaN | NaN | NaN | NaN | NaN | True | True |
8 | True | True | True | True | True | True | True | True | NaN | True | ... | True | True | True | NaN | NaN | NaN | NaN | NaN | True | True |
9 | True | True | True | True | True | True | NaN | NaN | NaN | True | ... | True | True | True | NaN | NaN | NaN | NaN | NaN | True | True |
11 | True | True | True | True | True | True | True | True | NaN | True | ... | True | True | True | NaN | NaN | NaN | NaN | NaN | True | True |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
808 | True | True | True | True | True | True | NaN | NaN | NaN | True | ... | True | True | True | NaN | NaN | NaN | NaN | NaN | True | True |
815 | True | True | True | True | True | True | True | True | NaN | True | ... | True | True | True | NaN | NaN | NaN | NaN | NaN | True | True |
816 | True | True | True | True | True | True | True | True | NaN | True | ... | True | True | True | NaN | NaN | NaN | NaN | NaN | True | True |
819 | True | True | True | True | True | True | True | True | True | True | ... | True | True | True | NaN | NaN | NaN | NaN | NaN | True | True |
821 | True | True | True | NaN | NaN | True | NaN | True | NaN | NaN | ... | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True |
311 rows × 37 columns
First, I used df.value_counts() to check for number for employees who resigned due to dissatisfaction. Then a function is created to check for NaN, '_' characters in those columns contributing to dissatisfaction of employees. Df.any() method is use to create columns in both tafe_resignations and dete_resignations dataframe. Finally, Df.copy is used to create a copy of the results and avoided the settingWithCopy Wanrning.
# To add column to each dataframe
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
# To combine columns using Concatenate dataframes horizontally (axis=1)
combined = pd.concat([dete_resignations_up, tafe_resignations_up],ignore_index = True)
#Verify the number of non null values in each column
combined.notnull().sum().sort_values()
torres_strait 0 south_sea 3 aboriginal 7 disability 8 nesb 9 business_unit 32 classification 161 region 265 role_start_date 271 dete_start_date 283 Employment Type. Employment Type 290 role_service 290 Gender. What is your Gender? 290 gender 302 employment_status 307 workload 311 work_life_balance 311 traumatic_incident 311 ill_health 311 study/travel 311 relocation 311 none_of_the_above 311 employment_conditions 311 work_location 311 lack_of_job_security 311 lack_of_recognition 311 physical_work_environment 311 dissatisfaction_with_the_department 311 job_dissatisfaction 311 interpersonal_conflicts 311 career_move_to_private_sector 311 career_move_to_public_sector 311 maternity/family 311 Contributing Factors. Career Move - Self-employment 332 Contributing Factors. Career Move - Private Sector 332 Contributing Factors. Career Move - Public Sector 332 Contributing Factors. Dissatisfaction 332 Contributing Factors. Other 332 Contributing Factors. Interpersonal Conflict 332 Contributing Factors. Study 332 Contributing Factors. Travel 332 Contributing Factors. Ill Health 332 Contributing Factors. NONE 332 Contributing Factors. Job Dissatisfaction 332 Contributing Factors. Maternity/Family 332 Institute 340 WorkArea 340 institute_service 563 age 596 position 598 cease_date 635 dissatisfied 643 institute 651 separationtype 651 id 651 dtype: int64
combined_updated = combined.dropna(thresh=500, axis = 1).copy()
Before combining the two columns (dete_resignations_up, tafe_resignations_up), I added column name(institute) for both dataframe. This easily allowed me to distinguish between the two dataframes. Then, I combined these columns using Concatenate dataframes horizontally (axis=1) and Verified the number of non null values in each column.Finally, Dataframe.dropna() method is used to drop any columns with less that 500 non null values as thresh parameter.
# Extracting years of service from institute_service column
combined_updated['institute_service'].value_counts(dropna = False) # To check the institute_service groups per service role in job
NaN 88 Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 5.0 23 1.0 22 7-10 21 0.0 20 3.0 20 6.0 17 4.0 16 2.0 14 9.0 14 7.0 13 More than 20 years 10 8.0 8 13.0 8 15.0 7 20.0 7 10.0 6 12.0 6 14.0 6 22.0 6 17.0 6 18.0 5 16.0 5 11.0 4 23.0 4 24.0 4 19.0 3 32.0 3 21.0 3 39.0 3 30.0 2 25.0 2 26.0 2 28.0 2 36.0 2 38.0 1 49.0 1 42.0 1 41.0 1 29.0 1 35.0 1 34.0 1 33.0 1 27.0 1 31.0 1 Name: institute_service, dtype: int64
# Extract the years of service and convert them to float
combined_updated['institute_service'] = combined_updated['institute_service'].astype('str').str.extract(r'(\d+)')
combined_updated['institute_service'] = combined_updated['institute_service'].astype(float)
combined_updated['institute_service'].value_counts()
1.0 159 3.0 83 5.0 56 7.0 34 11.0 30 0.0 20 20.0 17 6.0 17 4.0 16 9.0 14 2.0 14 13.0 8 8.0 8 15.0 7 17.0 6 10.0 6 12.0 6 14.0 6 22.0 6 16.0 5 18.0 5 24.0 4 23.0 4 39.0 3 19.0 3 21.0 3 32.0 3 28.0 2 36.0 2 25.0 2 30.0 2 26.0 2 29.0 1 38.0 1 42.0 1 27.0 1 41.0 1 35.0 1 49.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service, dtype: int64
def career_vals(x):
if x >= 11:
return 'Veteran'
elif 7 <= x <= 10:
return 'Establish'
elif 3 <= x <= 6:
return 'Experience'
elif pd.isnull(x):
return np.nan
else:
return ' New'
combined_updated['service_cat'] = combined_updated['institute_service'].apply(career_vals)
# Check the unique values after the updates
combined_updated['service_cat'].value_counts()
New 193 Experience 172 Veteran 136 Establish 62 Name: service_cat, dtype: int64
# To confirm that the number of True and False in dissatisfied column
combined_updated['dissatisfied'].value_counts(dropna = False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
#To replace missing values in dissatisfied column with value that occur more frequently in the column
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
dis_cat = combined_updated.pivot_table(index='service_cat', values='dissatisfied')
%matplotlib inline
dis_cat.plot(kind = 'bar', rot = 30)
<matplotlib.axes._subplots.AxesSubplot at 0x7f0f19ce3a90>
Based on inital analysis of bar plot, most employees who resigned because they were dissatisfied were new to the company (193 employee) and experience (172 employees). while veterans and Establish employees were the least to resign due to job dissatisfactions. However, vetarans and establish employee are likely to exit job due retirement
# To check tafe_survey age group
tafe_survey_updated['age'].value_counts(dropna=False) # To check the age groups per service role in job
56 or older 162 NaN 106 51-55 82 41 45 80 46 50 59 31 35 52 36 40 51 26 30 50 21 25 44 20 or younger 16 Name: age, dtype: int64
# Check dete_survey age group
dete_survey_updated['age'].value_counts(dropna=False)
61 or older 222 56-60 174 51-55 103 46-50 63 41-45 61 26-30 57 36-40 51 21-25 40 31-35 39 NaN 11 20 or younger 1 Name: age, dtype: int64
# Extract the age of employee and convert them to float for dete_survey
dete_survey_updated['age'] = dete_survey_updated['age'].astype('str').str.extract(r'(\d+)')
dete_survey_updated['age'] = dete_survey_updated['age'].astype(float)
dete_survey_updated['age'].value_counts()
61.0 222 56.0 174 51.0 103 46.0 63 41.0 61 26.0 57 36.0 51 21.0 40 31.0 39 20.0 1 Name: age, dtype: int64
# Extract the age of employee and convert them to float for tafe_survey group
tafe_survey_updated['age'] = tafe_survey_updated['age'].astype('str').str.extract(r'(\d+)')
tafe_survey_updated['age'] = tafe_survey_updated['age'].astype(float)
tafe_survey_updated['age'].value_counts()
56.0 162 51.0 82 41.0 80 46.0 59 31.0 52 36.0 51 26.0 50 21.0 44 20.0 16 Name: age, dtype: int64
# To add column to each dataframe for age colums
dete_resignations_up['age'] = 'DETE'
tafe_resignations_up['age'] = 'TAFE'
# To combine columns using Concatenate dataframes horizontally (axis=1)
combined_age = pd.concat([dete_survey_updated, tafe_survey_updated],ignore_index = True)
#Verify the number of non null values in each column
combined_age.notnull().sum().sort_values()
torres_strait 3 south_sea 7 aboriginal 16 disability 23 nesb 32 business_unit 126 Contributing Factors. Career Move - Public Sector 437 Contributing Factors. Career Move - Private Sector 437 Contributing Factors. Career Move - Self-employment 437 Contributing Factors. Ill Health 437 Contributing Factors. Maternity/Family 437 Contributing Factors. Dissatisfaction 437 Contributing Factors. Interpersonal Conflict 437 Contributing Factors. Job Dissatisfaction 437 Contributing Factors. Travel 437 Contributing Factors. Other 437 Contributing Factors. NONE 437 Contributing Factors. Study 437 classification 455 Gender. What is your Gender? 596 institute_service 596 Employment Type. Employment Type 596 role_service 596 WorkArea 702 Institute 702 region 717 role_start_date 724 dete_start_date 749 gender 798 employment_status 817 workload 822 none_of_the_above 822 career_move_to_public_sector 822 career_move_to_private_sector 822 interpersonal_conflicts 822 job_dissatisfaction 822 dissatisfaction_with_the_department 822 physical_work_environment 822 lack_of_job_security 822 lack_of_recognition 822 employment_conditions 822 maternity/family 822 relocation 822 study/travel 822 ill_health 822 traumatic_incident 822 work_life_balance 822 work_location 822 age 1407 position 1413 cease_date 1483 separationtype 1523 id 1524 dtype: int64
combined_age_updated = combined_age.dropna(thresh=500, axis = 1).copy()
def employee_retirement_age(age):
age = float(age) # Convert string to float before comparing
if age >= 50:
return 'Old Age'
elif 40 <= age < 50:
return 'Middle Age Adult'
elif 20 >= age < 40:
return 'Young Adult'
elif pd.isnull(age):
return np.nan
else:
return 'Adolescent'
combined_age_updated['Age_retirement_cat'] = combined_age_updated['age'].apply(employee_retirement_age)
# Check the unique values after the updates
combined_age_updated['Age_retirement_cat'].value_counts()
Old Age 743 Adolescent 384 Middle Age Adult 263 Young Adult 17 Name: Age_retirement_cat, dtype: int64
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.axis('equal')
Age_retirement_cat = ['Old Age', 'Adolescent', 'Middle Age Adult', 'Young Adult']
column =[60,49,39,19]
ax.pie(column, labels = Age_retirement_cat, autopct='%1.2f%%')
plt.show()
Based on pie-chart plot above, about 35.93% of employees are likely to exit job due to old age, probably due to retirement. 29.34% are adolescent age and might still be too young to work while both Middle age adult(23.35%) and young adults(11.38%) are less likely to ritire from job but these group are prone to changing job
From my point of view, Young and new employees are more likely to exit job due to job dissatisfaction. For Example, about 193 new employees and 29.34% young adolescent employees are likely to quit job. Veterans and establish employees at their old ages are likely to exit job due to retirement. However, Both middle and young experience employees are likely to change job due to either job dissatisfaction or find new challenges in other companies