In this project, I am going 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.
You can find two exit survey datasets from following links. However, We've made some slight modifications to these datasets to make them easier to work with.
Our main objective is to find the answers for following questions:
Following steps will be followed to reach the goal:
Findings of my analysis are as below:
# Import all required modules for the project
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Make cell output scroll horizontally
from IPython.core.display import HTML
display(HTML("<style>pre { white-space: pre !important; }</style>"))
# Reading two datasets
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
# Initial exploration of DETE survey dataset
dete_survey.info()
dete_survey.head()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 822 non-null int64 1 SeparationType 822 non-null object 2 Cease Date 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
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
# Exploring No. of missing values in each column of DETE Survey Dataset
print('No. of missing values in each column of DETE Survey Dataset')
print('------------------------------------------------------------')
dete_survey.isnull().sum()
No. of missing values in each column of DETE Survey Dataset ------------------------------------------------------------
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
# Grouping null value percentages in DETE survey dataset
(dete_survey.isnull().sum()/822).value_counts(normalize = True, bins = 10).sort_index(ascending = True)
(-0.001996, 0.0996] 0.857143 (0.0996, 0.199] 0.017857 (0.199, 0.299] 0.000000 (0.299, 0.399] 0.000000 (0.399, 0.498] 0.017857 (0.498, 0.598] 0.000000 (0.598, 0.697] 0.000000 (0.697, 0.797] 0.000000 (0.797, 0.897] 0.017857 (0.897, 0.996] 0.089286 dtype: float64
# Initial exploration of TAFE survey data set
tafe_survey.info()
tafe_survey.head()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 72 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Record ID 702 non-null float64 1 Institute 702 non-null object 2 WorkArea 702 non-null object 3 CESSATION YEAR 695 non-null float64 4 Reason for ceasing employment 701 non-null object 5 Contributing Factors. Career Move - Public Sector 437 non-null object 6 Contributing Factors. Career Move - Private Sector 437 non-null object 7 Contributing Factors. Career Move - Self-employment 437 non-null object 8 Contributing Factors. Ill Health 437 non-null object 9 Contributing Factors. Maternity/Family 437 non-null object 10 Contributing Factors. Dissatisfaction 437 non-null object 11 Contributing Factors. Job Dissatisfaction 437 non-null object 12 Contributing Factors. Interpersonal Conflict 437 non-null object 13 Contributing Factors. Study 437 non-null object 14 Contributing Factors. Travel 437 non-null object 15 Contributing Factors. Other 437 non-null object 16 Contributing Factors. NONE 437 non-null object 17 Main Factor. Which of these was the main factor for leaving? 113 non-null object 18 InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object 19 InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object 20 InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object 21 InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object 22 InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object 23 InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object 24 InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object 25 InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object 26 InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object 27 InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object 28 InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object 29 InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object 30 InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object 31 WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object 32 WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object 33 WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object 34 WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object 35 WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object 36 WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 609 non-null object 37 WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object 38 WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] 608 non-null object 39 WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object 40 WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object 41 WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object 42 WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object 43 WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object 44 WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object 45 WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 609 non-null object 46 WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object 47 WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object 48 Induction. Did you undertake Workplace Induction? 619 non-null object 49 InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object 50 InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object 51 InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object 52 InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 53 InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 54 InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object 55 InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object 56 InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object 57 InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object 58 InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object 59 InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object 60 InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object 61 Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object 62 Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object 63 Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object 64 Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object 65 Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object 66 Gender. What is your Gender? 596 non-null object 67 CurrentAge. Current Age 596 non-null object 68 Employment Type. Employment Type 596 non-null object 69 Classification. Classification 596 non-null object 70 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object 71 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(70) memory usage: 395.0+ KB
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | ... | Yes | Yes | Yes | Yes | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 72 columns
# Exploring No. of missing values in each column of TAFE survey Dataset
print('No. of missing values in each column of TAFE Survey Data set')
print('------------------------------------------------------------')
tafe_survey.isnull().sum()
No. of missing values in each column of TAFE Survey Data set ------------------------------------------------------------
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
# Grouping null value percentages in TAFE survey dataset
(tafe_survey.isnull().sum()/702).value_counts(normalize = True, bins = 10).sort_index(ascending = True)
(-0.001839, 0.0839] 0.069444 (0.0839, 0.168] 0.569444 (0.168, 0.252] 0.138889 (0.252, 0.336] 0.013889 (0.336, 0.42] 0.194444 (0.42, 0.503] 0.000000 (0.503, 0.587] 0.000000 (0.587, 0.671] 0.000000 (0.671, 0.755] 0.000000 (0.755, 0.839] 0.013889 dtype: float64
*DETE Survey Dataset*
null
values while around 9% of columns comprise more than 90% null
values.Not Stated
has used in some cells to indicate missing values but those values are not considered as null
values.*TAFE Survey Dataset*
null
values while about 1% of contain more than 75% of null
values.-
has used in some cells to indicate missing values but those values are not considered as null
values.Both the dataset contain many columns that we don't need to complete our analysis.
Each dataset contains many of the same columns, but the column names are different.
Purpose of this reading is to fix the missing values. In here we use pd.read_csv()
function to specify values that should be represented as NaN
.
# Reread the DETE dataset to specify values that should be represented as NaN
dete_survey = pd.read_csv('dete_survey.csv', na_values='Not Stated')
# Exploring DETE dataset
dete_survey.head()
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 56 columns
# Drop column No. 28 to 49 of DETE dataset and assign the result to dete_survey_updated
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)
# Drop column No. 17 to 66 of TAFE dataset and assign the result to tafe_survey_updated
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis = 1)
print("------------------------------------------------------ ")
print("| Description | Before | After |")
print("------------------------------------------------------ ")
print("|Shape of DETE survey dataset |", dete_survey.shape, "|" ,dete_survey_updated.shape, "|")
print("------------------------------------------------------ ")
print("|Shape of TAFE survey dataset |", tafe_survey.shape, "|" ,tafe_survey_updated.shape, "|")
print("------------------------------------------------------ ")
------------------------------------------------------ | Description | Before | After | ------------------------------------------------------ |Shape of DETE survey dataset | (822, 56) | (822, 35) | ------------------------------------------------------ |Shape of TAFE survey dataset | (702, 72) | (702, 23) | ------------------------------------------------------
We dropped some columns from each dataframe that we don't use in our analysis to make the dataframes easier to work with. By doing this we were able to reduce the sizes of both dataframes significantly.
# Modify all columns of DETE survey dataset
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(' ', '_')
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 some columns of TAFE survey dataset
new_col_nm = {'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(new_col_nm, axis=1, inplace=True)
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')
Each dataframe contains many of the same columns, but the column names are different. Some of the columns those will be used for our final analysis were renamed as eventually those columns want to be combined, the column names had to be standardized.
# Explore the separationtype column of DETE survey dataset to identify the resignation data
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
# Explore the separationtype column of TAFE survey dataset to identify the resignation data
tafe_survey_updated["separationtype"].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
# Filter out resignation data from DETE survey dataset and assign to dete_resignations dataframe
dete_resignations = dete_survey_updated[dete_survey_updated["separationtype"].str.contains("Resignation")].copy()
dete_resignations["separationtype"].value_counts()
Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Name: separationtype, dtype: int64
# Filter out resignation data from TAFE survey dataset and assign to tafe_resignations dataframe
tafe_resignations = tafe_survey_updated[tafe_survey_updated["separationtype"]=="Resignation"].copy()
tafe_resignations["separationtype"].value_counts()
Resignation 340 Name: separationtype, dtype: int64
Our objective is to analyze the reasons for resignation and therefore we'll only analyze survey respondents who resigned. However separationtype columns in each dataframe contains a couple of different separation types. So other separationtypes of the datasets were filtered out.
# Explore DETE resignation dataset to figure out the treatment required
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 07/2012 1 07/2006 1 09/2010 1 2010 1 Name: cease_date, dtype: int64
# Extract the ceased years from values in cease_date column and replace values in cease_date column with respective ceased years
p1 = r"([1-2][0-9]{3})"
dete_resignations["cease_date"] = dete_resignations["cease_date"].str.extract(p1).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
# Arrange values in dete_start_date column in ascending order to identify any unrealistic values in DETE resignation dataset
dete_resignations["dete_start_date"].value_counts().sort_index(ascending = True)
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
# Generate boxplot to identify any unrealistic values in DETE resignation dataset
dete_resignations.boxplot(column=["cease_date", "dete_start_date"])
<matplotlib.axes._subplots.AxesSubplot at 0x1d09a038550>
# Arrange values in cease_date column in ascending order to identify any unrealistic values in TAFE resignation dataset
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
# Generate boxplot to identify any unrealistic values in TAFE resignation dataset
tafe_resignations.boxplot(column="cease_date")
<matplotlib.axes._subplots.AxesSubplot at 0x1d09a79bb20>
According to the outputs above, it can be seen that there are no any unrealistic values in cease_date
and dete_start_date
columns of dete_resignations
dataset and in cease_date
column of cafe_resignations
dataset.
# Calculate the No. of years each employees served for DETE and assign the values to institute_service column
dete_resignations["institute_service"] = dete_resignations["cease_date"] - dete_resignations["dete_start_date"]
dete_resignations.head()
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | workload | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | institute_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 4 | Resignation-Other reasons | 2012.0 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN | 7.0 |
5 | 6 | Resignation-Other reasons | 2012.0 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | False | Female | 41-45 | NaN | NaN | NaN | NaN | NaN | 18.0 |
8 | 9 | Resignation-Other reasons | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | False | False | Female | 31-35 | NaN | NaN | NaN | NaN | NaN | 3.0 |
9 | 10 | Resignation-Other employer | 2012.0 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | ... | False | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN | 15.0 |
11 | 12 | Resignation-Move overseas/interstate | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | ... | False | False | Male | 31-35 | NaN | NaN | NaN | NaN | NaN | 3.0 |
5 rows × 36 columns
The length of time an employee spent in a workplace is essential for our analyze. The tafe_resignations
dataframe already contains these data in the column named institute_service
while the dete_resignations
dataframe doesn't contain such a data. However, it contain service started year and service ended year in dete_start_date
and cease_date
columns respectively. So these 02 columns were used to calculate the service and added it into the dataframe under the new column named institute_service
.
# Explore "Contributing Factors. Dissatisfaction" column to identify different types of values in that column
tafe_resignations["Contributing Factors. Dissatisfaction"].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
# Explore "Contributing Factors. Job Dissatisfaction" column to identify different types of values in that column
tafe_resignations["Contributing Factors. Job Dissatisfaction"].value_counts()
- 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
# Define a function to update the values in specific columns with "True", "False" and "NaN" values
def update_vals(element):
if pd.isnull(element):
return np.nan
elif element == '-':
return False
else:
return True
# Group the columns needed to apply the function above
tafe_factors = ["Contributing Factors. Dissatisfaction", "Contributing Factors. Job Dissatisfaction"]
# Apply the function defined above with "df.any()" method and add result to "dissatisfied" column
tafe_resignations["dissatisfied"] = tafe_resignations[tafe_factors].applymap(update_vals).any(1, skipna = False)
# Create a copy of "tafe_resignations" dataset and assign it to "tafe_resignations_up"
tafe_resignations_up = tafe_resignations.copy()
# Explore different value types and counts of each value types in "dissatisfied" column
tafe_resignations_up["dissatisfied"].value_counts(dropna=False)
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
# Group the columns needed to apply the "df.any()" method
dete_factors = ["job_dissatisfaction", "dissatisfaction_with_the_department", "physical_work_environment",
"lack_of_recognition", "lack_of_job_security", "work_location", "employment_conditions",
"work_life_balance", "workload"]
# Apply the "df.any()" method and add result to "dissatisfied" column
dete_resignations["dissatisfied"] = dete_resignations[dete_factors].any(1, skipna = False)
# Create a copy of "dete_resignations" dataset and assign it to "dete_resignations_up"
dete_resignations_up = dete_resignations.copy()
# Explore different value types and counts of each value types in "dissatisfied" column
dete_resignations_up["dissatisfied"].value_counts(dropna=False)
False 162 True 149 Name: dissatisfied, dtype: int64
In order to achieve the objective, we need to identify any employees who resigned because they were dissatisfied. In each dataframe there are several columns that can be used to categorize employees as dissatisfied
. If the employee indicated any of the factors in those columns caused them to resign, we maked them as dissatisfied
in a new column.
In this exercise, we had to convert Contributing Factors. Dissatisfaction
and Contributing Factors. Job Dissatisfaction
columns in the tafe_resignations
dataframe to True
, False
, or NaN
values.
# Add a column to each dataframe that will allow us to easily distinguish between the two datasets
dete_resignations_up["institute"] = "DETE"
tafe_resignations_up["institute"] = "TAFE"
# Combine the dataframes and assign the result to "combined"
combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index=True)
# Explore the "combined" dataframe
combined.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 651 entries, 0 to 650 Data columns (total 53 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 651 non-null float64 1 separationtype 651 non-null object 2 cease_date 635 non-null float64 3 dete_start_date 283 non-null float64 4 role_start_date 271 non-null float64 5 position 598 non-null object 6 classification 161 non-null object 7 region 265 non-null object 8 business_unit 32 non-null object 9 employment_status 597 non-null object 10 career_move_to_public_sector 311 non-null object 11 career_move_to_private_sector 311 non-null object 12 interpersonal_conflicts 311 non-null object 13 job_dissatisfaction 311 non-null object 14 dissatisfaction_with_the_department 311 non-null object 15 physical_work_environment 311 non-null object 16 lack_of_recognition 311 non-null object 17 lack_of_job_security 311 non-null object 18 work_location 311 non-null object 19 employment_conditions 311 non-null object 20 maternity/family 311 non-null object 21 relocation 311 non-null object 22 study/travel 311 non-null object 23 ill_health 311 non-null object 24 traumatic_incident 311 non-null object 25 work_life_balance 311 non-null object 26 workload 311 non-null object 27 none_of_the_above 311 non-null object 28 gender 592 non-null object 29 age 596 non-null object 30 aboriginal 7 non-null object 31 torres_strait 0 non-null object 32 south_sea 3 non-null object 33 disability 8 non-null object 34 nesb 9 non-null object 35 institute_service 563 non-null object 36 dissatisfied 643 non-null object 37 institute 651 non-null object 38 Institute 340 non-null object 39 WorkArea 340 non-null object 40 Contributing Factors. Career Move - Public Sector 332 non-null object 41 Contributing Factors. Career Move - Private Sector 332 non-null object 42 Contributing Factors. Career Move - Self-employment 332 non-null object 43 Contributing Factors. Ill Health 332 non-null object 44 Contributing Factors. Maternity/Family 332 non-null object 45 Contributing Factors. Dissatisfaction 332 non-null object 46 Contributing Factors. Job Dissatisfaction 332 non-null object 47 Contributing Factors. Interpersonal Conflict 332 non-null object 48 Contributing Factors. Study 332 non-null object 49 Contributing Factors. Travel 332 non-null object 50 Contributing Factors. Other 332 non-null object 51 Contributing Factors. NONE 332 non-null object 52 role_service 290 non-null object dtypes: float64(4), object(49) memory usage: 269.7+ KB
# Drop any columns with less than 500 non null values
combined_updated = combined.dropna(axis=1,thresh=500)
# Explore the "combined_updated" dataframe
combined_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 651 entries, 0 to 650 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 651 non-null float64 1 separationtype 651 non-null object 2 cease_date 635 non-null float64 3 position 598 non-null object 4 employment_status 597 non-null object 5 gender 592 non-null object 6 age 596 non-null object 7 institute_service 563 non-null object 8 dissatisfied 643 non-null object 9 institute 651 non-null object dtypes: float64(2), object(8) memory usage: 51.0+ KB
First, a column was added to each dataframe that allow us to easily distinguish between the two and then two dataframes were combined.
However,after combining the dataframes we still have some columns left in the combined
dataframe that we don't need to complete our analysis. Therefore we dropped all those column if each column didn't have 500 values or more.
This exercise caused to reduce the size of combined dataframe notably as it dropped 43 columns.
# Explore different value types and counts of each value types in "institute_service" column
combined_updated["institute_service"].value_counts(dropna=False)
NaN 88 Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 5.0 23 1.0 22 7-10 21 0.0 20 3.0 20 6.0 17 4.0 16 9.0 14 2.0 14 7.0 13 More than 20 years 10 8.0 8 13.0 8 15.0 7 20.0 7 10.0 6 12.0 6 14.0 6 17.0 6 22.0 6 18.0 5 16.0 5 11.0 4 23.0 4 24.0 4 19.0 3 32.0 3 39.0 3 21.0 3 28.0 2 30.0 2 26.0 2 36.0 2 25.0 2 29.0 1 31.0 1 27.0 1 34.0 1 35.0 1 38.0 1 41.0 1 42.0 1 49.0 1 33.0 1 Name: institute_service, dtype: int64
# Create a copy of "combined_updated" dataset and assign it to "combined_updated_up"
combined_updated_up = combined_updated.copy()
# Extract the years of service from each value in the "institute_service" column
combined_updated_up["institute_service"] = combined_updated_up["institute_service"].astype('str')
combined_updated_up["institute_service"] = combined_updated_up["institute_service"].str.extract(r'(\d+)')
combined_updated_up["institute_service"] = combined_updated_up["institute_service"].astype('float')
# Explore different value types and counts of each value types in "institute_service" column
combined_updated_up["institute_service"].value_counts(dropna=False)
1.0 159 NaN 88 3.0 83 5.0 56 7.0 34 11.0 30 0.0 20 20.0 17 6.0 17 4.0 16 9.0 14 2.0 14 13.0 8 8.0 8 15.0 7 22.0 6 10.0 6 17.0 6 14.0 6 12.0 6 16.0 5 18.0 5 24.0 4 23.0 4 21.0 3 39.0 3 32.0 3 19.0 3 36.0 2 30.0 2 25.0 2 26.0 2 28.0 2 42.0 1 29.0 1 35.0 1 27.0 1 41.0 1 49.0 1 38.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service, dtype: int64
# Define a function, that categorizes employees according to the amount of years spent in their workplace
def service_cats(val):
if pd.isnull(val):
return np.nan
elif val < 3:
return "New"
elif val <= 6:
return "Experienced"
elif val <= 10:
return "Established"
else:
return "Veteran"
# Apply the function defined above to "institute_service" column and assign the results to "service_cat" column
combined_updated_up["service_cat"] = combined_updated_up["institute_service"].apply(service_cats)
# Explore different value types and counts of each value types in "service_cat" column
combined_updated_up["service_cat"].value_counts(dropna = False)
New 193 Experienced 172 Veteran 136 NaN 88 Established 62 Name: service_cat, dtype: int64
First we analyzed the institute_service
column that contained values in a couple different forms as we need to apply right cleaning technique according to the form of data.
Then we cleaned the column and categorized all service years in to four categories based on definitions below:
Finally, we created a service_cat
column, that categorizes employees according to the amount of years spent in their workplace.
# Explore different value types and counts of each value types in "dissatisfied" column
combined_updated_up["dissatisfied"].value_counts(dropna=False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
# Replace the missing values in the "dissatisfied" column with the value that occurs most frequently in this column
combined_updated_up["dissatisfied"].fillna(value=False, inplace=True)
# Explore different value types and counts of each value types in "dissatisfied" column
combined_updated_up["dissatisfied"].value_counts(dropna=False)
False 411 True 240 Name: dissatisfied, dtype: int64
# Calculate the percentage of dissatisfied employees in each service_cat group
pvt_dst_ser = combined_updated_up.pivot_table(values="dissatisfied", index="service_cat")
# Plot the results with barplot
pvt_dst_ser.plot(kind="bar")
# Define Y-axis lable
plt.ylabel("Percentage of dissatisfied Employees")
# Define title of the graph
plt.title("Dissatisfied percentage - service category wise")
# Hide legend
plt.legend().set_visible(False)
# Explore different value types and counts of each value types in "age" column
combined_updated_up["age"].value_counts(dropna = False)
51-55 71 NaN 55 41-45 48 41 45 45 46-50 42 36-40 41 46 50 39 26-30 35 21 25 33 31 35 32 36 40 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
# Replace white space with dash and assign updated values to "age_group" column
combined_updated_up["age_group"] = combined_updated_up["age"].str.replace(" ", "-")
# Define a function to update some values
def age_groups(val):
if val == "56-60" or val == "61 or older":
return "56 or older"
elif pd.isnull(val):
return np.nan
else:
return val
# Apply function and dceate new column with updated values
combined_updated_up["age_group"] = combined_updated_up["age_group"].apply(age_groups)
# Explore different value types and counts of each value types in "age_group" column
combined_updated_up["age_group"].value_counts(dropna = False)
41-45 93 46-50 81 56 or older 78 36-40 73 51-55 71 26-30 67 21-25 62 31-35 61 NaN 55 20 or younger 10 Name: age_group, dtype: int64
# Replace the missing values in the "age_group" column by propagating non-null values forward
combined_updated_up["age_group"].fillna(method='ffill', inplace=True)
# Explore different value types and counts of each value types in "age_group" column
combined_updated_up["age_group"].value_counts(dropna = False)
41-45 101 46-50 92 56 or older 84 51-55 77 36-40 76 26-30 73 31-35 71 21-25 66 20 or younger 11 Name: age_group, dtype: int64
# Define a function, that categorizes employees according to their ages
def age_cats(val):
if val == "20 or younger" or val == "21-25" or val == "26-30":
return "Younger Age"
elif val == "31-35" or val == "36-40" or val == "41-45":
return "Middle Age"
else:
return "Older Age"
# Apply function and dceate new column with updated values
combined_updated_up["age_category"] = combined_updated_up["age_group"].apply(age_cats)
combined_updated_up["age_category"].value_counts(dropna = False)
Older Age 253 Middle Age 248 Younger Age 150 Name: age_category, dtype: int64
# Calculate the percentage of dissatisfied employees in each age_category group
pvt_dst_age = combined_updated_up.pivot_table(values="dissatisfied", index="age_category")
# Plot the results with barplot
pvt_dst_age.plot(kind="bar")
# Define Y-axis lable
plt.ylabel("Percentage of dissatisfied Employees")
# Define title of the graph
plt.title("Dissatisfied percentage - Age group wise")
# Hide legend
plt.legend().set_visible(False)
We have achieved our goal after so many data cleaning, combining, aggregation and analysis. Based on our analysis, we can conclude the output of the analysis as follows: