This project focus on cleaning and analysing exit surveys from employees of the [Department of Education, Training and Employment]('https://en.wikipedia.org/wiki/Department_of_Education_and_Training_(Queensland') (DETE) and the Technical and Further Education (TAFE) body of the Queensland government in Australia. The DETE exit survey can be found hear, meanwhile, the original dataset for the TAFE exit survey is no longer available. We, have made some slight modifications to the original datasets to make them easier to work with, including changing the encoding to UTF-8
(the original ones are encoded using cp1252
)
The objective of this analysis is to combine the results for both surveys to answer the following questions:
The stakeholders want us to combine results from both surveys and answer above questions. Although both surveys used the same template, one of them had customized answers.
Below are the available columns in both datasets
dete_survey.csv
ID
: An id used to identify the participant of the surveySeparationType
: The reason why the person's employment endedCease Date
: The year or month the person's employment endedDETE Start Date
: The year the person began employment with the DETEtafe_survey.csv
Record ID
: An id used to identify the participant of the survey.Reason for ceasing employment
: The reason why the person's employment ended.LengthofServiceOverall. Overall Length of Service at Institute (in years)
: The length of the person's employment (in years)Employees with more years of service have a higher likelihood of resigning due to dissatisfaction compare
# Import the needed libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Read the dete survey data
dete_survey = pd.read_csv('dete_survey.csv')
# View the first five records
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 | 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
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
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
# Read in the tafe survey data
tafe_survey = pd.read_csv("tafe_survey.csv")
# View the first five records
tafe_survey.head()
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | ... | Yes | Yes | Yes | Yes | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 72 columns
tafe_survey.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
tafe_survey.isna().sum()
Record ID 0 Institute 0 WorkArea 0 CESSATION YEAR 7 Reason for ceasing employment 1 ... CurrentAge. Current Age 106 Employment Type. Employment Type 106 Classification. Classification 106 LengthofServiceOverall. Overall Length of Service at Institute (in years) 106 LengthofServiceCurrent. Length of Service at current workplace (in years) 106 Length: 72, dtype: int64
The following observations be made from the above analysis.
Not Stated
values that indicate values are missing, but they aren't represented as NaN
.dete_survey
and tafe_survey
contain many columns that we don't need to complete our analysis.dete_survey
have 822 rows with 56 columns, while tafe_survey
have 702 rows with 72 columsFirst, we'll correct the Not Stated
values and drop some of the columns we don't need for our analysis.
# Read in the data again, but set the `Not Sated` values to `NaN`
dete_survey = pd.read_csv('dete_survey.csv', na_values='Not Stated')
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
Now, let's drop some columns from each dataframe that we won't use in our analysis to make the dataframes easier to work with.
# Remove the columns we don't need for our analysis
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis=1)
# Preview to confirm the columns were drpped
print(dete_survey_updated.columns)
print(tafe_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') 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')
In order to avoid working with columns that are needed, we dropped [28:48]
columns from dete_survey
data, we also dropped
[17:66]
columns from tafe_survey
data.
We will rename the column names to make them suitable for our analysis
# Clean the column names
dete_survey_updated.columns = dete_survey_updated.columns.str.strip().str.lower().str.replace(' ', '_')
# Check to confirm that the cloumn 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')
# Update column names to match the names in dete_survey_updated
mapping = {'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(mapping, axis = 1)
# Check that the specified column names were updated correctly
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')
Since our focus is on employees who resigned, we'll only analyze survey respondents who resigned, so we'll only select separation types containing the string 'Resignation
.
# Check the unique values for the separationtype column
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
# Check the unique values for the separationtype column
tafe_survey_updated['separationtype'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 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
It can be observed that both datasets have varying seperation types, hence we have to account for this variations so that we don’t unintentionally drop important data.
# Select only the resignation separation types from each dataframe
dete_resignation = dete_survey_updated[dete_survey_updated['separationtype'] == 'Resignation'].copy()
tafe_resignation = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
In each of dataframes, we select only the data for survey respondents who have a Resignation separation type.
The nest step is for us to clean and explore the cease_date
and dete_start_date
columns to make sure all of the years are in order. We'll use the following criteria:
cease_date
is the last year of the person's employment and the dete_start_date
is the person's first year of employment, it wouldn't practical to have years after the current date.dete_start_date
was before the year 1940.dete_resignation['cease_date'].value_counts()
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 11/2013 9 07/2013 9 10/2013 6 08/2013 4 05/2012 2 05/2013 2 07/2006 1 07/2012 1 09/2010 1 2010 1 Name: cease_date, dtype: int64
It could be observed from the data above that the year formats are not uniform, some are YYYY
, while some are MM/YYYY
format. Hence, goin Forward, we need to formarlise the date to have a uniform date format.
# Extract the years and convert them to a float data type
dete_resignation['cease_date'] = dete_resignation['cease_date'].str.split('/').str[-1]
dete_resignation['cease_date'] = dete_resignation['cease_date'].astype('float')
# Confirm the values again and look for outliers
dete_resignation['cease_date'].value_counts()
2013.0 146 2012.0 129 2014.0 22 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
# Check the unique values and look for outliers
dete_resignation['dete_start_date'].value_counts().sort_values()
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
# Check the unique values
tafe_resignation['cease_date'].value_counts().sort_values()
2009.0 2 2013.0 55 2010.0 68 2012.0 94 2011.0 116 Name: cease_date, dtype: int64
We can deduce from above that the years in both dataframes don't completely align. The tafe_survey_updated
dataframe contains some cease dates in 2009
, but the dete_survey_updated
dataframe does not. The tafe_survey_updated
dataframe also contains 68
cease dates in 2010
, while the dete_survey_updaed
contains only 2
.
Since we aren't so concerned with analyzing the results by year, we'll leave them as is.
Since our end goal is to answer the question below, we need a column containing the length of time an employee spent in their workplace, or years of service, in both dataframes.
The tafe_resignation
dataframe already contains a "service
" column, which we renamed to institute_service
.
Next step, we calculate the years of service in the dete_survey_updated
dataframe by subtracting the dete_start_date
from the cease_date
and create a new column named institute_service
.
# Calculate the year of service an employee spent in their respective workplace and create a new column
dete_resignation['institute_service'] = dete_resignation['cease_date'] - dete_resignation['dete_start_date']
# Check out the result
dete_resignation['institute_service'].head()
3 7.0 5 18.0 8 3.0 9 15.0 11 3.0 Name: institute_service, dtype: float64
Next, we'll identify employees who resigned because they were dissatisfied. Below are the columns we'll use to categorize employees as "dissatisfied" from each dataframe:
tafe_survey_updated:
Contributing Factors. Dissatisfaction
Contributing Factors. Job Dissatisfaction
dafe_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
If the employee indicated any of the factors above caused them to resign, we'll mark them as dissatisfied in a new column. After our changes, the new dissatisfied column will contain just the following values:
True
: indicates a person resigned because they were dissatisfied in some way
False
: indicates a person resigned because of a reason other than dissatisfaction with the job
NaN
: indicates the value is missing
# Check for unique values
tafe_resignation['Contributing Factors. Dissatisfaction'].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
# Check for unique values
tafe_resignation['Contributing Factors. Job Dissatisfaction'].value_counts()
- 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
# Update the values in the contributing factors columns to be either True, False, or NaN
def update_vals(x):
if x == '-':
return False
elif pd.isnull(x):
return np.nan
else:
return True
tafe_resignation['dissatisfied'] = tafe_resignation[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']].applymap(update_vals).any(1, skipna=False)
tafe_resignation_up = tafe_resignation.copy()
# Check the unique values after the updates
tafe_resignation_up['dissatisfied'].value_counts(dropna=False)
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
# Update the values in columns related to dissatisfaction to be either True, False, or NaN
dete_resignation['dissatisfied'] = dete_resignation[['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_resignation_up = dete_resignation.copy()
dete_resignation_up['dissatisfied'].value_counts(dropna=False)
False 162 True 149 Name: dissatisfied, dtype: int64
It appears DETE
have 149
True
for dissatisfaction, while TAFE
have 91
.
Below, we'll add an institute column so that we can differentiate the data from each survey after we combine them. Then, we'll combine the dataframes and drop any remaining columns we don't need.
# Add an institute column to both DataFrame
dete_resignation_up['institute'] = 'DETE'
tafe_resignation_up['institute'] = 'TAFE'
# Combine the 2 DataFrames
combined = pd.concat([dete_resignation, tafe_resignation], ignore_index=True)
# Verify the number of non null values in each column
combined.isnull().sum().sort_values()
id 0 separationtype 0 dissatisfied 8 cease_date 16 position 53 employment_status 54 age 55 gender 59 institute_service 88 Institute 311 WorkArea 311 Contributing Factors. Career Move - Public Sector 319 Contributing Factors. Career Move - Private Sector 319 Contributing Factors. Ill Health 319 Contributing Factors. Maternity/Family 319 Contributing Factors. Dissatisfaction 319 Contributing Factors. Job Dissatisfaction 319 Contributing Factors. Interpersonal Conflict 319 Contributing Factors. Study 319 Contributing Factors. Travel 319 Contributing Factors. Other 319 Contributing Factors. Career Move - Self-employment 319 Contributing Factors. NONE 319 workload 340 none_of_the_above 340 work_life_balance 340 ill_health 340 career_move_to_public_sector 340 career_move_to_private_sector 340 interpersonal_conflicts 340 job_dissatisfaction 340 dissatisfaction_with_the_department 340 physical_work_environment 340 traumatic_incident 340 lack_of_job_security 340 work_location 340 lack_of_recognition 340 maternity/family 340 relocation 340 study/travel 340 employment_conditions 340 role_service 361 dete_start_date 368 role_start_date 380 region 386 classification 490 business_unit 619 nesb 642 disability 643 aboriginal 644 south_sea 648 torres_strait 651 dtype: int64
Almost all the columns have null values, with the exception of id
and separationtype
dete_resignation['institute_service']
3 7.0 5 18.0 8 3.0 9 15.0 11 3.0 ... 808 3.0 815 2.0 816 2.0 819 5.0 821 NaN Name: institute_service, Length: 311, dtype: float64
# Drop columns with less than 500 non null value
combined_updated = combined.dropna(thresh=500, axis=1).copy()
Next, we'll clean the institute_service
column and categorize employees according to the following definitions:
New
: Less than 3 years in the workplaceExperienced
: 3-6 years in the workplaceEstablished
: 7-10 years in the workplaceVeteran
: 11 or more years in the workplaceOur analysis is based on this article, which agreed with the popular saying that Age is just a number
, the article went further to makes the argument that understanding employee's needs according to career stage instead of age is more effective
# Check the unique values in institute_service
combined_updated['institute_service'].value_counts(dropna=False).reset_index()
index | institute_service | |
---|---|---|
0 | NaN | 88 |
1 | Less than 1 year | 73 |
2 | 1-2 | 64 |
3 | 3-4 | 63 |
4 | 5-6 | 33 |
5 | 11-20 | 26 |
6 | 5 | 23 |
7 | 1 | 22 |
8 | 7-10 | 21 |
9 | 0 | 20 |
10 | 3 | 20 |
11 | 6 | 17 |
12 | 4 | 16 |
13 | 9 | 14 |
14 | 2 | 14 |
15 | 7 | 13 |
16 | More than 20 years | 10 |
17 | 8 | 8 |
18 | 13 | 8 |
19 | 15 | 7 |
20 | 20 | 7 |
21 | 10 | 6 |
22 | 12 | 6 |
23 | 14 | 6 |
24 | 22 | 6 |
25 | 17 | 6 |
26 | 18 | 5 |
27 | 16 | 5 |
28 | 11 | 4 |
29 | 23 | 4 |
30 | 24 | 4 |
31 | 19 | 3 |
32 | 21 | 3 |
33 | 39 | 3 |
34 | 32 | 3 |
35 | 28 | 2 |
36 | 30 | 2 |
37 | 26 | 2 |
38 | 36 | 2 |
39 | 25 | 2 |
40 | 27 | 1 |
41 | 29 | 1 |
42 | 31 | 1 |
43 | 33 | 1 |
44 | 34 | 1 |
45 | 35 | 1 |
46 | 38 | 1 |
47 | 41 | 1 |
48 | 42 | 1 |
49 | 49 | 1 |
We noticed that majority of the employess have worked for more than 20 years, whiel few have worked for less than 10 years.
Now, we'll extract the years of service from each value in the institute_service
column.
# Extract the years of service and convert the type to float
combined_updated['institute_service_up'] = combined_updated['institute_service'].astype('str').str.extract(r'(\d+)')
combined_updated['institute_service_up'] = combined_updated['institute_service_up'].astype('float')
# Check the years extracted are correct
combined_updated['institute_service_up'].value_counts().reset_index()
index | institute_service_up | |
---|---|---|
0 | 1.0 | 159 |
1 | 3.0 | 83 |
2 | 5.0 | 56 |
3 | 7.0 | 34 |
4 | 11.0 | 30 |
5 | 0.0 | 20 |
6 | 20.0 | 17 |
7 | 6.0 | 17 |
8 | 4.0 | 16 |
9 | 9.0 | 14 |
10 | 2.0 | 14 |
11 | 13.0 | 8 |
12 | 8.0 | 8 |
13 | 15.0 | 7 |
14 | 17.0 | 6 |
15 | 10.0 | 6 |
16 | 12.0 | 6 |
17 | 14.0 | 6 |
18 | 22.0 | 6 |
19 | 16.0 | 5 |
20 | 18.0 | 5 |
21 | 24.0 | 4 |
22 | 23.0 | 4 |
23 | 39.0 | 3 |
24 | 19.0 | 3 |
25 | 21.0 | 3 |
26 | 32.0 | 3 |
27 | 28.0 | 2 |
28 | 36.0 | 2 |
29 | 25.0 | 2 |
30 | 30.0 | 2 |
31 | 26.0 | 2 |
32 | 29.0 | 1 |
33 | 38.0 | 1 |
34 | 42.0 | 1 |
35 | 27.0 | 1 |
36 | 41.0 | 1 |
37 | 35.0 | 1 |
38 | 49.0 | 1 |
39 | 34.0 | 1 |
40 | 33.0 | 1 |
41 | 31.0 | 1 |
# Convert years of service to categories
def transform_service(val):
if val >= 11:
return "Veteran"
elif 7 <= val < 11:
return "Established"
elif 3 <= val < 7:
return "Experienced"
elif pd.isnull(val):
return np.nan
else:
return "New"
combined_updated['service_cat'] = combined_updated['institute_service_up'].apply(transform_service)
# Quick check of the update
combined_updated['service_cat'].value_counts().reset_index()
index | service_cat | |
---|---|---|
0 | New | 193 |
1 | Experienced | 172 |
2 | Veteran | 136 |
3 | Established | 62 |
Finally, we'll replace the missing values in the dissatisfied
column with the most frequent value, False. Then, we'll calculate the percentage of employees who resigned due to dissatisfaction in each service_cat
group and plot the results.
# Verify the unique values
combined_updated['dissatisfied'].value_counts(dropna=False).reset_index()
index | dissatisfied | |
---|---|---|
0 | False | 403 |
1 | True | 240 |
2 | NaN | 8 |
# Replace missing values with the most frequent value, False
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
# Calculate the percentage of employees who resigned due to dissatisfaction in each category
dis_pct = combined_updated.pivot_table(index='service_cat', values='dissatisfied')
# Plot the results
dis_pct.plot(kind='bar', title='Percentage of employees dissatisfaction', rot=30)
<matplotlib.axes._subplots.AxesSubplot at 0x7fd0c2c851c0>
# Calculate the percentage of employees who resigned due to dissatisfaction in each category
dis_pct = combined_updated.pivot_table(index='service_cat', values='dissatisfied')
# Plot the results
dis_pct.plot(kind='bar', title='Percentage of employees dissatisfaction', rot=30)
<matplotlib.axes._subplots.AxesSubplot at 0x7fd0c2a8fe80>
Generally, the results of our analysis shows that employees with at least 7 years of service are likely to resign due to dissatisfaction, while employees with less than 7 years of service are not likely to resign.
Going by the service categories, employees in Established and Veteran categories have a higher likelihood to resign.
NOTE: Other factors that could be considered for employees resignation are Age
, Role
, Contract type
among others.