There are countless reasons why employees resign or retire early. Reasons may include some dissatisfaction with employers or some concerns over family and health. Whatever reason it may be for an employee to resign, it is necessary for employers to understand the causes and be able to reduce workers exit or resignation. Surveys and staff feedback provide employers with valuable information on the reasons why their employees resign or retire. The information is used to inform attraction and retention initiatives and to improve work practices across an organization to ensure the organization is considered an employer of choice.
In this project, our goal is to provide answers to the project questions above.
In this project, we'll work with two datasets:
Both datasets originated from departments in Queensland, Australia. The DETE and TAFE Exit Surveys were developed to effectively canvass the opinions and attitudes of departing employees to identify a wide range of operational, organizational and personal variables affecting the decision to leave.
Data Dictionary
A dictionary wasn't provided with the datasets. However, for this project, we'll use our general knowledge to define some of the columns we're going to work with. We'll provide another combined dictionary later as we move forward.
dete_survey.csv
Column | Definition |
---|---|
ID | An id used to identify the participant of the survey |
SeparationType | The reason why the person's employment ended |
Cease Date | The year or month the person's employment ended |
DETE Start Date | The year the person began employment with the DETE |
tafe_survey.csv
Column | Definition |
---|---|
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 Legth of Service at Institute (in years) | The length of the person's employment (in years). |
In this project, it was found that the total number of workers who resigned due to some kinds of job dissatisfaction totalled 240. Out of this number, we found that, compared to employees with less than 7 years of service, more employees who have spent 7 or more years at a job (Established and Veteran categories) resigned due to some kinds of job dissatisfaction (with more than 60% of the total number of dissatisfied employees).
Further into the analysis, we noticed that more workers resigned as they increase in age. We observed a progressive increase in the percentage of workers that resigned due to job dissatisfaction as they move from their 30s to 40s etc. We found the highest percentage of dissatisfied workers among those who are 50 years of age and above. However, we found that more workers in their 20s and below resigned due to job dissatisfaction compared to those in their 30s.
# Import needed libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# Read-in datasets with pandas
dete_survey = pd.read_csv('dete_survey.csv', encoding="Latin-1")
tafe_survey = pd.read_csv('tafe_survey.csv', encoding="Latin-1")
The first action to carry out is to get an overview of what the two datasets look like. These initial and somewhat simple actions will direct our steps into the carrying out proper data cleaning and eventually, data analysis. We'll familiarize ourselves with the two datasets using some important methods. At the end, we'll have adequate knowledge to guide us as we move forward with the analysis and towards the set goals.
# This line of code helps to display full columns in the output
pd.options.display.max_columns = 150
# Familiarizing ourselves with the DETE dataset
dete_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): ID 822 non-null int64 SeparationType 822 non-null object Cease Date 822 non-null object DETE Start Date 822 non-null object Role Start Date 822 non-null object Position 817 non-null object Classification 455 non-null object Region 822 non-null object Business Unit 126 non-null object Employment Status 817 non-null object Career move to public sector 822 non-null bool Career move to private sector 822 non-null bool Interpersonal conflicts 822 non-null bool Job dissatisfaction 822 non-null bool Dissatisfaction with the department 822 non-null bool Physical work environment 822 non-null bool Lack of recognition 822 non-null bool Lack of job security 822 non-null bool Work location 822 non-null bool Employment conditions 822 non-null bool Maternity/family 822 non-null bool Relocation 822 non-null bool Study/Travel 822 non-null bool Ill Health 822 non-null bool Traumatic incident 822 non-null bool Work life balance 822 non-null bool Workload 822 non-null bool None of the above 822 non-null bool Professional Development 808 non-null object Opportunities for promotion 735 non-null object Staff morale 816 non-null object Workplace issue 788 non-null object Physical environment 817 non-null object Worklife balance 815 non-null object Stress and pressure support 810 non-null object Performance of supervisor 813 non-null object Peer support 812 non-null object Initiative 813 non-null object Skills 811 non-null object Coach 767 non-null object Career Aspirations 746 non-null object Feedback 792 non-null object Further PD 768 non-null object Communication 814 non-null object My say 812 non-null object Information 816 non-null object Kept informed 813 non-null object Wellness programs 766 non-null object Health & Safety 793 non-null object Gender 798 non-null object Age 811 non-null object Aboriginal 16 non-null object Torres Strait 3 non-null object South Sea 7 non-null object Disability 23 non-null object NESB 32 non-null object dtypes: bool(18), int64(1), object(37) memory usage: 258.6+ KB
dete_survey.head()
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | Career move to public sector | Career move to private sector | Interpersonal conflicts | Job dissatisfaction | Dissatisfaction with the department | Physical work environment | Lack of recognition | Lack of job security | Work location | Employment conditions | Maternity/family | Relocation | Study/Travel | Ill Health | Traumatic incident | Work life balance | Workload | None of the above | Professional Development | Opportunities for promotion | Staff morale | Workplace issue | Physical environment | Worklife balance | Stress and pressure support | Performance of supervisor | Peer support | Initiative | Skills | Coach | Career Aspirations | Feedback | Further PD | Communication | My say | Information | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984 | 2004 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | True | False | False | True | False | False | True | False | False | False | False | False | False | False | False | False | False | True | A | A | N | N | N | A | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | Not Stated | Not Stated | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | A | N | N | N | N | A | A | A | N | N | N | A | A | A | N | A | A | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011 | 2011 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | N | N | N | N | N | N | N | N | N | N | N | N | N | N | N | A | A | N | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005 | 2006 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | N | N | N | A | A | N | N | A | A | A | A | A | A | A | A | A | A | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970 | 1989 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | A | A | N | N | D | D | N | A | A | A | A | A | A | SA | SA | D | D | A | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
# Checking for unique entries in the column
dete_survey['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
# Getting the number of null values in the dataframe
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
# Familiarizing ourselves with the TAFE dataset
tafe_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 72 columns): Record ID 702 non-null int64 Institute 702 non-null object WorkArea 702 non-null object CESSATION YEAR 695 non-null float64 Reason for ceasing employment 701 non-null object Contributing Factors. Career Move - Public Sector 437 non-null object Contributing Factors. Career Move - Private Sector 437 non-null object Contributing Factors. Career Move - Self-employment 437 non-null object Contributing Factors. Ill Health 437 non-null object Contributing Factors. Maternity/Family 437 non-null object Contributing Factors. Dissatisfaction 437 non-null object Contributing Factors. Job Dissatisfaction 437 non-null object Contributing Factors. Interpersonal Conflict 437 non-null object Contributing Factors. Study 437 non-null object Contributing Factors. Travel 437 non-null object Contributing Factors. Other 437 non-null object Contributing Factors. NONE 437 non-null object Main Factor. Which of these was the main factor for leaving? 113 non-null object InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 609 non-null object WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] 608 non-null object WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 609 non-null object WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object Induction. Did you undertake Workplace Induction? 619 non-null object InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object Gender. What is your Gender? 596 non-null object CurrentAge. Current Age 596 non-null object Employment Type. Employment Type 596 non-null object Classification. Classification 596 non-null object LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(1), int64(1), object(70) memory usage: 395.0+ KB
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 | Contributing Factors. Dissatisfaction | Contributing Factors. Job Dissatisfaction | Contributing Factors. Interpersonal Conflict | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | Main Factor. Which of these was the main factor for leaving? | InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction | InstituteViews. Topic:2. I was given access to skills training to help me do my job better | InstituteViews. Topic:3. I was given adequate opportunities for personal development | InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% | InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had | InstituteViews. Topic:6. The organisation recognised when staff did good work | InstituteViews. Topic:7. Management was generally supportive of me | InstituteViews. Topic:8. Management was generally supportive of my team | InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me | InstituteViews. Topic:10. Staff morale was positive within the Institute | InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly | InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently | InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly | WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit | WorkUnitViews. Topic:15. I worked well with my colleagues | WorkUnitViews. Topic:16. My job was challenging and interesting | WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work | WorkUnitViews. Topic:18. I had sufficient contact with other people in my job | WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job | WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job | WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] | WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job | WorkUnitViews. Topic:23. My job provided sufficient variety | WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job | WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction | WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance | WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area | WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date | WorkUnitViews. Topic:29. There was adequate communication between staff in my unit | WorkUnitViews. Topic:30. Staff morale was positive within my work unit | Induction. Did you undertake Workplace Induction? | InductionInfo. Topic:Did you undertake a Corporate Induction? | InductionInfo. Topic:Did you undertake a Institute Induction? | InductionInfo. Topic: Did you undertake Team Induction? | InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? | InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? | InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? | InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? | InductionInfo. On-line Topic:Did you undertake a Institute Induction? | InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? | InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? | InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] | InductionInfo. Induction Manual Topic: Did you undertake Team Induction? | Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 634133009996094000 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Agree | Agree | Agree | Neutral | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Neutral | Agree | Agree | Yes | Yes | Yes | Yes | Face to Face | - | - | Face to Face | - | - | Face to Face | - | - | Yes | Yes | Yes | Yes | Yes | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 634133654064531000 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Agree | Agree | Agree | Disagree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Agree | Agree | Strongly Agree | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 634138845606563000 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | - | - | - | - | - | - | NONE | NaN | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Agree | Neutral | Neutral | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 634139903350000000 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Yes | No | Yes | Yes | - | - | - | NaN | - | - | - | - | - | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 634146578511788000 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | - | - | - | - | - | - | - | NaN | Agree | Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Yes | Yes | Yes | Yes | - | - | Induction Manual | Face to Face | - | - | Face to Face | - | - | Yes | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
# Checking for unique entries in the column
tafe_survey['Reason for ceasing employment'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: Reason for ceasing employment, dtype: int64
# Getting the number of null values in the dataframe
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
OBSERVATIONS
As we can see from the information above, the DETE
dataframe contains 56 columns and 822 rows of data. We also see that DETE Start Date
column in dete_survey
dataframe contains some missing/null values entered as Not Stated. This is recognized by pandas as a string and not a missing value.
The TAFE
dataframe also contains 72 columns and 702 rows of data. We also see that LengthofServiceOverall. Overall Length of Service at Institute (in years)
column in tafe_survey
dataframe contains some missing/null values seen as NaN
.
The two dataframes contain many of the same columns, but the column names are different.
Now we know that the dataframes contain many columns that are not needed to complete this project. Also, there are multiple columns/answers that indicate an employee resigned because they were dissatisfied.
Hence, before we proceed further with the data exploration, we'll read dete_survey.csv
CSV file into pandas again. This time around, the Not Stated
values will be specified as NAN
. We'll thereafter proceed to remove the unwanted columns.
# Makes pandas to identify specified missing values
dete_survey = pd.read_csv('dete_survey.csv', na_values='Not Stated', encoding="Latin-1")
# Removes unwanted columns from each dataframes
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)
# Checks that the columns were removed
print(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')
print(tafe_survey_updated.columns)
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')
From the new information above, we see that the dete_survey_updated
dataframe now contains 35 columns and 822 rows. We also see that missing/null values are now seen as NAN
. The tafe_survey_updated
dataframe now contains 23 columns and 702 rows.
Now that we have successfully made these changes, we'll get back on track to our data exploration and cleaning.
As stated earlier in our observation, each dataframe contains many of the same columns, but the column names are different. Below is a combined dictionary for some similar columns in the two datasets:
dete_survey | tafe_survey | Definition |
---|---|---|
ID | Record ID | An id used to identify the participant of the survey |
SeparationType | Reason for ceasing employment | The reason why the participant's employment ended |
Cease Date | CESSATION YEAR | The year or month the participant's employment ended |
DETE Start Date | The year the participant began employment with DETE | |
LengthofServiceOverall. Overall Length of Service at Institute (in years) | The length of the person's employment (in years) | |
Age | CurrentAge. Current Age | The age of the participant |
Gender | Gender. What is your Gender? | The gender of the participant |
We'll standardize the column names in both dataframes and make them uniform. This is because we'll later combine the two dataframes.
In some few cells below, we'll see the state of the column names before and after the cleaning process that was carried out. We do this in order to maintain the flow of the whole process.
# Initial form of the column names
print(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')
# Column head: BEFORE CLEANING
dete_survey_updated.head(2)
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | Career move to public sector | Career move to private sector | Interpersonal conflicts | Job dissatisfaction | Dissatisfaction with the department | Physical work environment | Lack of recognition | Lack of job security | Work location | Employment conditions | Maternity/family | Relocation | Study/Travel | Ill Health | Traumatic incident | Work life balance | Workload | None of the above | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | True | False | False | True | False | False | True | False | False | False | False | False | False | False | False | False | False | True | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
# Updates the column names to a standardized form
dete_survey_updated.columns = dete_survey_updated.columns.str.replace(' ', '_').str.strip().str.lower()
# Updated form of the column names
print(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')
# Column head: AFTER CLEANING
dete_survey_updated.head(2)
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | career_move_to_public_sector | career_move_to_private_sector | interpersonal_conflicts | job_dissatisfaction | dissatisfaction_with_the_department | physical_work_environment | lack_of_recognition | lack_of_job_security | work_location | employment_conditions | maternity/family | relocation | study/travel | ill_health | traumatic_incident | work_life_balance | workload | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | True | False | False | True | False | False | True | False | False | False | False | False | False | False | False | False | False | True | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
In the following cells, we'll update the column names in tafe_survey_updated
dataframe to be uniform with the dete_survey_updated
dataframe.
# A dictionary of name change
updated_names = {"Record ID": "id", "CESSATION YEAR": "cease_date", "Reason for ceasing employment": "separationtype", "Gender. What is your Gender?": "gender", "CurrentAge. Current Age": "age", "Employment Type. Employment Type": "employment_status", "Classification. Classification": "position", "LengthofServiceOverall. Overall Length of Service at Institute (in years)": "institute_service", "LengthofServiceCurrent. Length of Service at current workplace (in years)": "role_service" }
# Updates the column names in dataframe
tafe_survey_updated = tafe_survey_updated.rename(updated_names, 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', 'age', 'Employment Type. Employment Type', 'Classification. Classification', 'institute_service', 'role_service'], dtype='object')
We can see that the names of the columns representing the same information in both dataframes are now uniform. Thus, our analysis is made less confusing. This is especially important because we aim to combine both dataframes to complete our analysis.
Next, we'll remove more of the data we don't need to answer our questions - one of which is:
Again, let's take a look at the unique values in separationtype
columns in each dataframe:
dete_survey_updated['separationtype'].value_counts()
Age Retirement 285 Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
tafe_survey_updated['separationtype'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
We can see that each dataframe contains a couple of different separation types. However, for this particular analysis, we're only interested in survey respondents who resigned. Therefore, only those separation types that contains the string Resignation
will be analyzed.
We can also see that dete_survey_updated
dataframe contains multiple separation types with the string Resignation
:
# The pattern to search for and select
resignations = dete_survey_updated['separationtype'].str.contains(r"[Rr]esignation", na=False)
# Selects only data that have the desired string
dete_resignations = dete_survey_updated.copy()[resignations]
dete_resignations.head()
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
5 | 6 | Resignation-Other reasons | 05/2012 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | True | False | False | False | False | False | False | False | True | True | False | False | False | False | False | False | False | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
8 | 9 | Resignation-Other reasons | 07/2012 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
9 | 10 | Resignation-Other employer | 2012 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | False | False | True | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
11 | 12 | Resignation-Move overseas/interstate | 2012 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | True | True | False | False | False | False | False | False | Male | 31-35 | NaN | NaN | NaN | NaN | NaN |
# Separates and select Resignation from the strings
dete_resignations['separationtype'] = dete_survey_updated['separationtype'].str.split('-').str[0]
# Unique values in the updated column
dete_resignations['separationtype'].value_counts()
Resignation 311 Name: separationtype, dtype: int64
Above, we select only data whose separationtype
column contains the string - "Resignation". This is solely because we're only going to analyze the data of the respondents who resigned from their job.
We can see above that the dataframe dete_resignations
now contains 311 rows of data.
The separationtype
column in tafe_survey_updated
dataframe has only one type of resignation string. Next, we'll select those data with Resignation
directly into a new dataframe.
# Selects only data which have the desired string
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
tafe_resignations.head()
id | Institute | WorkArea | cease_date | separationtype | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | Contributing Factors. Dissatisfaction | Contributing Factors. Job Dissatisfaction | Contributing Factors. Interpersonal Conflict | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | gender | age | Employment Type. Employment Type | Classification. Classification | institute_service | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 634139903350000000 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 634146578511788000 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | - | - | - | - | - | - | - | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 | 634147506906311000 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | - | - | - | - | - | Other | - | Female | 56 or older | Contract/casual | Teacher (including LVT) | 7-10 | 7-10 |
6 | 634152007975694000 | Barrier Reef Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | Maternity/Family | - | - | - | - | - | Other | - | Male | 20 or younger | Temporary Full-time | Administration (AO) | 3-4 | 3-4 |
7 | 634153745310374000 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | - | - | - | - | - | Other | - | Male | 46 50 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
# Unique values in the updated column
tafe_resignations['separationtype'].value_counts()
Resignation 340 Name: separationtype, dtype: int64
After selecting just the rows that contains Resignation
in their separationtype
column, we can see above that the dataframe tafe_resignations
now contains 340 rows of data.
We'll continue by checking for other errors in the dataframes. We'll do this by examining each column we need for this anaysis:
Let's take a look at the cease_date
and dete_start_date
in the dete_resignations
dataframe and see whether there's need for any adjustments based on these:
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 make sense to have years after the current date.dete_start_date
was before the year 1940.So if we have years (in dete_start_date
) higher than the cease_date
or lower than 1940
, we wouldn't want to continue with the analysis. The reason is that there may be something very wrong with the data. However, if there are just a small amount of values that are high or low, we'll remove them.
dete_resignations['cease_date'].value_counts(ascending=True)
09/2010 1 2010 1 07/2006 1 07/2012 1 05/2013 2 05/2012 2 08/2013 4 10/2013 6 11/2013 9 07/2013 9 09/2013 11 06/2013 14 12/2013 17 01/2014 22 2013 74 2012 126 Name: cease_date, dtype: int64
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
The cease_date
column in dete_resignation
dataframe contains two different date formats: some only contain year while others contain month and year. There is a need to change these dates to take a single and similar form across the rows.
# Pattern for extracting date format
pattern = r"/?(?P<Year>[1-2][0-9]{3})"
dete_resignations['cease_date'] = dete_resignations['cease_date'].astype(str).str.extract(pattern).astype(float)
dete_resignations['cease_date'].value_counts().sort_index()
2006.0 1 2010.0 2 2012.0 129 2013.0 146 2014.0 22 Name: cease_date, dtype: int64
# Box plot to check for outliers
plt.figure(figsize=(10, 10))
dete_resignations.boxplot(column=['cease_date']).set_ylim(2005,2018)
(2005, 2018)
# Box plot to check for outliers
plt.figure(figsize=(10, 10))
dete_resignations.boxplot(column=['dete_start_date']).set_ylim(1960,2014)
(1960, 2014)
# Sorted values in the cease_date column
tafe_resignations['cease_date'].value_counts().sort_index()
2009.0 2 2010.0 68 2011.0 116 2012.0 94 2013.0 55 Name: cease_date, dtype: int64
# Box plot to check for outliers
plt.figure(figsize=(10, 10))
tafe_resignations.boxplot(column=['cease_date']).set_ylim(2005,2018)
(2005, 2018)
Here is what we can deduce from the information above:
dete_start_date
and cease_date
.dete_resignations
dataframe contains outliers (2006 and 2010) while tafe_resignations
has outliers at 2009 (a year absent from the other dataframe). The tafe_resignations
dataframe also contains more cease dates (68) in 2010 compared to the number of cease dates in dete_resignations
(2).Since we're not interested in analyzing the data according to year, we'll leave them as they are.
So far so good, we have rectified the years in the columns to follow a single uniform pattern in the two dataframes. We also didn't find any major issues with the years. Additionally, we saw that the years in each dataframe don't span the same number of years.
As a reminder, our goal is to answer this question:
In order to answer the questions, we'll use the now verified years in the dete_registrations
dataframe to create a new column. In the Human Resources field, the length of time an employee spent in a workplace is referred to as their years of service. The tafe_resignations
dataframe already contains a "service" column that we renamed to institute_service
. Therefore, in order to analyze both surveys together, we'll create a corresponding institute_service
column in dete_resignations
.
dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']
dete_resignations['institute_service'].head()
3 7.0 5 18.0 8 3.0 9 15.0 11 3.0 Name: institute_service, dtype: float64
We have successfully created a new corresponding column named institute_service
in the dete_resignations
dataframe. Thus, we can now analyze the survey respondents according to their length of employment. Firstly, we'll identify and classify employees who resigned because they were dissatisfied. The reason for which a worker's employment ended at both institutes are indicated by the column separationtype
in both datasets. Other factors that may have contributed to the job cessation are indicated in other columns of the datasets.
According to a dictionary meaning,
Dissatisfaction: The feeling of being displeased and discontent.
Based on that meaning, we'll select some columns from the columns in both dataframes to categorize employees as "dissatisfied". The selected columns are listed below
If the employee indicated any of the above caused them to resign, we'll mark them as dissatisfied in a new column as following:
Let's start by viewing the values in the two columns of Contributing Factors
in tafe_resignations
dataframe.
# Checks the unique values
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
# Checks the unique values
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()
- 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
There are 55 respondents who resigned due to dissatisfaction in the Contributing Factors. Dissatisfaction
column. In the other column (Contributing Factors. Job Dissatisfaction
), 62 respondents resigned due to dissatisfaction.
# Updates values in columns to either True, False, or NaN
def update_vals(value):
if pd.isnull(value):
return np.nan
elif value == '-':
return False
else:
return True
# Creates a new column for dissatisfaction state
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()
# Checks the unique values of the new column
tafe_resignations_up['dissatisfied'].value_counts(dropna=False)
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
# Creates a new column for dissatisfaction state
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()
# Checks the unique values of the new column
dete_resignations_up['dissatisfied'].value_counts(dropna=False)
False 162 True 149 Name: dissatisfied, dtype: int64
Hooray! We've successfully created a new column dissatisfied
in both dataframes classifying each respondent whose reason for resignation was based on job dissatisfaction (True
) or other reasons (False
). This action is necessary because we hope to combine the two dataframe and be able to make deductions based on this reason.
Up until this moment, we have worked to clean up and add valuable data. We've carried out these changes separately on each dataframe. Now, we're finally ready to combine the two datasets and take a big step towards answering our questions.
We're going to start by adding a column to each dataframe. This new column will allow us to easily distinguish between the two datasets after we've combined them.
# Adds a new column to the datasets
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
# Combines the datasets
combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index=True)
# Checks the number of non null values in each column
combined.notnull().sum().sort_values()
C:\Users\extraUNIQUEguy\Anaconda3\lib\site-packages\ipykernel_launcher.py:2: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version of pandas will change to not sort by default. To accept the future behavior, pass 'sort=False'. To retain the current behavior and silence the warning, pass 'sort=True'.
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 Classification. Classification 290 Employment Type. Employment Type 290 role_service 290 employment_status 307 position 308 none_of_the_above 311 ill_health 311 maternity/family 311 relocation 311 lack_of_recognition 311 lack_of_job_security 311 job_dissatisfaction 311 interpersonal_conflicts 311 study/travel 311 traumatic_incident 311 work_life_balance 311 physical_work_environment 311 employment_conditions 311 workload 311 dissatisfaction_with_the_department 311 career_move_to_public_sector 311 career_move_to_private_sector 311 work_location 311 Contributing Factors. Career Move - Private Sector 332 Contributing Factors. Career Move - Public Sector 332 Contributing Factors. Career Move - Self-employment 332 Contributing Factors. Dissatisfaction 332 Contributing Factors. Ill Health 332 Contributing Factors. Interpersonal Conflict 332 Contributing Factors. Job Dissatisfaction 332 Contributing Factors. Maternity/Family 332 Contributing Factors. NONE 332 Contributing Factors. Study 332 Contributing Factors. Travel 332 Contributing Factors. Other 332 Institute 340 WorkArea 340 institute_service 563 gender 592 age 596 cease_date 635 dissatisfied 643 separationtype 651 institute 651 id 651 dtype: int64
We've successfully combined the two datasets. Next, we checked for the number of non null values in order to know the other unnecessary columns to drop from the combined
dataframe.
# Columns prior to dropping
combined.columns
Index(['Classification. Classification', 'Contributing Factors. Career Move - Private Sector ', 'Contributing Factors. Career Move - Public Sector ', 'Contributing Factors. Career Move - Self-employment', 'Contributing Factors. Dissatisfaction', 'Contributing Factors. Ill Health', 'Contributing Factors. Interpersonal Conflict', 'Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Maternity/Family', 'Contributing Factors. NONE', 'Contributing Factors. Other', 'Contributing Factors. Study', 'Contributing Factors. Travel', 'Employment Type. Employment Type', 'Institute', 'WorkArea', 'aboriginal', 'age', 'business_unit', 'career_move_to_private_sector', 'career_move_to_public_sector', 'cease_date', 'classification', 'dete_start_date', 'disability', 'dissatisfaction_with_the_department', 'dissatisfied', 'employment_conditions', 'employment_status', 'gender', 'id', 'ill_health', 'institute', 'institute_service', 'interpersonal_conflicts', 'job_dissatisfaction', 'lack_of_job_security', 'lack_of_recognition', 'maternity/family', 'nesb', 'none_of_the_above', 'physical_work_environment', 'position', 'region', 'relocation', 'role_service', 'role_start_date', 'separationtype', 'south_sea', 'study/travel', 'torres_strait', 'traumatic_incident', 'work_life_balance', 'work_location', 'workload'], dtype='object')
# Removes columns with less than 500 non null values
combined_updated = combined.dropna(thresh=500, axis=1).copy()
# Columns after dropping
combined_updated.columns
Index(['age', 'cease_date', 'dissatisfied', 'gender', 'id', 'institute', 'institute_service', 'separationtype'], dtype='object')
We dropped all columns with less than 500 non null values above. This is necessary for the effective management and analysis of the data. Essentially, columns with less than 500 non null values can not influence the result of our analysis.
Before we proceed to proper analysis, we want to clean up the institute_service
column. Let's see the unique values in that particular column below:
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 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 14.0 6 10.0 6 12.0 6 17.0 6 22.0 6 16.0 5 18.0 5 24.0 4 11.0 4 23.0 4 19.0 3 39.0 3 21.0 3 32.0 3 25.0 2 26.0 2 36.0 2 28.0 2 30.0 2 33.0 1 38.0 1 35.0 1 34.0 1 31.0 1 49.0 1 29.0 1 27.0 1 42.0 1 41.0 1 Name: institute_service, dtype: int64
The column contains some very different forms of value. Tricky business! In order to analyze the data, we'll have to convert these values into categories below:
This analysis is based on this article, which makes the argument that understanding employee's needs according to career stage instead of age is more effective.
# Extracts years of service from column and converts 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')
# Confirms successful extraction
combined_updated['institute_service_up'].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_up, dtype: int64
# Sorts years into different categories
def categorize(value):
if value >= 11:
return "Veteran"
elif 3 <= value < 7:
return "Experienced"
elif 7 <= value < 11:
return "Established"
elif pd.isnull(value):
return np.nan
else:
return "New"
# Applies function to the column
combined_updated['service_cat'] = combined_updated['institute_service_up'].apply(categorize)
# Confirms the changes
combined_updated['service_cat'].value_counts()
New 193 Experienced 172 Veteran 136 Established 62 Name: service_cat, dtype: int64
We categorized the employees into four groups based on the number of years spent working. A new column service_cat
was created to contain the category each employee falls to. Now it is easy to analyze our data and answer some salient questions.
In order to get started with our data analysis, we'll perform a small piece of analysis. We'll first fill in missing values in the dissatisfied
column and then aggregate the data.
# Checks the unique values in the column
combined_updated['dissatisfied'].value_counts(dropna=False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
# Replaces missing values in the column
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
So far, we have found out the total number of workers who resigned due to some kinds of job dissatisfaction to be 240. To continue further with the analysis, we needed to fill the 8 missing values in the column with the most frequent category therein.
The False
group occurs most in the dissatisfied
column, we therefore replaced the missing values with 'False'.
All the values in dissatisfied
column now consists of Boolean values, meaning they're either True
or False
. Thus, we'll use a pivot table to aggregate the column and calculate the percentage of people in each group. This is possible because df.pivot_table()
method treats Boolean values as integers, so a True
value is considered to be 1
and a False
value is considered to be 0
.
# Calculate and visualize the percentage of dissatisfied employees
table_s_cat = combined_updated.pivot_table(index='service_cat', values='dissatisfied')
print(table_s_cat)
# Data to plot bar chart
plt.figure(figsize=(10,10))
sns.set_style('darkgrid')
ax = sns.barplot(x=table_s_cat.index, y=table_s_cat['dissatisfied'], palette=sns.color_palette('GnBu_d'))
ax.set_yticks([])
ax.set_title('Percentage of Dissatisfied Employee Based on Service Category')
ax.set_xlabel('Service Category')
ax.set_ylabel('Percentage')
ax.tick_params(bottom=True, top=False, left=False, right=False, labelbottom=True)
sns.despine(left=True)
dissatisfied service_cat Established 0.516129 Experienced 0.343023 New 0.295337 Veteran 0.485294
Let's also visualize this data using a pie chart:
# Data to plot pie chart
labels = 'Established', 'Experienced', 'New', 'Veteran'
colours = ['lightskyblue', 'yellowgreen', 'lightcoral', 'gold']
explode = (0.1, 0, 0, 0.1) # "Explodes" the first and last slice
plt.pie(table_s_cat, labels=labels, colors=colours, autopct='%1.1f%%', shadow=True, explode=explode, startangle=90)
plt.title('Percentage of Dissatisfied Employee Based on Service Category', bbox={'facecolor':'0.8', 'pad':5})
plt.axis('equal') # Ensures pie is drawn as a circle
plt.show()
C:\Users\extraUNIQUEguy\Anaconda3\lib\site-packages\ipykernel_launcher.py:5: MatplotlibDeprecationWarning: Non-1D inputs to pie() are currently squeeze()d, but this behavior is deprecated since 3.1 and will be removed in 3.3; pass a 1D array instead. """
Above, we aggregated the data in the dissatisfied
column and used the information to calculate the percentage of the people in each group. From this small analysis, we can presently conclude that more employees who have spent 7 or more years at a job (Established and Veteran categories) resigned due to some kinds of job dissatisfaction (with more than 60% of the total number of dissatisfied employees). Compared to the two aforementioned categories, employees with less than 7 years of service resigned less due to job dissatisfaction.
Next, we'll handle the missing values in the other categories. We want to know how many people resigned due to some kind of dissatisfaction and the percentage according to gender. First, let's take a closer look at each column and clean up the age
column, which is still in an untreatable form.
# Values in each column
gender_counts = combined_updated['gender'].value_counts(dropna=False)
print(gender_counts, '\n')
service_counts = combined_updated['service_cat'].value_counts(dropna=False)
print(service_counts, '\n')
age_counts = combined_updated['age'].value_counts(dropna=False)
print(age_counts)
Female 424 Male 168 NaN 59 Name: gender, dtype: int64 New 193 Experienced 172 Veteran 136 NaN 88 Established 62 Name: service_cat, dtype: int64 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 26 30 32 36 40 32 31 35 32 56 or older 29 31-35 29 21-25 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
# Extracts the first age in each age group
combined_updated['age'] = combined_updated['age'].str.extract(r"(\d+)").astype(float)
combined_updated['age'].value_counts(dropna=False)
41.0 93 46.0 81 36.0 73 51.0 71 26.0 67 21.0 62 31.0 61 NaN 55 56.0 55 61.0 23 20.0 10 Name: age, dtype: int64
The age
column now looks good and can be analyzed. We can also observe that there are 55 missing values in the column. Before we decide on what to do with these missing values, let's first look at the missing values across the columns we are interested in.
# Creates heatmap to locate missing values
map_index = combined_updated.set_index('dissatisfied')
sns.heatmap(map_index.isnull(), cbar=False)
<matplotlib.axes._subplots.AxesSubplot at 0x163034faa88>
Above, we created a heatmap to check the location of the missing values in our dataframe. We can observe that the essential columns for our next analysis (age
, gender
and service_cat
) all have roughly the same number of missing values and almost on the same rows of data. We'll continue by filling the missing age
data with the average value of all the ages. It is however unnecessary to try to fill the other missing values and thus, we'll leave the dataset as is.
# Calculate the mean age
mean = combined_updated['age'].mean()
# Fill missing age with the mean value
combined_updated['age'] = combined_updated['age'].fillna(mean)
# Display the unique values in the age column
combined_updated['age'].value_counts(dropna=False)
41.000000 93 46.000000 81 36.000000 73 51.000000 71 26.000000 67 21.000000 62 31.000000 61 39.271812 55 56.000000 55 61.000000 23 20.000000 10 Name: age, dtype: int64
Next, we'll group the ages into four different categories. These categories include workers in their:
# A function to group age
def categorize_age(age):
if age >= 50:
return '50s and above'
elif 40 <= age < 50:
return '40s'
elif 30 <= age < 40:
return '30s'
elif 20 <= age < 30:
return '20s and below'
# Apply function to categorize age into groups in a new column
combined_updated['age_cat'] = combined_updated['age'].apply(categorize_age)
combined_updated['age_cat'].value_counts()
30s 189 40s 174 50s and above 149 20s and below 139 Name: age_cat, dtype: int64
As we can now see, we have 139 workers who are in their 20s or below, 189 workers in their 30s, 174 workers in their 40s and 149 workers in their 50s or above. This categorization looks encompassing enough.
Earlier, we aggregated dissatisfied workers based on service category. Right now, we want to do the same, albeit based on age category.
# Calculate and visualize the percentage of dissatisfied employees
table_a_cat = combined_updated.pivot_table(index='age_cat', values='dissatisfied')
print(table_a_cat)
# Data to plot
plt.figure(figsize=(10,10))
sns.set_style('darkgrid')
ax = sns.barplot(x=table_a_cat.index, y=table_a_cat['dissatisfied'], palette=sns.color_palette('GnBu_d'))
ax.set_yticks([])
ax.set_title('Percentage of Dissatisfied Employee Based on Age Category')
ax.set_xlabel('Age Category')
ax.set_ylabel('Percentage')
ax.tick_params(bottom=True, top=False, left=False, right=False, labelbottom=True)
sns.despine(left=True)
dissatisfied age_cat 20s and below 0.352518 30s 0.328042 40s 0.379310 50s and above 0.422819
Let's also visualize this data using a pie chart below:
labels = '20s and below', '30s', '40s', '50s and above'
colours = ['lightcoral', 'lightskyblue', 'gold', 'yellowgreen']
explode = (0.05, 0.05, 0.05, 0.1)
plt.pie(table_a_cat, labels=labels, colors=colours, autopct='%1.1f%%', shadow=True, explode=explode, startangle=90)
plt.title('Percentage of Dissatisfied Employee Based on Age Category', bbox={'facecolor':'0.8', 'pad':5})
plt.axis('equal') # Ensures pie is drawn as a circle
plt.show()
C:\Users\extraUNIQUEguy\Anaconda3\lib\site-packages\ipykernel_launcher.py:4: MatplotlibDeprecationWarning: Non-1D inputs to pie() are currently squeeze()d, but this behavior is deprecated since 3.1 and will be removed in 3.3; pass a 1D array instead. after removing the cwd from sys.path.
In this project we set out to answer some important questions regarding worker's resignation. From the analysis we performed, we found the total number of workers who resigned due to some kinds of job dissatisfaction to be 240. We also found that compared to employees with less than 7 years of service, more employees who have spent 7 or more years at a job (Established and Veteran categories) resigned due to some kinds of job dissatisfaction (with more than 60% of the total number of dissatisfied employees).
Further into the analysis, we noticed that more workers resigned as they increase in age. We observed a progressive increase in the percentage of workers that resigned due to job dissatisfaction as they move from their 30s to 40s etc. We found the highest percentage of dissatisfied workers among those who are 50 years of age and above. However, we found that more workers in their 20s and below resigned due to job dissatisfaction compared to those in their 30s.
One of the reasons this might be so is that in many cases, most in their 20s are recent graduates, new entrants in the workforce, single (or at least unmarried). Many of these young individuals, fresh out of college, lockstep with peers, carry much expectation and expect a workplace to be ideal and a perfect fit. But then after these expectations are not met, they may become grumpy and dissatisfied with their jobs. However, when individuals attain their 30s they probably are now at peace, develop more emotional, moral and intellectual alignment with the world. These individuals have now attained another level of self-awareness and may put up more with some job dissatisfactions. Then, this character probably tends to simmer down as individuals become much older as seen by this analysis.
Here, we want to present the numbers of people in each service category and the gender differences.
# Dataframe of dissatisfied employees in the Veteran category
dissatisfied_veteran = combined_updated.loc[(combined_updated['dissatisfied'] == True) & (combined_updated['service_cat'] == 'Veteran')]
print('Total Dissatisfied Veteran Workers = ' + str(len(dissatisfied_veteran)))
Total Dissatisfied Veteran Workers = 66
dissatisfied_veteran['gender'].value_counts(dropna=False)
Female 45 Male 19 NaN 2 Name: gender, dtype: int64
# Dataframe of dissatisfied employees in the Established category
dissatisfied_established = combined_updated.loc[(combined_updated['dissatisfied'] == True) & (combined_updated['service_cat'] == 'Established')]
print('Total Dissatisfied Established Workers = ' + str(len(dissatisfied_established)))
Total Dissatisfied Established Workers = 32
dissatisfied_established['gender'].value_counts(dropna=False)
Female 24 Male 8 Name: gender, dtype: int64
# Dataframe of dissatisfied employees in the Experienced category
dissatisfied_experienced = combined_updated.loc[(combined_updated['dissatisfied'] == True) & (combined_updated['service_cat'] == 'Experienced')]
print('Total Dissatisfied Experienced Workers = ' + str(len(dissatisfied_experienced)))
Total Dissatisfied Experienced Workers = 59
dissatisfied_experienced['gender'].value_counts(dropna=False)
Female 44 Male 15 Name: gender, dtype: int64
# Dataframe of dissatisfied employees in the New category
dissatisfied_new = combined_updated.loc[(combined_updated['dissatisfied'] == True) & (combined_updated['service_cat'] == 'New')]
print('Total Dissatisfied New Workers = ' + str(len(dissatisfied_new)))
Total Dissatisfied New Workers = 57
dissatisfied_new['gender'].value_counts(dropna=False)
Female 37 Male 20 Name: gender, dtype: int64