Feedback from employee exit surveys can provide powerful insights into a company’s culture. It doesn't matter how excellent a company is, people are eventually going to leave. Exit surveys allow leaving employees to share their unique opinions. This can help companies in mitigating the many costs of losing other employees in the future.
Image source: Skywalk Group
In this Project, we'll work with 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) institute in Queensland, Australia.
The DETE exit survey data can be found here. However, the original TAFE survey data is no longer available. Some modifications have been made to the original datasets to make them easier to work with, especially changing the encoding from cp1252 to UTF-8.
We will play the role of data analysts and pretend our stakeholders want to know the following:
The stakeholders want us to combine results from both surveys and answer these questions. Although both surveys used the same template, one of them had customized answers.
A data dictionary wasn't provided with the dataset. In a job setting, we'd make sure to meet with a manager and confirm the definitions of the data. For this project, we'll use our general knowledge to define the columns.
From dete_survey.csv
, we will focus on the following columns:
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.
From tafe_survey.csv
, we will focus on the following columns:
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).
Age, gender, and length of service are important factors when it comes to employee satisfaction and retention, especially in the current world where employees have high expectations of sound workplace culture.
Young employees are less willing to leave a current employer while older employees pose a higher flight risk, perhaps, driven by a search for better career opportunities or a more challenging work environment with cross-functional collaboration. Younger employees generally seek to gain more experience, acquire new skills and advance their careers, which might explain their lower tendency to resign from dissatisfaction at the early stage of their careers.
In terms of gender, men posed a slightly higher flight risk than their female counterparts. They might likely be in search of higher-paying and career-accelerating opportunities to fend for their families.
We will start by importing some useful python libraries. Numpy
and Pandas
for performing mathematical operations and manipulating data; Tabulate
for pretty-printing pandas series and dataframes; and the Plotly
visualisation libraries for building informing visuals.
import numpy as np
import pandas as pd
from tabulate import tabulate
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
#read the DETE dataset
dete_survey = pd.read_csv('./dete_survey.csv')
# Ensure that all columns are printed in our output
pd.set_option("display.max_columns", None)
# preview the DETE dataset
dete_survey.info()
dete_survey.head()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 822 non-null int64 1 SeparationType 822 non-null object 2 Cease Date 822 non-null object 3 DETE Start Date 822 non-null object 4 Role Start Date 822 non-null object 5 Position 817 non-null object 6 Classification 455 non-null object 7 Region 822 non-null object 8 Business Unit 126 non-null object 9 Employment Status 817 non-null object 10 Career move to public sector 822 non-null bool 11 Career move to private sector 822 non-null bool 12 Interpersonal conflicts 822 non-null bool 13 Job dissatisfaction 822 non-null bool 14 Dissatisfaction with the department 822 non-null bool 15 Physical work environment 822 non-null bool 16 Lack of recognition 822 non-null bool 17 Lack of job security 822 non-null bool 18 Work location 822 non-null bool 19 Employment conditions 822 non-null bool 20 Maternity/family 822 non-null bool 21 Relocation 822 non-null bool 22 Study/Travel 822 non-null bool 23 Ill Health 822 non-null bool 24 Traumatic incident 822 non-null bool 25 Work life balance 822 non-null bool 26 Workload 822 non-null bool 27 None of the above 822 non-null bool 28 Professional Development 808 non-null object 29 Opportunities for promotion 735 non-null object 30 Staff morale 816 non-null object 31 Workplace issue 788 non-null object 32 Physical environment 817 non-null object 33 Worklife balance 815 non-null object 34 Stress and pressure support 810 non-null object 35 Performance of supervisor 813 non-null object 36 Peer support 812 non-null object 37 Initiative 813 non-null object 38 Skills 811 non-null object 39 Coach 767 non-null object 40 Career Aspirations 746 non-null object 41 Feedback 792 non-null object 42 Further PD 768 non-null object 43 Communication 814 non-null object 44 My say 812 non-null object 45 Information 816 non-null object 46 Kept informed 813 non-null object 47 Wellness programs 766 non-null object 48 Health & Safety 793 non-null object 49 Gender 798 non-null object 50 Age 811 non-null object 51 Aboriginal 16 non-null object 52 Torres Strait 3 non-null object 53 South Sea 7 non-null object 54 Disability 23 non-null object 55 NESB 32 non-null object dtypes: bool(18), int64(1), object(37) memory usage: 258.6+ KB
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | 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 |
Classification
, Business Unit
, Aboriginal
, Torres Strait
, South Sea
, Disability
and NESB
have over 50% missing data.ID
column is stored as an integer. Other columns are stored as object/string data.Cease Date
, DETE Start Date
and Role Start Date
) are stored as object/string data instead of datetime or numerical data.dete_survey.describe(include='all')
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 822.000000 | 822 | 822 | 822 | 822 | 817 | 455 | 822 | 126 | 817 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 808 | 735 | 816 | 788 | 817 | 815 | 810 | 813 | 812 | 813 | 811 | 767 | 746 | 792 | 768 | 814 | 812 | 816 | 813 | 766 | 793 | 798 | 811 | 16 | 3 | 7 | 23 | 32 |
unique | NaN | 9 | 25 | 51 | 46 | 15 | 8 | 9 | 14 | 5 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 2 | 10 | 1 | 1 | 1 | 1 | 1 |
top | NaN | Age Retirement | 2012 | Not Stated | Not Stated | Teacher | Primary | Metropolitan | Education Queensland | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | A | A | A | A | A | A | A | A | A | A | A | A | A | A | A | A | A | A | A | A | Female | 61 or older | Yes | Yes | Yes | Yes | Yes |
freq | NaN | 285 | 344 | 73 | 98 | 324 | 161 | 135 | 54 | 434 | 800 | 742 | 788 | 733 | 761 | 806 | 765 | 794 | 795 | 788 | 760 | 754 | 785 | 710 | 794 | 605 | 735 | 605 | 413 | 242 | 335 | 357 | 467 | 359 | 342 | 349 | 401 | 396 | 372 | 345 | 246 | 348 | 293 | 399 | 400 | 436 | 401 | 253 | 386 | 573 | 222 | 16 | 3 | 7 | 23 | 32 |
mean | 411.693431 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
std | 237.705820 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
min | 1.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
25% | 206.250000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
50% | 411.500000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
75% | 616.750000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
max | 823.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
SeparationType
column.Start Date
and Role Start Date
columns contain alot of 'Not Stated' entries. There could be a chance that this information wasn't provided by respondents at the time of completing the survey.Aboriginal
, Torres Strait
, South Sea
, Disability
and NESB
have only one unique value which is 'Yes'. This might explain why they have the highest proportion of null values. Null entries in these columns might have represented 'No'* at the time the survey was administered*.Professional Development
column to the Health & Safety
column is 'A'. This seems quite unusual as 'A' doesn't seem to represent anything. We will explore these columns further.To investigate the unusual 'A' entries, we can define a function count_values()
which computes the counts of all the unique values in a series. Next, we will apply the function to all columns from Professional Development
to Health & Safety
column using the Dataframe.apply()
method:
def count_values(column):
'''Computes the count of all unique values in a series'''
return column.value_counts()
# Extract the columns from Professional Development to Health and Safety using their indices.
flagged_columns = dete_survey.iloc[:, 28:49]
# Apply the function to the flagged columns
flagged_columns.apply(count_values)
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
A | 413 | 242 | 335 | 357 | 467 | 359 | 342 | 349 | 401 | 396 | 372 | 345 | 246 | 348 | 293 | 399 | 400 | 436 | 401 | 253 | 386 |
D | 60 | 83 | 112 | 77 | 61 | 107 | 95 | 77 | 37 | 34 | 59 | 65 | 108 | 78 | 77 | 76 | 52 | 45 | 60 | 105 | 50 |
M | 15 | 24 | 13 | 14 | 15 | 12 | 14 | 12 | 11 | 13 | 11 | 22 | 17 | 15 | 13 | 8 | 10 | 11 | 10 | 33 | 28 |
N | 103 | 230 | 158 | 160 | 99 | 116 | 168 | 120 | 95 | 95 | 94 | 141 | 183 | 138 | 179 | 129 | 116 | 120 | 130 | 225 | 153 |
SA | 184 | 100 | 121 | 115 | 148 | 162 | 124 | 179 | 243 | 244 | 228 | 157 | 130 | 156 | 149 | 144 | 177 | 165 | 162 | 78 | 141 |
SD | 33 | 56 | 77 | 65 | 27 | 59 | 67 | 76 | 25 | 31 | 47 | 37 | 62 | 57 | 57 | 58 | 57 | 39 | 50 | 72 | 35 |
A
, D
, M
,N
, SA
, SD
.tafe_survey = pd.read_csv('./tafe_survey.csv')
# preview dataset info
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
'Contributing Factor...'
columns.Record ID
and CESSATION YEAR
columns are stored as float types.CESSATION YEAR
, Reason for ceasing employment
, Gender
, CurrentAge
, and EmploymentType
.Let's look at some quick descriptive statistics for this dataset:
tafe_survey.describe(include='all')
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) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 7.020000e+02 | 702 | 702 | 695.000000 | 701 | 437 | 437 | 437 | 437 | 437 | 437 | 437 | 437 | 437 | 437 | 437 | 437 | 113 | 608 | 613 | 610 | 608 | 615 | 607 | 614 | 608 | 610 | 602 | 601 | 597 | 601 | 609 | 605 | 607 | 610 | 613 | 609 | 609 | 608 | 608 | 611 | 610 | 611 | 606 | 610 | 609 | 603 | 606 | 619 | 432 | 483 | 440 | 555 | 555 | 555 | 530 | 555 | 553 | 555 | 555 | 555 | 608 | 594 | 587 | 586 | 581 | 596 | 596 | 596 | 596 | 596 | 596 |
unique | NaN | 12 | 2 | NaN | 6 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 11 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 9 | 5 | 9 | 7 | 7 |
top | NaN | Brisbane North Institute of TAFE | Non-Delivery (corporate) | NaN | Resignation | - | - | - | - | - | - | - | - | - | - | - | - | Dissatisfaction with %[Institute]Q25LBL% | Agree | Agree | Agree | Neutral | Agree | Agree | Agree | Agree | Agree | Neutral | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Yes | Yes | Yes | Yes | - | - | - | - | - | - | - | - | - | Yes | Yes | Yes | Yes | Yes | Female | 56 or older | Permanent Full-time | Administration (AO) | Less than 1 year | Less than 1 year |
freq | NaN | 161 | 432 | NaN | 340 | 375 | 336 | 420 | 403 | 411 | 371 | 360 | 410 | 421 | 415 | 331 | 391 | 23 | 233 | 275 | 247 | 175 | 255 | 212 | 267 | 268 | 284 | 154 | 216 | 209 | 226 | 234 | 281 | 284 | 253 | 331 | 286 | 230 | 232 | 237 | 296 | 298 | 290 | 231 | 269 | 234 | 300 | 236 | 541 | 232 | 441 | 285 | 412 | 502 | 539 | 270 | 473 | 518 | 366 | 555 | 541 | 382 | 536 | 512 | 488 | 416 | 389 | 162 | 237 | 293 | 147 | 177 |
mean | 6.346026e+17 | NaN | NaN | 2011.423022 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
std | 2.515071e+14 | NaN | NaN | 0.905977 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
min | 6.341330e+17 | NaN | NaN | 2009.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
25% | 6.343954e+17 | NaN | NaN | 2011.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
50% | 6.345835e+17 | NaN | NaN | 2011.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
75% | 6.348005e+17 | NaN | NaN | 2012.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
max | 6.350730e+17 | NaN | NaN | 2013.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
"-"
. This might be a placeholder indicating that no answer was provided at the time ths survey was administered.Main Factor. Which of these was the main factor for leaving?
shows that the most frequent reason for employee exit is dissatisfaction. This column has over 80% missing entries.CurrentAge
column contains several age bins. Most respondents are 56 years or older.Both the dete_survey
and tafe_survey
datasets contain many columns that we wont be needing to answer our stakeholder questions.
dete_survey
data contains 'Not Stated' values that indicate values are missing, they should be represented as NaN.tafe_survey
there are many responses that point to resignation caused by dissatisfaction.Let's address these observations:
We can start by using the pd.read_csv()
method to specify values that should be represented as NaN. We will use the method to fix missing values in the dete_survey
. Next, we will drop columns that we don't need for our analysis. This includes columns that do not imply that an employee resigned due to dissatisfaction, columns that do not add relevant data to our analysis, and columns with too many missing entries.
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 | 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 | 1984.0 | 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 | 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 | 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 | 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 | 2011.0 | 2011.0 | 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 | 2005.0 | 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 | 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 | 1970.0 | 1989.0 | 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 |
For the DETE survey, we'll drop the object/string type columns from Professional Development [28]
to Health & Safety [48]
. These were the columns with the infamous Agree, Neutral, Strongly Agree, Disagree, Strongly Disagree and Not Applicable options.
# Verify and print out the unwanted columns
unwanted_dete = dete_survey.columns[28:49]
print('\033[1m' + '\033[4m' + '\033[95m' + 'Unwanted Columns in DETE Survey' + '\033[0m')
print(unwanted_dete)
Unwanted Columns in DETE Survey
Index(['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'],
dtype='object')
# Remove unwanted columns
dete_survey.drop(unwanted_dete, axis=1, inplace=True)
dete_survey.head(3)
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 |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | 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 | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
We will repeat the same process for TAFE, dropping columns containing similar "Agree/Disagree" data from Main Factor [17]
to Workplace Topic [65]
.
unwanted_tafe = tafe_survey.columns[17:66]
tafe_survey.drop(unwanted_tafe, axis=1, inplace=True)
tafe_survey.head(3)
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) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | - | - | - | - | - | - | NONE | NaN | NaN | NaN | NaN | NaN | NaN |
Now let's verify the number of remaining columns in both datasets:
print('\033[1m' + '\033[4m' + 'REMAINING COLUMNS' + '\033[0m')
print('\033[1m' + '\033[95m' + 'DETE: {} columns'.format(dete_survey.shape[1]) + '\033[0m')
print('\033[1m' + '\033[94m' + 'TAFE: {} columns'.format(tafe_survey.shape[1]) + '\033[0m')
REMAINING COLUMNS DETE: 35 columns TAFE: 23 columns
As observed earlier, both datasets contains many of the same columns, but the column names are different. Here are some columns we'd like to use for our final analysis of both datasets:
The plan is to end up combining the two datasets. To do this, we will have to standardize the column names. Let's start by formating the DETE survey column names to the proper snake case convention:
# Format column names
dete_survey.columns = (dete_survey.columns.str.lower()
.str.replace('separationtype', 'separation_type')
.str.replace(' ', '_')
.str.replace('/', '_')
.str.strip()
)
# Preview results
print('\033[1m' + '\033[4m' + '\033[95m' + 'Renamed DETE Columns' + '\033[0m')
print(dete_survey.columns)
Renamed DETE Columns
Index(['id', 'separation_type', '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')
Next, we will use the DataFrame.rename()
method to update the columns in tafe_survey
. We will focus on the similar columns for now, then handle the other columns later:
# Create a dictionary of columns to rename
similar_columns = {
'Record ID': 'id',
'CESSATION YEAR': 'cease_date',
'Reason for ceasing employment': 'separation_type',
'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'
}
# Rename the TAFE columns
tafe_survey.rename(similar_columns, axis=1, inplace=True)
# Preview renamed columns
print('\033[1m' + '\033[4m' + '\033[94m' + 'Renamed TAFE Columns' + '\033[0m')
print(tafe_survey.columns)
Renamed TAFE Columns
Index(['id', 'Institute', 'WorkArea', 'cease_date', 'separation_type',
'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')
One of our goals is to answer the following question:
Is some dissatisfaction causing newer and older employees to resign from the institute?
If we look at the unique values in the separation_type
columns in each dataframe, we'll see that each dataset contains varying entries for separation type:
names = ['DETE SURVEY DATA', 'TAFE SURVEY DATA']
# Create a selection of colors for output headers
colors = ['\033[95m','\033[94m']
# Pretty print unique values in the seperation_type column of both datasets
for df, name, color in zip([dete_survey, tafe_survey], names, colors):
print('\033[1m' + '\033[4m' + color + name + '\033[0m')
print(tabulate(df['separation_type'].value_counts(dropna=False).to_frame(),
headers=['Separation Type', 'Count'], tablefmt='psql'))
DETE SURVEY DATA +--------------------------------------+---------+ | Separation Type | Count | |--------------------------------------+---------| | 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 | +--------------------------------------+---------+ TAFE SURVEY DATA +--------------------------+---------+ | Separation Type | Count | |--------------------------+---------| | Resignation | 340 | | Contract Expired | 127 | | Retrenchment/ Redundancy | 104 | | Retirement | 82 | | Transfer | 25 | | Termination | 23 | | nan | 1 | +--------------------------+---------+
We will only analyze survey respondents who resigned. Their separation type contains the string 'Resignation'
. We can see multiple uses of the word in the different seperation types:
Resignation
Resignation-Other reasons
Resignation-Other employer
Resignation-Move overseas/interstate
We have to account for each of these variations so we don't unintentionally drop useful data.
# Select entries starting with resignation in both datasets.
dete_resignations = dete_survey[dete_survey['separation_type'].str.startswith('Resignation')].copy()
tafe_resignations = tafe_survey[tafe_survey['separation_type'].str.startswith('Resignation', na=False)].copy()
# Copy was added above to deal with settings with copy warnings.
# Pretty print unique values in the seperation_type column of both datasets
for df, name, color in zip([dete_resignations, tafe_resignations], names, colors):
print('\033[1m' + '\033[4m' + color + name + '\033[0m')
print(tabulate(df['separation_type'].value_counts(dropna=False).to_frame(),
headers=['Separation Type', 'Count'], tablefmt='psql'))
DETE SURVEY DATA +--------------------------------------+---------+ | Separation Type | Count | |--------------------------------------+---------| | Resignation-Other reasons | 150 | | Resignation-Other employer | 91 | | Resignation-Move overseas/interstate | 70 | +--------------------------------------+---------+ TAFE SURVEY DATA +-------------------+---------+ | Separation Type | Count | |-------------------+---------| | Resignation | 340 | +-------------------+---------+
In this step, we'll focus on verifying that the years in the cease_date
, dete_start_date
and role_start_date
are correctly entered.
dete_start_date
was before the year 1940.Lets start by taking a look at the cease_date
column in the DETE dataset:
# Define a function that pretty prints a pandas series to a readable format
def pretty_print(data, headings, color, title):
"""
Pretty-prints a Pandas series in a more readable format
Params:
:data (series): Pandas series of interest
:headings (list): List of column names to use in output
:color (string): Python formatted output color code
:title (string): Title of output table
Output:
Returns pretty-printed series with assigned column names.
"""
print('\033[1m' + '\033[4m' + color + title + '\033[0m')
print(tabulate(data.to_frame(), headers=headings, tablefmt='pretty', stralign='left'))
# Pretty print the cease dates in DETE data.
pretty_print(dete_resignations['cease_date'].value_counts(),
['cease_date', 'Count'], colors[0],
'DETE: Cease Date')
DETE: Cease Date
+------------+-------+
| cease_date | Count |
+------------+-------+
| 2012 | 126 |
| 2013 | 74 |
| 01/2014 | 22 |
| 12/2013 | 17 |
| 06/2013 | 14 |
| 09/2013 | 11 |
| 07/2013 | 9 |
| 11/2013 | 9 |
| 10/2013 | 6 |
| 08/2013 | 4 |
| 05/2012 | 2 |
| 05/2013 | 2 |
| 07/2012 | 1 |
| 2010 | 1 |
| 09/2010 | 1 |
| 07/2006 | 1 |
+------------+-------+
To avoid further confusion down the line, we will clean this column, extract only the year values and convert the datatype to float (float makes it easier to work with NaN entries).
# Create a regex to extract the year
year_pattern = r"([0-9]{4})"
# Extract the year and assign data type as float
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.extract(year_pattern)
dete_resignations['cease_date'] = dete_resignations['cease_date'].astype(float)
# Preview the modified column
pretty_print(dete_resignations['cease_date'].value_counts().sort_index(),
['Cease_date', 'Count'], colors[0],
'DETE: Cease Date - After Cleaning')
print('Datatype: {}'.format(dete_resignations['cease_date'].dtype))
DETE: Cease Date - After Cleaning
+------------+-------+
| Cease_date | Count |
+------------+-------+
| 2006.0 | 1 |
| 2010.0 | 2 |
| 2012.0 | 129 |
| 2013.0 | 146 |
| 2014.0 | 22 |
+------------+-------+
Datatype: float64
Next, we will explore the cease_date
column of TAFE resignation data.
pretty_print(tafe_resignations['cease_date'].value_counts().sort_index(),
['cease_date', 'Count'], colors[1],
'TAFE: Cease Date')
print('Datatype: {}'.format(tafe_resignations['cease_date'].dtype))
TAFE: Cease Date
+------------+-------+
| cease_date | Count |
+------------+-------+
| 2009.0 | 2 |
| 2010.0 | 68 |
| 2011.0 | 116 |
| 2012.0 | 94 |
| 2013.0 | 55 |
+------------+-------+
Datatype: float64
The TAFE cease dates look fine. They are uniformly formatted too. Let's dive-in to explore the dete_start_date
column of the DETE resignation data.
pretty_print(dete_resignations['dete_start_date'].value_counts().sort_index(),
['Start Date', 'Count'], colors[0],
'DETE: Start Date')
print('Datatype: {}'.format(dete_resignations['dete_start_date'].dtype))
DETE: Start Date
+------------+-------+
| Start Date | Count |
+------------+-------+
| 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 |
+------------+-------+
Datatype: float64
Again, the dates seem realistic and uniformly formatted. Nothing to do here. Let's explore the role_start_date
column of the DETE resignation data.
pretty_print(dete_resignations['role_start_date'].value_counts().sort_index(),
['Role Start Date', 'Count'], colors[0],
'DETE: Role Start Date')
print('Datatype: {}'.format(dete_resignations['role_start_date'].dtype))
DETE: Role Start Date
+-----------------+-------+
| Role Start Date | Count |
+-----------------+-------+
| 200.0 | 1 |
| 1976.0 | 2 |
| 1980.0 | 1 |
| 1982.0 | 1 |
| 1986.0 | 1 |
| 1987.0 | 2 |
| 1988.0 | 3 |
| 1989.0 | 5 |
| 1990.0 | 1 |
| 1991.0 | 1 |
| 1992.0 | 4 |
| 1993.0 | 3 |
| 1994.0 | 2 |
| 1996.0 | 3 |
| 1997.0 | 5 |
| 1998.0 | 4 |
| 1999.0 | 6 |
| 2000.0 | 1 |
| 2001.0 | 2 |
| 2002.0 | 7 |
| 2003.0 | 6 |
| 2004.0 | 10 |
| 2005.0 | 9 |
| 2006.0 | 7 |
| 2007.0 | 24 |
| 2008.0 | 21 |
| 2009.0 | 18 |
| 2010.0 | 27 |
| 2011.0 | 33 |
| 2012.0 | 37 |
| 2013.0 | 24 |
+-----------------+-------+
Datatype: float64
Since there is only one entry with this error, we can safely remove the record from our dataset:
# Eliminate the entry where the role start date is 200
dete_resignations = dete_resignations.query('role_start_date != 200')
pretty_print(dete_resignations['role_start_date'].value_counts().sort_index(),
['Role Start Date', 'Count'], colors[0],
'DETE: Role Start Date - After cleaning')
DETE: Role Start Date - After cleaning
+-----------------+-------+
| Role Start Date | Count |
+-----------------+-------+
| 1976.0 | 2 |
| 1980.0 | 1 |
| 1982.0 | 1 |
| 1986.0 | 1 |
| 1987.0 | 2 |
| 1988.0 | 3 |
| 1989.0 | 5 |
| 1990.0 | 1 |
| 1991.0 | 1 |
| 1992.0 | 4 |
| 1993.0 | 3 |
| 1994.0 | 2 |
| 1996.0 | 3 |
| 1997.0 | 5 |
| 1998.0 | 4 |
| 1999.0 | 6 |
| 2000.0 | 1 |
| 2001.0 | 2 |
| 2002.0 | 7 |
| 2003.0 | 6 |
| 2004.0 | 10 |
| 2005.0 | 9 |
| 2006.0 | 7 |
| 2007.0 | 24 |
| 2008.0 | 21 |
| 2009.0 | 18 |
| 2010.0 | 27 |
| 2011.0 | 33 |
| 2012.0 | 37 |
| 2013.0 | 24 |
+-----------------+-------+
dete_dates = dete_resignations[['dete_start_date', 'role_start_date', 'cease_date']]
fig = px.box(dete_dates, y=dete_dates.columns, width=500, height=600, template='plotly_white')
fig.update_layout(title='DETE Employees Who Resigned.<br><i>When did they join, when did they leave?')
fig.update_yaxes(dtick=5, color='gray', title='Year', showline=True, mirror=True)
fig.update_xaxes(title='', color='gray', showline=True, mirror=True)
fig.show('png')
Since we do not have detailed information on the job start dates from the TAFE resignation data. We cannot build a comprehensive visualization for the TAFE survey.
Now that we've verified the different date data from the two datasets. We can safely calculate the length of time that each survey respondent (employee) spent at the institute.
The tafe_resignations
dataframe already contains an institute_service
column. However, dete_resignations
does not contain such information at the moment. Luckily we can extrapolate this from the dete_start_date
and cease_date
columns. This will prove useful in the long run, when we have to analyze both surveys together.
# Compute the institute service years for the DETE resignation data
dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']
pretty_print(dete_resignations['institute_service'].value_counts(bins=5),
['Institute service', 'Count'], colors[0],
'DETE: Institute Service')
DETE: Institute Service
+-------------------+-------+
| Institute service | Count |
+-------------------+-------+
| (-0.05, 9.8] | 167 |
| (9.8, 19.6] | 55 |
| (19.6, 29.4] | 32 |
| (29.4, 39.2] | 15 |
| (39.2, 49.0] | 3 |
+-------------------+-------+
Let's explore the institute service pattern at TAFE:
pretty_print(tafe_resignations['institute_service'].value_counts(dropna=False),
['Institute service', 'Count'], colors[1],
'TAFE: Institute Service')
TAFE: Institute Service
+--------------------+-------+
| Institute service | Count |
+--------------------+-------+
| Less than 1 year | 73 |
| 1-2 | 64 |
| 3-4 | 63 |
| nan | 50 |
| 5-6 | 33 |
| 11-20 | 26 |
| 7-10 | 21 |
| More than 20 years | 10 |
+--------------------+-------+
Now, we will try to identify any employees who resigned because they were dissatisfied. Below are the columns we'll use to make this assessment:
TAFE
Contributing Factors. Dissatisfaction
Contributing Factors. Job Dissatisfaction
DETE
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 an employee indicated that any of the factors above caused them to resign, we'll mark them as dissatisfied in a new column. After our changes, the dissatisfied
column will contain just the following values:
True:
indicates a person resigned because they were dissatisfied with the job
False:
indicates a person resigned because of a reason other than dissatisfaction with the job
# DETE columns related to dissatisfaction
dissatisfied_dete = [
'job_dissatisfaction',
'dissatisfaction_with_the_department',
'physical_work_environment',
'lack_of_recognition',
'lack_of_job_security',
'work_location',
'employment_conditions',
'work_life_balance',
'workload'
]
# TAFE columns related to dissatisfaction
dissatisfied_tafe = [
'Contributing Factors. Dissatisfaction',
'Contributing Factors. Job Dissatisfaction'
]
# Preview the unique entries in the DETE columns
for column in dissatisfied_dete:
pretty_print(dete_resignations[column].value_counts(dropna=False),
['Unique Values', 'Count'], colors[0],
'DETE: '+ column)
DETE: job_dissatisfaction +---------------+-------+ | Unique Values | Count | +---------------+-------+ | False | 269 | | True | 41 | +---------------+-------+ DETE: dissatisfaction_with_the_department +---------------+-------+ | Unique Values | Count | +---------------+-------+ | False | 281 | | True | 29 | +---------------+-------+ DETE: physical_work_environment +---------------+-------+ | Unique Values | Count | +---------------+-------+ | False | 304 | | True | 6 | +---------------+-------+ DETE: lack_of_recognition +---------------+-------+ | Unique Values | Count | +---------------+-------+ | False | 277 | | True | 33 | +---------------+-------+ DETE: lack_of_job_security +---------------+-------+ | Unique Values | Count | +---------------+-------+ | False | 296 | | True | 14 | +---------------+-------+ DETE: work_location +---------------+-------+ | Unique Values | Count | +---------------+-------+ | False | 292 | | True | 18 | +---------------+-------+ DETE: employment_conditions +---------------+-------+ | Unique Values | Count | +---------------+-------+ | False | 287 | | True | 23 | +---------------+-------+ DETE: work_life_balance +---------------+-------+ | Unique Values | Count | +---------------+-------+ | False | 242 | | True | 68 | +---------------+-------+ DETE: workload +---------------+-------+ | Unique Values | Count | +---------------+-------+ | False | 283 | | True | 27 | +---------------+-------+
We won't need need to clean these DETE resignation columns further. They all appear to be in the right format. For now, let's explore the TAFE columns that we are interested in:
for column in dissatisfied_tafe:
pretty_print(tafe_resignations[column].value_counts(dropna=False),
['Unique Values', 'Count'], colors[0],
'TAFE: '+ column)
TAFE: Contributing Factors. Dissatisfaction +---------------------------------------+-------+ | Unique Values | Count | +---------------------------------------+-------+ | - | 277 | | Contributing Factors. Dissatisfaction | 55 | | nan | 8 | +---------------------------------------+-------+ TAFE: Contributing Factors. Job Dissatisfaction +---------------------+-------+ | Unique Values | Count | +---------------------+-------+ | - | 270 | | Job Dissatisfaction | 62 | | nan | 8 | +---------------------+-------+
We can easily intuit that the "-"
entries are analogous to a respondent answering as "False"
, while any other string entry will equate to "True"
. Let's update these columns to True
, False
or NaN
values:
# A function to update '-' as False and other string entries to True
def map_boolean(entry):
if entry == '-':
return False
elif pd.isnull(entry):
return np.nan
else:
return True
# Apply function and print preview
for column in dissatisfied_tafe:
tafe_resignations[column] = tafe_resignations[column].map(map_boolean)
pretty_print(tafe_resignations[column].value_counts(dropna=False),
['Unique Values', 'Count'], colors[0],
'TAFE: '+ column)
TAFE: Contributing Factors. Dissatisfaction +---------------+-------+ | Unique Values | Count | +---------------+-------+ | False | 277 | | True | 55 | | nan | 8 | +---------------+-------+ TAFE: Contributing Factors. Job Dissatisfaction +---------------+-------+ | Unique Values | Count | +---------------+-------+ | False | 270 | | True | 62 | | nan | 8 | +---------------+-------+
Finally, we can create the dissatisfied
column in both datasets. Remember, once any of the employee dissatisfaction questions equates to True, the dissatisfied column will also contain True, otherwise False. For ease, we will use the Dataframe.any()
method to make this possible.
# Create a dissatisfied column and evaluate to True or False
tafe_resignations['dissatisfied'] = tafe_resignations[dissatisfied_tafe].any(axis=1, skipna=False)
dete_resignations['dissatisfied'] = dete_resignations[dissatisfied_dete].any(axis=1, skipna=False)
# Preview the newly created column
for df, name, color in zip([dete_resignations, tafe_resignations], ['DETE', 'TAFE'], [0,1]):
pretty_print(df['dissatisfied'].value_counts(dropna=False),
['Unique Values', 'Count'], colors[color],
name+': Dissatisfied Column')
DETE: Dissatisfied Column +---------------+-------+ | Unique Values | Count | +---------------+-------+ | False | 161 | | True | 149 | +---------------+-------+ TAFE: Dissatisfied Column +---------------+-------+ | Unique Values | Count | +---------------+-------+ | False | 241 | | True | 99 | +---------------+-------+
Among others, our stakeholders expect us to answer the following question:
If a dissatisfaction is present, how does it vary within the different age groups at the instititute?
To accurately provide an answer to this, we need to ensure that age
information is properly formatted in both datasets. Let's start by previewing the entries for age.
pretty_print(dete_resignations['age'].value_counts(dropna=False).sort_index(),
['Age group', 'Count'], colors[0],
'DETE: Age Groups')
pretty_print(tafe_resignations['age'].value_counts(dropna=False).sort_index(),
['Age group', 'Count'], colors[1],
'TAFE: Age Groups')
DETE: Age Groups +---------------+-------+ | Age group | Count | +---------------+-------+ | 20 or younger | 1 | | 21-25 | 29 | | 26-30 | 35 | | 31-35 | 29 | | 36-40 | 41 | | 41-45 | 48 | | 46-50 | 41 | | 51-55 | 32 | | 56-60 | 26 | | 61 or older | 23 | | nan | 5 | +---------------+-------+ TAFE: Age Groups +---------------+-------+ | Age group | Count | +---------------+-------+ | 20 or younger | 9 | | 21 25 | 33 | | 26 30 | 32 | | 31 35 | 32 | | 36 40 | 32 | | 41 45 | 45 | | 46 50 | 39 | | 51-55 | 39 | | 56 or older | 29 | | nan | 50 | +---------------+-------+
Although, the age groups in the datasets are mostly similar, the formats are not exactly the same. The age groups in the TAFE dataset contain extra space characters e.g 21 25
. We should reformat these entries to agree with that of the DETE dataset e.g 21-25
.
In the TAFE data, age brackets end at 56 or older
while the DETE data has two extra age groups 56-60
and 61 or older
. We should make the age groups uniform in both datasets by formatting the two extra groups in DETE data to 56 or older
.
# Remove the extra space characters from TAFE age data
tafe_resignations['age'] = tafe_resignations['age'].str.replace(' ', '-')
# Format the extra age brackets in DETE data to 56 or older
dete_resignations['age'] = (dete_resignations['age'].str.replace('56-60', '56 or older')
.str.replace('61 or older', '56 or older')
)
# Re-examine the age columns again.
pretty_print(dete_resignations['age'].value_counts(dropna=False).sort_index(),
['Age group', 'Count'], colors[0],
'DETE: Age Groups - post cleaning')
pretty_print(tafe_resignations['age'].value_counts(dropna=False).sort_index(),
['Age group', 'Count'], colors[1],
'TAFE: Age Groups - post cleaning')
DETE: Age Groups - post cleaning +---------------+-------+ | Age group | Count | +---------------+-------+ | 20 or younger | 1 | | 21-25 | 29 | | 26-30 | 35 | | 31-35 | 29 | | 36-40 | 41 | | 41-45 | 48 | | 46-50 | 41 | | 51-55 | 32 | | 56 or older | 49 | | nan | 5 | +---------------+-------+ TAFE: Age Groups - post cleaning +---------------+-------+ | Age group | Count | +---------------+-------+ | 20 or younger | 9 | | 21-25 | 33 | | 26-30 | 32 | | 31-35 | 32 | | 36-40 | 32 | | 41-45 | 45 | | 46-50 | 39 | | 51-55 | 39 | | 56 or older | 29 | | nan | 50 | +---------------+-------+
Note: *The age groups are quite numerous, partly because they are mostly spaced at an interval of 5. This might make it difficult to observe some trends during analysis (since each group is not quite large enough). We will correct for these by creating an age structure later on.*
Combining will also mean combining columns that are not common to both datasets. This would lead to a lot of null values. It is better to investigate each dataset column, then select only the common columns that are useful for our analysis. To select the common columns, we will use the np.intersect1d()
method.
# Preview all columns in DETE data
print('\033[1m' + '\033[4m' + colors[0] + 'DETE Resignation Columns' + '\033[0m')
print(dete_resignations.columns)
print('')
# Preview all columns in TAFE data
print('\033[1m' + '\033[4m' + colors[1] + 'TAFE Resignation Columns' + '\033[0m')
print(tafe_resignations.columns)
print('')
# Find the intersect (common items) in both columns
common_columns = np.intersect1d(dete_resignations.columns, tafe_resignations.columns)
# Preview the common columns
print('\033[1m' + '\033[4m' + '\033[91m' + 'COMMON COLUMNS' + '\033[0m')
for num, column in zip(range(1, 10), common_columns):
print('\033[91m' + str(num) + ': ' + column + '\033[0m')
DETE Resignation Columns Index(['id', 'separation_type', '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', 'institute_service', 'dissatisfied'], dtype='object') TAFE Resignation Columns Index(['id', 'Institute', 'WorkArea', 'cease_date', 'separation_type', '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', 'dissatisfied'], dtype='object') COMMON COLUMNS 1: age 2: cease_date 3: dissatisfied 4: employment_status 5: gender 6: id 7: institute_service 8: position 9: separation_type
We are almost ready to combine our datasets. In a two step process, we will isolate the common columns from each dataset, then create an institute
column. This will help us distinguish the source of each data after combining:
# Select only common columns from each dataset
dete_updated = dete_resignations[common_columns].copy()
tafe_updated = tafe_resignations[common_columns].copy()
# Add an institute column in each dataset
dete_updated['institute'] = 'DETE'
tafe_updated['institute'] = 'TAFE'
dete_updated.head()
age | cease_date | dissatisfied | employment_status | gender | id | institute_service | position | separation_type | institute | |
---|---|---|---|---|---|---|---|---|---|---|
3 | 36-40 | 2012.0 | False | Permanent Full-time | Female | 4 | 7.0 | Teacher | Resignation-Other reasons | DETE |
5 | 41-45 | 2012.0 | True | Permanent Full-time | Female | 6 | 18.0 | Guidance Officer | Resignation-Other reasons | DETE |
8 | 31-35 | 2012.0 | False | Permanent Full-time | Female | 9 | 3.0 | Teacher | Resignation-Other reasons | DETE |
9 | 46-50 | 2012.0 | True | Permanent Part-time | Female | 10 | 15.0 | Teacher Aide | Resignation-Other employer | DETE |
11 | 31-35 | 2012.0 | False | Permanent Full-time | Male | 12 | 3.0 | Teacher | Resignation-Move overseas/interstate | DETE |
tafe_updated.head()
age | cease_date | dissatisfied | employment_status | gender | id | institute_service | position | separation_type | institute | |
---|---|---|---|---|---|---|---|---|---|---|
3 | NaN | 2010.0 | False | NaN | NaN | 6.341399e+17 | NaN | NaN | Resignation | TAFE |
4 | 41-45 | 2010.0 | False | Permanent Full-time | Male | 6.341466e+17 | 3-4 | Teacher (including LVT) | Resignation | TAFE |
5 | 56 or older | 2010.0 | False | Contract/casual | Female | 6.341475e+17 | 7-10 | Teacher (including LVT) | Resignation | TAFE |
6 | 20 or younger | 2010.0 | False | Temporary Full-time | Male | 6.341520e+17 | 3-4 | Administration (AO) | Resignation | TAFE |
7 | 46-50 | 2010.0 | False | Permanent Full-time | Male | 6.341537e+17 | 3-4 | Teacher (including LVT) | Resignation | TAFE |
From the output above, we'd notice that the institute_service
column currently contains entries that are not uniformly formatted accross both datasets. We will deal with this later. For now, we are ready to combine our datasets.
We can use the pd.concat()
function to stack our dataframes on one another, essentially combining them into one unit:
combined = pd.concat([dete_updated, tafe_updated])
combined.head(3)
age | cease_date | dissatisfied | employment_status | gender | id | institute_service | position | separation_type | institute | |
---|---|---|---|---|---|---|---|---|---|---|
3 | 36-40 | 2012.0 | False | Permanent Full-time | Female | 4.0 | 7.0 | Teacher | Resignation-Other reasons | DETE |
5 | 41-45 | 2012.0 | True | Permanent Full-time | Female | 6.0 | 18.0 | Guidance Officer | Resignation-Other reasons | DETE |
8 | 31-35 | 2012.0 | False | Permanent Full-time | Female | 9.0 | 3.0 | Teacher | Resignation-Other reasons | DETE |
combined.tail(3)
age | cease_date | dissatisfied | employment_status | gender | id | institute_service | position | separation_type | institute | |
---|---|---|---|---|---|---|---|---|---|---|
698 | NaN | 2013.0 | False | NaN | NaN | 6.350677e+17 | NaN | NaN | Resignation | TAFE |
699 | 51-55 | 2013.0 | False | Permanent Full-time | Female | 6.350704e+17 | 5-6 | Teacher (including LVT) | Resignation | TAFE |
701 | 26-30 | 2013.0 | False | Contract/casual | Female | 6.350730e+17 | 3-4 | Administration (AO) | Resignation | TAFE |
The ID
column does not add anything of value to our analysis. Let's drop it before we proceed.
combined.drop('id', axis=1, inplace=True)
Now that we have combined our dataframes and removed the id
column. The next step is to clean up institute_service
. Let's preview this column to have an idea of what we will be working with.
pretty_print(combined['institute_service'].value_counts(dropna=False),
['Institute service', 'Count'], colors[1],
'Institute Service Entries')
Institute Service Entries
+--------------------+-------+
| Institute service | Count |
+--------------------+-------+
| 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 |
| 3.0 | 20 |
| 0.0 | 20 |
| 6.0 | 17 |
| 4.0 | 16 |
| 9.0 | 14 |
| 2.0 | 14 |
| 7.0 | 13 |
| More than 20 years | 10 |
| 8.0 | 8 |
| 13.0 | 7 |
| 15.0 | 7 |
| 20.0 | 7 |
| 10.0 | 6 |
| 14.0 | 6 |
| 12.0 | 6 |
| 17.0 | 6 |
| 22.0 | 6 |
| 18.0 | 5 |
| 16.0 | 5 |
| 11.0 | 4 |
| 23.0 | 4 |
| 24.0 | 4 |
| 32.0 | 3 |
| 39.0 | 3 |
| 19.0 | 3 |
| 21.0 | 3 |
| 36.0 | 2 |
| 25.0 | 2 |
| 30.0 | 2 |
| 26.0 | 2 |
| 28.0 | 2 |
| 49.0 | 1 |
| 41.0 | 1 |
| 27.0 | 1 |
| 42.0 | 1 |
| 29.0 | 1 |
| 34.0 | 1 |
| 31.0 | 1 |
| 33.0 | 1 |
| 35.0 | 1 |
| 38.0 | 1 |
+--------------------+-------+
This column is tricky to clean because it currently contains values that are formatted in different ways. Rather than cleaning this column alone, we will further convert the numbers into various service categories.
We can draw insights from this article, which makes the argument that understanding employee's needs according to career stage instead of age is more effective.
We'll use the following definitions:
New:
Less than 3 years at a company
Experienced:
3-6 years at a company
Established:
7-10 years at a company
Veteran:
11 or more years at a company
We can now clean and categorize the values in the institute_service
column using the definitions above:
pattern = r"(\d+)" # matches one or more repetitions of numbers between the range [0-9]
# Extract values from institute service based on the defined pattern
combined['institute_service'] = (combined['institute_service'].astype('str')
.str.extract(pattern)
.astype(float)
)
# Preview results
pretty_print(combined['institute_service'].value_counts(dropna=False),
['Institute service', 'Count'], colors[1],
'Institute Service Entries - After cleaning')
Institute Service Entries - After cleaning
+-------------------+-------+
| Institute service | Count |
+-------------------+-------+
| 1.0 | 159 |
| nan | 88 |
| 3.0 | 83 |
| 5.0 | 56 |
| 7.0 | 34 |
| 11.0 | 30 |
| 0.0 | 20 |
| 6.0 | 17 |
| 20.0 | 17 |
| 4.0 | 16 |
| 2.0 | 14 |
| 9.0 | 14 |
| 8.0 | 8 |
| 15.0 | 7 |
| 13.0 | 7 |
| 17.0 | 6 |
| 22.0 | 6 |
| 14.0 | 6 |
| 12.0 | 6 |
| 10.0 | 6 |
| 18.0 | 5 |
| 16.0 | 5 |
| 23.0 | 4 |
| 24.0 | 4 |
| 21.0 | 3 |
| 39.0 | 3 |
| 19.0 | 3 |
| 32.0 | 3 |
| 30.0 | 2 |
| 26.0 | 2 |
| 36.0 | 2 |
| 28.0 | 2 |
| 25.0 | 2 |
| 27.0 | 1 |
| 34.0 | 1 |
| 29.0 | 1 |
| 42.0 | 1 |
| 49.0 | 1 |
| 41.0 | 1 |
| 38.0 | 1 |
| 33.0 | 1 |
| 35.0 | 1 |
| 31.0 | 1 |
+-------------------+-------+
Next, we'll map each value to one of the career stage definitions namely: New, experienced, established and veteran.
def map_career_state(value):
'''Maps value to a corresponding service category'''
if pd.isnull(value):
return np.nan
elif value < 3:
return 'New'
elif (value >=3 and value <=6):
return 'Experienced'
elif (value >=7 and value <=10):
return 'Established'
else:
return 'Veteran'
# Apply function to the combined dataframe
combined['service_category'] = combined['institute_service'].apply(map_career_state)
pretty_print(combined['service_category'].value_counts(dropna=False),
['Category', 'Count'], colors[1],
'Entries For Service Category')
Entries For Service Category
+-------------+-------+
| Category | Count |
+-------------+-------+
| New | 193 |
| Experienced | 172 |
| Veteran | 135 |
| nan | 88 |
| Established | 62 |
+-------------+-------+
combined.isnull().sum()
age 55 cease_date 16 dissatisfied 0 employment_status 54 gender 59 institute_service 88 position 53 separation_type 0 institute 0 service_category 88 dtype: int64
Having some information about age can help us estimate how long an employee may have served at an institute (institute service) and vice versa. However, we will notice that both information are missing from some records. In the absence of both data, these records will be hard to analyze. Hence will remove records with missing information for age and service category.
# Extract records where age and service category are missing
missing_service_data = combined[(combined['age'].isnull() & combined['service_category'].isnull())]
print('\033[1m' + '\033[91m' + str(missing_service_data.shape[0]) + ' records meet this criteria' + '\033[0m')
missing_service_data
53 records meet this criteria
age | cease_date | dissatisfied | employment_status | gender | institute_service | position | separation_type | institute | service_category | |
---|---|---|---|---|---|---|---|---|---|---|
405 | NaN | 2012.0 | False | NaN | NaN | NaN | Teacher | Resignation-Other reasons | DETE | NaN |
802 | NaN | 2013.0 | False | Permanent Part-time | NaN | NaN | Teacher Aide | Resignation-Move overseas/interstate | DETE | NaN |
821 | NaN | 2013.0 | False | NaN | NaN | NaN | Teacher Aide | Resignation-Move overseas/interstate | DETE | NaN |
3 | NaN | 2010.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
16 | NaN | 2010.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
18 | NaN | 2010.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
19 | NaN | 2010.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
20 | NaN | 2010.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
21 | NaN | 2010.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
26 | NaN | 2010.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
36 | NaN | 2010.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
37 | NaN | 2010.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
39 | NaN | 2010.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
51 | NaN | 2010.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
53 | NaN | 2010.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
54 | NaN | 2010.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
87 | NaN | 2010.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
91 | NaN | 2010.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
94 | NaN | 2011.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
97 | NaN | 2011.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
101 | NaN | 2010.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
102 | NaN | 2010.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
113 | NaN | NaN | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
130 | NaN | 2011.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
135 | NaN | NaN | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
138 | NaN | 2011.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
169 | NaN | 2011.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
204 | NaN | 2010.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
234 | NaN | 2010.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
243 | NaN | 2011.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
258 | NaN | 2011.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
276 | NaN | 2011.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
279 | NaN | 2011.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
287 | NaN | 2011.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
373 | NaN | 2011.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
412 | NaN | 2012.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
423 | NaN | 2012.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
437 | NaN | 2012.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
513 | NaN | NaN | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
530 | NaN | 2012.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
533 | NaN | 2012.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
535 | NaN | 2012.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
539 | NaN | 2012.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
579 | NaN | 2012.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
621 | NaN | 2013.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
625 | NaN | 2013.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
628 | NaN | 2013.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
665 | NaN | 2013.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
666 | NaN | 2013.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
670 | NaN | 2013.0 | True | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
690 | NaN | 2013.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
694 | NaN | 2013.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
698 | NaN | 2013.0 | False | NaN | NaN | NaN | NaN | Resignation | TAFE | NaN |
These 53 records contain a lot of missing data. It is clear that they wont be useful for our analysis, so we will drop them all from our dataframe:
# Select only records with information on age and service category
combined = combined[(combined['age'].notnull() & combined['service_category'].notnull())]
print('\033[1m' + '\033[4m' + '\033[94m' + 'Null Values in our Combined Dataframe' + '\033[0m')
combined.isnull().sum()
Null Values in our Combined Dataframe
age 0 cease_date 2 dissatisfied 0 employment_status 0 gender 5 institute_service 0 position 3 separation_type 0 institute 0 service_category 0 dtype: int64
By dealing with null values in the age and service_category columns. We have significantly reduced the number of null records from our dataset. Next, we will deal with the few null values left.
We won't be needing the cease_date
column to answer our stakeholder questions. However, the gender
and position
columns are important. We may decide to fill the missing gender records with the most common gender. However, this would not be a safe way to extrapolate the missing gender records, instead we will drop the 5 records with missing gender entries.
combined = combined[combined['gender'].notnull()]
print('\033[1m' + '\033[4m' + '\033[94m' + 'Null Values left after cleaning' + '\033[0m')
combined.isnull().sum()
Null Values left after cleaning
age 0 cease_date 2 dissatisfied 0 employment_status 0 gender 0 institute_service 0 position 3 separation_type 0 institute 0 service_category 0 dtype: int64
We will categorize entries in the position
column into teaching
and non-teaching staff
. We only have 3 missing records in this column so it will be safe to map them as non-teaching staff. Let's view the unique values in this column again:
pretty_print(combined['position'].value_counts(dropna=False),
['position', 'Count'], colors[1],
'Unique entries in the position column')
Unique entries in the position column
+---------------------------------------------------------+-------+
| position | Count |
+---------------------------------------------------------+-------+
| Administration (AO) | 148 |
| Teacher | 111 |
| Teacher (including LVT) | 95 |
| Teacher Aide | 49 |
| Cleaner | 33 |
| Public Servant | 27 |
| Professional Officer (PO) | 16 |
| Operational (OO) | 13 |
| Head of Curriculum/Head of Special Education | 10 |
| Technical Officer | 7 |
| Workplace Training Officer | 6 |
| Schools Officer | 6 |
| Technical Officer (TO) | 5 |
| School Administrative Staff | 5 |
| School Based Professional Staff (Therapist, nurse, etc) | 5 |
| Executive (SES/SO) | 4 |
| Guidance Officer | 3 |
| Other | 3 |
| nan | 3 |
| Tutor | 3 |
| Professional Officer | 2 |
| Business Service Manager | 1 |
+---------------------------------------------------------+-------+
Here is how we will proceed with our mapping: Any entry that contains the term Teacher, Tutor, Training, and Guidance will be mapped as Teaching staff
. All other entries will be recorded as Non-Teaching staff
. The mapped information will be stored in a new role
column.
teaching_roles = ['Teacher', 'Teacher (including LVT)', 'Teacher Aide', 'Guidance Officer', 'Tutor',
'Workplace Training Officer']
def map_position(entry):
''''categorizes entry under teaching or non-teaching staff'''
if pd.isnull(entry):
return 'Non-Teaching staff'
elif entry.strip() in teaching_roles:
return 'Teaching staff'
else:
return 'Non-Teaching staff'
# Apply function to the position column
combined['role'] = combined['position'].apply(map_position)
# Preview results
pretty_print(combined['role'].value_counts(dropna=False),
['Role', 'Count'], colors[1],
'Entries in the role column')
Entries in the role column
+--------------------+-------+
| Role | Count |
+--------------------+-------+
| Non-Teaching staff | 288 |
| Teaching staff | 267 |
+--------------------+-------+
pretty_print(combined['employment_status'].value_counts(dropna=False),
['Status', 'Count'], colors[1],
'Entries in the employment status column')
Entries in the employment status column
+---------------------+-------+
| Status | Count |
+---------------------+-------+
| Permanent Full-time | 240 |
| Permanent Part-time | 128 |
| Temporary Full-time | 119 |
| Temporary Part-time | 35 |
| Contract/casual | 29 |
| Casual | 4 |
+---------------------+-------+
There is not much to correct here. However, we can see that there is an overlap between the Contract/casual
group and the casual
group. For uniformity sake, we will reformat all the Casual
entries as Contract/casual
:
# Replace casual with contract/casual
combined['employment_status'] = combined['employment_status'].str.replace('Casual', 'Contract/casual')
pretty_print(combined['employment_status'].value_counts(dropna=False),
['Status', 'Count'], colors[1],
'Entries in the employment status column - After cleaning')
Entries in the employment status column - After cleaning
+---------------------+-------+
| Status | Count |
+---------------------+-------+
| Permanent Full-time | 240 |
| Permanent Part-time | 128 |
| Temporary Full-time | 119 |
| Temporary Part-time | 35 |
| Contract/casual | 33 |
+---------------------+-------+
As a final step in our data preparation and cleaning process, we will create a standard age structure to correct for the numerous age groups we currently have in our data. We will follow some standard structures used by indexmundi.com for classifying age in relation to demographics. A few modifications will be made to suit our use case:
25 years or less
: Early working age
26-55 years
: Prime working age
56 years or older
: Mature or elderly working age
We can create a dictionary with this information, and map it to a new column in our dataframe:
# Create the mapping dictionary
age_structure = {
'20 or younger': 'Early working age',
'21-25': 'Early working age',
'26-30': 'Prime working age',
'31-35': 'Prime working age',
'36-40': 'Prime working age',
'41-45': 'Prime working age',
'46-50': 'Prime working age',
'51-55': 'Prime working age',
'56 or older': 'Elderly working age'
}
# Map dictionary contents to a new column
combined['age_structure'] = combined['age'].map(age_structure)
# Preview results
pretty_print(combined['age_structure'].value_counts(dropna=False),
['Age structure', 'Count'], colors[1],
'Entries in the age structure column')
Entries in the age structure column
+---------------------+-------+
| Age structure | Count |
+---------------------+-------+
| Prime working age | 419 |
| Early working age | 69 |
| Elderly working age | 67 |
+---------------------+-------+
It is not too surprising that the majority of employees are in their prime working age. They are Probably* resigning to pursue other career interests, or further their current careers. We also have an essentially even distribution of early aged and mature/elderly aged employees*.
Lets take one final look at our combined and cleaned dataframe:
combined.reset_index(drop=True, inplace=True)
combined.head()
age | cease_date | dissatisfied | employment_status | gender | institute_service | position | separation_type | institute | service_category | role | age_structure | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 36-40 | 2012.0 | False | Permanent Full-time | Female | 7.0 | Teacher | Resignation-Other reasons | DETE | Established | Teaching staff | Prime working age |
1 | 41-45 | 2012.0 | True | Permanent Full-time | Female | 18.0 | Guidance Officer | Resignation-Other reasons | DETE | Veteran | Teaching staff | Prime working age |
2 | 31-35 | 2012.0 | False | Permanent Full-time | Female | 3.0 | Teacher | Resignation-Other reasons | DETE | Experienced | Teaching staff | Prime working age |
3 | 46-50 | 2012.0 | True | Permanent Part-time | Female | 15.0 | Teacher Aide | Resignation-Other employer | DETE | Veteran | Teaching staff | Prime working age |
4 | 31-35 | 2012.0 | False | Permanent Full-time | Male | 3.0 | Teacher | Resignation-Move overseas/interstate | DETE | Experienced | Teaching staff | Prime working age |
Great! Everything looks good. We are finally ready to dive into analysis.
To make working with the data easier, we will define two helper functions:
generate_table()
: Generates a pivot table based on provided inputs.
plot_table()
: Creates a pre-styled horizontal bar chart from passed arguments.
def generate_table(df, index_col, value_col):
"""
Builds a pivot table from provided arguments.
Params:
:df (dataframe): dataframe of interest
:index_col(string): name of index column
:value_col(string): name of column to aggregate (value column)
Output:
Pivot table showing the percentage of dissatisfied and satisfied employees.
"""
table = df.pivot_table(index = index_col, values = value_col).reset_index().sort_values(by=value_col)
table[value_col] = round(table[value_col]*100, 2)
table['not_dissatisfied'] = 100 - table[value_col]
return table
def plot_table(table, main_title=None, plot_color = '#0062CC'):
"""
Creates an horizontal bar chart formatted like a progress bar
Params:
:table(dataframe): dataframe of interest
:main_title(string): main chart title
:plot_color(string): hex code to style bar color
Output:
Bar chart generated from input table.
"""
x_val, y_val, ref_val = table.columns[1], table.columns[0], table.columns[2]
fig = px.bar(table, y=y_val, x=x_val,
orientation='h', text= x_val
)
fig.add_trace(go.Bar(y=table[y_val], x=table[ref_val],
orientation='h', marker_color='grey', opacity=0.1
))
fig.data[0].marker.color = '#0062CC'
fig.data[0].texttemplate='%{text:.0f}%'
fig.update_yaxes(showline=False, title='', ticksuffix=' ')
fig.update_xaxes(title='', showticklabels=False, showgrid=False, zeroline=False)
fig.update_layout(template='plotly_white', showlegend=False, font_family='arial', font_size=13,
title= '<i>'+main_title, bargap=0.35, margin_b=0)
return fig
Now, we can proceed to answer some interesting questions
institute_info = generate_table(combined, 'institute', 'dissatisfied')
fig = plot_table(institute_info, 'Percentage of dissatisfied employees by institute')
fig.update_layout(height=200, width=500)
fig.show('png')
The DETE Institute recorded a greater number of employees resigning due to dissatisfaction. Infact the dissatisfaction rates observed in DETE are almost twice as much as those recorded in the TAFE institute.
This huge difference in dissatisfaction rates has a potential to significantly skew our analysis results. To ensure that we obtain the best insights possible, we will first conduct a general analysis of the combined data, then dive deeper to explore the differences between DETE and TAFE for each analysis question.
service_info = generate_table(combined, 'service_category', 'dissatisfied')
fig = plot_table(service_info, 'Percentage of dissatisfied employees in each service category')
fig.update_layout(height=300, width=700)
fig.show('png')
It appears that employees who spend longer at these institutes are more likely to resign due to dissatisfaction. Established employees and Veterans are more likely to be dissatisfied than experienced or new employees. In otherwords, employees who have spent 7 years or more are more likely to resign due to dissatisfaction than those who have spent lesser than 7 years.
age_info = generate_table(combined, ['age'], 'dissatisfied')
age_info
fig = plot_table(age_info, 'Percentage of dissatisfied employees across different age groups')
fig.update_layout(height=500, width=700)
fig.show('png')
Our plot doesn't show a clear pattern here. Although we see that older employees are more likely to resign due to dissatisfaction, we cannot confidently conclude because younger employees of age 26-30 also show high dissatisfaction rates.
Perhaps we could turn our attention to the age structure we previously created. This may give us clearer insights into the underlying central pattern.
age_structure_info = generate_table(combined, ['age_structure'], 'dissatisfied')
age_structure_info
fig = plot_table(age_structure_info, 'Percentage of dissatisfied employees across different age structures')
fig.update_layout(height=250, width=700)
fig.show('png')
The age structure informs us clearer and better! Employee dissatisfaction increases with age, leading to more resignations among older employees. Younger employees are less likely to resign due to dissatisfaction.
gender_info = generate_table(combined, ['gender'], 'dissatisfied')
gender_info
fig = plot_table(gender_info, 'Percentage of dissatisfied employees by gender')
fig.update_layout(height=200, width=500)
fig.show('png')
It appears that gender doesn't exert so much influence on dissatisfaction. However, male employees have a marginally higher tendency to resign due to dissatisfaction than their female counterparts.
employment_info = generate_table(combined, ['employment_status'], 'dissatisfied')
employment_info
fig = plot_table(employment_info, 'Percentage of dissatisfied employees by contract type')
fig.update_layout(height=360, width=700)
fig.show('png')
As employee contracts become more permanent, dissatisfaction is more likely to occur. Permanent employees suffer a larger share of dissatisfaction than temporary employees and casual workers.
role_info = generate_table(combined, ['role'], 'dissatisfied')
role_info
fig = plot_table(role_info, 'Percentage of dissatisfied employees by role')
fig.update_layout(height=200, width=500)
fig.show('png')
Teaching staff are more likely to resign due to dissatisfaction than staff who carry out administrative or other non-teaching related roles.
In the second aspect of our analysis, we will consider and compare the influence of each factor on resignation across both institutes. This is primarily because of the largely unequal dissatisfaction rates that we obeserved between DETE and TAFE employees.
Again, we will define two helper functions. The create_subplot()
function helps to generate high quality subplots from both datasets while the sort_by_df()
function sorts a dataframe based on a specified columns in another dataframe: this is especially useful when we want our graphs to have the same arrangement of data labels.
def create_subplot(first, second, main_title):
"""
Builds a subplot from provided arguments.
Params:
:first(dataframe): first dataframe of interest
:second(dataframe): second dataframe of interest
:main_title(string): name of chart
Output:
Subplots containing barcharts from both dataframes.
"""
fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.2,
subplot_titles=('DETE', 'TAFE'))
x_val, y_val, ref_val = first.columns[1], first.columns[0], first.columns[2]
fig.add_trace(go.Bar(y=first[y_val], x=first[x_val],
orientation='h', marker_color='#2E7D9E', text= first[x_val]
), row=1, col=1)
fig.add_trace(go.Bar(y=first[y_val], x=first[ref_val],
orientation='h', marker_color='grey', opacity=0.1
), row=1, col=1)
fig.add_trace(go.Bar(y=second[y_val], x=second[x_val],
orientation='h', marker_color='#A36A69', text= second[x_val]
), row=1, col=2)
fig.add_trace(go.Bar(y=second[y_val], x=second[ref_val],
orientation='h', marker_color='grey', opacity=0.1
), row=1, col=2)
fig.data[0].texttemplate='%{text:.0f}%'
fig.data[2].texttemplate='%{text:.0f}%'
fig.update_yaxes(showline=False, title='', ticksuffix=' ')
fig.update_xaxes(title='', showticklabels=False, showgrid=False, zeroline=False)
fig.update_layout(template='plotly_white', showlegend=False, font_family='arial', font_size=13,
title= '<i>'+main_title, bargap=0.35, margin_b=0, barmode='stack')
return fig
def sort_by_df(input_df, sorting_column, sorting_df):
"""
Sorts a dataframe by a set column in another dataframe
Params:
:input_df (dataframe): dataframe to sort
:sorting_column(string): name of column in sorting_df to sort by
:sorting_df(dataframe): dataframe to sort from
Output:
An input dataframe sorted by the column specified in the sorting dataframe.
"""
result = input_df.set_index(sorting_column)
result = result.reindex(index=sorting_df[sorting_column])
result = result.reset_index()
return result
We will proceed to seperate DETE related data from TAFE related data by filtering the combined
dataframe:
dete = combined.query("institute == 'DETE'")
tafe = combined.query("institute == 'TAFE'")
# Use the generate_table function to pull relevant service_year information from both datasets
dete_service_info = generate_table(dete, ['service_category'], 'dissatisfied')
tafe_service_info = generate_table(tafe, ['service_category'], 'dissatisfied')
# Arrange the tafe service info dataset in the same order as dete service info
tafe_service_info = sort_by_df(tafe_service_info, 'service_category', dete_service_info)
# Generate subplot
fig = create_subplot(dete_service_info, tafe_service_info,
'Percentage of dissatisfied employees in each service category')
fig.update_layout(height=300, width=900)
fig.show('png')
Across both institutes, dissatisfaction tends to increase with the number of service years. This pattern is prominent among DETE employees (higher percentages) while subtle among TAFE employees (smaller percentages). New TAFE employees are more likely to resign due to dissatisfaction than experienced employees, although the difference is only marginal.
dete_age_info = generate_table(dete, ['age'], 'dissatisfied')
tafe_age_info = generate_table(tafe, ['age'], 'dissatisfied')
tafe_age_info = sort_by_df(tafe_age_info, 'age', dete_age_info)
fig = create_subplot(dete_age_info, tafe_age_info,
'Percentage of dissatisfied employees across different age groups')
fig.update_layout(height=500, width=950)
fig.show('png')
It is evident that both institutes conform to different of variations of dissatisfaction among different age groups. While older employees are more likely to be dissatisfied in DETE, dissatisfaction is rampant among younger TAFE employees. This variation, though present, is not very clear. We can obtain a more informed view by further considering the age structures across both institutions.
dete_age_structure = generate_table(dete, ['age_structure'], 'dissatisfied')
tafe_age_structure = generate_table(tafe, ['age_structure'], 'dissatisfied')
tafe_age_structure = sort_by_df(tafe_age_structure, 'age_structure', dete_age_structure)
fig = create_subplot(dete_age_structure, tafe_age_structure,
'Percentage of dissatisfied employees across different age structures')
fig.update_layout(height=260, width=950)
fig.show('png')
The age structure provides a clearer pattern. At DETE dissatisfaction rises with age, while at TAFE, it decreases with Age. Again, the levels of dissatisfaction obeserved at DETE are much higher than those observed at TAFE.
dete_gender_info = generate_table(dete, ['gender'], 'dissatisfied')
tafe_gender_info = generate_table(tafe, ['gender'], 'dissatisfied')
tafe_gender_info = sort_by_df(tafe_gender_info, 'gender', dete_gender_info)
fig = create_subplot(dete_gender_info, tafe_gender_info,
'Percentage of dissatisfied employees by gender')
fig.update_layout(height=220, width=950)
fig.show('png')
Males are more likely to get dissatisfied and resign in both institutes. The only difference is how much more. At DETE there is a considerable difference between the rates of dissatisfaction among males than females (55% vs 50%). At TAFE this difference is milder (28% of males vs 26% of females).
dete_employment_status_info = generate_table(dete, ['employment_status'], 'dissatisfied')
tafe_employment_status_info = generate_table(tafe, ['employment_status'], 'dissatisfied')
tafe_employment_status_info = sort_by_df(tafe_employment_status_info, 'employment_status',
dete_employment_status_info)
fig = create_subplot(dete_employment_status_info, tafe_employment_status_info,
'Percentage of dissatisfied employees by contract type')
fig.update_layout(height=360, width=950)
fig.show('png')
Both institutes show the same overall pattern: permanent employees showed higher dissatisfaction rates than temporary and casual/contract based employees. At TAFE, temporary part-time employees showed the least rates of dissatisfaction (7%).
dete_role_info = generate_table(dete, ['role'], 'dissatisfied')
tafe_role_info = generate_table(tafe, ['role'], 'dissatisfied')
tafe_role_info = sort_by_df(tafe_role_info, 'role', dete_role_info)
fig = create_subplot(dete_role_info, tafe_role_info,
'Percentage of dissatisfied employees by role')
fig.update_layout(height=220, width=950)
fig.show('png')
Teaching staff showed more dissatisfaction rates than non-teaching staff. This difference is not very pronounced in DETE (52% teaching vs 50% non-teaching). TAFE shows a more observable difference with 13% more teaching staff resigning due to dissatisfaction when compared to non-teaching staff (35% vs 22%).
From our analysis, we learned the relationship between various factors and resignation due to dissatisfaction. These factors included years of service, gender, age, contract type and job role.
In general, we observed that 51% of DETE's resigning workforce are leaving dissatisfied while at TAFE this percentage is 27%. Established and veteran employees are more likely to leave dissatisfied. A deeper dive into the data shows an interesting perspective: dissatisfaction positively correlates with employee age and length of service at the DETE institute while at TAFE, the reverse is the case (dissatisfaction correlated negatively with age and length of service).
Common to both institutes, gender doesn’t seem to exert much influence on employee dissatisfaction, but males showed slightly higher propensity to be dissatisfied than females. The majority of employees on permanent contracts reported more dissatisfaction than temporary or casually employed employees, and staff who worked in teaching roles resigned more, due to dissatisfaction, than non-teaching staff.
The DETE management could consider Improvements to, or addition of, wellness programs to help older employees. Employee Wellness Programs have been shown to be one of the most effective strategies to increase employee morale, health, and productivity. This can also help reduce stress and work/life balance Issues.
TAFE on the other hand, is struggling to retain its new employees. The management should invest in comprehensive onboarding for new employees. Leaders should teach them how things work, define what good looks like and build a sense of community. This fosters a positive work environment and reduces role overload.
Employees of all categories should feel like they are using their skills, knowledge and abilities to their fullest extent. Feedback and recognition should be regularly provided, so employees feel appreciated for the work they do.
While it might appear that DETE employees are more likely to resign due to dissatisfaction, there were simply more survey answers that corresponded to a 'dissatisfied' outcome in the DETE survey. The TAFE survey had fewer answers see data cleaning section 8. This inequality in the way the surveys were handled could pose a limitation in this project.
Dissatisfaction results were also handled as either 'dissatisfied' or 'not dissatisfied', as opposed to, for instance, having employees rate their dissatisfaction on a scale of 1-10. The latter might have reflected employee sentiment more accurately, as it represents a spectrum of satisfaction that tends to correspond better to a person's actual feelings.