In this guided project, I will work with exit surveys from employees of the [Department of Education, Training and Employment](https://en.wikipedia.org/wiki/Department_of_Education_(Queensland) (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. You can find the DETE exit survey data here. The original TAFE exit survey data is no longer available. Some slight modifications to the original datasets has been made to make them easier to work with, including changing the encoding to UTF-8 (the original ones are encoded using cp1252.)
In this project, I will play the role of a data analyst and pretend the stakeholders want to know the following:
They want me to combine the results for both surveys to answer these questions. However, although both used the same survey template, one of them customized some of the answers. The guided steps, aim to do most of the data cleaning and get me started analyzing the first question.
Since a data dictionary wasn't provided with the dataset. In a job setting, I will make sure to meet with a manager to confirm the definitions of the data. For this project, I will use my general knowledge to define the columns.
Below is a preview of a couple columns I will work with from the dete_survey.csv:
Below is a preview of a couple columns we'll work with from the tafe_survey.csv:
I will start by reading the datasets into pandas and exploring them.
# importing the required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
%matplotlib inline
# Using pandas to read in the datasets
"""
I will use the pd.read_csv() function to specify values that
should be represented as NaN and fix the missing values first.
"""
dete_survey = pd.read_csv("dete_survey.csv", na_values='Not Stated')
tafe_survey = pd.read_csv("tafe_survey.csv")
I will use the DataFrame.info() and DataFrame.head() methods to print information about both dataframes, as well as the first few rows. Furthermore, I will use the Series.value_counts() and DataFrame.isnull() methods to explore the data and figure out some next steps.
dete_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 822 non-null int64 1 SeparationType 822 non-null object 2 Cease Date 788 non-null object 3 DETE Start Date 749 non-null float64 4 Role Start Date 724 non-null float64 5 Position 817 non-null object 6 Classification 455 non-null object 7 Region 717 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), float64(2), int64(1), object(35) memory usage: 258.6+ KB
tafe_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 72 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Record ID 702 non-null float64 1 Institute 702 non-null object 2 WorkArea 702 non-null object 3 CESSATION YEAR 695 non-null float64 4 Reason for ceasing employment 701 non-null object 5 Contributing Factors. Career Move - Public Sector 437 non-null object 6 Contributing Factors. Career Move - Private Sector 437 non-null object 7 Contributing Factors. Career Move - Self-employment 437 non-null object 8 Contributing Factors. Ill Health 437 non-null object 9 Contributing Factors. Maternity/Family 437 non-null object 10 Contributing Factors. Dissatisfaction 437 non-null object 11 Contributing Factors. Job Dissatisfaction 437 non-null object 12 Contributing Factors. Interpersonal Conflict 437 non-null object 13 Contributing Factors. Study 437 non-null object 14 Contributing Factors. Travel 437 non-null object 15 Contributing Factors. Other 437 non-null object 16 Contributing Factors. NONE 437 non-null object 17 Main Factor. Which of these was the main factor for leaving? 113 non-null object 18 InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object 19 InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object 20 InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object 21 InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object 22 InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object 23 InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object 24 InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object 25 InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object 26 InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object 27 InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object 28 InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object 29 InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object 30 InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object 31 WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object 32 WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object 33 WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object 34 WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object 35 WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object 36 WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 609 non-null object 37 WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object 38 WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] 608 non-null object 39 WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object 40 WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object 41 WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object 42 WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object 43 WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object 44 WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object 45 WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 609 non-null object 46 WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object 47 WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object 48 Induction. Did you undertake Workplace Induction? 619 non-null object 49 InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object 50 InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object 51 InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object 52 InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 53 InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 54 InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object 55 InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object 56 InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object 57 InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object 58 InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object 59 InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object 60 InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object 61 Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object 62 Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object 63 Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object 64 Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object 65 Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object 66 Gender. What is your Gender? 596 non-null object 67 CurrentAge. Current Age 596 non-null object 68 Employment Type. Employment Type 596 non-null object 69 Classification. Classification 596 non-null object 70 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object 71 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(70) memory usage: 395.0+ KB
dete_survey.head()
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 56 columns
tafe_survey.head()
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | ... | Yes | Yes | Yes | Yes | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 72 columns
dete_survey.isnull()
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | True | True | True | True | False |
1 | False | False | False | True | True | False | False | False | False | False | ... | False | False | False | False | False | True | True | True | True | True |
2 | False | False | False | False | False | False | True | False | False | False | ... | False | False | False | False | False | True | True | True | True | True |
3 | False | False | False | False | False | False | False | False | True | False | ... | False | False | False | False | False | True | True | True | True | True |
4 | False | False | False | False | False | False | True | False | True | False | ... | False | False | False | False | False | True | True | True | True | True |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
817 | False | False | False | False | False | False | False | False | True | False | ... | False | False | False | False | False | True | True | True | True | True |
818 | False | False | False | False | False | False | False | False | True | False | ... | False | False | False | False | False | True | True | True | True | True |
819 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | True | True | True | True | True |
820 | False | False | False | False | False | False | False | False | True | False | ... | False | False | False | False | False | True | True | True | True | True |
821 | False | False | False | True | True | False | True | False | True | True | ... | True | True | True | True | True | True | True | True | True | True |
822 rows × 56 columns
tafe_survey.isnull()
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | False | False | False | False | False | True | True | True | True | True | ... | False | False | False | False | False | False | False | False | False | False |
1 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | True | True | True | True | True | True |
2 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | True | True | True | True | True | True |
3 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | True | True | True | True | True | True |
4 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
697 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
698 | False | False | False | False | False | False | False | False | False | False | ... | True | True | True | True | True | True | True | True | True | True |
699 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
700 | False | False | False | False | False | True | True | True | True | True | ... | False | False | False | False | False | False | False | False | False | False |
701 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
702 rows × 72 columns
From the DataFrame.info(), DataFrame.head() and DataFrame.isnull() methods the we will dediduce the following:
has 702 rows and 72 columns with float64 and Object/Strings datatypes.
From the work in the previous screen, I will start by handling the firts two issues. The pd.read_csv funtion can be used to specify values that should be represented as NaN. I will use this function to fix the missing values first. Then, drop columns we do not need for the analysis.
# Dropping columns from the dete_survey dataframe that are not needed for the analysis
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49]
, axis=1)
dete_survey_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 35 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 822 non-null int64 1 SeparationType 822 non-null object 2 Cease Date 788 non-null object 3 DETE Start Date 749 non-null float64 4 Role Start Date 724 non-null float64 5 Position 817 non-null object 6 Classification 455 non-null object 7 Region 717 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 Gender 798 non-null object 29 Age 811 non-null object 30 Aboriginal 16 non-null object 31 Torres Strait 3 non-null object 32 South Sea 7 non-null object 33 Disability 23 non-null object 34 NESB 32 non-null object dtypes: bool(18), float64(2), int64(1), object(14) memory usage: 123.7+ KB
# Dropping columns from the tafe_survey dataframe that are not needed for the analysis
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66]
, axis=1)
tafe_survey_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 23 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 Gender. What is your Gender? 596 non-null object 18 CurrentAge. Current Age 596 non-null object 19 Employment Type. Employment Type 596 non-null object 20 Classification. Classification 596 non-null object 21 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object 22 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(21) memory usage: 126.3+ KB
Now, I will turn my attention to the column names. Each dataframe contains many of the same columns, but the column names are different. Below are some of the columns we'd like to use for our final analysis:
dete_survey | tafe_survey | Definition | |
---|---|---|---|
ID | Record ID | An id used to identify the participant of the survey | |
SeparationType | Reason for ceasing employment | The reason why the participant's employment ended | |
Cease Date | CESSATION YEAR | The year or month the participant's employment ended | |
DETE Start Date | The year the participant began employment with the DETE | ||
LengthofServiceOverall. Overall Length of Service at Institute (in years) | The length of the person's employment (in years) | ||
Age | CurrentAge. Current Age | The age of the participant | |
Gender | Gender. What is your Gender? | The gender of the participant |
Since I want to combine the dataset, then I have to standardize the column names. The DataFrame.columns attribute will be used along with vectorized string methods to update all of the columns at once.
# Renaming remaining columns in the dete_survey_updated dataframe.
dete_survey_updated.columns= dete_survey_updated.columns.str.lower().str.strip().str.replace(' ','_')
dete_survey_updated.columns
Index(['id', 'separationtype', 'cease_date', 'dete_start_date', 'role_start_date', 'position', 'classification', 'region', 'business_unit', 'employment_status', 'career_move_to_public_sector', 'career_move_to_private_sector', 'interpersonal_conflicts', 'job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions', 'maternity/family', 'relocation', 'study/travel', 'ill_health', 'traumatic_incident', 'work_life_balance', 'workload', 'none_of_the_above', 'gender', 'age', 'aboriginal', 'torres_strait', 'south_sea', 'disability', 'nesb'], dtype='object')
#Creatig a mapping dictionary
mapping = {'Record ID': 'id', 'CESSATION YEAR': 'cease_date', 'Reason for ceasing employment': 'separationtype', 'Gender. What is your Gender?': 'gender', 'CurrentAge. Current Age': 'age',
'Employment Type. Employment Type': 'employment_status',
'Classification. Classification': 'position',
'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service',
'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'}
#Using the mapping dictionary to update the column names in the tafe_survey_updated dataframe
tafe_survey_updated = tafe_survey_updated.rename(mapping, axis=1)
dete_survey_updated.head()
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | 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 | ... | 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 | 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 | True | 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 | False | False | 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 | ... | True | False | False | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 35 columns
tafe_survey_updated.head()
id | Institute | WorkArea | cease_date | separationtype | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | gender | age | employment_status | position | institute_service | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | 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 |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | - | - | - | - | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 23 columns
*'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'
The columns to be used in our analysis were renamed for better understanding.
In the last screen, I renamed the columns that will be used in the analysis. Next, I will remove more of the data we don't need.
Recall that the end goal is to answer the following question:
Looking at the unique values in the separationtype columns in each dataframe, you will see that each contains a couple of different separation types. For this project, I will only analyze survey respondents who resigned, so their separation type contains the string 'Resignation'.
The dete_survey_updated dataframe contains multiple separation types with the string 'Resignation':
All these variations have to be accounted for do that no data will be dropped unintetionally!
We will use the Series.value_counts() method to review the unique values in the separationtype column in both dete_survey_updated and tafe_survey_updated.
dete_survey_updated['separationtype'].value_counts()
Age Retirement 285 Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
Resignation is the major reason people exit the company at the Department of Education, Training and Employment with a total of 311 employees. 150 employees resigned because of other reasons, 91 resigned because they got other employer and 70 resigned because they moved overseas or interstate.
tafe_survey_updated['separationtype'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
Resignation is also the major reason people exit the company at the Technical and Further Education (TAFE) institute in Queensland, Australia with a total of 340 employees.
Before selecting only the data for survey respondents who have a Resignation separation types, all separation types containing the word 'Resignation' in the dete_survey_updated have to be updated to only the string 'Resignation'. e.g 'Resignation-Other reasons' becomes 'Resignation'.
dete_survey_updated['separationtype']= dete_survey_updated['separationtype'].str.split('-').str[0]
#check if the resignationtype column is updated correctly
dete_survey_updated['separationtype'].value_counts()
Resignation 311 Age Retirement 285 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
Now, I will select only the data for survey who have Resignation separationtype in each dataframe.
dete_resignations=dete_survey_updated[dete_survey_updated['separationtype']=='Resignation'].copy()
tafe_resignations=tafe_survey_updated[tafe_survey_updated['separationtype']=='Resignation'].copy()
We have only selected the rows where employees have resigned because that is the only data relevant to answer the given questions.
Now, before I start cleaning and manipulating the rest of the data, I will verify that the data doesn't contain any major inconsistencies (to the best of my knowledge).
In this step, I will focus on verifying that the years in the cease_date and dete_start_date columns make sense.
If I have many years higher than the current date or lower than 1940, I wouldn't want to continue with my analysis, because it could mean there's something very wrong with the data. If there are a small amount of values that are unrealistically high or low, we can remove them.
below, I will check the years in each dataframe for logical consistencies
#1) Checking cease_date in dete_resignations
dete_resignations['cease_date'].value_counts()
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 07/2013 9 11/2013 9 10/2013 6 08/2013 4 05/2013 2 05/2012 2 2010 1 07/2012 1 07/2006 1 09/2010 1 Name: cease_date, dtype: int64
I will use the vectorized method to extract the years and use the Series.astype() method to convert the type to float.
# 1) Converting cease_date in dete_resignations
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.split('/').str[-1]
dete_resignations['cease_date'] = dete_resignations['cease_date'].astype("float")
# Confirming if the code did what I expected
dete_resignations['cease_date'].value_counts()
2013.0 146 2012.0 129 2014.0 22 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
#2 Checking dete_start_date in dete_resignations
dete_resignations['dete_start_date'].value_counts()
2011.0 24 2008.0 22 2007.0 21 2012.0 21 2010.0 17 2005.0 15 2004.0 14 2009.0 13 2006.0 13 2013.0 10 2000.0 9 1999.0 8 1996.0 6 2002.0 6 1992.0 6 1998.0 6 2003.0 6 1994.0 6 1993.0 5 1990.0 5 1980.0 5 1997.0 5 1991.0 4 1989.0 4 1988.0 4 1995.0 4 2001.0 3 1985.0 3 1986.0 3 1983.0 2 1976.0 2 1974.0 2 1971.0 1 1972.0 1 1984.0 1 1982.0 1 1987.0 1 1975.0 1 1973.0 1 1977.0 1 1963.0 1 Name: dete_start_date, dtype: int64
#3 Checking cease_date in tafe_resignations
tafe_resignations['cease_date'].value_counts()
2011.0 116 2012.0 94 2010.0 68 2013.0 55 2009.0 2 Name: cease_date, dtype: int64
# Checking for unique values
tafe_resignations['cease_date'].value_counts().sort_index(ascending=True)
2009.0 2 2010.0 68 2011.0 116 2012.0 94 2013.0 55 Name: cease_date, dtype: int64
dete_dates = dete_resignations[['dete_start_date', 'cease_date']]
fig = px.box(dete_dates, y=dete_dates.columns, width=500, height=500, template='plotly_white')
fig.update_layout(title='DETE Resignations: Start Date and Cease Date')
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()
From box plot above we observe that the majority of the employees who resigned joined the DETE between the late 1997 and 2010. Between year 2010 and 2014, a large proportion of these employees had resigned from the institution.
Now that I have verified the years in the dete_resignations dataframe, I will use them to create a new column. Recall that the end goal is to answer the following question:
In the Human Resources field, the length of time an employee spent in a workplace is referred to as their years of service.
Since the tafe_resignations dataframe already contains a "service" column, which we renamed to institute_service, then I have to create a corresponding institute_service column in dete_resignations in order to analyze both surveys together.
dete_resignations['institute_service']=dete_resignations['cease_date']-dete_resignations['dete_start_date']
#checking for values
dete_resignations['institute_service'].shape
(311,)
The institute_service in the dete_resignations dataframe was created by subtracting the dete_start_date from the cease_date column. This is the column I will use to analyze survey respondents according to their length of employment.
Next, I will identify any employees who resigned because they were dissatisfied.
Below are the columns I will use to categorize employees as "dissatisfied" from each dataframe.
tafe_survey_updated:
dete_survey_updated:
* workload
If the employee indicated any of the factors above caused them to resign, I will mark them as dissatisfied in a new column.
# Checking for Unique values
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
Only 55 employees resigned because they were dissatisfied.
# Checking for Unique values
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()
- 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
Only 62 employees resigned because they were dissatisfied with the job.
The function below will update values in 'Contributing Factors. Dissatisfaction' and 'Contributing Factors. Job Dissatisfaction' in the tafe_resignations dataframes so that each contain only True, False or NaN values.
def update_vals(x):
if x == '-':
return False
elif pd.isnull(x):
return np.nan
else:
return True
# Applying the function to update values in the tafe_resignation dataframe.
tafe_resignations['dissatisfied'] = tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']].applymap(update_vals).any(axis =1, skipna=False)
tafe_resignations_up = tafe_resignations.copy()
# Checking for unique vales
tafe_resignations_up['dissatisfied'].value_counts(dropna=False)
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
# Applying the function to update values in the dete_resignation dataframe.
dete_resignations['dissatisfied']=dete_resignations[['job_dissatisfaction',
'dissatisfaction_with_the_department', 'physical_work_environment',
'lack_of_recognition', 'lack_of_job_security', 'work_location',
'employment_conditions', 'work_life_balance', 'workload']].any(axis=1, skipna=False)
dete_resignations_up = dete_resignations.copy()
# Checking for unique vales
dete_resignations_up['dissatisfied'].value_counts(dropna = False)
False 162 True 149 Name: dissatisfied, dtype: int64
Now, I am finally ready to combine the datasets! The end goal is to aggregate the data according to the institute_service column.
First, I will add a column to each dataframe that will allow me to easily distinguish between the two.
dete_resignations_up['institution']='DETE'
tafe_resignations_up['institution']='TAFE'
# Combining the dataframes
combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index=True)
# Verifying the number of non null values in each column
combined.notnull().sum().sort_values()
torres_strait 0 south_sea 3 aboriginal 7 disability 8 nesb 9 business_unit 32 classification 161 region 265 role_start_date 271 dete_start_date 283 role_service 290 none_of_the_above 311 work_life_balance 311 traumatic_incident 311 ill_health 311 study/travel 311 relocation 311 maternity/family 311 employment_conditions 311 workload 311 lack_of_job_security 311 career_move_to_public_sector 311 career_move_to_private_sector 311 interpersonal_conflicts 311 work_location 311 dissatisfaction_with_the_department 311 physical_work_environment 311 lack_of_recognition 311 job_dissatisfaction 311 Contributing Factors. Job Dissatisfaction 332 Contributing Factors. Travel 332 Contributing Factors. Maternity/Family 332 Contributing Factors. Ill Health 332 Contributing Factors. Career Move - Self-employment 332 Contributing Factors. Career Move - Private Sector 332 Contributing Factors. Career Move - Public Sector 332 Contributing Factors. Dissatisfaction 332 Contributing Factors. Other 332 Contributing Factors. Interpersonal Conflict 332 Contributing Factors. NONE 332 Contributing Factors. Study 332 Institute 340 WorkArea 340 institute_service 563 gender 592 age 596 employment_status 597 position 598 cease_date 635 dissatisfied 643 separationtype 651 institution 651 id 651 dtype: int64
Below I will drop all columns with less than 500 non null values.
combined_updated = combined.dropna(thresh = 500, axis = 1).copy()
combined_updated.notnull().sum().sort_values()
institute_service 563 gender 592 age 596 employment_status 597 position 598 cease_date 635 dissatisfied 643 id 651 separationtype 651 institution 651 dtype: int64
Now I am are only left with 9 columns which are relevant for the analysis.
Now that I have combined the dataframes, I am almost at a place where I can perform some kind of analysis! First, though, I have to clean up the institute_service column. This column is tricky to clean because it currently contains values in a couple different forms.
To analyze the data, I will convert these numbers into categories. I will base my analysis on this article, which makes the argument that understanding employee's needs according to career stage instead of age is more effective.
I will use the slightly modified definitions below:
I will categorize the values in the institute_service column using the definitions above.
combined_updated['institute_service'].value_counts()
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 2.0 14 9.0 14 7.0 13 More than 20 years 10 8.0 8 13.0 8 15.0 7 20.0 7 17.0 6 22.0 6 12.0 6 14.0 6 10.0 6 16.0 5 18.0 5 23.0 4 11.0 4 24.0 4 19.0 3 21.0 3 39.0 3 32.0 3 26.0 2 28.0 2 30.0 2 25.0 2 36.0 2 27.0 1 29.0 1 31.0 1 33.0 1 34.0 1 41.0 1 35.0 1 42.0 1 49.0 1 38.0 1 Name: institute_service, dtype: int64
Below I will extract the years of service for each value in the institution_service column and convert the type to float.
combined_updated['institute_service'] = combined_updated['institute_service'].astype('str').str.extract(r'(\d+)')
combined_updated['institute_service'] = combined_updated['institute_service'].astype('float')
#Checking for unique values
combined_updated['institute_service'].value_counts()
1.0 159 3.0 83 5.0 56 7.0 34 11.0 30 0.0 20 20.0 17 6.0 17 4.0 16 9.0 14 2.0 14 13.0 8 8.0 8 15.0 7 17.0 6 10.0 6 12.0 6 14.0 6 22.0 6 16.0 5 18.0 5 24.0 4 23.0 4 39.0 3 19.0 3 21.0 3 32.0 3 28.0 2 36.0 2 25.0 2 30.0 2 26.0 2 29.0 1 38.0 1 42.0 1 27.0 1 41.0 1 35.0 1 49.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service, dtype: int64
Now, I will create a function that maps each year value to one of the career stages above.
def categorize(val):
if pd.isnull(val):
return np.nan
elif val<3:
return 'New'
elif 3<=val<=6:
return 'Experienced'
elif 7<=val<=10:
return 'Established'
else:
return 'Veteran'
# Applying the function to map each year value to one of the career stages above
combined_updated['service_cat'] = combined_updated['institute_service'].apply(categorize)
# Confirming the function did what I expected.
combined_updated['service_cat'].value_counts(dropna=False)
New 193 Experienced 172 Veteran 136 NaN 88 Established 62 Name: service_cat, dtype: int64
From the code above I created a service_cat column, that categorizes employees according to the number of years they spend at the componey: One can deduce that there are 193 New employees, 172 experienced employees, 62 established employees and 136 veteran employees. There are 88 missing values.
Now, I will finally do the first piece of analysis! I will fill in missing values in the dissatisfied column and then aggregate the data to get started, but there are still additional missing values left to deal with. This is meant to be an initial introduction to the analysis, not the final analysis.
I will use the Series.value_counts() method to confirm if the number of True and False in the dissatisfied column. I will set the dropna parameter to False to also confirm the number of missing values.
combined_updated['dissatisfied'].value_counts(dropna=False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
# Replacing the missing values with the most frequentely occuring value in the column.
# in this case, the most frequent value is False
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
I use the DataFrame.pivot_table() to calculate the percentage of dissatisfied employees in each service_cat group.
dissatisfied_resignations = combined_updated.pivot_table(values='dissatisfied', index = 'service_cat')
dissatisfied_resignations
dissatisfied | |
---|---|
service_cat | |
Established | 0.516129 |
Experienced | 0.343023 |
New | 0.295337 |
Veteran | 0.485294 |
Doing a the analysis between service category and dissatisfied employees, I observe that 51.61% of the employees who resigned from the DETE and TAFE were established (worked for 7-10 years), 34.3% were experienced (worked for 3-7 years), 29.53% were newbies (worked for less than 3 years) and 48.53% were veterans (worked for 11 years or more). One can infer that people that work in the DETE and TAFE institute become dissatisfied with their jobs due to some challenges they are faced.
# Calculating the percentage of employees who resigned due to dissatisfaction in each category
dissatisfied_resignations['dissatisfied']=dissatisfied_resignations['dissatisfied']*100
# Plotting the results
dissatisfied_resignations.plot(kind='bar', rot=45)
plt.ylabel('percentage (%)')
plt.title('Percentage of dissatisfied employees')
plt.show()
From the bar graph above, I can dedude that established employees are more likely to resign due to some dissatisfications. New employees are least likely to resign.
To turn back to our initial question: How many people in each age group resgined due to some kind of dissatisfaction? Instead of analyzing the survey results together, analyze each survey separately.
Did more employees in the DETE survey or TAFE survey end their employment because they were dissatisfied in some way?
# Checking for unique values in the age column
combined_updated['age'].value_counts().sort_index()
20 or younger 10 21 25 33 21-25 29 26 30 32 26-30 35 31 35 32 31-35 29 36 40 32 36-40 41 41 45 45 41-45 48 46 50 39 46-50 42 51-55 71 56 or older 29 56-60 26 61 or older 23 Name: age, dtype: int64
# Extracting the dataset for employees who only indicated True in the dissatisfied column
combined_dissatisfied = combined_updated.loc[combined_updated['dissatisfied']==True,]
# cleaning the age column
combined_updated['age'] = combined_updated['age'].str.replace(" ","-")
combined_updated['age'].value_counts().sort_index()
20 or younger 10 21-25 62 26-30 67 31-35 61 36-40 73 41-45 93 46-50 81 51-55 71 56 or older 29 56-60 26 61 or older 23 Name: age, dtype: int64
Looking at the age column, I notice some incinsisitencies in some age categories. 56 or older, 56-60 and 61 or older seems odd, therefore need cleaning. We will use the function below to haddle that issue.
def clean_age(element):
if element == "56-60" or element == "61 or older":
return "56 or older"
else:
return element
# Applying the function to clean the age column.
combined_updated['age'] = combined_updated['age'].map(clean_age)
# Checking for unique values
combined_updated['age'].value_counts(dropna=False).sort_index()
20 or younger 10 21-25 62 26-30 67 31-35 61 36-40 73 41-45 93 46-50 81 51-55 71 56 or older 78 NaN 55 Name: age, dtype: int64
Since we have cleaned up the age, now we can see if there is a relationship between age and employees resigning due to some kind of dissatisfication. I will first filter the dataset to only employees who indicated True in the dissatisfied column
combined_dissatisfied = combined_updated[combined_updated['dissatisfied']==True]
I will create a dataframe with age groups counts, number of dissatisfied people per age group and their respective percentages.
Age_DF = combined_updated['age'].value_counts().sort_index().to_frame(name='Total')
Age_DF['Dissatisfied'] = combined_dissatisfied ['age'].value_counts().sort_index()
Age_DF['Dissatisfied (%)'] = round(Age_DF['Dissatisfied'] / Age_DF['Total'],4)*100
Age_DF.index.name = 'Age'
Age_DF
Total | Dissatisfied | Dissatisfied (%) | |
---|---|---|---|
Age | |||
20 or younger | 10 | 2 | 20.00 |
21-25 | 62 | 19 | 30.65 |
26-30 | 67 | 28 | 41.79 |
31-35 | 61 | 23 | 37.70 |
36-40 | 73 | 25 | 34.25 |
41-45 | 93 | 35 | 37.63 |
46-50 | 81 | 31 | 38.27 |
51-55 | 71 | 30 | 42.25 |
56 or older | 78 | 33 | 42.31 |
labels = ['20 or younger','21-25','26-30','31-35', '36-40','41-45','46-50','51-55','56 or older' ]
sizes = [15, 30, 45, 10]
explode = (0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1) # only "explode" the 2nd slice (i.e. 'Hogs')
fig1, ax1 = plt.subplots()
ax1.pie(Age_DF['Dissatisfied (%)'], explode=explode, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
ax1.legend(labels, title="Age Group",
loc="center left",
bbox_to_anchor=(1, 0, 0.5, 1))
ax1.set_title("Dissatisfaction Distribution by Age\n", weight ='bold')
plt.show()
From the dissatisfaction distribution by age pie chart, one can deduce that older employees (51-55 years and 56 years or older) are the most likely to resign due to dissatisfications (42.25% and 42.31% respectively) than younger employees, with the exception of 26-30 year olds.