In this project, I will work with exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. You can find the TAFE exit survey here and the survey for the DETE here.
In this project, cleaning
and analysis
will be done on the two data sets in a bid to help certain stakeholders answer the following questions about their employees:
Although, a data dictionary wasn't provided with the dataset, below is a preview of a couple columns we'll work with from the dete_survey.csv
:
Column_Name | Description |
---|---|
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 |
Below is a preview of a couple columns we'll work with from the tafe_survey.csv
:
Column_Name | Description |
---|---|
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) |
## Import necessary modules and read in the dataset
import numpy as np
import pandas as pd
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
# display first five rows of dete_survey data set
dete_survey.head()
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984 | 2004 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | Not Stated | Not Stated | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011 | 2011 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005 | 2006 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970 | 1989 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 56 columns
## display dete_survey data set infos
dete_survey.info()
print(dete_survey.shape)
<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 (822, 56)
The result from the code cell above gives certain infos as regards the dete_survey
data set. It tell us:
822 rows
and 56 columns
ID
column is the only column stored as int
string objects
Career move to public sector
- None of the above
are stored as boolean
values## display exact number of missing values in each column
dete_survey.isnull().sum()
ID 0 SeparationType 0 Cease Date 0 DETE Start Date 0 Role Start Date 0 Position 5 Classification 367 Region 0 Business Unit 696 Employment Status 5 Career move to public sector 0 Career move to private sector 0 Interpersonal conflicts 0 Job dissatisfaction 0 Dissatisfaction with the department 0 Physical work environment 0 Lack of recognition 0 Lack of job security 0 Work location 0 Employment conditions 0 Maternity/family 0 Relocation 0 Study/Travel 0 Ill Health 0 Traumatic incident 0 Work life balance 0 Workload 0 None of the above 0 Professional Development 14 Opportunities for promotion 87 Staff morale 6 Workplace issue 34 Physical environment 5 Worklife balance 7 Stress and pressure support 12 Performance of supervisor 9 Peer support 10 Initiative 9 Skills 11 Coach 55 Career Aspirations 76 Feedback 30 Further PD 54 Communication 8 My say 10 Information 6 Kept informed 9 Wellness programs 56 Health & Safety 29 Gender 24 Age 11 Aboriginal 806 Torres Strait 819 South Sea 815 Disability 799 NESB 790 dtype: int64
The result from the code cell above shows:
Classification
,Business Unit
,Aboriginal
,Torres Strait
,South Sea
,Disability
and NESB
columns contain a lot of missing values# display first five rows of tafe_survey data set
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
## display tafe_survey data set infos
tafe_survey.info()
print(tafe_survey.shape)
<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 (702, 72)
The result from the code cell above gives certain infos as regards the dete_survey data set. It tell us:
702 rows
and 72 columns
are in data setRecord ID
and CESSATION YEAR
are the only columns stored as floats
while others are stored as string objects
More details as regards the number of missing values in each column is displayed in the code cell below
## display exact number of missing values in each column
tafe_survey.isnull().sum()
Record ID 0 Institute 0 WorkArea 0 CESSATION YEAR 7 Reason for ceasing employment 1 ... CurrentAge. Current Age 106 Employment Type. Employment Type 106 Classification. Classification 106 LengthofServiceOverall. Overall Length of Service at Institute (in years) 106 LengthofServiceCurrent. Length of Service at current workplace (in years) 106 Length: 72, dtype: int64
Exploring the data sets show:
dete_survey
dataframe contains 'Not Stated'
values that indicate values are missing, but they aren't represented as NaN.dete_survey
and tafe_survey
dataframes contain many columns that we don't need to complete the analysis.In the code cells below, the first two issues will be addressed.
# use the pd.read_csv() function to specify values that should be represented as NaN
dete_survey = pd.read_csv('dete_survey.csv', na_values = 'Not Stated')
#columns not needed for the analysis are dropped
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis = 1)
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis = 1)
In the code cell above, the changes made to the data sets will make working with the data sets for analysis purpose, easier
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_updated | tafe_survey_updated | 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 |
Because, the data sets are eventually going to be combined, the column names will have to be standerdized. Lets take a peek at the columns
in the dete_survey_updated
data set in the code cell below
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')
In the code cell below:
#rename columns in the dete_survey_updated dataframe
dete_survey_updated.columns = dete_survey_updated.columns.str.replace(' ','_').str.lower()
dete_survey_updated.columns
Index(['id', 'separationtype', 'cease_date', 'dete_start_date', 'role_start_date', 'position', 'classification', 'region', 'business_unit', 'employment_status', 'career_move_to_public_sector', 'career_move_to_private_sector', 'interpersonal_conflicts', 'job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions', 'maternity/family', 'relocation', 'study/travel', 'ill_health', 'traumatic_incident', 'work_life_balance', 'workload', 'none_of_the_above', 'gender', 'age', 'aboriginal', 'torres_strait', 'south_sea', 'disability', 'nesb'], dtype='object')
# rename concerned columns in the tafe_survey_updated data set
col_rename = {'Record ID': 'id',
'CESSATION YEAR': 'cease_date',
'Reason for ceasing employment': 'separationtype',
'Gender. What is your Gender?': 'gender',
'CurrentAge. Current Age': 'age',
'Employment Type. Employment Type': 'employment_status',
'Classification. Classification': 'position',
'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service',
'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'}
tafe_survey_updated = tafe_survey_updated.rename(columns = col_rename)
tafe_survey_updated.columns
Index(['id', 'Institute', 'WorkArea', 'cease_date', 'separationtype', 'Contributing Factors. Career Move - Public Sector ', 'Contributing Factors. Career Move - Private Sector ', 'Contributing Factors. Career Move - Self-employment', 'Contributing Factors. Ill Health', 'Contributing Factors. Maternity/Family', 'Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Interpersonal Conflict', 'Contributing Factors. Study', 'Contributing Factors. Travel', 'Contributing Factors. Other', 'Contributing Factors. NONE', 'gender', 'age', 'employment_status', 'position', 'institute_service', 'role_service'], dtype='object')
If we look at the unique values in the separationtype
columns in each dataframe, we'll see that each contains a couple of different separation types. For this project, we'll only analyze survey respondents who resigned, so their separation type contains the string 'Resignation'
.
dete_survey_updated['separationtype'].value_counts()
Age Retirement 285 Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
tafe_survey_updated['separationtype'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
dete_survey_updated['separationtype'] = dete_survey_updated['separationtype'].str.split('-').str[0]
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
dete_resignations = dete_survey_updated[dete_survey_updated['separationtype'] == 'Resignation'].copy()
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
The dete_resignations
and tafe_resignations
dataframes contain only data points with the Resignation
value form the seperationtype
column.
Before we start cleaning and manipulating the rest of our data, let's verify that the data doesn't contain any major inconsistencies (to the best of our knowledge).
We'll verifying that the years in the cease_date and dete_start_date columns make sense.
# display unique value counts in th cease_date column
dete_resignations['cease_date'].value_counts()
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 11/2013 9 07/2013 9 10/2013 6 08/2013 4 05/2013 2 05/2012 2 2010 1 09/2010 1 07/2012 1 07/2006 1 Name: cease_date, dtype: int64
# Extract and represent the data in a consistent format
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.split('/').str[-1].astype('float64')
dete_resignations['cease_date'].value_counts().sort_index(ascending=True)
2006.0 1 2010.0 2 2012.0 129 2013.0 146 2014.0 22 Name: cease_date, dtype: int64
dete_resignations['dete_start_date'].value_counts().sort_index(ascending=True)
1963.0 1 1971.0 1 1972.0 1 1973.0 1 1974.0 2 1975.0 1 1976.0 2 1977.0 1 1980.0 5 1982.0 1 1983.0 2 1984.0 1 1985.0 3 1986.0 3 1987.0 1 1988.0 4 1989.0 4 1990.0 5 1991.0 4 1992.0 6 1993.0 5 1994.0 6 1995.0 4 1996.0 6 1997.0 5 1998.0 6 1999.0 8 2000.0 9 2001.0 3 2002.0 6 2003.0 6 2004.0 14 2005.0 15 2006.0 13 2007.0 21 2008.0 22 2009.0 13 2010.0 17 2011.0 24 2012.0 21 2013.0 10 Name: dete_start_date, dtype: int64
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
tafe_resignations.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 340 entries, 3 to 701 Data columns (total 23 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 340 non-null float64 1 Institute 340 non-null object 2 WorkArea 340 non-null object 3 cease_date 335 non-null float64 4 separationtype 340 non-null object 5 Contributing Factors. Career Move - Public Sector 332 non-null object 6 Contributing Factors. Career Move - Private Sector 332 non-null object 7 Contributing Factors. Career Move - Self-employment 332 non-null object 8 Contributing Factors. Ill Health 332 non-null object 9 Contributing Factors. Maternity/Family 332 non-null object 10 Contributing Factors. Dissatisfaction 332 non-null object 11 Contributing Factors. Job Dissatisfaction 332 non-null object 12 Contributing Factors. Interpersonal Conflict 332 non-null object 13 Contributing Factors. Study 332 non-null object 14 Contributing Factors. Travel 332 non-null object 15 Contributing Factors. Other 332 non-null object 16 Contributing Factors. NONE 332 non-null object 17 gender 290 non-null object 18 age 290 non-null object 19 employment_status 290 non-null object 20 position 290 non-null object 21 institute_service 290 non-null object 22 role_service 290 non-null object dtypes: float64(2), object(21) memory usage: 63.8+ KB
# %matplotlib inline
dete_resignations['cease_date'].plot(kind='box', ylim=(2005,2015))
<matplotlib.axes._subplots.AxesSubplot at 0x2af9af3cdc8>
tafe_resignations['cease_date'].plot(kind='box', ylim=(2006,2015))
<matplotlib.axes._subplots.AxesSubplot at 0x2af9b686108>
The result form the code cells helps us verify that the date
columns from both data consists of reasonable values
# determinne the year span of each employee
dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']
dete_resignations['institute_service'].value_counts()
5.0 23 1.0 22 3.0 20 0.0 20 6.0 17 4.0 16 9.0 14 2.0 14 7.0 13 13.0 8 8.0 8 20.0 7 15.0 7 10.0 6 22.0 6 14.0 6 17.0 6 12.0 6 16.0 5 18.0 5 23.0 4 11.0 4 24.0 4 39.0 3 19.0 3 21.0 3 32.0 3 28.0 2 26.0 2 25.0 2 30.0 2 36.0 2 29.0 1 33.0 1 42.0 1 27.0 1 41.0 1 35.0 1 38.0 1 34.0 1 49.0 1 31.0 1 Name: institute_service, dtype: int64
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()
- 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
# Convert the values in the 'Contributing Factors. Dissatisfaction' and 'Contributing Factors. Job Dissatisfaction' columns in the tafe_resignations dataframe to True, False, or NaN values
def update_vals(val):
if val == '-':
return False
elif pd.isnull(val):
return np.NaN
else:
return True
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()
tafe_resignations_up['dissatisfied'].value_counts(dropna=False)
False 241 True 91 True 8 Name: dissatisfied, dtype: int64
tafe_resignations_up['dissatisfied'].unique()
array([False, True, nan], dtype=object)
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()
dete_resignations_up['dissatisfied'].value_counts(dropna=False)
False 162 True 149 Name: dissatisfied, dtype: int64
# add a column to each dataframe that will allow us to easily distinguish between the two.
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index = True)
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 institute 651 id 651 dtype: int64
# drop columns with less than 300 notnull values
combined_updated = combined.dropna(thresh = 300, axis = 1).copy()
combined_updated.shape
(651, 42)
# Extract and represent values in a consistent format
combined_updated['institute_service'] = combined_updated['institute_service'].astype('str')
combined_updated['institute_service_d'] = combined_updated['institute_service'].str.extract(r'(\d+)').astype('float')
combined_updated['institute_service_d'].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_d, dtype: int64
# define function for better representation of data
def map_val(x):
if pd.isnull(x):
return np.nan
elif x >= 11:
return 'Veteran'
elif 7 <= x <= 10:
return 'Established'
elif 3 <= x <= 6:
return 'Experienced'
else:
return 'New'
combined_updated['service_cat'] = combined_updated['institute_service_d'].apply(map_val)
combined_updated['service_cat'].value_counts()
New 193 Experienced 172 Veteran 136 Established 62 Name: service_cat, dtype: int64
# replace missing values with the modal value(false)
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
combined_updated['dissatisfied'].value_counts()
False 411 True 240 Name: dissatisfied, dtype: int64
to_plot = combined_updated.pivot_table(values = 'dissatisfied', index = 'service_cat')
to_plot
dissatisfied | |
---|---|
service_cat | |
Established | 0.516129 |
Experienced | 0.343023 |
New | 0.295337 |
Veteran | 0.485294 |
to_plot.plot(kind='bar', rot = 30, legend = False, title = 'Dissatisfied % by Service Category')
<matplotlib.axes._subplots.AxesSubplot at 0x2af9b7c5988>
Most employees that resign due to some kind of dissatisfaction are the Established(Over 11 years of service) and Veteran(7 - 10 years of service) categories
recall the question the analysis is to help us answer, from the introductory cell The bar plot blot displayed above helps -to an extent- answer the first question.
We will analyse the major ccontributing factors to employees resignation. We will consider the given factors from each institue in our combined data sets.
DETE Institute
:
TAFE Institue
:
These factors are represented as columns in our data set, although they contain a alot of missing values. We will clean (fix the missing values) and aggregate them with the service_cat
column
contributing factors
columns in DETE
¶# display column's unique value_counts info
print("Unique value_counts in Job dissatisfaction\n",combined_updated["job_dissatisfaction"].value_counts())
Unique value_counts in Job dissatisfaction False 270 True 41 Name: job_dissatisfaction, dtype: int64
# replace missing values with the value that occurs most frequently in this column
combined_updated["job_dissatisfaction"]= combined_updated["job_dissatisfaction"].fillna(False)
combined_updated["job_dissatisfaction"].value_counts(dropna=False)
False 610 True 41 Name: job_dissatisfaction, dtype: int64
# display column's unique value_counts
print("Unique value_counts in dissatisfaction_with_the_department\n",combined_updated["dissatisfaction_with_the_department"].value_counts())
Unique value_counts in dissatisfaction_with_the_department False 282 True 29 Name: dissatisfaction_with_the_department, dtype: int64
# replace missing values with the value that occurs most frequently in this column
combined_updated["dissatisfaction_with_the_department"]= combined_updated["dissatisfaction_with_the_department"].fillna(False)
combined_updated["dissatisfaction_with_the_department"].value_counts(dropna=False)
False 622 True 29 Name: dissatisfaction_with_the_department, dtype: int64
# display column's unique value_counts
print("Unique value_counts in physical_work_environment\n",combined_updated["physical_work_environment"].value_counts())
Unique value_counts in physical_work_environment False 305 True 6 Name: physical_work_environment, dtype: int64
# replace missing values with the value that occurs most frequently in this column
combined_updated["physical_work_environment"]= combined_updated["physical_work_environment"].fillna(False)
combined_updated["physical_work_environment"].value_counts(dropna=False)
False 645 True 6 Name: physical_work_environment, dtype: int64
# display column's unique value_counts
print("Unique value_counts in lack_of_recognition\n",combined_updated["lack_of_recognition"].value_counts())
Unique value_counts in lack_of_recognition False 278 True 33 Name: lack_of_recognition, dtype: int64
# replace missing values with the value that occurs most frequently in this column
combined_updated["lack_of_recognition"]= combined_updated["lack_of_recognition"].fillna(False)
combined_updated["lack_of_recognition"].value_counts(dropna=False)
False 618 True 33 Name: lack_of_recognition, dtype: int64
# display column's unique value_counts
print("Unique value_counts in lack_of_job_security\n",combined_updated["lack_of_job_security"].value_counts())
Unique value_counts in lack_of_job_security False 297 True 14 Name: lack_of_job_security, dtype: int64
# replace missing values with the value that occurs most frequently in this column
combined_updated["lack_of_job_security"]= combined_updated["lack_of_job_security"].fillna(False)
combined_updated["lack_of_job_security"].value_counts(dropna=False)
False 637 True 14 Name: lack_of_job_security, dtype: int64
# display column's unique value_counts
print("Unique value_counts in work_location\n",combined_updated["work_location"].value_counts())
Unique value_counts in work_location False 293 True 18 Name: work_location, dtype: int64
# replace missing values with the value that occurs most frequently in this column
combined_updated["work_location"]= combined_updated["work_location"].fillna(False)
combined_updated["work_location"].value_counts(dropna=False)
False 633 True 18 Name: work_location, dtype: int64
# display column's unique value_counts
print("Unique value_counts in employment_conditions\n",combined_updated["employment_conditions"].value_counts())
Unique value_counts in employment_conditions False 288 True 23 Name: employment_conditions, dtype: int64
# replace missing values with the value that occurs most frequently in this column
combined_updated["employment_conditions"]= combined_updated["employment_conditions"].fillna(False)
combined_updated["employment_conditions"].value_counts(dropna=False)
False 628 True 23 Name: employment_conditions, dtype: int64
# display column's unique value_counts
print("Unique value_counts in work_life_balance\n",combined_updated["work_life_balance"].value_counts())
Unique value_counts in work_life_balance False 243 True 68 Name: work_life_balance, dtype: int64
# replace missing values with the value that occurs most frequently in this column
combined_updated["work_life_balance"]= combined_updated["work_life_balance"].fillna(False)
combined_updated["work_life_balance"].value_counts(dropna=False)
False 583 True 68 Name: work_life_balance, dtype: int64
# display column's unique value_counts
print("Unique value_counts in workload\n",combined_updated["workload"].value_counts())
Unique value_counts in workload False 284 True 27 Name: workload, dtype: int64
# replace missing values with the value that occurs most frequently in this column
combined_updated["workload"]= combined_updated["workload"].fillna(False)
combined_updated["workload"].value_counts(dropna=False)
False 624 True 27 Name: workload, dtype: int64
# update the missing values using the `update_vals()` function we created earlier
combined_updated[['Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Dissatisfaction']] = (
combined_updated[['Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Dissatisfaction']]
.applymap(update_vals))
# display column's unique value_counts
print('Unique value counts in Contributing Factors. Job Dissatisfaction\n',combined_updated['Contributing Factors. Job Dissatisfaction'].value_counts())
Unique value counts in Contributing Factors. Job Dissatisfaction False 270 True 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
# replace missing values with the value that occurs most frequently in this column
combined_updated["Contributing Factors. Job Dissatisfaction"]= combined_updated["Contributing Factors. Job Dissatisfaction"].fillna(False)
combined_updated["Contributing Factors. Job Dissatisfaction"].value_counts(dropna=False)
False 589 True 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
# display column's unique value_counts
print('Unique value counts in Contributing Factors. Dissatisfaction\n',combined_updated['Contributing Factors. Dissatisfaction'].value_counts())
Unique value counts in Contributing Factors. Dissatisfaction False 277 True 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
# replace missing values with the value that occurs most frequently in this column
combined_updated["Contributing Factors. Dissatisfaction"]= combined_updated["Contributing Factors. Dissatisfaction"].fillna(False)
combined_updated["Contributing Factors. Dissatisfaction"].value_counts(dropna=False)
False 596 True 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
In a bid to aggregate the contributing factors
factors column, we had to clean the different columns involved, In the code cells above, we:
However, since the job dissatisfaction
and Contributing Factors. Job Dissatisfaction
columns of the DETE and TAFE data sets communicate thesame factors, we will combine the two columns in the code cell below.
combined_updated["DETE-TAFE Combined job_dissatisfaction"] = combined_updated[['job_dissatisfaction',
'Contributing Factors. Job Dissatisfaction']].any(axis=1, skipna=False)
combined_updated["DETE-TAFE Combined job_dissatisfaction"].value_counts()
False 548 True 103 Name: DETE-TAFE Combined job_dissatisfaction, dtype: int64
Now that we have cleaned the missing values, let's aggregate by the service_cat
column using a pivot_table
and analyse the results.
cols=[ 'dissatisfaction_with_the_department', 'physical_work_environment',
'lack_of_recognition', 'lack_of_job_security', 'work_location',
'employment_conditions', 'work_life_balance',
'workload','DETE-TAFE Combined job_dissatisfaction','Contributing Factors. Dissatisfaction']
dissatisfaction_result = combined_updated.pivot_table(index='service_cat', values=cols)
dissatisfaction_result
Contributing Factors. Dissatisfaction | DETE-TAFE Combined job_dissatisfaction | dissatisfaction_with_the_department | employment_conditions | lack_of_job_security | lack_of_recognition | physical_work_environment | work_life_balance | work_location | workload | |
---|---|---|---|---|---|---|---|---|---|---|
service_cat | ||||||||||
Established | 0.048387 | 0.225806 | 0.096774 | 0.064516 | 0.016129 | 0.112903 | 0.016129 | 0.161290 | 0.112903 | 0.064516 |
Experienced | 0.087209 | 0.151163 | 0.040698 | 0.034884 | 0.017442 | 0.052326 | 0.017442 | 0.081395 | 0.029070 | 0.034884 |
New | 0.082902 | 0.160622 | 0.005181 | 0.010363 | 0.010363 | 0.010363 | 0.005181 | 0.072539 | 0.005181 | 0.015544 |
Veteran | 0.066176 | 0.161765 | 0.102941 | 0.051471 | 0.051471 | 0.073529 | 0.007353 | 0.183824 | 0.029412 | 0.080882 |
Now, let us visualize
the data
dissatisfaction_result.plot(kind='bar',title = 'Dissatisfaction % by Factor', rot=30,figsize=(15,10),colormap='Paired').legend(bbox_to_anchor=(0.65, 1))
<matplotlib.legend.Legend at 0x2af9b8525c8>
Recall,the question that led to our further analysis.
From the we plot, we can say that:
DETE institue
, general job dissatisfaction
and work_life_balance
are the major contributing factors
for both employees with few and longer years of service.TAFE institute
, general job dissatisfaction
is the major contributing factor
for both employees with few and longer years of service.# check unique values in age column
combined_updated['age'].value_counts(dropna=False)
51-55 71 NaN 55 41-45 48 41 45 45 46-50 42 36-40 41 46 50 39 26-30 35 21 25 33 31 35 32 26 30 32 36 40 32 31-35 29 56 or older 29 21-25 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
There are missing values in this column and the data representation is inconsistent. Based on the result from the code cell above, we will perform the following cleaning steps:
float
objectsmean
age# use regex to create a consistent representation of data
combined_updated['age'] = combined_updated['age'].astype('str').str.extract(r'(\d+)')
combined_updated['age']= combined_updated['age'].astype('float')
#replace missing values with mean age
combined_updated['age'] = combined_updated['age'].fillna(int(combined_updated['age'].mean()))
#verify changes
combined_updated['age'].value_counts(dropna=False)
41.0 93 46.0 81 36.0 73 51.0 71 26.0 67 21.0 62 31.0 61 39.0 55 56.0 55 61.0 23 20.0 10 Name: age, dtype: int64
For proper and better visualization
of data:
# define a function to represent the various age groups and apply to the age column
def age_update(x):
if x >= 60:
return '60 plus'
elif 55 <= x <= 59:
return '55-59'
elif 50 <= x <= 54:
return '50-54'
elif 45 <= x <= 49:
return '45-49'
elif 40 <= x <= 44:
return '40-44'
elif 35 <= x <= 39:
return '35-39'
elif 30 <= x <= 34:
return '30-34'
elif 25 <= x <= 29:
return '25-29'
else:
return 'Less than 25'
combined_updated['age_updated']= combined_updated['age'].apply(age_update)
combined_updated['age_updated'].value_counts()
35-39 128 40-44 93 45-49 81 Less than 25 72 50-54 71 25-29 67 30-34 61 55-59 55 60 plus 23 Name: age_updated, dtype: int64
Since, the cleaning process is done and the age
column has been updated, we will aggregate the age_updated
column by the different contributing factors
column.
cols=[ 'dissatisfaction_with_the_department', 'physical_work_environment',
'lack_of_recognition', 'lack_of_job_security', 'work_location',
'employment_conditions', 'work_life_balance',
'workload','DETE-TAFE Combined job_dissatisfaction','Contributing Factors. Dissatisfaction']
# create a pivot table to aggregate data
age_group = combined_updated.pivot_table(values = cols, index = 'age_updated')
age_group
Contributing Factors. Dissatisfaction | DETE-TAFE Combined job_dissatisfaction | dissatisfaction_with_the_department | employment_conditions | lack_of_job_security | lack_of_recognition | physical_work_environment | work_life_balance | work_location | workload | |
---|---|---|---|---|---|---|---|---|---|---|
age_updated | ||||||||||
25-29 | 0.059701 | 0.194030 | 0.044776 | 0.074627 | 0.029851 | 0.089552 | 0.029851 | 0.074627 | 0.074627 | 0.059701 |
30-34 | 0.081967 | 0.180328 | 0.065574 | 0.032787 | 0.000000 | 0.032787 | 0.000000 | 0.131148 | 0.049180 | 0.016393 |
35-39 | 0.132812 | 0.132812 | 0.031250 | 0.023438 | 0.007812 | 0.015625 | 0.015625 | 0.062500 | 0.015625 | 0.031250 |
40-44 | 0.075269 | 0.129032 | 0.032258 | 0.043011 | 0.021505 | 0.053763 | 0.010753 | 0.129032 | 0.043011 | 0.021505 |
45-49 | 0.074074 | 0.148148 | 0.037037 | 0.037037 | 0.061728 | 0.049383 | 0.000000 | 0.111111 | 0.012346 | 0.037037 |
50-54 | 0.098592 | 0.239437 | 0.084507 | 0.028169 | 0.042254 | 0.056338 | 0.000000 | 0.140845 | 0.000000 | 0.056338 |
55-59 | 0.072727 | 0.072727 | 0.072727 | 0.018182 | 0.018182 | 0.072727 | 0.018182 | 0.127273 | 0.000000 | 0.090909 |
60 plus | 0.000000 | 0.260870 | 0.043478 | 0.086957 | 0.000000 | 0.043478 | 0.000000 | 0.130435 | 0.086957 | 0.130435 |
Less than 25 | 0.069444 | 0.152778 | 0.013889 | 0.013889 | 0.000000 | 0.069444 | 0.000000 | 0.083333 | 0.013889 | 0.013889 |
age_group.plot(kind = 'bar', rot = 30, figsize = (20,10), colormap = 'Paired').legend(bbox_to_anchor = (0.5,1))
<matplotlib.legend.Legend at 0x2af9bc8d948>
Over 25% of the older employees resign because of general dissatisfaction with their jobs, the trend is thesame for the younger employees too.
As earlier stated in the introduction of this project, we have been able to answer the posed questions:
general job dissatisfaction
and work_life_balance
are the major contributing factors for both employees with few(New
) and longer(Veteran
and Established
) years of service.general job dissatisfaction
is the major contributing factor for both employees with few and longer years of service.For the DETE institue:
General dissatisfaction
, Workload
and Work_balance_life
General dissatisfaction
and Workload
For the TAFE institute:
Both old and young employees resign due to General dissatisfaction
with their jobs