In this guided project, we'll 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.
Our aim is to clean the dataset and analyze the data. Let us preview a few columns from our dataset which has 2 csv
files, dete_survey.csv
and tafe_survey.csv
.
dete_survey.csv
¶ID
- An id used to identify the participant of the surveySeparationType
- The reason why the person's employment endedCease Date
- The year or month the person's employment endedDETE Start Date
- The year the person began employment with the DETEtafe_survey.csv
¶Record ID
- An id used to identify the participant of the surveyReason for ceasing employment
- The reason why the person's employment endedLengthofServiceOverall. Overall Length of Service at Institute (in years)
: The length of the person's employment (in years)Our goal is to clean the dataset and answer the following:
Are employees who have only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been at the job longer?
Are younger employees resigning due to some kind of dissatisfaction? What about older employees?
import numpy as np
import pandas as pd
import matplotlib.style as style
style.use('ggplot')
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
dete_survey.info()
print("\n\n First 5 rows in DETE \n")
dete_survey[:5]
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): ID 822 non-null int64 SeparationType 822 non-null object Cease Date 822 non-null object DETE Start Date 822 non-null object Role Start Date 822 non-null object Position 817 non-null object Classification 455 non-null object Region 822 non-null object Business Unit 126 non-null object Employment Status 817 non-null object Career move to public sector 822 non-null bool Career move to private sector 822 non-null bool Interpersonal conflicts 822 non-null bool Job dissatisfaction 822 non-null bool Dissatisfaction with the department 822 non-null bool Physical work environment 822 non-null bool Lack of recognition 822 non-null bool Lack of job security 822 non-null bool Work location 822 non-null bool Employment conditions 822 non-null bool Maternity/family 822 non-null bool Relocation 822 non-null bool Study/Travel 822 non-null bool Ill Health 822 non-null bool Traumatic incident 822 non-null bool Work life balance 822 non-null bool Workload 822 non-null bool None of the above 822 non-null bool Professional Development 808 non-null object Opportunities for promotion 735 non-null object Staff morale 816 non-null object Workplace issue 788 non-null object Physical environment 817 non-null object Worklife balance 815 non-null object Stress and pressure support 810 non-null object Performance of supervisor 813 non-null object Peer support 812 non-null object Initiative 813 non-null object Skills 811 non-null object Coach 767 non-null object Career Aspirations 746 non-null object Feedback 792 non-null object Further PD 768 non-null object Communication 814 non-null object My say 812 non-null object Information 816 non-null object Kept informed 813 non-null object Wellness programs 766 non-null object Health & Safety 793 non-null object Gender 798 non-null object Age 811 non-null object Aboriginal 16 non-null object Torres Strait 3 non-null object South Sea 7 non-null object Disability 23 non-null object NESB 32 non-null object dtypes: bool(18), int64(1), object(37) memory usage: 258.6+ KB First 5 rows in DETE
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
tafe_survey.info()
print("\n\n First 5 rows in TAFE \n")
tafe_survey[:5]
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 72 columns): Record ID 702 non-null float64 Institute 702 non-null object WorkArea 702 non-null object CESSATION YEAR 695 non-null float64 Reason for ceasing employment 701 non-null object Contributing Factors. Career Move - Public Sector 437 non-null object Contributing Factors. Career Move - Private Sector 437 non-null object Contributing Factors. Career Move - Self-employment 437 non-null object Contributing Factors. Ill Health 437 non-null object Contributing Factors. Maternity/Family 437 non-null object Contributing Factors. Dissatisfaction 437 non-null object Contributing Factors. Job Dissatisfaction 437 non-null object Contributing Factors. Interpersonal Conflict 437 non-null object Contributing Factors. Study 437 non-null object Contributing Factors. Travel 437 non-null object Contributing Factors. Other 437 non-null object Contributing Factors. NONE 437 non-null object Main Factor. Which of these was the main factor for leaving? 113 non-null object InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 609 non-null object WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] 608 non-null object WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 609 non-null object WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object Induction. Did you undertake Workplace Induction? 619 non-null object InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object Gender. What is your Gender? 596 non-null object CurrentAge. Current Age 596 non-null object Employment Type. Employment Type 596 non-null object Classification. Classification 596 non-null object LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(70) memory usage: 395.0+ KB First 5 rows in TAFE
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_nulls = dete_survey.isnull().sum()
dete_nulls
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
tafe_nulls = tafe_survey.isnull().sum()
tafe_nulls
Record ID 0 Institute 0 WorkArea 0 CESSATION YEAR 7 Reason for ceasing employment 1 Contributing Factors. Career Move - Public Sector 265 Contributing Factors. Career Move - Private Sector 265 Contributing Factors. Career Move - Self-employment 265 Contributing Factors. Ill Health 265 Contributing Factors. Maternity/Family 265 Contributing Factors. Dissatisfaction 265 Contributing Factors. Job Dissatisfaction 265 Contributing Factors. Interpersonal Conflict 265 Contributing Factors. Study 265 Contributing Factors. Travel 265 Contributing Factors. Other 265 Contributing Factors. NONE 265 Main Factor. Which of these was the main factor for leaving? 589 InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 94 InstituteViews. Topic:2. I was given access to skills training to help me do my job better 89 InstituteViews. Topic:3. I was given adequate opportunities for personal development 92 InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 94 InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 87 InstituteViews. Topic:6. The organisation recognised when staff did good work 95 InstituteViews. Topic:7. Management was generally supportive of me 88 InstituteViews. Topic:8. Management was generally supportive of my team 94 InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 92 InstituteViews. Topic:10. Staff morale was positive within the Institute 100 InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 101 InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 105 ... WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 91 WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 96 WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 92 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 93 WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 99 WorkUnitViews. Topic:30. Staff morale was positive within my work unit 96 Induction. Did you undertake Workplace Induction? 83 InductionInfo. Topic:Did you undertake a Corporate Induction? 270 InductionInfo. Topic:Did you undertake a Institute Induction? 219 InductionInfo. Topic: Did you undertake Team Induction? 262 InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 147 InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 147 InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 147 InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 172 InductionInfo. On-line Topic:Did you undertake a Institute Induction? 147 InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 149 InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 147 InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 147 InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 147 Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 94 Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 108 Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 115 Workplace. Topic:Does your workplace value the diversity of its employees? 116 Workplace. Topic:Would you recommend the Institute as an employer to others? 121 Gender. What is your Gender? 106 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
From the above data, we can observe the following:
DETE Start Date
and Role Start Date
have invalid data 'Not Stated'
included which may denote that the values are missing.tafe_survey
and dete_survey
dataframes.dete_survey
and tafe_survey
dataframes contain many columns that we don't need to complete our analysis.As we observed earlier, the dete_survey
has Not Stated
values in Start Date
and Role Start Date
columns. Let us change those values and read them as NaN
.
dete_survey = pd.read_csv('dete_survey.csv', na_values='Not Stated')
dete_survey[:5]
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 56 columns
Now, lets proceed to remove the columns from the dataframes that we won't be using in our analysis.
# Drop unwanted columns from dete_survey
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)
print(dete_survey_updated.shape)
print(dete_survey.shape)
(822, 35) (822, 56)
# Drop unwanted columns from tafe_survey
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis=1)
print(tafe_survey_updated.shape)
print(tafe_survey.shape)
(702, 23) (702, 72)
We confirm that the columns have been dropped from both the dataframes, as seen in the results cells above.
We want to combine the dataframes into one and hence it is a best practise to standardize the column names and make it uniform across both the dataframes.
Let's start by renaming the columns in dete_survey_updated
dataframe.
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.replace(' ','_').str.strip()
dete_survey_updated[:0]
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 rows × 35 columns
Now, let's rename the column names in tafe_survey
dataframe
columns_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_rename, axis=1)
tafe_survey_updated[:0]
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 rows × 23 columns
We are going to clean the dataframe further and remove the unwanted data.
Let's have a close look at the separationtype
column in both the dataframes
dete_survey_updated["separationtype"].head()
0 Ill Health Retirement 1 Voluntary Early Retirement (VER) 2 Voluntary Early Retirement (VER) 3 Resignation-Other reasons 4 Age Retirement Name: separationtype, dtype: object
tafe_survey_updated["separationtype"].head()
0 Contract Expired 1 Retirement 2 Retirement 3 Resignation 4 Resignation Name: separationtype, dtype: object
We can notoce that though the data may imply the same meaning, the values are not different. For example, Retirement
and Resignation
in tafe_survey
and the values in dete_survey
are string like Resignation-Other reasons
, Age Retirement
etc.
In this analysis, we are only concerned with the Resignation
separation type. Let's proceed to clean and filter separationtype
column.
# review the unique values
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
# review the unique values
tafe_survey_updated["separationtype"].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
We have 3 resignation types in dete_survey_updated
as seen from the above result cell.
Resignation-Other reasons
Resignation-Other employer
Resignation-Move overseas/interstate
From the values above, we only want to keep the data preceeding the -
sign.
dete_survey_updated['separationtype'] = dete_survey_updated["separationtype"].str.split('-').str[0]
# review the unique values
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
# copy results with separationtype = 'Resignation' to a new dataframe
dete_resignations = dete_survey_updated[dete_survey_updated['separationtype']=='Resignation'].copy()
tafe_resignations = tafe_survey_updated[tafe_survey_updated["separationtype"]=='Resignation'].copy()
# confirm that the copy was successful
dete_resignations.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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 4 | Resignation | 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 |
5 | 6 | Resignation | 05/2012 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | False | False | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
8 | 9 | Resignation | 07/2012 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
9 | 10 | Resignation | 2012 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | ... | False | False | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
11 | 12 | Resignation | 2012 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | ... | False | False | False | Male | 31-35 | NaN | NaN | NaN | NaN | NaN |
5 rows × 35 columns
In this step, let us verify and clean the years in cease_year
and dete_start_date
. First, let's view the unique values.
print("cease date in dete resignations \n", dete_resignations['cease_date'].value_counts())
print("dete start date in dete resignations \n",dete_resignations['dete_start_date'].value_counts())
cease date in dete resignations 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 07/2012 1 2010 1 09/2010 1 07/2006 1 Name: cease_date, dtype: int64 dete start date in dete resignations 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
There are month/year values in cease_date
column and the values in dete_start_date
are in float values. We will remove the inconsistencies and extract the year values and make both the columns to represent the same type of data(float
).
# extract year from cease_date values in dete_resignations and convert to float
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.split('/').str[-1].astype("float")
print("uniques in cease date - dete resignations \n")
dete_resignations['cease_date'].value_counts().sort_index(ascending=True)
uniques in cease date - dete resignations
2006.0 1 2010.0 2 2012.0 129 2013.0 146 2014.0 22 Name: cease_date, dtype: int64
print("uniques in dete_start_date - dete resignations \n")
dete_resignations['dete_start_date'].value_counts().sort_index(ascending=True)
uniques in dete_start_date - dete resignations
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
print("uniques in cease_date - tafe resignations\n")
tafe_resignations['cease_date'].value_counts().sort_index(ascending=True)
uniques in cease_date - tafe resignations
2009.0 2 2010.0 68 2011.0 116 2012.0 94 2013.0 55 Name: cease_date, dtype: int64
Now we have the columns cease_data
in both tafe_resignation
and dete_resignations
and dete_start_date
in dete_resignations
holding the consistent values
%matplotlib inline
dete_resignations["cease_date"].plot(kind="box", ylim=(2005,2015))
<matplotlib.axes._subplots.AxesSubplot at 0x7fb195ff8f60>
tafe_resignations["cease_date"].plot(kind="box", ylim=(2005,2015))
<matplotlib.axes._subplots.AxesSubplot at 0x7fb195ffe278>
We can verify that there are no major issues with the years in the dataframes. If we recall our end goal of the analysis, we have to answer if the employees worked for short period of time due to any kind of dissatisfaction. Let's jump straight into analysing that.
We already renamed service
column in tafe_resignations
to institute_service
but we do not have a cooresponding column in dete_resignations
. Let's go ahead and create one.
dete_resignations["institute_service"] = dete_resignations["cease_date"] - dete_resignations["dete_start_date"]
dete_resignations[:5]
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | workload | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | institute_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 4 | Resignation | 2012.0 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN | 7.0 |
5 | 6 | Resignation | 2012.0 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | False | Female | 41-45 | NaN | NaN | NaN | NaN | NaN | 18.0 |
8 | 9 | Resignation | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | False | False | Female | 31-35 | NaN | NaN | NaN | NaN | NaN | 3.0 |
9 | 10 | Resignation | 2012.0 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | ... | False | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN | 15.0 |
11 | 12 | Resignation | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | ... | False | False | Male | 31-35 | NaN | NaN | NaN | NaN | NaN | 3.0 |
5 rows × 36 columns
Next, we'll identify any employees who resigned because they were dissatisfied. We have Contributing Factors. Dissatisfaction
and Contributing Factors. Job Dissatisfaction
columns in tafe_survey
. Let's explore more.
print(tafe_resignations["Contributing Factors. Dissatisfaction"].value_counts())
print("\n",tafe_resignations["Contributing Factors. Job Dissatisfaction"].value_counts())
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64 - 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
We have 55 and 62 Contributing Factors. Dissatisfaction
and Contributing Factors. Job Dissatisfaction
respectively. Instead of these values, update the values to True
, False
or NaN
values. We accomplish that with the help of the function update_vals()
below.
def update_vals(value):
if pd.isnull(value):
return np.nan
elif value == "-":
return False
else:
return True
# update tafe_resignations
tafe_resignations['dissatisfied'] = tafe_resignations[['Contributing Factors. Dissatisfaction',
'Contributing Factors. Job Dissatisfaction']].applymap(update_vals).any(1, skipna=False)
tafe_resignations_up = tafe_resignations.copy()
tafe_resignations_up["dissatisfied"].value_counts()
False 241 True 91 Name: dissatisfied, dtype: int64
# dete_resignations
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()
False 162 True 149 Name: dissatisfied, dtype: int64
First, let's add a column to each dataframe that will allow us to easily distinguish between the two and then combine the datasets.
dete_resignations_up["institute"] = "DETE"
tafe_resignations_up["institute"] = "TAFE"
# combine the datasets
combined = pd.concat([dete_resignations_up,tafe_resignations_up])
# check the number of non null values
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 career_move_to_public_sector 311 employment_conditions 311 work_location 311 lack_of_job_security 311 job_dissatisfaction 311 dissatisfaction_with_the_department 311 workload 311 lack_of_recognition 311 interpersonal_conflicts 311 maternity/family 311 none_of_the_above 311 physical_work_environment 311 relocation 311 study/travel 311 traumatic_incident 311 work_life_balance 311 career_move_to_private_sector 311 ill_health 311 Contributing Factors. Career Move - Private Sector 332 Contributing Factors. Other 332 Contributing Factors. Career Move - Public Sector 332 Contributing Factors. Career Move - Self-employment 332 Contributing Factors. Travel 332 Contributing Factors. Study 332 Contributing Factors. Dissatisfaction 332 Contributing Factors. Ill Health 332 Contributing Factors. NONE 332 Contributing Factors. Maternity/Family 332 Contributing Factors. Job Dissatisfaction 332 Contributing Factors. Interpersonal Conflict 332 WorkArea 340 Institute 340 institute_service 563 gender 592 age 596 employment_status 597 position 598 cease_date 635 dissatisfied 643 id 651 separationtype 651 institute 651 dtype: int64
# drop columns with less than 300 non null values
combined_updated = combined.dropna(thresh=300, axis =1).copy()
combined_updated[:5]
Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Dissatisfaction | Contributing Factors. Ill Health | Contributing Factors. Interpersonal Conflict | Contributing Factors. Job Dissatisfaction | Contributing Factors. Maternity/Family | Contributing Factors. NONE | Contributing Factors. Other | ... | none_of_the_above | physical_work_environment | position | relocation | separationtype | study/travel | traumatic_incident | work_life_balance | work_location | workload | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | False | False | Teacher | False | Resignation | False | False | False | False | False |
5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | False | False | Guidance Officer | False | Resignation | False | False | False | False | False |
8 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | False | False | Teacher | False | Resignation | False | False | False | False | False |
9 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | False | False | Teacher Aide | False | Resignation | False | False | False | False | False |
11 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | False | False | Teacher | True | Resignation | False | False | False | False | False |
5 rows × 42 columns
Service
column and perform initial analysis¶Now that we've combined our dataframes,we'll have to clean up the institute_service
column because it contains data in different forms.
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 9.0 14 2.0 14 7.0 13 More than 20 years 10 13.0 8 8.0 8 20.0 7 15.0 7 14.0 6 17.0 6 12.0 6 10.0 6 22.0 6 18.0 5 16.0 5 24.0 4 23.0 4 11.0 4 39.0 3 19.0 3 21.0 3 32.0 3 25.0 2 26.0 2 36.0 2 28.0 2 30.0 2 42.0 1 49.0 1 35.0 1 34.0 1 38.0 1 33.0 1 29.0 1 27.0 1 41.0 1 31.0 1 Name: institute_service, dtype: int64
To analyse data, we convert these values into categories.
# extract service years and convert to float
combined_updated["institute_service_updated"] = combined_updated["institute_service"].astype('str').str.extract(r'(\d+)')
combined_updated["institute_service_updated"] = combined_updated["institute_service_updated"].astype('float')
#verify the changes and unique values
combined_updated["institute_service_updated"].value_counts(dropna=False)
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:2: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame) from ipykernel import kernelapp as app
1.0 159 NaN 88 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 22.0 6 10.0 6 17.0 6 14.0 6 12.0 6 16.0 5 18.0 5 24.0 4 23.0 4 21.0 3 39.0 3 32.0 3 19.0 3 36.0 2 30.0 2 25.0 2 26.0 2 28.0 2 42.0 1 29.0 1 35.0 1 27.0 1 41.0 1 49.0 1 38.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service_updated, dtype: int64
We can see that there are NaN
values in the column. Let's proceed further to clean the column further
def modify_definitions(val):
if val >= 11:
return "Veteran"
elif (val >=7 and val <=10):
return "Established"
elif (val >=3 and val <=6):
return "Experienced"
elif (val <= 3):
return "New"
elif pd.isnull(val):
return np.nan
combined_updated["service_cat"] = combined_updated["institute_service_updated"].apply(modify_definitions)
# verify the changes
combined_updated["service_cat"].value_counts(dropna=False)
New 193 Experienced 172 Veteran 136 NaN 88 Established 62 Name: service_cat, dtype: int64
Now let's find the missing values in dissatisfied
column and proceed with further analysis.
# check the unique values including missing values
combined_updated["dissatisfied"].value_counts(dropna=False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
We will replace the missing values in the dissatisfied
column with the value that occurs most frequently in this column ie, False
# replace missing values with value
# that occurs most frequently in this column
combined_updated["dissatisfied"] = combined_updated["dissatisfied"].fillna(False)
combined_updated["dissatisfied"].value_counts(dropna=False)
False 411 True 240 Name: dissatisfied, dtype: int64
Next, let us create a pivot table to calculate the percentage of dissatisfied employees in each category
# create pivot table - dissatisfied by service_cat
ptable = combined_updated.pivot_table(index="service_cat", values="dissatisfied")
ptable = ptable.sort_values(by='dissatisfied',ascending=False)
ptable
dissatisfied | |
---|---|
service_cat | |
Established | 0.516129 |
Veteran | 0.485294 |
Experienced | 0.343023 |
New | 0.295337 |
%matplotlib inline
ptable.plot(kind='bar', color="green", rot=75,figsize=(10,5))
<matplotlib.axes._subplots.AxesSubplot at 0x7fb195f4ebe0>
We can observe that more people who left the job due to dissatisfaction are from the Established (51%) and Veteran (close to 49%) categories.
In other words, approx. 50% of the employees with more than 7 years of experience and 29% of employees with less than 3 years of experience resigned due to some kind of dissatisfation.
Let's figure out employees under which of the given positions were most dissatisfied of all.
## create a pivot table which displays dissatisfation
# by job position in the combined_updates
index_columns =['position']
ptable_position = combined_updated.pivot_table(index=index_columns, values="dissatisfied")
ptable_position = ptable_position.reindex(ptable_position['dissatisfied'].sort_values(ascending=False).index)
ptable_position
dissatisfied | |
---|---|
position | |
Guidance Officer | 1.000000 |
Other | 0.666667 |
Public Servant | 0.600000 |
Teacher | 0.527132 |
Executive (SES/SO) | 0.500000 |
Head of Curriculum/Head of Special Education | 0.500000 |
Cleaner | 0.487179 |
Schools Officer | 0.428571 |
Technical Officer (TO) | 0.400000 |
Teacher (including LVT) | 0.378947 |
School Administrative Staff | 0.375000 |
Teacher Aide | 0.365079 |
Technical Officer | 0.250000 |
Operational (OO) | 0.230769 |
Administration (AO) | 0.216216 |
School Based Professional Staff (Therapist, nurse, etc) | 0.200000 |
Professional Officer (PO) | 0.125000 |
Professional Officer | 0.000000 |
Tutor | 0.000000 |
Business Service Manager | 0.000000 |
Workplace Training Officer | 0.000000 |
%matplotlib inline
ptable_position.plot(kind='bar',figsize=(10,5),fontsize=12, color='green')
<matplotlib.axes._subplots.AxesSubplot at 0x7fb195ee4cc0>
From the above plot, we observe that employees who were Guidance Officers were the most dissatisfied with the job. Business Service Managers, Tutors and Workplace Training Officers showed no dissatisfaction, as per the data above.
Let's clean and aggregate the rest of data to service_cat
column to find out how many people resigned due in each stage of their career.
# let's have a look at all the column names again
print(combined_updated[:0])
Empty DataFrame Columns: [Contributing Factors. Career Move - Private Sector , Contributing Factors. Career Move - Public Sector , Contributing Factors. Career Move - Self-employment, Contributing Factors. Dissatisfaction, Contributing Factors. Ill Health, Contributing Factors. Interpersonal Conflict, Contributing Factors. Job Dissatisfaction, Contributing Factors. Maternity/Family, Contributing Factors. NONE, Contributing Factors. Other, Contributing Factors. Study, Contributing Factors. Travel, Institute, WorkArea, age, career_move_to_private_sector, career_move_to_public_sector, cease_date, dissatisfaction_with_the_department, dissatisfied, employment_conditions, employment_status, gender, id, ill_health, institute, institute_service, interpersonal_conflicts, job_dissatisfaction, lack_of_job_security, lack_of_recognition, maternity/family, none_of_the_above, physical_work_environment, position, relocation, separationtype, study/travel, traumatic_incident, work_life_balance, work_location, workload, institute_service_updated, service_cat] Index: [] [0 rows x 44 columns]
Let's us analyse how many people in each career stage resigned due to some kind of dissatisfaction. Here, we are going to consider the dissatisfaction factors considered under DETE institute which are the following columns:
job_dissatisfaction,dissatisfaction_with_the_department,physical_work_environment,lack_of_recognition, lack_of_job_security,work_location,employment_conditions, work_life_balance,workload
To proceed, firstly we find the missing values, clean them and then aggregate them with service_cat
column.
Let's go ahead and clean the data in each column.
# check the missing values
print("Missing values in Job dissatisfaction\n",combined_updated["job_dissatisfaction"].value_counts(dropna=False))
# replace missing values with 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)
Missing values in Job dissatisfaction NaN 340 False 270 True 41 Name: job_dissatisfaction, dtype: int64
False 610 True 41 Name: job_dissatisfaction, dtype: int64
print(" \n\nMissing values in dissatisfaction_with_the_department\n",combined_updated["dissatisfaction_with_the_department"].value_counts(dropna=False))
combined_updated["dissatisfaction_with_the_department"]= combined_updated["dissatisfaction_with_the_department"].fillna(False)
combined_updated["dissatisfaction_with_the_department"].value_counts(dropna=False)
Missing values in dissatisfaction_with_the_department NaN 340 False 282 True 29 Name: dissatisfaction_with_the_department, dtype: int64
False 622 True 29 Name: dissatisfaction_with_the_department, dtype: int64
print(" \n\nMissing values in physical_work_environment\n",combined_updated["physical_work_environment"].value_counts(dropna=False))
combined_updated["physical_work_environment"]= combined_updated["physical_work_environment"].fillna(False)
combined_updated["physical_work_environment"].value_counts(dropna=False)
Missing values in physical_work_environment NaN 340 False 305 True 6 Name: physical_work_environment, dtype: int64
False 645 True 6 Name: physical_work_environment, dtype: int64
print(" \n\nMissing values in lack_of_recognition\n",combined_updated["lack_of_recognition"].value_counts(dropna=False))
combined_updated["lack_of_recognition"]= combined_updated["lack_of_recognition"].fillna(False)
combined_updated["lack_of_recognition"].value_counts(dropna=False)
Missing values in lack_of_recognition NaN 340 False 278 True 33 Name: lack_of_recognition, dtype: int64
False 618 True 33 Name: lack_of_recognition, dtype: int64
print(" \n\nMissing values in lack_of_job_security\n",combined_updated["lack_of_job_security"].value_counts(dropna=False))
combined_updated["lack_of_job_security"]= combined_updated["lack_of_job_security"].fillna(False)
combined_updated["lack_of_job_security"].value_counts(dropna=False)
Missing values in lack_of_job_security NaN 340 False 297 True 14 Name: lack_of_job_security, dtype: int64
False 637 True 14 Name: lack_of_job_security, dtype: int64
print(" \n\nMissing values in work_location\n",combined_updated["work_location"].value_counts(dropna=False))
combined_updated["work_location"]= combined_updated["work_location"].fillna(False)
combined_updated["work_location"].value_counts(dropna=False)
Missing values in work_location NaN 340 False 293 True 18 Name: work_location, dtype: int64
False 633 True 18 Name: work_location, dtype: int64
print(" \n\nMissing values in employment_conditions\n",combined_updated["employment_conditions"].value_counts(dropna=False))
combined_updated["employment_conditions"]= combined_updated["employment_conditions"].fillna(False)
combined_updated["employment_conditions"].value_counts(dropna=False)
Missing values in employment_conditions NaN 340 False 288 True 23 Name: employment_conditions, dtype: int64
False 628 True 23 Name: employment_conditions, dtype: int64
print(" \n\nMissing values in work_life_balance\n",combined_updated["work_life_balance"].value_counts(dropna=False))
combined_updated["work_life_balance"]= combined_updated["work_life_balance"].fillna(False)
combined_updated["work_life_balance"].value_counts(dropna=False)
Missing values in work_life_balance NaN 340 False 243 True 68 Name: work_life_balance, dtype: int64
False 583 True 68 Name: work_life_balance, dtype: int64
print(" \n\nMissing values in workload\n",combined_updated["workload"].value_counts(dropna=False))
combined_updated["workload"]= combined_updated["workload"].fillna(False)
combined_updated["workload"].value_counts(dropna=False)
Missing values in workload NaN 340 False 284 True 27 Name: workload, dtype: int64
False 624 True 27 Name: workload, dtype: int64
# Contributing Factors. Job Dissatisfaction - TAFE
print(" \n\nMissing values in Contributing Factors. Job Dissatisfaction\n",combined_updated["Contributing Factors. Job Dissatisfaction"].value_counts(dropna=False))
print(" \n\nMissing values in Contributing Factors. Dissatisfaction\n",combined_updated["Contributing Factors. Dissatisfaction"].value_counts(dropna=False))
# update the missing values using the `update_vals()` we created earlier
combined_updated[['Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Dissatisfaction']] = (
combined_updated[['Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Dissatisfaction']]
.applymap(update_vals))
combined_updated['Contributing Factors. Job Dissatisfaction']= combined_updated["Contributing Factors. Job Dissatisfaction"].fillna(False)
combined_updated['Contributing Factors. Dissatisfaction']= combined_updated["Contributing Factors. Dissatisfaction"].fillna(False)
# check the values
print('\n\n Cleaned column Contributing Factors. Job Dissatisfaction \n')
print(combined_updated['Contributing Factors. Job Dissatisfaction'].value_counts(dropna=False))
print('\n\n Cleaned column Contributing Factors. Dissatisfaction \n')
print(combined_updated['Contributing Factors. Dissatisfaction'].value_counts(dropna=False))
Missing values in Contributing Factors. Job Dissatisfaction NaN 319 - 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64 Missing values in Contributing Factors. Dissatisfaction NaN 319 - 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64 Cleaned column Contributing Factors. Job Dissatisfaction False 589 True 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64 Cleaned column Contributing Factors. Dissatisfaction False 596 True 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
Columns Contributing Factors. Job Dissatisfaction
and job_dissatisfaction
represent job dissatisfaction in TAFE and DETE institutes respectively. We can combine both to on single column and proceed with our analysis.
#
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 service_cat
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 = dissatisfaction_result.sort_values(by=cols,ascending=False)
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 |
dissatisfaction_result.plot(kind='bar', figsize=(15,10),colormap='Paired').legend(bbox_to_anchor=(0.8, 1))
<matplotlib.legend.Legend at 0x7fb195e379b0>
The chart above shows the results of aggregation of dissatisfaction factors among the employees in DETE and TAFE institutes by service category.
From the plot, we can say that lack of *work-life balance* is one of the major reasons for dissatisfaction with the job that 18% of *Established* employees in DETE resigned followed by 13% of the *New* employees were dissatisfied with their job as represented by the *Contributing Factors. Job Dissatisfaction* factor.
In general by looking at the chart, it is clear that the more *Established* employees resigned due to some kind of dissatisfaction and this supports previous analysis here.
# check the values
combined_updated["institute"].value_counts(dropna=False)
TAFE 340 DETE 311 Name: institute, dtype: int64
# create pivot table - dissatisfaction by institute
ptable_institute= combined_updated.pivot_table(index=['institute'], values="dissatisfied")
ptable_institute
dissatisfied | |
---|---|
institute | |
DETE | 0.479100 |
TAFE | 0.267647 |
ptable_institute.plot(kind='bar',figsize=(10,5),rot=75,color='green')
<matplotlib.axes._subplots.AxesSubplot at 0x7fb195c83358>
From a glance at the chart above, it shows that higher number of employees in DETE institute resigned due to dissatisfaction of some kind. However, recall that the factors leading to resignation due to dissatisfaction in DETE were more than those considered for TAFE, and are as follows:
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
tafe_resignations:
Contributing Factors. Dissatisfaction
Contributing Factors. Job Dissatisfaction
Since there are more factors to dissatisfaction in DETE, the survey results in the above chart represents resignation due to dissatisfation employees in DETE institute.
Let's analyse and find out employees of what age resigned due to some kind of dissatisfaction.
To proceed, let's first check the age
column and it's values.
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 36 40 32 26 30 32 31 35 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
We can notice that there are missing values and the data representation is inconsistent and we need to clean the data in the column. Let's extract the age values and to make it consistent, cast all to type float
.
# extract age values and convert to float
combined_updated["age_updated"] = combined_updated["age"].astype('str').str.extract(r'(\d+)')
combined_updated["age_updated"] = combined_updated["age_updated"].astype('float')
# #verify the changes and unique values
combined_updated["age_updated"].value_counts(dropna=True)
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:2: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame) from ipykernel import kernelapp as app
41.0 93 46.0 81 36.0 73 51.0 71 26.0 67 21.0 62 31.0 61 56.0 55 61.0 23 20.0 10 Name: age_updated, dtype: int64
def update_age(age):
if age >=60:
return '60 and above'
elif (age>=55)and(age<60):
return '55-59'
elif (age>=50)and(age<55):
return '50-54'
elif (age>=45)and(age<50):
return '45-49'
elif (age>=40)and(age<45):
return '40-44'
elif (age>=35)and(age<40):
return '35-39'
elif (age>=30)and(age<35):
return '30-34'
elif (age>=25)and(age<30):
return '25-29'
elif age<25:
return '25 and less'
combined_updated['age_updated']= combined_updated['age_updated'].apply(update_age)
combined_updated['age_updated'].value_counts()
40-44 93 45-49 81 35-39 73 25 and less 72 50-54 71 25-29 67 30-34 61 55-59 55 60 and above 23 Name: age_updated, dtype: int64
# create pivot table - age by dissatisfaction
age_table = combined_updated.pivot_table(index='age_updated', values='dissatisfied')
age_table = age_table.sort_values(by='dissatisfied', ascending=False)
age_table
dissatisfied | |
---|---|
age_updated | |
60 and above | 0.521739 |
50-54 | 0.422535 |
25-29 | 0.417910 |
45-49 | 0.382716 |
55-59 | 0.381818 |
30-34 | 0.377049 |
40-44 | 0.376344 |
35-39 | 0.342466 |
25 and less | 0.291667 |
age_table.plot(kind='bar', figsize=(10,5), color='green', rot=75)
<matplotlib.axes._subplots.AxesSubplot at 0x7fb195c2cd30>
52% of employees aged above 60 years resigned due to dissatisfation of some kind, followed by 42% of the employees in the age group 50-54.
To analyse even further, let us find the dissatisfaction levels by gender and age group.
col =['gender','age_updated']
age_gender = combined_updated.pivot_table(index= col, values='dissatisfied')
age_gender = age_gender.sort_values(by='dissatisfied', ascending=False)
age_gender
dissatisfied | ||
---|---|---|
gender | age_updated | |
Male | 60 and above | 0.750000 |
50-54 | 0.480000 | |
25-29 | 0.466667 | |
35-39 | 0.434783 | |
Female | 45-49 | 0.419355 |
60 and above | 0.416667 | |
Male | 40-44 | 0.416667 |
Female | 25-29 | 0.403846 |
Male | 55-59 | 0.400000 |
30-34 | 0.400000 | |
Female | 30-34 | 0.377778 |
50-54 | 0.377778 | |
55-59 | 0.371429 | |
40-44 | 0.362319 | |
25 and less | 0.346154 | |
35-39 | 0.300000 | |
Male | 45-49 | 0.277778 |
25 and less | 0.150000 |
age_gender.plot(kind='bar', figsize=(10,5), color='green', rot=75)
<matplotlib.axes._subplots.AxesSubplot at 0x7fb195cf6be0>
Clearly, 75% of the male employees aged above 60 years resigned because of some kind of dissatisfaction at work, followed by 48% of male with the age group of 50-54.
In a broader view, Male employees aged above 35 except those in age range 55-59, resigned due to dissatisfaction when compared to their Female counterparts.
age_cat = combined_updated.pivot_table(index=['service_cat','age_updated'], values='dissatisfied')
age_cat = age_cat.sort_values(by='dissatisfied', ascending=False)
age_cat
dissatisfied | ||
---|---|---|
service_cat | age_updated | |
Established | 30-34 | 0.750000 |
40-44 | 0.666667 | |
Veteran | 60 and above | 0.642857 |
50-54 | 0.600000 | |
Established | 25-29 | 0.545455 |
Veteran | 45-49 | 0.545455 |
Established | 50-54 | 0.500000 |
60 and above | 0.500000 | |
Experienced | 60 and above | 0.500000 |
Veteran | 40-44 | 0.500000 |
Experienced | 25-29 | 0.444444 |
40-44 | 0.413793 | |
Established | 35-39 | 0.400000 |
Veteran | 30-34 | 0.400000 |
New | 55-59 | 0.400000 |
Veteran | 55-59 | 0.387097 |
Experienced | 35-39 | 0.380952 |
New | 45-49 | 0.368421 |
Established | 45-49 | 0.363636 |
New | 35-39 | 0.347826 |
Established | 55-59 | 0.333333 |
New | 25-29 | 0.320000 |
50-54 | 0.318182 | |
Experienced | 30-34 | 0.300000 |
25 and less | 0.291667 | |
45-49 | 0.285714 | |
Veteran | 35-39 | 0.285714 |
New | 25 and less | 0.272727 |
Experienced | 55-59 | 0.250000 |
New | 30-34 | 0.250000 |
Experienced | 50-54 | 0.250000 |
New | 40-44 | 0.233333 |
Established | 25 and less | 0.000000 |
%matplotlib inline
age_cat.plot(kind='bar', figsize=(20,10), color='green')
<matplotlib.axes._subplots.AxesSubplot at 0x7fb195915400>
75% of the *Established* employees within the age group *30-34* with 7 to 10 years of experience, 64% of *Veterans* with 11 or more years of experience aged above 60, reported more dissatisfation during their exit survery. Also, taking a closer look at the data shows that we can say that employees who have been employed at the institutes for more than 7 years were more likely to resign due to some kind of dissatisfaction.
However, establised employees who were less than 25 years of age reported zero dissatisfaction.
Let's further breakdown the analysis and plot each dissatisfaction factor in consideration against age
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_age_result = combined_updated.pivot_table(index='age_updated', values=cols)
dissatisfaction_age_result = dissatisfaction_age_result.sort_index(ascending=False)
dissatisfaction_age_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 | |
---|---|---|---|---|---|---|---|---|---|---|
age_updated | ||||||||||
60 and above | 0.000000 | 0.260870 | 0.043478 | 0.086957 | 0.000000 | 0.043478 | 0.000000 | 0.130435 | 0.086957 | 0.130435 |
55-59 | 0.072727 | 0.072727 | 0.072727 | 0.018182 | 0.018182 | 0.072727 | 0.018182 | 0.127273 | 0.000000 | 0.090909 |
50-54 | 0.098592 | 0.239437 | 0.084507 | 0.028169 | 0.042254 | 0.056338 | 0.000000 | 0.140845 | 0.000000 | 0.056338 |
45-49 | 0.074074 | 0.148148 | 0.037037 | 0.037037 | 0.061728 | 0.049383 | 0.000000 | 0.111111 | 0.012346 | 0.037037 |
40-44 | 0.075269 | 0.129032 | 0.032258 | 0.043011 | 0.021505 | 0.053763 | 0.010753 | 0.129032 | 0.043011 | 0.021505 |
35-39 | 0.068493 | 0.136986 | 0.054795 | 0.041096 | 0.013699 | 0.027397 | 0.027397 | 0.109589 | 0.027397 | 0.054795 |
30-34 | 0.081967 | 0.180328 | 0.065574 | 0.032787 | 0.000000 | 0.032787 | 0.000000 | 0.131148 | 0.049180 | 0.016393 |
25-29 | 0.059701 | 0.194030 | 0.044776 | 0.074627 | 0.029851 | 0.089552 | 0.029851 | 0.074627 | 0.074627 | 0.059701 |
25 and less | 0.069444 | 0.152778 | 0.013889 | 0.013889 | 0.000000 | 0.069444 | 0.000000 | 0.083333 | 0.013889 | 0.013889 |
%matplotlib inline
dissatisfaction_age_result.plot(kind='bar',figsize=(20,10), colormap='Paired').legend(bbox_to_anchor=(0.7, 1))
<matplotlib.legend.Legend at 0x7fb195680780>
We notice that 26% of the employees above 60 years of age felt dissatisfied at work. If we take a closer look at the data, it can be noted that one of the highest reasons of dissatisfaction is dissatisfaction with the job across all the age groups, which backs our analysis earlier here.
In this project we analysed the exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia to figure out the the reason for resignation being dissatisfaction of some kind amoung the employees.
We can conclude that employees who have worked longer in the institutes were prone to resign due to some kind of dissatisfaction than those who worked for shorter periods of time. About 30% of the new employees and more than 50% of the established employees resigned due to some dissatisfaction.
It is also notable that employees who were older ie, above the age of 60 and those between 50 and 54 years of age citied more dissatisfacation. And 42% of the younger employees between 25-29 years resigned due to dissatisfaction. Our data also shows employees in the age group 30-34 and 40-44 also have experienced higher rates of dissatisfaction. Therefore, we cannot assert that the age is directly proportional to the dissatisfaction experienced by the employees.