In this guided project, we'll work with exit surveys from employees of [the Department of Education, Training and Employment](https://en.wikipedia.org/wiki/Department_of_Education_(Queensland) (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, we'll try to answer the following questions:
Are employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been there longer?
Are younger employees resigning due to some kind of dissatisfaction? What about older employees?
Below is a preview of a couple columns we'll work with from the *dete_survey.csv*:
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*:
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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
# read csv files
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
# general information on dete file:
dete_survey.info()
dete_survey.head()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 822 non-null int64 1 SeparationType 822 non-null object 2 Cease Date 822 non-null object 3 DETE Start Date 822 non-null object 4 Role Start Date 822 non-null object 5 Position 817 non-null object 6 Classification 455 non-null object 7 Region 822 non-null object 8 Business Unit 126 non-null object 9 Employment Status 817 non-null object 10 Career move to public sector 822 non-null bool 11 Career move to private sector 822 non-null bool 12 Interpersonal conflicts 822 non-null bool 13 Job dissatisfaction 822 non-null bool 14 Dissatisfaction with the department 822 non-null bool 15 Physical work environment 822 non-null bool 16 Lack of recognition 822 non-null bool 17 Lack of job security 822 non-null bool 18 Work location 822 non-null bool 19 Employment conditions 822 non-null bool 20 Maternity/family 822 non-null bool 21 Relocation 822 non-null bool 22 Study/Travel 822 non-null bool 23 Ill Health 822 non-null bool 24 Traumatic incident 822 non-null bool 25 Work life balance 822 non-null bool 26 Workload 822 non-null bool 27 None of the above 822 non-null bool 28 Professional Development 808 non-null object 29 Opportunities for promotion 735 non-null object 30 Staff morale 816 non-null object 31 Workplace issue 788 non-null object 32 Physical environment 817 non-null object 33 Worklife balance 815 non-null object 34 Stress and pressure support 810 non-null object 35 Performance of supervisor 813 non-null object 36 Peer support 812 non-null object 37 Initiative 813 non-null object 38 Skills 811 non-null object 39 Coach 767 non-null object 40 Career Aspirations 746 non-null object 41 Feedback 792 non-null object 42 Further PD 768 non-null object 43 Communication 814 non-null object 44 My say 812 non-null object 45 Information 816 non-null object 46 Kept informed 813 non-null object 47 Wellness programs 766 non-null object 48 Health & Safety 793 non-null object 49 Gender 798 non-null object 50 Age 811 non-null object 51 Aboriginal 16 non-null object 52 Torres Strait 3 non-null object 53 South Sea 7 non-null object 54 Disability 23 non-null object 55 NESB 32 non-null object dtypes: bool(18), int64(1), object(37) memory usage: 258.6+ KB
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | 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
# general information on tafe file:
tafe_survey.info()
tafe_survey.head()
<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
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
# NaN values in dete file:
dete_survey.isnull().sum().sort_values()
ID 0 Workload 0 Work life balance 0 Traumatic incident 0 Ill Health 0 Study/Travel 0 Relocation 0 Maternity/family 0 Employment conditions 0 Work location 0 Lack of job security 0 Lack of recognition 0 Physical work environment 0 Dissatisfaction with the department 0 None of the above 0 Interpersonal conflicts 0 Job dissatisfaction 0 SeparationType 0 Cease Date 0 DETE Start Date 0 Role Start Date 0 Career move to public sector 0 Career move to private sector 0 Region 0 Position 5 Employment Status 5 Physical environment 5 Information 6 Staff morale 6 Worklife balance 7 Communication 8 Kept informed 9 Initiative 9 Performance of supervisor 9 My say 10 Peer support 10 Age 11 Skills 11 Stress and pressure support 12 Professional Development 14 Gender 24 Health & Safety 29 Feedback 30 Workplace issue 34 Further PD 54 Coach 55 Wellness programs 56 Career Aspirations 76 Opportunities for promotion 87 Classification 367 Business Unit 696 NESB 790 Disability 799 Aboriginal 806 South Sea 815 Torres Strait 819 dtype: int64
# NaN values in tafe file:
tafe_survey.isnull().sum().sort_values()
Record ID 0 Institute 0 WorkArea 0 Reason for ceasing employment 1 CESSATION YEAR 7 ... Contributing Factors. Career Move - Public Sector 265 Contributing Factors. Maternity/Family 265 Contributing Factors. Ill Health 265 InductionInfo. Topic:Did you undertake a Corporate Induction? 270 Main Factor. Which of these was the main factor for leaving? 589 Length: 72, dtype: int64
# explore years of cessation
tafe_survey['CESSATION YEAR'].value_counts()
2011.0 268 2012.0 235 2010.0 103 2013.0 85 2009.0 4 Name: CESSATION YEAR, dtype: int64
# understand reasons for ceasing employment
tafe_survey['Reason for ceasing employment'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: Reason for ceasing employment, dtype: int64
Conclusion:
DETE
TAFE
We will update our datasets, by reading our csv files again and specifying "Not Stated" as Nan and then dropping some columns.
dete_survey = pd.read_csv('dete_survey.csv', na_values='Not Stated')
#drop columns in dete df
dete_survey_updated = dete_survey.drop(columns=dete_survey.columns[28:49], axis=1)
#check updated df
dete_survey_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 35 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 822 non-null int64 1 SeparationType 822 non-null object 2 Cease Date 788 non-null object 3 DETE Start Date 749 non-null float64 4 Role Start Date 724 non-null float64 5 Position 817 non-null object 6 Classification 455 non-null object 7 Region 717 non-null object 8 Business Unit 126 non-null object 9 Employment Status 817 non-null object 10 Career move to public sector 822 non-null bool 11 Career move to private sector 822 non-null bool 12 Interpersonal conflicts 822 non-null bool 13 Job dissatisfaction 822 non-null bool 14 Dissatisfaction with the department 822 non-null bool 15 Physical work environment 822 non-null bool 16 Lack of recognition 822 non-null bool 17 Lack of job security 822 non-null bool 18 Work location 822 non-null bool 19 Employment conditions 822 non-null bool 20 Maternity/family 822 non-null bool 21 Relocation 822 non-null bool 22 Study/Travel 822 non-null bool 23 Ill Health 822 non-null bool 24 Traumatic incident 822 non-null bool 25 Work life balance 822 non-null bool 26 Workload 822 non-null bool 27 None of the above 822 non-null bool 28 Gender 798 non-null object 29 Age 811 non-null object 30 Aboriginal 16 non-null object 31 Torres Strait 3 non-null object 32 South Sea 7 non-null object 33 Disability 23 non-null object 34 NESB 32 non-null object dtypes: bool(18), float64(2), int64(1), object(14) memory usage: 123.7+ KB
#drop columns in tafe df
tafe_survey_updated = tafe_survey.drop(columns=tafe_survey.columns[17:66], axis=1)
#check updated df
tafe_survey_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 23 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Record ID 702 non-null float64 1 Institute 702 non-null object 2 WorkArea 702 non-null object 3 CESSATION YEAR 695 non-null float64 4 Reason for ceasing employment 701 non-null object 5 Contributing Factors. Career Move - Public Sector 437 non-null object 6 Contributing Factors. Career Move - Private Sector 437 non-null object 7 Contributing Factors. Career Move - Self-employment 437 non-null object 8 Contributing Factors. Ill Health 437 non-null object 9 Contributing Factors. Maternity/Family 437 non-null object 10 Contributing Factors. Dissatisfaction 437 non-null object 11 Contributing Factors. Job Dissatisfaction 437 non-null object 12 Contributing Factors. Interpersonal Conflict 437 non-null object 13 Contributing Factors. Study 437 non-null object 14 Contributing Factors. Travel 437 non-null object 15 Contributing Factors. Other 437 non-null object 16 Contributing Factors. NONE 437 non-null object 17 Gender. What is your Gender? 596 non-null object 18 CurrentAge. Current Age 596 non-null object 19 Employment Type. Employment Type 596 non-null object 20 Classification. Classification 596 non-null object 21 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object 22 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(21) memory usage: 126.3+ KB
We are going to standardize the names of columns for both datasets.
#make lowercase, remove whitespace from the end of the strings, replace spaces with underscores ('_') in DETE df
dete_survey_updated.columns = dete_survey_updated.columns.str.replace(' ','_').str.strip().str.lower()
dete_survey_updated.head(2)
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | work_life_balance | workload | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | False | False | True | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | False | False | False | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 rows × 35 columns
# dict of new names
new_names = {'Record ID': 'id',
'CESSATION YEAR': 'cease_date',
'Reason for ceasing employment': 'separationtype',
'Gender. What is your Gender?': 'gender',
'CurrentAge. Current Age': 'age',
'Employment Type. Employment Type': 'employment_status',
'Classification. Classification': 'position',
'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service',
'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'}
# rename
tafe_survey_updated.rename(columns=new_names, inplace=True)
tafe_survey_updated.head(2)
id | Institute | WorkArea | cease_date | separationtype | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | gender | age | employment_status | position | institute_service | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
2 rows × 23 columns
Conclusion
We've made some common columns names for both datesets in order to combine the data and move further in our analysis.
# value_counts for dete df separationtype column:
print(dete_survey_updated['separationtype'].value_counts(ascending=True))
print('\n')
# value_counts for tafe df separationtype column:
print(tafe_survey_updated['separationtype'].value_counts(ascending=True))
Termination 15 Contract Expired 34 Other 49 Ill Health Retirement 61 Voluntary Early Retirement (VER) 67 Resignation-Move overseas/interstate 70 Resignation-Other employer 91 Resignation-Other reasons 150 Age Retirement 285 Name: separationtype, dtype: int64 Termination 23 Transfer 25 Retirement 82 Retrenchment/ Redundancy 104 Contract Expired 127 Resignation 340 Name: separationtype, dtype: int64
# make df with only "resignation" reason
pattern = r'Resignation'
dete_resignation = dete_survey_updated[dete_survey_updated['separationtype'].str.contains(pattern)].copy()
tafe_resignation = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
print(dete_resignation['separationtype'].value_counts())
print('\n')
print(tafe_resignation['separationtype'].value_counts())
Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Name: separationtype, dtype: int64 Resignation 340 Name: separationtype, dtype: int64
We will check the cease_date and dete_start_date columns for any logical inconsistencies or errors in order to be sure our analysis make sence.
# observations of 'cease_date' column:
dete_resignation['cease_date'].value_counts(ascending=True)
2010 1 07/2006 1 09/2010 1 07/2012 1 05/2012 2 05/2013 2 08/2013 4 10/2013 6 07/2013 9 11/2013 9 09/2013 11 06/2013 14 12/2013 17 01/2014 22 2013 74 2012 126 Name: cease_date, dtype: int64
# get format of the year '0000': cleaning, dtype changing(object-string-float)
pattern =r"([1-2][0-9][0-9][0-9])"
dete_resignation['cease_date'].astype('string')
dete_resignation['cease_date'] = dete_resignation['cease_date'].str.extract(pattern)
dete_resignation['cease_date'] = dete_resignation['cease_date'].astype('float')
# check our columns with years:
print(dete_resignation['cease_date'].value_counts(ascending=True))
print('\n')
print(dete_resignation['dete_start_date'].value_counts(ascending=True))
print('\n')
print(tafe_resignation['cease_date'].value_counts(ascending=True))
2006.0 1 2010.0 2 2014.0 22 2012.0 129 2013.0 146 Name: cease_date, dtype: int64 1963.0 1 1977.0 1 1973.0 1 1975.0 1 1987.0 1 1982.0 1 1984.0 1 1972.0 1 1971.0 1 1974.0 2 1976.0 2 1983.0 2 1986.0 3 1985.0 3 2001.0 3 1995.0 4 1988.0 4 1989.0 4 1991.0 4 1997.0 5 1980.0 5 1990.0 5 1993.0 5 1994.0 6 2003.0 6 1998.0 6 1992.0 6 2002.0 6 1996.0 6 1999.0 8 2000.0 9 2013.0 10 2006.0 13 2009.0 13 2004.0 14 2005.0 15 2010.0 17 2012.0 21 2007.0 21 2008.0 22 2011.0 24 Name: dete_start_date, dtype: int64 2009.0 2 2013.0 55 2010.0 68 2012.0 94 2011.0 116 Name: cease_date, dtype: int64
# make boxplots for columns with years:
dete_resignation.boxplot(column = 'cease_date',grid = False)
plt.show()
dete_resignation.boxplot(column = 'dete_start_date',grid = False)
plt.show()
tafe_resignation.boxplot(column = 'cease_date',grid = False)
<AxesSubplot:>
In this block we cleaned and changed data in 'cease_date' column. Now all three columns in both dataframes have the similiar format and ready for next operations. At the same time, we didn't find any special logical inconsistencies in our date data.
We are going to make some preperations for analysis years in workplace. We will make a new column that counts years in workplace in DETE dataframe. TAFE has already column institute_service.
#make a new column in dete df, that counts years in workplace:
dete_resignation['institute_service'] = dete_resignation['cease_date']-dete_resignation['dete_start_date']
dete_resignation.head()
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-Other reasons | 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-Other reasons | 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-Other reasons | 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-Other employer | 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-Move overseas/interstate | 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
Our next step will be to group information about dissatisfaction factors that caused employees to resign in one column dissatisfied.
print(tafe_resignation['Contributing Factors. Dissatisfaction'].value_counts())
print('\n')
print(tafe_resignation['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
# function to make rows filled with NaN, False or True in both dfs:
def update_vals(value):
if pd.isnull(value):
return np.nan
elif value == '-':
return False
else:
return True
# change data in dfs columns using:
# df.applymap(function) method
# df.any() method to return True or False for all columns
tafe_resignation['dissatisfied'] = (tafe_resignation[['Contributing Factors. Dissatisfaction',
'Contributing Factors. Job Dissatisfaction']]
.applymap(update_vals).any(axis=1, skipna=False))
dete_resignation['dissatisfied'] = (dete_resignation[['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))
# make copies of our dfs to avoid the SettingWithCopy Warning:
tafe_resignations_up = tafe_resignation.copy()
dete_resignations_up = dete_resignation.copy()
# check our new columns:
print(tafe_resignations_up['dissatisfied'].value_counts())
print('\n')
print(dete_resignations_up['dissatisfied'].value_counts())
False 241 True 91 Name: dissatisfied, dtype: int64 False 162 True 149 Name: dissatisfied, dtype: int64
We have made a new column in both dataframes that explains us whether an employee was dissatisfied or not.
# make a new column to distinguish organisations before combining:
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
# pd.concat to combine two dfs:
combined = pd.concat([dete_resignations_up, tafe_resignations_up])
# dropp all columns that have less than 500 non null values
combined_updated = combined.dropna(thresh=500, axis=1)
# check our new df:
combined_updated.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 651 entries, 3 to 701 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 651 non-null float64 1 separationtype 651 non-null object 2 cease_date 635 non-null float64 3 position 598 non-null object 4 employment_status 597 non-null object 5 gender 592 non-null object 6 age 596 non-null object 7 institute_service 563 non-null object 8 dissatisfied 643 non-null object 9 institute 651 non-null object dtypes: float64(2), object(8) memory usage: 55.9+ KB
The result of our operations above is a new dataframe combined_updated, combined from our previous dataframes dete_resignations_up and tafe_resignations_up, cleaned from columns with less than 500 non null values. Now we have 10 columns and 651 rows.
In this block we are going to group the data in institute_service under the following conditions:
# check for strings from tafe dataframe, that may appear in our new df:
combined_updated['institute_service'].value_counts(dropna=False)
NaN 88 Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 5.0 23 1.0 22 7-10 21 0.0 20 3.0 20 6.0 17 4.0 16 9.0 14 2.0 14 7.0 13 More than 20 years 10 8.0 8 13.0 8 15.0 7 20.0 7 10.0 6 12.0 6 14.0 6 22.0 6 17.0 6 18.0 5 16.0 5 11.0 4 23.0 4 24.0 4 19.0 3 21.0 3 39.0 3 32.0 3 28.0 2 30.0 2 26.0 2 36.0 2 25.0 2 27.0 1 29.0 1 31.0 1 33.0 1 34.0 1 35.0 1 38.0 1 41.0 1 42.0 1 49.0 1 Name: institute_service, dtype: int64
# as we caused a problem with SettingWithCopy Warning, let's make a copy:
combined_updated = combined_updated.copy()
#convert to string
#replace all ranges
#convert to float
combined_updated['institute_service'] = (combined_updated['institute_service'].
astype(str).
str.replace('More than 20 years', '20.0').
str.replace('Less than 1 year', '1.0').
str.replace('1-2', '2.0').
str.replace('3-4', '4.0').
str.replace('5-6', '6.0').
str.replace('11-20', '16.0').
str.replace('7-10', '9.0').
astype(float, errors='ignore'))
# check the result
combined_updated['institute_service'].value_counts(ascending=False)
1.0 95 4.0 79 2.0 78 6.0 50 9.0 35 12.0 32 5.0 23 3.0 20 0.0 20 20.0 17 7.0 13 13.0 8 8.0 8 15.0 7 17.0 6 22.0 6 14.0 6 10.0 6 18.0 5 16.0 5 11.0 4 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 34.0 1 49.0 1 33.0 1 31.0 1 Name: institute_service, dtype: int64
# function with conditions : less than 3 years, 3-6 years, 7-10 years, more than 11 years
def categories(val):
if pd.isnull(val):
return np.nan
elif val < 3 :
return 'New'
elif val < 7 :
return 'Experienced'
elif val < 11 :
return 'Established'
else:
return 'Veteran'
# apply function and get results in a new column 'service_cat'
combined_updated['service_cat'] = combined_updated['institute_service'].apply(categories)
# check the result
combined_updated.tail(10)
id | separationtype | cease_date | position | employment_status | gender | age | institute_service | dissatisfied | institute | service_cat | |
---|---|---|---|---|---|---|---|---|---|---|---|
689 | 6.350480e+17 | Resignation | 2013.0 | Teacher (including LVT) | Permanent Full-time | Male | 41 45 | 1.0 | True | TAFE | New |
690 | 6.350496e+17 | Resignation | 2013.0 | NaN | NaN | NaN | NaN | NaN | False | TAFE | NaN |
691 | 6.350496e+17 | Resignation | 2013.0 | Operational (OO) | Permanent Part-time | Female | 56 or older | 4.0 | False | TAFE | Experienced |
693 | 6.350599e+17 | Resignation | 2013.0 | Administration (AO) | Temporary Full-time | Female | 26 30 | 2.0 | False | TAFE | New |
694 | 6.350652e+17 | Resignation | 2013.0 | NaN | NaN | NaN | NaN | NaN | False | TAFE | NaN |
696 | 6.350660e+17 | Resignation | 2013.0 | Operational (OO) | Temporary Full-time | Male | 21 25 | 6.0 | False | TAFE | Experienced |
697 | 6.350668e+17 | Resignation | 2013.0 | Teacher (including LVT) | Temporary Full-time | Male | 51-55 | 2.0 | False | TAFE | New |
698 | 6.350677e+17 | Resignation | 2013.0 | NaN | NaN | NaN | NaN | NaN | False | TAFE | NaN |
699 | 6.350704e+17 | Resignation | 2013.0 | Teacher (including LVT) | Permanent Full-time | Female | 51-55 | 6.0 | False | TAFE | Experienced |
701 | 6.350730e+17 | Resignation | 2013.0 | Administration (AO) | Contract/casual | Female | 26 30 | 4.0 | False | TAFE | Experienced |
# check for Nan values
combined_updated['dissatisfied'].value_counts(dropna=False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
# fill Nan values with True
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(True)
combined_updated['dissatisfied']
3 False 5 True 8 False 9 True 11 False ... 696 False 697 False 698 False 699 False 701 False Name: dissatisfied, Length: 651, dtype: bool
# check if Nan values left
combined_updated['dissatisfied'].value_counts(dropna=False)
False 403 True 248 Name: dissatisfied, dtype: int64
# pivot table by categories
pv_table = combined_updated.pivot_table(values='dissatisfied', index='service_cat')
pv_table.plot(kind='barh')
<AxesSubplot:ylabel='service_cat'>
From our analysis we see that the highest amount of 'dissatisfied' among employees who worked for a long time.
We are going to explore the age column and compare with dissatisfaction
# general information about '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
# updating range format
combined_updated['age']= (combined_updated['age'].
str.strip().
str.replace(' ', '-'))
# check result
combined_updated['age'].value_counts(dropna=False)
41-45 93 46-50 81 36-40 73 51-55 71 26-30 67 21-25 62 31-35 61 NaN 55 56 or older 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
# group ages:
ages_grouped = {'20 or younger':'25 or younger','21-25':'25 or younger','26-30':'26-35',
'31-35':'26-35', '36-40':'36-45', '41-45':'36-45', '46-50':'46-55',
'51-55':'46-55', '56-60':'56 or older', '61 or older':'56 or older'}
combined_updated['by_age'] = combined_updated['age'].map(ages_grouped)
combined_updated['by_age'].value_counts(dropna=False)
36-45 166 46-55 152 26-35 128 NaN 84 25 or younger 72 56 or older 49 Name: by_age, dtype: int64
# apply seaborn to compare relations between institute_service and age with hue dissatisfied
sns.set_theme()
sns.relplot(data=combined_updated, x='institute_service', y='by_age', hue='dissatisfied',
palette='seismic', aspect=1.25)
plt.title('Pic 1')
plt.show()
# pivot table by role:
pv_table1 = combined_updated.pivot_table(values='dissatisfied', index='by_age')
pv_table1.plot(kind='barh')
plt.show()
In this block, we have cleared and grouped the age column. Then we carried out an analysis using the seaborn library and determined the following: more unsatisfied are older workers and those who have worked for more than 5 years in companies.
Let's have a look at position column.
# value counts in position column:
combined_updated['position'].value_counts()
Administration (AO) 148 Teacher 129 Teacher (including LVT) 95 Teacher Aide 63 Cleaner 39 Public Servant 30 Professional Officer (PO) 16 Operational (OO) 13 Head of Curriculum/Head of Special Education 10 Technical Officer 8 School Administrative Staff 8 Schools Officer 7 Workplace Training Officer 6 School Based Professional Staff (Therapist, nurse, etc) 5 Technical Officer (TO) 5 Executive (SES/SO) 4 Guidance Officer 3 Tutor 3 Other 3 Professional Officer 2 Business Service Manager 1 Name: position, dtype: int64
# mapping positions in a new columns 'by_role'
roles_grouped = {'Administration (AO)':'Administration',
'School Administrative Staff':'Administration',
'Teacher':'Teacher',
'Teacher (including LVT)':'Teacher',
'Teacher Aide':'Teacher',
'Tutor':'Teacher',
'Cleaner':'School Staff',
'Public Servant':'School Staff',
'School Based Professional Staff (Therapist, nurse, etc)':'School Staff',
'Other':'School Staff',
'Professional Officer (PO)':'Officer',
'Operational (OO)': 'Officer',
'Technical Officer':'Officer',
'Schools Officer':'Officer',
'Workplace Training Officer':'Officer',
'Technical Officer (TO)':'Officer',
'Guidance Officer':'Officer',
'Professional Officer':'Officer',
'Business Service Manager':'Manager',
'Executive (SES/SO)':'Manager',
'Head of Curriculum/Head of Special Education':'Manager'}
combined_updated['by_role'] = combined_updated['position'].map(roles_grouped)
combined_updated['by_role'].value_counts(dropna=False)
Teacher 290 Administration 156 School Staff 77 Officer 60 NaN 53 Manager 15 Name: by_role, dtype: int64
# seaborn to compare relations between institute_service and position with hue dissatisfied:
sns.set_theme()
sns.relplot(data=combined_updated, x='institute_service', y='by_role', hue='dissatisfied',
palette='seismic', aspect=1.25)
plt.title('Pic 2', size=14)
plt.show()
# pivot table by role:
pv_table2 = combined_updated.pivot_table(values='dissatisfied', index='by_role')
pv_table2.plot(kind='barh')
plt.show()
# seaborn to compare deeper position column:
sns.set_theme()
sns.relplot(data=combined_updated,
x='by_age',
y='position',
hue='dissatisfied',
size='gender',
style='institute',
palette='seismic',
aspect=2.0)
plt.title('Pic 3', size=14)
plt.show()
We will start our conclusion from answering the initial questions:
If we look at Pic 1 (first seaborn graph) we will see a lot of red circles (dissatisfied) on mark 0 (x-coordinate) and after 15-20 years in all age groups. So we can be sure that employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction as well as those who have been there longer.
To answer this question let's have a look at Pic 1 again that tell us that amount of red circles is larger among 40-60 age group (end of 36-45, 46-55, 56 and older) but younger age groups do dissatisfied as well.