In this work we'll analyze datasets of 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 datset for DETE exit survey here and dataset for the survey TAFE here.
Majors goals of this project - pretend that I am a data analyst and I must provide the following summary information for stakeholders of these organization :
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?
They want us to combine the results for both surveys to answer these questions. However, although both used the same survey template, one of them customized some of the answers. We'll aim to do most of the data cleaning and get you started analyzing the first question.
# Import required modules
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
#Import cvs to dataframes
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
# View head dete_survey
dete_survey.head()
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984 | 2004 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | Not Stated | Not Stated | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011 | 2011 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005 | 2006 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970 | 1989 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 56 columns
# Get info about dete_survey
dete_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 822 non-null int64 1 SeparationType 822 non-null object 2 Cease Date 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
dete_survey.isnull()
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | True | True | True | True | False |
1 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | True | True | True | True | True |
2 | False | False | False | False | False | False | True | False | False | False | ... | False | False | False | False | False | True | True | True | True | True |
3 | False | False | False | False | False | False | False | False | True | False | ... | False | False | False | False | False | True | True | True | True | True |
4 | False | False | False | False | False | False | True | False | True | False | ... | False | False | False | False | False | True | True | True | True | True |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
817 | False | False | False | False | False | False | False | False | True | False | ... | False | False | False | False | False | True | True | True | True | True |
818 | False | False | False | False | False | False | False | False | True | False | ... | False | False | False | False | False | True | True | True | True | True |
819 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | True | True | True | True | True |
820 | False | False | False | False | False | False | False | False | True | False | ... | False | False | False | False | False | True | True | True | True | True |
821 | False | False | False | False | False | False | True | False | True | True | ... | True | True | True | True | True | True | True | True | True | True |
822 rows × 56 columns
ax = dete_survey["SeparationType"].value_counts(sort=True, ascending=True).\
plot(kind = 'barh', figsize=(12, 10), sort_columns = True)
tafe_survey.head()
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | ... | Yes | Yes | Yes | Yes | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 72 columns
tafe_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 72 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Record ID 702 non-null float64 1 Institute 702 non-null object 2 WorkArea 702 non-null object 3 CESSATION YEAR 695 non-null float64 4 Reason for ceasing employment 701 non-null object 5 Contributing Factors. Career Move - Public Sector 437 non-null object 6 Contributing Factors. Career Move - Private Sector 437 non-null object 7 Contributing Factors. Career Move - Self-employment 437 non-null object 8 Contributing Factors. Ill Health 437 non-null object 9 Contributing Factors. Maternity/Family 437 non-null object 10 Contributing Factors. Dissatisfaction 437 non-null object 11 Contributing Factors. Job Dissatisfaction 437 non-null object 12 Contributing Factors. Interpersonal Conflict 437 non-null object 13 Contributing Factors. Study 437 non-null object 14 Contributing Factors. Travel 437 non-null object 15 Contributing Factors. Other 437 non-null object 16 Contributing Factors. NONE 437 non-null object 17 Main Factor. Which of these was the main factor for leaving? 113 non-null object 18 InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object 19 InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object 20 InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object 21 InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object 22 InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object 23 InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object 24 InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object 25 InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object 26 InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object 27 InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object 28 InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object 29 InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object 30 InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object 31 WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object 32 WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object 33 WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object 34 WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object 35 WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object 36 WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 609 non-null object 37 WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object 38 WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] 608 non-null object 39 WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object 40 WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object 41 WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object 42 WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object 43 WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object 44 WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object 45 WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 609 non-null object 46 WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object 47 WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object 48 Induction. Did you undertake Workplace Induction? 619 non-null object 49 InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object 50 InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object 51 InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object 52 InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 53 InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 54 InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object 55 InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object 56 InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object 57 InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object 58 InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object 59 InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object 60 InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object 61 Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object 62 Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object 63 Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object 64 Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object 65 Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object 66 Gender. What is your Gender? 596 non-null object 67 CurrentAge. Current Age 596 non-null object 68 Employment Type. Employment Type 596 non-null object 69 Classification. Classification 596 non-null object 70 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object 71 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(70) memory usage: 395.0+ KB
tafe_survey.isnull()
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | False | False | False | False | False | True | True | True | True | True | ... | False | False | False | False | False | False | False | False | False | False |
1 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | True | True | True | True | True | True |
2 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | True | True | True | True | True | True |
3 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | True | True | True | True | True | True |
4 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
697 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
698 | False | False | False | False | False | False | False | False | False | False | ... | True | True | True | True | True | True | True | True | True | True |
699 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
700 | False | False | False | False | False | True | True | True | True | True | ... | False | False | False | False | False | False | False | False | False | False |
701 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
702 rows × 72 columns
ax1 = tafe_survey["Reason for ceasing employment"].value_counts(sort=True, ascending=True).\
plot(kind = 'barh', figsize=(12, 10), sort_columns = True)
Import the dete_survey.csv file into pandas again and replace values 'Not Stated' to the NaN and drop columns from 28 to 48 and get general information for cleaned dataset.
# Replace Not Stated' to the NaN. .
dete_survey = pd.read_csv('dete_survey.csv', na_values = 'Not Stated')
# Drop columns from 28 to 48
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49],axis = 1)
# Veiw cleaned dete_survey_updated
dete_survey_updated.head()
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Work life balance | Workload | None of the above | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | False | False | True | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | False | False | False | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | False | True | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | True | False | False | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 35 columns
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
dete_survey_updated.isnull()
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 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | True | True | True | True | False |
1 | False | False | False | True | True | False | False | False | False | False | ... | False | False | False | False | False | True | True | True | True | True |
2 | False | False | False | False | False | False | True | False | False | False | ... | False | False | False | False | False | True | True | True | True | True |
3 | False | False | False | False | False | False | False | False | True | False | ... | False | False | False | False | False | True | True | True | True | True |
4 | False | False | False | False | False | False | True | False | True | False | ... | False | False | False | False | False | True | True | True | True | True |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
817 | False | False | False | False | False | False | False | False | True | False | ... | False | False | False | False | False | True | True | True | True | True |
818 | False | False | False | False | False | False | False | False | True | False | ... | False | False | False | False | False | True | True | True | True | True |
819 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | True | True | True | True | True |
820 | False | False | False | False | False | False | False | False | True | False | ... | False | False | False | False | False | True | True | True | True | True |
821 | False | False | False | True | True | False | True | False | True | True | ... | False | False | False | True | True | True | True | True | True | True |
822 rows × 35 columns
Delete columns from 17 to 65 for tafe_survey and get general information:
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66],axis = 1)
tafe_survey_updated.head()
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | - | - | - | NONE | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | - | - | - | - | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 23 columns
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
tafe_survey_updated.isnull()
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | False | False | False | False | False | True | True | True | True | True | ... | True | True | True | True | False | False | False | False | False | False |
1 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | True | True | True | True | True | True |
2 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | True | True | True | True | True | True |
3 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | True | True | True | True | True | True |
4 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
697 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
698 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | True | True | True | True | True | True |
699 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
700 | False | False | False | False | False | True | True | True | True | True | ... | True | True | True | True | False | False | False | False | False | False |
701 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
702 rows × 23 columns
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.replace(' ','_')
#Rename separationtype to standard PEP-8 variable name separation_type
mapping = {'separationtype':'separation_type'}
dete_survey_updated = dete_survey_updated.rename(columns=mapping)
dete_survey_updated.columns
Index(['id', 'separation_type', 'cease_date', 'dete_start_date', 'role_start_date', 'position', 'classification', 'region', 'business_unit', 'employment_status', 'career_move_to_public_sector', 'career_move_to_private_sector', 'interpersonal_conflicts', 'job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions', 'maternity/family', 'relocation', 'study/travel', 'ill_health', 'traumatic_incident', 'work_life_balance', 'workload', 'none_of_the_above', 'gender', 'age', 'aboriginal', 'torres_strait', 'south_sea', 'disability', 'nesb'], dtype='object')
dete_survey_updated.head()
id | separation_type | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | work_life_balance | workload | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | False | False | True | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | False | False | False | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | False | True | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | True | False | False | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 35 columns
create dictionary mapping and rename columns
mapping = {'Record ID': 'id', 'CESSATION YEAR': 'cease_date',
'Reason for ceasing employment': 'separation_type',
'Gender. What is your Gender?': 'gender',
'CurrentAge. Current Age': 'age',
'Employment Type. Employment Type': 'employment_status',
'Classification. Classification': 'position',
'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service',
'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'}
tafe_survey_updated = tafe_survey_updated.rename(columns=mapping)
tafe_survey_updated.columns
Index(['id', 'Institute', 'WorkArea', 'cease_date', 'separation_type', 'Contributing Factors. Career Move - Public Sector ', 'Contributing Factors. Career Move - Private Sector ', 'Contributing Factors. Career Move - Self-employment', 'Contributing Factors. Ill Health', 'Contributing Factors. Maternity/Family', 'Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Interpersonal Conflict', 'Contributing Factors. Study', 'Contributing Factors. Travel', 'Contributing Factors. Other', 'Contributing Factors. NONE', 'gender', 'age', 'employment_status', 'position', 'institute_service', 'role_service'], dtype='object')
tafe_survey_updated.head()
id | Institute | WorkArea | cease_date | separation_type | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | gender | age | employment_status | position | institute_service | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | - | - | - | NONE | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | - | - | - | - | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 23 columns
# Make RE pattern
resign = r"[Rr]esignation"
dete_resignations = dete_survey_updated[dete_survey_updated['separation_type'].\
str.contains(resign)].copy()
dete_resignations['separation_type'].value_counts()
Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Name: separation_type, dtype: int64
dete_resignations.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 311 entries, 3 to 821 Data columns (total 35 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 311 non-null int64 1 separation_type 311 non-null object 2 cease_date 300 non-null object 3 dete_start_date 283 non-null float64 4 role_start_date 271 non-null float64 5 position 308 non-null object 6 classification 161 non-null object 7 region 265 non-null object 8 business_unit 32 non-null object 9 employment_status 307 non-null object 10 career_move_to_public_sector 311 non-null bool 11 career_move_to_private_sector 311 non-null bool 12 interpersonal_conflicts 311 non-null bool 13 job_dissatisfaction 311 non-null bool 14 dissatisfaction_with_the_department 311 non-null bool 15 physical_work_environment 311 non-null bool 16 lack_of_recognition 311 non-null bool 17 lack_of_job_security 311 non-null bool 18 work_location 311 non-null bool 19 employment_conditions 311 non-null bool 20 maternity/family 311 non-null bool 21 relocation 311 non-null bool 22 study/travel 311 non-null bool 23 ill_health 311 non-null bool 24 traumatic_incident 311 non-null bool 25 work_life_balance 311 non-null bool 26 workload 311 non-null bool 27 none_of_the_above 311 non-null bool 28 gender 302 non-null object 29 age 306 non-null object 30 aboriginal 7 non-null object 31 torres_strait 0 non-null object 32 south_sea 3 non-null object 33 disability 8 non-null object 34 nesb 9 non-null object dtypes: bool(18), float64(2), int64(1), object(14) memory usage: 49.2+ KB
If we try assign tafe_resignations use expression tafe_survey_updated[tafe_survey_updated['separation_type'].str.contains(resign)].copy() it raising exception ValueError: Cannot mask with non-boolean array containing NA / NaN values hense we must replace NaN values from this columns to False (0).
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separation_type'].\
str.contains(resign, na = False)].copy()
tafe_resignations['separation_type'].value_counts()
Resignation 340 Name: separation_type, dtype: int64
tafe_resignations.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 340 entries, 3 to 701 Data columns (total 23 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 340 non-null float64 1 Institute 340 non-null object 2 WorkArea 340 non-null object 3 cease_date 335 non-null float64 4 separation_type 340 non-null object 5 Contributing Factors. Career Move - Public Sector 332 non-null object 6 Contributing Factors. Career Move - Private Sector 332 non-null object 7 Contributing Factors. Career Move - Self-employment 332 non-null object 8 Contributing Factors. Ill Health 332 non-null object 9 Contributing Factors. Maternity/Family 332 non-null object 10 Contributing Factors. Dissatisfaction 332 non-null object 11 Contributing Factors. Job Dissatisfaction 332 non-null object 12 Contributing Factors. Interpersonal Conflict 332 non-null object 13 Contributing Factors. Study 332 non-null object 14 Contributing Factors. Travel 332 non-null object 15 Contributing Factors. Other 332 non-null object 16 Contributing Factors. NONE 332 non-null object 17 gender 290 non-null object 18 age 290 non-null object 19 employment_status 290 non-null object 20 position 290 non-null object 21 institute_service 290 non-null object 22 role_service 290 non-null object dtypes: float64(2), object(21) memory usage: 63.8+ KB
dete_resignations['cease_date'].value_counts()
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 11/2013 9 07/2013 9 10/2013 6 08/2013 4 05/2013 2 05/2012 2 07/2006 1 09/2010 1 07/2012 1 2010 1 Name: cease_date, dtype: int64
As we see there mess from years and month. Extract year using vectorized string methods with Regular Expression and check it.
#Pattern Regilar Expression
year = r"([1-2][0-9]{3})"
# Add new column 'cease_date_updated'
dete_resignations['cease_date_updated'] = dete_resignations['cease_date'].\
str.extract(year,expand = False).astype(float)
dete_resignations['cease_date_updated'].value_counts()
2013.0 146 2012.0 129 2014.0 22 2010.0 2 2006.0 1 Name: cease_date_updated, dtype: int64
check column cease_date for TAFE
tafe_resignations['cease_date'].value_counts()
2011.0 116 2012.0 94 2010.0 68 2013.0 55 2009.0 2 Name: cease_date, dtype: int64
as we see TAFE column 'cease_date' has required format.
Check 'dete_start_date' column
dete_resignations['dete_start_date'].value_counts().sort_index()
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
as we see DETE column 'dete_start_date' has required format. Check with plot dete_resignations['cease_date_updated'], dete_resignations['dete_start_date'] and tafe_resignations['cease_date'] with plot:
ax3 = dete_resignations['cease_date_updated'].value_counts().sort_index().\
plot(kind = 'barh', figsize=(12, 10), sort_columns = True)
ax3 = dete_resignations['dete_start_date'].value_counts().sort_index().\
plot(kind = 'barh', figsize=(12, 10), sort_columns = True)
ax5 = tafe_resignations['cease_date'].value_counts().sort_index().\
plot(kind = 'barh', figsize=(12, 10), sort_columns = True)
as we see values in columns dete_resignations['cease_date_updated'], dete_resignations['dete_start_date'] and tafe_resignations['cease_date'] have required numeric format.
Let's create an institute_service column in dete_resignations and examine it.
dete_resignations['institute_service'] = dete_resignations['cease_date_updated'] - dete_resignations['dete_start_date']
dete_resignations['institute_service'].describe()
count 273.000000 mean 10.457875 std 9.931709 min 0.000000 25% 3.000000 50% 7.000000 75% 16.000000 max 49.000000 Name: institute_service, dtype: float64
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts(dropna = False)
- 277 Contributing Factors. Dissatisfaction 55 NaN 8 Name: Contributing Factors. Dissatisfaction, dtype: int64
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts(dropna = False)
- 270 Job Dissatisfaction 62 NaN 8 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
as we see these both columns contain three categories of values - string1='-', string2= 'Contributing Factors. Job Dissatisfaction' or 'Job Dissatisfaction' and NaN. Hence we can replace string1 to False, string2 to True and NaN to np.nan.
# Define function for convert NaN to np.nan, '-' to False, other non zero string to True
def update_vals(value):
if pd.isnull(value):
return np.nan
elif value == '-':
return False
else:
return True
# Create new column and extract converted values from columns 'Contributing Factors. Dissatisfaction'
# and 'Contributing Factors. Job Dissatisfaction' to 'dissatisfied'
tafe_resignations['dissatisfied'] = tafe_resignations[['Contributing Factors. Dissatisfaction',
'Contributing Factors. Job Dissatisfaction']].\
applymap(update_vals).any(axis=1, skipna=False)
tafe_resignations_up = tafe_resignations.copy()
tafe_resignations_up.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 340 entries, 3 to 701 Data columns (total 24 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 340 non-null float64 1 Institute 340 non-null object 2 WorkArea 340 non-null object 3 cease_date 335 non-null float64 4 separation_type 340 non-null object 5 Contributing Factors. Career Move - Public Sector 332 non-null object 6 Contributing Factors. Career Move - Private Sector 332 non-null object 7 Contributing Factors. Career Move - Self-employment 332 non-null object 8 Contributing Factors. Ill Health 332 non-null object 9 Contributing Factors. Maternity/Family 332 non-null object 10 Contributing Factors. Dissatisfaction 332 non-null object 11 Contributing Factors. Job Dissatisfaction 332 non-null object 12 Contributing Factors. Interpersonal Conflict 332 non-null object 13 Contributing Factors. Study 332 non-null object 14 Contributing Factors. Travel 332 non-null object 15 Contributing Factors. Other 332 non-null object 16 Contributing Factors. NONE 332 non-null object 17 gender 290 non-null object 18 age 290 non-null object 19 employment_status 290 non-null object 20 position 290 non-null object 21 institute_service 290 non-null object 22 role_service 290 non-null object 23 dissatisfied 332 non-null object dtypes: float64(2), object(22) memory usage: 66.4+ KB
and check values in new column 'dissatisfied' in the TAFE
tafe_resignations['dissatisfied'].value_counts()
False 241 True 91 Name: dissatisfied, dtype: int64
2. DETE
We going to extract data from next columns:
of dete_resignations dataset to new column dissatisfied using by pandas any() method.
# Define list of columns
dete_res_cols = ['job_dissatisfaction',
'dissatisfaction_with_the_department',
'physical_work_environment','lack_of_recognition',
'lack_of_job_security','work_location',
'employment_conditions','work_life_balance','workload']
# Extract data of these columns
dete_resignations['dissatisfied'] = dete_resignations[dete_res_cols].any(axis=1, skipna = False)
# Create copy of dataset
dete_resignations_up = dete_resignations.copy()
# Check values on new column
dete_resignations_up['dissatisfied'].value_counts()
False 162 True 149 Name: dissatisfied, dtype: int64
Look at summary values for columns in the tafe_resignations - was 332 values in the columns 'Contributing Factors. Dissatisfaction' and "Contributing Factors. Job Dissatisfaction' - became 332 in the column 'dissatisfied' tafe_resignations_up. Set of columns dete_res_cols of dete_resignations - was 311 values of set, became 311 values in the column 'dissatisfied' in the dete_resignations_up. Hence extracting and converting values was successful.
First step add a column to each dataframe that will allow us to easily distinguish between the two.
Second step - combine the dataframes and assign the result to combined
# Add new columns for both dataset
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
# Comcatenate datasets by rows
combined = pd.concat([dete_resignations_up,tafe_resignations_up], axis = 0, ignore_index=True)
# View combined dataset.
combined.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 651 entries, 0 to 650 Data columns (total 54 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 651 non-null float64 1 separation_type 651 non-null object 2 cease_date 635 non-null object 3 dete_start_date 283 non-null float64 4 role_start_date 271 non-null float64 5 position 598 non-null object 6 classification 161 non-null object 7 region 265 non-null object 8 business_unit 32 non-null object 9 employment_status 597 non-null object 10 career_move_to_public_sector 311 non-null object 11 career_move_to_private_sector 311 non-null object 12 interpersonal_conflicts 311 non-null object 13 job_dissatisfaction 311 non-null object 14 dissatisfaction_with_the_department 311 non-null object 15 physical_work_environment 311 non-null object 16 lack_of_recognition 311 non-null object 17 lack_of_job_security 311 non-null object 18 work_location 311 non-null object 19 employment_conditions 311 non-null object 20 maternity/family 311 non-null object 21 relocation 311 non-null object 22 study/travel 311 non-null object 23 ill_health 311 non-null object 24 traumatic_incident 311 non-null object 25 work_life_balance 311 non-null object 26 workload 311 non-null object 27 none_of_the_above 311 non-null object 28 gender 592 non-null object 29 age 596 non-null object 30 aboriginal 7 non-null object 31 torres_strait 0 non-null object 32 south_sea 3 non-null object 33 disability 8 non-null object 34 nesb 9 non-null object 35 cease_date_updated 300 non-null float64 36 institute_service 563 non-null object 37 dissatisfied 643 non-null object 38 institute 651 non-null object 39 Institute 340 non-null object 40 WorkArea 340 non-null object 41 Contributing Factors. Career Move - Public Sector 332 non-null object 42 Contributing Factors. Career Move - Private Sector 332 non-null object 43 Contributing Factors. Career Move - Self-employment 332 non-null object 44 Contributing Factors. Ill Health 332 non-null object 45 Contributing Factors. Maternity/Family 332 non-null object 46 Contributing Factors. Dissatisfaction 332 non-null object 47 Contributing Factors. Job Dissatisfaction 332 non-null object 48 Contributing Factors. Interpersonal Conflict 332 non-null object 49 Contributing Factors. Study 332 non-null object 50 Contributing Factors. Travel 332 non-null object 51 Contributing Factors. Other 332 non-null object 52 Contributing Factors. NONE 332 non-null object 53 role_service 290 non-null object dtypes: float64(4), object(50) memory usage: 274.8+ KB
Drop any columns with less than 500 non null values using the DataFrame.dropna() method.
combined_updated = combined.dropna(thresh= 500 , axis = 1)
combined_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 651 entries, 0 to 650 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 651 non-null float64 1 separation_type 651 non-null object 2 cease_date 635 non-null object 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(1), object(9) memory usage: 51.0+ KB
Us we see was 53 columns, remains 9 columns for further analyze.
Look at institute_service column:
combined_updated['institute_service'].unique()
array([7.0, 18.0, 3.0, 15.0, 14.0, 5.0, nan, 30.0, 32.0, 39.0, 17.0, 9.0, 6.0, 1.0, 35.0, 38.0, 36.0, 19.0, 4.0, 26.0, 10.0, 8.0, 2.0, 0.0, 23.0, 13.0, 16.0, 12.0, 21.0, 20.0, 24.0, 33.0, 22.0, 28.0, 49.0, 11.0, 41.0, 27.0, 42.0, 25.0, 29.0, 34.0, 31.0, '3-4', '7-10', '1-2', 'Less than 1 year', '11-20', '5-6', 'More than 20 years'], dtype=object)
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 3.0 20 0.0 20 6.0 17 4.0 16 2.0 14 9.0 14 7.0 13 More than 20 years 10 13.0 8 8.0 8 15.0 7 20.0 7 17.0 6 22.0 6 12.0 6 10.0 6 14.0 6 16.0 5 18.0 5 24.0 4 11.0 4 23.0 4 39.0 3 19.0 3 32.0 3 21.0 3 36.0 2 26.0 2 28.0 2 30.0 2 25.0 2 38.0 1 29.0 1 41.0 1 42.0 1 49.0 1 27.0 1 35.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service, dtype: int64
# https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
# changing the type of 'institute_service' to string
combined_updated.loc[ :, ('institute_service')] = combined_updated.loc[ :, ('institute_service')].astype(str, copy=True)
# Extract number for string to new column 'institute_service_up' usig regular expression pattern
years = r'(\d+)'
combined_updated.insert(loc=10, column = "institute_service_upd",
value=combined_updated.loc[ :, ('institute_service')].\
str.extract(years, expand = False).astype('float'), allow_duplicates=True)
combined_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 651 entries, 0 to 650 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 651 non-null float64 1 separation_type 651 non-null object 2 cease_date 635 non-null object 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 651 non-null object 8 dissatisfied 643 non-null object 9 institute 651 non-null object 10 institute_service_upd 563 non-null float64 dtypes: float64(2), object(9) memory usage: 56.1+ KB
/home/mvg/.pyenv/versions/ds387/lib/python3.8/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy self._setitem_single_column(ilocs[0], value, pi)
combined_updated["institute_service_upd"].value_counts(dropna = False)
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 2.0 14 9.0 14 8.0 8 13.0 8 15.0 7 14.0 6 10.0 6 12.0 6 22.0 6 17.0 6 18.0 5 16.0 5 24.0 4 23.0 4 32.0 3 39.0 3 19.0 3 21.0 3 25.0 2 28.0 2 36.0 2 26.0 2 30.0 2 49.0 1 33.0 1 34.0 1 29.0 1 35.0 1 42.0 1 27.0 1 31.0 1 41.0 1 38.0 1 Name: institute_service_upd, dtype: int64
Let's divide data in column institute_service_upd as definitions below:
categorize the values from the columns column using the definitions above and pandas.cut() method.
combined_updated['service_cat'] = pd.cut(combined_updated['institute_service_upd'],
bins = [0,1,3,7,10,float("inf")],
labels = ['Novichok','New','Experienced','Established','Veteran'],
right=False)
combined_updated['service_cat'].value_counts(dropna = False).sort_index()
<ipython-input-42-bb187242ce1e>:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy combined_updated['service_cat'] = pd.cut(combined_updated['institute_service_upd'],
Novichok 20 New 173 Experienced 172 Established 56 Veteran 142 NaN 88 Name: service_cat, dtype: int64
combined_updated['dissatisfied'].value_counts(dropna = False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
we see NaN vales and as the great part of the values are False, we have to fill NaN with True.
combined_updated.loc[ : ,('dissatisfied')] = combined_updated.loc[ :, ('dissatisfied')].fillna(True)
combined_updated['dissatisfied'].value_counts(dropna = False)
False 403 True 248 Name: dissatisfied, dtype: int64
As we see dissatisfaction people are rough 2/3 of all people.
# combined_updated.reset_index(level=0, drop=True,inplace=True)
pvt_dissatified = pd.pivot_table(data = combined_updated,
index = ['service_cat'],
values = 'dissatisfied',
margins = True,
dropna=False)
pvt_dissatified
dissatisfied | |
---|---|
service_cat | |
Novichok | 0.550000 |
New | 0.265896 |
Experienced | 0.343023 |
Established | 0.553571 |
Veteran | 0.471831 |
All | 0.380952 |
pvt_dissatified.plot(kind = 'barh')
<AxesSubplot:ylabel='service_cat'>
As we see the most of issues are in the Novichok, Veteran, Established categories. Lets analyze dispersions by DETE and TAFE - create pivot table and plot.
pvt_dissatified_org = pd.pivot_table(data = combined_updated,
index = ['service_cat', 'institute'],
values = 'dissatisfied',
margins = True,
dropna=False)
pvt_dissatified_org
dissatisfied | ||
---|---|---|
service_cat | institute | |
Novichok | DETE | 0.550000 |
TAFE | NaN | |
New | DETE | 0.277778 |
TAFE | 0.262774 | |
Experienced | DETE | 0.460526 |
TAFE | 0.250000 | |
Established | DETE | 0.685714 |
TAFE | 0.333333 | |
Veteran | DETE | 0.537736 |
TAFE | 0.277778 | |
All | 0.380952 |
pvt_dissatified_org.plot(kind = 'barh')
<AxesSubplot:ylabel='service_cat,institute'>
Us we see dissatisfaction of work resigned employees on DATE by three - four times more than employees on TAFE. In fact all categories of resigned employees had dissatisfaction of work in DATE. The dispersions of dissatisfaction of work resigned employees from TAFE has almost evenly by all categories.
Let's view column age
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 36 40 32 26 30 32 31-35 29 21-25 29 56 or older 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
Carefully look at the values in the age column - us we see some ages category have different name. Rename values contains of column age with dictionary for avoiding duplicates.
# Dictionary for rename ages
rename_ages = {"20 or younger": "20-=<" , "21 25": "21-25",
"26 30": "26-30", "31 35" : "31-35", "36 40": "36-40",
"41 45": "41-45", "46 50" : "46-50", "56 or older": "56-60",
"61 or older": "60->= "}
combined_updated["age"].replace (rename_ages, inplace=True)
combined_updated['age'].value_counts(dropna = False).sort_index()
/home/mvg/.pyenv/versions/ds387/lib/python3.8/site-packages/pandas/core/series.py:4479: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy return super().replace(
20-=< 10 21-25 62 26-30 67 31-35 61 36-40 73 41-45 93 46-50 81 51-55 71 56-60 55 60->= 23 NaN 55 Name: age, dtype: int64
As we see we have 55 NaN values. Drop it.
combined_updated.dropna(subset=['age'], inplace = True)
combined_updated.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 596 entries, 0 to 650 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 596 non-null float64 1 separation_type 596 non-null object 2 cease_date 583 non-null object 3 position 593 non-null object 4 employment_status 594 non-null object 5 gender 590 non-null object 6 age 596 non-null object 7 institute_service 596 non-null object 8 dissatisfied 596 non-null bool 9 institute 596 non-null object 10 institute_service_upd 561 non-null float64 11 service_cat 561 non-null category dtypes: bool(1), category(1), float64(2), object(8) memory usage: 52.6+ KB
<ipython-input-51-7f1d52c6e8c9>:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy combined_updated.dropna(subset=['age'], inplace = True)
pvt_dissatified_age = pd.pivot_table(data = combined_updated,
index = ['age', 'institute'],
values = ['dissatisfied'],
margins = True,
dropna=False)
pvt_dissatified_age
dissatisfied | ||
---|---|---|
age | institute | |
20-=< | DETE | 0.000000 |
TAFE | 0.222222 | |
21-25 | DETE | 0.310345 |
TAFE | 0.303030 | |
26-30 | DETE | 0.571429 |
TAFE | 0.250000 | |
31-35 | DETE | 0.551724 |
TAFE | 0.218750 | |
36-40 | DETE | 0.390244 |
TAFE | 0.281250 | |
41-45 | DETE | 0.479167 |
TAFE | 0.266667 | |
46-50 | DETE | 0.452381 |
TAFE | 0.307692 | |
51-55 | DETE | 0.593750 |
TAFE | 0.282051 | |
56-60 | DETE | 0.576923 |
TAFE | 0.206897 | |
60->= | DETE | 0.521739 |
TAFE | NaN | |
All | 0.379195 |
pvt_dissatified_age.plot(kind = 'barh', figsize = (15, 10))
<AxesSubplot:ylabel='age,institute'>
pvt_dissatified_age1 = pd.pivot_table(data = combined_updated,
index = ['age', 'institute'],
values = [ 'dissatisfied'],
margins = True,
dropna=False)
pvt_dissatified_age1
dissatisfied | ||
---|---|---|
age | institute | |
20-=< | DETE | 0.000000 |
TAFE | 0.222222 | |
21-25 | DETE | 0.310345 |
TAFE | 0.303030 | |
26-30 | DETE | 0.571429 |
TAFE | 0.250000 | |
31-35 | DETE | 0.551724 |
TAFE | 0.218750 | |
36-40 | DETE | 0.390244 |
TAFE | 0.281250 | |
41-45 | DETE | 0.479167 |
TAFE | 0.266667 | |
46-50 | DETE | 0.452381 |
TAFE | 0.307692 | |
51-55 | DETE | 0.593750 |
TAFE | 0.282051 | |
56-60 | DETE | 0.576923 |
TAFE | 0.206897 | |
60->= | DETE | 0.521739 |
TAFE | NaN | |
All | 0.379195 |
pvt_dissatified_age1.plot(kind = 'barh', figsize = (12,9))
<AxesSubplot:ylabel='age,institute'>
As we see general representation dispersion of dissatisfaction resigned employees by age for DATE and TAFE seems at the dissatisfaction resigned employ in service category in the p.p No 10.2.
# create service category list
service_category = ['Novichok','New','Experienced','Established','Veteran']
# Loop for printing or plot pivot tables
for category in service_category :
#create a df filtering by cat
current_df = combined_updated[combined_updated['service_cat'] == category]
pvt_age = pd.pivot_table(data = current_df,
index = ['age','institute'],
values = 'dissatisfied',
margins = True,
dropna=True)
pvt_age.plot(kind = 'barh', title = category, figsize = (12,9))
How we see:
New - 36 - 40 year.
Experienced - <= 20, 21 - 25, 41 - 45 year.
Established - 31 - 35 year.
Veteran 51 - 55 year.
Novichok - 51 - 55, 55 - 60 year.
New - 46 – 50, 36-40, 31 – 35 year.
Experienced - 6-40, 26-30, 41-45, 56-60,60->= year.
Established - 31 – 35, 51-55. 36 -40 year.
Veteran 60->=, 41-45, 46 - 50, 51 -55 year.
We haven't detailed information about reason of reassignment for each employees in the combined dataset and can't say exactly about these reasons. Detailed information about reason dissatisfaction contained in the original DETE dataset but for for compatibility with TAFE is has been simplified.
May be reasons on low salary, high load, lack of social elevator, terrible psychological climate, a lot of writing the unnecessary reporting - but we can't assert something from above haven't reliable data of these reasons.
Created on Feb 20, 2021
@author: Vadim Maklakov, used some ideas from public Internet resources.
© 3-clause BSD License
Software environment: Debian 10.7, Python 3.8.7
required next preinstalled python modules: numpy, pandas , matplotlib