In this project we will be working with two datasets named dete_survey.csv and tafe_survey.csv. These datasets has the records of who exited TAFE i.e Technical and Further Education and DETE i.e Department of Education, Training and Employment. We will be cleanining these datasets, combining and by analysing them we will try to answer a few question.
The questions are :-
Q1 ) 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?
Q2 ) Are younger employees resigning due to some kind of dissatisfaction? What about older employees?
So lets start !
import numpy as np
import pandas as pd
dete_survey = pd.read_csv("dete_survey.csv")
tafe_survey = pd.read_csv("tafe_survey.csv")
dete_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): ID 822 non-null int64 SeparationType 822 non-null object Cease Date 822 non-null object DETE Start Date 822 non-null object Role Start Date 822 non-null object Position 817 non-null object Classification 455 non-null object Region 822 non-null object Business Unit 126 non-null object Employment Status 817 non-null object Career move to public sector 822 non-null bool Career move to private sector 822 non-null bool Interpersonal conflicts 822 non-null bool Job dissatisfaction 822 non-null bool Dissatisfaction with the department 822 non-null bool Physical work environment 822 non-null bool Lack of recognition 822 non-null bool Lack of job security 822 non-null bool Work location 822 non-null bool Employment conditions 822 non-null bool Maternity/family 822 non-null bool Relocation 822 non-null bool Study/Travel 822 non-null bool Ill Health 822 non-null bool Traumatic incident 822 non-null bool Work life balance 822 non-null bool Workload 822 non-null bool None of the above 822 non-null bool Professional Development 808 non-null object Opportunities for promotion 735 non-null object Staff morale 816 non-null object Workplace issue 788 non-null object Physical environment 817 non-null object Worklife balance 815 non-null object Stress and pressure support 810 non-null object Performance of supervisor 813 non-null object Peer support 812 non-null object Initiative 813 non-null object Skills 811 non-null object Coach 767 non-null object Career Aspirations 746 non-null object Feedback 792 non-null object Further PD 768 non-null object Communication 814 non-null object My say 812 non-null object Information 816 non-null object Kept informed 813 non-null object Wellness programs 766 non-null object Health & Safety 793 non-null object Gender 798 non-null object Age 811 non-null object Aboriginal 16 non-null object Torres Strait 3 non-null object South Sea 7 non-null object Disability 23 non-null object NESB 32 non-null object dtypes: bool(18), int64(1), object(37) memory usage: 258.6+ KB
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
In the above dataset there are 822 rows and 56 columns. There are many columns will null values. They are Position, Classification, Business Unit, Employment Status, Professional Development, Staff morale,Workplace issue, Physical environment, Worklife balance, Stress and pressure support ,Performance of supervisor, Peer support, Initiative , Skills, Coach, Career Aspirations, Feedback, Further PD, Communication, My say, Information, Kept informed, Wellness programs, Health & Safety, Gender, Age, Aboriginal, Torres Strait, South Sea, Disability, NESB. 32 columns have NULL values.
tafe_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 72 columns): Record ID 702 non-null float64 Institute 702 non-null object WorkArea 702 non-null object CESSATION YEAR 695 non-null float64 Reason for ceasing employment 701 non-null object Contributing Factors. Career Move - Public Sector 437 non-null object Contributing Factors. Career Move - Private Sector 437 non-null object Contributing Factors. Career Move - Self-employment 437 non-null object Contributing Factors. Ill Health 437 non-null object Contributing Factors. Maternity/Family 437 non-null object Contributing Factors. Dissatisfaction 437 non-null object Contributing Factors. Job Dissatisfaction 437 non-null object Contributing Factors. Interpersonal Conflict 437 non-null object Contributing Factors. Study 437 non-null object Contributing Factors. Travel 437 non-null object Contributing Factors. Other 437 non-null object Contributing Factors. NONE 437 non-null object Main Factor. Which of these was the main factor for leaving? 113 non-null object InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 609 non-null object WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] 608 non-null object WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 609 non-null object WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object Induction. Did you undertake Workplace Induction? 619 non-null object InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object Gender. What is your Gender? 596 non-null object CurrentAge. Current Age 596 non-null object Employment Type. Employment Type 596 non-null object Classification. Classification 596 non-null object LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(70) memory usage: 395.0+ KB
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
The above dataset has 702 rows and 72 columns. Except for Record ID, Institute and Work Area, all other columns have NULL values.
dete_survey = pd.read_csv("dete_survey.csv",na_values="Not Stated")
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49],axis=1)
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66],axis=1)
In dete_survey.csv dataset missing values are denoted by "Not Stated" other than NaN. So to replace them with NaN we read it stating that where ever there is Not Stated in the dataset replace it by NaN. Next we removed columns that were not necessary for our analysis from both the datasets.
dete_survey_updated.columns
Index(['ID', 'SeparationType', 'Cease Date', 'DETE Start Date', 'Role Start Date', 'Position', 'Classification', 'Region', 'Business Unit', 'Employment Status', 'Career move to public sector', 'Career move to private sector', 'Interpersonal conflicts', 'Job dissatisfaction', 'Dissatisfaction with the department', 'Physical work environment', 'Lack of recognition', 'Lack of job security', 'Work location', 'Employment conditions', 'Maternity/family', 'Relocation', 'Study/Travel', 'Ill Health', 'Traumatic incident', 'Work life balance', 'Workload', 'None of the above', 'Gender', 'Age', 'Aboriginal', 'Torres Strait', 'South Sea', 'Disability', 'NESB'], dtype='object')
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.replace(' ','_')
dete_survey_updated.columns
Index(['id', 'separationtype', 'cease_date', 'dete_start_date', 'role_start_date', 'position', 'classification', 'region', 'business_unit', 'employment_status', 'career_move_to_public_sector', 'career_move_to_private_sector', 'interpersonal_conflicts', 'job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions', 'maternity/family', 'relocation', 'study/travel', 'ill_health', 'traumatic_incident', 'work_life_balance', 'workload', 'none_of_the_above', 'gender', 'age', 'aboriginal', 'torres_strait', 'south_sea', 'disability', 'nesb'], dtype='object')
mapper = {'Record ID': 'id',
'CESSATION YEAR': 'cease_date',
'Reason for ceasing employment': 'separationtype',
'Gender. What is your Gender?': 'gender',
'CurrentAge. Current Age': 'age',
'Employment Type. Employment Type': 'employment_status',
'Classification. Classification': 'position',
'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service',
'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'}
tafe_survey_updated.rename(mapper=mapper,axis=1,inplace=True)
tafe_survey_updated.head()
id | Institute | WorkArea | cease_date | separationtype | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | gender | age | employment_status | position | institute_service | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | - | - | - | NONE | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | - | - | - | - | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 23 columns
For the dete_survey_updated dataset we standardised all the column names by lower casing all of them and replacing spaces(" ") with underscore("_").
For the tafe_survey_updated dataset we renamed some columns as they were very big and typing them again and again could be a tedious task.
dete_survey_updated["separationtype"].value_counts()
Age Retirement 285 Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
tafe_survey_updated["separationtype"].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
dete_resignation = dete_survey_updated[dete_survey_updated["separationtype"].str.contains("Resignation")]
dete_resignation.head()
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | work_life_balance | workload | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 4 | Resignation-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 |
5 | 6 | Resignation-Other reasons | 05/2012 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | False | False | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
8 | 9 | Resignation-Other reasons | 07/2012 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
9 | 10 | Resignation-Other employer | 2012 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | ... | False | False | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
11 | 12 | Resignation-Move overseas/interstate | 2012 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | ... | False | False | False | Male | 31-35 | NaN | NaN | NaN | NaN | NaN |
5 rows × 35 columns
tafe_resignation = tafe_survey_updated[tafe_survey_updated["separationtype"]=="Resignation"]
tafe_resignation.head()
id | Institute | WorkArea | cease_date | separationtype | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | gender | age | employment_status | position | institute_service | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | 6.341475e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | ... | - | - | Other | - | Female | 56 or older | Contract/casual | Teacher (including LVT) | 7-10 | 7-10 |
6 | 6.341520e+17 | Barrier Reef Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | Maternity/Family | ... | - | - | Other | - | Male | 20 or younger | Temporary Full-time | Administration (AO) | 3-4 | 3-4 |
7 | 6.341537e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | ... | - | - | Other | - | Male | 46 50 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 23 columns
We want to analyse about reasons of resignation of the employees. So we separated out data whose separation type is Resignation.
pd.set_option('mode.chained_assignment', None) # this lines shuts down SettingWithCopyWarning
pattern = r"([1-2][0-9]{3})"
dete_resignation['cease_date'] = dete_resignation['cease_date'].str.split('/').str.get(-1).astype('float')
dete_resignation["cease_date"].value_counts(ascending = False)
2013.0 146 2012.0 129 2014.0 22 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
tafe_resignation['cease_date'].value_counts()
2011.0 116 2012.0 94 2010.0 68 2013.0 55 2009.0 2 Name: cease_date, dtype: int64
All dates for dete_resignation are in range 1940 - 2020.
dete_resignation["institute_service"] = dete_resignation["cease_date"]-dete_resignation["dete_start_date"]
This column will denote the year of service an employee gave to the institution. A similar column is also availabe for tafe_resignation dataframe.
tafe_resignation["Contributing Factors. Job Dissatisfaction"].value_counts(dropna=False)
- 270 Job Dissatisfaction 62 NaN 8 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
tafe_resignation["Contributing Factors. Dissatisfaction"].value_counts(dropna=False)
- 277 Contributing Factors. Dissatisfaction 55 NaN 8 Name: Contributing Factors. Dissatisfaction, dtype: int64
def update_values(num):
if pd.isnull(num):
return
elif num == '-':
return False
else:
return True
tafe_resignation['dissatisfied'] = tafe_resignation[["Contributing Factors. Job Dissatisfaction","Contributing Factors. Dissatisfaction"]].applymap(update_values).any(axis=1,skipna = False)
print(tafe_resignation['dissatisfied'].value_counts())
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)
print(dete_resignation['dissatisfied'].value_counts())
False 241 True 91 Name: dissatisfied, dtype: int64 False 162 True 149 Name: dissatisfied, dtype: int64
dete_resignation['job_dissatisfaction'].value_counts()
False 270 True 41 Name: job_dissatisfaction, dtype: int64
dete_resignation['dissatisfaction_with_the_department'].value_counts()
False 282 True 29 Name: dissatisfaction_with_the_department, dtype: int64
dete_resignation_up = dete_resignation.copy()
tafe_resignation_up = tafe_resignation.copy()
dete_resignation_up['institute'] = 'DETE'
tafe_resignation_up['institute'] = 'TAFE'
print(dete_resignation_up.head())
print(tafe_resignation_up.head())
id separationtype cease_date dete_start_date \ 3 4 Resignation-Other reasons 2012.0 2005.0 5 6 Resignation-Other reasons 2012.0 1994.0 8 9 Resignation-Other reasons 2012.0 2009.0 9 10 Resignation-Other employer 2012.0 1997.0 11 12 Resignation-Move overseas/interstate 2012.0 2009.0 role_start_date position classification region \ 3 2006.0 Teacher Primary Central Queensland 5 1997.0 Guidance Officer NaN Central Office 8 2009.0 Teacher Secondary North Queensland 9 2008.0 Teacher Aide NaN NaN 11 2009.0 Teacher Secondary Far North Queensland business_unit employment_status ... gender age aboriginal \ 3 NaN Permanent Full-time ... Female 36-40 NaN 5 Education Queensland Permanent Full-time ... Female 41-45 NaN 8 NaN Permanent Full-time ... Female 31-35 NaN 9 NaN Permanent Part-time ... Female 46-50 NaN 11 NaN Permanent Full-time ... Male 31-35 NaN torres_strait south_sea disability nesb institute_service \ 3 NaN NaN NaN NaN 7.0 5 NaN NaN NaN NaN 18.0 8 NaN NaN NaN NaN 3.0 9 NaN NaN NaN NaN 15.0 11 NaN NaN NaN NaN 3.0 dissatisfied institute 3 False DETE 5 True DETE 8 False DETE 9 True DETE 11 False DETE [5 rows x 38 columns] id Institute \ 3 6.341399e+17 Mount Isa Institute of TAFE 4 6.341466e+17 Southern Queensland Institute of TAFE 5 6.341475e+17 Southern Queensland Institute of TAFE 6 6.341520e+17 Barrier Reef Institute of TAFE 7 6.341537e+17 Southern Queensland Institute of TAFE WorkArea cease_date separationtype \ 3 Non-Delivery (corporate) 2010.0 Resignation 4 Delivery (teaching) 2010.0 Resignation 5 Delivery (teaching) 2010.0 Resignation 6 Non-Delivery (corporate) 2010.0 Resignation 7 Delivery (teaching) 2010.0 Resignation Contributing Factors. Career Move - Public Sector \ 3 - 4 - 5 - 6 - 7 - Contributing Factors. Career Move - Private Sector \ 3 - 4 Career Move - Private Sector 5 - 6 Career Move - Private Sector 7 - Contributing Factors. Career Move - Self-employment \ 3 - 4 - 5 - 6 - 7 - Contributing Factors. Ill Health Contributing Factors. Maternity/Family \ 3 - - 4 - - 5 - - 6 - Maternity/Family 7 - - ... Contributing Factors. Other Contributing Factors. NONE gender \ 3 ... - - NaN 4 ... - - Male 5 ... Other - Female 6 ... Other - Male 7 ... Other - Male age employment_status position \ 3 NaN NaN NaN 4 41 45 Permanent Full-time Teacher (including LVT) 5 56 or older Contract/casual Teacher (including LVT) 6 20 or younger Temporary Full-time Administration (AO) 7 46 50 Permanent Full-time Teacher (including LVT) institute_service role_service dissatisfied institute 3 NaN NaN False TAFE 4 3-4 3-4 False TAFE 5 7-10 7-10 False TAFE 6 3-4 3-4 False TAFE 7 3-4 3-4 False TAFE [5 rows x 25 columns]
combined = pd.concat([dete_resignation_up,tafe_resignation_up])
C:\Users\Sourav Saraf\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version of pandas will change to not sort by default. To accept the future behavior, pass 'sort=False'. To retain the current behavior and silence the warning, pass 'sort=True'. """Entry point for launching an IPython kernel.
combined.head()
Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Dissatisfaction | Contributing Factors. Ill Health | Contributing Factors. Interpersonal Conflict | Contributing Factors. Job Dissatisfaction | Contributing Factors. Maternity/Family | Contributing Factors. NONE | Contributing Factors. Other | ... | role_service | role_start_date | separationtype | south_sea | study/travel | torres_strait | traumatic_incident | work_life_balance | work_location | workload | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 2006.0 | Resignation-Other reasons | NaN | False | NaN | False | False | False | False |
5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 1997.0 | Resignation-Other reasons | NaN | False | NaN | False | False | False | False |
8 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 2009.0 | Resignation-Other reasons | NaN | False | NaN | False | False | False | False |
9 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 2008.0 | Resignation-Other employer | NaN | False | NaN | False | False | False | False |
11 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 2009.0 | Resignation-Move overseas/interstate | NaN | False | NaN | False | False | False | False |
5 rows × 53 columns
#combined_updated.loc[:,"institute_service"] = combined_updated["institute_service"].astype('str').str.extract(r'(\d+)')
combined_updated["institute_service"]
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-28-05aa2f08b982> in <module> ----> 1 combined_updated["institute_service"] NameError: name 'combined_updated' is not defined
#dete_resignation_up.shape
tafe_resignation_up.shape
(340, 25)