Clean and analyse employee exit surveys¶
This project entails working with exit surveys from employees of the Department of Education, training and Employment(DETE) and the Technical and Further Education(TAFE) institue in Queensland, AUstralia.
project goal?
Present to the stakeholders some key important answers to some questions which include:
Are employees who worked for a period of short time resigning due to a kind of dissatisfaction? considering the employees who have worked there for a longer period of time.
Are younger employees resigning due to some kind of dissatisfaction while considering the older employees.
In [1]:
#start by first reading in the datasets into pandas. this will be done by first importing the necessary libraries(pandas and numpy)
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from importlib import reload
reload(plt)
#i will read in the csv files right now using pandas
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
#lets access the information in our files using the dataframe method
In [2]:
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
In [3]:
dete_survey.head(20)
Out[3]:
| 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 | 6 | Resignation-Other reasons | 05/2012 | 1994 | 1997 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | D | D | NaN | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
| 6 | 7 | Age Retirement | 05/2012 | 1972 | 2007 | Teacher | Secondary | Darling Downs South West | NaN | Permanent Part-time | ... | D | D | SD | Female | 56-60 | NaN | NaN | NaN | NaN | NaN |
| 7 | 8 | Age Retirement | 05/2012 | 1988 | 1990 | Teacher Aide | NaN | North Coast | NaN | Permanent Part-time | ... | SA | NaN | SA | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
| 8 | 9 | Resignation-Other reasons | 07/2012 | 2009 | 2009 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | A | D | N | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
| 9 | 10 | Resignation-Other employer | 2012 | 1997 | 2008 | Teacher Aide | NaN | Not Stated | NaN | Permanent Part-time | ... | SD | SD | SD | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
| 10 | 11 | Age Retirement | 2012 | 1999 | 1999 | Teacher | Primary | Central Office | Education Queensland | Permanent Full-time | ... | A | NaN | A | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
| 11 | 12 | Resignation-Move overseas/interstate | 2012 | 2009 | 2009 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | ... | N | N | N | Male | 31-35 | NaN | NaN | NaN | NaN | NaN |
| 12 | 13 | Resignation-Other reasons | 2012 | 1998 | 1998 | Teacher | Primary | Far North Queensland | NaN | Permanent Full-time | ... | SA | A | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
| 13 | 14 | Age Retirement | 2012 | 1967 | 2000 | Teacher | Primary | Metropolitan | NaN | Permanent Part-time | ... | A | D | A | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
| 14 | 15 | Resignation-Other employer | 2012 | 2007 | 2010 | Teacher | Secondary | Central Queensland | NaN | Permanent Full-time | ... | SA | N | SA | Male | 31-35 | NaN | NaN | NaN | NaN | NaN |
| 15 | 16 | Voluntary Early Retirement (VER) | 2012 | 1995 | 2004 | Teacher | Secondary | Central Queensland | NaN | Permanent Full-time | ... | A | N | A | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
| 16 | 17 | Resignation-Other reasons | 2012 | Not Stated | Not Stated | Teacher Aide | NaN | South East | NaN | Permanent Part-time | ... | M | M | M | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
| 17 | 18 | Age Retirement | 2012 | 1996 | 1996 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | A | A | Female | 56-60 | NaN | NaN | NaN | NaN | NaN |
| 18 | 19 | Age Retirement | 2012 | 2006 | 2006 | Cleaner | NaN | Central Office | Education Queensland | Permanent Full-time | ... | A | A | A | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
| 19 | 20 | Age Retirement | 2012 | 1989 | 1989 | Cleaner | NaN | Central Office | Education Queensland | Permanent Full-time | ... | A | A | A | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
20 rows × 56 columns
In [4]:
#let us look closer to each columns missing value sum
dete_survey['Classification'].value_counts()
Out[4]:
Primary 161 Secondary 124 A01-A04 66 AO5-AO7 46 Special Education 33 AO8 and Above 14 PO1-PO4 8 Middle 3 Name: Classification, dtype: int64
In [5]:
#let us take a look at the age variation
dete_survey['Age'].value_counts()
Out[5]:
61 or older 222 56-60 174 51-55 103 46-50 63 41-45 61 26-30 57 36-40 51 21-25 40 31-35 39 20 or younger 1 Name: Age, dtype: int64
In [6]:
#let us look at disability column
dete_survey['Disability'].value_counts()
Out[6]:
Yes 23 Name: Disability, dtype: int64
In [7]:
#lets see how many missing values each columns have in our dete_survey
dete_survey.isnull().sum()
Out[7]:
ID 0 SeparationType 0 Cease Date 0 DETE Start Date 0 Role Start Date 0 Position 5 Classification 367 Region 0 Business Unit 696 Employment Status 5 Career move to public sector 0 Career move to private sector 0 Interpersonal conflicts 0 Job dissatisfaction 0 Dissatisfaction with the department 0 Physical work environment 0 Lack of recognition 0 Lack of job security 0 Work location 0 Employment conditions 0 Maternity/family 0 Relocation 0 Study/Travel 0 Ill Health 0 Traumatic incident 0 Work life balance 0 Workload 0 None of the above 0 Professional Development 14 Opportunities for promotion 87 Staff morale 6 Workplace issue 34 Physical environment 5 Worklife balance 7 Stress and pressure support 12 Performance of supervisor 9 Peer support 10 Initiative 9 Skills 11 Coach 55 Career Aspirations 76 Feedback 30 Further PD 54 Communication 8 My say 10 Information 6 Kept informed 9 Wellness programs 56 Health & Safety 29 Gender 24 Age 11 Aboriginal 806 Torres Strait 819 South Sea 815 Disability 799 NESB 790 dtype: int64
I will explore the dete_survey with large NaN values to get a deep view of what is going on
In [8]:
dete_survey['Classification'].value_counts()
Out[8]:
Primary 161 Secondary 124 A01-A04 66 AO5-AO7 46 Special Education 33 AO8 and Above 14 PO1-PO4 8 Middle 3 Name: Classification, dtype: int64
In [9]:
#lets see what the tafe_survey has to tell us
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
In [10]:
tafe_survey.head()
Out[10]:
| 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
In [11]:
tafe_survey.isnull().sum()
Out[11]:
Record ID 0
Institute 0
WorkArea 0
CESSATION YEAR 7
Reason for ceasing employment 1
Contributing Factors. Career Move - Public Sector 265
Contributing Factors. Career Move - Private Sector 265
Contributing Factors. Career Move - Self-employment 265
Contributing Factors. Ill Health 265
Contributing Factors. Maternity/Family 265
Contributing Factors. Dissatisfaction 265
Contributing Factors. Job Dissatisfaction 265
Contributing Factors. Interpersonal Conflict 265
Contributing Factors. Study 265
Contributing Factors. Travel 265
Contributing Factors. Other 265
Contributing Factors. NONE 265
Main Factor. Which of these was the main factor for leaving? 589
InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 94
InstituteViews. Topic:2. I was given access to skills training to help me do my job better 89
InstituteViews. Topic:3. I was given adequate opportunities for personal development 92
InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 94
InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 87
InstituteViews. Topic:6. The organisation recognised when staff did good work 95
InstituteViews. Topic:7. Management was generally supportive of me 88
InstituteViews. Topic:8. Management was generally supportive of my team 94
InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 92
InstituteViews. Topic:10. Staff morale was positive within the Institute 100
InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 101
InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 105
...
WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 91
WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 96
WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 92
WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 93
WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 99
WorkUnitViews. Topic:30. Staff morale was positive within my work unit 96
Induction. Did you undertake Workplace Induction? 83
InductionInfo. Topic:Did you undertake a Corporate Induction? 270
InductionInfo. Topic:Did you undertake a Institute Induction? 219
InductionInfo. Topic: Did you undertake Team Induction? 262
InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 147
InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 147
InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 147
InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 172
InductionInfo. On-line Topic:Did you undertake a Institute Induction? 147
InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 149
InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 147
InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 147
InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 147
Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 94
Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 108
Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 115
Workplace. Topic:Does your workplace value the diversity of its employees? 116
Workplace. Topic:Would you recommend the Institute as an employer to others? 121
Gender. What is your Gender? 106
CurrentAge. Current Age 106
Employment Type. Employment Type 106
Classification. Classification 106
LengthofServiceOverall. Overall Length of Service at Institute (in years) 106
LengthofServiceCurrent. Length of Service at current workplace (in years) 106
Length: 72, dtype: int64
from the explored data, we could see that in out dete_survey
a) We have an unclean data and we need to do a lot of data cleaning
b) Both datasets have quite disimilar data recording and contains a lot of columns we may not necessary need
c) Some NaN values are not explicitly shown as Nan but as 'Not stated' which appeared in any format different from Nan
d) some rows repeated the reason for resignation.
e) Each dataset contains many of thesame columns but the columns have different names.
Now, let us start by handling the NaN and extra not needed columns in our analysis.
cleaning column names¶
In [12]:
# we will start by reading the dete_survey again as csv but this time,
# we will specify what should be counted as our NaN value
dete_survey = pd.read_csv('dete_survey.csv', na_values='Not Stated')
In [13]:
# let us view our dete_survey again
dete_survey.head()
Out[13]:
| ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
| 1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
| 2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
| 3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
| 4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 56 columns
In [14]:
# let us drop some columns that is not necessary. we wont actually need
# from column 28 to 49
dete_survey.columns[28:49]
Out[14]:
Index(['Professional Development', 'Opportunities for promotion',
'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'],
dtype='object')
In [15]:
# let us drop some columns that is not necessary. we wont actually need
# from column 28 to 49 dete_survey
dete_to_drop = dete_survey.columns[28:49]
dete_survey_updated = dete_survey.drop(dete_to_drop, axis=1)
In [16]:
dete_survey_updated.head()
Out[16]:
| 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
Let us work on cleaning our column name to the choice we want. we want to start our cleaning from the columns names.
In [17]:
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(' ', '_')
dete_survey_updated.columns
Out[17]:
Index(['id', 'separationtype', 'cease_date', 'dete_start_date',
'role_start_date', 'position', 'classification', 'region',
'business_unit', 'employment_status', 'career_move_to_public_sector',
'career_move_to_private_sector', 'interpersonal_conflicts',
'job_dissatisfaction', 'dissatisfaction_with_the_department',
'physical_work_environment', 'lack_of_recognition',
'lack_of_job_security', 'work_location', 'employment_conditions',
'maternity/family', 'relocation', 'study/travel', 'ill_health',
'traumatic_incident', 'work_life_balance', 'workload',
'none_of_the_above', 'gender', 'age', 'aboriginal', 'torres_strait',
'south_sea', 'disability', 'nesb'],
dtype='object')
In [18]:
# let us drop some columns that is not necessary. we wont actually need
# from column 17:66 for tafe_survey
tafe_to_drop = tafe_survey.columns[17:66]
tafe_survey_updated = tafe_survey.drop(tafe_to_drop, axis=1)
From the above code, i first set my NaN values to be all missing values from the dataset which is not seen as string NaN.
I had to first identify some columns that is not necessary for both of my datasets analysis for both datsets.
After the identification, i had to put each of them(differently) in a variable which is based on the columns not needed, and called them using the dataframe drop method since i am working with a dataframe with many columns
working on the cleaning of the columns names for tafe_survey dataset.
In [19]:
#let's assign the columns we want to rename to overcome ambigious nature of the column names
#we will start by assigning them to a seperate variable, for ease.
tafe_rename = {'Record ID': 'id','CESSATION YEAR': 'cease_date',
'Reason for ceasing employment': 'separationtype',
'Gender. What is your Gender?': 'gender',
'CurrentAge. Current Age': 'age',
'Employment Type. Employment Type': 'employment_status',
'Classification. Classification': 'position',
'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service',
'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'
}
tafe_survey_updated = tafe_survey_updated.rename(tafe_rename, axis=1)
#let's view our columns head
tafe_survey_updated.head()
Out[19]:
| 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
In [20]:
tafe_survey_updated['age'].value_counts()
Out[20]:
56 or older 162 51-55 82 41 45 80 46 50 59 31 35 52 36 40 51 26 30 50 21 25 44 20 or younger 16 Name: age, dtype: int64
Generally, what i have done so far, is looking at my data and trying to understand it fully.
This data was gotten from different sources with different survey pattern. so what insights are the survey saying? which should be my goal (knowing the employee exit survey)
To analyze this data so far, what i have done is
importing both pandas and numpy
reading my csv files(both dete_survey and tafe_survey) with pandas
looking at my column headers and checking for null(NaN) or missing data in my dataset, which i had many of them
Dropped some columns which it is not helpful to my endpoint, or my goal.
Tried working on my columns for each dataset, where in dete, i replaced some formats and in tafe, i renamed some headers.
Filtering the Data¶
In [21]:
#let us first look at the reason for the employee exit from the separationtype
#column by looking at the unique value counts for each reason.
print(dete_survey_updated['separationtype'].value_counts())
print(tafe_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 Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
The goal of our project is to analyse the resignation type of employee exit, which from the separation type column for both dataset, we could see that Resignation for dete_survey accounts for (150+91+70) which is dependent on the reason for resignation, and that of tafe_survey is 340
In [22]:
#let us reuse our series.value_counts() method to look at what happens to
#our separationtype column for dete_survey_updated
dete_survey_updated['separationtype'].value_counts()
Out[22]:
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
In [23]:
#now since we have different resignations orders, let us convert them to one word which is resignation
#i will first split the resignation type and remove other words that is not resignation
dete_survey_updated['separationtype'] = dete_survey_updated['separationtype'].str.split('-').str[0]
#lets confirm if our data was updated
dete_survey_updated['separationtype'].value_counts()
#confrimed, our resignation is now together.
Out[23]:
Resignation 311 Age Retirement 285 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
In [24]:
#let us reuse our series.value_counts() method to look at what happens to
#our separationtype column for tafe_survey_updated
tafe_survey_updated['separationtype'].value_counts()
Out[24]:
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
In [25]:
#since we are interested in the resignation of the employee exit type,
# we will work with resignation type from the separation column. but we will use dataframe.copy()
#this is so because we want to avoid the problem ofsettingwithCopy Warning.
dete_resignation = dete_survey_updated[dete_survey_updated['separationtype']=='Resignation'].copy()
In [26]:
#since we are interested in the resignation of the employee exit type,
# we will work with resignation type from the separation column. but we will use dataframe.copy()
#this is so because we want to avoid the problem ofsettingwithCopy Warning.
tafe_resignation = tafe_survey_updated[tafe_survey_updated['separationtype']=='Resignation'].copy()
At this point, i introudced a dataframe.copy method to copy seperately the findings from the resignation type in the separationtype column. This is so because i would encouter a SettingwithCopy warning. This warning arises as a result of chained assignment error.
Verifying the Data¶
when working with real life data, it is good we avoid the error of assuming that the data we are analyzing isn't corrupted. catching all the errors at once might not always be possible, but we have to make the data look reasonable. i will start be verifying the cease_date and dete_start_date columns to see if the years make any meaningful sense.
observations? yes, there is inconsistency in my date format type. But we are only interested in the years.
we will extract the year from the cease_date column
In [27]:
dete_resignation['cease_date'].value_counts()
Out[27]:
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 07/2013 9 11/2013 9 10/2013 6 08/2013 4 05/2013 2 05/2012 2 07/2006 1 07/2012 1 09/2010 1 2010 1 Name: cease_date, dtype: int64
In [28]:
#let's look at the dete_start_date column to check out for outliers
dete_resignation['dete_start_date'].value_counts().sort_index(ascending=True)
Out[28]:
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
In [29]:
#let us check out for tafe_resignation column
tafe_resignation['cease_date'].value_counts(ascending=True)
Out[29]:
2009.0 2 2013.0 55 2010.0 68 2012.0 94 2011.0 116 Name: cease_date, dtype: int64
In [30]:
# Extract the years and convert them to a float type
dete_resignation['cease_date'] = dete_resignation['cease_date'].str.split('/').str[-1]
dete_resignation['cease_date'] = dete_resignation['cease_date'].astype("float")
# Check the values again and look for outliers
dete_resignation['cease_date'].value_counts()
Out[30]:
2013.0 146 2012.0 129 2014.0 22 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
Boxplot representation¶
let us use boxplot to reresent our date column data to point out outliers
In [31]:
dete_resignation['cease_date'].describe()
Out[31]:
count 300.00000 mean 2012.60000 std 0.75403 min 2006.00000 25% 2012.00000 50% 2013.00000 75% 2013.00000 max 2014.00000 Name: cease_date, dtype: float64
box plot for dete_resignation cease_date¶
ax = dete_resignation.boxplot(column=['cease_date']) plt.title("dete cease employee year") ax.set_ylim(2004,2016) plt.ylabel("Year") plt.show
The box plot for dete_resignation and statistics of cease_date suggests that we recorded large number of resignation within 2012 - 2014 alone, while we have an outlier in 2006
In [32]:
#lets work for start date on dete_survey
ax = dete_resignation.boxplot(column = ['dete_start_date'])
plt.title("dete start date")
ax.set_ylim(1963,2013)
plt.ylabel("year")
plt.show()
In [33]:
dete_resignation['dete_start_date'].describe()
Out[33]:
count 283.000000 mean 2002.067138 std 9.914479 min 1963.000000 25% 1997.000000 50% 2005.000000 75% 2010.000000 max 2013.000000 Name: dete_start_date, dtype: float64
As can be seen, most of our start date occured in the late 1997 to 2010, while we outliers in periods below 1997
In [34]:
tafe_resignation['cease_date'].describe()
Out[34]:
count 335.000000 mean 2011.394030 std 1.005952 min 2009.000000 25% 2011.000000 50% 2011.000000 75% 2012.000000 max 2013.000000 Name: cease_date, dtype: float64
In [35]:
#lets work on tafe_resignation
ax = tafe_resignation.boxplot(column=['cease_date'])
plt.title("tafe employee cease date")
ax.set_ylim(2009,2013)
plt.ylabel("year")
plt.show()
tafe_resignation suggests that people resigned mostly betwen 2011-2012
Summary on date values¶
I did not see any years listed after the current date, and I did not see any start dates before the year 1940. There were many outliers present, but nothing that seemed inherently incorrect.
Therefore, I can verify that there aren't any major issues with the years.
Creating a new column, institute_service in dete_resignation¶
In [36]:
# let us review our dete_resignation columns again.
dete_resignation.head()
Out[36]:
| 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 | 2012.0 | 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 | 2012.0 | 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 | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
| 9 | 10 | Resignation | 2012.0 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | ... | False | False | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
| 11 | 12 | Resignation | 2012.0 | 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
Having our goal in mind which is checking out on whether resignation of employees was as a result of dissatisfaction. we will do it by comparing both employees with long and short service rate.(which will be done by substracting start date and cease date of the various employees) we will create a column in dete_resignation column, and term it institue_service, since we already have a corresponding column description in tafe_resignation.
from the dete_resignation column ahead, we can see that we can infer length of service from the difference between cease_date and dete_start_date.
Now let us create a new column that would refer to the length of service
In [37]:
#creating length of service column will be done by subtracting the dete_start_date from the cease_date
institute_service = dete_resignation['cease_date'] - dete_resignation['dete_start_date']
#let us assign this variable as a column in our dete_resignation data
dete_resignation['institute_service'] = institute_service
print(dete_resignation['institute_service'])
3 7.0
5 18.0
8 3.0
9 15.0
11 3.0
12 14.0
14 5.0
16 NaN
20 30.0
21 32.0
22 15.0
23 39.0
25 17.0
27 7.0
33 9.0
34 6.0
37 1.0
39 NaN
40 35.0
41 38.0
42 1.0
43 36.0
48 3.0
50 3.0
51 19.0
55 4.0
57 9.0
61 1.0
69 6.0
71 1.0
...
747 6.0
751 8.0
752 15.0
753 9.0
755 1.0
762 0.0
766 7.0
769 5.0
770 NaN
771 12.0
774 NaN
784 0.0
786 20.0
788 NaN
789 31.0
790 6.0
791 NaN
794 NaN
797 NaN
798 NaN
802 NaN
803 10.0
804 6.0
806 8.0
807 9.0
808 3.0
815 2.0
816 2.0
819 5.0
821 NaN
Name: institute_service, Length: 311, dtype: float64
In [38]:
ax = dete_resignation.boxplot(column=['institute_service'])
plt.title("dete work years")
plt.ylabel("year")
plt.show()
In [39]:
dete_resignation['institute_service'].describe()
Out[39]:
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
dete_resignation insitute_service boxplot suggests that majority of recorded years work lies between 3 - 16 years.
We have successfully created a new column in our dete_resignation column known as institute_service, which takes care of the difference between the start and end work date of the exited employees (which is known as years of service).
Observations made from the result:
from our observations, we can see that though we have succesfully crated a column termed institute_service, which calculates the length of service by finding the years range between start and end work date of employees(years of service), some rows still shows NaN.
tafe institute service¶
In [40]:
tafe_resignation['institute_service'].value_counts()
Out[40]:
Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 7-10 21 More than 20 years 10 Name: institute_service, dtype: int64
Identifying Dissatisfied Employees¶
In [41]:
dete_survey_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 35 columns): id 822 non-null int64 separationtype 822 non-null object cease_date 788 non-null object dete_start_date 749 non-null float64 role_start_date 724 non-null float64 position 817 non-null object classification 455 non-null object region 717 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 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), float64(2), int64(1), object(14) memory usage: 123.7+ KB
In [42]:
tafe_survey_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 23 columns): id 702 non-null float64 Institute 702 non-null object WorkArea 702 non-null object cease_date 695 non-null float64 separationtype 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 gender 596 non-null object age 596 non-null object employment_status 596 non-null object position 596 non-null object institute_service 596 non-null object role_service 596 non-null object dtypes: float64(2), object(21) memory usage: 126.2+ KB
We are going to select the columns necessary to categorie employees as 'dissatisfied'.
Going through the tafe_survey_updated, i think, 3 columns would be necessary for this.
Contributing Factors: Dissatisfaction(are they tired of the whole system?)
Contributing Facctors: Job Dissatisfaction(Are they tired of the job?) and if so, why?? the next step to this question is, is it because of education advancement(if they are young), this will make me to go for the next column selection which will be
for dete_survey_updated i will select the following:
job_dissatisfaction
dissatisfaction_with_the_department
physical_work_environment
lack_of_recognition
lack_of_job_security
work_location
employment_conditions
work_life_balance
workload
Hence, our next step is to see if any of the employees matches this discription (respectively for each survey) and we will classlify them as dissatisfied in a new column.
in Summary: True: indicates a person resigned because they were dissatisfied in some way
False: indicates a person resigned because of a reason other than dissatisfaction with the job
NaN: indicates the value is missing
In [43]:
#let us verify the unique content of each selected contributing factors in tafe_resignation
tafe_resignation['Contributing Factors. Dissatisfaction'].value_counts()
#shows that contributing factor.dissatifaction numbers 55 recorded cases, while others are represented as (-)
Out[43]:
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
In [44]:
##let us verify the unique content of each selected contributing factors in tafe_resignation
tafe_resignation['Contributing Factors. Job Dissatisfaction'].value_counts()
#shows that contributing factor. job dissatifaction numbers 62 recorded cases, while others are represented as (-)
Out[44]:
- 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
In [45]:
# let us define a function that would carry out this operation for us.
def considered_value(val):
if val == '-':
return False
elif pd.isnull(val):
return np.nan
else:
return True
#let us now create a column termed dissatisfied that fits in with this defined function
tafe_resignation['dissatisfied'] = tafe_resignation[['Contributing Factors. Dissatisfaction',
'Contributing Factors. Job Dissatisfaction',
]].applymap(considered_value).any(axis=1, skipna=False)
tafe_resignation_up = tafe_resignation.copy()
tafe_resignation_up['dissatisfied'].value_counts(dropna=False)
Out[45]:
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
from this, it can be seen that i have 91 cases of dissatisfaction for tafe_resignation
In [46]:
# lets do the same for dete_resignation
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)
dete_resignation_up = dete_resignation.copy()
dete_resignation_up['dissatisfied'].value_counts(dropna=False)
Out[46]:
False 162 True 149 Name: dissatisfied, dtype: int64
from this, it can be seen that i have 149 cases of dissatisfaction for dete_resignation.
So far, we have created a new column(with required or needed variables) seperately for our analysis for both data survey respectively which shows the reason (which surrounds dissatisfaction) of employees exit
Combining the Data¶
Summary of what i have done so far:
- Renamed the columns
- Dropped any data not needed for our analysis
- Verified the quality of our data
- Created a new institute_service column
- Cleaned the Contributing Factors columns
- Created a new column indicating if an employee resigned because they were dissatisfied in some way
Let us try to aggregate the datasets. Our end goal is aggregating data according to the institute_service column(which we earlier on created)
In [47]:
#we will add a new column to be able to differentiate between the intitute_service column of both data surveys or dataframes easily.
dete_resignation_up['institute'] = "DETE"
tafe_resignation_up['institute'] = "TAFE"
#combining the dataframe
combined = pd.concat([dete_resignation_up, tafe_resignation_up], ignore_index=True)
#let us verify our data concatenation
combined.notnull().sum().sort_values()
Out[47]:
torres_strait 0 south_sea 3 aboriginal 7 disability 8 nesb 9 business_unit 32 classification 161 region 265 role_start_date 271 dete_start_date 283 role_service 290 career_move_to_public_sector 311 employment_conditions 311 work_location 311 lack_of_job_security 311 job_dissatisfaction 311 dissatisfaction_with_the_department 311 workload 311 lack_of_recognition 311 interpersonal_conflicts 311 maternity/family 311 none_of_the_above 311 physical_work_environment 311 relocation 311 study/travel 311 traumatic_incident 311 work_life_balance 311 career_move_to_private_sector 311 ill_health 311 Contributing Factors. Career Move - Private Sector 332 Contributing Factors. Other 332 Contributing Factors. Career Move - Public Sector 332 Contributing Factors. Career Move - Self-employment 332 Contributing Factors. Travel 332 Contributing Factors. Study 332 Contributing Factors. Dissatisfaction 332 Contributing Factors. Ill Health 332 Contributing Factors. NONE 332 Contributing Factors. Maternity/Family 332 Contributing Factors. Job Dissatisfaction 332 Contributing Factors. Interpersonal Conflict 332 WorkArea 340 Institute 340 institute_service 563 gender 592 age 596 employment_status 597 position 598 cease_date 635 dissatisfied 643 id 651 separationtype 651 institute 651 dtype: int64
In [48]:
#since we only need the relevant data for our analyses, let us drop those which are not relevant
#from observation, we are going to drop data with less than 500 non null values because they are not important for our amalyses.
combined_update = combined.dropna(thresh=500, axis=1).copy()
combined_update.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 651 entries, 0 to 650 Data columns (total 10 columns): age 596 non-null object cease_date 635 non-null float64 dissatisfied 643 non-null object employment_status 597 non-null object gender 592 non-null object id 651 non-null float64 institute 651 non-null object institute_service 563 non-null object position 598 non-null object separationtype 651 non-null object dtypes: float64(2), object(8) memory usage: 50.9+ KB
In [49]:
combined_update['institute'].value_counts()
Out[49]:
TAFE 340 DETE 311 Name: institute, dtype: int64
in combining the data, we first created a column, which would make it easier to identify and spot each dataset(between DETE and TAFE)
After that, we were able to remove the data not necessary for our analyses by using dropna method
Cleaning the Service column¶
In [50]:
combined['institute_service'].value_counts()
Out[50]:
Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 5.0 23 1.0 22 7-10 21 3.0 20 0.0 20 6.0 17 4.0 16 9.0 14 2.0 14 7.0 13 More than 20 years 10 13.0 8 8.0 8 20.0 7 15.0 7 14.0 6 17.0 6 12.0 6 10.0 6 22.0 6 18.0 5 16.0 5 24.0 4 23.0 4 11.0 4 19.0 3 39.0 3 21.0 3 32.0 3 36.0 2 25.0 2 26.0 2 28.0 2 30.0 2 42.0 1 35.0 1 49.0 1 34.0 1 31.0 1 33.0 1 29.0 1 27.0 1 41.0 1 38.0 1 Name: institute_service, dtype: int64
working of the institute service column in important since there are variations in the years range.
hence, we will convert this numbers into categories. Hence, our analysis of this article(https://www.businesswire.com/news/home/20171108006002/en/Age-Number-Engage-Employees-Career-Stage) which makes cases of understanding employeees needs based on career stage than age.
our classification will be:
New: Less than 3 years at a company
Experienced: 3-6 years at a company
Established: 7-10 years at a company
Veteran: 11 or more years at a company
now let us categorize the institute_service column
In [51]:
#let us extract the years of service from the institute_service and change the type to str which is based on regex method
combined_update['institute_service_up'] = combined_update['institute_service'].astype('str').str.extract(r'(\d+)', expand=False)
combined_update['institute_service_up'] = combined_update['institute_service_up'].astype('float')
#let us verify our changes and compare with the combined_update['institute_service']
combined_update['institute_service_up'].value_counts()
Out[51]:
1.0 159 3.0 83 5.0 56 7.0 34 11.0 30 0.0 20 20.0 17 6.0 17 4.0 16 9.0 14 2.0 14 13.0 8 8.0 8 15.0 7 17.0 6 10.0 6 12.0 6 14.0 6 22.0 6 16.0 5 18.0 5 24.0 4 23.0 4 39.0 3 19.0 3 21.0 3 32.0 3 28.0 2 36.0 2 25.0 2 30.0 2 26.0 2 29.0 1 38.0 1 42.0 1 27.0 1 41.0 1 35.0 1 49.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service_up, dtype: int64
In [52]:
# Change values to float
combined_update['institute_service_up'] = combined_update['institute_service_up'].astype(float)
combined_update['institute_service_up'].describe()
Out[52]:
count 563.000000 mean 7.067496 std 8.251974 min 0.000000 25% 1.000000 50% 4.000000 75% 10.000000 max 49.000000 Name: institute_service_up, dtype: float64
In [53]:
combined_update['institute_service_up'].value_counts(dropna=False)
Out[53]:
1.0 159 NaN 88 3.0 83 5.0 56 7.0 34 11.0 30 0.0 20 20.0 17 6.0 17 4.0 16 9.0 14 2.0 14 13.0 8 8.0 8 15.0 7 22.0 6 10.0 6 17.0 6 14.0 6 12.0 6 16.0 5 18.0 5 24.0 4 23.0 4 21.0 3 39.0 3 32.0 3 19.0 3 36.0 2 30.0 2 25.0 2 26.0 2 28.0 2 42.0 1 29.0 1 35.0 1 27.0 1 41.0 1 49.0 1 38.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service_up, dtype: int64
we have succesfully converted the institute_service_up to float values with characters.
Now let us analyse our data with the above grouped classification for age, by creating a function that will do the mapping. the grouping will be like:
New: Less than 3 years at a company
Experienced: 3-6 years at a company
Established: 7-10 years at a company
Veteran: 11 or more years at a company
In [54]:
#let us create a function that will map the age classification.
def institute_map(element):
if element < 3:
return "New"
elif element >=3 and element <= 6:
return "Experienced"
elif element >=7 and element <= 10:
return "Established"
elif pd.isnull(element):
return np.nan
else:
return "Veteran"
combined_update['service_cat'] = combined_update['institute_service_up'].apply(institute_map)
combined_update['service_cat'].value_counts(dropna=False)
Out[54]:
New 193 Experienced 172 Veteran 136 NaN 88 Established 62 Name: service_cat, dtype: int64
I have created a column category service_cat that categorizes employees according to the amount of years spent in their workplace.
Performing Analysis¶
In [55]:
#verifying our dissatisfied column
#we have 392 false value, 251 true and 8 missing values
combined_update['dissatisfied'].value_counts(dropna=False)
Out[55]:
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
In [56]:
#since i have missing values as nan, i will update my false values to include nan
#since the false values appears most
combined_update['dissatisfied'] = combined_update['dissatisfied'].fillna(False)
In [57]:
#calculating the percentage of dissastisfied employees in each service_cat group
dissatisfied_percent = combined_update.pivot_table(index='service_cat', values='dissatisfied')
%matplotlib inline
dissatisfied_percent.plot(kind='bar', rot=30)
Out[57]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f2d9a195390>
consdering the job dissatisfaction from both dataset, Established(more than 7-10years) seems like they are more likely to exit due to dissatisaction. we will look on the next dataset to finalize
In [58]:
gender_dist = combined_update['gender'].value_counts(dropna=False)
print(gender_dist)
Female 424 Male 168 NaN 59 Name: gender, dtype: int64
In [59]:
combined_update.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 651 entries, 0 to 650 Data columns (total 12 columns): age 596 non-null object cease_date 635 non-null float64 dissatisfied 651 non-null bool employment_status 597 non-null object gender 592 non-null object id 651 non-null float64 institute 651 non-null object institute_service 563 non-null object position 598 non-null object separationtype 651 non-null object institute_service_up 563 non-null float64 service_cat 563 non-null object dtypes: bool(1), float64(3), object(8) memory usage: 56.7+ KB
In [60]:
combined_update['age'].value_counts(dropna=False)
Out[60]:
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 26 30 32 36 40 32 31 35 32 21-25 29 56 or older 29 31-35 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
In order to answer the question about dissatisfaction and age, I need to evaluate and clean the "age" column, since my age column seems not to be good and neat for analysis.
from the age column, there are irregularities in the different rows, some contain "-" while others dont. i will make it in uniform by converting those without "-" to contain "-", and i will replace 56 or older to 56-60.
In [61]:
# first, let me convert the age column to a string type
combined_update['age'] = combined_update['age'].astype(str)
combined_update['age_cleaned'] = combined['age'].str.replace(" ", "-").str.replace("56 or older", "56-60")
combined_update['age_cleaned'].value_counts(dropna=False)
Out[61]:
41-45 93 46-50 81 36-40 73 51-55 71 26-30 67 21-25 62 31-35 61 56-60 55 NaN 55 61 or older 23 20 or younger 10 Name: age_cleaned, dtype: int64
This section, I cleaned the age column in order to have consistent values across the dataset. i will use this information in analysing the dissastifaction rate of each employee catgory
Analysis on dissastifaction of the employees¶
We are going to look on the resignation of the employees under the following headings
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?
In [62]:
combined_update['dissatisfied'].value_counts()
Out[62]:
False 411 True 240 Name: dissatisfied, dtype: int64
Career stage dissastifaction plot¶
We will look on young resignation against old resignation category. Are new employees resigning more than the veterans or old employees? what should be the cause? for us to that, we will have to convert our data to pivot_table
In [63]:
#let us calculate the percentage of dissastified employees with there category
category_per_dis = combined_update.pivot_table(index = 'service_cat', values='dissatisfied')
print(category_per_dis)
dissatisfied service_cat Established 0.516129 Experienced 0.343023 New 0.295337 Veteran 0.485294
In [64]:
#lets plot this using bar chart
category_per_dis.plot(kind='bar', legend=False, rot=30, title='Resignation: Career Stage and Dissatisfaction')
plt.xlabel("Employee stage")
plt.ylabel("percent dissatisfied")
plt.show()
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?
New employees of about 29.5% resigned due to dissastifaction, while Established of about 7-10 years of experience has the highest resigning rate with about 51.6% while veterans with about 48% rate resigned and Experienced at about 34% resigned
Summary, New employees have a lesser resigning rate than those with higher work years experience.¶
Age dissastifaction rate¶
lets work with the age range. recall that we have a NaN value, lets drop it from this plot.
In [65]:
combined_update['age_cleaned'].value_counts()
Out[65]:
41-45 93 46-50 81 36-40 73 51-55 71 26-30 67 21-25 62 31-35 61 56-60 55 61 or older 23 20 or younger 10 Name: age_cleaned, dtype: int64
In [66]:
#lets convert the age catgory to pivot table to ease the plot
age_dis_per = combined_update.pivot_table(index='age_cleaned', values='dissatisfied')
age_dis_per
Out[66]:
| dissatisfied | |
|---|---|
| age_cleaned | |
| 20 or younger | 0.200000 |
| 21-25 | 0.306452 |
| 26-30 | 0.417910 |
| 31-35 | 0.377049 |
| 36-40 | 0.342466 |
| 41-45 | 0.376344 |
| 46-50 | 0.382716 |
| 51-55 | 0.422535 |
| 56-60 | 0.381818 |
| 61 or older | 0.521739 |
In [67]:
# lets visualize the plot
age_dis_per.plot(kind='bar', rot=90, title='Resignation: Age and Dissatisfaction', legend=False)
plt.xlabel('Age')
plt.ylabel('dissatisfied percent')
plt.show()
Employees 20 years younger are the least to resign at the rate of 20%, while those with more than 61 years of age resign the most at the rate of 52%
Summary¶
The analysis so far has showed that the groups most at risk of resigning due to dissatisfaction are the older employees (61 or older) and those who have been at the company 7 to 10 years (Established). The groups least at risk are the employees who are 21 and younger and those who have been at the company less than three years (New).
Age Impact and Career dissatisfaction rate¶
I want to investigate into that a bit further, and look at which combination of career stage and age is most likely to claim leaving due to dissatisfaction. I will also look into the effects of institute and position.
In [68]:
# let me create a pivot table for age, career stage and dissastifaction
age_car_dis_pt = combined_update.pivot_table(index="service_cat",columns="age_cleaned",values="dissatisfied")
age_car_dis_pt.head()
Out[68]:
| age_cleaned | 20 or younger | 21-25 | 26-30 | 31-35 | 36-40 | 41-45 | 46-50 | 51-55 | 56-60 | 61 or older |
|---|---|---|---|---|---|---|---|---|---|---|
| service_cat | ||||||||||
| Established | NaN | 0.000000 | 0.545455 | 0.75 | 0.400000 | 0.666667 | 0.363636 | 0.500000 | 0.333333 | 0.500000 |
| Experienced | 0.333333 | 0.285714 | 0.444444 | 0.30 | 0.380952 | 0.413793 | 0.285714 | 0.250000 | 0.250000 | 0.500000 |
| New | 0.142857 | 0.297297 | 0.320000 | 0.25 | 0.347826 | 0.233333 | 0.368421 | 0.318182 | 0.400000 | NaN |
| Veteran | NaN | NaN | NaN | 0.40 | 0.285714 | 0.500000 | 0.545455 | 0.600000 | 0.387097 | 0.642857 |
In [69]:
# now lets plot this into a figure plot.
fig = plt.figure(figsize=(16,16))
fig.suptitle('Dissatisfied Employee by Career stage and Age', fontsize=16)
#let us plot for New
ax = plt.subplot('221')
#to select only the new role,
age_car_dis_pt.iloc[2,:].plot(kind='bar', ax= ax)
plt.ylabel("Percent Dissatisfied")
ax.set_title("New: Less than 3 years")
ax.set_ylim([0, .8])
#lets plot for established
ax = plt.subplot('222')
#to select only the new role,
age_car_dis_pt.iloc[0,:].plot(kind='bar', ax= ax)
ax.set_title("Established: 3 - 6 years")
ax.set_ylim([0, .8])
#lets plot for experienced
ax = plt.subplot('223')
#to select only the new role,
age_car_dis_pt.iloc[1,:].plot(kind='bar', ax= ax)
plt.ylabel("Percent Dissatisfied")
ax.set_title("Experienced: 7 - 10 years")
ax.set_ylim([0, .8])
#lets plot for veteran
ax = plt.subplot('224')
#to select only the new role,
age_car_dis_pt.iloc[3,:].plot(kind='bar', ax= ax)
ax.set_title("New: more than 11 years")
ax.set_ylim([0, .8])
plt.show()
In [70]:
#using for loop to achieve the same result
fig = plt.figure(figsize=(16,16))
fig.suptitle('Dissatisfied Employee by Career stage and Age', fontsize=16)
for_plot = (("221", 2, "New: less than 3 years"),
("222", 0, "Established: 3-6 years"),
("223", 1, "Experienced: 7-10 years"),
("224", 3, "veteran: more than 11 years")
)
for sn, id, title in for_plot:
ax = plt.subplot(sn)
age_car_dis_pt.iloc[id,:].plot(kind="bar", ax= ax)
ax.set_title(title)
ax.set_ylim([0, .8])
plt.xlabel("Employee Age")
plt.ylabel("Percent Dissatisfied")
plt.show()
The new plot, Experienced and Veteran showed that the resignation rate rises on the inxrease for those workers with more than 61 of years.
Thus this was different for Established(between 3-6 years) which sees themselves in the high rsignation
DETE Resignation and TAFE rsignation¶
Now the question still rooms, amongst the different institute, which recorded the highest resigning rate?
I will work in comparing the resignation rate between the two institute.
let us first view the institute column
In [71]:
#lets view the total data in each institute survey
combined_update['institute'].value_counts()
Out[71]:
TAFE 340 DETE 311 Name: institute, dtype: int64
In [72]:
#We can see that Tafe seems to record more survey, lets see how it will affect the resignation rate
institute_plot = combined_update.pivot_table(index='institute', values='dissatisfied')
#let us put our data in a plot visual
institute_plot.plot(kind='bar', rot=0, title='Resignation due to Dissatisfaction per Institute', legend=False)
plt.xlabel='Institute'
plt.ylabel='Percent Dissatisfied'
plt.show()
Effects of Career Stage on Dissatisfaction per Institute¶
In [73]:
#let us first convert the require column to pivot table
inst_car_plot = combined_update.pivot_table(index='service_cat', columns='institute', values='dissatisfied')
inst_car_plot.head()
Out[73]:
| institute | DETE | TAFE |
|---|---|---|
| service_cat | ||
| Established | 0.609756 | 0.333333 |
| Experienced | 0.460526 | 0.250000 |
| New | 0.375000 | 0.262774 |
| Veteran | 0.560000 | 0.277778 |
In [74]:
#let us now plot our career stage on dissatisfaction per institute
inst_car_plot.plot(kind='bar', rot=0, title='Effects of Career Stage on Dissatisfaction per Institute')
plt.xlabel='career stage'
plt.ylabel='percent dissatisfied'
plt.legend(loc='upper right', fontsize='small')
plt.show()
Dete Established saw the highest resigning rate from the dete data survey by about 60% based on career stage category, which is followed by the veteran(more than 61 years older) and in Tafe, it seems that resignation is relatively constant across the levels but, established saw the highest resigning rate.
It could be looked further for the reason of resignation of established.
In [75]:
combined_update['gender'].value_counts()
Out[75]:
Female 424 Male 168 Name: gender, dtype: int64
In [76]:
gen_plot = combined_update.pivot_table(index='service_cat', columns='gender', values='dissatisfied')
gen_plot
Out[76]:
| gender | Female | Male |
|---|---|---|
| service_cat | ||
| Established | 0.545455 | 0.444444 |
| Experienced | 0.372881 | 0.283019 |
| New | 0.262411 | 0.384615 |
| Veteran | 0.478723 | 0.500000 |
In [77]:
gen_plot.plot(kind='bar', rot=0, title='gender resignation rate by service category')
plt.ylabel='percent dissatisfied'
plt.xlabel='gender resignation'
plt.legend(loc='best', fontsize='small')
plt.show()
More females contributed to the resignation rate in the employee exit.
Institute Summary¶
The differences between the institutes look very significant, but it is important to remember that there were 9 columns to describe dissatisfaction in the DETE survey, and two in TAFE.
Resignation effect on position¶
lets us look ath row position played in this resignation of employees to see which position in each survey got tired quickly
let us look at our position setting for both survey.
In [78]:
combined_update['position'].value_counts()
Out[78]:
Administration (AO) 148 Teacher 129 Teacher (including LVT) 95 Teacher Aide 63 Cleaner 39 Public Servant 30 Professional Officer (PO) 16 Operational (OO) 13 Head of Curriculum/Head of Special Education 10 School Administrative Staff 8 Technical Officer 8 Schools Officer 7 Workplace Training Officer 6 School Based Professional Staff (Therapist, nurse, etc) 5 Technical Officer (TO) 5 Executive (SES/SO) 4 Guidance Officer 3 Tutor 3 Other 3 Professional Officer 2 Business Service Manager 1 Name: position, dtype: int64
To get a bigger picture of what each survey is saying, and how to further clean or relace position names which are not matching or will ease our analysis, we will have to seperate and look at each survey independently
In [79]:
print('Dete position')
combined_update.loc[combined_update['institute']=="DETE", "position"].value_counts()
Dete position
Out[79]:
Teacher 129 Teacher Aide 63 Cleaner 39 Public Servant 30 Head of Curriculum/Head of Special Education 10 School Administrative Staff 8 Technical Officer 8 Schools Officer 7 School Based Professional Staff (Therapist, nurse, etc) 5 Guidance Officer 3 Other 3 Professional Officer 2 Business Service Manager 1 Name: position, dtype: int64
In [80]:
combined_update.loc[combined_update['institute']=="TAFE", "position"].value_counts()
Out[80]:
Administration (AO) 148 Teacher (including LVT) 95 Professional Officer (PO) 16 Operational (OO) 13 Workplace Training Officer 6 Technical Officer (TO) 5 Executive (SES/SO) 4 Tutor 3 Name: position, dtype: int64
Now we can see clearly that our Institute position in tafe survey needs some cleaning. we have to rename some measures.
In [81]:
tafe_pos_rename = {'Administration (AO)' : 'Administration',
'Teacher (including LVT)' : 'Teacher',
'Professional Officer (PO)' : 'Professional Officer',
'Operational (OO)' : 'Operational',
'Technical Officer (TO)' : 'Technical Officer',
'Executive (SES/SO)' : 'Executive'}
combined_update = combined_update.replace({"position" : tafe_pos_rename})
combined_update.loc[combined_update['institute']=="TAFE", "position"].value_counts()
Out[81]:
Administration 148 Teacher 95 Professional Officer 16 Operational 13 Workplace Training Officer 6 Technical Officer 5 Executive 4 Tutor 3 Name: position, dtype: int64
The position for Tafe dateset column has been corrected. Now lets see what th data shows for position level and the rate of resignation.
we will convert it into pivot_table first before we start visualizing
In [82]:
from importlib import reload
reload(plt)
pos_val = combined_update.pivot_table(index="position",values="dissatisfied") # mean is default arg
pos_val.plot(kind="bar", rot = 90, legend=False,
title="Resignation due to Dissatisfaction per Position and Institute")
plt.xlabel("Position")
plt.ylabel("Percent Dissatisfied")
plt.show()
From the graph, we could see that the Guidance value recorded the highest value to 1 while others ranges between 0 and 1. This could be as a large response of the data.
Nows visualize individually.
In [83]:
pos_num_counts = combined_update["position"].value_counts()
pos_num_counts.plot(kind="bar", color = "green", title="Employees per Position")
plt.xlabel("Position")
plt.ylabel("Number of Employees")
plt.show()
We could now finalize that from our combined dataset, Teachers showed the highest form of dissatisfaction rate with their resignations
Now let us visualize it indivudually and see what indiviual dataset has to say about the resignation
In [84]:
pos_inst_cat = combined_update.pivot_table(index="position",columns="institute",values="dissatisfied") # mean is default arg
pos_inst_cat.plot(kind="bar", rot = 90,
title="Resignation due to Dissatisfaction per Position and Institute")
plt.xlabel("Position")
plt.ylabel("Percent Dissatisfied")
plt.legend(loc='best',fontsize="small")
plt.show()
We have analysed the age, position and institute category to finalize the resignation of employees and its dissatisaction rate