** EMPLOYEE EXIT SURVEYS**
The aim of this project is to analyze the exit surveys from employess of Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. The objective is to help stakeholders to answer below questions:
To accomplish this we will be working with 2 separate datasets, TAFE exit survey and survey for DETE. A data dictionary was not provided for the datasets by the source but using general knowledge, below is a preview of a couple of columns we will work with. From DETE survey:
ID: An id used to identify the participant of the survey SeparationType: The reason why the person's employment ended Cease Date: The year or month the person's employment ended DETE Start Date: The year the person began employment with the DETE
From TAFE survey:
Record ID: An id used to identify the participant of the survey Reason for ceasing employment: The reason why the person's employment ended LengthofServiceOverall. Overall Length of Service at Institute (in years): The length of the person's employment (in years)
Let's import the pandas and NumPy libraries we will be using for this analysis and read our datasets into
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
Let's explore our datasets to gain an insight to the data we are working with.
print(dete_survey.info())
print(dete_survey.shape)
dete_survey.head()
<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 None (822, 56)
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
dete_survey.isnull().sum()
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
print(tafe_survey.info())
print(tafe_survey.shape)
tafe_survey.head()
<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 None (702, 72)
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | ... | Yes | Yes | Yes | Yes | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 72 columns
tafe_survey.isnull().sum()
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
tafe_survey['CESSATION YEAR'].value_counts(dropna=False)
2011.0 268 2012.0 235 2010.0 103 2013.0 85 NaN 7 2009.0 4 Name: CESSATION YEAR, dtype: int64
dete_survey['Cease Date'].value_counts(dropna=False)
2012 344 2013 200 01/2014 43 12/2013 40 Not Stated 34 09/2013 34 06/2013 27 07/2013 22 10/2013 20 11/2013 16 08/2013 12 05/2013 7 05/2012 6 02/2014 2 07/2014 2 04/2014 2 04/2013 2 08/2012 2 07/2012 1 2014 1 11/2012 1 09/2010 1 07/2006 1 2010 1 09/2014 1 Name: Cease Date, dtype: int64
From the exploration above we see that observe as follows;
dete_survey
tafe_survey
Both datasets has multiple columns that are not needed for our analysis.Both datasets also contains many of the same columns but different names.
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)
We started the cleaning by rereading the 'dete_survey.csv' and also read the 'Not stated' values in as 'NaN'
Next we deleted the columns we do not need for the analysis that is columns 28 to 48 for dete_survey and columns 17 to 65 for the tafe_survey, then save them as dete_survey_updated and tafe_survey_updated respectively.
Next we clean the column names
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(' ', '_')
dete_survey_updated.head()
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | work_life_balance | workload | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | False | False | True | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | False | False | False | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | False | True | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | True | False | False | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 35 columns
tafe_survey_updated.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'}, 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
Since we will have to combine both datasets for our analysis, let's standardize the column names.
We started by making all capitilazition lowercase, removing any trailing whitespace from end of strings an replacing spaces with underscores in the dete_survey_updated dataset.
We then converted a few of the column names for tafe_survey_updated to align with that of dete_survey_updated.
print(dete_survey_updated['separationtype'].value_counts())
dete_bool1 = (dete_survey_updated['separationtype'] == 'Resignation-Other reasons') #or (dete_survey_updated['separationtype'] == 'Resignation-Other employer') or (dete_survey_updated['separationtype'] == 'Resignation-Move overseas/interstate')
dete_bool2 = (dete_survey_updated['separationtype'] == 'Resignation-Other employer')
dete_bool3 = (dete_survey_updated['separationtype'] == 'Resignation-Move overseas/interstate')
dete_bool = dete_bool1 | dete_bool2 | dete_bool3
dete_resignations = dete_survey_updated[dete_bool]
dete_resignations['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-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Name: separationtype, dtype: int64
tafe_survey_updated['separationtype'].value_counts()
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation']
tafe_resignations['separationtype'].value_counts()
Resignation 340 Name: separationtype, dtype: int64
Considering that we are only interested in the respondents who resigned, we will select only the rows in both datasets that their separation type has to do with resignation.
we achieved this by first using the Series.value_counts()
to see the unique values and select based on those that has to do with resignation.
dete_resignations['cease_date'].value_counts()
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 05/2012 2 05/2013 2 09/2010 1 07/2006 1 2010 1 07/2012 1 Name: cease_date, dtype: int64
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.split('/').str[-1]
dete_resignations['cease_date'] = dete_resignations['cease_date'].astype('float')
dete_resignations['cease_date'].value_counts()
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy if __name__ == '__main__': /dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy from ipykernel import kernelapp as app
2013.0 146 2012.0 129 2014.0 22 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
dete_resignations['dete_start_date'].value_counts().sort_index(ascending=False)
2013.0 10 2012.0 21 2011.0 24 2010.0 17 2009.0 13 2008.0 22 2007.0 21 2006.0 13 2005.0 15 2004.0 14 2003.0 6 2002.0 6 2001.0 3 2000.0 9 1999.0 8 1998.0 6 1997.0 5 1996.0 6 1995.0 4 1994.0 6 1993.0 5 1992.0 6 1991.0 4 1990.0 5 1989.0 4 1988.0 4 1987.0 1 1986.0 3 1985.0 3 1984.0 1 1983.0 2 1982.0 1 1980.0 5 1977.0 1 1976.0 2 1975.0 1 1974.0 2 1973.0 1 1972.0 1 1971.0 1 1963.0 1 Name: dete_start_date, dtype: int64
tafe_resignations['cease_date'].value_counts().sort_index(ascending=False)
2013.0 55 2012.0 94 2011.0 116 2010.0 68 2009.0 2 Name: cease_date, dtype: int64
We first analysed the cease_date column of the dete_resignations and noticed that some of the dates have months. We then extracted only the years so that it will be in uniformity with the other dates.
We continued our analysis on the dete_start_date column of dete_resigantions and cease_date column of tafe_resignations and observed that the dates seems to be in other.
dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy if __name__ == '__main__':
Since the analysis we are doing has to do with the lenght of time an employee spent in a workplace, called service, we have to calculate that for dete_resignations dataframe. Since we already have a column with the information on the tafe_resignation dataframe called institute_service, we will also named the new column *institute_service'.
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts(dropna=False)
- 277 Contributing Factors. Dissatisfaction 55 NaN 8 Name: Contributing Factors. Dissatisfaction, dtype: int64
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts(dropna=False)
- 270 Job Dissatisfaction 62 NaN 8 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
def update_vals(val):
if pd.isnull(val):
return np.nan
elif val == '-':
return False
else:
return True
tafe_resignations['dissatisfied'] = tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']]. applymap(update_vals).any(axis=1, skipna=False)
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:9: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
tafe_resignations['dissatisfied'].value_counts(dropna=False)
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
dete_resignations['dissatisfied'] = dete_resignations[['job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions', 'work_life_balance', 'workload']].any(axis=1, skipna=False).copy()
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy if __name__ == '__main__':
dete_resignations_up = dete_resignations.copy()
tafe_resignations_up = tafe_resignations.copy()
dete_resignations_up['dissatisfied'].value_counts(dropna=False)
False 162 True 149 Name: dissatisfied, dtype: int64
Our analysis is concerning employees that resigned because they wre dissatisfied. We then identify the columns that specified that employees resigned because thay are dissatified as below:
tafe_survey_updated:
dete_survey_updated:
Having identified above columns, we converted the tafe_resignation columns values to True or False or NaN to make it easier to analuse. We the created a new column called dissatisfied each for both dataframes and used the DataFrame.any()
to assign True if any of the element in selected column is True, False if none is True and NaN if the value is NaN
We later created a copy of the results and assigned it to dete_resignations_up and tafe_resignations_up
Now let's combine the two dataframes. But bfore we do so, let's add a column that will enable us distinguish between the dataframes even after combining them. In order to achieve this we will create a column institute and each row will contain either DETE or TAFE depending on the dataframe.
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
combined = pd.concat([dete_resignations_up, tafe_resignations_up])
combined.notnull().sum().sort_values()
torres_strait 0 south_sea 3 aboriginal 7 disability 8 nesb 9 business_unit 32 classification 161 region 265 role_start_date 271 dete_start_date 283 role_service 290 career_move_to_public_sector 311 employment_conditions 311 work_location 311 lack_of_job_security 311 job_dissatisfaction 311 dissatisfaction_with_the_department 311 workload 311 lack_of_recognition 311 interpersonal_conflicts 311 maternity/family 311 none_of_the_above 311 physical_work_environment 311 relocation 311 study/travel 311 traumatic_incident 311 work_life_balance 311 career_move_to_private_sector 311 ill_health 311 Contributing Factors. Career Move - Private Sector 332 Contributing Factors. Other 332 Contributing Factors. Career Move - Public Sector 332 Contributing Factors. Career Move - Self-employment 332 Contributing Factors. Travel 332 Contributing Factors. Study 332 Contributing Factors. Dissatisfaction 332 Contributing Factors. Ill Health 332 Contributing Factors. NONE 332 Contributing Factors. Maternity/Family 332 Contributing Factors. Job Dissatisfaction 332 Contributing Factors. Interpersonal Conflict 332 WorkArea 340 Institute 340 institute_service 563 gender 592 age 596 employment_status 597 position 598 cease_date 635 dissatisfied 643 id 651 separationtype 651 institute 651 dtype: int64
combined_updated = combined.dropna(thresh=500, axis=1)
combined_updated.notnull().sum().sort_values()
institute_service 563 gender 592 age 596 employment_status 597 position 598 cease_date 635 dissatisfied 643 id 651 institute 651 separationtype 651 dtype: int64
combined_updated
age | cease_date | dissatisfied | employment_status | gender | id | institute | institute_service | position | separationtype | |
---|---|---|---|---|---|---|---|---|---|---|
3 | 36-40 | 2012.0 | False | Permanent Full-time | Female | 4.000000e+00 | DETE | 7 | Teacher | Resignation-Other reasons |
5 | 41-45 | 2012.0 | True | Permanent Full-time | Female | 6.000000e+00 | DETE | 18 | Guidance Officer | Resignation-Other reasons |
8 | 31-35 | 2012.0 | False | Permanent Full-time | Female | 9.000000e+00 | DETE | 3 | Teacher | Resignation-Other reasons |
9 | 46-50 | 2012.0 | True | Permanent Part-time | Female | 1.000000e+01 | DETE | 15 | Teacher Aide | Resignation-Other employer |
11 | 31-35 | 2012.0 | False | Permanent Full-time | Male | 1.200000e+01 | DETE | 3 | Teacher | Resignation-Move overseas/interstate |
12 | 36-40 | 2012.0 | False | Permanent Full-time | Female | 1.300000e+01 | DETE | 14 | Teacher | Resignation-Other reasons |
14 | 31-35 | 2012.0 | True | Permanent Full-time | Male | 1.500000e+01 | DETE | 5 | Teacher | Resignation-Other employer |
16 | 61 or older | 2012.0 | True | Permanent Part-time | Male | 1.700000e+01 | DETE | NaN | Teacher Aide | Resignation-Other reasons |
20 | 56-60 | 2012.0 | False | Permanent Full-time | Male | 2.100000e+01 | DETE | 30 | Teacher | Resignation-Other employer |
21 | 51-55 | 2012.0 | False | Permanent Part-time | Female | 2.200000e+01 | DETE | 32 | Cleaner | Resignation-Other reasons |
22 | 46-50 | 2012.0 | True | Permanent Part-time | Female | 2.300000e+01 | DETE | 15 | School Administrative Staff | Resignation-Other reasons |
23 | 61 or older | 2012.0 | True | Permanent Full-time | Female | 2.400000e+01 | DETE | 39 | Teacher | Resignation-Other reasons |
25 | 41-45 | 2012.0 | True | Permanent Part-time | Female | 2.600000e+01 | DETE | 17 | Teacher | Resignation-Other reasons |
27 | 21-25 | 2012.0 | False | Permanent Full-time | Female | 2.800000e+01 | DETE | 7 | Public Servant | Resignation-Other employer |
33 | 36-40 | 2012.0 | True | Permanent Full-time | Male | 3.400000e+01 | DETE | 9 | Teacher | Resignation-Other reasons |
34 | 61 or older | 2012.0 | True | Permanent Part-time | Male | 3.500000e+01 | DETE | 6 | Cleaner | Resignation-Other reasons |
37 | 21-25 | 2012.0 | False | Temporary Part-time | Female | 3.800000e+01 | DETE | 1 | Teacher Aide | Resignation-Other reasons |
39 | 21-25 | 2012.0 | True | Permanent Full-time | Female | 4.000000e+01 | DETE | NaN | Teacher | Resignation-Move overseas/interstate |
40 | 56-60 | 2012.0 | False | Permanent Full-time | Male | 4.100000e+01 | DETE | 35 | Teacher | Resignation-Other employer |
41 | 51-55 | 2012.0 | True | Permanent Full-time | Female | 4.200000e+01 | DETE | 38 | Head of Curriculum/Head of Special Education | Resignation-Other reasons |
42 | 41-45 | 2012.0 | False | Permanent Part-time | Female | 4.300000e+01 | DETE | 1 | Cleaner | Resignation-Move overseas/interstate |
43 | 51-55 | 2012.0 | True | Permanent Full-time | Male | 4.400000e+01 | DETE | 36 | Teacher | Resignation-Other reasons |
48 | 21-25 | 2012.0 | False | Permanent Full-time | Male | 4.900000e+01 | DETE | 3 | Cleaner | Resignation-Move overseas/interstate |
50 | 21-25 | 2012.0 | False | Permanent Full-time | Male | 5.100000e+01 | DETE | 3 | Cleaner | Resignation-Move overseas/interstate |
51 | 61 or older | 2012.0 | False | Permanent Full-time | Female | 5.200000e+01 | DETE | 19 | Cleaner | Resignation-Other reasons |
55 | 26-30 | 2012.0 | False | Permanent Part-time | Female | 5.600000e+01 | DETE | 4 | Teacher Aide | Resignation-Other employer |
57 | 46-50 | 2012.0 | False | Permanent Full-time | Male | 5.800000e+01 | DETE | 9 | Teacher | Resignation-Other employer |
61 | 31-35 | 2012.0 | False | Temporary Part-time | Female | 6.200000e+01 | DETE | 1 | Schools Officer | Resignation-Other reasons |
69 | 36-40 | 2012.0 | True | Permanent Full-time | Female | 7.000000e+01 | DETE | 6 | Public Servant | Resignation-Other reasons |
71 | 36-40 | 2012.0 | False | Permanent Part-time | Female | 7.200000e+01 | DETE | 1 | Teacher Aide | Resignation-Other reasons |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
659 | 46 50 | 2013.0 | False | Temporary Part-time | Female | 6.349985e+17 | TAFE | 1-2 | Administration (AO) | Resignation |
660 | 41 45 | 2013.0 | False | Permanent Part-time | Female | 6.349994e+17 | TAFE | 3-4 | Administration (AO) | Resignation |
661 | 46 50 | 2013.0 | True | Permanent Full-time | Female | 6.350003e+17 | TAFE | 5-6 | Administration (AO) | Resignation |
665 | NaN | 2013.0 | False | NaN | NaN | 6.350055e+17 | TAFE | NaN | NaN | Resignation |
666 | NaN | 2013.0 | False | NaN | NaN | 6.350055e+17 | TAFE | NaN | NaN | Resignation |
669 | 26 30 | 2013.0 | False | Temporary Full-time | Female | 6.350108e+17 | TAFE | 3-4 | Administration (AO) | Resignation |
670 | NaN | 2013.0 | NaN | NaN | NaN | 6.350124e+17 | TAFE | NaN | NaN | Resignation |
671 | 46 50 | 2013.0 | True | Temporary Full-time | Female | 6.350127e+17 | TAFE | Less than 1 year | Teacher (including LVT) | Resignation |
675 | 51-55 | 2013.0 | True | Temporary Full-time | Male | 6.350175e+17 | TAFE | Less than 1 year | Teacher (including LVT) | Resignation |
676 | 41 45 | 2013.0 | False | Contract/casual | Female | 6.350194e+17 | TAFE | 1-2 | Administration (AO) | Resignation |
677 | 36 40 | 2013.0 | False | Temporary Full-time | Female | 6.350219e+17 | TAFE | Less than 1 year | Administration (AO) | Resignation |
678 | 51-55 | 2013.0 | False | Permanent Full-time | Male | 6.350253e+17 | TAFE | 3-4 | Administration (AO) | Resignation |
679 | 56 or older | 2013.0 | False | Temporary Part-time | Female | 6.350279e+17 | TAFE | 1-2 | Operational (OO) | Resignation |
681 | 26 30 | 2013.0 | False | Temporary Full-time | Female | 6.350314e+17 | TAFE | Less than 1 year | Administration (AO) | Resignation |
682 | 26 30 | 2013.0 | False | Permanent Part-time | Female | 6.350357e+17 | TAFE | Less than 1 year | Administration (AO) | Resignation |
683 | 41 45 | 2013.0 | False | Temporary Full-time | Female | 6.350374e+17 | TAFE | Less than 1 year | Administration (AO) | Resignation |
684 | 41 45 | 2013.0 | False | Contract/casual | Male | 6.350375e+17 | TAFE | 3-4 | Administration (AO) | Resignation |
685 | 26 30 | 2013.0 | True | Temporary Full-time | Female | 6.350402e+17 | TAFE | 1-2 | Technical Officer (TO) | Resignation |
686 | 41 45 | 2013.0 | False | Temporary Full-time | Female | 6.350426e+17 | TAFE | 5-6 | Administration (AO) | Resignation |
688 | 46 50 | 2013.0 | False | Permanent Part-time | Female | 6.350479e+17 | TAFE | 5-6 | Professional Officer (PO) | Resignation |
689 | 41 45 | 2013.0 | True | Permanent Full-time | Male | 6.350480e+17 | TAFE | Less than 1 year | Teacher (including LVT) | Resignation |
690 | NaN | 2013.0 | False | NaN | NaN | 6.350496e+17 | TAFE | NaN | NaN | Resignation |
691 | 56 or older | 2013.0 | False | Permanent Part-time | Female | 6.350496e+17 | TAFE | 3-4 | Operational (OO) | Resignation |
693 | 26 30 | 2013.0 | False | Temporary Full-time | Female | 6.350599e+17 | TAFE | 1-2 | Administration (AO) | Resignation |
694 | NaN | 2013.0 | False | NaN | NaN | 6.350652e+17 | TAFE | NaN | NaN | Resignation |
696 | 21 25 | 2013.0 | False | Temporary Full-time | Male | 6.350660e+17 | TAFE | 5-6 | Operational (OO) | Resignation |
697 | 51-55 | 2013.0 | False | Temporary Full-time | Male | 6.350668e+17 | TAFE | 1-2 | Teacher (including LVT) | Resignation |
698 | NaN | 2013.0 | False | NaN | NaN | 6.350677e+17 | TAFE | NaN | NaN | Resignation |
699 | 51-55 | 2013.0 | False | Permanent Full-time | Female | 6.350704e+17 | TAFE | 5-6 | Teacher (including LVT) | Resignation |
701 | 26 30 | 2013.0 | False | Contract/casual | Female | 6.350730e+17 | TAFE | 3-4 | Administration (AO) | Resignation |
651 rows × 10 columns
After combining the dataframes, we noticed that a good number of the columns contained null values and those columns are not needed for our analysis anyways. We then droppped them using the Dataframe.dropna() method while setting the thresh parameter to 500.
combined_updated['institute_service'].value_counts(dropna=False)
NaN 88 Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 5.0 23 1.0 22 7-10 21 0.0 20 3.0 20 6.0 17 4.0 16 2.0 14 9.0 14 7.0 13 More than 20 years 10 8.0 8 13.0 8 15.0 7 20.0 7 12.0 6 22.0 6 17.0 6 10.0 6 14.0 6 18.0 5 16.0 5 23.0 4 24.0 4 11.0 4 39.0 3 21.0 3 32.0 3 19.0 3 36.0 2 30.0 2 26.0 2 28.0 2 25.0 2 29.0 1 31.0 1 49.0 1 33.0 1 34.0 1 35.0 1 38.0 1 41.0 1 42.0 1 27.0 1 Name: institute_service, dtype: int64
Next we will have to clean up the institute_service column because it contains values in different forms. We will base our analysis on this article.
We will use slightly modified definitions below:
#combined_updated['institute_service_up'] = combined_updated['institute_service'].astype("str")#.str.replace(r'[a-z]+', '').str.replace(r'[A-Z]+', '').str.strip().str.split('-').str[0].str.strip()
#I tried above code but it did not give me the result I wanted because of NaN and I don't understand why.
combined_updated['institute_service_up'] = combined_updated['institute_service'].astype("str").str.extract(r'(\d+)')
combined_updated['institute_service_up'] = combined_updated['institute_service_up'].astype("float")
combined_updated['institute_service_up'].value_counts(dropna=False)
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:4: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame) /dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy /dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
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
def map_fun(val):
if val < 3:
return 'New'
elif val >= 3 and val <=6:
return 'Experienced'
elif val >= 7 and val <= 10:
return 'Established'
elif pd.isnull(val):
pass
else:
return 'Veteran'
combined_updated['service_cat'] = combined_updated['institute_service_up'].apply(map_fun)
combined_updated['service_cat'].value_counts()
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy if __name__ == '__main__':
New 193 Experienced 172 Veteran 136 Established 62 Name: service_cat, dtype: int64
We wrote a function to enable us apply the definitions and used the Series.apply()
method to apply. The categories where then assigned to a new column service_cat.
combined_updated['dissatisfied'].value_counts(dropna=False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
combined_updated['dissatisfied'].value_counts(dropna=False)
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy if __name__ == '__main__':
False 411 True 240 Name: dissatisfied, dtype: int64
pv_combined = combined_updated.pivot_table(values='dissatisfied', index='service_cat')
pv_combined.plot(kind='bar', title='Exited dissatisfied employees by service category', ylim=(0,1), legend=False)
<matplotlib.axes._subplots.AxesSubplot at 0x7ffb62501cc0>
From the pivot table and the bar chart above we can answer the first question of our project. 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?
Fewer employees who worked for the institutes for a short period of time resign due to some kind of dissatisfaction while the resignation due to a kind of dissatisfaction grew as the employees put in more years of service.