In this guided project, we'll work with exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia.
** Below are the questions we hope to answer after cleaning and analyzing the data:**
Dictionary:
Below is a preview of a couple columns we'll work with from the dete_survey.csv
:
ID
: An id used to identify the participant of the surveySeparationType
: The reason why the person's employment endedCease Date
: The year or month the person's employment endedDETE Start Date
: The year the person began employment with the DETEBelow is a preview of a couple columns we'll work with from the tafe_survey.csv
:
Record ID
: An id used to identify the participant of the surveyReason for ceasing employment
: The reason why the person's employment endedLengthofServiceOverall. Overall Length of Service at Institute (in years)
: The length of the person's employment (in years)import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
dete_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): ID 822 non-null int64 SeparationType 822 non-null object Cease Date 822 non-null object DETE Start Date 822 non-null object Role Start Date 822 non-null object Position 817 non-null object Classification 455 non-null object Region 822 non-null object Business Unit 126 non-null object Employment Status 817 non-null object Career move to public sector 822 non-null bool Career move to private sector 822 non-null bool Interpersonal conflicts 822 non-null bool Job dissatisfaction 822 non-null bool Dissatisfaction with the department 822 non-null bool Physical work environment 822 non-null bool Lack of recognition 822 non-null bool Lack of job security 822 non-null bool Work location 822 non-null bool Employment conditions 822 non-null bool Maternity/family 822 non-null bool Relocation 822 non-null bool Study/Travel 822 non-null bool Ill Health 822 non-null bool Traumatic incident 822 non-null bool Work life balance 822 non-null bool Workload 822 non-null bool None of the above 822 non-null bool Professional Development 808 non-null object Opportunities for promotion 735 non-null object Staff morale 816 non-null object Workplace issue 788 non-null object Physical environment 817 non-null object Worklife balance 815 non-null object Stress and pressure support 810 non-null object Performance of supervisor 813 non-null object Peer support 812 non-null object Initiative 813 non-null object Skills 811 non-null object Coach 767 non-null object Career Aspirations 746 non-null object Feedback 792 non-null object Further PD 768 non-null object Communication 814 non-null object My say 812 non-null object Information 816 non-null object Kept informed 813 non-null object Wellness programs 766 non-null object Health & Safety 793 non-null object Gender 798 non-null object Age 811 non-null object Aboriginal 16 non-null object Torres Strait 3 non-null object South Sea 7 non-null object Disability 23 non-null object NESB 32 non-null object dtypes: bool(18), int64(1), object(37) memory usage: 258.6+ KB
dete_survey.head()
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984 | 2004 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | Not Stated | Not Stated | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011 | 2011 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005 | 2006 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970 | 1989 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 56 columns
dete_survey.describe(include = 'all')
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 822.000000 | 822 | 822 | 822 | 822 | 817 | 455 | 822 | 126 | 817 | ... | 813 | 766 | 793 | 798 | 811 | 16 | 3 | 7 | 23 | 32 |
unique | NaN | 9 | 25 | 51 | 46 | 15 | 8 | 9 | 14 | 5 | ... | 6 | 6 | 6 | 2 | 10 | 1 | 1 | 1 | 1 | 1 |
top | NaN | Age Retirement | 2012 | Not Stated | Not Stated | Teacher | Primary | Metropolitan | Education Queensland | Permanent Full-time | ... | A | A | A | Female | 61 or older | Yes | Yes | Yes | Yes | Yes |
freq | NaN | 285 | 344 | 73 | 98 | 324 | 161 | 135 | 54 | 434 | ... | 401 | 253 | 386 | 573 | 222 | 16 | 3 | 7 | 23 | 32 |
mean | 411.693431 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
std | 237.705820 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
min | 1.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
25% | 206.250000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
50% | 411.500000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
75% | 616.750000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
max | 823.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
11 rows × 56 columns
# Find the number of missing values in columns and sort by descending order
find_null_dete = pd.Series(dete_survey.isnull().sum())
missing_dete = find_null_dete[find_null_dete !=0]
missing_dete.sort_values(ascending=False), missing_dete.shape
(Torres Strait 819 South Sea 815 Aboriginal 806 Disability 799 NESB 790 Business Unit 696 Classification 367 Opportunities for promotion 87 Career Aspirations 76 Wellness programs 56 Coach 55 Further PD 54 Workplace issue 34 Feedback 30 Health & Safety 29 Gender 24 Professional Development 14 Stress and pressure support 12 Age 11 Skills 11 My say 10 Peer support 10 Performance of supervisor 9 Initiative 9 Kept informed 9 Communication 8 Worklife balance 7 Staff morale 6 Information 6 Physical environment 5 Employment Status 5 Position 5 dtype: int64, (32,))
# Check on columns that has significant amount of missing values
dete_survey['Torres Strait'].value_counts()
Yes 3 Name: Torres Strait, dtype: int64
dete_survey['South Sea'].value_counts()
Yes 7 Name: South Sea, dtype: int64
dete_survey['Aboriginal'].value_counts()
Yes 16 Name: Aboriginal, dtype: int64
dete_survey['Disability'].value_counts()
Yes 23 Name: Disability, dtype: int64
dete_survey['NESB'].value_counts()
Yes 32 Name: NESB, dtype: int64
dete_survey['Business Unit'].value_counts()
Education Queensland 54 Information and Technologies 26 Training and Tertiary Education Queensland 12 Other 11 Human Resources 6 Corporate Strategy and Peformance 5 Early Childhood Education and Care 3 Policy, Research, Legislation 2 Infrastructure 2 Calliope State School 1 Corporate Procurement 1 Finance 1 Pacific Pines SHS 1 Indigenous Education and Training Futures 1 Name: Business Unit, dtype: int64
dete_survey['Classification'].value_counts()
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
# Create heatmap to have a better look on distribution of missing data
fig, ax = plt.subplots(figsize = (10,5))
ax = sns.heatmap(dete_survey.isnull(), cbar = False)
Observations:
tafe_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 72 columns): Record ID 702 non-null float64 Institute 702 non-null object WorkArea 702 non-null object CESSATION YEAR 695 non-null float64 Reason for ceasing employment 701 non-null object Contributing Factors. Career Move - Public Sector 437 non-null object Contributing Factors. Career Move - Private Sector 437 non-null object Contributing Factors. Career Move - Self-employment 437 non-null object Contributing Factors. Ill Health 437 non-null object Contributing Factors. Maternity/Family 437 non-null object Contributing Factors. Dissatisfaction 437 non-null object Contributing Factors. Job Dissatisfaction 437 non-null object Contributing Factors. Interpersonal Conflict 437 non-null object Contributing Factors. Study 437 non-null object Contributing Factors. Travel 437 non-null object Contributing Factors. Other 437 non-null object Contributing Factors. NONE 437 non-null object Main Factor. Which of these was the main factor for leaving? 113 non-null object InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 609 non-null object WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] 608 non-null object WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 609 non-null object WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object Induction. Did you undertake Workplace Induction? 619 non-null object InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object Gender. What is your Gender? 596 non-null object CurrentAge. Current Age 596 non-null object Employment Type. Employment Type 596 non-null object Classification. Classification 596 non-null object LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(70) memory usage: 395.0+ KB
tafe_survey.head()
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | ... | Yes | Yes | Yes | Yes | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 72 columns
tafe_survey.describe(include='all')
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) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 7.020000e+02 | 702 | 702 | 695.000000 | 701 | 437 | 437 | 437 | 437 | 437 | ... | 594 | 587 | 586 | 581 | 596 | 596 | 596 | 596 | 596 | 596 |
unique | NaN | 12 | 2 | NaN | 6 | 2 | 2 | 2 | 2 | 2 | ... | 2 | 2 | 2 | 2 | 2 | 9 | 5 | 9 | 7 | 7 |
top | NaN | Brisbane North Institute of TAFE | Non-Delivery (corporate) | NaN | Resignation | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | Female | 56 or older | Permanent Full-time | Administration (AO) | Less than 1 year | Less than 1 year |
freq | NaN | 161 | 432 | NaN | 340 | 375 | 336 | 420 | 403 | 411 | ... | 536 | 512 | 488 | 416 | 389 | 162 | 237 | 293 | 147 | 177 |
mean | 6.346026e+17 | NaN | NaN | 2011.423022 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
std | 2.515071e+14 | NaN | NaN | 0.905977 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
min | 6.341330e+17 | NaN | NaN | 2009.000000 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
25% | 6.343954e+17 | NaN | NaN | 2011.000000 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
50% | 6.345835e+17 | NaN | NaN | 2011.000000 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
75% | 6.348005e+17 | NaN | NaN | 2012.000000 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
max | 6.350730e+17 | NaN | NaN | 2013.000000 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
11 rows × 72 columns
# Find the number of missing values in columns and sort by descending order
find_null_tafe = pd.Series(tafe_survey.isnull().sum())
missing_tafe = find_null_tafe[find_null_tafe !=0]
missing_tafe.sort_values(ascending=False), missing_tafe.shape
(Main Factor. Which of these was the main factor for leaving? 589 InductionInfo. Topic:Did you undertake a Corporate Induction? 270 Contributing Factors. Ill Health 265 Contributing Factors. Other 265 Contributing Factors. Maternity/Family 265 Contributing Factors. Career Move - Public Sector 265 Contributing Factors. Travel 265 Contributing Factors. NONE 265 Contributing Factors. Career Move - Private Sector 265 Contributing Factors. Career Move - Self-employment 265 Contributing Factors. Dissatisfaction 265 Contributing Factors. Job Dissatisfaction 265 Contributing Factors. Interpersonal Conflict 265 Contributing Factors. Study 265 InductionInfo. Topic: Did you undertake Team Induction? 262 InductionInfo. Topic:Did you undertake a Institute Induction? 219 InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 172 InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 149 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 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 InductionInfo. On-line Topic:Did you undertake a Institute Induction? 147 Workplace. Topic:Would you recommend the Institute as an employer to others? 121 Workplace. Topic:Does your workplace value the diversity of its employees? 116 Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 115 Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 108 LengthofServiceOverall. Overall Length of Service at Institute (in years) 106 ... WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 99 WorkUnitViews. Topic:15. I worked well with my colleagues 97 WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 96 WorkUnitViews. Topic:30. Staff morale was positive within my work unit 96 WorkUnitViews. Topic:16. My job was challenging and interesting 95 InstituteViews. Topic:6. The organisation recognised when staff did good work 95 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] 94 WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 94 InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 94 InstituteViews. Topic:8. Management was generally supportive of my team 94 Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 94 InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 94 WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 93 WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 93 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:14. I was satisfied with the quality of the management and supervision within my work unit 93 WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 92 WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 92 InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 92 WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 92 InstituteViews. Topic:3. I was given adequate opportunities for personal development 92 WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 91 WorkUnitViews. Topic:23. My job provided sufficient variety 91 InstituteViews. Topic:2. I was given access to skills training to help me do my job better 89 WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 89 InstituteViews. Topic:7. Management was generally supportive of me 88 InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 87 Induction. Did you undertake Workplace Induction? 83 CESSATION YEAR 7 Reason for ceasing employment 1 Length: 69, dtype: int64, (69,))
# Check out columns with significant amount of missing values and suspicious ones
tafe_survey['Main Factor. Which of these was the main factor for leaving?'].value_counts()
Dissatisfaction with %[Institute]Q25LBL% 23 Job Dissatisfaction 22 Other 18 Career Move - Private Sector 16 Interpersonal Conflict 9 Career Move - Public Sector 8 Maternity/Family 6 Career Move - Self-employment 4 Ill Health 3 Travel 2 Study 2 Name: Main Factor. Which of these was the main factor for leaving?, dtype: int64
tafe_survey['Contributing Factors. Travel'].value_counts()
- 415 Travel 22 Name: Contributing Factors. Travel, dtype: int64
tafe_survey['Contributing Factors. Travel'].head()
0 NaN 1 Travel 2 - 3 Travel 4 - Name: Contributing Factors. Travel, dtype: object
# Create heatmap to have a better look on distribution of missing data
fig, ax = plt.subplots(figsize = (10,10))
ax = sns.heatmap(tafe_survey.isnull(), cbar = False)
Observations:
# Reread the dete_survey.csv CSV file with 'Not Stated' values as NaN
dete_survey = pd.read_csv('dete_survey.csv', na_values= 'Not Stated')
# Drop irrelevant columns
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)
dete_survey.columns, tafe_survey.columns
(Index(['ID', 'SeparationType', 'Cease Date', 'DETE Start Date', 'Role Start Date', 'Position', 'Classification', 'Region', 'Business Unit', 'Employment Status', 'Career move to public sector', 'Career move to private sector', 'Interpersonal conflicts', 'Job dissatisfaction', 'Dissatisfaction with the department', 'Physical work environment', 'Lack of recognition', 'Lack of job security', 'Work location', 'Employment conditions', 'Maternity/family', 'Relocation', 'Study/Travel', 'Ill Health', 'Traumatic incident', 'Work life balance', 'Workload', 'None of the above', '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', 'Gender', 'Age', 'Aboriginal', 'Torres Strait', 'South Sea', 'Disability', 'NESB'], dtype='object'), Index(['Record ID', 'Institute', 'WorkArea', 'CESSATION YEAR', 'Reason for ceasing employment', 'Contributing Factors. Career Move - Public Sector ', 'Contributing Factors. Career Move - Private Sector ', 'Contributing Factors. Career Move - Self-employment', 'Contributing Factors. Ill Health', 'Contributing Factors. Maternity/Family', 'Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Interpersonal Conflict', 'Contributing Factors. Study', 'Contributing Factors. Travel', 'Contributing Factors. Other', 'Contributing Factors. NONE', 'Main Factor. Which of these was the main factor for leaving?', 'InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction', 'InstituteViews. Topic:2. I was given access to skills training to help me do my job better', 'InstituteViews. Topic:3. I was given adequate opportunities for personal development', 'InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL%', 'InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had', 'InstituteViews. Topic:6. The organisation recognised when staff did good work', 'InstituteViews. Topic:7. Management was generally supportive of me', 'InstituteViews. Topic:8. Management was generally supportive of my team', 'InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me', 'InstituteViews. Topic:10. Staff morale was positive within the Institute', 'InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly', 'InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently', 'InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly', 'WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit', 'WorkUnitViews. Topic:15. I worked well with my colleagues', 'WorkUnitViews. Topic:16. My job was challenging and interesting', 'WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work', 'WorkUnitViews. Topic:18. I had sufficient contact with other people in my job', 'WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job', 'WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job', '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]', 'WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job', 'WorkUnitViews. Topic:23. My job provided sufficient variety', 'WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job', 'WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction', 'WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance', 'WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area', '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', 'WorkUnitViews. Topic:29. There was adequate communication between staff in my unit', 'WorkUnitViews. Topic:30. Staff morale was positive within my work unit', 'Induction. Did you undertake Workplace Induction?', 'InductionInfo. Topic:Did you undertake a Corporate Induction?', 'InductionInfo. Topic:Did you undertake a Institute Induction?', 'InductionInfo. Topic: Did you undertake Team Induction?', 'InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted?', 'InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted?', 'InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction?', 'InductionInfo. Face to Face Topic:Did you undertake a Institute Induction?', 'InductionInfo. On-line Topic:Did you undertake a Institute Induction?', 'InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction?', 'InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category?', 'InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.]', 'InductionInfo. Induction Manual Topic: Did you undertake Team Induction?', 'Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)?', '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)'], dtype='object'))
# Reform column names in dete_survey to snake_casing, remove trailing white space
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.rstrip().str.replace('\s', '_')
dete_survey_updated.columns
Index(['id', 'separationtype', 'cease_date', 'dete_start_date', 'role_start_date', 'position', 'classification', 'region', 'business_unit', 'employment_status', 'career_move_to_public_sector', 'career_move_to_private_sector', 'interpersonal_conflicts', 'job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions', 'maternity/family', 'relocation', 'study/travel', 'ill_health', 'traumatic_incident', 'work_life_balance', 'workload', 'none_of_the_above', 'gender', 'age', 'aboriginal', 'torres_strait', 'south_sea', 'disability', 'nesb'], dtype='object')
# Change column names in tafe_survey to conform to dete_survey
column_map = {'Record ID': 'id',
'CESSATION YEAR': 'cease_date',
'Reason for ceasing employment': 'separationtype',
'Gender. What is your Gender?': 'gender',
'CurrentAge. Current Age': 'age',
'Employment Type. Employment Type': 'employment_status',
'Classification. Classification': 'position',
'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service',
'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'}
tafe_survey_updated.rename(column_map, axis = 1, inplace = True)
tafe_survey_updated.columns
Index(['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. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Interpersonal Conflict', 'Contributing Factors. Study', 'Contributing Factors. Travel', 'Contributing Factors. Other', 'Contributing Factors. NONE', 'gender', 'age', 'employment_status', 'position', 'institute_service', 'role_service'], dtype='object')
# Check out unique values in separationtype column in both data sets
dete_survey_updated['separationtype'].value_counts()
Age Retirement 285 Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
tafe_survey_updated['separationtype'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
# Keep only separationtype with 'Resignation' values in dete_survey_updated
dete_resignations = dete_survey_updated[dete_survey_updated['separationtype'].str.contains('Resignation')]
dete_resignations['separationtype'].value_counts()
Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Name: separationtype, dtype: int64
# Keep only entires with separationtype 'Resignation' in tafe_survey_updated
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation']
tafe_resignations['separationtype'].value_counts()
Resignation 340 Name: separationtype, dtype: int64
# Check unique values in 'cease_date' column
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 05/2013 2 05/2012 2 07/2006 1 2010 1 07/2012 1 09/2010 1 Name: cease_date, dtype: int64
# Check unique values in 'dete_start_date' column
dete_resignations['dete_start_date'].value_counts(dropna=False).sort_index(ascending = True)
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 NaN 28 Name: dete_start_date, dtype: int64
Since 'dete_start_date' column contains only the year information, in float type, we can convert the 'cease_date' to year only too for comparison.
# Extract year in dete_resignations['cease_date']
# Create a copy of dete_resignations to avoid SettingWithCopyWarning
dete_resignations = dete_resignations.copy()
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.extract(r'(20\d{2})', expand = False).astype(float)
dete_resignations['cease_date'].value_counts(dropna=False).sort_index(ascending = True)
2006.0 1 2010.0 2 2012.0 129 2013.0 146 2014.0 22 NaN 11 Name: cease_date, dtype: int64
tafe_resignations['cease_date'].value_counts(dropna=False).sort_index(ascending = True)
2009.0 2 2010.0 68 2011.0 116 2012.0 94 2013.0 55 NaN 5 Name: cease_date, dtype: int64
dete_resignations['dete_start_date'].plot.box()
<matplotlib.axes._subplots.AxesSubplot at 0x7f43f84b25f8>
# Check if there are entries with cease_date earlier than 'dete_start_date'
year_dif = dete_resignations['cease_date'] - dete_resignations['dete_start_date']
year_dif.value_counts(dropna = False)
NaN 38 5.0 23 1.0 22 3.0 20 0.0 20 6.0 17 4.0 16 9.0 14 2.0 14 7.0 13 13.0 8 8.0 8 20.0 7 15.0 7 12.0 6 22.0 6 17.0 6 10.0 6 14.0 6 16.0 5 18.0 5 24.0 4 23.0 4 11.0 4 39.0 3 32.0 3 19.0 3 21.0 3 36.0 2 30.0 2 25.0 2 28.0 2 26.0 2 29.0 1 42.0 1 38.0 1 27.0 1 41.0 1 35.0 1 49.0 1 34.0 1 33.0 1 31.0 1 dtype: int64
# Create a copy of dete_resignations to avoid SettingWithCopyWarning
dete_resignations = dete_resignations.copy()
dete_resignations['institute_service'] = year_dif
# Create a new 'dissatisfied' column to record if an empolyee resigned because of some dissatisfaction
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
# Create a function to convert corresponding dissatisfatcation values to True/False/NaN
def update_vals(val):
if pd.isnull(val):
return np.nan
elif val == '-':
return False
else:
return True
# Apply the function above to the two dissatisfaction columns in tafe_resignition & assign back
# Create a copy to avoid SettingWithCopyWarning
tafe_resignations_up = tafe_resignations.copy()
tafe_resignations_up[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']] = tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']].applymap(update_vals)
tafe_resignations_up['Contributing Factors. Job Dissatisfaction'].value_counts()
False 270 True 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
# Create a new column 'dissatisfied' in both df based on the associated-column results
tafe_resignations_up['dissatisfied'] = tafe_resignations_up[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']].any(axis = 1, skipna = False)
tafe_resignations_up['dissatisfied'].value_counts(dropna=False)
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
# Check out columns in dete_resignitions
dete_resignations.columns
Index(['id', 'separationtype', 'cease_date', 'dete_start_date', 'role_start_date', 'position', 'classification', 'region', 'business_unit', 'employment_status', 'career_move_to_public_sector', 'career_move_to_private_sector', 'interpersonal_conflicts', 'job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions', 'maternity/family', 'relocation', 'study/travel', 'ill_health', 'traumatic_incident', 'work_life_balance', 'workload', 'none_of_the_above', 'gender', 'age', 'aboriginal', 'torres_strait', 'south_sea', 'disability', 'nesb', 'institute_service'], dtype='object')
# Pick the columns that are related to possible dissatisfactions
col = ['job_dissatisfaction', 'dissatisfaction_with_the_department',
'physical_work_environment', 'lack_of_recognition',
'lack_of_job_security', 'work_location', 'employment_conditions', 'work_life_balance', 'workload',]
dete_resignations[col].info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 311 entries, 3 to 821 Data columns (total 9 columns): job_dissatisfaction 311 non-null bool dissatisfaction_with_the_department 311 non-null bool physical_work_environment 311 non-null bool lack_of_recognition 311 non-null bool lack_of_job_security 311 non-null bool work_location 311 non-null bool employment_conditions 311 non-null bool work_life_balance 311 non-null bool workload 311 non-null bool dtypes: bool(9) memory usage: 5.2 KB
Since the values are already in bool
, we can proceed to create dissatisfied column
# Create a new column 'dissatisfied' based on the associated-column results
dete_resignations_up = dete_resignations.copy()
dete_resignations_up['dissatisfied'] = dete_resignations[col].any(axis = 1, skipna = False)
dete_resignations_up['dissatisfied'].value_counts(dropna = False)
False 162 True 149 Name: dissatisfied, dtype: int64
# Create a new column 'institute' for both df to record the institution info
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
tafe_resignations_up.columns
Index(['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. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Interpersonal Conflict', 'Contributing Factors. Study', 'Contributing Factors. Travel', 'Contributing Factors. Other', 'Contributing Factors. NONE', 'gender', 'age', 'employment_status', 'position', 'institute_service', 'role_service', 'dissatisfied', 'institute'], dtype='object')
# Combine two dfs
combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index = True)
combined.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 651 entries, 0 to 650 Data columns (total 53 columns): Contributing Factors. Career Move - Private Sector 332 non-null object Contributing Factors. Career Move - Public Sector 332 non-null object Contributing Factors. Career Move - Self-employment 332 non-null object Contributing Factors. Dissatisfaction 332 non-null object Contributing Factors. Ill Health 332 non-null object Contributing Factors. Interpersonal Conflict 332 non-null object Contributing Factors. Job Dissatisfaction 332 non-null object Contributing Factors. Maternity/Family 332 non-null object Contributing Factors. NONE 332 non-null object Contributing Factors. Other 332 non-null object Contributing Factors. Study 332 non-null object Contributing Factors. Travel 332 non-null object Institute 340 non-null object WorkArea 340 non-null object aboriginal 7 non-null object age 596 non-null object business_unit 32 non-null object career_move_to_private_sector 311 non-null object career_move_to_public_sector 311 non-null object cease_date 635 non-null float64 classification 161 non-null object dete_start_date 283 non-null float64 disability 8 non-null object dissatisfaction_with_the_department 311 non-null object dissatisfied 643 non-null object employment_conditions 311 non-null object employment_status 597 non-null object gender 592 non-null object id 651 non-null float64 ill_health 311 non-null object institute 651 non-null object institute_service 563 non-null object interpersonal_conflicts 311 non-null object job_dissatisfaction 311 non-null object lack_of_job_security 311 non-null object lack_of_recognition 311 non-null object maternity/family 311 non-null object nesb 9 non-null object none_of_the_above 311 non-null object physical_work_environment 311 non-null object position 598 non-null object region 265 non-null object relocation 311 non-null object role_service 290 non-null object role_start_date 271 non-null float64 separationtype 651 non-null object south_sea 3 non-null object study/travel 311 non-null object torres_strait 0 non-null object traumatic_incident 311 non-null object work_life_balance 311 non-null object work_location 311 non-null object workload 311 non-null object dtypes: float64(4), object(49) memory usage: 269.6+ KB
combined_updated = combined.dropna(axis = 1, thresh = 500)
combined_updated.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
# Check out unique values in column 'institute_service'
combined_updated['institute_service'].value_counts()
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
# Extract years from 'institute_service'
# Since all the irregular years in the column all fall into the same category with either end, keep only one number
# Regex '\d+' for one or more digits(greedy)
# Create a copy to avoid SettingWithCopyWarning
combined_updated = combined_updated.copy()
combined_updated['institute_service'] = combined_updated['institute_service'].astype(str).str.split('.').str[0].str.extract('(\d+)', expand = False).astype(float)
# Create a function to categorize 'institute_service' values into levels
def exp_level(val):
if pd.isnull(val):
return np.nan
elif val < 3:
return 'New'
elif (val >= 3) and (val <= 6):
return 'Experienced'
elif (val > 6) and (val < 11):
return 'Established'
else:
return 'Veteran'
combined_updated['service_cat'] = combined_updated['institute_service'].apply(exp_level)
combined_updated['service_cat'].value_counts()
New 193 Experienced 172 Veteran 136 Established 62 Name: service_cat, dtype: int64
# Check out unique values in 'dissatisfied' column
combined_updated['dissatisfied'].value_counts(dropna = False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
# Fill null values in the column with the predominant value
combined_updated['dissatisfied'].fillna(value = False, inplace = True)
# Group data by 'service_cat' with 'dissatisfied' values and calculate mean of each group
#(Since df.pivot_table treats True as '1', False as '0', the mean also happens to be the percentage of dissatified employee in each group)
cat_percentage = combined_updated.pivot_table(index = 'service_cat', values = 'dissatisfied')
cat_percentage
dissatisfied | |
---|---|
service_cat | |
Established | 0.516129 |
Experienced | 0.343023 |
New | 0.295337 |
Veteran | 0.485294 |
cat_percentage.mean()
dissatisfied 0.409946 dtype: float64
# Plot the result
import matplotlib.pyplot as plt
%matplotlib inline
cat_percentage.plot.bar(rot = 0)
<matplotlib.axes._subplots.AxesSubplot at 0x7f43f7a67898>
Observations:
cat_num = combined_updated.pivot_table(index = 'service_cat', values = 'dissatisfied', aggfunc = np.sum)
cat_num
dissatisfied | |
---|---|
service_cat | |
Established | 32.0 |
Experienced | 59.0 |
New | 57.0 |
Veteran | 66.0 |
# Plot the result
cat_num.plot.bar(rot = 0)
<matplotlib.axes._subplots.AxesSubplot at 0x7f43f81b7828>
# Check out 'age' column
combined_updated['age'].value_counts(dropna = False)
51-55 71 NaN 55 41-45 48 41 45 45 46-50 42 36-40 41 46 50 39 26-30 35 21 25 33 26 30 32 31 35 32 36 40 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
Divide 'age' columns into groups of:
By this standard, we only need to extract the first digit in each value, and create a new column to record age group.
# Create a function to sort values in 'age' column into different age gruops
def age_cat(val):
if pd.isnull(val):
return np.nan
elif val == 2:
return 'Young'
elif (val == 3) or (val == 4) :
return 'Middle-aged'
else:
return 'Old'
# Extract first digit in 'age' column to apply age_cat function
combined_updated['age_cat'] = combined_updated['age'].str.extract('(\d)', expand = False).astype(float).apply(age_cat)
combined_updated['age_cat'].value_counts(dropna = False)
Middle-aged 308 Old 149 Young 139 NaN 55 Name: age_cat, dtype: int64
# Group data by 'age_cat' with 'dissatisfied' values and calculate mean of each group
#(Since df.pivot_table treats True as '1', False as '0', the mean also happens to be the percentage of dissatified employee in each group)
age_percentage = combined_updated.pivot_table(index = 'age_cat', values = 'dissatisfied')
age_percentage
dissatisfied | |
---|---|
age_cat | |
Middle-aged | 0.370130 |
Old | 0.422819 |
Young | 0.352518 |
# Plot result
age_percentage.plot.barh(rot = 0, legend = False, title = 'Percentage of Dissatisfied Former Employee in Age Groups')
<matplotlib.axes._subplots.AxesSubplot at 0x7f43f80d8dd8>
By now, we can answer the two questions we had at the beginning of this 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?*
*Are younger employees resigning due to some kind of dissatisfaction? What about older employees?*