BACKGROUND:
TARGET:
Below is a preview of a couple columns we'll work with from the dete_survey.csv:
Below is a preview of a couple columns we'll work with from the tafe_survey.csv:
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',na_values='Not Stated')
tafe_survey = pd.read_csv('tafe_survey.csv')
Get Informationabout content, shape, size of data sets
dete_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 822 non-null int64 1 SeparationType 822 non-null object 2 Cease Date 788 non-null object 3 DETE Start Date 749 non-null float64 4 Role Start Date 724 non-null float64 5 Position 817 non-null object 6 Classification 455 non-null object 7 Region 717 non-null object 8 Business Unit 126 non-null object 9 Employment Status 817 non-null object 10 Career move to public sector 822 non-null bool 11 Career move to private sector 822 non-null bool 12 Interpersonal conflicts 822 non-null bool 13 Job dissatisfaction 822 non-null bool 14 Dissatisfaction with the department 822 non-null bool 15 Physical work environment 822 non-null bool 16 Lack of recognition 822 non-null bool 17 Lack of job security 822 non-null bool 18 Work location 822 non-null bool 19 Employment conditions 822 non-null bool 20 Maternity/family 822 non-null bool 21 Relocation 822 non-null bool 22 Study/Travel 822 non-null bool 23 Ill Health 822 non-null bool 24 Traumatic incident 822 non-null bool 25 Work life balance 822 non-null bool 26 Workload 822 non-null bool 27 None of the above 822 non-null bool 28 Professional Development 808 non-null object 29 Opportunities for promotion 735 non-null object 30 Staff morale 816 non-null object 31 Workplace issue 788 non-null object 32 Physical environment 817 non-null object 33 Worklife balance 815 non-null object 34 Stress and pressure support 810 non-null object 35 Performance of supervisor 813 non-null object 36 Peer support 812 non-null object 37 Initiative 813 non-null object 38 Skills 811 non-null object 39 Coach 767 non-null object 40 Career Aspirations 746 non-null object 41 Feedback 792 non-null object 42 Further PD 768 non-null object 43 Communication 814 non-null object 44 My say 812 non-null object 45 Information 816 non-null object 46 Kept informed 813 non-null object 47 Wellness programs 766 non-null object 48 Health & Safety 793 non-null object 49 Gender 798 non-null object 50 Age 811 non-null object 51 Aboriginal 16 non-null object 52 Torres Strait 3 non-null object 53 South Sea 7 non-null object 54 Disability 23 non-null object 55 NESB 32 non-null object dtypes: bool(18), float64(2), int64(1), object(35) memory usage: 258.6+ KB
Check NaN/null values in DETE dataframe
sorted = dete_survey.set_index('Region').sort_values(['Region', 'SeparationType'])
sns.heatmap(sorted.isnull(), cbar=False)
<matplotlib.axes._subplots.AxesSubplot at 0x7ffa849c6610>
dete_survey.isnull().sum()
ID 0 SeparationType 0 Cease Date 34 DETE Start Date 73 Role Start Date 98 Position 5 Classification 367 Region 105 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
dete_survey.columns[dete_survey.isnull().sum()>100]
Index(['Classification', 'Region', 'Business Unit', 'Aboriginal', 'Torres Strait', 'South Sea', 'Disability', 'NESB'], dtype='object')
Check for columns with remaining 'Not Stated' elements (fixed via na_values parameter of read_csv() )
for col in dete_survey.columns:
if ('Not Stated' in dete_survey[col].unique().tolist()):
print(col)
Check for total amount of columns that contain NaN
dete_survey.columns[dete_survey.isnull().sum()>0].shape
(36,)
Check Data Types of columns
dt = dete_survey.dtypes
dt.value_counts()
object 35 bool 18 float64 2 int64 1 dtype: int64
tafe_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 72 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Record ID 702 non-null float64 1 Institute 702 non-null object 2 WorkArea 702 non-null object 3 CESSATION YEAR 695 non-null float64 4 Reason for ceasing employment 701 non-null object 5 Contributing Factors. Career Move - Public Sector 437 non-null object 6 Contributing Factors. Career Move - Private Sector 437 non-null object 7 Contributing Factors. Career Move - Self-employment 437 non-null object 8 Contributing Factors. Ill Health 437 non-null object 9 Contributing Factors. Maternity/Family 437 non-null object 10 Contributing Factors. Dissatisfaction 437 non-null object 11 Contributing Factors. Job Dissatisfaction 437 non-null object 12 Contributing Factors. Interpersonal Conflict 437 non-null object 13 Contributing Factors. Study 437 non-null object 14 Contributing Factors. Travel 437 non-null object 15 Contributing Factors. Other 437 non-null object 16 Contributing Factors. NONE 437 non-null object 17 Main Factor. Which of these was the main factor for leaving? 113 non-null object 18 InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object 19 InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object 20 InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object 21 InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object 22 InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object 23 InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object 24 InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object 25 InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object 26 InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object 27 InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object 28 InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object 29 InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object 30 InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object 31 WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object 32 WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object 33 WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object 34 WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object 35 WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object 36 WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 609 non-null object 37 WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object 38 WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] 608 non-null object 39 WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object 40 WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object 41 WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object 42 WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object 43 WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object 44 WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object 45 WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 609 non-null object 46 WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object 47 WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object 48 Induction. Did you undertake Workplace Induction? 619 non-null object 49 InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object 50 InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object 51 InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object 52 InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 53 InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 54 InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object 55 InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object 56 InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object 57 InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object 58 InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object 59 InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object 60 InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object 61 Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object 62 Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object 63 Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object 64 Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object 65 Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object 66 Gender. What is your Gender? 596 non-null object 67 CurrentAge. Current Age 596 non-null object 68 Employment Type. Employment Type 596 non-null object 69 Classification. Classification 596 non-null object 70 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object 71 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(70) memory usage: 395.0+ KB
tafe_survey.columns
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')
tafe_survey.sample()
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) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
582 | 6.348832e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2012.0 | Termination | NaN | NaN | NaN | NaN | NaN | ... | No | Yes | Yes | No | Female | 20 or younger | Contract/casual | Teacher (including LVT) | 7-10 | 7-10 |
1 rows × 72 columns
Check NaN/null values in TAFE dataframe
sorted = tafe_survey.set_index('Institute').sort_values(['Institute', 'Reason for ceasing employment'])
sns.heatmap(sorted.isnull(), cbar=False)
<matplotlib.axes._subplots.AxesSubplot at 0x7ffa84ab8b20>
tafe_survey.columns[tafe_survey.isnull().sum()>0]
Index(['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')
tafe_survey.columns[tafe_survey.isnull().sum()>100].shape
(38,)
dt = tafe_survey.dtypes
dt.value_counts()
object 70 float64 2 dtype: int64
Drop columns that are not needed for the analysis. Those columns contain work place and career related topics.
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49],axis=1)
Format column names to lowercase, remove spaces - to have common naming between DETE and TAFE data set
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(' ','_')
dete_survey_updated.columns
Index(['id', 'separationtype', 'cease_date', 'dete_start_date', 'role_start_date', 'position', 'classification', 'region', 'business_unit', 'employment_status', 'career_move_to_public_sector', 'career_move_to_private_sector', 'interpersonal_conflicts', 'job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions', 'maternity/family', 'relocation', 'study/travel', 'ill_health', 'traumatic_incident', 'work_life_balance', 'workload', 'none_of_the_above', 'gender', 'age', 'aboriginal', 'torres_strait', 'south_sea', 'disability', 'nesb'], dtype='object')
Drop columns that are not needed for the analysis. Those columns contain detailed questions/answers regarding the reason to leave the institute
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66],axis=1)
Rename column names - to have common naming between DETE and TAFE data set
col_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(col_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')
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
Extract only Resignation cases (seperationtype entry starts with Resignation...)
dete_resignations = dete_survey_updated[dete_survey_updated.separationtype.str.contains('^Resignation.*')].copy()
dete_resignations.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 311 entries, 3 to 821 Data columns (total 35 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 311 non-null int64 1 separationtype 311 non-null object 2 cease_date 300 non-null object 3 dete_start_date 283 non-null float64 4 role_start_date 271 non-null float64 5 position 308 non-null object 6 classification 161 non-null object 7 region 265 non-null object 8 business_unit 32 non-null object 9 employment_status 307 non-null object 10 career_move_to_public_sector 311 non-null bool 11 career_move_to_private_sector 311 non-null bool 12 interpersonal_conflicts 311 non-null bool 13 job_dissatisfaction 311 non-null bool 14 dissatisfaction_with_the_department 311 non-null bool 15 physical_work_environment 311 non-null bool 16 lack_of_recognition 311 non-null bool 17 lack_of_job_security 311 non-null bool 18 work_location 311 non-null bool 19 employment_conditions 311 non-null bool 20 maternity/family 311 non-null bool 21 relocation 311 non-null bool 22 study/travel 311 non-null bool 23 ill_health 311 non-null bool 24 traumatic_incident 311 non-null bool 25 work_life_balance 311 non-null bool 26 workload 311 non-null bool 27 none_of_the_above 311 non-null bool 28 gender 302 non-null object 29 age 306 non-null object 30 aboriginal 7 non-null object 31 torres_strait 0 non-null object 32 south_sea 3 non-null object 33 disability 8 non-null object 34 nesb 9 non-null object dtypes: bool(18), float64(2), int64(1), object(14) memory usage: 49.2+ KB
tafe_survey_updated.separationtype.value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
Extract only cases where seperationtype column gives Resignation value
tafe_resignations = tafe_survey_updated[tafe_survey_updated.separationtype=='Resignation'].copy()
tafe_resignations.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 340 entries, 3 to 701 Data columns (total 23 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 340 non-null float64 1 Institute 340 non-null object 2 WorkArea 340 non-null object 3 cease_date 335 non-null float64 4 separationtype 340 non-null object 5 Contributing Factors. Career Move - Public Sector 332 non-null object 6 Contributing Factors. Career Move - Private Sector 332 non-null object 7 Contributing Factors. Career Move - Self-employment 332 non-null object 8 Contributing Factors. Ill Health 332 non-null object 9 Contributing Factors. Maternity/Family 332 non-null object 10 Contributing Factors. Dissatisfaction 332 non-null object 11 Contributing Factors. Job Dissatisfaction 332 non-null object 12 Contributing Factors. Interpersonal Conflict 332 non-null object 13 Contributing Factors. Study 332 non-null object 14 Contributing Factors. Travel 332 non-null object 15 Contributing Factors. Other 332 non-null object 16 Contributing Factors. NONE 332 non-null object 17 gender 290 non-null object 18 age 290 non-null object 19 employment_status 290 non-null object 20 position 290 non-null object 21 institute_service 290 non-null object 22 role_service 290 non-null object dtypes: float64(2), object(21) memory usage: 63.8+ KB
dete_resignations.cease_date.value_counts()
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 11/2013 9 07/2013 9 10/2013 6 08/2013 4 05/2012 2 05/2013 2 09/2010 1 2010 1 07/2012 1 07/2006 1 Name: cease_date, dtype: int64
Convert "cease_date" column from string to float, extracting only year
pattern = r"(2\d{3})"
cease_yr = dete_resignations['cease_date'].str.extract(pattern)
dete_resignations['cease_date'] = cease_yr.squeeze().astype('float') # convert to float and squeeze to column array/Series
# check resulting cease date column
print(dete_resignations['cease_date'] .value_counts().sort_index())
print('Contained NaN values: {}'.format(dete_resignations['cease_date'].isnull().sum()))
2006.0 1 2010.0 2 2012.0 129 2013.0 146 2014.0 22 Name: cease_date, dtype: int64 Contained NaN values: 11
#check start date column
print(dete_resignations['dete_start_date'] .value_counts().sort_index())
print('Contained NaN values: {}'.format(dete_resignations['dete_start_date'] .isnull().sum()))
1963.0 1 1971.0 1 1972.0 1 1973.0 1 1974.0 2 1975.0 1 1976.0 2 1977.0 1 1980.0 5 1982.0 1 1983.0 2 1984.0 1 1985.0 3 1986.0 3 1987.0 1 1988.0 4 1989.0 4 1990.0 5 1991.0 4 1992.0 6 1993.0 5 1994.0 6 1995.0 4 1996.0 6 1997.0 5 1998.0 6 1999.0 8 2000.0 9 2001.0 3 2002.0 6 2003.0 6 2004.0 14 2005.0 15 2006.0 13 2007.0 21 2008.0 22 2009.0 13 2010.0 17 2011.0 24 2012.0 21 2013.0 10 Name: dete_start_date, dtype: int64 Contained NaN values: 28
#check tafe cease_date
print(tafe_resignations['cease_date'].value_counts().sort_index())
print('Contained NaN values:{}'.format(tafe_resignations['cease_date'].isnull().sum()))
2009.0 2 2010.0 68 2011.0 116 2012.0 94 2013.0 55 Name: cease_date, dtype: int64 Contained NaN values:5
import matplotlib.pyplot as plt
fig,ax = plt.subplots(1,2)
dete_resignations.boxplot(column='cease_date',ax=ax[0])
tafe_resignations.boxplot(column='cease_date',ax=ax[1])
ax[0].set_title('DETE')
ax[1].set_title('TAFE')
fig.tight_layout()
Observations :
tafe_resignations['institute_service'].value_counts()
Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 7-10 21 More than 20 years 10 Name: institute_service, dtype: int64
Create new column, calculated time worked at DETE from difference of "cease_date" - "dete_start_date"
dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']
#check results
print(dete_resignations['institute_service'].value_counts().sort_index())
print('Contained NaN values: {}'.format(dete_resignations['institute_service'].isnull().sum()))
0.0 20 1.0 22 2.0 14 3.0 20 4.0 16 5.0 23 6.0 17 7.0 13 8.0 8 9.0 14 10.0 6 11.0 4 12.0 6 13.0 8 14.0 6 15.0 7 16.0 5 17.0 6 18.0 5 19.0 3 20.0 7 21.0 3 22.0 6 23.0 4 24.0 4 25.0 2 26.0 2 27.0 1 28.0 2 29.0 1 30.0 2 31.0 1 32.0 3 33.0 1 34.0 1 35.0 1 36.0 2 38.0 1 39.0 3 41.0 1 42.0 1 49.0 1 Name: institute_service, dtype: int64 Contained NaN values: 38
dete_resignations.boxplot(column='institute_service')
<matplotlib.axes._subplots.AxesSubplot at 0x7ffa840571c0>
Observations:
Convert 'Contributing Factors. Dissatisfaction' column to boolean values
## ALTERNATIVE CODE
#rp_dict = {'Contributing Factors. Dissatisfaction ':True,
# '-':False}
#tafe_resignations['Contributing Factors. Dissatisfaction'] = tafe_resignations['Contributing Factors. Dissatisfaction'].replace(rp_dict)
#tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()
#rp_dict = {'Job Dissatisfaction':True,
# '-':False}
#tafe_resignations['Contributing Factors. Job Dissatisfaction'] = tafe_resignations['Contributing Factors. Job Dissatisfaction'].replace(rp_dict)
#tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()
print(tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts())
print(tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts())
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64 - 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
def update_vals(element):
if pd.isnull(element):
return np.nan
elif element == '-':
return False
else:
return True
Update columns 'Contruting Factors. Job Dissatisfaction' and ' Contibuting Factor. Dissatisfaction' with boolean values (True/False) or NaN
tafe_resignations[['Contributing Factors. Job Dissatisfaction','Contributing Factors. Dissatisfaction']] = tafe_resignations[['Contributing Factors. Job Dissatisfaction','Contributing Factors. Dissatisfaction']].applymap(update_vals)
Create a single column dissatisfaction containig boolean value in function of the two columns:
factors = ['Contributing Factors. Job Dissatisfaction','Contributing Factors. Dissatisfaction']
tafe_resignations['dissatisfied'] = tafe_resignations[factors].any(axis=1,skipna=False)
Check if NaN elements are skipped >> represented as NaN
ii_nan = (tafe_resignations[factors[0]].isnull()) | (tafe_resignations[factors[1]].isnull())
tafe_resignations.loc[ii_nan,["dissatisfied","Contributing Factors. Dissatisfaction","Contributing Factors. Dissatisfaction"]]
dissatisfied | Contributing Factors. Dissatisfaction | Contributing Factors. Dissatisfaction | |
---|---|---|---|
16 | NaN | NaN | NaN |
18 | NaN | NaN | NaN |
51 | NaN | NaN | NaN |
258 | NaN | NaN | NaN |
276 | NaN | NaN | NaN |
437 | NaN | NaN | NaN |
513 | NaN | NaN | NaN |
670 | NaN | NaN | NaN |
display(tafe_resignations[factors[0]].value_counts(dropna=False))
display(tafe_resignations[factors[1]].value_counts(dropna=False))
display(tafe_resignations['dissatisfied'].value_counts(dropna=False))
False 270 True 62 NaN 8 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
False 277 True 55 NaN 8 Name: Contributing Factors. Dissatisfaction, dtype: int64
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
tafe_resignations_up = tafe_resignations.copy()
Create a single column 'dissatisfied' containing boolean value in function of the below mentioned 9 columns indicating dissatisfaction:
factors = ['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['dissatisfied'] = dete_resignations[factors].any(axis=1,skipna=False)
ii_nan = (dete_resignations[factors[0]].isnull()) | \
(dete_resignations[factors[1]].isnull()) | \
(dete_resignations[factors[2]].isnull()) | \
(dete_resignations[factors[3]].isnull()) | \
(dete_resignations[factors[4]].isnull()) | \
(dete_resignations[factors[5]].isnull()) | \
(dete_resignations[factors[6]].isnull()) | \
(dete_resignations[factors[7]].isnull()) | \
(dete_resignations[factors[8]].isnull())
Check for NaN in the 9 "dissatified" columns
[dete_resignations[el].isnull().sum() for el in factors]
[0, 0, 0, 0, 0, 0, 0, 0, 0]
dc_str = [el for el in factors]
dc_str.append("dissatisfied")
dete_resignations.loc[ii_nan,dc_str]
job_dissatisfaction | dissatisfaction_with_the_department | physical_work_environment | lack_of_recognition | lack_of_job_security | work_location | employment_conditions | work_life_balance | workload | dissatisfied |
---|
dete_resignations_up = dete_resignations.copy()
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
dete_resignations_up['age'].value_counts()
41-45 48 46-50 42 36-40 41 26-30 35 51-55 32 31-35 29 21-25 29 56-60 26 61 or older 23 20 or younger 1 Name: age, dtype: int64
tafe_resignations_up['age'].value_counts()
41 45 45 46 50 39 51-55 39 21 25 33 36 40 32 26 30 32 31 35 32 56 or older 29 20 or younger 9 Name: age, dtype: int64
Examine common columns between both data sets before joining
set.intersection(set(dete_resignations_up.keys()),set(tafe_resignations_up.keys()))
{'age', 'cease_date', 'dissatisfied', 'employment_status', 'gender', 'id', 'institute', 'institute_service', 'position', 'separationtype'}
Concatenate data sets
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): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 651 non-null float64 1 separationtype 651 non-null object 2 cease_date 635 non-null float64 3 dete_start_date 283 non-null float64 4 role_start_date 271 non-null float64 5 position 598 non-null object 6 classification 161 non-null object 7 region 265 non-null object 8 business_unit 32 non-null object 9 employment_status 597 non-null object 10 career_move_to_public_sector 311 non-null object 11 career_move_to_private_sector 311 non-null object 12 interpersonal_conflicts 311 non-null object 13 job_dissatisfaction 311 non-null object 14 dissatisfaction_with_the_department 311 non-null object 15 physical_work_environment 311 non-null object 16 lack_of_recognition 311 non-null object 17 lack_of_job_security 311 non-null object 18 work_location 311 non-null object 19 employment_conditions 311 non-null object 20 maternity/family 311 non-null object 21 relocation 311 non-null object 22 study/travel 311 non-null object 23 ill_health 311 non-null object 24 traumatic_incident 311 non-null object 25 work_life_balance 311 non-null object 26 workload 311 non-null object 27 none_of_the_above 311 non-null object 28 gender 592 non-null object 29 age 596 non-null object 30 aboriginal 7 non-null object 31 torres_strait 0 non-null object 32 south_sea 3 non-null object 33 disability 8 non-null object 34 nesb 9 non-null object 35 institute_service 563 non-null object 36 dissatisfied 643 non-null object 37 institute 651 non-null object 38 Institute 340 non-null object 39 WorkArea 340 non-null object 40 Contributing Factors. Career Move - Public Sector 332 non-null object 41 Contributing Factors. Career Move - Private Sector 332 non-null object 42 Contributing Factors. Career Move - Self-employment 332 non-null object 43 Contributing Factors. Ill Health 332 non-null object 44 Contributing Factors. Maternity/Family 332 non-null object 45 Contributing Factors. Dissatisfaction 332 non-null object 46 Contributing Factors. Job Dissatisfaction 332 non-null object 47 Contributing Factors. Interpersonal Conflict 332 non-null object 48 Contributing Factors. Study 332 non-null object 49 Contributing Factors. Travel 332 non-null object 50 Contributing Factors. Other 332 non-null object 51 Contributing Factors. NONE 332 non-null object 52 role_service 290 non-null object dtypes: float64(4), object(49) memory usage: 269.7+ KB
Drop all columns with more then 500 NaN
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): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 651 non-null float64 1 separationtype 651 non-null object 2 cease_date 635 non-null float64 3 position 598 non-null object 4 employment_status 597 non-null object 5 gender 592 non-null object 6 age 596 non-null object 7 institute_service 563 non-null object 8 dissatisfied 643 non-null object 9 institute 651 non-null object dtypes: float64(2), object(8) memory usage: 51.0+ KB
Checking contents of ["institute_service"] column
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 39.0 3 19.0 3 21.0 3 32.0 3 25.0 2 26.0 2 36.0 2 28.0 2 30.0 2 42.0 1 49.0 1 35.0 1 34.0 1 38.0 1 33.0 1 29.0 1 27.0 1 41.0 1 31.0 1 Name: institute_service, dtype: int64
combined_updated['institute_service'].astype('str').value_counts()
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 13.0 8 8.0 8 15.0 7 20.0 7 17.0 6 14.0 6 10.0 6 22.0 6 12.0 6 18.0 5 16.0 5 11.0 4 24.0 4 23.0 4 39.0 3 32.0 3 21.0 3 19.0 3 28.0 2 25.0 2 26.0 2 30.0 2 36.0 2 41.0 1 38.0 1 49.0 1 34.0 1 35.0 1 33.0 1 42.0 1 31.0 1 29.0 1 27.0 1 Name: institute_service, dtype: int64
# map function - calculating YEAR info
# - based on extracted> float-converted 2 columns of numbers
def calc_yrs(ycol):
if ycol[1]==0: # if second number is zero (original value notes as float e.g. 3.0)
return ycol[0] # return 1st column
else:
return (ycol[0]+ycol[1])/2 # return average of 1st+2nd number
Extract the years worked at service in numeric/float format
yrs_str = combined_updated['institute_service'].astype('str') # convert to string
patt = r"([0-9]+)[-\.]?([0-9]+)?" #regular exp pattern to extract up to 2 numbers
yrs_extr = yrs_str.str.extract(patt) # extract numbers
yrs_cln = yrs_extr.dropna(how='all') # remove all lines with both elements NaN - 88 lines(ref above)
yrs_calc = yrs_cln.fillna('0').astype('float').apply(calc_yrs,axis=1) #fill single column NaN with '0' >> convert to float >> deploy calc_yrs function
Mapping function for Carreer stages
# map function to convert YEAR info to carreer stages
def carr_stage(el):
if el<3.0:
return 'New'
elif (el>=3.0) & (el<7.0):
return 'Experienced'
elif (el>=7.0) & (el<11.0):
return 'Established'
elif el>=11.0:
return 'Veteran'
combined_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 651 entries, 0 to 650 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 651 non-null float64 1 separationtype 651 non-null object 2 cease_date 635 non-null float64 3 position 598 non-null object 4 employment_status 597 non-null object 5 gender 592 non-null object 6 age 596 non-null object 7 institute_service 563 non-null object 8 dissatisfied 643 non-null object 9 institute 651 non-null object dtypes: float64(2), object(8) memory usage: 51.0+ KB
Create a new colum based on the calculated years at service Series mapped with the carreer stages
# add column "service_cat"
#combined_updated['service_cat'] = yrs_calc
#combined_updated['service_cat'].map(carr_stage,inplace=True)
#combined_updated['service_cat'] = yrs_calc.map(carr_stage)#.copy()
combined_updated.loc[:,'service_cat'] = yrs_calc.map(carr_stage).copy()
combined_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 651 entries, 0 to 650 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 651 non-null float64 1 separationtype 651 non-null object 2 cease_date 635 non-null float64 3 position 598 non-null object 4 employment_status 597 non-null object 5 gender 592 non-null object 6 age 596 non-null object 7 institute_service 563 non-null object 8 dissatisfied 643 non-null object 9 institute 651 non-null object 10 service_cat 563 non-null object dtypes: float64(2), object(9) memory usage: 56.1+ KB
combined_updated.dissatisfied.value_counts(dropna=False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
Fill remaining NaN in ["dissatisfied"] column with "False", as it is the most occuring value
combined_updated.loc[:,'dissatisfied'] = combined_updated.dissatisfied.fillna(value=False).copy()
combined_updated.dissatisfied.value_counts(dropna=False)
False 411 True 240 Name: dissatisfied, dtype: int64
Cross-Check values vs pivot_table/mean approach
def dissatisfied_perc(group):
n = len(group)
diss_n = group[group==True].value_counts()
return diss_n/n*100
combined_updated.pivot_table(values='dissatisfied',index='service_cat',aggfunc=dissatisfied_perc)
dissatisfied | |
---|---|
service_cat | |
Established | 51.612903 |
Experienced | 34.302326 |
New | 29.533679 |
Veteran | 48.529412 |
combined_updated.pivot_table(values='dissatisfied',index='service_cat')*100
dissatisfied | |
---|---|
service_cat | |
Established | 51.612903 |
Experienced | 34.302326 |
New | 29.533679 |
Veteran | 48.529412 |
combined_updated.pivot_table(values='dissatisfied',index='service_cat').reindex(['New','Experienced','Established','Veteran']).plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x7ffa84362430>
Answering the first question:
" 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?"
Therefore we can say that "New" employees are not resigning due to dissatisfaction with higher rates then other seniority levels.
The highest rates were found for the "Established" level.
combined_updated.pivot_table(values='dissatisfied',index=['institute','service_cat']).reindex([['DETE','DETE','DETE','DETE','TAFE','TAFE','TAFE','TAFE'],['New','Experienced','Established','Veteran','New','Experienced','Established','Veteran']]).plot(kind='bar',stacked=True)
<matplotlib.axes._subplots.AxesSubplot at 0x7ffa84322160>
This can be confirmed in the alternative plot below in which we displayed the data from DETE (blue) and TAFE (red) as stacked bar graphs
fig,axi = plt.subplots(1,1)
combined_updated[combined_updated.institute=='DETE'].pivot_table(values='dissatisfied',index='service_cat').reindex(['New','Experienced','Established','Veteran']).plot(kind='bar',ax=axi,color='b',stacked=True)
combined_updated[combined_updated.institute=='TAFE'].pivot_table(values='dissatisfied',index='service_cat').reindex(['New','Experienced','Established','Veteran']).plot(kind='bar',ax=axi,color='r',stacked=True)
<matplotlib.axes._subplots.AxesSubplot at 0x7ffa7d794250>
combined_updated['age'].value_counts()
51-55 71 41-45 48 41 45 45 46-50 42 36-40 41 46 50 39 26-30 35 21 25 33 36 40 32 26 30 32 31 35 32 21-25 29 56 or older 29 31-35 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
rp_age = {'41 45':'41-45',
'46 50':'46-50',
'21 25':'21-25',
'36 40':'36-40',
'31 35':'31-35',
'26 30':'26-30'}
combined_updated['age'].replace(rp_age,inplace=True)
combined_updated.pivot_table(values='dissatisfied',index='age').plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x7ffa7d234250>
Answering the second question:
"Are younger employees resigning due to some kind of dissatisfaction?
"What about older employees?"
Difficult to detect a significant trend for younger employees (<30years), but dissatisfaction seems to peak with the age group of 26-30 years. As this is as well the age group where most people become parents for a first time being aged 25-34 years:
https://aifs.gov.au/facts-and-figures/births-in-australia
It would have been interesting to investigate the influence of an upcoming child birth on the resigning rate in that age group
We abosere a rather big jump in resigning rate for the age groups above 56years
Quick visualization of remaining "NaN/Null" Values
sns.heatmap(combined_updated.isnull(), cbar=False)
<matplotlib.axes._subplots.AxesSubplot at 0x7ffa84802a90>