The TAFE exit survey responses can be downloaded here
The DETE exit survey responses can be downloaded here
Thank you to DataQuest for changing the encoding to UTF-8 from cp1252.
A quick preview of the individual DETE and TAFE data sets
import pandas as pd
import numpy as np
dete_survey = pd.read_csv("dete_survey.csv")
tafe_survey = pd.read_csv("tafe_survey.csv")
The data has a lot of columns = 56.
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 822 non-null object 3 DETE Start Date 822 non-null object 4 Role Start Date 822 non-null object 5 Position 817 non-null object 6 Classification 455 non-null object 7 Region 822 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), int64(1), object(37) memory usage: 258.6+ KB
dete_survey[0:5]
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984 | 2004 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | Not Stated | Not Stated | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011 | 2011 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005 | 2006 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970 | 1989 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 56 columns
dete_survey.isnull().sum()
ID 0 SeparationType 0 Cease Date 0 DETE Start Date 0 Role Start Date 0 Position 5 Classification 367 Region 0 Business Unit 696 Employment Status 5 Career move to public sector 0 Career move to private sector 0 Interpersonal conflicts 0 Job dissatisfaction 0 Dissatisfaction with the department 0 Physical work environment 0 Lack of recognition 0 Lack of job security 0 Work location 0 Employment conditions 0 Maternity/family 0 Relocation 0 Study/Travel 0 Ill Health 0 Traumatic incident 0 Work life balance 0 Workload 0 None of the above 0 Professional Development 14 Opportunities for promotion 87 Staff morale 6 Workplace issue 34 Physical environment 5 Worklife balance 7 Stress and pressure support 12 Performance of supervisor 9 Peer support 10 Initiative 9 Skills 11 Coach 55 Career Aspirations 76 Feedback 30 Further PD 54 Communication 8 My say 10 Information 6 Kept informed 9 Wellness programs 56 Health & Safety 29 Gender 24 Age 11 Aboriginal 806 Torres Strait 819 South Sea 815 Disability 799 NESB 790 dtype: int64
This data set is even 'busier'.
It has more columns = 72, many of which are probably not relevant
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.isnull().sum()
Record ID 0 Institute 0 WorkArea 0 CESSATION YEAR 7 Reason for ceasing employment 1 ... CurrentAge. Current Age 106 Employment Type. Employment Type 106 Classification. Classification 106 LengthofServiceOverall. Overall Length of Service at Institute (in years) 106 LengthofServiceCurrent. Length of Service at current workplace (in years) 106 Length: 72, dtype: int64
tafe_survey.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.loc[tafe_survey["Reason for ceasing employment"] == "Resignation"].isnull().sum()
Record ID 0 Institute 0 WorkArea 0 CESSATION YEAR 5 Reason for ceasing employment 0 .. CurrentAge. Current Age 50 Employment Type. Employment Type 50 Classification. Classification 50 LengthofServiceOverall. Overall Length of Service at Institute (in years) 50 LengthofServiceCurrent. Length of Service at current workplace (in years) 50 Length: 72, dtype: int64
Because "Not Stated" was being used to denote missing values, the below re-reads the data file using that information.
dete_survey = pd.read_csv("dete_survey.csv", na_values="Not Stated")
Thank you for telling me which columns are irrelevant and can be dropped!
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)
dete_survey_updated.head()
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Work life balance | Workload | None of the above | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | False | False | True | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | False | False | False | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | False | True | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | True | False | False | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 35 columns
It appears we do not need to make any manipulations for '-' or 'NaN' values in the TAFE survey. In retropspect I understand these are the expected ways of indicating missing values for int/float and strings respectively.
Thank you for proving me again with the approx. 50 irrelevant columns to drop from this dataset.
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis=1)
tafe_survey_updated.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 | ... | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | 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 | ... | NaN | NaN | NaN | NaN | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | - | - | - | NONE | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | - | - | - | - | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 23 columns
The column names are not too long to work with so the only modifications really required is to standardize the case and spacing.
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(" *", "_")
dete_header=dete_survey_updated.columns
dete_header
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')
This dataset does not have very suitable column names to work with. Below I will gladly replace them with the names of equivalent column in the DETE data set. Where there is no equivalent, I'll use simplified, non-redundant names.
tafe_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(columns=tafe_col_map, inplace=True)
tafe_header=tafe_survey_updated.columns
tafe_header
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')
We are interested in learning more about resignations resulting from job satisfactions so we can isolate these entries to focus on and disregard reasons for leaving such as retirement or reaching the end of a contract.
This information is likely stored in the separationtype column and I expect we will have different values for this column in the 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
dete_resignations = dete_survey_updated[dete_survey_updated["separationtype"].str.contains("Resignation")].copy()
dete_resignations["separationtype"].value_counts()
Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 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
The following line gives me an error about null values so I need to check the TAFE data for null values in the separationtype column.
tafe_resignations = tafe_survey_updated[tafe_survey_updated["separationtype"].str.contains("Resignation")].copy() tafe_resignations["separationtype"].value_counts()
temp_missing = tafe_survey_updated['separationtype'].isnull()
tafe_survey_updated[temp_missing]
id | Institute | WorkArea | cease_date | separationtype | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | gender | age | employment_status | position | institute_service | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
324 | 6.345804e+17 | Sunshine Coast Institute of TAFE | Non-Delivery (corporate) | 2011.0 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
1 rows × 23 columns
There is almost no information in this entry to use so we will drop it from the tafe_survey_updated dataset.
tafe_survey_updated.dropna(subset=["separationtype"], axis=0, inplace=True)
tafe_survey_updated.reset_index()
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. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | gender | age | employment_status | position | institute_service | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | ... | - | - | - | NONE | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | ... | - | - | - | - | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
696 | 697 | 6.350668e+17 | Barrier Reef Institute of TAFE | Delivery (teaching) | 2013.0 | Resignation | Career Move - Public Sector | - | - | - | ... | - | - | - | - | Male | 51-55 | Temporary Full-time | Teacher (including LVT) | 1-2 | 1-2 |
697 | 698 | 6.350677e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2013.0 | Resignation | Career Move - Public Sector | - | - | - | ... | - | - | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
698 | 699 | 6.350704e+17 | Tropical North Institute of TAFE | Delivery (teaching) | 2013.0 | Resignation | - | - | - | - | ... | - | - | Other | - | Female | 51-55 | Permanent Full-time | Teacher (including LVT) | 5-6 | 1-2 |
699 | 700 | 6.350712e+17 | Southbank Institute of Technology | Non-Delivery (corporate) | 2013.0 | Contract Expired | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | Female | 41 45 | Temporary Full-time | Professional Officer (PO) | 1-2 | 1-2 |
700 | 701 | 6.350730e+17 | Tropical North Institute of TAFE | Non-Delivery (corporate) | 2013.0 | Resignation | - | - | Career Move - Self-employment | - | ... | - | Travel | - | - | Female | 26 30 | Contract/casual | Administration (AO) | 3-4 | 1-2 |
701 rows × 24 columns
Check the row was successfully removed:
temp2_missing = tafe_survey_updated['separationtype'].isnull()
tafe_survey_updated[temp2_missing]
id | Institute | WorkArea | cease_date | separationtype | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | gender | age | employment_status | position | institute_service | role_service |
---|
0 rows × 23 columns
Now I try again to isolate resignations from the TAFE data and now it works:
tafe_resignations = tafe_survey_updated[tafe_survey_updated["separationtype"].str.contains("Resignation")].copy()
tafe_resignations["separationtype"].value_counts()
Resignation 340 Name: separationtype, dtype: int64
In order to verify the integrity of the data, I want to make sure the date values of the resignations are consistent with logical expectation.
The cease_date values in the DETE data set seem valid but some include the month while others to not. I would like to extract just the year.
The TAFE data set only provides the year of the cease_date so I am just ensuring it is also stored as a float.
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 05/2013 2 05/2012 2 2010 1 09/2010 1 07/2006 1 07/2012 1 Name: cease_date, dtype: int64
I noticed there are missing values for cease_date. I discover below that the vectorized string methods used to extract the year keep the NaN values but ignore them when performing their operations.
dete_resignations[dete_resignations["cease_date"].isnull()]
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | work_life_balance | workload | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
683 | 685 | Resignation-Other employer | NaN | 2011.0 | 2012.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | False | False | False | Male | 21-25 | NaN | NaN | NaN | NaN | NaN |
694 | 696 | Resignation-Other reasons | NaN | 2012.0 | NaN | Teacher Aide | NaN | Metropolitan | NaN | Casual | ... | False | False | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
704 | 706 | Resignation-Other reasons | NaN | 2006.0 | 2007.0 | Teacher Aide | NaN | Darling Downs South West | NaN | Permanent Full-time | ... | False | False | False | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
709 | 711 | Resignation-Other employer | NaN | NaN | NaN | Teacher | Primary | Central Office | Education Queensland | Permanent Full-time | ... | True | True | False | Female | 51-55 | NaN | NaN | NaN | NaN | NaN |
724 | 726 | Resignation-Other reasons | NaN | 1984.0 | NaN | Teacher | Primary | Darling Downs South West | NaN | Permanent Full-time | ... | False | False | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
770 | 772 | Resignation-Other reasons | NaN | 1987.0 | 1987.0 | Cleaner | NaN | Darling Downs South West | NaN | Permanent Part-time | ... | False | False | True | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
774 | 776 | Resignation-Other employer | NaN | 2005.0 | 2005.0 | Teacher Aide | NaN | Central Queensland | NaN | Permanent Part-time | ... | False | False | True | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
788 | 790 | Resignation-Other employer | NaN | 1990.0 | 2010.0 | Teacher | Secondary | Metropolitan | NaN | Permanent Full-time | ... | False | False | False | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
791 | 793 | Resignation-Other reasons | NaN | 2007.0 | 2007.0 | Public Servant | A01-A04 | Metropolitan | NaN | Permanent Part-time | ... | False | False | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
797 | 799 | Resignation-Move overseas/interstate | NaN | 2000.0 | 2013.0 | Public Servant | A01-A04 | South East | NaN | Permanent Part-time | ... | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
798 | 800 | Resignation-Move overseas/interstate | NaN | 1995.0 | NaN | Teacher Aide | NaN | Darling Downs South West | NaN | Permanent Part-time | ... | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
11 rows × 35 columns
dete_resignations["cease_year"] = dete_resignations["cease_date"].str.extract(r"([0-9]{4})").astype(float)
dete_resignations["cease_year"]
3 2012.0 5 2012.0 8 2012.0 9 2012.0 11 2012.0 ... 808 2013.0 815 2014.0 816 2014.0 819 2014.0 821 2013.0 Name: cease_year, Length: 311, dtype: float64
dete_resignations["cease_year"].value_counts().sort_index(ascending=False)
2014.0 22 2013.0 146 2012.0 129 2010.0 2 2006.0 1 Name: cease_year, dtype: int64
tafe_resignations["cease_year"] = tafe_resignations["cease_date"].astype(float)
tafe_resignations["cease_year"].value_counts().sort_index(ascending=False)
2013.0 55 2012.0 94 2011.0 116 2010.0 68 2009.0 2 Name: cease_year, dtype: int64
The DETE data doesn't have an equivalent to the TAFE data's institute_service column storing the time the employee spent at DETE so we will create this column using the dates corresponding to the beginning and end of the employee's service.
Now it's time to check the integrity of the dete_start_date column. I used the cut method to make 10 groups of 5 years each to quickly get a sense of the values. No value is after 2013 which is also good sign.
dete_resignations["dete_start_date"] = dete_resignations["dete_start_date"].astype(float)
pd.cut(dete_resignations["dete_start_date"], 10).value_counts()
(2008.0, 2013.0] 85 (2003.0, 2008.0] 85 (1998.0, 2003.0] 32 (1993.0, 1998.0] 27 (1988.0, 1993.0] 24 (1983.0, 1988.0] 12 (1978.0, 1983.0] 8 (1973.0, 1978.0] 6 (1968.0, 1973.0] 3 (1962.95, 1968.0] 1 Name: dete_start_date, dtype: int64
dete_resignations["institute_service"] = dete_resignations["cease_year"] - dete_resignations["dete_start_date"].astype(float)
pd.cut(dete_resignations["institute_service"], 7).value_counts()
(-0.049, 7.0] 145 (7.0, 14.0] 52 (14.0, 21.0] 36 (21.0, 28.0] 21 (28.0, 35.0] 10 (35.0, 42.0] 8 (42.0, 49.0] 1 Name: institute_service, dtype: int64
Looks like someone has a negative length of service. the way the cut displays but if we look closer no institute_service column values are actually negative.
dete_resignations.loc[dete_resignations["institute_service"] <0]
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | cease_year | institute_service |
---|
0 rows × 37 columns
The column headings and the values are the same long strings so I will store them as variables to reference.
The values in "Contributing Factors. [Job] Dissatisfaction" are not as described in the instructions. They are not True, False or NaN. They are "[Job] Description" or "-". I interpret "-" to be therefore False, not NaN.
The instructions have a a lot of instructions ... I feel my solution below is a more elegent way of doing the same thing. Unless it didn'nt actually do the same thing, that is!
disstr = 'Contributing Factors. Dissatisfaction'
job_disstr = 'Contributing Factors. Job Dissatisfaction'
tafe_resignations["my_dissatisfied"] = (tafe_resignations[disstr].str.contains("Dissatisfaction") | tafe_resignations[job_disstr].str.contains("Dissatisfaction"))
tafe_resignations["my_dissatisfied"].value_counts()
False 249 True 91 Name: my_dissatisfied, dtype: int64
I compared with the recommended method to find NaNs and there is in fact a difference in the result.
So '-' is a specifically False response, not a no response as I originally rationalized.
tafe_resignations[disstr].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
tafe_resignations[job_disstr].value_counts()
- 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
def update_vals (val) :
if pd.isnull(val) :
return (np.nan)
elif val == "-" :
return False
else :
return True
tafe_resignations[[disstr, job_disstr]] = tafe_resignations[[disstr, job_disstr]].applymap(update_vals)
tafe_resignations["dissatisfied"] = tafe_resignations[[disstr, job_disstr]].any(axis=1, skipna=False)
tafe_resignations["dissatisfied"].value_counts()
False 241 True 91 Name: dissatisfied, dtype: int64
There are multiple columns and apparently every Resignation is has one of these reasons indicated (where indicated).
discols = ["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[discols] = dete_resignations[discols].applymap(update_vals)
dete_resignations["dissatisfied"] = dete_resignations[discols].any(axis=1, skipna=False)
dete_resignations["dissatisfied"].value_counts()
True 311 Name: dissatisfied, dtype: int64
dete_resignations_up = dete_resignations.copy()
tafe_resignations_up = tafe_resignations.copy()
In order to easily identify which set the data came from originally, I'm going to add a column to store the name of the institute.
dete_resignations_up["institute"] = "DETE"
tafe_resignations_up["institute"] = "TAFE"
dd
In the instructions it says to combine the two data sets on "institute_service" column and then drop.na.
combined = pd.concat([dete_resignations_up,
tafe_resignations_up], join="outer")
combined["institute"].value_counts()
TAFE 340 DETE 311 Name: institute, dtype: int64
combined.describe(include='all')
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | 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 | role_service | my_dissatisfied | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 6.510000e+02 | 651 | 635 | 283.000000 | 271.000000 | 598 | 161 | 265 | 32 | 597 | ... | 332 | 332 | 332 | 332 | 332 | 332 | 332 | 332 | 290 | 340 |
unique | NaN | 4 | 21 | NaN | NaN | 21 | 8 | 8 | 9 | 6 | ... | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 7 | 2 |
top | NaN | Resignation | 2012 | NaN | NaN | Administration (AO) | Primary | Central Queensland | Education Queensland | Permanent Full-time | ... | - | False | False | - | - | - | - | - | Less than 1 year | False |
freq | NaN | 340 | 126 | NaN | NaN | 148 | 58 | 45 | 14 | 256 | ... | 312 | 277 | 270 | 308 | 316 | 315 | 246 | 316 | 92 | 249 |
mean | 3.314265e+17 | NaN | NaN | 2002.067138 | 1999.653137 | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
std | 3.172210e+17 | NaN | NaN | 9.914479 | 109.965675 | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
min | 4.000000e+00 | NaN | NaN | 1963.000000 | 200.000000 | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
25% | 4.525000e+02 | NaN | NaN | 1997.000000 | 2004.000000 | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
50% | 6.341820e+17 | NaN | NaN | 2005.000000 | 2009.000000 | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
75% | 6.345770e+17 | NaN | NaN | 2010.000000 | 2011.000000 | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
max | 6.350730e+17 | NaN | NaN | 2013.000000 | 2013.000000 | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
11 rows × 55 columns
Now I'll drop any columns with not enough non-null values. A threshold of 500 has been provided persumably because that will include all columns that were common to the 2 data sets but not anything specific to just one data set.
combined_updated = combined.dropna(axis=1, thresh=500)
combined_updated.describe(include='all')
id | separationtype | cease_date | position | employment_status | gender | age | cease_year | institute_service | dissatisfied | institute | |
---|---|---|---|---|---|---|---|---|---|---|---|
count | 6.510000e+02 | 651 | 635 | 598 | 597 | 592 | 596 | 635.000000 | 563 | 643 | 651 |
unique | NaN | 4 | 21 | 21 | 6 | 2 | 17 | NaN | 49 | 2 | 2 |
top | NaN | Resignation | 2012 | Administration (AO) | Permanent Full-time | Female | 51-55 | NaN | Less than 1 year | True | TAFE |
freq | NaN | 340 | 126 | 148 | 256 | 424 | 71 | NaN | 73 | 402 | 340 |
mean | 3.314265e+17 | NaN | NaN | NaN | NaN | NaN | NaN | 2011.963780 | NaN | NaN | NaN |
std | 3.172210e+17 | NaN | NaN | NaN | NaN | NaN | NaN | 1.079028 | NaN | NaN | NaN |
min | 4.000000e+00 | NaN | NaN | NaN | NaN | NaN | NaN | 2006.000000 | NaN | NaN | NaN |
25% | 4.525000e+02 | NaN | NaN | NaN | NaN | NaN | NaN | 2011.000000 | NaN | NaN | NaN |
50% | 6.341820e+17 | NaN | NaN | NaN | NaN | NaN | NaN | 2012.000000 | NaN | NaN | NaN |
75% | 6.345770e+17 | NaN | NaN | NaN | NaN | NaN | NaN | 2013.000000 | NaN | NaN | NaN |
max | 6.350730e+17 | NaN | NaN | NaN | NaN | NaN | NaN | 2014.000000 | NaN | NaN | NaN |
In order to normalize the years as service and then group them, I want to first verify the format of the values.
Give the formats, I made these changes:
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 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 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"] = combined_updated["institute_service"].astype(str).str.replace("Less than 1 year","0").str.lower().str.replace("[a-z]*", "").str.replace("\..*","").str.replace("-.*","").str.strip()
<ipython-input-47-aec85db4c104>:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy combined_updated["institute_service"] = combined_updated["institute_service"].astype(str).str.replace("Less than 1 year","0").str.lower().str.replace("[a-z]*", "").str.replace("\..*","").str.replace("-.*","").str.strip()
A simple way of filling NaN values so I can convert to float.
combined_updated.loc[combined_updated["institute_service"]== '', "institute_service"] = "NaN"
/dataquest/system/env/python3/lib/python3.8/site-packages/pandas/core/indexing.py:966: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy self.obj[item] = s
combined_updated["institute_service"].value_counts()
0 93 NaN 88 1 86 3 83 5 56 7 34 11 30 6 17 20 17 4 16 9 14 2 14 13 8 8 8 15 7 22 6 17 6 12 6 10 6 14 6 18 5 16 5 24 4 23 4 32 3 19 3 21 3 39 3 28 2 26 2 25 2 36 2 30 2 49 1 29 1 41 1 34 1 42 1 31 1 38 1 27 1 33 1 35 1 Name: institute_service, dtype: int64
combined_updated["institute_service"] = combined_updated["institute_service"].astype(float)
<ipython-input-50-2a554ebc0312>:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy combined_updated["institute_service"] = combined_updated["institute_service"].astype(float)
def years_to_stage (years) :
if years < 3 :
return "New"
elif years < 7 :
return "Experienced"
elif years < 11 :
return "Established"
elif years >= 11 :
return "Veteran"
else :
return "NaN"
combined_updated["service_cat"] = combined_updated["institute_service"].apply(years_to_stage)
combined_updated["service_cat"].value_counts(dropna=False)
<ipython-input-52-5c8916c54d27>:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy combined_updated["service_cat"] = combined_updated["institute_service"].apply(years_to_stage)
New 193 Experienced 172 Veteran 136 NaN 88 Established 62 Name: service_cat, dtype: int64
A quick check at Established column data indicates institute_serivce durations of 7,8,9 and 10 years were included and they were.
combined_updated.loc[combined_updated["service_cat"] == "Established", "institute_service"].value_counts()
7.0 34 9.0 14 8.0 8 10.0 6 Name: institute_service, dtype: int64
Before creating the pivot table I need to fill in missing values. The method in the instructions required finding the most frequent reponse and filling the na values with that. Most frequent response ended up being "True".
combined_updated["dissatisfied"].value_counts(dropna=False)
True 402 False 241 NaN 8 Name: dissatisfied, dtype: int64
combined_updated["dissatisfied"] = combined_updated["dissatisfied"].fillna(value=True)
combined_updated["dissatisfied"].value_counts()
<ipython-input-55-dfde65416531>:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy combined_updated["dissatisfied"] = combined_updated["dissatisfied"].fillna(value=True)
True 410 False 241 Name: dissatisfied, dtype: int64
Now I can check the frequency of dissatisfaction depending on the duration of serivce by creating a pivot table and plot.
True is equal to 1 and False is equal to 0. The pivot table will provide the mean of the values.
pt = combined_updated.pivot_table(index="service_cat", values="dissatisfied")
pt
dissatisfied | |
---|---|
service_cat | |
Established | 0.774194 |
Experienced | 0.581395 |
NaN | 0.681818 |
New | 0.476684 |
Veteran | 0.808824 |
Since there are just 5 categories of values a bar graph should display the information well.
%matplotlib inline
pt.plot(kind="bar", ylim=(0,1))
<matplotlib.axes._subplots.AxesSubplot at 0x7fb519b2af10>
It would be easier to see trends if the bars were ordered by the service duration categories:
["New", "Experienced", "Established", "Veteran"] and then show "NaN" at the end.
I found this solution on stackoverflow:
weekdays = ['Mon', 'Tues', 'Weds', 'Thurs', 'Fri', 'Sat', 'Sun']
mapping = {day: i for i, day in enumerate(weekdays)}
key = df['day'].map(mapping)
And the sorting is simple:
df.iloc[key.argsort()]
ser_cats = ["New", "Experienced", "Established", "Veteran", "NaN"]
mapping = {cat: i+1 for i, cat in enumerate(ser_cats)}
combined_updated["num_service_cat"] = combined_updated["service_cat"].map(mapping)
<ipython-input-58-63de5dbcd4b0>:7: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy combined_updated["num_service_cat"] = combined_updated["service_cat"].map(mapping)
pt2 = combined_updated.pivot_table(index="num_service_cat", values="dissatisfied")
pt2
dissatisfied | |
---|---|
num_service_cat | |
1 | 0.476684 |
2 | 0.581395 |
3 | 0.774194 |
4 | 0.808824 |
5 | 0.681818 |
pt2.plot(kind='bar', ylim=(0,1), title="Employees are more likely to resign due to dissatisfaction the longer their service.\n 1=New 2=Experienced 3=Established 4-Veteren 5-Unknown")
<matplotlib.axes._subplots.AxesSubplot at 0x7fb5503a01c0>
After a lot of cleaning I was able to create a bar graph indicating the longer employee was in service, the more likely they are to have resigned with some some feelings of dissatisfaction.
Improve how to deal with missing values: filling based on most popular response might not be the best strategy
Introduce a level of dissatisfaction: instead of using the any function to see if any of the contributing factors are present, count up the contributing factors to make a dissatisfaction score to plot the mean of.
Find correlations with age.