In this project I'll try to answer the following questions:
I'll be basing the analysis on exit surveys from Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia.
We don't have a complete data dictionary for the datasets, so we'll have to rely on some general knowledge.
Below is a preview of a couple columns we'll work with from the dete_survey.csv:
ID
: An id used to identify the participant of the surveySeparationType
: The reason why the person's employment endedCease Date
: The year or month the person's employment endedDETE Start Date
: The year the person began employment with the DETEBelow is a preview of a couple columns we'll work with from the tafe_survey.csv:
Record ID
: An id used to identify the participant of the surveyReason for ceasing employment
: The reason why the person's employment endedLengthofServiceOverall. Overall Length of Service at Institute (in years)
: The length of the person's employment (in years)This notebook stores prepared data for analysis as pickled dataframes.
combined_updated.pickle
contains a subset of the exit survey data.import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
%config InlineBackend.figure_format='retina'
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
dete_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): # 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.head()
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984 | 2004 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | Not Stated | Not Stated | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011 | 2011 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005 | 2006 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970 | 1989 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 56 columns
The DETE survey dataset contains 822 rows and 56 columns. They key columns of interest have been imported with no null values, but it is clear from a cursory glance at some of the rows that some of those columns have other values, like "Not Stated" for missing/unknown data. Many columns have a Dtype of 'object', some of which can be converted into boolean values. Many columns have null values. Most of those with null values are less than 25% null, but some have more, and some have a >75% of null values.
Essential Columns:
ID
: 0 missing values. int64. Values shouldn't need manipulation.SeparationType
: 0 missing values. object. It appears these may be categorical values. It remains to be seen whether they are consistent, or need to be cleaned.Cease Date
: 0 missing values. Object. Format appers to be MM/YYYY.DETE Start Date
: 0 missing values. Object. Format appears to be YYYY. Values include "Not Stated"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.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
The TAFE survey dataset contains 702 rows and 72 columns. Key columns of interest have missing values. The LengthofServiceOverall. Overall Length of Service at Institute (in years)
column has over 1/7th of the values missing. Non-null values in some columns include other values, like a dash, that seem to indicate missing information. Most columns have a dtype of 'object', many of which look like they can be converted into Boolean values. Some appear to contain a numeric range. Many column names are long and unweildy; they appear to include the full text of survey questions.
EssentialColumns:
Record ID
: 0 missing values. Float64. Float64 is certainly not the best representation of this, but it may not matter for the purposes of our analysis.Reason for ceasing employment
: 1 missing value. Object. Appears to be categorical. It is not clear whether the categories are represented consistently, or whether they'll need cleaning.LengthofServiceOverall. Overall Length of Service at Institute (in years)
: 106 missing. Object. Appears to be categorical giving a rage of years.It will take some work to merge these datasets for even the limited set of columns listed above.
dete_survey = pd.read_csv('dete_survey.csv', na_values=['Not Stated'])
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis=1)
dete_survey_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 35 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 Gender 798 non-null object 29 Age 811 non-null object 30 Aboriginal 16 non-null object 31 Torres Strait 3 non-null object 32 South Sea 7 non-null object 33 Disability 23 non-null object 34 NESB 32 non-null object dtypes: bool(18), float64(2), int64(1), object(14) memory usage: 123.7+ KB
tafe_survey_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 23 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 Gender. What is your Gender? 596 non-null object 18 CurrentAge. Current Age 596 non-null object 19 Employment Type. Employment Type 596 non-null object 20 Classification. Classification 596 non-null object 21 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object 22 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(21) memory usage: 126.3+ KB
dete_survey.csv
so value "Not Stated" is recognized as NaN.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')
dete_survey_updated.head()
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | work_life_balance | workload | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | False | False | True | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | False | False | False | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | False | True | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | True | False | False | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 35 columns
tafe_column_map = {
'Record ID': 'id',
'CESSATION YEAR': 'cease_date',
'Reason for ceasing employment': 'separationtype',
'Gender. What is your Gender?': 'gender',
'CurrentAge. Current Age': 'age',
'Employment Type. Employment Type': 'employment_status',
'Classification. Classification': 'position',
'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service',
'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'
}
tafe_survey_updated = tafe_survey_updated.rename(tafe_column_map, axis=1)
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')
tafe_survey_updated.head()
id | Institute | WorkArea | cease_date | separationtype | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | gender | age | employment_status | position | institute_service | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | - | - | - | NONE | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | - | - | - | - | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 23 columns
dete_survey_updated
dataframes.tafe_survey_updated
dataframe to match. Untouched column names need additional work.dete_survey_updated['separationtype'].value_counts()
Age Retirement 285 Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
tafe_survey_updated['separationtype'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
dete_resignations = dete_survey_updated[dete_survey_updated['separationtype'].str.contains("Resignation")].copy()
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
dete_resignations['cease_date'].value_counts()
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 07/2013 9 11/2013 9 10/2013 6 08/2013 4 05/2013 2 05/2012 2 09/2010 1 07/2012 1 2010 1 07/2006 1 Name: cease_date, dtype: int64
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.extract('/?(20[01][0-9])')[0].astype(float)
dete_resignations['cease_date'].value_counts().sort_index()
2006.0 1 2010.0 2 2012.0 129 2013.0 146 2014.0 22 Name: cease_date, dtype: int64
Extracted year and converted into a numeric value.
dete_resignations['dete_start_date'].value_counts().sort_index()
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
dete_resignations['dete_start_date'].isnull().sum()
28
No cleaning appears necessary at this point.
dete_resignations['cease_date'].value_counts().sort_index()
2006.0 1 2010.0 2 2012.0 129 2013.0 146 2014.0 22 Name: cease_date, dtype: int64
dete_resignations[['cease_date', 'dete_start_date']].plot.box(subplots=True)
cease_date AxesSubplot(0.125,0.125;0.352273x0.755) dete_start_date AxesSubplot(0.547727,0.125;0.352273x0.755) dtype: object
tafe_resignations['cease_date'].value_counts().sort_index()
2009.0 2 2010.0 68 2011.0 116 2012.0 94 2013.0 55 Name: cease_date, dtype: int64
tafe_resignations['cease_date'].plot.box()
<AxesSubplot:>
dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']
dete_resignations['institute_service'].isnull().sum()
38
tafe_resignations['institute_service'].isnull().sum()
50
institute_service
to dete_resignations, in order compliment the column of the same name in tafe_resignations, and populated it with calculated values.tafe_dissatisfaction_columns = [
'Contributing Factors. Dissatisfaction',
'Contributing Factors. Job Dissatisfaction'
]
dete_dissatisfaction_columns = [
'job_dissatisfaction',
'dissatisfaction_with_the_department',
'physical_work_environment',
'lack_of_recognition',
'lack_of_job_security',
'work_location',
'employment_conditions',
'work_life_balance',
'workload'
]
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()
- 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
def update_vals(value):
if pd.isnull(value):
return np.nan
elif value == '-':
return False
else:
return True
tafe_resignations[tafe_dissatisfaction_columns] = tafe_resignations[tafe_dissatisfaction_columns].applymap(update_vals)
tafe_resignations['dissatisfied'] = tafe_resignations[tafe_dissatisfaction_columns].any(axis=1, skipna=False)
dete_resignations['dissatisfied'] = dete_resignations[dete_dissatisfaction_columns].any(axis=1, skipna=False)
tafe_resignations
so values were represented as 'True' 'False' or 'NaN,' for consistency with dete_resignations
dataset, and general best practices.dissatisfied
in both datasets.dete_other_reason_columns = set(dete_resignations.columns[10:27]) - set(dete_dissatisfaction_columns)
dete_other_reason_columns
{'career_move_to_private_sector', 'career_move_to_public_sector', 'ill_health', 'interpersonal_conflicts', 'maternity/family', 'relocation', 'study/travel', 'traumatic_incident'}
dete_resignations[dete_other_reason_columns]
maternity/family | traumatic_incident | ill_health | career_move_to_public_sector | career_move_to_private_sector | interpersonal_conflicts | relocation | study/travel | |
---|---|---|---|---|---|---|---|---|
3 | False | False | False | False | True | False | False | False |
5 | True | False | False | False | True | False | False | False |
8 | False | False | False | False | True | False | False | False |
9 | False | False | False | False | False | True | False | False |
11 | True | False | False | False | False | False | True | False |
... | ... | ... | ... | ... | ... | ... | ... | ... |
808 | True | False | False | False | False | False | False | False |
815 | False | False | False | False | True | False | False | False |
816 | False | False | False | False | False | False | False | True |
819 | True | False | False | False | False | False | True | False |
821 | False | False | False | False | False | False | False | False |
311 rows × 8 columns
tafe_other_reason_columns = set(tafe_resignations.columns[5:16]) - set(tafe_dissatisfaction_columns)
tafe_other_reason_columns
{'Contributing Factors. Career Move - Private Sector ', 'Contributing Factors. Career Move - Public Sector ', 'Contributing Factors. Career Move - Self-employment', 'Contributing Factors. Ill Health', 'Contributing Factors. Interpersonal Conflict', 'Contributing Factors. Maternity/Family', 'Contributing Factors. Other', 'Contributing Factors. Study', 'Contributing Factors. Travel'}
tafe_resignations[list(tafe_other_reason_columns)] = tafe_resignations[tafe_other_reason_columns].applymap(update_vals)
tafe_resignations[tafe_other_reason_columns]
Contributing Factors. Study | Contributing Factors. Career Move - Private Sector | Contributing Factors. Ill Health | Contributing Factors. Career Move - Self-employment | Contributing Factors. Other | Contributing Factors. Maternity/Family | Contributing Factors. Career Move - Public Sector | Contributing Factors. Interpersonal Conflict | Contributing Factors. Travel | |
---|---|---|---|---|---|---|---|---|---|
3 | False | False | False | False | False | False | False | False | True |
4 | False | True | False | False | False | False | False | False | False |
5 | False | False | False | False | True | False | False | False | False |
6 | False | True | False | False | True | True | False | False | False |
7 | False | False | False | False | True | False | False | False | False |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
696 | False | True | False | False | False | False | False | False | False |
697 | False | False | False | False | False | False | True | False | False |
698 | False | False | False | False | False | False | True | False | False |
699 | False | False | False | False | True | False | False | False | False |
701 | False | False | False | True | False | False | False | False | True |
340 rows × 9 columns
tafe_resignations['other_reasons'] = tafe_resignations[tafe_other_reason_columns].any(axis=1, skipna=False)
dete_resignations['other_reasons'] = dete_resignations[dete_other_reason_columns].any(axis=1, skipna=False)
tafe_resignations_up = tafe_resignations.copy()
dete_resignations_up = dete_resignations.copy()
dete_resignations_up['institute'] = "DETE"
tafe_resignations_up['institute'] = "TAFE"
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 54 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 other_reasons 643 non-null object 38 institute 651 non-null object 39 Institute 340 non-null object 40 WorkArea 340 non-null object 41 Contributing Factors. Career Move - Public Sector 332 non-null object 42 Contributing Factors. Career Move - Private Sector 332 non-null object 43 Contributing Factors. Career Move - Self-employment 332 non-null object 44 Contributing Factors. Ill Health 332 non-null object 45 Contributing Factors. Maternity/Family 332 non-null object 46 Contributing Factors. Dissatisfaction 332 non-null object 47 Contributing Factors. Job Dissatisfaction 332 non-null object 48 Contributing Factors. Interpersonal Conflict 332 non-null object 49 Contributing Factors. Study 332 non-null object 50 Contributing Factors. Travel 332 non-null object 51 Contributing Factors. Other 332 non-null object 52 Contributing Factors. NONE 332 non-null object 53 role_service 290 non-null object dtypes: float64(4), object(50) memory usage: 274.8+ KB
#If I don't make this a copy I get warning when I do an assignment below. I'm not sure why.
combined_updated = combined.dropna(thresh=500, axis=1).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 other_reasons 643 non-null object 10 institute 651 non-null object dtypes: float64(2), object(9) memory usage: 56.1+ KB
In preparation for combining both datasets into one, I created a column called institute
in each dataset and populated it with an identifier. This will allow the origin of individual rows to be identified once the datasets are combined. The datasets were concatenated together. Afterwords, the number of null values in all columns was inspected. All necessary columns had at least 500 non-null values. Any other columns with less than 500 non-null values were dropped as they were not necessary or useful in completing our analysis.
The institute_service
column currently contains a mishmash of categorical values and numeric values. I'm going to rework them into the following categories:
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
The existing categorical values have different spans than the categories I plan to use, but fortunately, all of the existing spans fit within them. This means I can just concern myself with extracting the starting value.
combined_updated['institute_service'] = combined_updated['institute_service'].astype(str).str.extract('(\d+)').astype(float)
combined_updated['institute_service'].value_counts(dropna=False)
1.0 159 NaN 88 3.0 83 5.0 56 7.0 34 11.0 30 0.0 20 20.0 17 6.0 17 4.0 16 9.0 14 2.0 14 13.0 8 8.0 8 15.0 7 22.0 6 10.0 6 17.0 6 14.0 6 12.0 6 16.0 5 18.0 5 24.0 4 23.0 4 21.0 3 39.0 3 32.0 3 19.0 3 36.0 2 30.0 2 25.0 2 26.0 2 28.0 2 42.0 1 29.0 1 35.0 1 27.0 1 41.0 1 49.0 1 38.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service, dtype: int64
def calc_service_cat(value):
if pd.isnull(value):
return np.NAN
elif value < 3:
return "New"
elif 3 <= value < 7:
return "Experienced"
elif 7 <= value < 11:
return "Established"
elif 11 <= value:
return "Veteran"
else:
return np.NAN
combined_updated['service_cat'] = combined_updated['institute_service'].apply(calc_service_cat)
combined_updated['service_cat'].value_counts(dropna=False)
New 193 Experienced 172 Veteran 136 NaN 88 Established 62 Name: service_cat, dtype: int64
The institute_service
column has been converted into consistent categories using the criteria described earlier.
combined_updated['dissatisfied'].value_counts(dropna=False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
#fill NaN with most common value (False)
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(value=False).astype(bool)
combined_updated['other_reasons'].value_counts(dropna=False)
True 486 False 157 NaN 8 Name: other_reasons, dtype: int64
combined_updated['other_reasons'] = combined_updated['other_reasons'].fillna(value=False).astype(bool)
combined_updated['age'].value_counts(dropna=False)
51-55 71 NaN 55 41-45 48 41 45 45 46-50 42 36-40 41 46 50 39 26-30 35 21 25 33 26 30 32 31 35 32 36 40 32 21-25 29 56 or older 29 31-35 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
combined_updated['age'] = (combined_updated['age']
.str.strip()
.str.replace(' ', '-')
.str.replace('56-60', '56 or older')
.str.replace('61 or older', '56 or older')
)
combined_updated['age'].value_counts(dropna=False)
41-45 93 46-50 81 56 or older 78 36-40 73 51-55 71 26-30 67 21-25 62 31-35 61 NaN 55 20 or younger 10 Name: age, dtype: int64
combined_updated.loc[combined_updated['institute_service'].isnull(),'institute'].value_counts()
TAFE 50 DETE 38 Name: institute, dtype: int64
combined_updated.pivot_table(index='institute', values='institute_service', aggfunc=['count'])
count | |
---|---|
institute_service | |
institute | |
DETE | 273 |
TAFE | 290 |
We are missing institute_service
values for ~20% of resignations. Our analysis only looks at the subset where this column is populated, so filling this column or dropping rows is unnecessary.
combined_updated.to_pickle('combined_updated.pickle')