In this project, we'll work with exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. We'll aim to do most of the data cleaning and get you started analyzing the questions:
Are employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been there longer?
Are younger employees resigning due to some kind of dissatisfaction? What about older employees?
Below is a preview of a couple columns we'll work with from the dete_survey.csv
and tafe_survey.csv
datasets:
Department of Education, Training and Employment (DETE)
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 DETETechnical and Further Education (TAFE)
Record ID:
An id used to identify the participant of the surveyReason for ceasing employment:
The reason why the person's employment endedLengthofServiceOverall. Overall Length of Service at Institute (in years):
The length of the person's employment (in years)# import the pandas and NumPy libraries
import pandas as pd
import numpy as np
# read the csv files into pandas
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
# information about dete_survey
dete_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): ID 822 non-null int64 SeparationType 822 non-null object Cease Date 822 non-null object DETE Start Date 822 non-null object Role Start Date 822 non-null object Position 817 non-null object Classification 455 non-null object Region 822 non-null object Business Unit 126 non-null object Employment Status 817 non-null object Career move to public sector 822 non-null bool Career move to private sector 822 non-null bool Interpersonal conflicts 822 non-null bool Job dissatisfaction 822 non-null bool Dissatisfaction with the department 822 non-null bool Physical work environment 822 non-null bool Lack of recognition 822 non-null bool Lack of job security 822 non-null bool Work location 822 non-null bool Employment conditions 822 non-null bool Maternity/family 822 non-null bool Relocation 822 non-null bool Study/Travel 822 non-null bool Ill Health 822 non-null bool Traumatic incident 822 non-null bool Work life balance 822 non-null bool Workload 822 non-null bool None of the above 822 non-null bool Professional Development 808 non-null object Opportunities for promotion 735 non-null object Staff morale 816 non-null object Workplace issue 788 non-null object Physical environment 817 non-null object Worklife balance 815 non-null object Stress and pressure support 810 non-null object Performance of supervisor 813 non-null object Peer support 812 non-null object Initiative 813 non-null object Skills 811 non-null object Coach 767 non-null object Career Aspirations 746 non-null object Feedback 792 non-null object Further PD 768 non-null object Communication 814 non-null object My say 812 non-null object Information 816 non-null object Kept informed 813 non-null object Wellness programs 766 non-null object Health & Safety 793 non-null object Gender 798 non-null object Age 811 non-null object Aboriginal 16 non-null object Torres Strait 3 non-null object South Sea 7 non-null object Disability 23 non-null object NESB 32 non-null object dtypes: bool(18), int64(1), object(37) memory usage: 258.6+ KB
# return the first rows of dete_survey
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
# information about tafe_survey
tafe_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 72 columns): Record ID 702 non-null float64 Institute 702 non-null object WorkArea 702 non-null object CESSATION YEAR 695 non-null float64 Reason for ceasing employment 701 non-null object Contributing Factors. Career Move - Public Sector 437 non-null object Contributing Factors. Career Move - Private Sector 437 non-null object Contributing Factors. Career Move - Self-employment 437 non-null object Contributing Factors. Ill Health 437 non-null object Contributing Factors. Maternity/Family 437 non-null object Contributing Factors. Dissatisfaction 437 non-null object Contributing Factors. Job Dissatisfaction 437 non-null object Contributing Factors. Interpersonal Conflict 437 non-null object Contributing Factors. Study 437 non-null object Contributing Factors. Travel 437 non-null object Contributing Factors. Other 437 non-null object Contributing Factors. NONE 437 non-null object Main Factor. Which of these was the main factor for leaving? 113 non-null object InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 609 non-null object WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] 608 non-null object WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 609 non-null object WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object Induction. Did you undertake Workplace Induction? 619 non-null object InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object Gender. What is your Gender? 596 non-null object CurrentAge. Current Age 596 non-null object Employment Type. Employment Type 596 non-null object Classification. Classification 596 non-null object LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(70) memory usage: 395.0+ KB
# return the first rows of tafe_survey
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
As can be seen from the results, we can make the following observations:
The dete_survey
dataframe contains 'Not Stated' values that indicate values are missing, but they aren't represented as NaN.
Both the dete_survey
and tafe_survey
dataframes contain many columns that we don't need to complete our analysis. The dete_survey has 822 rows x 56 columns and the tafe_survey has 702 rows x 72 columns.
Each dataframe contains many of the same columns, but the column names are different.
There are multiple columns/answers that indicate an employee resigned because they were dissatisfied.
To start, we'll handle the first two issues. Recall that we can use the pd.read_csv()
function to specify values that should be represented as NaN. We'll use this function to fix the missing values first. Then, we'll drop columns we know we don't need for our analysis to make the dataframes easier to work with.
# read the dete_survey but this time set 'Not Stated' to `NaN'
dete_survey = pd.read_csv('dete_survey.csv', na_values='Not Stated')
# quick exploration
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 | 1984.0 | 2004.0 | 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 | NaN | NaN | 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 | 2011.0 | 2011.0 | 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 | 2005.0 | 2006.0 | 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 | 1970.0 | 1989.0 | 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
# drop columns from each dataframe
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)
# quick exploration of dete_survey_updated
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
# quick exploration of tafe_survey_updated
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
Let's turn our attention to the column names. Each dataframe contains many of the same columns, but the column names are different.
Because we eventually want to combine them, we'll have to standardize the column names. Recall that we can use the DataFrame.columns attribute along with vectorized string methods to update all of the columns at once.
# rename columns in the dete_survey_updated dataframe
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.replace(' ', '_').str.strip()
# check the columns updated
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
# rename columns in the tafe_survey_updated dataframe
columns_updated = {'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(columns_updated, axis=1)
# check columns updated
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
We'll remove more of the data we don't need to answer our two questions which are our end goal.
If we look at the unique values in the separationtype
columns in each dataframe, we'll see that each contains a couple of different separation types. We'll only analyze survey respondents who resigned, so their separation type contains the string Resignation
.
Note that dete_survey_updated dataframe contains multiple separation types with the string 'Resignation':
# check the values in separationtype
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
# check the values in separationtype
tafe_survey_updated['separationtype'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
# avoid the SettingWithCopy Warning
dete_resignations = dete_survey_updated[dete_survey_updated['separationtype'].str.contains('Resignation')].copy()
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
# check the datafram dete_resignation
dete_resignations.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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
5 | 6 | Resignation-Other reasons | 05/2012 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | False | False | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
8 | 9 | Resignation-Other reasons | 07/2012 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
9 | 10 | Resignation-Other employer | 2012 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | ... | False | False | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
11 | 12 | Resignation-Move overseas/interstate | 2012 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | ... | False | False | False | Male | 31-35 | NaN | NaN | NaN | NaN | NaN |
5 rows × 35 columns
# check the datafram dete_resignation
tafe_resignations.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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | 6.341475e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | ... | - | - | Other | - | Female | 56 or older | Contract/casual | Teacher (including LVT) | 7-10 | 7-10 |
6 | 6.341520e+17 | Barrier Reef Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | Maternity/Family | ... | - | - | Other | - | Male | 20 or younger | Temporary Full-time | Administration (AO) | 3-4 | 3-4 |
7 | 6.341537e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | ... | - | - | Other | - | Male | 46 50 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 23 columns
Before we start cleaning and manipulating the rest of our data, let's verify that the data doesn't contain any major inconsistencies (to the best of our knowledge).
In this step, we'll focus on verifying that the years in the cease_date
and dete_start_date
columns make sense.
cease_date
is the last year of the person's employment and the dete_start_date
is the person's first year of employment, it wouldn't make sense to have years after the current datedete_start_date
was before the year 1940If we have many years higher than the current date or lower than 1940, we wouldn't want to continue with our analysis, because it could mean there's something very wrong with the data. If there are a small amount of values that are unrealistically high or low, we can remove them.
# check the years
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 07/2006 1 2010 1 07/2012 1 09/2010 1 Name: cease_date, dtype: int64
# extract the year
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.split('/').str[-1]
# convert the type to a float
dete_resignations['cease_date'] = dete_resignations['cease_date'].astype(float)
# Function to extract the year
# def extract_year(element):
# element = str(element)
# if element[2] == '/':
# year = element[3:]
# else:
# year = element[:4]
# return year
# extract the year
# dete_resignations['cease_date'] = dete_resignations['cease_date'].apply(extract_year)
# convert the type to a float
# dete_resignations['cease_date'] = dete_resignations['cease_date'].astype(float)
# check the values
dete_resignations['cease_date'].value_counts()
2013.0 146 2012.0 129 2014.0 22 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
# check the values in descending order
dete_resignations['cease_date'].value_counts().sort_index(ascending=False)
2014.0 22 2013.0 146 2012.0 129 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
# check the values in descending order
dete_resignations['dete_start_date'].value_counts().sort_index(ascending=False)
2013.0 10 2012.0 21 2011.0 24 2010.0 17 2009.0 13 2008.0 22 2007.0 21 2006.0 13 2005.0 15 2004.0 14 2003.0 6 2002.0 6 2001.0 3 2000.0 9 1999.0 8 1998.0 6 1997.0 5 1996.0 6 1995.0 4 1994.0 6 1993.0 5 1992.0 6 1991.0 4 1990.0 5 1989.0 4 1988.0 4 1987.0 1 1986.0 3 1985.0 3 1984.0 1 1983.0 2 1982.0 1 1980.0 5 1977.0 1 1976.0 2 1975.0 1 1974.0 2 1973.0 1 1972.0 1 1971.0 1 1963.0 1 Name: dete_start_date, dtype: int64
# check the values in descending order
tafe_resignations['cease_date'].value_counts().sort_index(ascending=False)
2013.0 55 2012.0 94 2011.0 116 2010.0 68 2009.0 2 Name: cease_date, dtype: int64
According to the results, the years in both dataframes are not aligned. For example, TAFE dataframe contains years such as 2009 and 2011 which DEFE doesn't have.
# boxplot
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(15,5))
ax1 = fig.add_subplot(1,3,1)
ax2 = fig.add_subplot(1,3,2)
ax3 = fig.add_subplot(1,3,3)
ax1.boxplot(dete_resignations['cease_date'].astype(float))
ax1.set_title('dete cease_date')
ax2.boxplot(dete_resignations['dete_start_date'].astype(float))
ax2.set_title('dete_cease_date')
ax3.boxplot(tafe_resignations['cease_date'].astype(float))
ax3.set_title('tafe cease_date')
plt.show()
C:\Users\User\Anaconda3\lib\site-packages\numpy\lib\function_base.py:3826: RuntimeWarning: Invalid value encountered in percentile interpolation=interpolation) C:\Users\User\Anaconda3\lib\site-packages\matplotlib\cbook\__init__.py:1316: RuntimeWarning: invalid value encountered in less_equal wiskhi = x[x <= hival] C:\Users\User\Anaconda3\lib\site-packages\matplotlib\cbook\__init__.py:1323: RuntimeWarning: invalid value encountered in greater_equal wisklo = x[x >= loval] C:\Users\User\Anaconda3\lib\site-packages\matplotlib\cbook\__init__.py:1331: RuntimeWarning: invalid value encountered in less x[x < stats['whislo']], C:\Users\User\Anaconda3\lib\site-packages\matplotlib\cbook\__init__.py:1332: RuntimeWarning: invalid value encountered in greater x[x > stats['whishi']], C:\Users\User\Anaconda3\lib\site-packages\numpy\lib\function_base.py:3826: RuntimeWarning: Invalid value encountered in percentile interpolation=interpolation) C:\Users\User\Anaconda3\lib\site-packages\matplotlib\cbook\__init__.py:1316: RuntimeWarning: invalid value encountered in less_equal wiskhi = x[x <= hival] C:\Users\User\Anaconda3\lib\site-packages\matplotlib\cbook\__init__.py:1323: RuntimeWarning: invalid value encountered in greater_equal wisklo = x[x >= loval] C:\Users\User\Anaconda3\lib\site-packages\matplotlib\cbook\__init__.py:1331: RuntimeWarning: invalid value encountered in less x[x < stats['whislo']], C:\Users\User\Anaconda3\lib\site-packages\matplotlib\cbook\__init__.py:1332: RuntimeWarning: invalid value encountered in greater x[x > stats['whishi']], C:\Users\User\Anaconda3\lib\site-packages\numpy\lib\function_base.py:3826: RuntimeWarning: Invalid value encountered in percentile interpolation=interpolation) C:\Users\User\Anaconda3\lib\site-packages\matplotlib\cbook\__init__.py:1316: RuntimeWarning: invalid value encountered in less_equal wiskhi = x[x <= hival] C:\Users\User\Anaconda3\lib\site-packages\matplotlib\cbook\__init__.py:1323: RuntimeWarning: invalid value encountered in greater_equal wisklo = x[x >= loval] C:\Users\User\Anaconda3\lib\site-packages\matplotlib\cbook\__init__.py:1331: RuntimeWarning: invalid value encountered in less x[x < stats['whislo']], C:\Users\User\Anaconda3\lib\site-packages\matplotlib\cbook\__init__.py:1332: RuntimeWarning: invalid value encountered in greater x[x > stats['whishi']],
<Figure size 1500x500 with 3 Axes>
Since our end goal is to answer the following question:
In the Human Resources field, the length of time an employee spent in a workplace is referred to as their years of service.
The tafe_resignations
dataframe already contains a service
column, which we renamed to institute_service
. In order to analyze both surveys together, we'll have to create a corresponding institute_service
column in dete_resignations
.
# create an institute_service column
dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']
dete_resignations['institute_service'].head()
3 7.0 5 18.0 8 3.0 9 15.0 11 3.0 Name: institute_service, dtype: float64
We created a new institute_service
column that we'll use to analyze survey respondents according to their length of employment. Next, we'll identify any employees who resigned because they were dissatisfied.
Below are the columns we'll use to categorize employees as dissatisfied
from each dataframe.
If the employee indicated any of the factors above caused them to resign, we'll mark them as dissatisfied
in a new column.
# check the values of the serie
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
# check the values of the serie
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()
- 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
# function to to change values to True, False or NaN
def update_vals(value):
if pd.isnull(value):
return np.nan
elif value == '-':
return False
else:
return True
# update the values
tafe_resignations['dissatisfied'] = tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']].applymap(update_vals).any(axis=1, skipna=False)
tafe_resignations['dissatisfied']
3 False 4 False 5 False 6 False 7 False ... 696 False 697 False 698 False 699 False 701 False Name: dissatisfied, Length: 340, dtype: object
# avoid the SettingWithCopy Warning
tafe_resignations_up = tafe_resignations.copy()
# check the values
tafe_resignations_up['dissatisfied'].value_counts(dropna=False)
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
# update the values
dete_resignations['dissatisfied'] = dete_resignations[['job_dissatisfaction',
'dissatisfaction_with_the_department',
'physical_work_environment',
'lack_of_recognition',
'lack_of_job_security',
'work_location',
'employment_conditions',
'work_life_balance',
'workload']].any(axis=1, skipna=False)
dete_resignations['dissatisfied']
3 False 5 True 8 False 9 True 11 False ... 808 False 815 False 816 False 819 True 821 False Name: dissatisfied, Length: 311, dtype: bool
# avoid the SettingWithCopy Warning
dete_resignations_up = dete_resignations.copy()
# check the values
dete_resignations_up['dissatisfied'].value_counts(dropna=False)
False 162 True 149 Name: dissatisfied, dtype: int64
Now, we're finally ready to combine our datasets. Our end goal is to aggregate the data according to the institute_service
column, so when we combine the data, think about how to get the data into a form that's easy to aggregate.
# add a column to each dataframe to distinguish between them
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
# combine the dataframes
combined = pd.concat([dete_resignations_up, tafe_resignations_up])
# check the dataframe
combined.head()
C:\Users\User\Anaconda3\lib\site-packages\ipykernel_launcher.py:2: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version of pandas will change to not sort by default. To accept the future behavior, pass 'sort=False'. To retain the current behavior and silence the warning, pass 'sort=True'.
Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Dissatisfaction | Contributing Factors. Ill Health | Contributing Factors. Interpersonal Conflict | Contributing Factors. Job Dissatisfaction | Contributing Factors. Maternity/Family | Contributing Factors. NONE | Contributing Factors. Other | ... | role_service | role_start_date | separationtype | south_sea | study/travel | torres_strait | traumatic_incident | work_life_balance | work_location | workload | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 2006.0 | Resignation-Other reasons | NaN | False | NaN | False | False | False | False |
5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 1997.0 | Resignation-Other reasons | NaN | False | NaN | False | False | False | False |
8 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 2009.0 | Resignation-Other reasons | NaN | False | NaN | False | False | False | False |
9 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 2008.0 | Resignation-Other employer | NaN | False | NaN | False | False | False | False |
11 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 2009.0 | Resignation-Move overseas/interstate | NaN | False | NaN | False | False | False | False |
5 rows × 53 columns
We still have some columns left in the dataframe that we don't need to complete our analysis. We'll drop any columns with less than 500 non null values.
# drop columns with less than 500 non-null values
combined_updated = combined.dropna(axis=1, thresh=500)
# check the dataframe
combined_updated.head()
age | cease_date | dissatisfied | employment_status | gender | id | institute | institute_service | position | separationtype | |
---|---|---|---|---|---|---|---|---|---|---|
3 | 36-40 | 2012.0 | False | Permanent Full-time | Female | 4.0 | DETE | 7 | Teacher | Resignation-Other reasons |
5 | 41-45 | 2012.0 | True | Permanent Full-time | Female | 6.0 | DETE | 18 | Guidance Officer | Resignation-Other reasons |
8 | 31-35 | 2012.0 | False | Permanent Full-time | Female | 9.0 | DETE | 3 | Teacher | Resignation-Other reasons |
9 | 46-50 | 2012.0 | True | Permanent Part-time | Female | 10.0 | DETE | 15 | Teacher Aide | Resignation-Other employer |
11 | 31-35 | 2012.0 | False | Permanent Full-time | Male | 12.0 | DETE | 3 | Teacher | Resignation-Move overseas/interstate |
We'll have to clean up the institute_service
column. This column is tricky to clean because it currently contains values in a couple different forms:
# check the column institute_service
combined_updated['institute_service'].value_counts(dropna=False)
NaN 88 Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 5.0 23 1.0 22 7-10 21 0.0 20 3.0 20 6.0 17 4.0 16 9.0 14 2.0 14 7.0 13 More than 20 years 10 13.0 8 8.0 8 15.0 7 20.0 7 17.0 6 12.0 6 10.0 6 14.0 6 22.0 6 16.0 5 18.0 5 23.0 4 24.0 4 11.0 4 19.0 3 39.0 3 21.0 3 32.0 3 26.0 2 28.0 2 30.0 2 25.0 2 36.0 2 27.0 1 29.0 1 31.0 1 33.0 1 42.0 1 34.0 1 35.0 1 49.0 1 38.0 1 41.0 1 Name: institute_service, dtype: int64
To analyze the data, we'll convert these numbers into categories, which makes the argument that understanding employee's needs according to career stage instead of age is more effective.
New:
Less than 3 years at a companyExperienced:
3-6 years at a companyEstablished:
7-10 years at a companyVeteran:
11 or more years at a company# extract the year of service
combined_updated['institute_service_catg'] = combined_updated['institute_service'].astype(str)
combined_updated['institute_service_catg'] = combined_updated['institute_service_catg'].str.extract(r'(?P<digit>\d+)')
combined_updated['institute_service_catg'] = combined_updated['institute_service_catg'].astype(float)
# check values
combined_updated['institute_service_catg'].value_counts()
C:\Users\User\Anaconda3\lib\site-packages\ipykernel_launcher.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy C:\Users\User\Anaconda3\lib\site-packages\ipykernel_launcher.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy This is separate from the ipykernel package so we can avoid doing imports until C:\Users\User\Anaconda3\lib\site-packages\ipykernel_launcher.py:4: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy after removing the cwd from sys.path.
1.0 159 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 17.0 6 10.0 6 12.0 6 14.0 6 22.0 6 16.0 5 18.0 5 24.0 4 23.0 4 39.0 3 19.0 3 21.0 3 32.0 3 28.0 2 36.0 2 25.0 2 30.0 2 26.0 2 29.0 1 38.0 1 42.0 1 27.0 1 41.0 1 35.0 1 49.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service_catg, dtype: int64
# function that maps each year value to one of the career stage
def map_year(val):
if pd.isnull(val):
return np.nan
elif val < 3:
return 'New'
elif (3 <= val < 7):
return 'Experienced'
elif (7 <= val < 11):
return 'Established'
else:
return 'Veteran'
# apply the function
combined_updated['service_cat'] = combined_updated['institute_service_catg'].apply(map_year)
combined_updated['service_cat'].value_counts(dropna=False)
C:\Users\User\Anaconda3\lib\site-packages\ipykernel_launcher.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
New 193 Experienced 172 Veteran 136 NaN 88 Established 62 Name: service_cat, dtype: int64
# check the number of missing values
combined_updated['dissatisfied'].value_counts(dropna=False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
# replace the missing values
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(True)
C:\Users\User\Anaconda3\lib\site-packages\ipykernel_launcher.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
# check the dataframe combined_updated
combined_updated.head()
age | cease_date | dissatisfied | employment_status | gender | id | institute | institute_service | position | separationtype | institute_service_catg | service_cat | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 36-40 | 2012.0 | False | Permanent Full-time | Female | 4.0 | DETE | 7 | Teacher | Resignation-Other reasons | 7.0 | Established |
5 | 41-45 | 2012.0 | True | Permanent Full-time | Female | 6.0 | DETE | 18 | Guidance Officer | Resignation-Other reasons | 18.0 | Veteran |
8 | 31-35 | 2012.0 | False | Permanent Full-time | Female | 9.0 | DETE | 3 | Teacher | Resignation-Other reasons | 3.0 | Experienced |
9 | 46-50 | 2012.0 | True | Permanent Part-time | Female | 10.0 | DETE | 15 | Teacher Aide | Resignation-Other employer | 15.0 | Veteran |
11 | 31-35 | 2012.0 | False | Permanent Full-time | Male | 12.0 | DETE | 3 | Teacher | Resignation-Move overseas/interstate | 3.0 | Experienced |
# calculate the percentage of dissatisfied employees in each 'service_cat' group
pivot_combined = combined_updated.pivot_table(values='dissatisfied', index='service_cat')
# plot the results
%matplotlib inline
pivot_combined.plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x2a880bdd908>
We'll continue with the following steps:
service_cat
column again. How many people in each career stage resigned due to some kind of dissatisfaction?# group by service_cat and dissatisfied pivot_table
diss_values = combined_updated.pivot_table(values='dissatisfied', index='service_cat', aggfunc=np.sum)
diss_values
dissatisfied | |
---|---|
service_cat | |
Established | 32.0 |
Experienced | 59.0 |
New | 57.0 |
Veteran | 66.0 |
# group by
service_grouped = combined_updated.groupby(['service_cat', 'dissatisfied'])['dissatisfied']
grouped = service_grouped.agg('count')
grouped
service_cat dissatisfied Established False 30 True 32 Experienced False 113 True 59 New False 136 True 57 Veteran False 70 True 66 Name: dissatisfied, dtype: int64
The veterans and the experienced are the most likely people which leave a position due to dissatisfaction with 66 and 59 people respectively.
# check the ages
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 36 40 32 31 35 32 56 or older 29 31-35 29 21-25 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
# function to classify the ages
def class_age(val):
if pd.isnull(val):
return np.nan
elif val == 20:
return '20 >='
elif (20 < val <= 30):
return '21-30'
elif (30 < val <= 40):
return '31-40'
elif (40 < val <= 50):
return '41-50'
else:
return '50 <'
# classify the ages
combined_updated['age_clas'] = combined_updated['age'].str.replace(' ', '-').str.replace(' ', '-').str.extract(r'([0-9]{2})', expand=True).astype(float)
combined_updated['age_clas'] = combined_updated['age_clas'].apply(class_age)
# check the values
combined_updated['age_clas'].value_counts(dropna=False)
C:\Users\User\Anaconda3\lib\site-packages\ipykernel_launcher.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy C:\Users\User\Anaconda3\lib\site-packages\ipykernel_launcher.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy This is separate from the ipykernel package so we can avoid doing imports until
41-50 174 50 < 149 31-40 134 21-30 129 NaN 55 20 >= 10 Name: age_clas, dtype: int64
# calculate the percentage of dissatisfied employees in each 'service_cat' group
pivot_ages = combined_updated.pivot_table(values='dissatisfied', index='age_clas')
# plot the results
pivot_ages.plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x2a8820148c8>
The plot shows that the most age group resigned as the result of dissatisfaction is in the range above 50 years old.
# calculate the percentage of dissatisfied employees in each 'service_cat' group
pivot_survey = combined_updated.pivot_table(values='dissatisfied', index='institute')
# plot the results
pivot_survey.plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x2a88208d548>
The bar chart gives information about DETE survey has more employees who end their empoytment because they were dissatisfied.