In this project, we will try to investigate the following:
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?
import pandas as pd
import numpy as np
dete_survey = pd.read_csv("dete_survey.csv", index_col=0)
tafe_survey = pd.read_csv("tafe_survey.csv", index_col=0)
print(dete_survey.info())
print(dete_survey.head())
print(tafe_survey.info())
print(tafe_survey.head())
<class 'pandas.core.frame.DataFrame'> Int64Index: 822 entries, 1 to 823 Data columns (total 55 columns): 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), object(37) memory usage: 258.5+ KB None SeparationType Cease Date DETE Start Date \ ID 1 Ill Health Retirement 08/2012 1984 2 Voluntary Early Retirement (VER) 08/2012 Not Stated 3 Voluntary Early Retirement (VER) 05/2012 2011 4 Resignation-Other reasons 05/2012 2005 5 Age Retirement 05/2012 1970 Role Start Date Position \ ID 1 2004 Public Servant 2 Not Stated Public Servant 3 2011 Schools Officer 4 2006 Teacher 5 1989 Head of Curriculum/Head of Special Education Classification Region Business Unit \ ID 1 A01-A04 Central Office Corporate Strategy and Peformance 2 AO5-AO7 Central Office Corporate Strategy and Peformance 3 NaN Central Office Education Queensland 4 Primary Central Queensland NaN 5 NaN South East NaN Employment Status Career move to public sector ... Kept informed \ ID ... 1 Permanent Full-time True ... N 2 Permanent Full-time False ... N 3 Permanent Full-time False ... N 4 Permanent Full-time False ... A 5 Permanent Full-time False ... N Wellness programs Health & Safety Gender Age Aboriginal \ ID 1 N N Male 56-60 NaN 2 N N Male 56-60 NaN 3 N N Male 61 or older NaN 4 N A Female 36-40 NaN 5 A M Female 61 or older NaN Torres Strait South Sea Disability NESB ID 1 NaN NaN NaN Yes 2 NaN NaN NaN NaN 3 NaN NaN NaN NaN 4 NaN NaN NaN NaN 5 NaN NaN NaN NaN [5 rows x 55 columns] <class 'pandas.core.frame.DataFrame'> Float64Index: 702 entries, 6.34133009996094e+17 to 6.35073030973791e+17 Data columns (total 71 columns): 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(1), object(70) memory usage: 394.9+ KB None Institute WorkArea \ Record ID 6.341330e+17 Southern Queensland Institute of TAFE Non-Delivery (corporate) 6.341337e+17 Mount Isa Institute of TAFE Non-Delivery (corporate) 6.341388e+17 Mount Isa Institute of TAFE Delivery (teaching) 6.341399e+17 Mount Isa Institute of TAFE Non-Delivery (corporate) 6.341466e+17 Southern Queensland Institute of TAFE Delivery (teaching) CESSATION YEAR Reason for ceasing employment \ Record ID 6.341330e+17 2010.0 Contract Expired 6.341337e+17 2010.0 Retirement 6.341388e+17 2010.0 Retirement 6.341399e+17 2010.0 Resignation 6.341466e+17 2010.0 Resignation Contributing Factors. Career Move - Public Sector \ Record ID 6.341330e+17 NaN 6.341337e+17 - 6.341388e+17 - 6.341399e+17 - 6.341466e+17 - Contributing Factors. Career Move - Private Sector \ Record ID 6.341330e+17 NaN 6.341337e+17 - 6.341388e+17 - 6.341399e+17 - 6.341466e+17 Career Move - Private Sector Contributing Factors. Career Move - Self-employment \ Record ID 6.341330e+17 NaN 6.341337e+17 - 6.341388e+17 - 6.341399e+17 - 6.341466e+17 - Contributing Factors. Ill Health \ Record ID 6.341330e+17 NaN 6.341337e+17 - 6.341388e+17 - 6.341399e+17 - 6.341466e+17 - Contributing Factors. Maternity/Family \ Record ID 6.341330e+17 NaN 6.341337e+17 - 6.341388e+17 - 6.341399e+17 - 6.341466e+17 - Contributing Factors. Dissatisfaction \ Record ID 6.341330e+17 NaN 6.341337e+17 - 6.341388e+17 - 6.341399e+17 - 6.341466e+17 - ... \ Record ID ... 6.341330e+17 ... 6.341337e+17 ... 6.341388e+17 ... 6.341399e+17 ... 6.341466e+17 ... Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? \ Record ID 6.341330e+17 Yes 6.341337e+17 Yes 6.341388e+17 Yes 6.341399e+17 Yes 6.341466e+17 Yes Workplace. Topic:Does your workplace promote and practice the principles of employment equity? \ Record ID 6.341330e+17 Yes 6.341337e+17 Yes 6.341388e+17 Yes 6.341399e+17 Yes 6.341466e+17 Yes Workplace. Topic:Does your workplace value the diversity of its employees? \ Record ID 6.341330e+17 Yes 6.341337e+17 Yes 6.341388e+17 Yes 6.341399e+17 Yes 6.341466e+17 Yes Workplace. Topic:Would you recommend the Institute as an employer to others? \ Record ID 6.341330e+17 Yes 6.341337e+17 Yes 6.341388e+17 Yes 6.341399e+17 Yes 6.341466e+17 Yes Gender. What is your Gender? CurrentAge. Current Age \ Record ID 6.341330e+17 Female 26 30 6.341337e+17 NaN NaN 6.341388e+17 NaN NaN 6.341399e+17 NaN NaN 6.341466e+17 Male 41 45 Employment Type. Employment Type Classification. Classification \ Record ID 6.341330e+17 Temporary Full-time Administration (AO) 6.341337e+17 NaN NaN 6.341388e+17 NaN NaN 6.341399e+17 NaN NaN 6.341466e+17 Permanent Full-time Teacher (including LVT) LengthofServiceOverall. Overall Length of Service at Institute (in years) \ Record ID 6.341330e+17 1-2 6.341337e+17 NaN 6.341388e+17 NaN 6.341399e+17 NaN 6.341466e+17 3-4 LengthofServiceCurrent. Length of Service at current workplace (in years) Record ID 6.341330e+17 1-2 6.341337e+17 NaN 6.341388e+17 NaN 6.341399e+17 NaN 6.341466e+17 3-4 [5 rows x 71 columns]
From preliminary observation, there is a lot of missing data in both dataframes. It should also be noted that both dataframes contain many columns that we don't need to complete our analysis.
The dete_survey dataframe contains 'Not Stated' values that indicate values are missing, but they aren't represented as NaN.
Moreover, each dataframe contains many of the same columns, but the column names are different. There are many columns/answers that indicate an employee resigned because they were dissatisfied.
# We will read the dete_survey.csv CSV file into pandas again, but this time read the Not Stated values in as NaN.
dete_survey = pd.read_csv("dete_survey.csv", index_col=0, na_values = 'Not Stated')
# drop some columns from each dataframe that won't be used in the analysis
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)
In the above, after our data exploration, we've chosen to drop columns from each dataframe that we won't use in our analysis to make the dataframes easier to work with.
dete_survey_updated.columns = dete_survey_updated.columns.str.replace(' ', '_').str.replace('\s+', '').str.lower()
print(dete_survey_updated.columns)
mapping = {'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=mapping)
tafe_survey_updated.head()
Index(['separationtype', 'cease_date', 'dete_start_date', 'role_start_date', 'position', 'classification', 'region', 'business_unit', 'employment_status', 'career_move_to_public_sector', 'career_move_to_private_sector', 'interpersonal_conflicts', 'job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions', 'maternity/family', 'relocation', 'study/travel', 'ill_health', 'traumatic_incident', 'work_life_balance', 'workload', 'none_of_the_above', 'professional_development', 'age', 'aboriginal', 'torres_strait', 'south_sea', 'disability', 'nesb'], dtype='object')
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. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | Main Factor. Which of these was the main factor for leaving? | age | employment_status | position | institute_service | role_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Record ID | |||||||||||||||||||||
6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | - | ... | - | - | - | NONE | NaN | NaN | NaN | NaN | NaN | NaN |
6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | - | ... | - | - | - | - | NaN | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 22 columns
dete_survey_updated.head()
separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | career_move_to_public_sector | ... | work_life_balance | workload | none_of_the_above | professional_development | age | aboriginal | torres_strait | south_sea | disability | nesb | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ID | |||||||||||||||||||||
1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | True | ... | False | False | True | A | 56-60 | NaN | NaN | NaN | NaN | Yes |
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 | False | A | 56-60 | NaN | NaN | NaN | NaN | NaN |
3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | ... | False | False | True | N | 61 or older | NaN | NaN | NaN | NaN | NaN |
4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | ... | False | False | False | A | 36-40 | NaN | NaN | NaN | NaN | NaN |
5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | False | ... | True | False | False | A | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 34 columns
We've renamed the columns that we'll use in our analysis. Next, let's remove more of the data we don't need.
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. For this project, we'll only analyze survey respondents who resigned, so their separation type contains the string 'Resignation'.
There are 3 separation types with the string 'Resignation', namely:
# Review unique values in the 'separationtype' column for both dataframes
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
pattern = r"([Resignation])"
dete_resignations = dete_survey_updated[
dete_survey_updated['separationtype'].str.contains('Resignation')].copy()
tafe_resignations = tafe_survey_updated[
tafe_survey_updated['separationtype'].str.contains('Resignation', na = False)].copy()
tafe_resignations.info()
<class 'pandas.core.frame.DataFrame'> Float64Index: 340 entries, 6.3413990335e+17 to 6.35073030973791e+17 Data columns (total 22 columns): Institute 340 non-null object WorkArea 340 non-null object cease_date 335 non-null float64 separationtype 340 non-null object Contributing Factors. Career Move - Public Sector 332 non-null object Contributing Factors. Career Move - Private Sector 332 non-null object Contributing Factors. Career Move - Self-employment 332 non-null object Contributing Factors. Ill Health 332 non-null object Contributing Factors. Maternity/Family 332 non-null object Contributing Factors. Dissatisfaction 332 non-null object Contributing Factors. Job Dissatisfaction 332 non-null object Contributing Factors. Interpersonal Conflict 332 non-null object Contributing Factors. Study 332 non-null object Contributing Factors. Travel 332 non-null object Contributing Factors. Other 332 non-null object Contributing Factors. NONE 332 non-null object Main Factor. Which of these was the main factor for leaving? 96 non-null object age 290 non-null object employment_status 290 non-null object position 290 non-null object institute_service 290 non-null object role_service 290 non-null object dtypes: float64(1), object(21) memory usage: 61.1+ KB
For the TAFE data set, there were NaN values which we replaced with False for the Boolean indexing to be done without errors.
Now, 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). We'll focus on verifying that the years in the cease_date and dete_start_date columns make sense.
We will be using the following guiding principles:
Since the 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 date.
Given that most people in this field start working in their 20s, it's also unlikely that the dete_start_date was before the year 1940.
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 07/2012 1 2010 1 07/2006 1 09/2010 1 Name: cease_date, dtype: int64
# Extracing the year using vectorized string methods
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.split('/').str.get(-1).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
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
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
Based on the findings above, it seems that the data generally looks fine. We could scrutinize the years further as the distribution is slightly different across the dataframes, but the effort is not warranted considering the data quality is decent
dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']
dete_resignations['institute_service'].value_counts().sort_index()
0.0 20 1.0 22 2.0 14 3.0 20 4.0 16 5.0 23 6.0 17 7.0 13 8.0 8 9.0 14 10.0 6 11.0 4 12.0 6 13.0 8 14.0 6 15.0 7 16.0 5 17.0 6 18.0 5 19.0 3 20.0 7 21.0 3 22.0 6 23.0 4 24.0 4 25.0 2 26.0 2 27.0 1 28.0 2 29.0 1 30.0 2 31.0 1 32.0 3 33.0 1 34.0 1 35.0 1 36.0 2 38.0 1 39.0 3 41.0 1 42.0 1 49.0 1 Name: institute_service, dtype: int64
By performing the step above, we can directly scan through all the employees' length of time spent in the workplace, aka years of service. The data suggests that churn rate is higher for those serving < 10 years, as the numbers are in the double digits. The peak of churn comes in after about 5 years of service.
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.
From tafe_survey_updated:
From detesurveyupdated:
If the employee indicated any of the factors above caused them to resign, we'll mark them as 'dissatisfied' in a new column.
# We will convert values in 'Contributing Factors. Dissatisfaction'
# and 'Contributing Factors. Job Dissatisfaction' columns in the
# 'tafe_resignations' dataframe to True, False, or NaN values.
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
# We'll create a version to do the updating
def update_vals(val):
if pd.isnull(val):
return np.nan
elif val == '-':
return False
else:
return True
tafe_resignations['dissatisfied'] = tafe_resignations[[
'Contributing Factors. Dissatisfaction',
'Contributing Factors. Job Dissatisfaction']].applymap(update_vals).any(
axis=1, skipna=False)
dete_res_col = ['job_dissatisfaction',
'dissatisfaction_with_the_department',
'physical_work_environment','lack_of_recognition',
'lack_of_job_security','work_location',
'employment_conditions','work_life_balance','workload']
dete_resignations['dissatisfied'] = dete_resignations[
dete_res_col].applymap(update_vals).any(
axis=1, skipna=False)
tafe_resignations_up = tafe_resignations.copy()
dete_resignations_up = dete_resignations.copy()
tafe_resignations_up.head()
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. Travel | Contributing Factors. Other | Contributing Factors. NONE | Main Factor. Which of these was the main factor for leaving? | age | employment_status | position | institute_service | role_service | dissatisfied | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Record ID | |||||||||||||||||||||
6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | - | ... | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN | False |
6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | - | ... | - | - | - | NaN | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 | False |
6.341475e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | - | ... | - | Other | - | NaN | 56 or older | Contract/casual | Teacher (including LVT) | 7-10 | 7-10 | False |
6.341520e+17 | Barrier Reef Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | Maternity/Family | - | ... | - | Other | - | Career Move - Private Sector | 20 or younger | Temporary Full-time | Administration (AO) | 3-4 | 3-4 | False |
6.341537e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | - | ... | - | Other | - | NaN | 46 50 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 | False |
5 rows × 23 columns
dete_resignations_up.head()
separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | career_move_to_public_sector | ... | none_of_the_above | professional_development | age | aboriginal | torres_strait | south_sea | disability | nesb | institute_service | dissatisfied | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ID | |||||||||||||||||||||
4 | Resignation-Other reasons | 2012.0 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | ... | False | A | 36-40 | NaN | NaN | NaN | NaN | NaN | 7.0 | True |
6 | Resignation-Other reasons | 2012.0 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | ... | False | SD | 41-45 | NaN | NaN | NaN | NaN | NaN | 18.0 | True |
9 | Resignation-Other reasons | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | False | ... | False | D | 31-35 | NaN | NaN | NaN | NaN | NaN | 3.0 | True |
10 | Resignation-Other employer | 2012.0 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | False | ... | False | SD | 46-50 | NaN | NaN | NaN | NaN | NaN | 15.0 | True |
12 | Resignation-Move overseas/interstate | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | False | ... | False | N | 31-35 | NaN | NaN | NaN | NaN | NaN | 3.0 | True |
5 rows × 36 columns
Now, we are 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, we will think about ways to get the data into a form that's easy to aggregate.
# Before merging, we will add a column to each of our new dataframes so that we can distinguish between the two.
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
dete_resignations_up.head()
separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | career_move_to_public_sector | ... | professional_development | age | aboriginal | torres_strait | south_sea | disability | nesb | institute_service | dissatisfied | institute | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ID | |||||||||||||||||||||
4 | Resignation-Other reasons | 2012.0 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | ... | A | 36-40 | NaN | NaN | NaN | NaN | NaN | 7.0 | True | DETE |
6 | Resignation-Other reasons | 2012.0 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | ... | SD | 41-45 | NaN | NaN | NaN | NaN | NaN | 18.0 | True | DETE |
9 | Resignation-Other reasons | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | False | ... | D | 31-35 | NaN | NaN | NaN | NaN | NaN | 3.0 | True | DETE |
10 | Resignation-Other employer | 2012.0 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | False | ... | SD | 46-50 | NaN | NaN | NaN | NaN | NaN | 15.0 | True | DETE |
12 | Resignation-Move overseas/interstate | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | False | ... | N | 31-35 | NaN | NaN | NaN | NaN | NaN | 3.0 | True | DETE |
5 rows × 37 columns
tafe_resignations_up.head()
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. Other | Contributing Factors. NONE | Main Factor. Which of these was the main factor for leaving? | age | employment_status | position | institute_service | role_service | dissatisfied | institute | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Record ID | |||||||||||||||||||||
6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | - | ... | - | - | NaN | NaN | NaN | NaN | NaN | NaN | False | TAFE |
6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | - | ... | - | - | NaN | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 | False | TAFE |
6.341475e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | - | ... | Other | - | NaN | 56 or older | Contract/casual | Teacher (including LVT) | 7-10 | 7-10 | False | TAFE |
6.341520e+17 | Barrier Reef Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | Maternity/Family | - | ... | Other | - | Career Move - Private Sector | 20 or younger | Temporary Full-time | Administration (AO) | 3-4 | 3-4 | False | TAFE |
6.341537e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | - | - | - | - | - | ... | Other | - | NaN | 46 50 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 | False | TAFE |
5 rows × 24 columns
# Combining the data sets by stacking them
combined = pd.concat([dete_resignations_up,tafe_resignations_up], axis = 0, ignore_index=True)
combined.head()
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 2006.0 | Resignation-Other reasons | NaN | False | NaN | False | False | False | False |
1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 1997.0 | Resignation-Other reasons | NaN | False | NaN | False | False | False | False |
2 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 2009.0 | Resignation-Other reasons | NaN | False | NaN | False | False | False | False |
3 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 2008.0 | Resignation-Other employer | NaN | False | NaN | False | False | False | False |
4 | 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
# Dropping columns with < 500 non null values
combined_updated = combined.dropna(thresh= 500 , axis = 1)
combined_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 651 entries, 0 to 650 Data columns (total 8 columns): age 596 non-null object cease_date 635 non-null float64 dissatisfied 643 non-null object employment_status 597 non-null object institute 651 non-null object institute_service 563 non-null object position 598 non-null object separationtype 651 non-null object dtypes: float64(1), object(7) memory usage: 40.8+ KB
After our dataframes have been combined, we will want to clean up the 'institute_service' column. Let's examine this column in greater detail.
combined_updated['institute_service'].value_counts()
Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 5.0 23 1.0 22 7-10 21 3.0 20 0.0 20 6.0 17 4.0 16 9.0 14 2.0 14 7.0 13 More than 20 years 10 13.0 8 8.0 8 20.0 7 15.0 7 14.0 6 17.0 6 12.0 6 10.0 6 22.0 6 18.0 5 16.0 5 24.0 4 23.0 4 11.0 4 19.0 3 39.0 3 21.0 3 32.0 3 36.0 2 25.0 2 26.0 2 28.0 2 30.0 2 42.0 1 35.0 1 49.0 1 34.0 1 31.0 1 33.0 1 29.0 1 27.0 1 41.0 1 38.0 1 Name: institute_service, dtype: int64
To analyze the data, we'll convert these numbers into the following categories:
# Change the entries in 'institute_service' to strings
combined_updated['institute_service'].astype(str)
yrs = r"(?P<yrs_service>[0-9]?[0-9])"
# Use vectorized str method to extract the years of service from each pattern
combined_updated['institute_service'] = combined_updated['institute_service'].str.extract(yrs, expand=False).astype(float)
# Check that we did not miss out any digits in the extraction
combined_updated['institute_service'].value_counts(dropna=False)
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:8: 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/indexing.html#indexing-view-versus-copy
NaN 361 1.0 137 3.0 63 5.0 33 11.0 26 7.0 21 20.0 10 Name: institute_service, dtype: int64
# Create a function that maps each year value to one of the career stages we defined
def career_staging(val):
if pd.isnull(val):
return 'No Info Available'
elif val < 3:
return 'New'
elif val < 7:
return 'Experienced'
elif val < 11:
return 'Established'
else:
return 'Veteran'
combined_updated['service_cat'] = combined_updated['institute_service'].apply(career_staging)
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:15: 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/indexing.html#indexing-view-versus-copy
combined_updated['service_cat'].value_counts()
No Info Available 361 New 137 Experienced 96 Veteran 36 Established 21 Name: service_cat, dtype: int64
Now we can start our analysis by filling in the missing values under the 'dissatisfied' column and aggregating the data (such as the count/percentage in each of our defined groups).
# Confirm the number of True and False in the 'dissatisfied' column
combined_updated['dissatisfied'].value_counts(dropna=False)
True 402 False 241 NaN 8 Name: dissatisfied, dtype: int64
# Since True appears most frequently in this column, let's assume all the missing values are True
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(True)
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.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/indexing.html#indexing-view-versus-copy app.launch_new_instance()
# Calculate the % of dissatisfied employees in each 'service_cat' group by using pivot_table() method
# Since a True value is considered to be 1, calculating the mean will also calculate the percentage of dissatisfied employees.
combinedpivot = combined_updated.pivot_table(
values = 'dissatisfied', index = 'service_cat')
# Plotting the bar chart results
%matplotlib inline
combinedpivot.plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x7f0213a46eb8>
From our findings, it seems that those who are new tend to be less dissatisfied, and the dissatisfaction grows as employees serve longer and longer in the company.
It is also interesting to note that from our earlier analysis, the churn rate for resignations reaches a peak around the Experienced group and tapers off afterwards, so this might suggest that dissatisfaction does not necessarily lead to resignation.
Alternatively, it could also suggest that there is certain crucial data we left out which might change the dissatisfaction bar graph here.