In this guided 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. You can find the TAFE exit survey here and the survey for the DETE here.
In this project, we'll try to answer the following questions:
We will combine the results for both surveys to answer these questions. You can find description of the columns of the datasets in the README file.
Let's start by reading in the datasets and exploring them.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('fivethirtyeight')
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
dete_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): 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
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
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
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
From the above few cells we see:
dete_survey
dataframe contains 'Not Stated' values that indicate values are missing, but they aren't represented as NaN.dete_survey
and tafe_survey
dataframes contain many columns that we don't need to complete our analysis.To start, we'll handle the first two issues.
#First, let's re-open the dete_survey dataset
#but this time replace the 'Not stated' values with NaN
dete_survey = pd.read_csv('dete_survey.csv', na_values='Not Stated')
dete_survey.head(3)
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 rows × 56 columns
#Let's drop the columns which we will not use from the DETE survey:
columns_to_drop = dete_survey.iloc[:,28:49]
dete_survey_updated = dete_survey.drop(columns_to_drop, axis=1)
#Let's drop the columns which we will not use from the TAFE survey:
drop_columns = tafe_survey.iloc[:, 17:66]
tafe_survey_updated = tafe_survey.drop(drop_columns, axis=1)
Above, we preformed some data cleaning steps:
dete_survey_updated
and tafe_survey_updated
.Next, let's turn our attention to the column names. Each dataframe contains many of the same columns, but the column names are different.
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.replace(' ', '_')
dete_survey_updated.head(3)
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 rows × 35 columns
tafe_survey_updated = tafe_survey_updated.rename(columns={
'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.head(2)
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 rows × 23 columns
In the above few cells, the names of the columns in both dataframes were updated.
Next, let's remove more of the data we don't need. Our end goal is to answer the following question:
This means that we are only interesed in employees who have resigned.
dete_survey_updated['separationtype'].unique()
##There are a few types of Resignation in the DETE survey
array(['Ill Health Retirement', 'Voluntary Early Retirement (VER)', 'Resignation-Other reasons', 'Age Retirement', 'Resignation-Other employer', 'Resignation-Move overseas/interstate', 'Other', 'Contract Expired', 'Termination'], dtype=object)
tafe_survey_updated['separationtype'].unique()
array(['Contract Expired', 'Retirement', 'Resignation', 'Retrenchment/ Redundancy', 'Termination', 'Transfer', nan], dtype=object)
dete_survey_updated['separationtype'].value_counts()
Age Retirement 285 Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
tafe_survey_updated['separationtype'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
dete_survey_updated['separationtype'] = dete_survey_updated['separationtype'].str.split('-').str[0]
dete_survey_updated['separationtype'].value_counts()
Resignation 311 Age Retirement 285 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
dete_resignations = dete_survey_updated[dete_survey_updated['separationtype']=='Resignation'].copy()
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
Above, two new dataframes were created - dete_resignations and tafe_resignations. They hold data only for those employees who resigned.
Next, 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 date.dete_start_date
was before the year 1940.If 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.
dete_resignations['cease_date'].value_counts()
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 07/2013 9 11/2013 9 10/2013 6 08/2013 4 05/2013 2 05/2012 2 07/2012 1 09/2010 1 07/2006 1 2010 1 Name: cease_date, dtype: int64
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.split('/').str[-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(ascending=True)
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
dete_resignations[['dete_start_date','cease_date']].plot(kind='box')
<matplotlib.axes._subplots.AxesSubplot at 0x7fb46f88f320>
Above, we cleaned the columns of both dataframes which contain the start and cease date of the resigned employees. There do not appear to be any major issues with the values. The span of the cease years for both dataframes is a bit different:
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 dataset contains a column called institute_service
. Unfortunately, the DETE dataset does not have such a column. We do, however, have the needed data to create this column. It should contain the difference between the cease_date
and the dete_start_date columns
.
dete_resignations['institute_service'] = dete_resignations['cease_date'] - dete_resignations['dete_start_date']
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.
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()
- 277 Contributing Factors. Dissatisfaction 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()
- 270 Job Dissatisfaction 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
def updated_vals(x):
if pd.isnull(x):
return np.nan
elif x == '-':
return False
else:
return True
tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']] = tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']].applymap(updated_vals)
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts()
False 277 True 55 Name: Contributing Factors. Dissatisfaction, dtype: int64
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts()
False 270 True 62 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
tafe_resignations['dissatisfied'] = tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']].any(axis='columns', skipna=False)
tafe_resignations['dissatisfied'].value_counts()
False 241 True 91 Name: dissatisfied, dtype: int64
dete_diss_columns = ['job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment',
'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions',
'work_life_balance', 'workload']
dete_resignations['dissatisfied'] = dete_resignations[dete_diss_columns].any(axis='columns', skipna=False)
dete_resignations['dissatisfied'].value_counts()
False 162 True 149 Name: dissatisfied, dtype: int64
dete_resignations_up = dete_resignations.copy()
tafe_resignations_up = tafe_resignations.copy()
Above, we created a dissatisfied
column in both dataframes. The values of the columns are either True or False based on the emplyees' response to the questions in the columns we identified above.
Additionally, we created copies of each dataframe.
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index=True)
combined_updated = combined.dropna(thresh=500, axis=1).copy()
In the above few cells we did the following:
institute
column in each dataset indicating where the employee worked;Next we need to clean the institute_service
column as it contains values in a couple of different formats. To analyze the data, we'll convert these numbers into categories. We'll base our anlaysis on this article, which makes the argument that understanding employee's needs according to career stage instead of age is more effective.
We'll use the slightly modified definitions below:
combined_updated['institute_service'].unique()
array([7.0, 18.0, 3.0, 15.0, 14.0, 5.0, nan, 30.0, 32.0, 39.0, 17.0, 9.0, 6.0, 1.0, 35.0, 38.0, 36.0, 19.0, 4.0, 26.0, 10.0, 8.0, 2.0, 0.0, 23.0, 13.0, 16.0, 12.0, 21.0, 20.0, 24.0, 33.0, 22.0, 28.0, 49.0, 11.0, 41.0, 27.0, 42.0, 25.0, 29.0, 34.0, 31.0, '3-4', '7-10', '1-2', 'Less than 1 year', '11-20', '5-6', 'More than 20 years'], dtype=object)
combined_updated['institute_service'] = combined_updated['institute_service'].astype('str')
combined_updated['institute_service'] = combined_updated['institute_service'].str.extract(r'(\d+)')
combined_updated['institute_service'] = combined_updated['institute_service'].astype(float)
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:2: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame)
combined_updated['institute_service'].value_counts()
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, dtype: int64
def career_stages(x):
if pd.isnull(x):
return np.nan
elif x < 3:
return 'New'
elif 3 <= x <= 6:
return 'Experienced'
elif 6 < x <= 10:
return 'Established'
elif x > 10:
return 'Veteran'
combined_updated['service_cat'] = combined_updated['institute_service'].apply(career_stages)
combined_updated['service_cat'].value_counts(dropna=False)
New 193 Experienced 172 Veteran 136 NaN 88 Established 62 Name: service_cat, dtype: int64
Above, we cleaned the institute_service
column. We used the value from that column in order to determine in which category the employee falls. We created a new column - service_cat
- where we see the category of the employee.
combined_updated['dissatisfied'].value_counts(dropna=False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
table = pd.pivot_table(combined_updated, index='service_cat', values='dissatisfied')
table = table.sort_values(by=['dissatisfied'])
table.plot(kind='barh', legend=False, figsize=(10,5), fontsize=12)
<matplotlib.axes._subplots.AxesSubplot at 0x7fb46f80cc88>
Above, we created a pivot table which calcualtes the percentage of dissatisfied employees for each service category. Afterwards, we plotted the results on a horizontal bar chart.
We can see that of the employees who took the two serveys, Established and Veteran employees are more likely to resign due to dissatisfaction. New employees are least likely to do so.
diss_count = pd.pivot_table(combined_updated, index='service_cat', values='dissatisfied', aggfunc='sum')
diss_count = diss_count.sort_values(by=['dissatisfied'])
diss_count = diss_count.rename(columns={'dissatisfied':'dissatisfied_count'})
diss_count
dissatisfied_count | |
---|---|
service_cat | |
Established | 32.0 |
New | 57.0 |
Experienced | 59.0 |
Veteran | 66.0 |
Above we see the number of people in each service category who left due to dissatisfaction.
Below we will clean the age
column by grouping the employees in age groups. Afterwards, we will answer the question:
In order to clean the data, we will divide the age groups like this:
combined_updated['age'].value_counts(dropna=False)
51-55 71 NaN 55 41-45 48 41 45 45 46-50 42 36-40 41 46 50 39 26-30 35 21 25 33 26 30 32 31 35 32 36 40 32 21-25 29 31-35 29 56 or older 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
combined_updated['age'] = combined_updated['age'].astype('str')
def age(s):
if s[0] == '2':
return '20s'
elif s[0] == '3':
return '30s'
elif s[0] == '4':
return '40s'
elif s[0] == '5':
return '50s'
elif s[0] == '6':
return '60s'
elif s == 'nan':
return np.nan
combined_updated['age'] = combined_updated['age'].apply(age)
combined_updated['age'].value_counts()
40s 174 20s 139 30s 134 50s 126 60s 23 Name: age, dtype: int64
age_diss_count = pd.pivot_table(combined_updated, index='age', values='dissatisfied', aggfunc='sum')
age_diss_count = age_diss_count.sort_values(by=['dissatisfied'])
age_diss_count = age_diss_count.rename(columns={'dissatisfied': 'dissatisfied_count'})
age_diss_count
dissatisfied_count | |
---|---|
age | |
60s | 12.0 |
30s | 48.0 |
20s | 49.0 |
50s | 51.0 |
40s | 66.0 |
age_perc = pd.pivot_table(combined_updated, index='age', values='dissatisfied')
age_perc = age_perc.sort_values(by=['dissatisfied'])
age_perc.plot(kind='barh', legend=False, figsize=(10,10), fontsize=12)
<matplotlib.axes._subplots.AxesSubplot at 0x7fb46f8256a0>
In the charts above we see:
Overall, the number of young employees of the two institues who resigned due to job dissatisfaction is lowest. This might be due to the fact that they were early in their career development and were still looking for a career path to follow.
institute_count = pd.pivot_table(combined_updated, index='institute', values='dissatisfied', aggfunc='sum')
institute_count = institute_count.rename(columns={'dissatisfied': 'dissatisfied_count'})
institute_count
dissatisfied_count | |
---|---|
institute | |
DETE | 149.0 |
TAFE | 91.0 |
by_perc = pd.pivot_table(combined_updated, index='institute', values='dissatisfied')
by_perc.plot(kind='bar', rot=360, figsize=(10,5), fontsize=12, legend=False)
<matplotlib.axes._subplots.AxesSubplot at 0x7fb46f789320>
In the charts above we see:
It seems that DETE employees have resigned due to job dissatisfaction more often than TAFE employees.
by_gender = pd.pivot_table(combined_updated, index='gender', values='dissatisfied')
by_gender.plot(kind='bar', rot=360, figsize=(10,5), fontsize=12, ylim=[0, 0.5], legend=False)
<matplotlib.axes._subplots.AxesSubplot at 0x7fb46f7140b8>
by_status = pd.pivot_table(combined_updated, index='employment_status', values='dissatisfied')
by_status = by_status.sort_values(by=['dissatisfied'])
by_status.plot(kind='barh', figsize=(10,5), fontsize=12, legend=False)
<matplotlib.axes._subplots.AxesSubplot at 0x7fb46f6c2860>
by_position = pd.pivot_table(combined_updated, index='position', values='dissatisfied')
by_position = by_position.sort_values(by=['dissatisfied'])
by_position.plot(kind='barh', figsize=(15, 10), fontsize=12, legend=False, xlim=[0.05, 1.01])
<matplotlib.axes._subplots.AxesSubplot at 0x7fb46f6d6e48>
gender_age = pd.pivot_table(combined_updated, index=['gender', 'age'], values='dissatisfied')
gender_age
dissatisfied | ||
---|---|---|
gender | age | |
Female | 20s | 0.375000 |
30s | 0.336842 | |
40s | 0.389313 | |
50s | 0.375000 | |
60s | 0.416667 | |
Male | 20s | 0.285714 |
30s | 0.421053 | |
40s | 0.357143 | |
50s | 0.444444 | |
60s | 0.750000 |
gender_age = gender_age.sort_values(by=['dissatisfied'])
gender_age.plot(kind='barh', figsize=(15, 10), legend=False, xlim=[0, 0.60])
<matplotlib.axes._subplots.AxesSubplot at 0x7fb46f6960f0>
status_age = pd.pivot_table(combined_updated, index=['employment_status', 'age'], values='dissatisfied')
status_age = status_age.sort_values(by=['dissatisfied'])
status_age.plot(kind='barh', figsize=(15, 10), legend=False, xlim=[0, 0.7])
<matplotlib.axes._subplots.AxesSubplot at 0x7fb46f5782e8>
In this project, we analyzed the exit surveys of employees of the DETE and TAFE institutes. We focused on those who resiged due to some sort of job dissatisfaction and concluded that: