In this project, we'll play the role of data analyst and pretend our stakeholders want to know the following:
They want us to combine the results for both surveys to answer these questions. However, although both used the same survey template, one of them customized some of the answers. In the following steps, we'll aim to do most of the data cleaning and get started analyzing the first question.
Below is a preview of a couple columns we'll work with from the dete_survey.csv
:
ID
: An id used to identify the participant of the surveySeparationType
: The reason why the person's employment endedCease Date
: The year or month the person's employment endedDETE Start Date
: The year the person began employment with the DETEBelow is a preview of a couple columns we'll work with from the tafe_survey.csv
:
Record ID
: An id used to identify the participant of the surveyReason for ceasing employment
: The reason why the person's employment endedLengthofServiceOverall. Overall Length of Service at Institute (in years)
: The length of the person's employment (in years)import numpy as np
import pandas as pd
dete_survey = pd.read_csv("dete_survey.csv")
tafe_survey = pd.read_csv("tafe_survey.csv")
# Let's deep into the first survey
print(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 None
print(dete_survey.head(3))
ID SeparationType Cease Date DETE Start Date \ 0 1 Ill Health Retirement 08/2012 1984 1 2 Voluntary Early Retirement (VER) 08/2012 Not Stated 2 3 Voluntary Early Retirement (VER) 05/2012 2011 Role Start Date Position Classification Region \ 0 2004 Public Servant A01-A04 Central Office 1 Not Stated Public Servant AO5-AO7 Central Office 2 2011 Schools Officer NaN Central Office Business Unit Employment Status ... \ 0 Corporate Strategy and Peformance Permanent Full-time ... 1 Corporate Strategy and Peformance Permanent Full-time ... 2 Education Queensland Permanent Full-time ... Kept informed Wellness programs Health & Safety Gender Age \ 0 N N N Male 56-60 1 N N N Male 56-60 2 N N N Male 61 or older Aboriginal Torres Strait South Sea Disability NESB 0 NaN NaN NaN NaN Yes 1 NaN NaN NaN NaN NaN 2 NaN NaN NaN NaN NaN [3 rows x 56 columns]
# Let's deep into the second survey
print(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 None
print(tafe_survey.head(3))
Record ID Institute \ 0 6.341330e+17 Southern Queensland Institute of TAFE 1 6.341337e+17 Mount Isa Institute of TAFE 2 6.341388e+17 Mount Isa Institute of TAFE WorkArea CESSATION YEAR Reason for ceasing employment \ 0 Non-Delivery (corporate) 2010.0 Contract Expired 1 Non-Delivery (corporate) 2010.0 Retirement 2 Delivery (teaching) 2010.0 Retirement Contributing Factors. Career Move - Public Sector \ 0 NaN 1 - 2 - Contributing Factors. Career Move - Private Sector \ 0 NaN 1 - 2 - Contributing Factors. Career Move - Self-employment \ 0 NaN 1 - 2 - Contributing Factors. Ill Health Contributing Factors. Maternity/Family \ 0 NaN NaN 1 - - 2 - - ... \ 0 ... 1 ... 2 ... Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? \ 0 Yes 1 Yes 2 Yes Workplace. Topic:Does your workplace promote and practice the principles of employment equity? \ 0 Yes 1 Yes 2 Yes Workplace. Topic:Does your workplace value the diversity of its employees? \ 0 Yes 1 Yes 2 Yes Workplace. Topic:Would you recommend the Institute as an employer to others? \ 0 Yes 1 Yes 2 Yes Gender. What is your Gender? CurrentAge. Current Age \ 0 Female 26 30 1 NaN NaN 2 NaN NaN Employment Type. Employment Type Classification. Classification \ 0 Temporary Full-time Administration (AO) 1 NaN NaN 2 NaN NaN LengthofServiceOverall. Overall Length of Service at Institute (in years) \ 0 1-2 1 NaN 2 NaN LengthofServiceCurrent. Length of Service at current workplace (in years) 0 1-2 1 NaN 2 NaN [3 rows x 72 columns]
tafe_survey["Main Factor. Which of these was the main factor for leaving?"].value_counts()
Dissatisfaction with %[Institute]Q25LBL% 23 Job Dissatisfaction 22 Other 18 Career Move - Private Sector 16 Interpersonal Conflict 9 Career Move - Public Sector 8 Maternity/Family 6 Career Move - Self-employment 4 Ill Health 3 Study 2 Travel 2 Name: Main Factor. Which of these was the main factor for leaving?, dtype: int64
tafe_survey["Workplace. Topic:Would you recommend the Institute as an employer to others?"].value_counts()
Yes 416 No 165 Name: Workplace. Topic:Would you recommend the Institute as an employer to others?, dtype: int64
Observation : It seems that on the second survey (TAFE), employees were given some Multiple Choice Questions (MCQ), with sometimes the possibility to answer only by "Yes" or "No".
Other observations :
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, using pd.read_csv()
function to specify values that should be represented as NaN
. Then, we'll drop columns we know we don't need for our analysis.
dete_survey = pd.read_csv("dete_survey.csv", na_values="Not Stated")
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis=1)
dete_survey_updated.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')
tafe_survey_updated.columns
Index(['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. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Interpersonal Conflict', '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)'], dtype='object')
Observation : above, we saw that we only kept relevant columns concerning contract duration, resignation and age. These columns are the only ones relevant in order to answer are two questions stated at the beginning of this project.
Next, let's turn our attention to the column names. Each dataframe contains many of the same columns, but the column names are different.
Below are some of the columns we'd like to use for our final analysis:
dete_survey_updated.columns = dete_survey_updated.columns.str.replace(" ","_").str.lower()
print(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')
tafe_survey_updated.rename({"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"
},
inplace=True,axis =1)
print(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')
Recall that our end goal is to answer the following question:
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'
.
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
Observation : we notice that dete_survey_updated
dataframe contains multiple separation types with the string 'Resignation'
:
We'll have to account for each of these variations so we need to be careful about not unintentionally dropping data!
dete_resignations = dete_survey_updated[
(dete_survey_updated["separationtype"] == "Resignation-Other reasons")
| (dete_survey_updated["separationtype"] == "Resignation-Other employer")
| (dete_survey_updated["separationtype"] == "Resignation-Move overseas/interstate")
].copy()
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
tafe_resignations = tafe_survey_updated[
(tafe_survey_updated["separationtype"] == "Resignation")
].copy()
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
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).
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 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(dropna=False)
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 NaN 11 09/2013 11 11/2013 9 07/2013 9 10/2013 6 08/2013 4 05/2013 2 05/2012 2 07/2006 1 07/2012 1 09/2010 1 2010 1 Name: cease_date, dtype: int64
# Let's extract only the year
dete_resignations["cease_date"] = dete_resignations["cease_date"].str.extract(r"(20[0-1][0-6])", expand=False)
dete_resignations["cease_date"].value_counts(dropna=False).sort_index(ascending = False)
2014 22 2013 146 2012 129 2010 2 2006 1 NaN 11 Name: cease_date, dtype: int64
dete_resignations["dete_start_date"].value_counts(dropna=False).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 NaN 28 Name: dete_start_date, dtype: int64
tafe_resignations["cease_date"].value_counts(dropna=False).sort_index(ascending = False)
2013.0 55 2012.0 94 2011.0 116 2010.0 68 2009.0 2 NaN 5 Name: cease_date, dtype: int64
Observation: From the work we did in the last screen, we can verify:
Now that we've verified the years in the dete_resignations
dataframe, we'll use them to create a new column. Recall that 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.
We can notice that the tafe_resignations
dataframe already contains a "service" column, which name is institute_service
. In order to analyze both surveys together, we'll have to create a corresponding institute_service
column in dete_resignations
.
# First we change the type of our dates to be able to do computations
dete_resignations["cease_date"] = dete_resignations["cease_date"].astype(float)
dete_resignations["dete_start_date"] = dete_resignations["dete_start_date"].astype(float)
dete_resignations["institute_service"] = dete_resignations["cease_date"] - dete_resignations["dete_start_date"]
dete_resignations["institute_service"].value_counts(dropna=False).sort_index(ascending=False)
49.0 1 42.0 1 41.0 1 39.0 3 38.0 1 36.0 2 35.0 1 34.0 1 33.0 1 32.0 3 31.0 1 30.0 2 29.0 1 28.0 2 27.0 1 26.0 2 25.0 2 24.0 4 23.0 4 22.0 6 21.0 3 20.0 7 19.0 3 18.0 5 17.0 6 16.0 5 15.0 7 14.0 6 13.0 8 12.0 6 11.0 4 10.0 6 9.0 14 8.0 8 7.0 13 6.0 17 5.0 23 4.0 16 3.0 20 2.0 14 1.0 22 0.0 20 NaN 38 Name: institute_service, dtype: int64
Previously, 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.
A) tafe_resignations
Contributing Factors. Dissatisfaction
Contributing Factors. Job Dissatisfaction
B) 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
If the employee indicated any of the factors above caused them to resign, we'll mark them as dissatisfied
in a new column.
# Let's start with a view of the values contained in the columns we want to analyze
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
Observation : we will now change the datas contained in the two columns stated above from tafe_resignations
, to have False
when the value is '_'
and True
for any other value. Then, we'll use any()
function to return our dissatisfied
column.
def update_vals(cell):
if pd.isnull(cell) is True:
return np.nan
elif cell == "-":
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)
print(tafe_resignations["dissatisfied"].value_counts(dropna=False))
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
# Then, let's deep into our second dataset
dete_resignations["job_dissatisfaction"].value_counts()
False 270 True 41 Name: job_dissatisfaction, dtype: int64
Observation : the columns we need in dete_resignations
already contained "False" and "True" values. Thus, we don't need to perform transformation using update_vals()
, unlike tafe_resignations
dete_resignations.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', 'institute_service'], dtype='object')
columns_needed = ["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[columns_needed].any(axis=1, skipna= False)
print(dete_resignations["dissatisfied"].value_counts(dropna=False))
False 162 True 149 Name: dissatisfied, dtype: int64
# We create a copy to avoid the SettingWithCopy Warning
tafe_resignations_up = tafe_resignations.copy()
dete_resignations_up = dete_resignations.copy()
To recap, we've accomplished the following:
institute_service
columnContributing Factors
columnsNow, we're finally ready to combine our datasets! Our end goal is to aggregate the data according to the institute_service
column.
institute
" in both datasets¶First, let's add a column to each dataframe that will allow us to easily distinguish between the two.
dete_resignations_up["institute"] = "DETE"
tafe_resignations_up["institute"] = "TAFE"
combined = pd.concat([dete_resignations_up, tafe_resignations_up], join="inner")
combined.columns
Index(['id', 'separationtype', 'cease_date', 'position', 'employment_status', 'gender', 'age', 'institute_service', 'dissatisfied', 'institute'], dtype='object')
combined.head()
id | separationtype | cease_date | position | employment_status | gender | age | institute_service | dissatisfied | institute | |
---|---|---|---|---|---|---|---|---|---|---|
3 | 4.0 | Resignation-Other reasons | 2012.0 | Teacher | Permanent Full-time | Female | 36-40 | 7 | False | DETE |
5 | 6.0 | Resignation-Other reasons | 2012.0 | Guidance Officer | Permanent Full-time | Female | 41-45 | 18 | True | DETE |
8 | 9.0 | Resignation-Other reasons | 2012.0 | Teacher | Permanent Full-time | Female | 31-35 | 3 | False | DETE |
9 | 10.0 | Resignation-Other employer | 2012.0 | Teacher Aide | Permanent Part-time | Female | 46-50 | 15 | True | DETE |
11 | 12.0 | Resignation-Move overseas/interstate | 2012.0 | Teacher | Permanent Full-time | Male | 31-35 | 3 | False | DETE |
combined.tail()
id | separationtype | cease_date | position | employment_status | gender | age | institute_service | dissatisfied | institute | |
---|---|---|---|---|---|---|---|---|---|---|
696 | 6.350660e+17 | Resignation | 2013.0 | Operational (OO) | Temporary Full-time | Male | 21 25 | 5-6 | False | TAFE |
697 | 6.350668e+17 | Resignation | 2013.0 | Teacher (including LVT) | Temporary Full-time | Male | 51-55 | 1-2 | False | TAFE |
698 | 6.350677e+17 | Resignation | 2013.0 | NaN | NaN | NaN | NaN | NaN | False | TAFE |
699 | 6.350704e+17 | Resignation | 2013.0 | Teacher (including LVT) | Permanent Full-time | Female | 51-55 | 5-6 | False | TAFE |
701 | 6.350730e+17 | Resignation | 2013.0 | Administration (AO) | Contract/casual | Female | 26 30 | 3-4 | False | TAFE |
combined.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 651 entries, 3 to 701 Data columns (total 10 columns): id 651 non-null float64 separationtype 651 non-null object cease_date 635 non-null float64 position 598 non-null object employment_status 597 non-null object gender 592 non-null object age 596 non-null object institute_service 563 non-null object dissatisfied 643 non-null object institute 651 non-null object dtypes: float64(2), object(8) memory usage: 55.9+ KB
Observation : thanks to our previous work concerning the alignment of columns' names of both datasets, we were able to concatenate them using an "inner join" (i.e. keeping only the columns which are present in both datasets and adding rows vertically)
institute_service
" column¶Now that we've combined our dataframes, we're almost at a place where we can perform some kind of analysis! First, though, 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:
combined["institute_service"] = combined["institute_service"].astype(str)
combined["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 2.0 14 9.0 14 7.0 13 More than 20 years 10 13.0 8 8.0 8 20.0 7 15.0 7 14.0 6 12.0 6 22.0 6 10.0 6 17.0 6 16.0 5 18.0 5 11.0 4 23.0 4 24.0 4 19.0 3 39.0 3 32.0 3 21.0 3 28.0 2 36.0 2 25.0 2 26.0 2 30.0 2 31.0 1 27.0 1 29.0 1 34.0 1 41.0 1 42.0 1 35.0 1 49.0 1 38.0 1 33.0 1 Name: institute_service, dtype: int64
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:
Let's now categorize the values in the institute_service
column using the definitions above.
# First we extract only the first number of years when the value is a range
combined["institute_service"] = combined["institute_service"].str.extract(r"(\d+)", expand=False).astype(float)
combined["institute_service"].value_counts(dropna=False).sort_index(ascending=True)
0.0 20 1.0 159 2.0 14 3.0 83 4.0 16 5.0 56 6.0 17 7.0 34 8.0 8 9.0 14 10.0 6 11.0 30 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 17 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 NaN 88 Name: institute_service, dtype: int64
# Then we create the function who will be able to do a mapping between the values and the career stages above
def map_career(val):
if val < 3:
return "0_New"
elif 3 <= val <= 6:
return "1_Experienced"
elif 7 <= val <= 10:
return "2_Established"
elif 11 <= val:
return "3_Veteran"
elif pd.isnull(val) is True:
return np.nan
# At the end, we apply it to the institute_service column
combined["service_cat"] = combined["institute_service"].apply(map_career)
combined["service_cat"].value_counts(dropna=False)
0_New 193 1_Experienced 172 3_Veteran 136 NaN 88 2_Established 62 Name: service_cat, dtype: int64
combined["service_cat"].head()
3 2_Established 5 3_Veteran 8 1_Experienced 9 3_Veteran 11 1_Experienced Name: service_cat, dtype: object
dissatisfied
column¶combined["dissatisfied"].value_counts(dropna=False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
# We replace the missing values in the dissatisfied column with the value that occurs most frequently in this column
combined["dissatisfied"] = combined["dissatisfied"].fillna(False)
combined["dissatisfied"].value_counts(dropna=False)
False 411 True 240 Name: dissatisfied, dtype: int64
# At the end, we calculate the percentage of dissastified people of each group
result = combined.pivot_table(values="dissatisfied",index="service_cat", aggfunc=np.mean)
print(result)
dissatisfied service_cat 0_New 0.295337 1_Experienced 0.343023 2_Established 0.516129 3_Veteran 0.485294
%matplotlib inline
graph = result.plot(kind="bar", legend=False, color="red", title="Analysis of dissatisfaction as reason of resignation")
graph.set_xlabel("x").set_visible(False)
graph.set_xticklabels(result.index, rotation = 0)
graph.set_ylabel("Percentage of dissatisfied people")
graph.tick_params(right='off',top="off")
graph.spines["right"].set_visible(False)
graph.spines["top"].set_visible(False)
To conclude, it seems that the higher the length of service, the more "dissatisfaction" is a reason of resignation. Yet, this idea does not explain why "Veteran" resignations are a bit less explained by dissatisfaction than "Established" group of former employees.
To answer the second question "Are younger employees resigning due to some kind of dissatisfaction? What about older employees?", we would do relatively the same work of cleaning and analyzing data, inverting "service_cat"
with group of people made from the "age"
column.