Credit to David Chung, May 7th, 2020
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 DETE exit survey here and the survey for the TAFE here
The encoding was changed from cp1252
to UTF-8
to make them easier to work with.
In this project, we'll play the role of data analyst and pretend our stackholders want to know the following questions:
Let's start by reading the datasets into pandas and exploring them.
# import libraries as below to begin with
% matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# read csv file into pandas
dete_survey = pd.read_csv("dete_survey.csv")
tafe_survey = pd.read_csv("tafe_survey.csv")
# review information of dete_survey
dete_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): ID 822 non-null int64 SeparationType 822 non-null object Cease Date 822 non-null object DETE Start Date 822 non-null object Role Start Date 822 non-null object Position 817 non-null object Classification 455 non-null object Region 822 non-null object Business Unit 126 non-null object Employment Status 817 non-null object Career move to public sector 822 non-null bool Career move to private sector 822 non-null bool Interpersonal conflicts 822 non-null bool Job dissatisfaction 822 non-null bool Dissatisfaction with the department 822 non-null bool Physical work environment 822 non-null bool Lack of recognition 822 non-null bool Lack of job security 822 non-null bool Work location 822 non-null bool Employment conditions 822 non-null bool Maternity/family 822 non-null bool Relocation 822 non-null bool Study/Travel 822 non-null bool Ill Health 822 non-null bool Traumatic incident 822 non-null bool Work life balance 822 non-null bool Workload 822 non-null bool None of the above 822 non-null bool Professional Development 808 non-null object Opportunities for promotion 735 non-null object Staff morale 816 non-null object Workplace issue 788 non-null object Physical environment 817 non-null object Worklife balance 815 non-null object Stress and pressure support 810 non-null object Performance of supervisor 813 non-null object Peer support 812 non-null object Initiative 813 non-null object Skills 811 non-null object Coach 767 non-null object Career Aspirations 746 non-null object Feedback 792 non-null object Further PD 768 non-null object Communication 814 non-null object My say 812 non-null object Information 816 non-null object Kept informed 813 non-null object Wellness programs 766 non-null object Health & Safety 793 non-null object Gender 798 non-null object Age 811 non-null object Aboriginal 16 non-null object Torres Strait 3 non-null object South Sea 7 non-null object Disability 23 non-null object NESB 32 non-null object dtypes: bool(18), int64(1), object(37) memory usage: 258.6+ KB
dete_survey.head(5)
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
dete_survey.isnull().sum().head(5)
ID 0 SeparationType 0 Cease Date 0 DETE Start Date 0 Role Start Date 0 dtype: int64
In dete_survey, we take a look into ID
, SeperationType
, Cease Date
and DETE Start Date
and here's my observations:
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(5)
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
tafe_survey.isnull().sum()
Record ID 0 Institute 0 WorkArea 0 CESSATION YEAR 7 Reason for ceasing employment 1 Contributing Factors. Career Move - Public Sector 265 Contributing Factors. Career Move - Private Sector 265 Contributing Factors. Career Move - Self-employment 265 Contributing Factors. Ill Health 265 Contributing Factors. Maternity/Family 265 Contributing Factors. Dissatisfaction 265 Contributing Factors. Job Dissatisfaction 265 Contributing Factors. Interpersonal Conflict 265 Contributing Factors. Study 265 Contributing Factors. Travel 265 Contributing Factors. Other 265 Contributing Factors. NONE 265 Main Factor. Which of these was the main factor for leaving? 589 InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 94 InstituteViews. Topic:2. I was given access to skills training to help me do my job better 89 InstituteViews. Topic:3. I was given adequate opportunities for personal development 92 InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 94 InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 87 InstituteViews. Topic:6. The organisation recognised when staff did good work 95 InstituteViews. Topic:7. Management was generally supportive of me 88 InstituteViews. Topic:8. Management was generally supportive of my team 94 InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 92 InstituteViews. Topic:10. Staff morale was positive within the Institute 100 InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 101 InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 105 ... WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 91 WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 96 WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 92 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 93 WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 99 WorkUnitViews. Topic:30. Staff morale was positive within my work unit 96 Induction. Did you undertake Workplace Induction? 83 InductionInfo. Topic:Did you undertake a Corporate Induction? 270 InductionInfo. Topic:Did you undertake a Institute Induction? 219 InductionInfo. Topic: Did you undertake Team Induction? 262 InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 147 InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 147 InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 147 InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 172 InductionInfo. On-line Topic:Did you undertake a Institute Induction? 147 InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 149 InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 147 InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 147 InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 147 Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 94 Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 108 Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 115 Workplace. Topic:Does your workplace value the diversity of its employees? 116 Workplace. Topic:Would you recommend the Institute as an employer to others? 121 Gender. What is your Gender? 106 CurrentAge. Current Age 106 Employment Type. Employment Type 106 Classification. Classification 106 LengthofServiceOverall. Overall Length of Service at Institute (in years) 106 LengthofServiceCurrent. Length of Service at current workplace (in years) 106 Length: 72, dtype: int64
In tefe_survey, we take a look into Recoded ID
, Reason for ceasing employment
and LengthofServiceOverall. Overall Length of Service at Institute (in years)
and here's my observations:
LengthofServiceOverall
is a string of year range, maybe we can replace them as mean number for future analysis.# read the dete_survey file into pandas again
# but this time read the "Not Stated" values in as "NaN"
dete_survey = pd.read_csv("dete_survey.csv", na_values="Not Stated")
# drop columns which we won't use for analysis
# dete_survey
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)
# tafe_survey
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis=1)
# rename the remaining columns in lowercase,
# remove whitespace and replace space with underline
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(" ","_")
# use dataframe.rename() to update column names
# for tefe_survey_updated
cols_dict = {"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 = cols_dict)
# look at the current state of both dataframe
print(dete_survey_updated.head(5))
print("-------------------------")
print(tafe_survey_updated.head(5))
id separationtype cease_date dete_start_date \ 0 1 Ill Health Retirement 08/2012 1984.0 1 2 Voluntary Early Retirement (VER) 08/2012 NaN 2 3 Voluntary Early Retirement (VER) 05/2012 2011.0 3 4 Resignation-Other reasons 05/2012 2005.0 4 5 Age Retirement 05/2012 1970.0 role_start_date position \ 0 2004.0 Public Servant 1 NaN Public Servant 2 2011.0 Schools Officer 3 2006.0 Teacher 4 1989.0 Head of Curriculum/Head of Special Education classification region business_unit \ 0 A01-A04 Central Office Corporate Strategy and Peformance 1 AO5-AO7 Central Office Corporate Strategy and Peformance 2 NaN Central Office Education Queensland 3 Primary Central Queensland NaN 4 NaN South East NaN employment_status ... work_life_balance workload none_of_the_above \ 0 Permanent Full-time ... False False True 1 Permanent Full-time ... False False False 2 Permanent Full-time ... False False True 3 Permanent Full-time ... False False False 4 Permanent Full-time ... True False False gender age aboriginal torres_strait south_sea disability nesb 0 Male 56-60 NaN NaN NaN NaN Yes 1 Male 56-60 NaN NaN NaN NaN NaN 2 Male 61 or older NaN NaN NaN NaN NaN 3 Female 36-40 NaN NaN NaN NaN NaN 4 Female 61 or older NaN NaN NaN NaN NaN [5 rows x 35 columns] ------------------------- 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 3 6.341399e+17 Mount Isa Institute of TAFE 4 6.341466e+17 Southern Queensland Institute of TAFE WorkArea cease_date separationtype \ 0 Non-Delivery (corporate) 2010.0 Contract Expired 1 Non-Delivery (corporate) 2010.0 Retirement 2 Delivery (teaching) 2010.0 Retirement 3 Non-Delivery (corporate) 2010.0 Resignation 4 Delivery (teaching) 2010.0 Resignation Contributing Factors. Career Move - Public Sector \ 0 NaN 1 - 2 - 3 - 4 - Contributing Factors. Career Move - Private Sector \ 0 NaN 1 - 2 - 3 - 4 Career Move - Private Sector Contributing Factors. Career Move - Self-employment \ 0 NaN 1 - 2 - 3 - 4 - Contributing Factors. Ill Health Contributing Factors. Maternity/Family \ 0 NaN NaN 1 - - 2 - - 3 - - 4 - - ... Contributing Factors. Study Contributing Factors. Travel \ 0 ... NaN NaN 1 ... - Travel 2 ... - - 3 ... - Travel 4 ... - - Contributing Factors. Other Contributing Factors. NONE gender age \ 0 NaN NaN Female 26 30 1 - - NaN NaN 2 - NONE NaN NaN 3 - - NaN NaN 4 - - Male 41 45 employment_status position institute_service role_service 0 Temporary Full-time Administration (AO) 1-2 1-2 1 NaN NaN NaN NaN 2 NaN NaN NaN NaN 3 NaN NaN NaN NaN 4 Permanent Full-time Teacher (including LVT) 3-4 3-4 [5 rows x 23 columns]
The main reason to change column names in both surveys is that we can make plots and directly compare with values with each other.
The column names of Tafe_survey_updated is much more different with Dete_survey_updated. So we need to build a dictionary to change name of Tafe while we only need to use function for Dete.
# review the unique values in both dataframe
# dete_survey_updated
print(dete_survey_updated["separationtype"].value_counts())
print()
# tafe_survey_updated
print(tafe_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 Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
In order to analyze the dissatisfaction status of employees working short or long, we'd like to select the data with "Resignation" only. Remember there're three kinds of resignation in dete_survey_updated.
# dete_survey_updated
# select rows based on multiple values
# df[df["column_name].isin(["value_1", "value_2"])]
resignation_list = ["Resignation-Other reasons","Resignation-Other employer", "Resignation-Move overseas/interstate"]
dete_resignations = dete_survey_updated[dete_survey_updated["separationtype"].isin(resignation_list)].copy()
# tafe_survey_updated
tafe_resignations = tafe_survey_updated[tafe_survey_updated["separationtype"]=="Resignation"].copy()
We use Series.value_counts()
to review unique values. There's 311 employees exit for resignation in dete_survey, and 340 employees in tafe_survey.
Since we'd like to analyze further for employees exit for resignation, we have to select the data with "resignation" in separation
column.
dete_survey has 3 kinds of resignation so we need to select rows based on multiple values. After searching, we decide to use df[df["column_name"].isin(["value_1","value_2"])]
method.
For tafe_survey, since there's only one kind of resignation, we can easily use boolean indexing to select rows.
# view the unique values in cease_date
# select only year part and convert the float type
dete_resignations["cease_date"].value_counts()
dete_resignations["cease_date"] = dete_resignations["cease_date"].str[-4:].astype(float)
# Use series.value_counts().sort_values()
# to check the values
dete_resignations["cease_date"].value_counts().sort_values(ascending=False)
2013.0 146 2012.0 129 2014.0 22 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
# Use series.value_counts().sort_index()
# to check the values by index
s1 = dete_resignations["dete_start_date"].value_counts().sort_index(ascending=False)
print(s1.head(10))
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 Name: dete_start_date, dtype: int64
tafe_resignations["cease_date"].value_counts().sort_index(ascending=False)
2013.0 55 2012.0 94 2011.0 116 2010.0 68 2009.0 2 Name: cease_date, dtype: int64
The year in cease_date
column of dete_resignation is string type. Some are {yyyy} format and some are {mm/yyyy}, and we'd like to unite the format to be {yyyy}. First, we use series.str()
to select the last four words. Second, we chain series.astype(float)
to change type of value.
Then we review values in cease_date
and dete_start_date
whether it's beyond our acceptance. (start_date earlier than 1940 or cease_date later than current date.) Luckily, there's no values beyond boundaries so we can move on.
# subtract the "dete_start_date" from "cease_date"
# assign the result to a new column "institute_service"
dete_resignations["institute_service"] = dete_resignations["cease_date"] - dete_resignations["dete_start_date"]
We have to keep in mind that our final goal is to answer:
So we need to calculate the service length by subtracting dete_start_date
from cease_date
, and assign the result to a new column institute_service
.
# Convert the values to True, False or NaN
# # "Contributing Factors. Dissatisfaction" column
# tafe_resignations["Contributing Factors. Dissatisfaction"] = tafe_resignations["Contributing Factors. Dissatisfaction"].str.replace("-","False").str.replace("Contributing Factors. Dissatisfaction","True")
# # "Contributing Factors. Job Dissatisfaction" column
# tafe_resignations["Contributing Factors. Job Dissatisfaction"] = tafe_resignations["Contributing Factors. Job Dissatisfaction"].str.replace("-","False").str.replace("Job Dissatisfaction","True")
tafe_cols = ["Contributing Factors. Dissatisfaction",
"Contributing Factors. Job Dissatisfaction"]
dete_cols = ["job_dissatisfaction", "dissatisfaction_with_the_department",
"physical_work_environment","lack_of_recognition",
"lack_of_job_security","work_location",
"employment_conditions","work_life_balance","workload"]
# write a function which transform NaN to np.nan; "-" to False
# other situation to True
def update_vals(val):
if pd.isnull(val):
return np.nan
elif val == "-":
return False
else:
return True
# tafe_resignations
# use df.applymap(function).any(axis=1,skipna=False)
tafe_resignations["dissatisfied"] = tafe_resignations[tafe_cols].applymap(update_vals).any(axis=1, skipna=False)
tafe_resignations_up = tafe_resignations.copy()
# dete_resignations
dete_resignations["dissatisfied"] = dete_resignations[dete_cols].any(axis=1, skipna=False)
dete_resignations_up = dete_resignations.copy()
# TAFE
tafe_resignations_up["dissatisfied"].value_counts(dropna=False)
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
# DETE
dete_resignations_up["dissatisfied"].value_counts(dropna=False)
False 162 True 149 Name: dissatisfied, dtype: int64
We write a function to transform value into the format we expected and sort the resignation reasons of dissatisfied into groups.
# Add a column "institute" to dete_resignations_up
dete_resignations_up["institute"] = "DETE"
# Do the same to tafe_resignations_up
tafe_resignations_up["institute"] = "TAFE"
# Combine both dataframes, assign to combined
combined = pd.concat([dete_resignations_up ,tafe_resignations_up])
# use df.dropna(thresh=x) to remove any columns(axis=1)
# with less than 500 non-null values, x=500
combined = combined.dropna(axis=1, thresh=500)
# df.notnull().sum() to check if result is what we expected
combined.notnull().sum()
age 596 cease_date 635 dissatisfied 643 employment_status 597 gender 592 id 651 institute 651 institute_service 563 position 598 separationtype 651 dtype: int64
After many cleaning steps, we're finally ready to combine both datasets. Our goal is to aggregate the data according to institute_service(how many years working here) column.
We want to remove some columns with too many null values. Since there're 651 datas and we decide to keep columns with more than 500 not-null values to make further analysis.
Classify the employees into four groups:
# convert value into string type, extract number only
# convert the result back to float and assign to new column
years = combined["institute_service"].astype("str").str.extract(r"(\d+)", expand=True)
combined["institute_service_up"] = years.astype(float)
# write a function to classify into groups
# remember to classify null value seperately
def classify_year(val):
if pd.isnull(val):
return np.nan
elif val < 3:
return "Newbie"
elif 3 <= val <7:
return "Sophomore"
elif 7 <= val <11:
return "Tenured"
else:
return "Sage"
# apply function and assign into new column
combined["service_cat"] = combined["institute_service_up"].apply(classify_year)
# check if the result is what we expect
# combined[combined["service_cat"] == "Tenured"]
# combined[combined["service_cat"] == "Sage"]
# combined[combined["service_cat"] == "Newbie"]
# combined[combined["service_cat"] == "Sophomore"]
If we look into institute_service
column, we can find the format is inconsistent and hard to analyze. So we convert values into string dtype, extract only digits and convert back to float dtype. Finally, we assign new values into new column institute_service_up
We write a function in order to classify values of institute_service_up
into four groups: Newbie, Sophomore, Tenured and Sage. Use series.apply() to activate the function and assign it into new column service_cat
Remember to check if the result is what we expected.
# confirm the number of True & False in dissatisfied column
# use dropna=False to also confirm number of missing value
combined["dissatisfied"].value_counts(dropna=False)
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
# use DataFrame.fillna() to replace missing value
# with the value that occurs most frequently
combined["dissatisfied"] = combined["dissatisfied"].fillna(False)
# use DataFrame.pivot_table(df, index, column, value, aggfunc)
# default aggfunc is mean
# since True is considered as 1 and False as 0
# mean value in this situation can be considered as percentage
dissatisfied_by_year = pd.pivot_table(combined, index=["service_cat"], values=["dissatisfied"])
dissatisfied_by_year
dissatisfied | |
---|---|
service_cat | |
Newbie | 0.295337 |
Sage | 0.485294 |
Sophomore | 0.343023 |
Tenured | 0.516129 |
dissatisfied_by_year.plot(kind="bar", legend=None, title="Dissatisfaction Percentage by Service Year")
plt.tick_params(right="off", top="off")
plt.show()
We replace missing values of column dissatisfied with most frequent values False
. Then, use pivot_table() to generate a table. True is considered as 1 while False as 0, plus the defalut aggfunc of pivot_table is mean
. We don't need to set aggfunc and we can get mean values which can be considered as percentage in this situation.
We can plot the table and make some brief conclusion:
According to this brief conclusion, if we're manager of these companies, we should take opinions from employees who worked for many years seriously or we'll easily lose their loyalty.
# 算出各個階段因為不滿意現況而離職的人數
grouped = combined.groupby(["service_cat","dissatisfied"])["service_cat"].agg("count")
print(grouped)
service_cat dissatisfied Newbie False 136 True 57 Sage False 70 True 66 Sophomore False 113 True 59 Tenured False 30 True 32 Name: service_cat, dtype: int64
The number exit for dissatisfied of Newbie: 57
The number exit for dissatisfied of Sophomore: 59
The number exit for dissatisfied of Tenurated: 32
The number exit for dissatisfied of Newbie: 66
# review the value status and figure out how to clean data
combined["age"]
3 36-40 5 41-45 8 31-35 9 46-50 11 31-35 12 36-40 14 31-35 16 61 or older 20 56-60 21 51-55 22 46-50 23 61 or older 25 41-45 27 21-25 33 36-40 34 61 or older 37 21-25 39 21-25 40 56-60 41 51-55 42 41-45 43 51-55 48 21-25 50 21-25 51 61 or older 55 26-30 57 46-50 61 31-35 69 36-40 71 36-40 ... 659 46 50 660 41 45 661 46 50 665 NaN 666 NaN 669 26 30 670 NaN 671 46 50 675 51-55 676 41 45 677 36 40 678 51-55 679 56 or older 681 26 30 682 26 30 683 41 45 684 41 45 685 26 30 686 41 45 688 46 50 689 41 45 690 NaN 691 56 or older 693 26 30 694 NaN 696 21 25 697 51-55 698 NaN 699 51-55 701 26 30 Name: age, Length: 651, dtype: object
combined["age_up"] = combined["age"].str.extract(r"(\d+)", expand=True).astype(float)
combined["age_up"].value_counts()
41.0 93 46.0 81 36.0 73 51.0 71 26.0 67 21.0 62 31.0 61 56.0 55 61.0 23 20.0 10 Name: age_up, dtype: int64
def classify_age(val):
if pd.isnull(val):
return np.nan
elif val < 30:
return "30 or less"
elif 30< val< 40:
return "30s"
elif 40< val< 50:
return "40s"
else:
return "50 or more"
combined["age_cat"] = combined["age_up"].apply(classify_age)
combined["age_cat"].value_counts(dropna=False)
40s 174 50 or more 149 30 or less 139 30s 134 NaN 55 Name: age_cat, dtype: int64
# Use groupby to see how many people by age are dissatisfied
combined.groupby(["age_cat","dissatisfied"])["age_cat"].agg("count")
age_cat dissatisfied 30 or less False 90 True 49 30s False 86 True 48 40s False 108 True 66 50 or more False 86 True 63 Name: age_cat, dtype: int64
The number exit for dissatisfied of 30 or less: 49
The number exit for dissatisfied of 30s: 48
The number exit for dissatisfied of 40s: 66
The number exit for dissatisfied of 50 or more: 63
# recall there's a "institute" column stored
# where this data comes from
# see total amounts
combined.groupby(["institute","dissatisfied"])["institute"].agg("count")
institute dissatisfied DETE False 162 True 149 TAFE False 249 True 91 Name: institute, dtype: int64
# see percentage
pd.pivot_table(combined, index=["institute"], values=["dissatisfied"])
dissatisfied | |
---|---|
institute | |
DETE | 0.479100 |
TAFE | 0.267647 |
From both total amounts(149>91) and percentage(47%>26%), we can realize more employees from DETE resigned due to dissatisfaction reasons.