This project will analyze employee exit surveys taken from workers in the Department of Education, Training, and Employment (DETE) and the Technical and Further Education (TAFE) institute that are located in Queensland, Australia. Will explore reasons for resignation along the lines of age and tenure, looking at the dissatisfaction levels of different employee demographics.
Will import the dete_survey.csv and tafe_survey.csv files into pandas.
import pandas as pd
dete_survey = pd.read_csv("dete_survey.csv")
tafe_survey = pd.read_csv("tafe_survey.csv")
Will examine the DETE survey first. Print the first five rows.
print(dete_survey.head())
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 3 4 Resignation-Other reasons 05/2012 2005 4 5 Age Retirement 05/2012 1970 Role Start Date Position \ 0 2004 Public Servant 1 Not Stated Public Servant 2 2011 Schools Officer 3 2006 Teacher 4 1989 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 ... Kept informed Wellness programs \ 0 Permanent Full-time ... N N 1 Permanent Full-time ... N N 2 Permanent Full-time ... N N 3 Permanent Full-time ... A N 4 Permanent Full-time ... N A Health & Safety Gender Age Aboriginal Torres Strait South Sea \ 0 N Male 56-60 NaN NaN NaN 1 N Male 56-60 NaN NaN NaN 2 N Male 61 or older NaN NaN NaN 3 A Female 36-40 NaN NaN NaN 4 M Female 61 or older NaN NaN NaN Disability NESB 0 NaN Yes 1 NaN NaN 2 NaN NaN 3 NaN NaN 4 NaN NaN [5 rows x 56 columns]
Print info about the DETE survey dataframe.
print(dete_survey.info())
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 822 non-null int64 1 SeparationType 822 non-null object 2 Cease Date 822 non-null object 3 DETE Start Date 822 non-null object 4 Role Start Date 822 non-null object 5 Position 817 non-null object 6 Classification 455 non-null object 7 Region 822 non-null object 8 Business Unit 126 non-null object 9 Employment Status 817 non-null object 10 Career move to public sector 822 non-null bool 11 Career move to private sector 822 non-null bool 12 Interpersonal conflicts 822 non-null bool 13 Job dissatisfaction 822 non-null bool 14 Dissatisfaction with the department 822 non-null bool 15 Physical work environment 822 non-null bool 16 Lack of recognition 822 non-null bool 17 Lack of job security 822 non-null bool 18 Work location 822 non-null bool 19 Employment conditions 822 non-null bool 20 Maternity/family 822 non-null bool 21 Relocation 822 non-null bool 22 Study/Travel 822 non-null bool 23 Ill Health 822 non-null bool 24 Traumatic incident 822 non-null bool 25 Work life balance 822 non-null bool 26 Workload 822 non-null bool 27 None of the above 822 non-null bool 28 Professional Development 808 non-null object 29 Opportunities for promotion 735 non-null object 30 Staff morale 816 non-null object 31 Workplace issue 788 non-null object 32 Physical environment 817 non-null object 33 Worklife balance 815 non-null object 34 Stress and pressure support 810 non-null object 35 Performance of supervisor 813 non-null object 36 Peer support 812 non-null object 37 Initiative 813 non-null object 38 Skills 811 non-null object 39 Coach 767 non-null object 40 Career Aspirations 746 non-null object 41 Feedback 792 non-null object 42 Further PD 768 non-null object 43 Communication 814 non-null object 44 My say 812 non-null object 45 Information 816 non-null object 46 Kept informed 813 non-null object 47 Wellness programs 766 non-null object 48 Health & Safety 793 non-null object 49 Gender 798 non-null object 50 Age 811 non-null object 51 Aboriginal 16 non-null object 52 Torres Strait 3 non-null object 53 South Sea 7 non-null object 54 Disability 23 non-null object 55 NESB 32 non-null object dtypes: bool(18), int64(1), object(37) memory usage: 258.6+ KB None
Lot of boolean True false columns for factors that could lead to resignation. Few columns at the end have lots of null values.
Will examine the TAFE survey dataframe. Print the first five rows.
print(tafe_survey.head())
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 3 6.341399e+17 Mount Isa Institute of TAFE 4 6.341466e+17 Southern Queensland 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 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 - - ... \ 0 ... 1 ... 2 ... 3 ... 4 ... Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? \ 0 Yes 1 Yes 2 Yes 3 Yes 4 Yes Workplace. Topic:Does your workplace promote and practice the principles of employment equity? \ 0 Yes 1 Yes 2 Yes 3 Yes 4 Yes Workplace. Topic:Does your workplace value the diversity of its employees? \ 0 Yes 1 Yes 2 Yes 3 Yes 4 Yes Workplace. Topic:Would you recommend the Institute as an employer to others? \ 0 Yes 1 Yes 2 Yes 3 Yes 4 Yes Gender. What is your Gender? CurrentAge. Current Age \ 0 Female 26 30 1 NaN NaN 2 NaN NaN 3 NaN NaN 4 Male 41 45 Employment Type. Employment Type Classification. Classification \ 0 Temporary Full-time Administration (AO) 1 NaN NaN 2 NaN NaN 3 NaN NaN 4 Permanent Full-time Teacher (including LVT) LengthofServiceOverall. Overall Length of Service at Institute (in years) \ 0 1-2 1 NaN 2 NaN 3 NaN 4 3-4 LengthofServiceCurrent. Length of Service at current workplace (in years) 0 1-2 1 NaN 2 NaN 3 NaN 4 3-4 [5 rows x 72 columns]
Print information about the TAFE survey dataframe.
print(tafe_survey.info())
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 72 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Record ID 702 non-null float64 1 Institute 702 non-null object 2 WorkArea 702 non-null object 3 CESSATION YEAR 695 non-null float64 4 Reason for ceasing employment 701 non-null object 5 Contributing Factors. Career Move - Public Sector 437 non-null object 6 Contributing Factors. Career Move - Private Sector 437 non-null object 7 Contributing Factors. Career Move - Self-employment 437 non-null object 8 Contributing Factors. Ill Health 437 non-null object 9 Contributing Factors. Maternity/Family 437 non-null object 10 Contributing Factors. Dissatisfaction 437 non-null object 11 Contributing Factors. Job Dissatisfaction 437 non-null object 12 Contributing Factors. Interpersonal Conflict 437 non-null object 13 Contributing Factors. Study 437 non-null object 14 Contributing Factors. Travel 437 non-null object 15 Contributing Factors. Other 437 non-null object 16 Contributing Factors. NONE 437 non-null object 17 Main Factor. Which of these was the main factor for leaving? 113 non-null object 18 InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object 19 InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object 20 InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object 21 InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object 22 InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object 23 InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object 24 InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object 25 InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object 26 InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object 27 InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object 28 InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object 29 InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object 30 InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object 31 WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object 32 WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object 33 WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object 34 WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object 35 WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object 36 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 37 WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object 38 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 39 WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object 40 WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object 41 WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object 42 WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object 43 WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object 44 WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object 45 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 46 WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object 47 WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object 48 Induction. Did you undertake Workplace Induction? 619 non-null object 49 InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object 50 InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object 51 InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object 52 InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 53 InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object 54 InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object 55 InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object 56 InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object 57 InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object 58 InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object 59 InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object 60 InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object 61 Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object 62 Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object 63 Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object 64 Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object 65 Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object 66 Gender. What is your Gender? 596 non-null object 67 CurrentAge. Current Age 596 non-null object 68 Employment Type. Employment Type 596 non-null object 69 Classification. Classification 596 non-null object 70 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object 71 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(70) memory usage: 395.0+ KB None
TAFE survey data has a lot more columns that are titled differently than the DETE survey. Will have to rename columns to merge data.
Read the DETE survey file again, but set values that are Not Stated to null.
dete_survey = pd.read_csv("dete_survey.csv", na_values = "Not Stated")
Drop unnecessary columns from DETE and TAFE survey dataframes.
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)
print(dete_survey_updated.columns)
print(tafe_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') 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')
Want to match columns for DETE and TAFE survey dataframes so can combine into one.
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(" ","_")
tafe_survey_updated.rename({"Record ID":"id","CESSATION YEAR":"cease_date","Reason for ceasing employment":
"separationtype","CurrentAge. Current Age":"age","Employment Type. Employment Type":
"employment_status","Classification. Classification":"position","LengthofService" +
"Overall. Overall Length of Service at Institute (in years)":"institute_service",
"LengthofServiceCurrent. Length of Service at current workplace (in years)":
"role_service","Gender. What is your Gender?":"gender"}, axis=1, inplace=True)
Current state of DETE survey dataframe.
print(dete_survey_updated.head())
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]
Current state of TAFE survey dataframe.
print(tafe_survey_updated.head())
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]
Want to select only resignation data for each dataframe.
dete_resignations = dete_survey_updated[dete_survey_updated["separationtype"].str.contains(r"Resignation")]
tafe_resignations = tafe_survey_updated[tafe_survey_updated["separationtype"] == "Resignation"]
print(dete_resignations["separationtype"].value_counts(),"\n")
print(tafe_resignations["separationtype"].value_counts())
Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Name: separationtype, dtype: int64 Resignation 340 Name: separationtype, dtype: int64
Want to make sure that the years values in each dataframe are logical.
pd.options.mode.chained_assignment = None
dete_resignations["cease_date"] = dete_resignations["cease_date"].str.extract(r"(2[0-9]{3})")
dete_resignations["cease_date"] = dete_resignations["cease_date"].astype(float)
print(dete_resignations["cease_date"].value_counts().sort_index(ascending=True))
print(tafe_resignations["cease_date"].value_counts().sort_index(ascending = True))
2006.0 1 2010.0 2 2012.0 129 2013.0 146 2014.0 22 Name: cease_date, dtype: int64 2009.0 2 2010.0 68 2011.0 116 2012.0 94 2013.0 55 Name: cease_date, dtype: int64
No issues with the years data values.
Add an institute_service column to the dete_resignations dataframe. This will allow for merging of the DETE and TAFE resignations dataframes.
dete_resignations["institute_service"] = dete_resignations["cease_date"] - dete_resignations["dete_start_date"]
print(dete_resignations["institute_service"].value_counts().sort_index().head())
0.0 20 1.0 22 2.0 14 3.0 20 4.0 16 Name: institute_service, dtype: int64
Using the institute_service column, can classify respondents based on their length of employment to ananlyze the data.
Want to create a dissatisfaction column that has True, False, or null entries based on whether or not the employee was dissatisfied or not.
import numpy as np
def update_vals(element):
if str(element) == "-":
return False
elif pd.isnull(element):
return np.nan
return True
two_cols = tafe_resignations[["Contributing Factors. Dissatisfaction","Contributing Factors." +
" Job Dissatisfaction"]]
tafe_resignations[["Contributing Factors. Dissatisfaction","Contributing Factors." +
" Job Dissatisfaction"]] = two_cols.applymap(update_vals)
two_cols = tafe_resignations[["Contributing Factors. Dissatisfaction","Contributing Factors." +
" Job Dissatisfaction"]]
#print(tafe_resignations["Contributing Factors. Dissatisfaction"].value_counts(),"\n")
#print(tafe_resignations["Contributing Factors. Job Dissatisfaction"].value_counts())
tafe_resignations["dissatisfied"] = two_cols.any(axis=1,skipna=False)
cols_dete = 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"]]
dete_resignations["dissatisfied"] = cols_dete.any(axis=1,skipna=False)
print(tafe_resignations["dissatisfied"].value_counts())
print(dete_resignations["dissatisfied"].value_counts())
False 241 True 91 Name: dissatisfied, dtype: int64 False 162 True 149 Name: dissatisfied, dtype: int64
Add institute column to distinguish between the two when merged.
dete_resignations["institute"] = "DETE"
tafe_resignations["institute"] = "TAFE"
Combine the two dataframes.
combined = pd.concat([dete_resignations,tafe_resignations])
combined = combined.dropna(axis=1,thresh=500)
print(combined.head())
id separationtype cease_date position \ 3 4.0 Resignation-Other reasons 2012.0 Teacher 5 6.0 Resignation-Other reasons 2012.0 Guidance Officer 8 9.0 Resignation-Other reasons 2012.0 Teacher 9 10.0 Resignation-Other employer 2012.0 Teacher Aide 11 12.0 Resignation-Move overseas/interstate 2012.0 Teacher employment_status gender age institute_service dissatisfied \ 3 Permanent Full-time Female 36-40 7.0 False 5 Permanent Full-time Female 41-45 18.0 True 8 Permanent Full-time Female 31-35 3.0 False 9 Permanent Part-time Female 46-50 15.0 True 11 Permanent Full-time Male 31-35 3.0 False institute 3 DETE 5 DETE 8 DETE 9 DETE 11 DETE
combined["institute_service"] = combined["institute_service"].astype(str).str.extract(r'(\d+)')
combined["institute_service"] = combined["institute_service"].astype(float)
def career_map(element):
if element < 3:
return "New"
elif 3 <= element <= 6:
return "Experienced"
elif 7 <= element <= 10:
return "Established"
else:
return "Veteran"
combined["service_cat"] = combined["institute_service"].apply(career_map)
print(combined["service_cat"].value_counts())
Veteran 224 New 193 Experienced 172 Established 62 Name: service_cat, dtype: int64
Created a new column that takes the number of years of service for each employee and categorizes them.
combined["dissatisfied"].fillna(False)
print(combined["dissatisfied"].value_counts(dropna=False))
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
def bool_num(element):
if element:
return 1
return 0
combined["dissatisfied_num"] = combined["dissatisfied"].apply(bool_num)
pv_dissatisfied = combined.pivot_table(values="dissatisfied_num", index="service_cat")
import matplotlib.pyplot as plt
%matplotlib inline
pv_dissatisfied.plot(kind="bar",title="Percent Dissatisfied by Service Category",legend=False,rot=0)
plt.ylabel("Percentage Dissatisfied")
plt.xlabel("Service Type")
plt.yticks(ticks=[0,0.1,0.2,0.3,0.4,0.5], labels=["0%","10%","20%","30%","40%","50%"])
plt.show()
Established workers tend to be the most dissatisfied, while veteran workers are the least dissatisfied.
Want to clean the age column now.
print(combined["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 31 35 32 26 30 32 36 40 32 21-25 29 56 or older 29 31-35 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
combined["age"] = combined["age"].astype(str).str.replace(" ","-")
combined.loc[combined["age"] == "61 or older","age"] = "56+"
combined.loc[combined["age"] == "56 or older","age"] = "56+"
combined.loc[combined["age"] == "56-60","age"] = "56+"
combined.loc[combined["age"] == "20 or younger","age"] = "20-"
age = combined[combined["age"].isna() == False]
pv_comb_age = age.pivot_table(index="age",values="dissatisfied_num")
pv_comb_age.plot(kind="bar",title="Percent Dissatisfaction by Age",legend=False,rot=40)
plt.yticks(ticks=[0,0.1,0.2,0.3,0.4,0.5,0.6],labels=["0%","10%","20%","30%","40%","50%","60%"])
plt.show()
Workers aged 21-25 have the highest rates of dissatisfaction, while workers aged under 20 have the lowest.
Thanks for looking through my project, please let me know if you have any feedback.