In this project, we'll play the role of data analyst and pretend our stakeholders want to know the following:
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.
We will import the databases and study them to understand the info they have.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
print (dete_survey.info())
print (dete_survey.head(5))
print (dete_survey.isnull().sum())
<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 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] ID 0 SeparationType 0 Cease Date 0 DETE Start Date 0 Role Start Date 0 Position 5 Classification 367 Region 0 Business Unit 696 Employment Status 5 Career move to public sector 0 Career move to private sector 0 Interpersonal conflicts 0 Job dissatisfaction 0 Dissatisfaction with the department 0 Physical work environment 0 Lack of recognition 0 Lack of job security 0 Work location 0 Employment conditions 0 Maternity/family 0 Relocation 0 Study/Travel 0 Ill Health 0 Traumatic incident 0 Work life balance 0 Workload 0 None of the above 0 Professional Development 14 Opportunities for promotion 87 Staff morale 6 Workplace issue 34 Physical environment 5 Worklife balance 7 Stress and pressure support 12 Performance of supervisor 9 Peer support 10 Initiative 9 Skills 11 Coach 55 Career Aspirations 76 Feedback 30 Further PD 54 Communication 8 My say 10 Information 6 Kept informed 9 Wellness programs 56 Health & Safety 29 Gender 24 Age 11 Aboriginal 806 Torres Strait 819 South Sea 815 Disability 799 NESB 790 dtype: int64
print (tafe_survey.info())
print (tafe_survey.head(5))
print (tafe_survey.isnull().sum())
<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 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] Record ID 0 Institute 0 WorkArea 0 CESSATION YEAR 7 Reason for ceasing employment 1 ... 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
Both the dete_survey and tafe_survey dataframes contain many columns that seem unnecesary for the analysis requested.
It must be noted that column names are different in both dataframes.
A question we must answer:
Also in Dete Survey, there are values declared as Not Stated that must be cleaned to reflect a NaN value. We accomplish this by rereading the 'dete_survey.csv' and indicating to read the 'Not stated' values as 'NaN'
Next we will delete columns we do not need for the analysis:
We will create new files named dete_survey_updated and tafe_survey_updated respectively.
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)
print (dete_survey_updated.info())
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 35 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 822 non-null int64 1 SeparationType 822 non-null object 2 Cease Date 788 non-null object 3 DETE Start Date 749 non-null float64 4 Role Start Date 724 non-null float64 5 Position 817 non-null object 6 Classification 455 non-null object 7 Region 717 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 Gender 798 non-null object 29 Age 811 non-null object 30 Aboriginal 16 non-null object 31 Torres Strait 3 non-null object 32 South Sea 7 non-null object 33 Disability 23 non-null object 34 NESB 32 non-null object dtypes: bool(18), float64(2), int64(1), object(14) memory usage: 123.7+ KB None
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis = 1)
print (tafe_survey_updated.info())
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 23 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 Gender. What is your Gender? 596 non-null object 18 CurrentAge. Current Age 596 non-null object 19 Employment Type. Employment Type 596 non-null object 20 Classification. Classification 596 non-null object 21 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object 22 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(21) memory usage: 126.3+ KB None
Now we need to standardize the column names
dete_survey_updated.columns = dete_survey_updated.columns.str.strip().str.replace('\s', '_').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')
mapping = {'Record ID': 'id','CESSATION YEAR': 'cease_date','Reason for ceasing employment': 'separationtype','Gender. What is your Gender?': 'gender','CurrentAge. Current Age': 'age','Employment Type. Employment Type': 'employment_status','Classification. Classification': 'position','LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service','LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'}
tafe_survey_updated = tafe_survey_updated.rename(mapping, 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')
print (dete_survey_updated.head())
print (tafe_survey_updated.head())
print (dete_survey_updated['separationtype'].head(15))
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] 0 Ill Health Retirement 1 Voluntary Early Retirement (VER) 2 Voluntary Early Retirement (VER) 3 Resignation-Other reasons 4 Age Retirement 5 Resignation-Other reasons 6 Age Retirement 7 Age Retirement 8 Resignation-Other reasons 9 Resignation-Other employer 10 Age Retirement 11 Resignation-Move overseas/interstate 12 Resignation-Other reasons 13 Age Retirement 14 Resignation-Other employer Name: separationtype, dtype: object
Changes to dete_survey_updated:
Changes to tafe_survey_updated:
Rename columns as dete_survey_updated:
'Reason for ceasing employment': 'separationtype'
print (dete_survey_updated['separationtype'].value_counts())
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
tafe_resignations= tafe_survey_updated[tafe_survey_updated['separationtype']=='Resignation'].copy()
print (tafe_resignations['separationtype'].value_counts())
Resignation 340 Name: separationtype, dtype: int64
res_type = {'Resignation-Other reasons','Resignation-Other employer','Resignation-Move overseas/interstate'}
dete_resignations = dete_survey_updated.copy()[(dete_survey_updated['separationtype'].isin(res_type))]
print (dete_resignations['separationtype'].value_counts())
Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Name: separationtype, dtype: int64
In each dataframe only the regisgnation rows in the separationtype column were selected and assigned to a new dataframe (dete_resignations (311 rows) and tafe_resignations (340 rows)) to answer the question:
print (dete_resignations['cease_date'].value_counts(dropna=False))
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 NaN 11 11/2013 9 07/2013 9 10/2013 6 08/2013 4 05/2012 2 05/2013 2 07/2012 1 2010 1 09/2010 1 07/2006 1 Name: cease_date, dtype: int64
dete_resignations['cease_date'] = dete_resignations.cease_date.str.extract(r'([2][0-9][0-9][0-9])', expand=True)
dete_resignations['cease_date']=dete_resignations['cease_date'].astype('float64')
print (dete_resignations['cease_date'].value_counts(dropna=False))
2013.0 146 2012.0 129 2014.0 22 NaN 11 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
print (dete_resignations['cease_date'].value_counts(dropna=False).sort_index(ascending=True))
print (dete_resignations['dete_start_date'].value_counts(dropna=False).sort_index(ascending=True))
print (tafe_resignations['cease_date'].value_counts(dropna=False).sort_index(ascending=True))
2006.0 1 2010.0 2 2012.0 129 2013.0 146 2014.0 22 NaN 11 Name: cease_date, dtype: int64 1963.0 1 1971.0 1 1972.0 1 1973.0 1 1974.0 2 1975.0 1 1976.0 2 1977.0 1 1980.0 5 1982.0 1 1983.0 2 1984.0 1 1985.0 3 1986.0 3 1987.0 1 1988.0 4 1989.0 4 1990.0 5 1991.0 4 1992.0 6 1993.0 5 1994.0 6 1995.0 4 1996.0 6 1997.0 5 1998.0 6 1999.0 8 2000.0 9 2001.0 3 2002.0 6 2003.0 6 2004.0 14 2005.0 15 2006.0 13 2007.0 21 2008.0 22 2009.0 13 2010.0 17 2011.0 24 2012.0 21 2013.0 10 NaN 28 Name: dete_start_date, dtype: int64 2009.0 2 2010.0 68 2011.0 116 2012.0 94 2013.0 55 NaN 5 Name: cease_date, dtype: int64
There are a lot of NaN values in the 3 columns
We will align time of service in both databases, using the tafe institute_service column as a guide.
print (tafe_resignations['institute_service'].value_counts(dropna=False).sort_index(ascending=True))
dete_resignations['institute_service'] = dete_resignations['cease_date']-dete_resignations['dete_start_date']
print (dete_resignations['institute_service'].value_counts(dropna=False).sort_index(ascending=True))
1-2 64 11-20 26 3-4 63 5-6 33 7-10 21 Less than 1 year 73 More than 20 years 10 NaN 50 Name: institute_service, dtype: int64 0.0 20 1.0 22 2.0 14 3.0 20 4.0 16 5.0 23 6.0 17 7.0 13 8.0 8 9.0 14 10.0 6 11.0 4 12.0 6 13.0 8 14.0 6 15.0 7 16.0 5 17.0 6 18.0 5 19.0 3 20.0 7 21.0 3 22.0 6 23.0 4 24.0 4 25.0 2 26.0 2 27.0 1 28.0 2 29.0 1 30.0 2 31.0 1 32.0 3 33.0 1 34.0 1 35.0 1 36.0 2 38.0 1 39.0 3 41.0 1 42.0 1 49.0 1 NaN 38 Name: institute_service, dtype: int64
di = {0.0: "Less than 1 year", 1.0: "1-2", 2.0:"1-2",3.0:"3-4",4.0:"3-4", 5.0:"5-6", 6.0:"5-6",7.0:"7-10", 8.0:"7-10", 9.0:"7-10",10.0:"7-10", 11.0:"11-20",12.0:"11-20",13.0:"11-20",14.0:"11-20",15.0:"11-20",16.0:"11-20",17.0:"11-20",18.0:"11-20",19.0:"11-20",20.0:"11-20",21:"More than 20 years",22:"More than 20 years",23:"More than 20 years",24:"More than 20 years",25:"More than 20 years",26:"More than 20 years",27:"More than 20 years",28:"More than 20 years",29:"More than 20 years",30:"More than 20 years",31:"More than 20 years",32:"More than 20 years",33:"More than 20 years",34:"More than 20 years",35:"More than 20 years",36:"More than 20 years",38:"More than 20 years",39:"More than 20 years",41:"More than 20 years",42:"More than 20 years",49:"More than 20 years"}
dete_resignations['institute_service'] = dete_resignations['institute_service'].map(di)
print (dete_resignations['institute_service'].value_counts(dropna=False).sort_index(ascending=True))
1-2 36 11-20 57 3-4 36 5-6 40 7-10 41 Less than 1 year 20 More than 20 years 43 NaN 38 Name: institute_service, dtype: int64
tafe_survey_updated:
dete_survey_updated:
Will create a new column called dissatisfied in each dataframe that will map if in any column there is a True, a False or NaN, and then copy to a new dataframe with ending _up.
print (tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts(dropna=False).sort_index(ascending=True))
print (tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts(dropna=False).sort_index(ascending=True))
- 277 Contributing Factors. Dissatisfaction 55 NaN 8 Name: Contributing Factors. Dissatisfaction, dtype: int64 - 270 Job Dissatisfaction 62 NaN 8 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
def update_vals(x):
if pd.isnull(x):
return np.nan
if x == '-':
return False
else:
return True
factors = ['Contributing Factors. Dissatisfaction','Contributing Factors. Job Dissatisfaction']
tafe_resignations[factors] = tafe_resignations[factors].applymap(update_vals)
print (tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts(dropna=False).sort_index(ascending=True))
print (tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts(dropna=False).sort_index(ascending=True))
False 277 True 55 NaN 8 Name: Contributing Factors. Dissatisfaction, dtype: int64 False 270 True 62 NaN 8 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
print (tafe_resignations.info())
<class 'pandas.core.frame.DataFrame'> Int64Index: 340 entries, 3 to 701 Data columns (total 23 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 340 non-null float64 1 Institute 340 non-null object 2 WorkArea 340 non-null object 3 cease_date 335 non-null float64 4 separationtype 340 non-null object 5 Contributing Factors. Career Move - Public Sector 332 non-null object 6 Contributing Factors. Career Move - Private Sector 332 non-null object 7 Contributing Factors. Career Move - Self-employment 332 non-null object 8 Contributing Factors. Ill Health 332 non-null object 9 Contributing Factors. Maternity/Family 332 non-null object 10 Contributing Factors. Dissatisfaction 332 non-null object 11 Contributing Factors. Job Dissatisfaction 332 non-null object 12 Contributing Factors. Interpersonal Conflict 332 non-null object 13 Contributing Factors. Study 332 non-null object 14 Contributing Factors. Travel 332 non-null object 15 Contributing Factors. Other 332 non-null object 16 Contributing Factors. NONE 332 non-null object 17 gender 290 non-null object 18 age 290 non-null object 19 employment_status 290 non-null object 20 position 290 non-null object 21 institute_service 290 non-null object 22 role_service 290 non-null object dtypes: float64(2), object(21) memory usage: 63.8+ KB None
tafe_resignations['dissatisfied']= tafe_resignations[factors].any(axis=1, skipna=False)
print (tafe_resignations['dissatisfied'].value_counts(dropna=False).sort_index(ascending=True))
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
factors_dete = ['job_dissatisfaction', 'dissatisfaction_with_the_department', 'physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location','employment_conditions','work_life_balance','workload']
print (dete_resignations[factors_dete[5]].value_counts(dropna=False).sort_index(ascending=True))
dete_resignations['dissatisfied']= dete_resignations[factors_dete].any(axis=1, skipna=False)
print (dete_resignations['dissatisfied'].value_counts(dropna=False).sort_index(ascending=True))
False 293 True 18 Name: work_location, dtype: int64 False 162 True 149 Name: dissatisfied, dtype: int64
dete_resignations_up = dete_resignations.copy()
tafe_resignations_up = tafe_resignations.copy()
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
print (dete_resignations_up.head())
print (tafe_resignations_up.head())
id separationtype cease_date dete_start_date \ 3 4 Resignation-Other reasons 2012.0 2005.0 5 6 Resignation-Other reasons 2012.0 1994.0 8 9 Resignation-Other reasons 2012.0 2009.0 9 10 Resignation-Other employer 2012.0 1997.0 11 12 Resignation-Move overseas/interstate 2012.0 2009.0 role_start_date position classification region \ 3 2006.0 Teacher Primary Central Queensland 5 1997.0 Guidance Officer NaN Central Office 8 2009.0 Teacher Secondary North Queensland 9 2008.0 Teacher Aide NaN NaN 11 2009.0 Teacher Secondary Far North Queensland business_unit employment_status ... gender age aboriginal \ 3 NaN Permanent Full-time ... Female 36-40 NaN 5 Education Queensland Permanent Full-time ... Female 41-45 NaN 8 NaN Permanent Full-time ... Female 31-35 NaN 9 NaN Permanent Part-time ... Female 46-50 NaN 11 NaN Permanent Full-time ... Male 31-35 NaN torres_strait south_sea disability nesb institute_service \ 3 NaN NaN NaN NaN 7-10 5 NaN NaN NaN NaN 11-20 8 NaN NaN NaN NaN 3-4 9 NaN NaN NaN NaN 11-20 11 NaN NaN NaN NaN 3-4 dissatisfied institute 3 False DETE 5 True DETE 8 False DETE 9 True DETE 11 False DETE [5 rows x 38 columns] id Institute \ 3 6.341399e+17 Mount Isa Institute of TAFE 4 6.341466e+17 Southern Queensland Institute of TAFE 5 6.341475e+17 Southern Queensland Institute of TAFE 6 6.341520e+17 Barrier Reef Institute of TAFE 7 6.341537e+17 Southern Queensland Institute of TAFE WorkArea cease_date separationtype \ 3 Non-Delivery (corporate) 2010.0 Resignation 4 Delivery (teaching) 2010.0 Resignation 5 Delivery (teaching) 2010.0 Resignation 6 Non-Delivery (corporate) 2010.0 Resignation 7 Delivery (teaching) 2010.0 Resignation Contributing Factors. Career Move - Public Sector \ 3 - 4 - 5 - 6 - 7 - Contributing Factors. Career Move - Private Sector \ 3 - 4 Career Move - Private Sector 5 - 6 Career Move - Private Sector 7 - Contributing Factors. Career Move - Self-employment \ 3 - 4 - 5 - 6 - 7 - Contributing Factors. Ill Health Contributing Factors. Maternity/Family \ 3 - - 4 - - 5 - - 6 - Maternity/Family 7 - - ... Contributing Factors. Other Contributing Factors. NONE gender \ 3 ... - - NaN 4 ... - - Male 5 ... Other - Female 6 ... Other - Male 7 ... Other - Male age employment_status position \ 3 NaN NaN NaN 4 41 45 Permanent Full-time Teacher (including LVT) 5 56 or older Contract/casual Teacher (including LVT) 6 20 or younger Temporary Full-time Administration (AO) 7 46 50 Permanent Full-time Teacher (including LVT) institute_service role_service dissatisfied institute 3 NaN NaN False TAFE 4 3-4 3-4 False TAFE 5 7-10 7-10 False TAFE 6 3-4 3-4 False TAFE 7 3-4 3-4 False TAFE [5 rows x 25 columns]
print (dete_resignations_up['institute_service'].value_counts(dropna=False).sort_index(ascending=True))
print (dete_resignations_up['institute_service'].count())
print (tafe_resignations_up['institute_service'].value_counts(dropna=False).sort_index(ascending=True))
print (tafe_resignations_up['institute_service'].count())
1-2 36 11-20 57 3-4 36 5-6 40 7-10 41 Less than 1 year 20 More than 20 years 43 NaN 38 Name: institute_service, dtype: int64 273 1-2 64 11-20 26 3-4 63 5-6 33 7-10 21 Less than 1 year 73 More than 20 years 10 NaN 50 Name: institute_service, dtype: int64 290
combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index=True)
print (combined.shape)
print (combined.count())
(651, 53) id 651 separationtype 651 cease_date 635 dete_start_date 283 role_start_date 271 position 598 classification 161 region 265 business_unit 32 employment_status 597 career_move_to_public_sector 311 career_move_to_private_sector 311 interpersonal_conflicts 311 job_dissatisfaction 311 dissatisfaction_with_the_department 311 physical_work_environment 311 lack_of_recognition 311 lack_of_job_security 311 work_location 311 employment_conditions 311 maternity/family 311 relocation 311 study/travel 311 ill_health 311 traumatic_incident 311 work_life_balance 311 workload 311 none_of_the_above 311 gender 592 age 596 aboriginal 7 torres_strait 0 south_sea 3 disability 8 nesb 9 institute_service 563 dissatisfied 643 institute 651 Institute 340 WorkArea 340 Contributing Factors. Career Move - Public Sector 332 Contributing Factors. Career Move - Private Sector 332 Contributing Factors. Career Move - Self-employment 332 Contributing Factors. Ill Health 332 Contributing Factors. Maternity/Family 332 Contributing Factors. Dissatisfaction 332 Contributing Factors. Job Dissatisfaction 332 Contributing Factors. Interpersonal Conflict 332 Contributing Factors. Study 332 Contributing Factors. Travel 332 Contributing Factors. Other 332 Contributing Factors. NONE 332 role_service 290 dtype: int64
combined_updated = combined.dropna(axis=1,thresh=500)
print (combined_updated.info())
<class 'pandas.core.frame.DataFrame'> RangeIndex: 651 entries, 0 to 650 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 651 non-null float64 1 separationtype 651 non-null object 2 cease_date 635 non-null float64 3 position 598 non-null object 4 employment_status 597 non-null object 5 gender 592 non-null object 6 age 596 non-null object 7 institute_service 563 non-null object 8 dissatisfied 643 non-null object 9 institute 651 non-null object dtypes: float64(2), object(8) memory usage: 51.0+ KB None
Combined the dataframes, drop any columns with less than 500 non null values and assigned to combined_update
print (combined_updated['institute_service'].value_counts())
print (combined_updated['institute_service'].count())
1-2 100 3-4 99 Less than 1 year 93 11-20 83 5-6 73 7-10 62 More than 20 years 53 Name: institute_service, dtype: int64 563
di = {'Less than 1 year':'New','1-2':'New','3-4':'Experienced','5-6':'Experienced','7-10':'Established', '11-20':'Veteran', 'More than 20 years':'Veteran'}
new_col = combined_updated['institute_service'].copy()
new_col = new_col.map(di)
combined_updated = combined_updated.assign(service_cat=new_col.values)
print (combined_updated['service_cat'].value_counts())
print (combined_updated['service_cat'].count())
New 193 Experienced 172 Veteran 136 Established 62 Name: service_cat, dtype: int64 563
print (combined_updated['dissatisfied'].value_counts(dropna=False))
combined_updated.loc[:,'dissatisfied']=combined_updated['dissatisfied'].fillna(value=False)
print (combined_updated['dissatisfied'].value_counts(dropna=False))
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64 False 411 True 240 Name: dissatisfied, dtype: int64
pv_melt = pd.pivot_table(combined_updated,index = 'service_cat', values = 'dissatisfied')
print(pv_melt)
pv_melt.plot(kind ='bar', y= 'dissatisfied', legend = False)
dissatisfied service_cat Established 0.516129 Experienced 0.343023 New 0.295337 Veteran 0.485294
<matplotlib.axes._subplots.AxesSubplot at 0x7f6881e02ee0>
From the pivot table and the bar chart above we can answer the first question of our project and it is that the more experienced you are the more dissatisfied you will be. The level of dissatisfaction is lower for New and Experienced while for Veterain and Established they have the highest level of dissatisfied employees.
We will try to answer also the question of dissatisfaction related to employee age. For that we will conduct a similar exercise cleaning and then creating a pivot table and plot.
print (combined_updated['age'].value_counts(dropna=False).sort_index(ascending=True))
20 or younger 10 21 25 33 21-25 29 26 30 32 26-30 35 31 35 32 31-35 29 36 40 32 36-40 41 41 45 45 41-45 48 46 50 39 46-50 42 51-55 71 56 or older 29 56-60 26 61 or older 23 NaN 55 Name: age, dtype: int64
di = {'20 or younger':'20 or younger','21 25':'21-30','21-25':'21-30','26 30':'21-30','26-30':'21-30','31 35':'31-40','31-35':'31-40','36 40':'31-40','36-40':'31-40','41 45':'41-50','41-45':'41-50','46 50':'41-50','46-50':'41-50','51-55':'51 or older','56 or older':'51 or older','56-60':'51 or older','61 or older':'51 or older'}
combined_updated.loc[:,'age'] = combined_updated['age'].map(di)
print (combined_updated['age'].value_counts(dropna=False).sort_index(ascending=True))
20 or younger 10 21-30 129 31-40 134 41-50 174 51 or older 149 NaN 55 Name: age, dtype: int64
pv_melt = pd.pivot_table(combined_updated,index = 'age', values = 'dissatisfied')
%matplotlib inline
print(pv_melt)
pv_melt.plot(kind ='bar', y= 'dissatisfied', legend = False)
dissatisfied age 20 or younger 0.200000 21-30 0.364341 31-40 0.358209 41-50 0.379310 51 or older 0.422819
<matplotlib.axes._subplots.AxesSubplot at 0x7f684525f760>
From the pivot table and the bar chart above we can answer the new question of our project and it is that the older you are the more dissatisfied you will be.
We will try to answer also the question of dissatisfaction related to institute of employment. For that we will conduct a similar exercise creating a pivot table and plot using as variable the institute of employment.
pv_melt = pd.pivot_table(combined_updated,index = 'institute', values = 'dissatisfied')
print(pv_melt)
pv_melt.plot(kind ='bar', y= 'dissatisfied', legend = False)
dissatisfied institute DETE 0.479100 TAFE 0.267647
<matplotlib.axes._subplots.AxesSubplot at 0x7f6845269520>
Fewer employees who worked for TAFE have some kind of dissatisfaction while DETE employees have more dissatisfaction.