We will take a look on employee exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia.
Our aim is to answer on following questions:
We'll work on combined 2 data sets (dete_survey.csv and tafe_survey.csv) to try answer on questions above.
A data dictionary wasn't provided with the dataset. For this project, we'll use our general knowledge to define couple columns.
Column name | Description |
---|---|
ID | An id used to identify the participant of the survey |
SeparationType | The reason why the person's employment ended |
Cease Date | The year or month the person's employment ended |
DETE Start Date | The year the person began employment with the DETE |
Column name | Description |
---|---|
Record ID | An id used to identify the participant of the survey |
Reason for ceasing employment | The reason why the person's employment ended |
LengthofServiceOverall. Overall Length of Service at Institute (in years) | The length of the person's employment (in years) |
You can find the TAFE exit survey here and the survey for the DETE here.
Let's start with opening data sets and take first look at both of them.
#import pandas liblary
import pandas as pd
pd.options.display.max_columns = 150 # to avoid truncated output
#opening dete_survey.csv file
dete_survey = pd.read_csv('dete_survey.csv', encoding='UTF-8')
#opening tafe_survey.csv file
tafe_survey = pd.read_csv('tafe_survey.csv', encoding='UTF-8')
#display info about dete_survey columns
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
Dete_survey data set contains with 822 rows and 56 columns. Only the first column ID has numeric values, the rest are object or bool type. Some of columns have many missing values, like Torres Strait and South Sea. We have to examine columns more closely.
#display first 5 rows in dete_survey
dete_survey.head()
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 | Professional Development | Opportunities for promotion | Staff morale | Workplace issue | Physical environment | Worklife balance | Stress and pressure support | Performance of supervisor | Peer support | Initiative | Skills | Coach | Career Aspirations | Feedback | Further PD | Communication | My say | Information | 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 | True | False | False | True | False | False | True | False | False | False | False | False | False | False | False | False | False | True | A | A | N | N | N | A | A | A | A | N | N | N | A | A | A | N | A | A | 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 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | A | N | N | N | N | A | A | A | N | N | N | A | A | A | N | A | A | 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 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | N | N | N | N | N | N | N | N | N | N | N | N | N | N | N | A | A | N | 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 | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | A | N | N | N | A | A | N | N | A | A | A | A | A | A | A | A | A | A | 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 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | A | A | N | N | D | D | N | A | A | A | A | A | A | SA | SA | D | D | A | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
#display info about tafe_survey columns
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
#display first 5 rows in tafe_survey
tafe_survey.head()
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | Contributing Factors. Dissatisfaction | Contributing Factors. Job Dissatisfaction | Contributing Factors. Interpersonal Conflict | Contributing Factors. Study | Contributing Factors. Travel | Contributing Factors. Other | Contributing Factors. NONE | Main Factor. Which of these was the main factor for leaving? | InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction | InstituteViews. Topic:2. I was given access to skills training to help me do my job better | InstituteViews. Topic:3. I was given adequate opportunities for personal development | InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% | InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had | InstituteViews. Topic:6. The organisation recognised when staff did good work | InstituteViews. Topic:7. Management was generally supportive of me | InstituteViews. Topic:8. Management was generally supportive of my team | InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me | InstituteViews. Topic:10. Staff morale was positive within the Institute | InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly | InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently | InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly | WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit | WorkUnitViews. Topic:15. I worked well with my colleagues | WorkUnitViews. Topic:16. My job was challenging and interesting | WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work | WorkUnitViews. Topic:18. I had sufficient contact with other people in my job | WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job | WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job | 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] | WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job | WorkUnitViews. Topic:23. My job provided sufficient variety | WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job | WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction | WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance | WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area | 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 | WorkUnitViews. Topic:29. There was adequate communication between staff in my unit | WorkUnitViews. Topic:30. Staff morale was positive within my work unit | Induction. Did you undertake Workplace Induction? | InductionInfo. Topic:Did you undertake a Corporate Induction? | InductionInfo. Topic:Did you undertake a Institute Induction? | InductionInfo. Topic: Did you undertake Team Induction? | InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? | InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? | InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? | InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? | InductionInfo. On-line Topic:Did you undertake a Institute Induction? | InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? | InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? | InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] | InductionInfo. Induction Manual Topic: Did you undertake Team Induction? | Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? | 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 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Agree | Agree | Agree | Neutral | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Neutral | Agree | Agree | Yes | Yes | Yes | Yes | Face to Face | - | - | Face to Face | - | - | Face to Face | - | - | Yes | 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 | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Agree | Agree | Agree | Disagree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Agree | Agree | Strongly Agree | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Yes | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | - | - | - | - | - | - | NONE | NaN | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Agree | Agree | Agree | Agree | Neutral | Neutral | Strongly Agree | Strongly Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | No | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Yes | 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 | - | - | - | - | - | - | - | - | - | Travel | - | - | NaN | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Yes | No | Yes | Yes | - | - | - | NaN | - | - | - | - | - | Yes | 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 | - | - | - | - | - | - | - | - | - | - | NaN | Agree | Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Yes | Yes | Yes | Yes | - | - | Induction Manual | Face to Face | - | - | Face to Face | - | - | Yes | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
#display Nan values in tafe_survey
tafe_survey.isnull().sum().sort_values(ascending=False)
Main Factor. Which of these was the main factor for leaving? 589 InductionInfo. Topic:Did you undertake a Corporate Induction? 270 Contributing Factors. Ill Health 265 Contributing Factors. Maternity/Family 265 Contributing Factors. Career Move - Public Sector 265 ... CESSATION YEAR 7 Reason for ceasing employment 1 WorkArea 0 Institute 0 Record ID 0 Length: 72, dtype: int64
First look at both data sets, give us following observations:
'Not Stated'
values which indicate values are missing, but they aren't represented as NaN
,'-'
character and they are not marked as NaN
values,We will read our data set again, but this time we'll add na_values
parameter.
#opening dete_survey with na_values parameter. 'Not Stated' values are treated as NaN now'
dete_survey = pd.read_csv('dete_survey.csv', na_values='Not Stated')
We will remove some columns, because many of them will not have big influence on our goals. We will focous on these columns which contains similar factors in both data sets.
#delete columns which we won't use in analysys in dete_survey
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)
#delete columns which we won't use in analysys in tafe_survey
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis=1)
#check updated dete_survey columns
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
Our dete_survey
data set contains 35 columns now and almost half of them are bool type.
#check updated tafe_survey columns
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
tafe_survey
data set has 23 columns after change.
As we mentioned above, both data sets have similar contributing factors, but column names are different. We'll change their names to combaine date sets.
First, we will do standardize the column names in dete_survey_updated
.
#standardization column names in dete_survey_updated
dete_survey_updated.columns = (dete_survey_updated.columns.str.lower() #lowercase capitalization
.str.strip() #Remove any trailing whitespace from the end of the strings.
.str.replace(' ','_') #Replace spaces with underscores ('_')
)
#display columns after standardization in dete_survey_updated
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')
Next, we'll change some column names in tafe_survey_updated
. We could noticed that many column names are written as whole question from survey and they're too long.
#changing column names in tafe_survey_updated
tafe_survey_updated = tafe_survey_updated.rename(columns={'Record ID': 'id',
'CESSATION YEAR': 'cease_date',
'Reason for ceasing employment': 'separationtype',
'Gender. What is your Gender?': 'gender',
'CurrentAge. Current Age': 'age',
'Employment Type. Employment Type': 'employment_status',
'Classification. Classification': 'position',
'LengthofServiceOverall. Overall Length of Service at Institute (in years)': 'institute_service',
'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'
})
#display columns after renaming in tafe_survey_updated
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')
Let's check separationtype
column in both data set
#checking unique values in dete_survey_updated
dete_survey_updated['separationtype'].value_counts()
Age Retirement 285 Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
#checking unique values in tafe_survey_updated
tafe_survey_updated['separationtype'].value_counts()
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 Name: separationtype, dtype: int64
We see that there are 3 types of Resignation in dete_survey_updated
. In tafe_survey_updated
there is one value with resigantion.
To work easier with both data sets we will treat these 3 types of Resignation in dete_survey_updated
as one value - Resignation
.
#convert all values which contains world Resignation in one value
dete_survey_updated['separationtype'] = dete_survey_updated['separationtype'].str.split('-').str.get(0)
#display values in separationtype column in dete_survey_updated after changes
dete_survey_updated['separationtype'].value_counts()
Resignation 311 Age Retirement 285 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
After changes, we can select only the data for survey respondents who have a Resignation separation type in both data sets.
#selecting rows with Resignation value in separationtype column for each DataFrame
#make copy to avoid SettingWithCopy Warning
dete_resignations = dete_survey_updated[dete_survey_updated['separationtype'] == 'Resignation'].copy()
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation'].copy()
Let's look at cease_date
and dete_start_date
columns to verify if the years look reliably.
First, we have to convert values to float in dete_resignations
dataframe.
#check unique values
dete_resignations['cease_date'].value_counts()
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 09/2013 11 11/2013 9 07/2013 9 10/2013 6 08/2013 4 05/2013 2 05/2012 2 09/2010 1 07/2012 1 07/2006 1 2010 1 Name: cease_date, dtype: int64
Many values contain also number of mounth, we can remove them at leave only year.
#convering cease_date column to float
dete_resignations['cease_date'] = (dete_resignations['cease_date'].str.split('/') #split by character '/'
.str.get(-1) #get last item in each value
.astype(float) #convert to float
)
#sort unique values in cease_date column
dete_resignations_end_sorted = dete_resignations['cease_date'].value_counts().sort_index(ascending = False)
#check values after converting
dete_resignations_end_sorted
2014.0 22 2013.0 146 2012.0 129 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
#sort unique values in dete_start_date column
dete_resignations_start_sorted = dete_resignations['dete_start_date'].value_counts().sort_index(ascending = False)
dete_resignations_start_sorted
2013.0 10 2012.0 21 2011.0 24 2010.0 17 2009.0 13 2008.0 22 2007.0 21 2006.0 13 2005.0 15 2004.0 14 2003.0 6 2002.0 6 2001.0 3 2000.0 9 1999.0 8 1998.0 6 1997.0 5 1996.0 6 1995.0 4 1994.0 6 1993.0 5 1992.0 6 1991.0 4 1990.0 5 1989.0 4 1988.0 4 1987.0 1 1986.0 3 1985.0 3 1984.0 1 1983.0 2 1982.0 1 1980.0 5 1977.0 1 1976.0 2 1975.0 1 1974.0 2 1973.0 1 1972.0 1 1971.0 1 1963.0 1 Name: dete_start_date, dtype: int64
#sort unique values in cease_date column
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
#plot values of cease_date column in tafe_resignations
tafe_resignations.boxplot(column=['cease_date'])
<matplotlib.axes._subplots.AxesSubplot at 0x7f175f11e910>
#plot values of cease_date column in dete_resignations
dete_resignations.boxplot(column=['cease_date'])
<matplotlib.axes._subplots.AxesSubplot at 0x7f175f11e910>
We compared cease_date
column in both Dataframes. All values seem to be real.
However, we see that some values are not a equal. For example, in tafe_resignations
there are some values with 2009 year and there not in dete_resignations
. Most popular resigantion year is 2013 in dete_resignations
and 2011 in tafe_resignations
.
One of our goal, as a remainder is:
To ask on question above, we need an information about period of employment. In tafe_resignations
column with these values already exist.
Let' calculate the period of service each employee in dete_resignations
. We have 2 columns here: dete_start_date
and cease_date
. They contain floats so we have to only substract them.
Let's first check if dates are real. Period of employment can't be a negative value, so we will check if any value in dete_start_date
column is bigger than cease_date
column.
#compare values in cease_date column with dete_start_date column
dete_resignations[dete_resignations.cease_date < dete_resignations.dete_start_date].copy()
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 |
---|
All look ok, so we can calculate period of employment.
#Subtracting values in cease_date column from cease_date and create new column -> institute_service
dete_resignations['institute_service'] = dete_resignations.cease_date - dete_resignations.dete_start_date
#display 5 first rows
dete_resignations.head()
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | career_move_to_public_sector | career_move_to_private_sector | interpersonal_conflicts | job_dissatisfaction | dissatisfaction_with_the_department | physical_work_environment | lack_of_recognition | lack_of_job_security | work_location | employment_conditions | maternity/family | relocation | study/travel | ill_health | traumatic_incident | work_life_balance | workload | none_of_the_above | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | institute_service | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 4 | Resignation | 2012.0 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN | 7.0 |
5 | 6 | Resignation | 2012.0 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | False | True | False | False | False | False | False | False | False | True | True | False | False | False | False | False | False | False | Female | 41-45 | NaN | NaN | NaN | NaN | NaN | 18.0 |
8 | 9 | Resignation | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | Female | 31-35 | NaN | NaN | NaN | NaN | NaN | 3.0 |
9 | 10 | Resignation | 2012.0 | 1997.0 | 2008.0 | Teacher Aide | NaN | NaN | NaN | Permanent Part-time | False | False | True | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | Female | 46-50 | NaN | NaN | NaN | NaN | NaN | 15.0 |
11 | 12 | Resignation | 2012.0 | 2009.0 | 2009.0 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | False | False | False | False | False | False | False | False | False | False | True | True | False | False | False | False | False | False | Male | 31-35 | NaN | NaN | NaN | NaN | NaN | 3.0 |
Now, we will identify dissatisfied employees. To do this, we have chosen following columns, which in our opinion most contributes to dissatisfaction:
First, we'll look at tafe_resignations
DataFrame.
# check unique values in Contributing Factors. Dissatisfaction column
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts(dropna=False) #set dropna parameter to see NaN values
- 277 Contributing Factors. Dissatisfaction 55 NaN 8 Name: Contributing Factors. Dissatisfaction, dtype: int64
# check unique values in Contributing Factors. Job Dissatisfaction
tafe_resignations['Contributing Factors. Job Dissatisfaction'].value_counts(dropna=False) #set dropna parameter to see NaN values
- 270 Job Dissatisfaction 62 NaN 8 Name: Contributing Factors. Job Dissatisfaction, dtype: int64
There are only 2 values in both columns. One: '-'
character, which means False and second values is the same like column name, so it means that this is True. There are also few NaN
values.
We are going to create a new column with marking if employee's resigantion is due to any kind of dissatisfaction.
First, we have to make a function to convert values on True
or False
.
#import numpy liblary to use NaN values
import numpy as np
#creating update_vals function
def update_vals(value):
if pd.isnull(value):
return np.nan ##use NaN values
elif value == '-':
return False
else:
return True
Our created funcation is ready to apply. We can simply convert values to bool type. Next, we can use df.any()
function. If any column in tafe_resignations
(we chosen columns above) will has a True
value the row in dissatisfied
column will be True also. If all values are False
, then it means that the resigantion is not due to dissatisfaction and it is marked aas False. NaN
values will remain unchanged.
tafe_resignations['dissatisfied'] = (tafe_resignations[['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']]
.applymap(update_vals) #apply function update_vals
.any(axis=1, skipna=False) #set row in dissatisfied column (True, False or NaN (skipna parameter))
)
tafe_resignations_up = tafe_resignations.copy() #make copy to avoid SettingWithCopy Warning
#dispaly values in dissatisfied column
tafe_resignations_up['dissatisfied'].value_counts(dropna=False)
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
#show columns in dete_resignations
dete_resignations.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 311 entries, 3 to 821 Data columns (total 36 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 311 non-null int64 1 separationtype 311 non-null object 2 cease_date 300 non-null float64 3 dete_start_date 283 non-null float64 4 role_start_date 271 non-null float64 5 position 308 non-null object 6 classification 161 non-null object 7 region 265 non-null object 8 business_unit 32 non-null object 9 employment_status 307 non-null object 10 career_move_to_public_sector 311 non-null bool 11 career_move_to_private_sector 311 non-null bool 12 interpersonal_conflicts 311 non-null bool 13 job_dissatisfaction 311 non-null bool 14 dissatisfaction_with_the_department 311 non-null bool 15 physical_work_environment 311 non-null bool 16 lack_of_recognition 311 non-null bool 17 lack_of_job_security 311 non-null bool 18 work_location 311 non-null bool 19 employment_conditions 311 non-null bool 20 maternity/family 311 non-null bool 21 relocation 311 non-null bool 22 study/travel 311 non-null bool 23 ill_health 311 non-null bool 24 traumatic_incident 311 non-null bool 25 work_life_balance 311 non-null bool 26 workload 311 non-null bool 27 none_of_the_above 311 non-null bool 28 gender 302 non-null object 29 age 306 non-null object 30 aboriginal 7 non-null object 31 torres_strait 0 non-null object 32 south_sea 3 non-null object 33 disability 8 non-null object 34 nesb 9 non-null object 35 institute_service 273 non-null float64 dtypes: bool(18), float64(4), int64(1), object(13) memory usage: 51.6+ KB
All columns which we have chosen to our analysy are bool type, so only we have to do is creat also new column dissatisfied
in dete_resignations
and use df.any()
.
#create dissatisfied column and apply True, False or NaN value to each row
dete_resignations['dissatisfied'] = 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']].any(axis=1, skipna=False)
dete_resignations_up = dete_resignations.copy() #make copy to avoid SettingWithCopy Warning
#dispaly values in dissatisfied column
dete_resignations_up['dissatisfied'].value_counts(dropna=False)
False 162 True 149 Name: dissatisfied, dtype: int64
tafe_resignations_up.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 340 entries, 3 to 701 Data columns (total 24 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 23 dissatisfied 332 non-null object dtypes: float64(2), object(22) memory usage: 66.4+ KB
It's time to combine both DataFrames. First, we will add new column institute
with DATE
value for dete_resignations_up
and TAFE
for tafe_resignations_up
. We'll know which row come from which DataFrame.
dete_resignations_up['institute'] = 'DATE' #create new column and add DATE value to each row
tafe_resignations_up['institute'] = 'TAFE' #create new column and add TAFE value to each row
#combining DataFrames with pd.concat
combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index=True) #set ignore_index on True to sort out indexes
#check columns in combined
combined.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 651 entries, 0 to 650 Data columns (total 53 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 dete_start_date 283 non-null float64 4 role_start_date 271 non-null float64 5 position 598 non-null object 6 classification 161 non-null object 7 region 265 non-null object 8 business_unit 32 non-null object 9 employment_status 597 non-null object 10 career_move_to_public_sector 311 non-null object 11 career_move_to_private_sector 311 non-null object 12 interpersonal_conflicts 311 non-null object 13 job_dissatisfaction 311 non-null object 14 dissatisfaction_with_the_department 311 non-null object 15 physical_work_environment 311 non-null object 16 lack_of_recognition 311 non-null object 17 lack_of_job_security 311 non-null object 18 work_location 311 non-null object 19 employment_conditions 311 non-null object 20 maternity/family 311 non-null object 21 relocation 311 non-null object 22 study/travel 311 non-null object 23 ill_health 311 non-null object 24 traumatic_incident 311 non-null object 25 work_life_balance 311 non-null object 26 workload 311 non-null object 27 none_of_the_above 311 non-null object 28 gender 592 non-null object 29 age 596 non-null object 30 aboriginal 7 non-null object 31 torres_strait 0 non-null object 32 south_sea 3 non-null object 33 disability 8 non-null object 34 nesb 9 non-null object 35 institute_service 563 non-null object 36 dissatisfied 643 non-null object 37 institute 651 non-null object 38 Institute 340 non-null object 39 WorkArea 340 non-null object 40 Contributing Factors. Career Move - Public Sector 332 non-null object 41 Contributing Factors. Career Move - Private Sector 332 non-null object 42 Contributing Factors. Career Move - Self-employment 332 non-null object 43 Contributing Factors. Ill Health 332 non-null object 44 Contributing Factors. Maternity/Family 332 non-null object 45 Contributing Factors. Dissatisfaction 332 non-null object 46 Contributing Factors. Job Dissatisfaction 332 non-null object 47 Contributing Factors. Interpersonal Conflict 332 non-null object 48 Contributing Factors. Study 332 non-null object 49 Contributing Factors. Travel 332 non-null object 50 Contributing Factors. Other 332 non-null object 51 Contributing Factors. NONE 332 non-null object 52 role_service 290 non-null object dtypes: float64(4), object(49) memory usage: 269.7+ KB
There are many columns which we don't need in our analysys, so we we will drop columns which contain les than 500 non null values.
#check which columns have less than 500 no null values
combined.notnull().sum().sort_values()
torres_strait 0 south_sea 3 aboriginal 7 disability 8 nesb 9 business_unit 32 classification 161 region 265 role_start_date 271 dete_start_date 283 role_service 290 none_of_the_above 311 work_life_balance 311 traumatic_incident 311 ill_health 311 study/travel 311 relocation 311 maternity/family 311 employment_conditions 311 workload 311 lack_of_job_security 311 career_move_to_public_sector 311 career_move_to_private_sector 311 interpersonal_conflicts 311 work_location 311 dissatisfaction_with_the_department 311 physical_work_environment 311 lack_of_recognition 311 job_dissatisfaction 311 Contributing Factors. Job Dissatisfaction 332 Contributing Factors. Travel 332 Contributing Factors. Maternity/Family 332 Contributing Factors. Ill Health 332 Contributing Factors. Career Move - Self-employment 332 Contributing Factors. Career Move - Private Sector 332 Contributing Factors. Career Move - Public Sector 332 Contributing Factors. Dissatisfaction 332 Contributing Factors. Other 332 Contributing Factors. Interpersonal Conflict 332 Contributing Factors. NONE 332 Contributing Factors. Study 332 Institute 340 WorkArea 340 institute_service 563 gender 592 age 596 employment_status 597 position 598 cease_date 635 dissatisfied 643 separationtype 651 institute 651 id 651 dtype: int64
#droping columns with less than 500 no null values
combined_updated = combined.dropna(axis=1, thresh=500).copy() #make copy to avoid SettingWithCopy Warning
combined_updated.head()
id | separationtype | cease_date | position | employment_status | gender | age | institute_service | dissatisfied | institute | |
---|---|---|---|---|---|---|---|---|---|---|
0 | 4.0 | Resignation | 2012.0 | Teacher | Permanent Full-time | Female | 36-40 | 7 | False | DATE |
1 | 6.0 | Resignation | 2012.0 | Guidance Officer | Permanent Full-time | Female | 41-45 | 18 | True | DATE |
2 | 9.0 | Resignation | 2012.0 | Teacher | Permanent Full-time | Female | 31-35 | 3 | False | DATE |
3 | 10.0 | Resignation | 2012.0 | Teacher Aide | Permanent Part-time | Female | 46-50 | 15 | True | DATE |
4 | 12.0 | Resignation | 2012.0 | Teacher | Permanent Full-time | Male | 31-35 | 3 | False | DATE |
We'll check all values in institute_service
column if there any tricky values.
#checking all values in institute_service column
combined_updated['institute_service'].value_counts(dropna=False) #check also NaN values
NaN 88 Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 5.0 23 1.0 22 7-10 21 0.0 20 3.0 20 6.0 17 4.0 16 2.0 14 9.0 14 7.0 13 More than 20 years 10 8.0 8 13.0 8 15.0 7 20.0 7 12.0 6 22.0 6 17.0 6 10.0 6 14.0 6 18.0 5 16.0 5 23.0 4 24.0 4 11.0 4 39.0 3 21.0 3 32.0 3 19.0 3 36.0 2 30.0 2 26.0 2 28.0 2 25.0 2 29.0 1 31.0 1 49.0 1 33.0 1 34.0 1 35.0 1 38.0 1 41.0 1 42.0 1 27.0 1 Name: institute_service, dtype: int64
Most values are ok, but some of them are written in range, like 1-2
. There are also 2 values with some words, More than 20 years
and Less than 1 year
.
We have to convert all values to get one numeric value which will indicates on peroid of employment.
We'll group all values on four categories:
This modification help us with our analysys. Moreover, problem with choosing number of years goes away, because we can chose the first number from the range.
For example, for range:
7-10
We'll choose 7
.
#extract only numbers from string and then convert it to float
combined_updated['institute_service'] = (combined_updated['institute_service'].astype(str)
.str.extract('(\d+)')
.astype(float))
#check all values in institute_service column after converting
combined_updated['institute_service'].value_counts(dropna=False)
1.0 159 NaN 88 3.0 83 5.0 56 7.0 34 11.0 30 0.0 20 20.0 17 6.0 17 4.0 16 9.0 14 2.0 14 13.0 8 8.0 8 15.0 7 22.0 6 10.0 6 17.0 6 14.0 6 12.0 6 16.0 5 18.0 5 24.0 4 23.0 4 21.0 3 39.0 3 32.0 3 19.0 3 36.0 2 30.0 2 25.0 2 26.0 2 28.0 2 42.0 1 29.0 1 35.0 1 27.0 1 41.0 1 49.0 1 38.0 1 34.0 1 33.0 1 31.0 1 Name: institute_service, dtype: int64
As we mentioned above, we will divide values from institute_service
column info four categories.
We'll create a function to compare each value and add category each of them.
#creating function set_category
def set_category(value):
if value < 3:
return 'New'
elif value >= 3 and value <= 6:
return 'Experienced'
elif value > 6 and value <=10:
return 'Established'
elif pd.isnull(value):
return np.nan #return NaN value if it was NaN
else:
return 'Veteran'
#create new column service_cat and add category for each row
combined_updated['service_cat'] = combined_updated['institute_service'].apply(set_category)
#check values after change
combined_updated['service_cat'].value_counts(dropna=False)
New 193 Experienced 172 Veteran 136 NaN 88 Established 62 Name: service_cat, dtype: int64
We can start do our initial analysys. Let's first check all values in dissatisfied
column.
#checking all values in dissatisfied column
combined_updated['dissatisfied'].value_counts(dropna=False) #check also NaN values
False 403 True 240 NaN 8 Name: dissatisfied, dtype: int64
We noticed that most values are False, almost 60% more than True values. There are also 8 NaN
values. We'll replace them on False.
#replacing all missing values with False
combined_updated['dissatisfied'] = combined_updated['dissatisfied'].fillna(False)
#check all values in dissatisfied column after replacing
combined_updated['dissatisfied'].value_counts(dropna=False) #check also NaN values
False 411 True 240 Name: dissatisfied, dtype: int64
#create pivot table to calcualte percentage of dissatisfied employee in each group
dissatisfied_perc = combined_updated.pivot_table(index='service_cat', values='dissatisfied')
#display plot in Jupyter
%matplotlib inline
#create bar plot
dissatisfied_perc.plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x7f175e5d7040>
As we can see, the most employees whose resignaed due to dissatisfaction are from group Established
(7-10 years at a company) and Veteran
(11 or more years at a company). We can say that every second person who leave job, because they were dissatisfied has 7 years exeprience or more.
Let's check also age of each employee who letf the job due to dissatisfaction.
#check all values in age column
combined_updated['age'].value_counts(dropna=False)
51-55 71 NaN 55 41-45 48 41 45 45 46-50 42 36-40 41 46 50 39 26-30 35 21 25 33 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
As we can see, we have many ranges with age. We decaided to divide all employees on 8 categories:
#convering values in age column to float
combined_updated['age'] = (combined_updated['age'].astype(str)
.str.extract('(\d+)')
.astype(float)
)
#see new values after change
combined_updated['age'].value_counts(dropna=False) #display NaN values also
41.0 93 46.0 81 36.0 73 51.0 71 26.0 67 21.0 62 31.0 61 NaN 55 56.0 55 61.0 23 20.0 10 Name: age, dtype: int64
Now, we can creat function to assign each age to each range.
#creating set_age function
def set_age(value):
if value < 25:
return '18 - 25'
elif value >= 26 and value <= 30:
return '26 - 30'
elif value >= 31 and value <= 35:
return '31 - 35'
elif value >= 36 and value <= 40:
return '36 - 40'
elif value >= 41 and value <= 45:
return '41 - 45'
elif value >= 46 and value <= 50:
return '46 - 45'
elif value >= 51 and value <= 55:
return '51 - 55'
elif value >= 56:
return '56 and more'
elif pd.isnull(value): #return NaN
return np.nan
#create new column and apply each value
combined_updated['age_range'] = combined_updated['age'].apply(set_age)
#see new values in age_range column after change
combined_updated['age_range'].value_counts(dropna=False).sort_index()
18 - 25 72 26 - 30 67 31 - 35 61 36 - 40 73 41 - 45 93 46 - 45 81 51 - 55 71 56 and more 78 NaN 55 Name: age_range, dtype: int64
#create pivot table
pv_age = pd.pivot_table(combined_updated, index='age_range', values='dissatisfied')
pv_age
dissatisfied | |
---|---|
age_range | |
18 - 25 | 0.291667 |
26 - 30 | 0.417910 |
31 - 35 | 0.377049 |
36 - 40 | 0.342466 |
41 - 45 | 0.376344 |
46 - 45 | 0.382716 |
51 - 55 | 0.422535 |
56 and more | 0.423077 |
#create plot
pv_age.plot(kind='bar', rot=45, ylim=(0,0.5), legend=False)
<matplotlib.axes._subplots.AxesSubplot at 0x7f175ded6d60>
As we can noticed, most often employees who left the job due to dissatisfaction are in 3 age ranges:
Let's create a plot for each group of service to check what is most common age range of dissatisfied employees.
#import matplotlib liblary
import matplotlib.pyplot as plt
#creating plots
plt.figure(figsize=(12,10))
plt.subplot(2,2,1)
(combined_updated[(combined_updated['service_cat'] == 'New') & (combined_updated['dissatisfied'] == True)]['age_range'].value_counts(normalize=True)).plot(kind='bar', rot=45)
plt.title('New')
plt.subplot(2,2,2)
(combined_updated[(combined_updated['service_cat'] == 'Experienced') & (combined_updated['dissatisfied'] == True)]['age_range'].value_counts(normalize=True)).plot(kind='bar', rot=45)
plt.title('Experienced')
plt.subplot(2,2,3)
(combined_updated[(combined_updated['service_cat'] == 'Established') & (combined_updated['dissatisfied'] == True)]['age_range'].value_counts(normalize=True)).plot(kind='bar', rot=45)
plt.title('Established')
plt.subplot(2,2,4)
(combined_updated[(combined_updated['service_cat'] == 'Veteran') & (combined_updated['dissatisfied'] == True)]['age_range'].value_counts(normalize=True)).plot(kind='bar', rot=45)
plt.title('Veteran')
plt.show()
We can noticed some observations:
New
category every fifth person who left job and was dissatisfied has from 18 till 25 years.Let's ask on one more question:
#creat pivot table
pv_institute = combined_updated.pivot_table(index = 'institute', values = 'dissatisfied')
pv_institute
dissatisfied | |
---|---|
institute | |
DATE | 0.479100 |
TAFE | 0.267647 |
Let's summarize our analysys. We're ready on answer following questions:
As we can see, the most employees whose resignaed due to dissatisfaction are from group Established
(7-10 years at a company) and Veteran
(11 or more years at a company). We can say that every second person who leave job, because they were dissatisfied has 7 years exeprience or more.
Dissatisfied employees, most often letf the job due to some kind of dissatisfaction in age 51 or older and in age 26-30 . In each of gropu, more than 40% employees letf the job who are not be satisfied. Whereas, less than 30% of people in age 18-25 leave job due to dissatisfaction.
During whole analysys we also found that:
DETE
survey end their employment almost twice as much as employees from TAFE
survey.New
category every fifth person who left job and was dissatisfied has from 18 till 25 years.