We'll work with exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. You can find the TAFE exit survey here and the survey for the DETE here.
We'll try to answer the following questions:
Are employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction?
What about employees who have been there longer?
Are younger employees resigning due to some kind of dissatisfaction?
What about older employees?
Index of contents:
A. READING AND EXPLORING OUR DATAFRAMES
B. TRANSFORMING OUR DATA
C. COMBINING OUR DATAFRAMES
D. VISUALIZING CLEANED DATA
E. CONCLUSIONS
Summary of conclusions:
Only a 30% approximately of "New" employees (Less than 3 years at their workplace) were dissatisfied with their jobs when they exit. Employees belonging to "Established" and Veteran categories, with more than 7 years and 11 years of servis respectively, were the most dissatisfied.
However grouping the data by the age of employees, there are very little differences between groups, in relation to their percentaje of dissatisfaction when they resigned.
import numpy as np
import pandas as pd
#read both files and assing them to variables. For this proyect both are coded in UTF-8 (the original ones are encoded using cp1252.)
dete_survey = pd.read_csv("dete_survey.csv")
tafe_survey = pd.read_csv("tafe_survey.csv")
dete_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 56 columns): ID 822 non-null int64 SeparationType 822 non-null object Cease Date 822 non-null object DETE Start Date 822 non-null object Role Start Date 822 non-null object Position 817 non-null object Classification 455 non-null object Region 822 non-null object Business Unit 126 non-null object Employment Status 817 non-null object Career move to public sector 822 non-null bool Career move to private sector 822 non-null bool Interpersonal conflicts 822 non-null bool Job dissatisfaction 822 non-null bool Dissatisfaction with the department 822 non-null bool Physical work environment 822 non-null bool Lack of recognition 822 non-null bool Lack of job security 822 non-null bool Work location 822 non-null bool Employment conditions 822 non-null bool Maternity/family 822 non-null bool Relocation 822 non-null bool Study/Travel 822 non-null bool Ill Health 822 non-null bool Traumatic incident 822 non-null bool Work life balance 822 non-null bool Workload 822 non-null bool None of the above 822 non-null bool Professional Development 808 non-null object Opportunities for promotion 735 non-null object Staff morale 816 non-null object Workplace issue 788 non-null object Physical environment 817 non-null object Worklife balance 815 non-null object Stress and pressure support 810 non-null object Performance of supervisor 813 non-null object Peer support 812 non-null object Initiative 813 non-null object Skills 811 non-null object Coach 767 non-null object Career Aspirations 746 non-null object Feedback 792 non-null object Further PD 768 non-null object Communication 814 non-null object My say 812 non-null object Information 816 non-null object Kept informed 813 non-null object Wellness programs 766 non-null object Health & Safety 793 non-null object Gender 798 non-null object Age 811 non-null object Aboriginal 16 non-null object Torres Strait 3 non-null object South Sea 7 non-null object Disability 23 non-null object NESB 32 non-null object dtypes: bool(18), int64(1), object(37) memory usage: 258.6+ KB
dete_survey.head(2)
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984 | 2004 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | Not Stated | Not Stated | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 rows × 56 columns
dete_survey.describe(include = "all").iloc[0:5, 0:12] # for a view of the 5 first lines from the 12 first columns
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 822.000000 | 822 | 822 | 822 | 822 | 817 | 455 | 822 | 126 | 817 | 822 | 822 |
unique | NaN | 9 | 25 | 51 | 46 | 15 | 8 | 9 | 14 | 5 | 2 | 2 |
top | NaN | Age Retirement | 2012 | Not Stated | Not Stated | Teacher | Primary | Metropolitan | Education Queensland | Permanent Full-time | False | False |
freq | NaN | 285 | 344 | 73 | 98 | 324 | 161 | 135 | 54 | 434 | 800 | 742 |
mean | 411.693431 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
dete_survey.describe(include = "all").iloc[0:5, 10:28] # columns 10-27
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 | 822 |
unique | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
top | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False |
freq | 800 | 742 | 788 | 733 | 761 | 806 | 765 | 794 | 795 | 788 | 760 | 754 | 785 | 710 | 794 | 605 | 735 | 605 |
mean | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
dete_survey.describe(include = "all").iloc[0:5, 28:49]
Professional Development | Opportunities for promotion | Staff morale | Workplace issue | Physical environment | Worklife balance | Stress and pressure support | Performance of supervisor | Peer support | Initiative | ... | Coach | Career Aspirations | Feedback | Further PD | Communication | My say | Information | Kept informed | Wellness programs | Health & Safety | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 808 | 735 | 816 | 788 | 817 | 815 | 810 | 813 | 812 | 813 | ... | 767 | 746 | 792 | 768 | 814 | 812 | 816 | 813 | 766 | 793 |
unique | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | ... | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 |
top | A | A | A | A | A | A | A | A | A | A | ... | A | A | A | A | A | A | A | A | A | A |
freq | 413 | 242 | 335 | 357 | 467 | 359 | 342 | 349 | 401 | 396 | ... | 345 | 246 | 348 | 293 | 399 | 400 | 436 | 401 | 253 | 386 |
mean | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 21 columns
dete_survey["Professional Development"].value_counts(dropna = False)
A 413 SA 184 N 103 D 60 SD 33 M 15 NaN 14 Name: Professional Development, dtype: int64
dete_survey.describe(include = "all").iloc[0:5, 49:]
Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|
count | 798 | 811 | 16 | 3 | 7 | 23 | 32 |
unique | 2 | 10 | 1 | 1 | 1 | 1 | 1 |
top | Female | 61 or older | Yes | Yes | Yes | Yes | Yes |
freq | 573 | 222 | 16 | 3 | 7 | 23 | 32 |
mean | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Almost all data are strings or boolean, there is only a column with numeric values that is ID column.
There are "Not Stated" values instead of NaN (e.g. in DETE start date column).
The names of columns combines upper and lower character. Should be standarize to upper for better management.
We can clasified four groups of columns for their type of information and values:
columns 0-12: general information related to the position and the cease. Type of values, strings.
columns 12-27: causes of cease. Type of values, boolean.
columns 28-49: evaluation of the Department. Type of values, string ranking from M to A (with 6 categories between these both).
columns 49-56: personal information of the employee (Gender and Age). The last 5 columns are Y or N and have very few values. We should decide if drop them because doesn't apport any information for our aims.
There are many columns we don't need in this proyect. The columns which seem necesary for answering our initial questions are: DETE Start date, Cese dat, Age, and the group of columns related to causes of cease.
tafe_survey.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 72 columns): Record ID 702 non-null float64 Institute 702 non-null object WorkArea 702 non-null object CESSATION YEAR 695 non-null float64 Reason for ceasing employment 701 non-null object Contributing Factors. Career Move - Public Sector 437 non-null object Contributing Factors. Career Move - Private Sector 437 non-null object Contributing Factors. Career Move - Self-employment 437 non-null object Contributing Factors. Ill Health 437 non-null object Contributing Factors. Maternity/Family 437 non-null object Contributing Factors. Dissatisfaction 437 non-null object Contributing Factors. Job Dissatisfaction 437 non-null object Contributing Factors. Interpersonal Conflict 437 non-null object Contributing Factors. Study 437 non-null object Contributing Factors. Travel 437 non-null object Contributing Factors. Other 437 non-null object Contributing Factors. NONE 437 non-null object Main Factor. Which of these was the main factor for leaving? 113 non-null object InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 609 non-null object WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] 608 non-null object WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 609 non-null object WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object Induction. Did you undertake Workplace Induction? 619 non-null object InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object Gender. What is your Gender? 596 non-null object CurrentAge. Current Age 596 non-null object Employment Type. Employment Type 596 non-null object Classification. Classification 596 non-null object LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(70) memory usage: 395.0+ KB
tafe_survey.head(2)
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.341330e+17 | Southern Queensland Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Contract Expired | NaN | NaN | NaN | NaN | NaN | ... | Yes | Yes | Yes | Yes | Female | 26 30 | Temporary Full-time | Administration (AO) | 1-2 | 1-2 |
1 | 6.341337e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
2 rows × 72 columns
tafe_survey.describe(include = "all")[0:5]
Record ID | Institute | WorkArea | CESSATION YEAR | Reason for ceasing employment | Contributing Factors. Career Move - Public Sector | Contributing Factors. Career Move - Private Sector | Contributing Factors. Career Move - Self-employment | Contributing Factors. Ill Health | Contributing Factors. Maternity/Family | ... | Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? | Workplace. Topic:Does your workplace promote and practice the principles of employment equity? | Workplace. Topic:Does your workplace value the diversity of its employees? | Workplace. Topic:Would you recommend the Institute as an employer to others? | Gender. What is your Gender? | CurrentAge. Current Age | Employment Type. Employment Type | Classification. Classification | LengthofServiceOverall. Overall Length of Service at Institute (in years) | LengthofServiceCurrent. Length of Service at current workplace (in years) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 7.020000e+02 | 702 | 702 | 695.000000 | 701 | 437 | 437 | 437 | 437 | 437 | ... | 594 | 587 | 586 | 581 | 596 | 596 | 596 | 596 | 596 | 596 |
unique | NaN | 12 | 2 | NaN | 6 | 2 | 2 | 2 | 2 | 2 | ... | 2 | 2 | 2 | 2 | 2 | 9 | 5 | 9 | 7 | 7 |
top | NaN | Brisbane North Institute of TAFE | Non-Delivery (corporate) | NaN | Resignation | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | Female | 56 or older | Permanent Full-time | Administration (AO) | Less than 1 year | Less than 1 year |
freq | NaN | 161 | 432 | NaN | 340 | 375 | 336 | 420 | 403 | 411 | ... | 536 | 512 | 488 | 416 | 389 | 162 | 237 | 293 | 147 | 177 |
mean | 6.346026e+17 | NaN | NaN | 2011.423022 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 72 columns
tafe_survey["Contributing Factors. Career Move - Public Sector "].value_counts(dropna = False)
- 375 NaN 265 Career Move - Public Sector 62 Name: Contributing Factors. Career Move - Public Sector , dtype: int64
The information of this file is much less clear:
The name of columns are too long for a good management of their information. It seems they used just the questions of this survey as columns names.
Almost of types of data are objects except for two columns: Record ID and CESSATION YEAR.
It seems it would be better select only the columns we will need for this proyect: CESSATION YEAR, LengthofServiceOverall, CurrentAge, and the gropu of Contributing factors.
dete_survey = pd.read_csv("dete_survey.csv", na_values = "Not Stated")
dete_survey.head(2) # only the 2 first rows
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984.0 | 2004.0 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | NaN | NaN | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 rows × 56 columns
dete_survey["DETE Start Date"].value_counts(dropna = False)
NaN 73 2011.0 40 2007.0 34 2008.0 31 2010.0 27 2012.0 27 2009.0 24 2006.0 23 1970.0 21 1975.0 21 2013.0 21 2005.0 20 1990.0 20 1999.0 19 1996.0 19 1992.0 18 1991.0 18 2000.0 18 2004.0 18 1989.0 17 1978.0 15 2003.0 15 1988.0 15 1976.0 15 2002.0 15 1974.0 14 1997.0 14 1998.0 14 1979.0 14 1995.0 14 1980.0 14 1993.0 13 1972.0 12 1986.0 12 1977.0 11 1971.0 10 1984.0 10 1994.0 10 1969.0 10 2001.0 10 1983.0 9 1981.0 9 1973.0 8 1985.0 8 1987.0 7 1982.0 4 1963.0 4 1968.0 3 1967.0 2 1965.0 1 1966.0 1 Name: DETE Start Date, dtype: int64
We don't have "Not Stated" values in this column as before...
dete_survey_updated = dete_survey.drop(axis = 1, labels = dete_survey.columns[28:49]) #use a subset of the list of columns to drop
dete_survey_updated.info() # take a look of the remainings columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 822 entries, 0 to 821 Data columns (total 35 columns): ID 822 non-null int64 SeparationType 822 non-null object Cease Date 788 non-null object DETE Start Date 749 non-null float64 Role Start Date 724 non-null float64 Position 817 non-null object Classification 455 non-null object Region 717 non-null object Business Unit 126 non-null object Employment Status 817 non-null object Career move to public sector 822 non-null bool Career move to private sector 822 non-null bool Interpersonal conflicts 822 non-null bool Job dissatisfaction 822 non-null bool Dissatisfaction with the department 822 non-null bool Physical work environment 822 non-null bool Lack of recognition 822 non-null bool Lack of job security 822 non-null bool Work location 822 non-null bool Employment conditions 822 non-null bool Maternity/family 822 non-null bool Relocation 822 non-null bool Study/Travel 822 non-null bool Ill Health 822 non-null bool Traumatic incident 822 non-null bool Work life balance 822 non-null bool Workload 822 non-null bool None of the above 822 non-null bool Gender 798 non-null object Age 811 non-null object Aboriginal 16 non-null object Torres Strait 3 non-null object South Sea 7 non-null object Disability 23 non-null object NESB 32 non-null object dtypes: bool(18), float64(2), int64(1), object(14) memory usage: 123.7+ KB
We have eliminated all columns related to evaluate the department, which we didn't need.
tafe_survey_updated = tafe_survey.drop(columns = tafe_survey.columns[17:66]) #in this case we use columns parameter instead of label + axis
tafe_survey_updated.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 702 entries, 0 to 701 Data columns (total 23 columns): Record ID 702 non-null float64 Institute 702 non-null object WorkArea 702 non-null object CESSATION YEAR 695 non-null float64 Reason for ceasing employment 701 non-null object Contributing Factors. Career Move - Public Sector 437 non-null object Contributing Factors. Career Move - Private Sector 437 non-null object Contributing Factors. Career Move - Self-employment 437 non-null object Contributing Factors. Ill Health 437 non-null object Contributing Factors. Maternity/Family 437 non-null object Contributing Factors. Dissatisfaction 437 non-null object Contributing Factors. Job Dissatisfaction 437 non-null object Contributing Factors. Interpersonal Conflict 437 non-null object Contributing Factors. Study 437 non-null object Contributing Factors. Travel 437 non-null object Contributing Factors. Other 437 non-null object Contributing Factors. NONE 437 non-null object Gender. What is your Gender? 596 non-null object CurrentAge. Current Age 596 non-null object Employment Type. Employment Type 596 non-null object Classification. Classification 596 non-null object LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object dtypes: float64(2), object(21) memory usage: 126.2+ KB
In the case of tafe_survey the reduction of columns is even bigger.
Because we eventually want to combine them, we'll have to standardize the column names of our interest.
We will use the following criteria to update the column names:
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(" ", "_")
print(dete_survey_updated.columns) # to check the result
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')
print(tafe_survey_updated.columns)
Index(['Record ID', 'Institute', 'WorkArea', 'CESSATION YEAR', 'Reason for ceasing employment', 'Contributing Factors. Career Move - Public Sector ', 'Contributing Factors. Career Move - Private Sector ', 'Contributing Factors. Career Move - Self-employment', 'Contributing Factors. Ill Health', 'Contributing Factors. Maternity/Family', 'Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Interpersonal Conflict', 'Contributing Factors. Study', 'Contributing Factors. Travel', 'Contributing Factors. Other', 'Contributing Factors. NONE', 'Gender. What is your Gender?', 'CurrentAge. Current Age', 'Employment Type. Employment Type', 'Classification. Classification', 'LengthofServiceOverall. Overall Length of Service at Institute (in years)', 'LengthofServiceCurrent. Length of Service at current workplace (in years)'], dtype='object')
# to update the columns name in tafe_survey_updated, according to dete_survey_updated columns names.
tafe_survey_updated = (tafe_survey_updated.rename(mapper = {'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'},
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')
Some of our end goals is to answer the following questions:
For this project, we'll only analyze survey respondents who resigned, so their separation type column, contains the string 'Resignation'.
dete_survey_updated["separationtype"].value_counts(dropna = False)
Age Retirement 285 Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Voluntary Early Retirement (VER) 67 Ill Health Retirement 61 Other 49 Contract Expired 34 Termination 15 Name: separationtype, dtype: int64
tafe_survey_updated["separationtype"].value_counts(dropna = False)
Resignation 340 Contract Expired 127 Retrenchment/ Redundancy 104 Retirement 82 Transfer 25 Termination 23 NaN 1 Name: separationtype, dtype: int64
# to make a boolean index from dete_separation column with all 3 types of resignations
dete_separationtype_resign = ((dete_survey_updated["separationtype"] == "Resignation-Other reasons") |
(dete_survey_updated["separationtype"] == "Resignation-Other employer") |
(dete_survey_updated["separationtype"] == "Resignation-Move overseas/interstate")
)
# make a subset of a copy of dete_survey_updated (for avoiding SettingWithCopy Warning later)
dete_resignations = dete_survey_updated.copy()[dete_separationtype_resign]
dete_resignations["separationtype"].value_counts(dropna = False)
Resignation-Other reasons 150 Resignation-Other employer 91 Resignation-Move overseas/interstate 70 Name: separationtype, dtype: int64
# the same operation with tafe_survey_updated dataframe
tafe_resignations = tafe_survey_updated.copy()[tafe_survey_updated["separationtype"] == "Resignation"]
tafe_resignations["separationtype"].value_counts(dropna = False)
Resignation 340 Name: separationtype, dtype: int64
Now, before we start cleaning and manipulating the rest of our data, let's verify that the data doesn't contain any major inconsistencies.
dete_resignations["cease_date"].value_counts(dropna = False)
2012 126 2013 74 01/2014 22 12/2013 17 06/2013 14 NaN 11 09/2013 11 11/2013 9 07/2013 9 10/2013 6 08/2013 4 05/2012 2 05/2013 2 07/2012 1 2010 1 09/2010 1 07/2006 1 Name: cease_date, dtype: int64
We need to clean this data from this column (cease_date): extract the year and convert its type to float.
pattern = r"(2[0-9]{3})" # regular expresion for extracting years from 2000 year.
dete_resignations["cease_date"] = dete_resignations["cease_date"].str.extract(pattern).astype(float)
dete_resignations["cease_date"].value_counts(dropna = False) # to check changes
/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:3: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame)
2013.0 146 2012.0 129 2014.0 22 NaN 11 2010.0 2 2006.0 1 Name: cease_date, dtype: int64
dete_resignations['dete_start_date'].value_counts(dropna = False).sort_index(ascending = True) # sort by year (index), acending
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
tafe_resignations['cease_date'].value_counts(dropna = False)
2011.0 116 2012.0 94 2010.0 68 2013.0 55 NaN 5 2009.0 2 Name: cease_date, dtype: int64
Values at these two column (dete_start_date and cease_date in tafe) are ok. There is no need to clean it.
Cheching all this columns we can see that there are no cease dates in dete before 2006, and it's odd because there are many star dates since 1963. Surely have had ceases during all this time that haven't been appointed until 2006 in this database.
We can calculate years of service in dete survey by subtracting the values in dete_start_date from the cease_date. We'll assign the result to a new column in dete named institute_service.
dete_resignations["institute_service"] = (dete_resignations["cease_date"] -
dete_resignations['dete_start_date'])
dete_resignations["institute_service"].describe() # to view its basics statistic values
count 273.000000 mean 10.457875 std 9.931709 min 0.000000 25% 3.000000 50% 7.000000 75% 16.000000 max 49.000000 Name: institute_service, dtype: float64
Next we must choose what factors or columns appointed in these surveys are the adecuated for considerign that an employee quit because thet were dissatisfied.
From dete survey we'll choose the following columns:'interpersonal_conflicts','job_dissatisfaction', 'dissatisfaction_with_the_department','physical_work_environment', 'lack_of_recognition', 'lack_of_job_security', 'work_location', 'employment_conditions', 'work_life_balance' and 'workload'.
And from tafe survey:'Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction', 'Contributing Factors. Interpersonal Conflict'.
If the employee indicated any of the factors above caused them to resign, we'll mark them as dissatisfied in a new column.
# as example for a view of the original values of these columns in dete survey
dete_resignations['interpersonal_conflicts'].value_counts(dropna = False)
False 291 True 20 Name: interpersonal_conflicts, dtype: int64
# as example for a view of the original values of these columns in tefe survey
tafe_resignations['Contributing Factors. Dissatisfaction'].value_counts(dropna = False)
- 277 Contributing Factors. Dissatisfaction 55 NaN 8 Name: Contributing Factors. Dissatisfaction, dtype: int64
At this point we'll create a function to transform the data in selected tafe columns from their actual values, to boolean values.
tafe_dissat_columns = (['Contributing Factors. Dissatisfaction',
'Contributing Factors. Job Dissatisfaction',
'Contributing Factors. Interpersonal Conflict'])
def to_boolean(element):
if element == "-":
return False
elif pd.isnull(element): #pandas function
return np.nan #numpy function
else:
return True
tafe_resignations[tafe_dissat_columns] = tafe_resignations[tafe_dissat_columns].applymap(to_boolean)
# to view the results use df.apply() method
tafe_resignations[tafe_dissat_columns].apply(pd.value_counts, dropna = False)
Contributing Factors. Dissatisfaction | Contributing Factors. Job Dissatisfaction | Contributing Factors. Interpersonal Conflict | |
---|---|---|---|
False | 277 | 270 | 308 |
True | 55 | 62 | 24 |
NaN | 8 | 8 | 8 |
# selected columns at dete survey:
dete_dissat_columns = (['interpersonal_conflicts','job_dissatisfaction',
'dissatisfaction_with_the_department','physical_work_environment',
'lack_of_recognition', 'lack_of_job_security', 'work_location',
'employment_conditions', 'work_life_balance', 'workload'])
# to check values of all selected columns :
dete_resignations[dete_dissat_columns].apply(pd.value_counts, dropna = False)
interpersonal_conflicts | job_dissatisfaction | dissatisfaction_with_the_department | physical_work_environment | lack_of_recognition | lack_of_job_security | work_location | employment_conditions | work_life_balance | workload | |
---|---|---|---|---|---|---|---|---|---|---|
False | 291 | 270 | 282 | 305 | 278 | 297 | 293 | 288 | 243 | 284 |
True | 20 | 41 | 29 | 6 | 33 | 14 | 18 | 23 | 68 | 27 |
Following we'll create a dissatisfied column in BOTH the tafe_resignations and dete_resignations dataframes.
If any of the columns of its respective dataframes listed above contain a True value, we'll add a True value to a new column named dissatisfied. To accomplish this, we'll use the DataFrame.any() method to do the following:
We also will use the df.copy() method to create a copy of the results and avoid the SettingWithCopy Warning. Assign the results to dete_resignations_up and tafe_resignations_up.
#assign True to dissatisfied columns if any element in the selected columns above is True, and NaN if the value is NaN
dete_resignations["dissatisfied"] = dete_resignations[dete_dissat_columns].any(axis = 1, skipna = False)
#make a copy:
dete_resignations_up = dete_resignations.copy()
#to check the results
dete_resignations_up["dissatisfied"].value_counts(dropna = False)
False 157 True 154 Name: dissatisfied, dtype: int64
# the same for tafe columns
tafe_resignations["dissatisfied"] = tafe_resignations[tafe_dissat_columns].any(axis = 1, skipna = False)
tafe_resignations_up = tafe_resignations.copy()
tafe_resignations_up["dissatisfied"].value_counts(dropna = False)
False 235 True 97 NaN 8 Name: dissatisfied, dtype: int64
Now, we're ready to combine our datasets! Our end goal is to aggregate the data according to the institute_service column, so when you combine the data, think about how to get the data into a form that's easy to aggregate.
Add a column named institute to dete_resignations_up. Each row should contain the value DETE.
Add a column named institute to tafe_resignations_up. Each row should contain the value TAFE.
Combine the dataframes. Assign the result to combined.
We still have some columns left in the dataframe that we don't need to complete our analysis. We'll use the DataFrame.dropna() method to drop any columns with less than 500 non null values, and will assign the result to combined_updated.
dete_resignations_up["institute"] = "DETE"
# to check the results and take a view of dete_resignations_up dataframe
dete_resignations_up.head(2)
id | separationtype | cease_date | dete_start_date | role_start_date | position | classification | region | business_unit | employment_status | ... | gender | age | aboriginal | torres_strait | south_sea | disability | nesb | institute_service | dissatisfied | institute | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 4 | Resignation-Other reasons | 2012.0 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | Female | 36-40 | NaN | NaN | NaN | NaN | NaN | 7.0 | False | DETE |
5 | 6 | Resignation-Other reasons | 2012.0 | 1994.0 | 1997.0 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | Female | 41-45 | NaN | NaN | NaN | NaN | NaN | 18.0 | True | DETE |
2 rows × 38 columns
tafe_resignations_up["institute"] = "DETE"
# to check the results and take a view of dete_resignations_up dataframe
tafe_resignations_up.head(2)
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. Other | Contributing Factors. NONE | gender | age | employment_status | position | institute_service | role_service | dissatisfied | institute | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | - | - | NaN | NaN | NaN | NaN | NaN | NaN | False | DETE |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | - | - | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 | False | DETE |
2 rows × 25 columns
# concatenate dataframes horizontally
combined = pd.concat([dete_resignations_up, tafe_resignations_up])
combined.shape
(651, 53)
# Calculate the number of missing values in each column
combined.isnull().sum().sort_values(ascending = False)
torres_strait 651 south_sea 648 aboriginal 644 disability 643 nesb 642 business_unit 619 classification 490 region 386 role_start_date 380 dete_start_date 368 role_service 361 dissatisfaction_with_the_department 340 work_location 340 employment_conditions 340 workload 340 job_dissatisfaction 340 career_move_to_public_sector 340 career_move_to_private_sector 340 ill_health 340 interpersonal_conflicts 340 physical_work_environment 340 relocation 340 work_life_balance 340 lack_of_job_security 340 traumatic_incident 340 lack_of_recognition 340 maternity/family 340 study/travel 340 none_of_the_above 340 Contributing Factors. Other 319 Contributing Factors. Career Move - Public Sector 319 Contributing Factors. NONE 319 Contributing Factors. Travel 319 Contributing Factors. Career Move - Self-employment 319 Contributing Factors. Dissatisfaction 319 Contributing Factors. Maternity/Family 319 Contributing Factors. Job Dissatisfaction 319 Contributing Factors. Interpersonal Conflict 319 Contributing Factors. Ill Health 319 Contributing Factors. Study 319 Contributing Factors. Career Move - Private Sector 319 Institute 311 WorkArea 311 institute_service 88 gender 59 age 55 employment_status 54 position 53 cease_date 16 dissatisfied 8 id 0 institute 0 separationtype 0 dtype: int64
The columns of our interest: dissatisfied, age, institute_service, are lower in Nan values
We are going to drop any columns with less than 500 non null values, and will assign the result to combined_updated.
combined_updated = combined.copy().dropna(thresh = 500, axis=1) # create a copy for avoid SettingWithCopyWarning later
# to check results
combined_updated.isnull().sum().sort_values(ascending = False)
institute_service 88 gender 59 age 55 employment_status 54 position 53 cease_date 16 dissatisfied 8 separationtype 0 institute 0 id 0 dtype: int64
This column is tricky to clean because it currently contains values in different forms.
combined_updated["institute_service"].value_counts(dropna = False)
NaN 88 Less than 1 year 73 1-2 64 3-4 63 5-6 33 11-20 26 5.0 23 1.0 22 7-10 21 0.0 20 3.0 20 6.0 17 4.0 16 2.0 14 9.0 14 7.0 13 More than 20 years 10 13.0 8 8.0 8 15.0 7 20.0 7 14.0 6 12.0 6 17.0 6 22.0 6 10.0 6 16.0 5 18.0 5 11.0 4 23.0 4 24.0 4 19.0 3 32.0 3 39.0 3 21.0 3 28.0 2 30.0 2 26.0 2 36.0 2 25.0 2 29.0 1 31.0 1 27.0 1 34.0 1 35.0 1 38.0 1 41.0 1 42.0 1 49.0 1 33.0 1 Name: institute_service, dtype: int64
We will tranform this column:
Drop its rows with Nan values
Convert their values into categories
# get the index of rows with nan values on column "institute_service"
index_rows = combined_updated[combined_updated["institute_service"].isnull()].index
# drop rows with null values. Create a new dataframe for not lossing these values in combined_updated
combined_service_cat = combined_updated.copy().drop(labels = index_rows)
combined_service_cat["institute_service"].value_counts(dropna = False)
Less than 1 year 67 3-4 63 1-2 61 5-6 32 11-20 25 1.0 21 7-10 20 3.0 19 5.0 19 0.0 18 6.0 16 4.0 15 9.0 14 2.0 12 7.0 12 More than 20 years 10 13.0 8 8.0 7 15.0 7 22.0 6 17.0 6 12.0 6 20.0 6 14.0 5 16.0 5 10.0 4 18.0 4 23.0 4 24.0 4 21.0 3 11.0 3 39.0 3 19.0 2 25.0 2 26.0 2 28.0 2 36.0 2 32.0 2 33.0 1 35.0 1 34.0 1 49.0 1 29.0 1 42.0 1 31.0 1 27.0 1 41.0 1 38.0 1 Name: institute_service, dtype: int64
# Change all values to string and remove ".0" character. Note that dot needs go between []
combined_service_cat["institute_service"] = (combined_service_cat["institute_service"].astype(str).
str.replace(r"([.]0)", ""))
combined_service_cat["institute_service"].value_counts(dropna = False).sort_index(ascending = False) #sorted for a better view
More than 20 years 10 Less than 1 year 67 9 14 8 7 7-10 20 7 12 6 16 5-6 32 5 19 49 1 42 1 41 1 4 15 39 3 38 1 36 2 35 1 34 1 33 1 32 2 31 1 3-4 63 3 19 29 1 28 2 27 1 26 2 25 2 24 4 23 4 22 6 21 3 20 6 2 12 19 2 18 4 17 6 16 5 15 7 14 5 13 8 12 6 11-20 25 11 3 10 4 1-2 61 1 21 0 18 Name: institute_service, dtype: int64
In the following code we'll extract all years of "institute_service" column in combined_service_cat dataframe, and will store them in a new dataframe called "years".
Lately we'll calculate years["service_cat"] column as the arithmetic mean of all years stracted.
Finally we'll map this columnd and will transform numbers into cathegories.
# regular expresion for extracting all the years of "institute_service" column
pattern = r"(?P<First_Year>[0-9][0-9]?)-?(?P<Second_Year>[1-9][0-9]?)?" # question mark, ?, after a group
# to indicate that a match for those groups
# is optional.Only extracting characters
# between brakets ()
# dataframe which store all years extracted
years = combined_service_cat.copy()["institute_service"].str.extractall(pattern) # make a copy of combined_updated
# a view of all values stored in columns of years
years.apply(pd.value_counts, dropna = False)
First_Year | Second_Year | |
---|---|---|
0 | 18.0 | NaN |
1 | 149.0 | NaN |
10 | 4.0 | 20.0 |
11 | 28.0 | NaN |
12 | 6.0 | NaN |
13 | 8.0 | NaN |
14 | 5.0 | NaN |
15 | 7.0 | NaN |
16 | 5.0 | NaN |
17 | 6.0 | NaN |
18 | 4.0 | NaN |
19 | 2.0 | NaN |
2 | 12.0 | 61.0 |
20 | 16.0 | 25.0 |
21 | 3.0 | NaN |
22 | 6.0 | NaN |
23 | 4.0 | NaN |
24 | 4.0 | NaN |
25 | 2.0 | NaN |
26 | 2.0 | NaN |
27 | 1.0 | NaN |
28 | 2.0 | NaN |
29 | 1.0 | NaN |
3 | 82.0 | NaN |
31 | 1.0 | NaN |
32 | 2.0 | NaN |
33 | 1.0 | NaN |
34 | 1.0 | NaN |
35 | 1.0 | NaN |
36 | 2.0 | NaN |
38 | 1.0 | NaN |
39 | 3.0 | NaN |
4 | 15.0 | 63.0 |
41 | 1.0 | NaN |
42 | 1.0 | NaN |
49 | 1.0 | NaN |
5 | 51.0 | NaN |
6 | 16.0 | 32.0 |
7 | 32.0 | NaN |
8 | 7.0 | NaN |
9 | 14.0 | NaN |
NaN | NaN | 326.0 |
years.head()
First_Year | Second_Year | ||
---|---|---|---|
match | |||
5 | 0 | 18 | NaN |
8 | 0 | 3 | NaN |
9 | 0 | 15 | NaN |
11 | 0 | 3 | NaN |
12 | 0 | 14 | NaN |
# because years has a MultiIndex, reset it, remove the index level "match" and convert it in a column.
years = years.reset_index(level=["match"])
years.head()
match | First_Year | Second_Year | |
---|---|---|---|
5 | 0 | 18 | NaN |
8 | 0 | 3 | NaN |
9 | 0 | 15 | NaN |
11 | 0 | 3 | NaN |
12 | 0 | 14 | NaN |
# drop column match, because is useless
years = years.drop("match", axis = 1)
# change all values of its column to float
years = years.astype(float)
# transfor Nan values in Second_Year to 0
years["Second_Year"] = years["Second_Year"].fillna(0)
# create a new column in year based in First_Year values
years["service_cat"] = years["First_Year"]
# for rows which have values in its Second_Year. The value of "service_cat" It's the arithmetic mean
years.loc[years["Second_Year"] > 0,"service_cat"] = ((years.loc[years["Second_Year"] > 0, "First_Year"]
+ years.loc[years["Second_Year"] > 0, "Second_Year"])
/ 2)
years.head()
First_Year | Second_Year | service_cat | |
---|---|---|---|
5 | 18.0 | 0.0 | 18.0 |
8 | 3.0 | 0.0 | 3.0 |
9 | 15.0 | 0.0 | 15.0 |
11 | 3.0 | 0.0 | 3.0 |
12 | 14.0 | 0.0 | 14.0 |
# there is no rows with Nan values, we drop them before
years["service_cat"].isnull().sum()
0
To analyze the data, we'll convert numbers into categories.
We'll use the slightly modified definitions below:
# create a function to transform the values year's service_cat column into categories
def to_categories(val):
if val < 3:
return "New"
elif val >=3 and val <=6:
return "Experienced"
elif val >6 and val <=10: # minimun lower than definition to evoid gaps
return "Established"
else:
return "Veteran"
years["service_cat"] = years["service_cat"].map(to_categories)
years["service_cat"].value_counts()
New 179 Experienced 164 Veteran 127 Established 57 Name: service_cat, dtype: int64
# drop this two columns of years that we don't need more
years = years.drop(["First_Year", "Second_Year"], axis = 1)
Following we join the column "service_cat" of years dataframe to combined_service_cat dataframe
Firsly we'll check if they both continue having the same index after all the former changes...
# to check if this two dataframes have the same number of rows to combine
print(combined_service_cat.shape)
print(years.shape)
(527, 10) (527, 1)
combined_service_cat.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 527 entries, 5 to 701 Data columns (total 10 columns): age 525 non-null object cease_date 525 non-null float64 dissatisfied 527 non-null object employment_status 527 non-null object gender 522 non-null object id 527 non-null float64 institute 527 non-null object institute_service 527 non-null object position 524 non-null object separationtype 527 non-null object dtypes: float64(2), object(8) memory usage: 45.3+ KB
# to check if these dataframe have the same index
years.index == combined_service_cat.index
array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True])
merged = pd.merge(left = combined_service_cat, right = years, left_index = True, right_index = True)
merged.shape
(727, 11)
merged.head()
age | cease_date | dissatisfied | employment_status | gender | id | institute | institute_service | position | separationtype | service_cat | |
---|---|---|---|---|---|---|---|---|---|---|---|
4 | 41 45 | 2010.0 | False | Permanent Full-time | Male | 6.341466e+17 | DETE | 3-4 | Teacher (including LVT) | Resignation | Experienced |
5 | 41-45 | 2012.0 | True | Permanent Full-time | Female | 6.000000e+00 | DETE | 18 | Guidance Officer | Resignation-Other reasons | Veteran |
5 | 41-45 | 2012.0 | True | Permanent Full-time | Female | 6.000000e+00 | DETE | 18 | Guidance Officer | Resignation-Other reasons | Established |
5 | 56 or older | 2010.0 | False | Contract/casual | Female | 6.341475e+17 | DETE | 7-10 | Teacher (including LVT) | Resignation | Veteran |
5 | 56 or older | 2010.0 | False | Contract/casual | Female | 6.341475e+17 | DETE | 7-10 | Teacher (including LVT) | Resignation | Established |
It is supose it merges 2 dataframes by its index. That is the same index for both. But instead of maintaining the number of rows, create rows duplicated with the same index number.
On the contrary the code bellow seems to work fine...
combined_service_cat = pd.concat([combined_service_cat, years], axis = 1)
combined_service_cat.head()
age | cease_date | dissatisfied | employment_status | gender | id | institute | institute_service | position | separationtype | service_cat | |
---|---|---|---|---|---|---|---|---|---|---|---|
5 | 41-45 | 2012.0 | True | Permanent Full-time | Female | 6.0 | DETE | 18 | Guidance Officer | Resignation-Other reasons | Veteran |
8 | 31-35 | 2012.0 | False | Permanent Full-time | Female | 9.0 | DETE | 3 | Teacher | Resignation-Other reasons | Experienced |
9 | 46-50 | 2012.0 | True | Permanent Part-time | Female | 10.0 | DETE | 15 | Teacher Aide | Resignation-Other employer | Veteran |
11 | 31-35 | 2012.0 | False | Permanent Full-time | Male | 12.0 | DETE | 3 | Teacher | Resignation-Move overseas/interstate | Experienced |
12 | 36-40 | 2012.0 | False | Permanent Full-time | Female | 13.0 | DETE | 14 | Teacher | Resignation-Other reasons | Veteran |
combined_service_cat.shape
(527, 11)
We get a frame with the same rows of combined_updated dataframe plus the service_cat column
# a preview of values in this 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 31-35 29 56 or older 29 56-60 26 61 or older 23 20 or younger 10 Name: age, dtype: int64
As in the case of "institute_service" column, this column is tricky to clean because it currently contains values in different forms. Because values are similar, we can use the same method that before.
# get the index of rows with nan values on column "institute_service"
index_rows = combined_updated[combined_updated["age"].isnull()].index
# drop rows with null values. Create a new dataframe for not lossing these values in combined_updated
combined_age= combined_updated.copy().drop(labels = index_rows)
combined_age["age"].value_counts(dropna = False)
51-55 65 41 45 45 41-45 44 46-50 41 36-40 39 46 50 39 21 25 33 26-30 33 36 40 32 31 35 32 26 30 32 56 or older 29 31-35 26 21-25 24 56-60 24 61 or older 20 20 or younger 10 Name: age, dtype: int64
# Change to string and spaces to "-"
combined_age["age"] = (combined_age["age"].astype(str).str.replace(" ", "-"))
combined_age["age"].value_counts(dropna = False)
51-55 65 41--45 45 41-45 44 46-50 41 46--50 39 36-40 39 26-30 33 21--25 33 36--40 32 26--30 32 31--35 32 56-or-older 29 31-35 26 56-60 24 21-25 24 61-or-older 20 20-or-younger 10 Name: age, dtype: int64
# regular expresion for extracting all the ages of "age" column
pattern = r"(?P<First_Age>[0-9][0-9]?)-?--?(?P<Second_Age>[1-9][0-9]?)?" # question mark, ?, after a group
# to indicate that a match for those groups
# is optional.Only extracting characters
# between brakets ()
# dataframe which store all years extracted
ages = combined_age.copy()["age"].str.extractall(pattern) # make a copy of combined_age
# a view of all values stored in columns of ages
ages.apply(pd.value_counts, dropna = False)
First_Age | Second_Age | |
---|---|---|
20 | 10.0 | NaN |
21 | 57.0 | NaN |
26 | 65.0 | NaN |
30 | NaN | 65.0 |
31 | 58.0 | NaN |
36 | 71.0 | NaN |
40 | NaN | 71.0 |
41 | 89.0 | NaN |
45 | NaN | 89.0 |
46 | 80.0 | NaN |
50 | NaN | 80.0 |
51 | 65.0 | NaN |
55 | NaN | 65.0 |
56 | 53.0 | NaN |
61 | 20.0 | NaN |
NaN | NaN | 59.0 |
35 | NaN | 58.0 |
25 | NaN | 57.0 |
60 | NaN | 24.0 |
It seems, looking at this table above, that all ages have been stracted.
ages.head()
First_Age | Second_Age | ||
---|---|---|---|
match | |||
5 | 0 | 41 | 45 |
8 | 0 | 31 | 35 |
9 | 0 | 46 | 50 |
11 | 0 | 31 | 35 |
12 | 0 | 36 | 40 |
# because years has a MultiIndex, reset it, remove the index level "match" and convert it in a column.
ages = ages.reset_index(level=["match"])
ages.head()
match | First_Age | Second_Age | |
---|---|---|---|
5 | 0 | 41 | 45 |
8 | 0 | 31 | 35 |
9 | 0 | 46 | 50 |
11 | 0 | 31 | 35 |
12 | 0 | 36 | 40 |
# drop column match, because is useless
ages = ages.drop("match", axis = 1)
# change all values of its column to float
ages = ages.astype(float)
# transfor Nan values in Second_Age to 0
ages["Second_Age"] = ages["Second_Age"].fillna(0)
# create a new column in year based in First_Age values
ages["ages_mean"] = ages["First_Age"]
# for rows which have values in its Second_Age. The value of "ages_mean" It's the arithmetic mean
ages.loc[ages["Second_Age"] > 0,"ages_mean"] = ((ages.loc[ages["Second_Age"] > 0, "First_Age"]
+ ages.loc[ages["Second_Age"] > 0, "Second_Age"])
/ 2)
ages.head()
First_Age | Second_Age | ages_mean | |
---|---|---|---|
5 | 41.0 | 45.0 | 43.0 |
8 | 31.0 | 35.0 | 33.0 |
9 | 46.0 | 50.0 | 48.0 |
11 | 31.0 | 35.0 | 33.0 |
12 | 36.0 | 40.0 | 38.0 |
# drop this two columns of ages that we don't need more
ages = ages.drop(["First_Age", "Second_Age"], axis = 1)
# there is no rows with Nan values
ages["ages_mean"].isnull().sum()
0
# a view of the distribution of ages' values
ages["ages_mean"].describe()
count 568.000000 mean 41.005282 std 11.370852 min 20.000000 25% 33.000000 50% 43.000000 75% 48.000000 max 61.000000 Name: ages_mean, dtype: float64
According to this distribution, as in the case of years of service, we'll convert numbers in this column into categories.
We'll use the three categories below:
# create a function to transform the values year's service_cat column into categories
def to_categories(val):
if val < 33:
return "Young"
elif val >=33 and val <=48:
return "Middle age"
else:
return "Senior"
ages["ages_mean"] = ages["ages_mean"].map(to_categories)
ages["ages_mean"].value_counts()
Middle age 298 Senior 138 Young 132 Name: ages_mean, dtype: int64
We'll use here the same methon we use before to join "service_cat" column to combined_service_cat dataframe.
combined_age = pd.concat([combined_age, ages], axis = 1)
combined_age.tail()
age | cease_date | dissatisfied | employment_status | gender | id | institute | institute_service | position | separationtype | ages_mean | |
---|---|---|---|---|---|---|---|---|---|---|---|
693 | 26--30 | 2013.0 | False | Temporary Full-time | Female | 6.350599e+17 | DETE | 1-2 | Administration (AO) | Resignation | Young |
696 | 21--25 | 2013.0 | False | Temporary Full-time | Male | 6.350660e+17 | DETE | 5-6 | Operational (OO) | Resignation | Young |
697 | 51-55 | 2013.0 | False | Temporary Full-time | Male | 6.350668e+17 | DETE | 1-2 | Teacher (including LVT) | Resignation | Senior |
699 | 51-55 | 2013.0 | False | Permanent Full-time | Female | 6.350704e+17 | DETE | 5-6 | Teacher (including LVT) | Resignation | Senior |
701 | 26--30 | 2013.0 | False | Contract/casual | Female | 6.350730e+17 | DETE | 3-4 | Administration (AO) | Resignation | Young |
We can look at here that ages_mean occupies the last column, and their join with combine_age dataframe seems fine.
# for this last task we'll import matplop library
import matplotlib.pyplot as plt
%matplotlib inline
Firstly we'll check the data in the columns of our interest.
combined_service_cat["service_cat"].value_counts(dropna = False)
New 179 Experienced 164 Veteran 127 Established 57 Name: service_cat, dtype: int64
combined_service_cat["dissatisfied"].value_counts(dropna = False)
False 316 True 211 Name: dissatisfied, dtype: int64
There are no missing values in these columns above
# this is needed because if not code bellow doesn't work
combined_service_cat["dissatisfied"] = combined_service_cat["dissatisfied"].astype(bool)
combined_service_pv = combined_service_cat.pivot_table(index = "service_cat", values = "dissatisfied")
# draw a bar plot
combined_service_pv.plot(kind = "bar", title = "Percentaje of dissatisfied by service categories")
plt.show()
GRAPH 1. Percentajes of dissatisfied grouping by service_cat (years of service) categories
Firstly we'll check the data in the columns of our interest.
combined_age["ages_mean"].value_counts(dropna = False)
Middle age 298 Senior 138 Young 132 Name: ages_mean, dtype: int64
combined_age["dissatisfied"].value_counts(dropna = False)
False 343 True 225 Name: dissatisfied, dtype: int64
There are no missing values in these columns above
# this is needed because if not code bellow doesn't work
combined_age["dissatisfied"] = combined_age["dissatisfied"].astype(bool)
combined_age_pv = combined_age.pivot_table(index = "ages_mean", values = "dissatisfied")
# draw a bar plot
combined_age_pv.plot(kind = "bar", title = "Percentaje of dissatisfied by age")
plt.show()
According to GRAPH 1 above: only a 30% approximately of "New" employees (Less than 3 years at their workplace) were dissatisfied with their jobs when they exit. The were the lowest dissatisfied of all groups, classified by ages of service, in this two surveys.
On the other hand, employees belonging to "Established" and Veteran cathegories, with more than 7 years and 11 years of servis respectively, were the most dissatisfied. With more than 50% employees dissatisfied in some way, when they resigned.
According to GRAPH 2 above: a 36% approximately of "Young" employees (less than 33 years old) were dissatisfied with their jobs when they exit.
On the other hand, employees classified as "Senior", with more than 48 years old, were the most dissatisfied of all groups of age. With more than 42% dissatisfied in some way, when they resigned.
Nevertheless, we can see there are very little differences between this three groups of age, in relation to their percentaje of dissatisfaction when they resigned.