The goal of this project is to answer the following questions:
For this project we work with exit surveys from employees of the Department of Education, Training and Employment (DETE and the Technical Further Education (TAFE) institute in Queensland, Australia. The idea of this project is to combine both surveys and apply data cleaning and analysis techniques
To start, the libraries are imporated and the data is imported.
# import libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style("whitegrid", {'axes.grid' : False})
%matplotlib inline
# import csv files of surveys
dete_survey = pd.read_csv("dete_survey.csv")
tafe_survey = pd.read_csv("tafe_survey.csv")
# mute SettingWithCopyWarning
pd.options.mode.chained_assignment = None # default='warn'
Next, we are getting acquainted with the data and investigage the first rows, different types of values and proportion of missing data. Let us investigate the DETE data first, and subsequently the TAFE data.
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.dtypes.value_counts()
object 37 bool 18 int64 1 dtype: int64
dete_survey.head(50)
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Kept informed | Wellness programs | Health & Safety | Gender | Age | Aboriginal | Torres Strait | South Sea | Disability | NESB | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Ill Health Retirement | 08/2012 | 1984 | 2004 | Public Servant | A01-A04 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | Yes |
1 | 2 | Voluntary Early Retirement (VER) | 08/2012 | Not Stated | Not Stated | Public Servant | AO5-AO7 | Central Office | Corporate Strategy and Peformance | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011 | 2011 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | N | N | N | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005 | 2006 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | N | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970 | 1989 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | N | A | M | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 | 6 | Resignation-Other reasons | 05/2012 | 1994 | 1997 | Guidance Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | D | D | NaN | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
6 | 7 | Age Retirement | 05/2012 | 1972 | 2007 | Teacher | Secondary | Darling Downs South West | NaN | Permanent Part-time | ... | D | D | SD | Female | 56-60 | NaN | NaN | NaN | NaN | NaN |
7 | 8 | Age Retirement | 05/2012 | 1988 | 1990 | Teacher Aide | NaN | North Coast | NaN | Permanent Part-time | ... | SA | NaN | SA | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
8 | 9 | Resignation-Other reasons | 07/2012 | 2009 | 2009 | Teacher | Secondary | North Queensland | NaN | Permanent Full-time | ... | A | D | N | Female | 31-35 | NaN | NaN | NaN | NaN | NaN |
9 | 10 | Resignation-Other employer | 2012 | 1997 | 2008 | Teacher Aide | NaN | Not Stated | NaN | Permanent Part-time | ... | SD | SD | SD | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
10 | 11 | Age Retirement | 2012 | 1999 | 1999 | Teacher | Primary | Central Office | Education Queensland | Permanent Full-time | ... | A | NaN | A | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
11 | 12 | Resignation-Move overseas/interstate | 2012 | 2009 | 2009 | Teacher | Secondary | Far North Queensland | NaN | Permanent Full-time | ... | N | N | N | Male | 31-35 | NaN | NaN | NaN | NaN | NaN |
12 | 13 | Resignation-Other reasons | 2012 | 1998 | 1998 | Teacher | Primary | Far North Queensland | NaN | Permanent Full-time | ... | SA | A | A | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
13 | 14 | Age Retirement | 2012 | 1967 | 2000 | Teacher | Primary | Metropolitan | NaN | Permanent Part-time | ... | A | D | A | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
14 | 15 | Resignation-Other employer | 2012 | 2007 | 2010 | Teacher | Secondary | Central Queensland | NaN | Permanent Full-time | ... | SA | N | SA | Male | 31-35 | NaN | NaN | NaN | NaN | NaN |
15 | 16 | Voluntary Early Retirement (VER) | 2012 | 1995 | 2004 | Teacher | Secondary | Central Queensland | NaN | Permanent Full-time | ... | A | N | A | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
16 | 17 | Resignation-Other reasons | 2012 | Not Stated | Not Stated | Teacher Aide | NaN | South East | NaN | Permanent Part-time | ... | M | M | M | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
17 | 18 | Age Retirement | 2012 | 1996 | 1996 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | A | A | Female | 56-60 | NaN | NaN | NaN | NaN | NaN |
18 | 19 | Age Retirement | 2012 | 2006 | 2006 | Cleaner | NaN | Central Office | Education Queensland | Permanent Full-time | ... | A | A | A | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
19 | 20 | Age Retirement | 2012 | 1989 | 1989 | Cleaner | NaN | Central Office | Education Queensland | Permanent Full-time | ... | A | A | A | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
20 | 21 | Resignation-Other employer | 2012 | 1982 | 1982 | Teacher | Secondary | Central Queensland | NaN | Permanent Full-time | ... | A | SD | A | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
21 | 22 | Resignation-Other reasons | 2012 | 1980 | 2009 | Cleaner | NaN | Darling Downs South West | NaN | Permanent Part-time | ... | SA | NaN | SA | Female | 51-55 | NaN | NaN | NaN | NaN | NaN |
22 | 23 | Resignation-Other reasons | 2012 | 1997 | 1998 | School Administrative Staff | NaN | Metropolitan | NaN | Permanent Part-time | ... | N | D | D | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
23 | 24 | Resignation-Other reasons | 2012 | 1973 | 2012 | Teacher | Primary | North Queensland | NaN | Permanent Full-time | ... | D | SD | SD | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
24 | 25 | Age Retirement | 2012 | 1981 | 1981 | Teacher Aide | NaN | North Coast | NaN | Permanent Part-time | ... | A | N | A | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
25 | 26 | Resignation-Other reasons | 2012 | 1995 | 2002 | Teacher | Primary | South East | NaN | Permanent Part-time | ... | A | SD | A | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
26 | 27 | Age Retirement | 2012 | 1974 | 1977 | Teacher | Primary | Central Office | Education Queensland | Permanent Full-time | ... | A | D | A | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
27 | 28 | Resignation-Other employer | 2012 | 2005 | 2011 | Public Servant | AO5-AO7 | Central Office | Information and Technologies | Permanent Full-time | ... | A | A | A | Female | 21-25 | Yes | NaN | NaN | NaN | NaN |
28 | 29 | Age Retirement | 2012 | 1989 | 1989 | Teacher Aide | NaN | Darling Downs South West | NaN | Permanent Part-time | ... | SA | SA | SA | Female | 56-60 | NaN | NaN | NaN | NaN | NaN |
29 | 30 | Age Retirement | 2012 | 1975 | 2003 | Teacher | Special Education | South East | NaN | Permanent Full-time | ... | SA | A | A | Female | 56-60 | NaN | NaN | NaN | NaN | NaN |
30 | 31 | Age Retirement | 2012 | 1989 | 1989 | Teacher | Primary | North Coast | NaN | Permanent Full-time | ... | A | SD | SA | Female | 56-60 | NaN | NaN | NaN | NaN | NaN |
31 | 32 | Age Retirement | 2012 | 1978 | 1978 | Teacher | Secondary | Metropolitan | NaN | Permanent Part-time | ... | A | D | A | Female | 51-55 | NaN | NaN | NaN | NaN | NaN |
32 | 33 | Age Retirement | 2012 | 1975 | 1992 | Head of Curriculum/Head of Special Education | NaN | Not Stated | NaN | Permanent Full-time | ... | A | D | A | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
33 | 34 | Resignation-Other reasons | 2012 | 2003 | 2003 | Teacher | Secondary | Not Stated | NaN | Permanent Full-time | ... | N | D | N | Male | 36-40 | NaN | NaN | NaN | Yes | NaN |
34 | 35 | Resignation-Other reasons | 2012 | 2006 | 2009 | Cleaner | NaN | Central Office | Education Queensland | Permanent Part-time | ... | A | A | A | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
35 | 36 | Ill Health Retirement | 2012 | 2000 | 2000 | Teacher | Special Education | Not Stated | NaN | Permanent Full-time | ... | SD | SD | D | Female | 51-55 | NaN | NaN | NaN | NaN | NaN |
36 | 37 | Age Retirement | 2012 | Not Stated | 1997 | Schools Officer | NaN | Metropolitan | NaN | Permanent Full-time | ... | SA | N | SA | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
37 | 38 | Resignation-Other reasons | 2012 | 2011 | 2011 | Teacher Aide | NaN | Central Queensland | NaN | Temporary Part-time | ... | SA | N | N | Female | 21-25 | NaN | NaN | NaN | NaN | NaN |
38 | 39 | Other | 2012 | 1998 | 1998 | Teacher Aide | NaN | Metropolitan | NaN | Permanent Part-time | ... | N | SD | A | Female | 51-55 | NaN | NaN | NaN | NaN | NaN |
39 | 40 | Resignation-Move overseas/interstate | 2012 | Not Stated | Not Stated | Teacher | NaN | Central Queensland | NaN | Permanent Full-time | ... | N | SD | N | Female | 21-25 | NaN | NaN | NaN | NaN | NaN |
40 | 41 | Resignation-Other employer | 2012 | 1977 | 1980 | Teacher | Primary | South East | NaN | Permanent Full-time | ... | SA | A | SA | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
41 | 42 | Resignation-Other reasons | 2012 | 1974 | 1994 | Head of Curriculum/Head of Special Education | NaN | Metropolitan | NaN | Permanent Full-time | ... | N | N | SA | Female | 51-55 | NaN | NaN | NaN | NaN | NaN |
42 | 43 | Resignation-Move overseas/interstate | 2012 | 2011 | 2011 | Cleaner | NaN | North Coast | NaN | Permanent Part-time | ... | SA | NaN | NaN | Female | 41-45 | NaN | NaN | NaN | NaN | NaN |
43 | 44 | Resignation-Other reasons | 2012 | 1976 | 1976 | Teacher | Primary | North Coast | NaN | Permanent Full-time | ... | SA | N | A | Male | 51-55 | NaN | NaN | NaN | NaN | NaN |
44 | 45 | Age Retirement | 2012 | 1985 | 1991 | Teacher | Primary | Metropolitan | NaN | Permanent Part-time | ... | A | N | N | Female | 51-55 | NaN | NaN | NaN | NaN | NaN |
45 | 46 | Voluntary Early Retirement (VER) | 2012 | 1999 | 2001 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | A | D | N | Female | 46-50 | NaN | NaN | NaN | NaN | NaN |
46 | 47 | Voluntary Early Retirement (VER) | 2012 | 2008 | 2008 | Cleaner | NaN | South East | NaN | Permanent Full-time | ... | N | N | N | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
47 | 48 | Age Retirement | 2012 | 1980 | 1993 | Teacher | Secondary | Not Stated | NaN | Permanent Full-time | ... | A | D | A | Female | 56-60 | NaN | NaN | NaN | NaN | NaN |
48 | 49 | Resignation-Move overseas/interstate | 2012 | 2009 | 2010 | Cleaner | NaN | South East | NaN | Permanent Full-time | ... | A | A | A | Male | 21-25 | NaN | NaN | NaN | NaN | NaN |
49 | 50 | Age Retirement | 2012 | 1963 | 2007 | Teacher | Primary | Darling Downs South West | NaN | Permanent Full-time | ... | A | D | A | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
50 rows × 56 columns
At first sight, we can make the following observations:
True
or False
valuesNot Stated
is answered referring to missing information (see Region
column)Now let's check what kind of values we have in these columns and explore whether we have a lot of missing or unworkable data.
dete_survey.isnull().sum() # Summing all missing data per column
ID 0 SeparationType 0 Cease Date 0 DETE Start Date 0 Role Start Date 0 Position 5 Classification 367 Region 0 Business Unit 696 Employment Status 5 Career move to public sector 0 Career move to private sector 0 Interpersonal conflicts 0 Job dissatisfaction 0 Dissatisfaction with the department 0 Physical work environment 0 Lack of recognition 0 Lack of job security 0 Work location 0 Employment conditions 0 Maternity/family 0 Relocation 0 Study/Travel 0 Ill Health 0 Traumatic incident 0 Work life balance 0 Workload 0 None of the above 0 Professional Development 14 Opportunities for promotion 87 Staff morale 6 Workplace issue 34 Physical environment 5 Worklife balance 7 Stress and pressure support 12 Performance of supervisor 9 Peer support 10 Initiative 9 Skills 11 Coach 55 Career Aspirations 76 Feedback 30 Further PD 54 Communication 8 My say 10 Information 6 Kept informed 9 Wellness programs 56 Health & Safety 29 Gender 24 Age 11 Aboriginal 806 Torres Strait 819 South Sea 815 Disability 799 NESB 790 dtype: int64
dete_survey.apply(pd.Series.value_counts)
/dataquest/system/env/python3/lib/python3.4/site-packages/pandas/core/indexes/api.py:87: RuntimeWarning: unorderable types: int() < str(), sort order is undefined for incomparable objects /dataquest/system/env/python3/lib/python3.4/site-packages/pandas/core/frame.py:6206: RuntimeWarning: unorderable types: int() < str(), sort order is undefined for incomparable objects /dataquest/system/env/python3/lib/python3.4/site-packages/pandas/core/frame.py:6206: RuntimeWarning: unorderable types: str() < int(), sort order is undefined for incomparable objects
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
823 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
270 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
280 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
279 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
278 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
277 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
276 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
275 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
274 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
273 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
272 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
271 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
269 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
282 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
268 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
267 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
266 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
265 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
264 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
263 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
262 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
261 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
260 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
259 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
281 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
283 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
308 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
296 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
306 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
305 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
Pacific Pines SHS | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Finance | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Calliope State School | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Corporate Procurement | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Indigenous Education and Training Futures | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Permanent Full-time | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 434.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Permanent Part-time | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 308.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Temporary Full-time | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 41.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Temporary Part-time | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 24.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Casual | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 10.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
False | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
A | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 401.0 | 253.0 | 386.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
SA | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 162.0 | 78.0 | 141.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 130.0 | 225.0 | 153.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
D | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 60.0 | 105.0 | 50.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
SD | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 50.0 | 72.0 | 35.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
M | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 10.0 | 33.0 | 28.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Female | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | 573.0 | NaN | NaN | NaN | NaN | NaN | NaN |
Male | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | 225.0 | NaN | NaN | NaN | NaN | NaN | NaN |
61 or older | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 222.0 | NaN | NaN | NaN | NaN | NaN |
56-60 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 174.0 | NaN | NaN | NaN | NaN | NaN |
51-55 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 103.0 | NaN | NaN | NaN | NaN | NaN |
46-50 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 63.0 | NaN | NaN | NaN | NaN | NaN |
41-45 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 61.0 | NaN | NaN | NaN | NaN | NaN |
26-30 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 57.0 | NaN | NaN | NaN | NaN | NaN |
36-40 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 51.0 | NaN | NaN | NaN | NaN | NaN |
21-25 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 40.0 | NaN | NaN | NaN | NaN | NaN |
31-35 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 39.0 | NaN | NaN | NaN | NaN | NaN |
20 or younger | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 1.0 | NaN | NaN | NaN | NaN | NaN |
Yes | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 16.0 | 3.0 | 7.0 | 23.0 | 32.0 |
972 rows × 56 columns
Observations that can be made from exploring the DETE data:
Now let's consider the TAFE data and follow similar exploration techniques.
tafe_survey.info()
tafe_survey.head(5)
tafe_survey.dtypes.value_counts()
<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
object 70 float64 2 dtype: int64
Findings:
tafe_survey.isnull().sum() # Summing all missing data per column
Record ID 0 Institute 0 WorkArea 0 CESSATION YEAR 7 Reason for ceasing employment 1 Contributing Factors. Career Move - Public Sector 265 Contributing Factors. Career Move - Private Sector 265 Contributing Factors. Career Move - Self-employment 265 Contributing Factors. Ill Health 265 Contributing Factors. Maternity/Family 265 Contributing Factors. Dissatisfaction 265 Contributing Factors. Job Dissatisfaction 265 Contributing Factors. Interpersonal Conflict 265 Contributing Factors. Study 265 Contributing Factors. Travel 265 Contributing Factors. Other 265 Contributing Factors. NONE 265 Main Factor. Which of these was the main factor for leaving? 589 InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 94 InstituteViews. Topic:2. I was given access to skills training to help me do my job better 89 InstituteViews. Topic:3. I was given adequate opportunities for personal development 92 InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 94 InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 87 InstituteViews. Topic:6. The organisation recognised when staff did good work 95 InstituteViews. Topic:7. Management was generally supportive of me 88 InstituteViews. Topic:8. Management was generally supportive of my team 94 InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 92 InstituteViews. Topic:10. Staff morale was positive within the Institute 100 InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 101 InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 105 ... WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 91 WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 96 WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 92 WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 93 WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 99 WorkUnitViews. Topic:30. Staff morale was positive within my work unit 96 Induction. Did you undertake Workplace Induction? 83 InductionInfo. Topic:Did you undertake a Corporate Induction? 270 InductionInfo. Topic:Did you undertake a Institute Induction? 219 InductionInfo. Topic: Did you undertake Team Induction? 262 InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 147 InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 147 InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 147 InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 172 InductionInfo. On-line Topic:Did you undertake a Institute Induction? 147 InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 149 InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 147 InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 147 InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 147 Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 94 Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 108 Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 115 Workplace. Topic:Does your workplace value the diversity of its employees? 116 Workplace. Topic:Would you recommend the Institute as an employer to others? 121 Gender. What is your Gender? 106 CurrentAge. Current Age 106 Employment Type. Employment Type 106 Classification. Classification 106 LengthofServiceOverall. Overall Length of Service at Institute (in years) 106 LengthofServiceCurrent. Length of Service at current workplace (in years) 106 Length: 72, dtype: int64
tafe_survey.apply(pd.Series.value_counts)
/dataquest/system/env/python3/lib/python3.4/site-packages/pandas/core/indexes/api.py:87: RuntimeWarning: unorderable types: float() < str(), sort order is undefined for incomparable objects /dataquest/system/env/python3/lib/python3.4/site-packages/pandas/core/frame.py:6206: RuntimeWarning: unorderable types: float() < str(), sort order is undefined for incomparable objects /dataquest/system/env/python3/lib/python3.4/site-packages/pandas/core/frame.py:6206: RuntimeWarning: unorderable types: str() < float(), sort order is undefined for incomparable objects
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) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6.34219394328258e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34992898379375e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34208063783969e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34595096448594e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34171929735346e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34329804601719e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34260719918915e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34568388536875e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34638198687656e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34577056474375e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34568317773125e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34982246838125e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34867257481719e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34849850334844e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34794857718438e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34480996232969e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34733451073235e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.3432311502146e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34552909616094e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34541462557656e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34587326853281e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34684858305469e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34753321028079e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34735049954641e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34743600339485e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34852503880156e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34761898333619e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34570821134531e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.34686807219219e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
6.3458231357125e+17 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
56 or older | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 162.0 | NaN | NaN | NaN | NaN |
51-55 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 82.0 | NaN | NaN | NaN | NaN |
41 45 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 80.0 | NaN | NaN | NaN | NaN |
46 50 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 59.0 | NaN | NaN | NaN | NaN |
31 35 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 52.0 | NaN | NaN | NaN | NaN |
36 40 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 51.0 | NaN | NaN | NaN | NaN |
26 30 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 50.0 | NaN | NaN | NaN | NaN |
21 25 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 44.0 | NaN | NaN | NaN | NaN |
20 or younger | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 16.0 | NaN | NaN | NaN | NaN |
Permanent Full-time | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 237.0 | NaN | NaN | NaN |
Temporary Full-time | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 177.0 | NaN | NaN | NaN |
Contract/casual | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 71.0 | NaN | NaN | NaN |
Permanent Part-time | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 59.0 | NaN | NaN | NaN |
Temporary Part-time | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 52.0 | NaN | NaN | NaN |
Administration (AO) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 293.0 | NaN | NaN |
Teacher (including LVT) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 201.0 | NaN | NaN |
Professional Officer (PO) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 33.0 | NaN | NaN |
Operational (OO) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 24.0 | NaN | NaN |
Tutor | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 16.0 | NaN | NaN |
Workplace Training Officer | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 11.0 | NaN | NaN |
Technical Officer (TO) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 10.0 | NaN | NaN |
Executive (SES/SO) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6.0 | NaN | NaN |
Apprentice | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | NaN | NaN |
Less than 1 year | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 147.0 | 177.0 |
1-2 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 102.0 | 113.0 |
3-4 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 96.0 | 86.0 |
11-20 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 89.0 | 82.0 |
More than 20 years | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 71.0 | 54.0 |
5-6 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 48.0 | 40.0 |
7-10 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 43.0 | 44.0 |
784 rows × 72 columns
Observations:
Next, we determine which data we need to leave out and which data needs to be modified in order to be useful. We have observed that Not Stated
needs to be transformed to NaN
, so we will first re-read the .csv file. In addition, we can reduce the dataset to data that we explicitly need for our analysis by eliminating columns that we do not use.
dete_survey = pd.read_csv("dete_survey.csv", na_values="Not Stated")
# Drop irrelevant columns
dete_survey_updated = dete_survey.drop(dete_survey.columns[28:49], axis=1)
tafe_survey_updated = tafe_survey.drop(tafe_survey.columns[17:66], axis=1)
# Explore our new datasets
dete_survey_updated.head() # Change to 50 to see that Region has NaN values now
ID | SeparationType | Cease Date | DETE Start Date | Role Start Date | Position | Classification | Region | Business Unit | Employment Status | ... | Work life balance | Workload | None of the above | 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 | ... | False | False | True | 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 | ... | False | False | False | Male | 56-60 | NaN | NaN | NaN | NaN | NaN |
2 | 3 | Voluntary Early Retirement (VER) | 05/2012 | 2011.0 | 2011.0 | Schools Officer | NaN | Central Office | Education Queensland | Permanent Full-time | ... | False | False | True | Male | 61 or older | NaN | NaN | NaN | NaN | NaN |
3 | 4 | Resignation-Other reasons | 05/2012 | 2005.0 | 2006.0 | Teacher | Primary | Central Queensland | NaN | Permanent Full-time | ... | False | False | False | Female | 36-40 | NaN | NaN | NaN | NaN | NaN |
4 | 5 | Age Retirement | 05/2012 | 1970.0 | 1989.0 | Head of Curriculum/Head of Special Education | NaN | South East | NaN | Permanent Full-time | ... | True | False | False | Female | 61 or older | NaN | NaN | NaN | NaN | NaN |
5 rows × 35 columns
tafe_survey_updated.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. 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) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | 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 | NaN | NaN | NaN | NaN | NaN |
2 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | - | - | - | NONE | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | - | Travel | - | - | NaN | NaN | NaN | NaN | NaN | NaN |
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 |
5 rows × 23 columns
Subsequently, let's focus on the differences between columns names of the above presented data structures. It appears that both data sets use different names for similar subjects. For example:
ID
versus Record ID
Age
versus CurrentAge. Current Age
Gender
versus Gender. What is your Gender?
Let's consider both data sets separately and update the column names to a single format.
dete_survey_updated.columns = dete_survey_updated.columns.str.lower().str.strip().str.replace(' ', '_')
tafe_survey_updated = tafe_survey_updated.rename(
{'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
)
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')
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')
In the above cells it can be seen that the columns that we are willing to evaluate for our research question have similar notations (e.g. id
, cease_date
). This is important to compare both data sets on column values and derive meaningful conclusions.
Now, let's count the reasons for leaving an employer for both data sets. We are only focusing on employees who have resignated and therefore filter these data from the data sets.
dete_survey_updated['separationtype'].value_counts()
dete_resignations = dete_survey_updated[dete_survey_updated['separationtype'].str.contains('Resignation')]
tafe_survey_updated['separationtype'].value_counts()
tafe_resignations = tafe_survey_updated[tafe_survey_updated['separationtype'] == 'Resignation']
The above operations for both data sets derive each row with a clear resignation reason as separation. These are the rows that we will continue work with.
Next, the starting and ending dates that are filled in the survey are evaluated. It would not make sense if the starting date is in the future or the beginning date is prior to 1940. The latter is not realiastic when we assume that a person is approximately 20 years when he/she begins and would now be over 100 years. Therefore, for those two exceptions we assume that the data is inaccurate and must be removed from the data sets.
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 2010 1 07/2006 1 07/2012 1 Name: cease_date, dtype: int64
It appears that besides the year as input, some respondents have entered the month in which they have started. Let's delete the months and solemny focus on the years that the respondents started to work.
# Converting years to strings to strip unwanted characters and store years
dete_resignations['cease_date'] = dete_resignations['cease_date'].astype('str')
dete_resignations['cease_date'] = dete_resignations['cease_date'].str.strip().replace("/","")
dete_resignations['cease_date'] = dete_resignations['cease_date'].str[-4:]
# Restore as integers and check whether there are remaining missing values
dete_resignations['cease_date'] = dete_resignations['cease_date'].astype('float')
dete_resignations['cease_date'].value_counts(dropna=False).sort_index(ascending=True)
2006.0 1 2010.0 2 2012.0 129 2013.0 146 2014.0 22 NaN 11 Name: cease_date, dtype: int64
dete_resignations['dete_start_date'].value_counts(dropna=False).sort_index(ascending=True)
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
dete_start = dete_resignations['dete_start_date']
dete_cease = dete_resignations['cease_date']
tafe_cease = tafe_resignations['cease_date']
df_resignations = pd.concat([dete_start, dete_cease, tafe_cease], axis=1)
df_resignations = df_resignations.dropna(subset=['dete_start_date',
'cease_date']
)
# PLEASE RUN THE LINE BELOW TO SEE MY QUESTION
ax = sns.boxplot(data = df_resignations, palette="Blues_d")
# ax = sns.boxplot(df_resignations.dete_start,
# df_resignations.dete_cease,
# df_resignations.tafe_cease,
# palette="Blues_d")
ax.set_xticklabels(ax.get_xticklabels(),rotation=30)
/dataquest/system/env/python3/lib/python3.4/site-packages/seaborn/categorical.py:454: FutureWarning: remove_na is deprecated and is a private function. Do not use.
[<matplotlib.text.Text at 0x7f297872eac8>, <matplotlib.text.Text at 0x7f29787313c8>, <matplotlib.text.Text at 0x7f2978715ba8>, <matplotlib.text.Text at 0x7f29787186a0>, <matplotlib.text.Text at 0x7f297871c198>]
In the above lines we have stored the starting and ending dates in series and combined them in a DataFrame. It is interesting to see that most ceasing dates are in 2012 or 2013, while, obviously, the starting dates are spear more evenly from now until 1980.
Are employees who have only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been at the job longer?
To answer this question, let's see what perion our respondents have been working at their institutions.
dete_resignations = dete_resignations.dropna(subset=['dete_start_date',
'cease_date',]
)
dete_resignations['institute_service'] = dete_cease.sub(dete_start, fill_value=0)
dete_resignations['institute_service'].value_counts()
5.0 23 1.0 22 3.0 20 0.0 20 6.0 17 4.0 16 9.0 14 2.0 14 7.0 13 13.0 8 8.0 8 20.0 7 15.0 7 10.0 6 22.0 6 14.0 6 17.0 6 12.0 6 16.0 5 18.0 5 23.0 4 11.0 4 24.0 4 39.0 3 19.0 3 21.0 3 32.0 3 28.0 2 26.0 2 25.0 2 30.0 2 36.0 2 29.0 1 33.0 1 42.0 1 27.0 1 41.0 1 35.0 1 38.0 1 34.0 1 49.0 1 31.0 1 Name: institute_service, dtype: int64
Above the results are shown which indictes two things:
Due to the large number of cessations observed in the first years of working, it is good to check the underlying reasons for this. Below we create a function to update values and check the dissatisfaction reasons of the cessations.
def update_vals(val):
if pd.isnull(val):
return np.nan
elif val == '-':
return False
else:
return True
tafe_dissatisfied = tafe_resignations[['Contributing Factors. Dissatisfaction',
'Contributing Factors. Job Dissatisfaction']].applymap(update_vals)
In the above cells it can be seen that our function distinguishes the reasons for which employees tend to be dissatisfied and might leave their employer. Now, let's seperate the data from the
tafe_resignations['dissatisfied'] = tafe_dissatisfied.any(axis=1, skipna=False)
tafe_resignations_up = tafe_resignations.copy()
tafe_resignations_up['dissatisfied'].value_counts(dropna=False)
False 241 True 91 NaN 8 Name: dissatisfied, dtype: int64
From the outcomes observed above we see that 91 respondents resigned since they qualify as being dissatisfied with their job in the tafe survey. For 241 respondents the resignation reason is something else and therefore we cannot assume with certainty that these respondents have been dissatisfied with their job.
Now let's perform the same steps for the tafe survey.
cols_dissatisfied = ['job_dissatisfaction',
'dissatisfaction_with_the_department',
'physical_work_environment',
'lack_of_recognition',
'lack_of_job_security',
'work_location',
'employment_conditions',
'work_life_balance',
'workload']
dete_resignations[cols_dissatisfied].head(5)
job_dissatisfaction | dissatisfaction_with_the_department | physical_work_environment | lack_of_recognition | lack_of_job_security | work_location | employment_conditions | work_life_balance | workload | |
---|---|---|---|---|---|---|---|---|---|
3 | False | False | False | False | False | False | False | False | False |
5 | False | False | False | False | False | False | True | False | False |
8 | False | False | False | False | False | False | False | False | False |
9 | True | True | False | False | False | False | False | False | False |
11 | False | False | False | False | False | False | False | False | False |
dete_resignations['dissatisfied'] = dete_resignations[cols_dissatisfied].any(axis=1, skipna=False)
dete_resignations_up = dete_resignations.copy()
dete_resignations_up['dissatisfied'].value_counts(dropna=False)
True 137 False 136 Name: dissatisfied, dtype: int64
From the dete resignations, it appears that there are 149 respondents that left their employers due to dissatisfaction with their job. In contrast to the tafe survey, we observe that almost 50% of these respondents have been dissatisfied. To avoid drawing conclusions to quickly, we must understand that there are more columns in which respondents can provide signs of dissatisfaction, hence resulting in a higher dissatisfaction rate than the tafe survey.
Now that our data is workable and we have erased most of the common flaws such as missing values and unrealistic dates, let's combine the datasets together to work from the same dataset.
Our end goal is to aggregate the data according to the institute_service
column, which is performed in the next steps.
dete_resignations_up['institute'] = 'DETE'
tafe_resignations_up['institute'] = 'TAFE'
combined = pd.concat([dete_resignations_up, tafe_resignations_up], axis=0)
print(combined.shape)
combined_updated = combined.dropna(axis=1, thresh=500)
print(combined_updated.shape)
(613, 53) (613, 10)
We finally have combined the tafe and dete survey into one dataframe. Since values were still missing in a a number of columns, we dropped the ones that provide limited information in that sense. It is noticable that we have removed 43 columns that qualified as having fewer than 500 non-null cells that we consider as the threshold for being meaningful.
combined_updated
age | cease_date | dissatisfied | employment_status | gender | id | institute | institute_service | position | separationtype | |
---|---|---|---|---|---|---|---|---|---|---|
3 | 36-40 | 2012.0 | False | Permanent Full-time | Female | 4.000000e+00 | DETE | 7 | Teacher | Resignation-Other reasons |
5 | 41-45 | 2012.0 | True | Permanent Full-time | Female | 6.000000e+00 | DETE | 18 | Guidance Officer | Resignation-Other reasons |
8 | 31-35 | 2012.0 | False | Permanent Full-time | Female | 9.000000e+00 | DETE | 3 | Teacher | Resignation-Other reasons |
9 | 46-50 | 2012.0 | True | Permanent Part-time | Female | 1.000000e+01 | DETE | 15 | Teacher Aide | Resignation-Other employer |
11 | 31-35 | 2012.0 | False | Permanent Full-time | Male | 1.200000e+01 | DETE | 3 | Teacher | Resignation-Move overseas/interstate |
12 | 36-40 | 2012.0 | False | Permanent Full-time | Female | 1.300000e+01 | DETE | 14 | Teacher | Resignation-Other reasons |
14 | 31-35 | 2012.0 | True | Permanent Full-time | Male | 1.500000e+01 | DETE | 5 | Teacher | Resignation-Other employer |
20 | 56-60 | 2012.0 | False | Permanent Full-time | Male | 2.100000e+01 | DETE | 30 | Teacher | Resignation-Other employer |
21 | 51-55 | 2012.0 | False | Permanent Part-time | Female | 2.200000e+01 | DETE | 32 | Cleaner | Resignation-Other reasons |
22 | 46-50 | 2012.0 | True | Permanent Part-time | Female | 2.300000e+01 | DETE | 15 | School Administrative Staff | Resignation-Other reasons |
23 | 61 or older | 2012.0 | True | Permanent Full-time | Female | 2.400000e+01 | DETE | 39 | Teacher | Resignation-Other reasons |
25 | 41-45 | 2012.0 | True | Permanent Part-time | Female | 2.600000e+01 | DETE | 17 | Teacher | Resignation-Other reasons |
27 | 21-25 | 2012.0 | False | Permanent Full-time | Female | 2.800000e+01 | DETE | 7 | Public Servant | Resignation-Other employer |
33 | 36-40 | 2012.0 | True | Permanent Full-time | Male | 3.400000e+01 | DETE | 9 | Teacher | Resignation-Other reasons |
34 | 61 or older | 2012.0 | True | Permanent Part-time | Male | 3.500000e+01 | DETE | 6 | Cleaner | Resignation-Other reasons |
37 | 21-25 | 2012.0 | False | Temporary Part-time | Female | 3.800000e+01 | DETE | 1 | Teacher Aide | Resignation-Other reasons |
40 | 56-60 | 2012.0 | False | Permanent Full-time | Male | 4.100000e+01 | DETE | 35 | Teacher | Resignation-Other employer |
41 | 51-55 | 2012.0 | True | Permanent Full-time | Female | 4.200000e+01 | DETE | 38 | Head of Curriculum/Head of Special Education | Resignation-Other reasons |
42 | 41-45 | 2012.0 | False | Permanent Part-time | Female | 4.300000e+01 | DETE | 1 | Cleaner | Resignation-Move overseas/interstate |
43 | 51-55 | 2012.0 | True | Permanent Full-time | Male | 4.400000e+01 | DETE | 36 | Teacher | Resignation-Other reasons |
48 | 21-25 | 2012.0 | False | Permanent Full-time | Male | 4.900000e+01 | DETE | 3 | Cleaner | Resignation-Move overseas/interstate |
50 | 21-25 | 2012.0 | False | Permanent Full-time | Male | 5.100000e+01 | DETE | 3 | Cleaner | Resignation-Move overseas/interstate |
51 | 61 or older | 2012.0 | False | Permanent Full-time | Female | 5.200000e+01 | DETE | 19 | Cleaner | Resignation-Other reasons |
55 | 26-30 | 2012.0 | False | Permanent Part-time | Female | 5.600000e+01 | DETE | 4 | Teacher Aide | Resignation-Other employer |
57 | 46-50 | 2012.0 | False | Permanent Full-time | Male | 5.800000e+01 | DETE | 9 | Teacher | Resignation-Other employer |
61 | 31-35 | 2012.0 | False | Temporary Part-time | Female | 6.200000e+01 | DETE | 1 | Schools Officer | Resignation-Other reasons |
69 | 36-40 | 2012.0 | True | Permanent Full-time | Female | 7.000000e+01 | DETE | 6 | Public Servant | Resignation-Other reasons |
71 | 36-40 | 2012.0 | False | Permanent Part-time | Female | 7.200000e+01 | DETE | 1 | Teacher Aide | Resignation-Other reasons |
87 | 26-30 | 2012.0 | False | Permanent Full-time | Female | 8.800000e+01 | DETE | 5 | Teacher | Resignation-Move overseas/interstate |
90 | 41-45 | 2012.0 | False | Permanent Part-time | Female | 9.100000e+01 | DETE | 26 | Teacher Aide | Resignation-Other employer |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
659 | 46 50 | 2013.0 | False | Temporary Part-time | Female | 6.349985e+17 | TAFE | 1-2 | Administration (AO) | Resignation |
660 | 41 45 | 2013.0 | False | Permanent Part-time | Female | 6.349994e+17 | TAFE | 3-4 | Administration (AO) | Resignation |
661 | 46 50 | 2013.0 | True | Permanent Full-time | Female | 6.350003e+17 | TAFE | 5-6 | Administration (AO) | Resignation |
665 | NaN | 2013.0 | False | NaN | NaN | 6.350055e+17 | TAFE | NaN | NaN | Resignation |
666 | NaN | 2013.0 | False | NaN | NaN | 6.350055e+17 | TAFE | NaN | NaN | Resignation |
669 | 26 30 | 2013.0 | False | Temporary Full-time | Female | 6.350108e+17 | TAFE | 3-4 | Administration (AO) | Resignation |
670 | NaN | 2013.0 | NaN | NaN | NaN | 6.350124e+17 | TAFE | NaN | NaN | Resignation |
671 | 46 50 | 2013.0 | True | Temporary Full-time | Female | 6.350127e+17 | TAFE | Less than 1 year | Teacher (including LVT) | Resignation |
675 | 51-55 | 2013.0 | True | Temporary Full-time | Male | 6.350175e+17 | TAFE | Less than 1 year | Teacher (including LVT) | Resignation |
676 | 41 45 | 2013.0 | False | Contract/casual | Female | 6.350194e+17 | TAFE | 1-2 | Administration (AO) | Resignation |
677 | 36 40 | 2013.0 | False | Temporary Full-time | Female | 6.350219e+17 | TAFE | Less than 1 year | Administration (AO) | Resignation |
678 | 51-55 | 2013.0 | False | Permanent Full-time | Male | 6.350253e+17 | TAFE | 3-4 | Administration (AO) | Resignation |
679 | 56 or older | 2013.0 | False | Temporary Part-time | Female | 6.350279e+17 | TAFE | 1-2 | Operational (OO) | Resignation |
681 | 26 30 | 2013.0 | False | Temporary Full-time | Female | 6.350314e+17 | TAFE | Less than 1 year | Administration (AO) | Resignation |
682 | 26 30 | 2013.0 | False | Permanent Part-time | Female | 6.350357e+17 | TAFE | Less than 1 year | Administration (AO) | Resignation |
683 | 41 45 | 2013.0 | False | Temporary Full-time | Female | 6.350374e+17 | TAFE | Less than 1 year | Administration (AO) | Resignation |
684 | 41 45 | 2013.0 | False | Contract/casual | Male | 6.350375e+17 | TAFE | 3-4 | Administration (AO) | Resignation |
685 | 26 30 | 2013.0 | True | Temporary Full-time | Female | 6.350402e+17 | TAFE | 1-2 | Technical Officer (TO) | Resignation |
686 | 41 45 | 2013.0 | False | Temporary Full-time | Female | 6.350426e+17 | TAFE | 5-6 | Administration (AO) | Resignation |
688 | 46 50 | 2013.0 | False | Permanent Part-time | Female | 6.350479e+17 | TAFE | 5-6 | Professional Officer (PO) | Resignation |
689 | 41 45 | 2013.0 | True | Permanent Full-time | Male | 6.350480e+17 | TAFE | Less than 1 year | Teacher (including LVT) | Resignation |
690 | NaN | 2013.0 | False | NaN | NaN | 6.350496e+17 | TAFE | NaN | NaN | Resignation |
691 | 56 or older | 2013.0 | False | Permanent Part-time | Female | 6.350496e+17 | TAFE | 3-4 | Operational (OO) | Resignation |
693 | 26 30 | 2013.0 | False | Temporary Full-time | Female | 6.350599e+17 | TAFE | 1-2 | Administration (AO) | Resignation |
694 | NaN | 2013.0 | False | NaN | NaN | 6.350652e+17 | TAFE | NaN | NaN | Resignation |
696 | 21 25 | 2013.0 | False | Temporary Full-time | Male | 6.350660e+17 | TAFE | 5-6 | Operational (OO) | Resignation |
697 | 51-55 | 2013.0 | False | Temporary Full-time | Male | 6.350668e+17 | TAFE | 1-2 | Teacher (including LVT) | Resignation |
698 | NaN | 2013.0 | False | NaN | NaN | 6.350677e+17 | TAFE | NaN | NaN | Resignation |
699 | 51-55 | 2013.0 | False | Permanent Full-time | Female | 6.350704e+17 | TAFE | 5-6 | Teacher (including LVT) | Resignation |
701 | 26 30 | 2013.0 | False | Contract/casual | Female | 6.350730e+17 | TAFE | 3-4 | Administration (AO) | Resignation |
613 rows × 10 columns
We observe that values in the institute_service
column still have values that are difficult to compare. For example, it can be seen that there are cells that include words such as less than
or more than
in the cells. In addition, we want to categorize values based on years at a company and do this as follows:
combined_updated = combined_updated.dropna(subset=['institute_service'])
combined_updated['institute_service'].value_counts()
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 20.0 7 15.0 7 10.0 6 12.0 6 14.0 6 17.0 6 22.0 6 16.0 5 18.0 5 11.0 4 24.0 4 23.0 4 19.0 3 21.0 3 39.0 3 32.0 3 25.0 2 26.0 2 28.0 2 30.0 2 36.0 2 27.0 1 29.0 1 33.0 1 35.0 1 38.0 1 31.0 1 41.0 1 42.0 1 49.0 1 34.0 1 Name: institute_service, dtype: int64
We observe that there are two values that are recurring in which string characters are found: Less than 1 year
and More than 20 years
. If we follow the previous mentioned categorization, these values can be modified to end up in new
and veteran
categrories, respectively. Lets filter out all the string characters als leave the digits.
# Set data type to string to perform string operations
combined_updated['institute_service'] = combined_updated['institute_service'].astype('str')
combined_updated['institute_service'] = combined_updated['institute_service'].str.split(pat='-').str[0]
combined_updated['institute_service'] = combined_updated['institute_service'].str.split(pat='.').str[0]
# Function that only stores digits of a string
def digit_only(s):
s = ''.join(filter(str.isdigit, s))
return s
# Apply function and set data type back to float
combined_updated['institute_service'] = combined_updated['institute_service'].apply(digit_only)
combined_updated['institute_service'] = combined_updated['institute_service'].astype('float')
combined_updated['institute_service'].head()
3 7.0 5 18.0 8 3.0 9 15.0 11 3.0 Name: institute_service, dtype: float64
Now the institute_service
data is removed from unwanted characters and can be grouped according to the previously announced categorizations.
def career_cat(year):
if year < 3:
return 'New'
elif (year > 2) and (year < 7):
return 'Experienced'
elif (year > 6) and (year < 10):
return 'Established'
else:
return 'Veteran'
combined_updated['service_cat'] = combined_updated['institute_service'].map(career_cat)
counts = combined_updated['service_cat'].value_counts()
ax = sns.countplot(data=combined_updated, x='service_cat', hue='gender',
palette="Blues_d")
/dataquest/system/env/python3/lib/python3.4/site-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.
We observe that there are much more women participated in the studies.
Finally we are able to use the combined data set to determine the dissatisfaction among participants of the surveys.
combined_updated['dissatisfied'].value_counts(dropna=False)
False 349 True 214 Name: dissatisfied, dtype: int64
pd.pivot_table(combined_updated, index=['service_cat'],
values=['dissatisfied'], aggfunc='sum')
dissatisfied | |
---|---|
service_cat | |
Established | 31 |
Experienced | 59 |
New | 57 |
Veteran | 67 |
ax = sns.countplot(data=combined_updated,
x='dissatisfied',
hue='service_cat',
palette="Blues_d")
/dataquest/system/env/python3/lib/python3.4/site-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.
combined_updated.columns
Index(['age', 'cease_date', 'dissatisfied', 'employment_status', 'gender', 'id', 'institute', 'institute_service', 'position', 'separationtype', 'service_cat'], dtype='object')
ax = sns.countplot(data=combined_updated, x='position',
hue='dissatisfied',
palette="Blues_d")
ax.set_xticklabels(ax.get_xticklabels(),rotation=90)
/dataquest/system/env/python3/lib/python3.4/site-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.
[<matplotlib.text.Text at 0x7f2978a95cc0>, <matplotlib.text.Text at 0x7f29789a8160>, <matplotlib.text.Text at 0x7f2978b25208>, <matplotlib.text.Text at 0x7f2978b0e940>, <matplotlib.text.Text at 0x7f2978b0eef0>, <matplotlib.text.Text at 0x7f2978c470f0>, <matplotlib.text.Text at 0x7f2978c3feb8>, <matplotlib.text.Text at 0x7f2978c3fb00>, <matplotlib.text.Text at 0x7f2979e15518>, <matplotlib.text.Text at 0x7f2978bf0160>, <matplotlib.text.Text at 0x7f2979df5748>, <matplotlib.text.Text at 0x7f2978b7f9b0>, <matplotlib.text.Text at 0x7f2978b86278>, <matplotlib.text.Text at 0x7f2978b861d0>, <matplotlib.text.Text at 0x7f2978c1eb38>, <matplotlib.text.Text at 0x7f2979cb82e8>, <matplotlib.text.Text at 0x7f2979cb8390>, <matplotlib.text.Text at 0x7f2978bfe4a8>, <matplotlib.text.Text at 0x7f2978b97208>, <matplotlib.text.Text at 0x7f2978b97ac8>, <matplotlib.text.Text at 0x7f2978ba3a58>]
ax = sns.countplot(data=combined_updated, x='institute',
hue='dissatisfied',
palette="Blues_d")
ax.set_xticklabels(ax.get_xticklabels(),rotation=90)
/dataquest/system/env/python3/lib/python3.4/site-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.
[<matplotlib.text.Text at 0x7f2978ae6f98>, <matplotlib.text.Text at 0x7f297a846908>]
This project has cleaned, aggregated and analysed two institute data sets to determine workplace dissatisfaction. Main observations are:
In conclusion, there are multiple indicators that show correlations with workplace dissatisfaction. If dissatisfied respondents are more willing to participate in surveys that may bias the results is an interesting future study.