In this guided project, 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.
In this project, we'll play the role of data analyst and pretend our stakeholders want to know the following:
They want us to combine the results for both surveys to answer these questions. However, although both used the same survey template, one of them customized some of the answers. In the guided steps, we'll aim to do most of the data cleaning and get you started analyzing the first question.
Below is a preview of a couple columns we'll work with from the dete_survey.csv:
Below is a preview of a couple columns we'll work with from the tafe_survey.csv:
# Importing the libraries needed and reading the datasets.
import pandas as pd
import numpy as np
dete_survey = pd.read_csv('dete_survey.csv')
tafe_survey = pd.read_csv('tafe_survey.csv')
# Getting details about the dete_survey dataset.
print("Dataset rows: ",dete_survey.shape[0]," Dataset columns: ",dete_survey.shape[1])
print('\n')
dete_survey.info()
Dataset rows: 822 Dataset columns: 56 <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()
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 rows × 56 columns
# Getting details about the tafe_survey dataset.
print("Dataset rows: ",tafe_survey.shape[0]," Dataset columns: ",tafe_survey.shape[1])
print('\n')
tafe_survey.info()
Dataset rows: 702 Dataset columns: 72 <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()
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 | 6.341388e+17 | Mount Isa Institute of TAFE | Delivery (teaching) | 2010.0 | Retirement | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
3 | 6.341399e+17 | Mount Isa Institute of TAFE | Non-Delivery (corporate) | 2010.0 | Resignation | - | - | - | - | - | ... | Yes | Yes | Yes | Yes | NaN | NaN | NaN | NaN | NaN | NaN |
4 | 6.341466e+17 | Southern Queensland Institute of TAFE | Delivery (teaching) | 2010.0 | Resignation | - | Career Move - Private Sector | - | - | - | ... | Yes | Yes | Yes | Yes | Male | 41 45 | Permanent Full-time | Teacher (including LVT) | 3-4 | 3-4 |
5 rows × 72 columns
At first sight my remarks about these datasets are:
> The dete_survey dataframe contains 'Not Stated' values that indicate values are missing, but they aren't represented as NaN.
> Both the dete_survey and tafe_survey dataframes contain many columns that we don't need to complete our analysis.
> Each dataframe contains many of the same columns, but the column names are different.
There are multiple columns/answers that indicate an employee resigned because they were dissatisfied.
# Reading 'Not Stated' values in as NAN and dropping not used columns from dete_survey dataset.
dete_survey = pd.read_csv('dete_survey.csv',na_values='Not Stated')
columns_to_drop = dete_survey.columns[28:49]
dete_survey.drop(columns_to_drop,axis=1,inplace=True)
print("Dataset rows: ",dete_survey.shape[0]," Dataset columns: ",dete_survey.shape[1])
print('\n')
Dataset rows: 822 Dataset columns: 35
# Dropping not used columns from dete_survey dataset.
columns_to_drop = tafe_survey.columns[17:66]
tafe_survey.drop(columns_to_drop,axis=1,inplace=True)
print("Dataset rows: ",tafe_survey.shape[0]," Dataset columns: ",tafe_survey.shape[1])
print('\n')
Dataset rows: 702 Dataset columns: 23
Changed the missing values from "Not Stated" into "NAN" in the dete_survey dataset to meet the standard, and I dropped several not used columns from both datasets.
Next, let's turn our attention to the column names. Each dataframe contains many of the same columns, but the column names are different. Below are some of the columns we'd like to use for our final analysis:
Because we eventually want to combine them, we'll have to standardize the column names.
Here are the criteria that will be used to rename the dete_survey columns':
* Make all the capitalization lowercase.
* Remove any trailing whitespace from the end of the strings.
* Replace spaces with underscores ('_').
# Standardizing the columns' names.
dete_survey.columns = dete_survey.columns.str.lower().str.strip().str.replace(' ','_')
# Checking the new column name.
dete_survey.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')
For dete_survey we will update the tafe_survey columns':
* 'Record ID': 'id'
* 'CESSATION YEAR': 'cease_date'
* 'Reason for ceasing employment': 'separationtype'
* 'Gender. What is your Gender?': 'gender'
* 'CurrentAge. Current Age': 'age'
* 'Employment Type. Employment Type': 'employment_status'
* 'Classification. Classification': 'position'
* 'LengthofServiceOverall. Overall Length of Service at Institute (in years)':
'institute_service'
* 'LengthofServiceCurrent. Length of Service at current workplace (in years)': 'role_service'
# Mapping old column names to new names.
columns_update = {
"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'
}
# Renaming columns. Not touched columns will be left as they are.
tafe_survey.rename(columns=columns_update,inplace=True)
tafe_survey.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')
The last step was relevant because we had two distinguished datasets in terms of columns names, and now, we have the important columns with the same name in both datasets.
Recall that our end goal is to answer the following questions:
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?
Are younger employees resigning due to some kind of dissatisfaction? What about older employees?
To answer this question, we have to filter out the data getting only the rows for employees who have resigned.
# Fetching employees who have resigned from tafe_dataset.
# I used the DataFrame.copy() method to solve de warning and prevent problems with SettingWithCopyWarning
tafe_resignations = tafe_survey.loc[tafe_survey["separationtype"] == "Resignation",:].copy()
# Using regex to get employees who have resigned from dete_survey dataset
pattern = "Resignation"
dete_resignations = dete_survey.loc[dete_survey["separationtype"].str.contains(pattern,na=False),:].copy()
#dete_survey[dete_survey["separationtype"].str.contains(pattern,na=False)]["separationtype"].value_counts()
#dete_survey["separationtype"].value_counts()
I have to filter out the two datasets to work only with the data I need, which is data for employees who have resigned.
# Validating the cease_date column from dete_resignations dataset.
#dete_resignations["cease_date"].str[-4:].astype(float).value_counts()
#dete_resignations["cease_date"].str[-4:].astype(float).sort_index(ascending= False)
print("Max job start year: ",dete_resignations["dete_start_date"].max())
print("Min job start year: ",dete_resignations["dete_start_date"].min())
print("Min resignation year: ",dete_resignations["cease_date"].str[-4:].astype(float).min())
print("Max resignation year: ",dete_resignations["cease_date"].str[-4:].astype(float).max())
print("Min resignation year: ",dete_resignations["cease_date"].str[-4:].astype(float).min())
print("Null Values: ",dete_resignations["cease_date"].str[-4:].isna().sum())
print("Total: ",dete_resignations["cease_date"].value_counts().sum())
Max job start year: 2013.0 Min job start year: 1963.0 Min resignation year: 2006.0 Max resignation year: 2014.0 Min resignation year: 2006.0 Null Values: 11 Total: 300
# Validating the cease_date column from dete_resignations dataset.
print("Max resignation year: ", tafe_resignations["cease_date"].max())
print("Min resignation year: ", tafe_resignations["cease_date"].min())
print("Null Values: ", tafe_resignations["cease_date"].isna().sum())
print("Total: ", tafe_resignations["cease_date"].value_counts().sum())
Max resignation year: 2013.0 Min resignation year: 2009.0 Null Values: 5 Total: 335
There aren't any major issues with the years.
The years in each dataframe don't span quite the same number of years.
In the Human Resources field, the length of time an employee spent in a workplace is referred to as their years of service.
You may have noticed that the tafe_resignations dataframe already contains a "service" column, which we renamed to institute_service. In order to analyze both surveys together, we'll have to create a corresponding institute_service column in dete_resignations
# Creating an institute_service column in dete_resignations.
dete_resignations.loc[:,"institute_service"] = dete_resignations.loc[:,"cease_date"].str[-4:].astype(float) - dete_resignations.loc[:,"dete_start_date"]
Below are the columns we'll use to categorize employees as "dissatisfied" from each dataframe.
The columns of dete_regisignations already are in the boolean format (True, False, or NAN) indicating that the employee left the company not satisfied. However, the tafe_resignations need to be transformed in order to represent the data in the same format, that's the operation performed down below.
# Function to format the columns in Boolean.
import numpy as np
def update_vals(var):
if pd.isnull(var):
return np.nan
elif var == '-':
return False
else:
return True
# Using the function to modify the columns passed in into boolean..
tafe_resignations[["Contributing Factors. Dissatisfaction","Contributing Factors. Job Dissatisfaction"]] = tafe_resignations.loc[:,["Contributing Factors. Dissatisfaction","Contributing Factors. Job Dissatisfaction"]].applymap(update_vals)
tafe_resignations.loc[683:690,["Contributing Factors. Dissatisfaction","Contributing Factors. Job Dissatisfaction"]]
# tafe_resignations["Contributing Factors. Dissatisfaction"] = tafe_resignations.loc[:,["Contributing Factors. Dissatisfaction","Contributing Factors. Job Dissatisfaction"]].applymap(update_vals)["Contributing Factors. Dissatisfaction"]
# tafe_resignations["Contributing Factors. Job Dissatisfaction"] = tafe_resignations.loc[:,["Contributing Factors. Dissatisfaction","Contributing Factors. Job Dissatisfaction"]].applymap(update_vals)["Contributing Factors. Job Dissatisfaction"]
Contributing Factors. Dissatisfaction | Contributing Factors. Job Dissatisfaction | |
---|---|---|
683 | False | False |
684 | False | False |
685 | True | True |
686 | False | False |
688 | False | False |
689 | False | True |
690 | False | False |
Now the columns in each dataset tell if the employee left the company unsatisfied or not if one column is flagged as "True" the column indicates job dissatisfaction. Therefore, it makes sense to compile this information into only one column for analyzing. The column will be named as dissatisfied.
causes = ["job_dissatisfaction","dissatisfaction_with_the_department","physical_work_environment","lack_of_recognition","lack_of_job_security","work_location","employment_conditions","work_life_balance","workload"]
dete_resignations["dissatisfied"] = dete_resignations.loc[:,causes].any(axis=1)
tafe_resignations["dissatisfied"] = tafe_resignations.loc[:,["Contributing Factors. Dissatisfaction","Contributing Factors. Job Dissatisfaction"]].any(axis=1)
Now we have the information needed to meet our goal, and it is time to combine both datasets in one but, to keep track of the information I will add a new column into each dataset indicating the row origin before combining the datasets, and I'm going to drop some needless columns.
# Flagging the dataset before combining them.
dete_resignations["institute"] = "DETE"
tafe_resignations["institute"] = "TAFE"
# Keeping only the column necessary.
tafe_resignations_final = tafe_resignations.loc[:,["dissatisfied","institute_service","institute"]]
dete_resignations_final = dete_resignations.loc[:,["dissatisfied","institute_service","institute"]]
print("Tafe shape:", tafe_resignations_final.shape)
print("Dete shape:", dete_resignations_final.shape)
Tafe shape: (340, 3) Dete shape: (311, 3)
# Combining the two datasets.
combined = pd.concat([tafe_resignations_final,dete_resignations_final],axis=0)
combined.head()
dissatisfied | institute_service | institute | |
---|---|---|---|
3 | False | NaN | TAFE |
4 | False | 3-4 | TAFE |
5 | False | 7-10 | TAFE |
6 | False | 3-4 | TAFE |
7 | False | 3-4 | TAFE |
Now that we've combined our data frames, we're almost at a place where we can perform some kind of analysis! First, though, we'll have to clean up the institute_service column. This column is tricky to clean because it currently contains values in a couple of different forms:
combined["institute_service"].head()
3 NaN 4 3-4 5 7-10 6 3-4 7 3-4 Name: institute_service, dtype: object
And based on this article https://bwnews.pr/36Bi8s4 I changed my assumption that understanding employee's needs according to career stage instead of age is more effective. I'll use the slightly modified definitions below:
But first, I have to standardize the institute_service column.
# Standardizing the institute_service column.
combined['institute_service'] = combined.loc[:,'institute_service'].astype('str').str.extract(r'(\d+)').astype('float')
# Function to work as a classifier.
import numpy as np
def classifier(ternure):
if pd.isnull(ternure):
return np.nan
elif ternure <3:
return "New"
elif 3 <= ternure <=6:
return "Experienced"
elif 7 <= ternure <= 10:
return "Established"
else:
return "Veteran"
combined["service_cat"] = combined.loc[:,["institute_service"]].applymap(classifier)
combined["service_cat"].value_counts(dropna=False)
New 193 Experienced 172 Veteran 136 NaN 88 Established 62 Name: service_cat, dtype: int64
The last two lines above convert the working years into several buckets, making it easier for drawing the analyses.
combined.loc[combined["service_cat"] == "New","dissatisfied"].value_counts(dropna=False)
False 136 True 57 Name: dissatisfied, dtype: int64
combined.loc[combined["service_cat"] == "Experienced", "dissatisfied"].value_counts(dropna=False)
False 113 True 59 Name: dissatisfied, dtype: int64
combined.loc[combined["service_cat"] == "Established", "dissatisfied"].value_counts(dropna=False)
True 32 False 30 Name: dissatisfied, dtype: int64
combined["service_cat"].isnull().sum()
88
combined["dissatisfied"].value_counts(dropna=False)
False 411 True 240 Name: dissatisfied, dtype: int64
# Calculating the percentage of employees who resigned due to dissatisfaction in each category
dis_pct = combined.pivot_table(index='service_cat', values='dissatisfied')
# Plot the results
%matplotlib inline
dis_pct.plot(kind='bar', rot=30)
<matplotlib.axes._subplots.AxesSubplot at 0x9627160>