Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
The Data Analysis Workshop

You're reading from   The Data Analysis Workshop Solve business problems with state-of-the-art data analysis models, developing expert data analysis skills along the way

Arrow left icon
Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781839211386
Length 626 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (3):
Arrow left icon
Konstantin Palagachev Konstantin Palagachev
Author Profile Icon Konstantin Palagachev
Konstantin Palagachev
Gururajan Govindan Gururajan Govindan
Author Profile Icon Gururajan Govindan
Gururajan Govindan
Shubhangi Hora Shubhangi Hora
Author Profile Icon Shubhangi Hora
Shubhangi Hora
Arrow right icon
View More author details
Toc

Table of Contents (12) Chapters Close

Preface
1. Bike Sharing Analysis 2. Absenteeism at Work FREE CHAPTER 3. Analyzing Bank Marketing Campaign Data 4. Tackling Company Bankruptcy 5. Analyzing the Online Shopper's Purchasing Intention 6. Analysis of Credit Card Defaulters 7. Analyzing the Heart Disease Dataset 8. Analyzing Online Retail II Dataset 9. Analysis of the Energy Consumed by Appliances 10. Analyzing Air Quality Appendix

Importing the Data

Before we begin with the actual analysis, we will need to import the required packages as follows:

# Import basic libraries
import numpy as np 
import pandas as pd 
# import visualization libraries
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

Next, read/import the dataset into the work environment:

df = pd.read_excel('default_credit.xls')
df.head(5)

The output will be as follows:

Figure 6.2: Top five rows of the DataFrame

Figure 6.2: Top five rows of the DataFrame

Check the metadata of the DataFrame:

# Getting Meta Data Information about the dataset
df.info()

The output will be similar to the image shown below:

Figure 6.3: Information of the DataFrame

Figure 6.3: Information of the DataFrame

Check the descriptive statistics for the numerical columns in the DataFrame:

df.describe().T

The output will be as follows:

Figure 6.4: Descriptive statistics of the DataFrame

Figure 6.4: Descriptive statistics of the DataFrame

Next, check for null values:

...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime