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
Exploratory Data Analysis with Python Cookbook

You're reading from   Exploratory Data Analysis with Python Cookbook Over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data

Arrow left icon
Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781803231105
Length 382 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Ayodele Oluleye Ayodele Oluleye
Author Profile Icon Ayodele Oluleye
Ayodele Oluleye
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Generating Summary Statistics 2. Chapter 2: Preparing Data for EDA FREE CHAPTER 3. Chapter 3: Visualizing Data in Python 4. Chapter 4: Performing Univariate Analysis in Python 5. Chapter 5: Performing Bivariate Analysis in Python 6. Chapter 6: Performing Multivariate Analysis in Python 7. Chapter 7: Analyzing Time Series Data in Python 8. Chapter 8: Analysing Text Data in Python 9. Chapter 9: Dealing with Outliers and Missing Values 10. Chapter 10: Performing Automated Exploratory Data Analysis in Python 11. Index 12. Other Books You May Enjoy

Finding bivariate outliers

Bivariate outliers are usually large or small values that occur in two variables simultaneously. In simple terms, these values differ from other observations when we examine the two variables together. Individually, the values in each variable may or may not be outliers; however, collectively, they are outliers.

To detect bivariate outliers, we typically need to check the relationship between the two variables. One primary method is to visualize the relationship using a scatter plot. Sometimes, we may be interested in identifying extreme values in a numerical variable across categories of a categorical variable or discrete values; in this case, a boxplot can be used. Using the boxplot, we can easily identify contextual outliers, which are usually observations considered anomalous given a specific context. The contextual outlier significantly deviates from the rest of the data points within a specific context. For example, when analyzing house prices, we...

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 $19.99/month. Cancel anytime