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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

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Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781803231105
Length 382 pages
Edition 1st Edition
Languages
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Author (1):
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Ayodele Oluleye Ayodele Oluleye
Author Profile Icon Ayodele Oluleye
Ayodele Oluleye
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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...

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