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

Product type Paperback

Published in Jun 2023

Publisher Packt

ISBN-13 9781803231105

Length 382 pages

Edition 1st Edition

Languages

Python

Tools

Matplotlib

Concepts

Data Analysis

Author (1):

Ayodele Oluleye

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Table of Contents (13) Chapters

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

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12. Other Books You May Enjoy

Visualizing data in Seaborn

Seaborn is another common Python data visualization library. It is based on matplotlib and integrates well with pandas data structures. Seaborn is primarily used for making statistical graphics, and this makes it a very good candidate for performing EDA. It uses matplotlib to draw its charts; however, this is done behind the scenes. Unlike matplotlib, seaborn’s high-level API makes it easier and faster to use. As mentioned earlier, common tasks can sometimes be cumbersome in matplotlib and take several lines of code. Even though matplotlib is highly customizable, the settings can sometimes be hard to tweak. However, seaborn provides settings that are easier to tweak and understand.

Many of the important terms considered under the previous recipe also apply to seaborn, such as axes, ticks, legends, titles, labels, and so on. We will explore seaborn through some examples.