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 Bokeh

Bokeh is another popular data visualization library in Python. Bokeh provides interactive and aesthetically appealing charts. These charts allow users to probe many scenarios interactively. Bokeh also supports custom JavaScript for special and advanced visualization use cases. With it, charts can be easily embedded into web pages.

Glyphs are the building blocks of bokeh visualizations. A glyph is a geometrical shape or marker that is used to represent data and create plots in bokeh. Typically, a plot consists of one or many geometrical shapes such as line, square, circle, rectangle, and so on. These shapes (glyphs) have visual information about the corresponding set of data.

Many of the important terms considered under the matplotlib recipe also apply to bokeh, and the syntax is also quite similar. We will explore bokeh through some examples.