Book Image

Matplotlib 2.x By Example

By : Allen Yu, Claire Chung, Aldrin Yim
Book Image

Matplotlib 2.x By Example

By: Allen Yu, Claire Chung, Aldrin Yim

Overview of this book

Big data analytics are driving innovations in scientific research, digital marketing, policy-making and much more. Matplotlib offers simple but powerful plotting interface, versatile plot types and robust customization. Matplotlib 2.x By Example illustrates the methods and applications of various plot types through real world examples. It begins by giving readers the basic know-how on how to create and customize plots by Matplotlib. It further covers how to plot different types of economic data in the form of 2D and 3D graphs, which give insights from a deluge of data from public repositories, such as Quandl Finance. You will learn to visualize geographical data on maps and implement interactive charts. By the end of this book, you will become well versed with Matplotlib in your day-to-day work to perform advanced data visualization. This book will guide you to prepare high quality figures for manuscripts and presentations. You will learn to create intuitive info-graphics and reshaping your message crisply understandable.
Table of Contents (15 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Title and legend

Title and legend are pieces of text that facilitate quick comprehension of the data context. Although a title is not required or recommended, sometimes, such as in inline figures of many scientific publications, adding a title for your plot often helps make the message clear, especially when your figure is not accompanied by explanatory text. For plots with multiple datasets, it is a good practice to keep a data legend with a distinct color or pattern code labeled with the corresponding identities.

Adding a title to your figure

The title of a figure can be set by pyplot.title() or axes.set_title(). Text properties can be supplied as keyword arguments.

Adding a legend

Adding a legend of data labels in Matplotlib is as simple as setting label='yourlabel' when plotting and adding pyplot.legend() before By default, Matplotlib finds the "best" location to prevent the legend from overlapping with data. You may also specify a location using pyplot.legend(loc='3', **kwargs...