Book Image

Matplotlib for Python Developers - Second Edition

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

Matplotlib for Python Developers - Second Edition

By: Aldrin Yim, Claire Chung, Allen Yu

Overview of this book

Python is a general-purpose programming language increasingly being used for data analysis and visualization. Matplotlib is a popular data visualization package in Python used to design effective plots and graphs. This is a practical, hands-on resource to help you visualize data with Python using the Matplotlib library. Matplotlib for Python Developers, Second Edition shows you how to create attractive graphs, charts, and plots using Matplotlib. You will also get a quick introduction to third-party packages, Seaborn, Pandas, Basemap, and Geopandas, and learn how to use them with Matplotlib. After that, you’ll embed and customize your plots in third-party tools such as GTK+3, Qt 5, and wxWidgets. You’ll also be able to tweak the look and feel of your visualization with the help of practical examples provided in this book. Further on, you’ll explore Matplotlib 2.1.x on the web, from a cloud-based platform using third-party packages such as Django. Finally, you will integrate interactive, real-time visualization techniques into your current workflow with the help of practical real-world examples. By the end of this book, you’ll be thoroughly comfortable with using the popular Python data visualization library Matplotlib 2.1.x and leveraging its power to build attractive, insightful, and powerful visualizations.
Table of Contents (16 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
Index

Saving plots to a file


To save a figure, we put plt.savefig(outputpath) at the end of plotting commands. It can be used in place of plt.show(), to directly save the figure without displaying it.

If you want to save the figure as a file as well as display it on the notebook output, you can call both plt.savefig() and plt.show().

Note

Reversing the order can result in the plot elements being cleaned up, leaving a blank canvas for the saved figure file.

Setting the output format

plt.savefig() automatically detects the file extension of the specified output path, and generates the corresponding file format if it is supported. If no file extension is specified in the input, a PNG format file would be obtained as output with the default backend. This supports a number of image formats, including PNG, JPG, PDF, and PostScript:

import numpy as np
import matplotlib.pyplot as plt
y = np.linspace(1,2000)
x = 1.0/np.sin(y)
plt.plot(x,y,'green')
plt.xlim(-20,20)
plt.ylim(1000,2400)
plt.show()
plt.savefig(...