Book Image

Python Data Visualization Cookbook (Second Edition)

Book Image

Python Data Visualization Cookbook (Second Edition)

Overview of this book

Python Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that will guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts. Python Data Visualization Cookbook starts by showing how to set up matplotlib and the related libraries that are required for most parts of the book, before moving on to discuss some of the lesser-used diagrams and charts such as Gantt Charts or Sankey diagrams. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python.
Table of Contents (16 chapters)
Python Data Visualization Cookbook Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Displaying images with other plots in the figure


This recipe will show how we can make simple yet effective usage of Python matplotlib library to process image channels and display the per-channel histogram of an external image.

Getting ready

We have provided some sample images, but the code is ready to load any image file, provided it is supported by matplotlib's imread function.

In this recipe, you will learn how to combine different matplotlib plots to achieve functionality of a simple image viewer that displays an image histogram for red, green, and blue channels.

How to do it...

To show how to build an image histogram viewer, we are going to implement a simple class named ImageViewer, and that class will contain helper methods to:

  1. Load image.

  2. Separate RGB channels from image matrix.

  3. Configure figure and axes (subplots).

  4. Plot channel histograms.

  5. Plot the image.

The following code shows how to build an image histogram viewer:

import matplotlib.pyplot as plt
import matplotlib.image as mplimage
import...