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

Creating 3D histograms


Similarly to 3D bars, we might want to create 3D histograms. These are useful for easily spotting correlation between three independent variables. They can be used to extract information from images in which the third dimension could be the intensity of a channel in the x, y space of the image under analysis.

In this recipe, you will learn how to create 3D histograms.

Getting ready

To recall, a histogram represents the number of occurrences of some value in a particular column—usually called bin. A 3D histogram, then, represents the number of occurrences in a grid. This grid is rectangular, over two variables represented by the data in the two columns.

How to do it...

For this computation we will:

  1. Use NumPy's help, as it has a function for computing the histogram of two variables.

  2. Generate x and y from normal distributions, but with different parameters, to be able to distinguish the correlation in the resulting histogram.

  3. Plot the scatter plot of the same dataset, to demonstrate...