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

Animating with OpenGL


The motivation to use OpenGL stems from limitations of CPU processing power when we are faced with the task of visualizing millions of data points and doing it fast (sometimes even in real time).

Modern computers have powerful GPUs that are made for fast visualization-related computations (such as games), and there is no reason why they can't be used for science-related visualizations.

Actually, there is at least one drawback of writing hardware-accelerated software that is hardware dependent. Modern graphics cards require proprietary drivers which are sometimes not available on the target platform/machine (the user's laptop, for example). Even when available, sometimes installing the required dependencies on-site is not what you want to spend your time on, while all you want is to present your findings and demonstrate your research results. This is not a showstopper but you should bear this in mind, and measure the benefits and costs of introducing this complexity in...