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

Using colormaps


Color coding the data can have great impact on how your visualizations are perceived by the viewer, as they come with assumptions about color and what that color represents.

Being explicit if the color is used to add additional information to the data is always good. To know when and how to use color in your visualizations is even better.

Getting ready

If your data is not naturally color coded (such as earth/terrain altitudes or object temperature), it's better not to make any artificial mappings to natural coloring. We want to understand the data appropriately and make a choice of color to help the reader decode data easily. We don't want readers constantly trying to suppress learned mapping of color for temperatures, if we are representing financial data that has no connection with Kelvins or Celsius.

If possible, avoid the usual red/green associations, if there are no strong correlations in the data to associate them with those colors.

To help you pick the right color mapping...