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 contour plots


A contour plot displays the isolines of a matrix. Isolines are curves where a function of two variables has the same value.

In this recipe, you will learn how to create contour plots.

Getting ready

Contours are represented as contour plots of the matrix Z, where Z is interpreted as height with respect to the XY plane. Z is of minimum size 2 and must contain at least two different values.

The problem with contour plots is that if they are coded without labeling the isolines, they are rendered pretty useless as we cannot decode the high points from the low points or find local minimas.

Here, we need to label the contour as well. The labeling of isolines can be done by using either labels (clabel()) or colormaps. If your output medium permits the use of color, colormaps are preferred because viewers will be able to decode data more easily.

The other risk with contour plots is in choosing the number of isolines to plot. If we choose too many, the plot becomes too dense to decode...