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

Drawing streamlines of vector flow


Stream plots are used to visualize flow in vector fields. Examples from science and nature include fields of magnetic and gravitational forces or movement of liquid materials.

Vector field can be visualized in such a way, where we assign a line and one or more arrows to every point. The intensity can be represented by the line length, and direction by arrow pointing in particular direction.

Usually, the intensity of the force is visualized with the length of a particular streamline, but density can also be used for the same purpose.

Getting ready

To visualize vector fields, we will use matplotlib's matplotlib.pyplot.streamplot function. This function creates plots from streamlines of a flow uniformly filling the domain. The velocities field is interpolated and streamlines are integrated. The original source for this function is to visualize wind patterns or liquid flow, hence we don't need strict vector lines but uniform representation of the vector field.

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