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

Adding legends and annotations


Legends and annotations explain data plots clearly and in context. By assigning each plot a short description about what data it represents, we are enabling an easier mental model in the reader's (viewer's) head. This recipe will show how to annotate specific points on our figures and how to create and position data legends.

Getting ready

How many times have you looked at a chart and wondered what the data represents? More often than not, newspapers and other daily and weekly publications create plots that don't contain appropriate legends, thus leaving the reader free to interpret the representation. This creates ambiguity for the readers and increases the possibility of error.

How to do it...

Let's demonstrate how to add legends and annotations with the following example:

from matplotlib.pyplot import *

# generate different normal distributions
x1 =np.random.normal(30, 3, 100)
x2 = np.random.normal(20, 2, 100)
x3 = np.random.normal(10, 3, 100)

# plot them
plot...