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

Plotting with filled areas


In this recipe, we will show you how to fill the area under a curve or in between two different curves.

Getting ready

Library matplotlib allows us to fill areas in between and under the curves with color so that we can display the value of that area to the spectator. Sometimes, it is necessary for readers (viewers) to comprehend the given specialization.

How to do it...

Here's one example of how to fill areas between two contours:

from matplotlib.pyplot import figure, show, gca
import numpy as np

x = np.arange(0.0, 2, 0.01)

# two different signals are measured
y1 = np.sin(2*np.pi*x)
y2 = 1.2*np.sin(4*np.pi*x)

fig = figure()
ax = gca()

# plot and
# fill between y1 and y2 where a logical condition is met
ax.plot(x, y1, x, y2, color='black')

ax.fill_between(x, y1, y2, where=y2>=y1, facecolor='darkblue', interpolate=True)
ax.fill_between(x, y1, y2, where=y2<=y1, facecolor='deeppink', interpolate=True)

ax.set_title('filled between')

show()

How it works...

After...