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

Moving spines to the center


This recipe will demonstrate how to move spines to the center.

Spines define data area boundaries; they connect the axis tick marks. There are four spines. We can place them wherever we want; by default, they are placed on the border of the axis, hence we see a box around our data plot.

How to do it...

To move the spines to the center of the plot, we need to remove two spines, making them hidden (set color to none). After that, we move two others to coordinate (0,0). The coordinates are specified in data space coordinates.

The following code shows how to do this:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-np.pi, np.pi, 500, endpoint=True)
y = np.sin(x)

plt.plot(x, y)

ax = plt.gca()

# hide two spines
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')

# move bottom and left spine to 0,0
ax.spines['bottom'].set_position(('data',0))
ax.spines['left'].set_position(('data',0))

# move ticks positions
ax.xaxis.set_ticks_position...