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

Defining axis lengths and limits


This recipe will demonstrate a variety of useful axis properties around limits and lengths that we can configure in matplotlib.

Getting ready

For this recipe, we want to fire up IPython:

$ ipython

After this, we need to import the plotting functions right away:

from matplotlib.pylab import *

How to do it...

Start experimenting with various properties of axes. Just calling an empty axis() function will return the default values for the axis:

In [1]: axis()
Out[1]: (0.0, 1.0, 0.0, 1.0)

Note that if you are in interactive mode and are using a windowing backend, a figure with an empty axis will be displayed.

Here the values represent xmin, xmax, ymin, and ymax respectively. Similarly, we can set values for the x and y axes:

In [2]: l = [-1, 1, -10, 10]

In [3]: axis(l)
Out[3]: [-1, 1, -10, 10]

Again, if you are in an interactive mode, this will update the same figure. Furthermore, we can also update any value separately using keyword arguments (**kwargs), setting...