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

Setting the transparency and size of axis labels


The Axes label describes what the data in the figure represents and is quite important for the viewer's understanding of the figure itself. By providing labels to the axes background, we help the viewer comprehend the information in an appropriate way.

Getting ready

Before we dive into the code, it is important to understand how matplotlib organizes our figures.

At the top level, there is a Figure instance containing all that we see and some more (that we don't see). The figure contains, among other things, instances of the Axes class as a Figure.axes field. The Axes instances contain almost everything we care about: all the lines, points, ticks, and labels. So, when we call plot(), we are adding a line (matplotlib.lines.Line2D) to the Axes.lines list. If we plot a histogram (hist()), we are adding rectangles to the list of Axes.patches ("patches" is the term inherited from MATLAB®, and it represents the "patch of color" concept).

An instance...