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 ticks, labels, and grids


In this recipe, we will continue with setting axis and line properties and adding more data to our figure and charts.

Getting ready

Let's learn a bit about figures and subplots.

In matplotlib, figure() is used to explicitly create a figure, which represents a user interface window. Figures are created implicitly just by calling plot() or similar functions. This is fine for simple charts, but having the ability to explicitly create a figure and get a reference to its instance is very useful for more advanced use.

A figure contains one or more subplots. Subplots allow us to arrange plots in a regular grid. We already used subplot(), in which we specify the number of rows and columns and the number of the plot we are referring to.

If we want more control, we need to use axes instances from the matplotlib.axes.Axes class. They allow us to place plots at any location in the figure. An example of this would be to put a smaller plot inside a bigger one.

How to do it....