Book Image

Python Data Visualization Cookbook

By : Igor Milovanovic
Book Image

Python Data Visualization Cookbook

By: Igor Milovanovic

Overview of this book

Today, data visualization is a hot topic as a direct result of the vast amount of data created every second. Transforming that data into information is a complex task for data visualization professionals, who, at the same time, try to understand the data and objectively transfer that understanding to others. This book is a set of practical recipes that strive to help the reader get a firm grasp of the area of data visualization using Python and its popular visualization and data libraries. 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 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 you 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. During the book, we go from simple plots and charts to more advanced ones, thoroughly explaining why we used them and how not to use them. As we go through the book, we will also discuss 3D diagrams. We will peep into animations just to show you what it takes to go into that area. Maps are irreplaceable for displaying geo-spatial data, so we also show you how to build them. In the last chapter, we show you how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python. This book will help those who already know how to program in Python to explore a new field – one of data visualization. As this book is all about recipes that explain how to do something, code samples are abundant, and they are followed by visual diagrams and charts to help you understand the logic and compare your own results with what is explained in the book.
Table of Contents (15 chapters)
Python Data Visualization Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Filling an under-plot area


The basic way to draw a filled polygon in matplotlib is to use matplotlib.pyplot.fill. This function accepts similar arguments as matplotlib.pyplot.plot—multiple x and y pairs and other Line2D properties. This function returns the list of Patch instances that were added.

In this recipe we will learn how to shade certain areas of plot intersections.

Getting ready

matplotlib provides several functions to help us plot filled figures, apart from plotting functions that are inherently plotting closed filled polygons, such as histogram (), of course.

We already mentioned one—matplotlib.pyplot.fill—but there are the matplotlib.pyplot.fill_between() and matplotlib.pyploy.fill_betweenx() functions too. These functions fill the polygons between two curves. The main difference between fill_between() and fill_betweenx() is that the latter fills between the x axis values, whereas the former fills between the y axis values.

The function fill_between accepts argument x—an x axis...