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

Creating stem plot


A two-dimensional stem plot displays data as lines extending from a baseline along the x-axis. A circle (the default) or the other marker's y-position represents the data value that terminates each stem.

In this recipe, we will be discussing about how to create a stem plot.

Do not confuse stem with stem and leaf plots, which is a method of representing data by separating the last important digit of values as leaves and higher order values as stems.

Getting ready

For this kind of plot, we need to use a sequence of discrete data, where ordinary an line plots wouldn't make sense anyway.

Plot discrete sequences as stems, where data values are represented as markers at the end of each stem. Stems extend from baseline (usually at y=0) to the data point value.

How to do it...

We will use matplotlib to plot stem plots using the stem() function. This function can use just a series of y values when x values are generated as a simple sequence from 0 to len(y)—1. If we provide the stem function...