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Python Data Visualization Cookbook (Second Edition)

You're reading from   Python Data Visualization Cookbook (Second Edition) Visualize data using Python's most popular libraries

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781784396695
Length 302 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (11) Chapters Close

Preface 1. Preparing Your Working Environment FREE CHAPTER 2. Knowing Your Data 3. Drawing Your First Plots and Customizing Them 4. More Plots and Customizations 5. Making 3D Visualizations 6. Plotting Charts with Images and Maps 7. Using the Right Plots to Understand Data 8. More on matplotlib Gems 9. Visualizations on the Clouds with Plot.ly 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...

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