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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

Defining plot line styles, properties, and format strings

This recipe shows how we can change various line properties such as styles, colors, or width. Having lines set up appropriately according to the information presented and distinct enough for target audiences (if the audience is a younger population, we may want to target them with more vivid colors; if they are older, we may want to use more contrasting colors) can make the difference between being barely noticeable and leaving a great impact on the viewer.

Getting ready

Although we stressed how important it is to aesthetically tune your presentation, we first must learn how to do it.

If you don't have a particular eye for color matching, there are free and commercial online tools that can generate color sets for you. One of the most well known is Colorbrewer2, which can be found at http://colorbrewer2.org/.

Some serious research has been conducted on the usage of color in data visualizations, but explaining that theory is out of...

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