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Jupyter Cookbook

You're reading from   Jupyter Cookbook Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788839440
Length 238 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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 Toomey Toomey
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Toomey
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Toc

Table of Contents (17) Chapters Close

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. Installation and Setting up the Environment FREE CHAPTER 2. Adding an Engine 3. Accessing and Retrieving Data 4. Visualizing Your Analytics 5. Working with Widgets 6. Jupyter Dashboards 7. Sharing Your Code 8. Multiuser Jupyter 9. Interacting with Big Data 10. Jupyter Security 11. Jupyter Labs Index

Visualizing with Julia


Julia is a programming language specifically built for numerical computing. There are several features that make it a great fit for use as a Jupyter scripting language. We will use several of the available packages for visualization.

Of special note is that Julia has direct access to most R packages, so those can be used as well.

Note

A word of caution: I could not produce most Julia visualizations on a Windows PC. For this section, I used a Mac, and even then I had to try adding packages several times before things started to work.

Getting ready

Before using Julia scripts, you should add some standard packages and update all packages to the current levels. You can do that with these commands directly in the Julia console rather than in a Notebook:

Note

I would recommend that you add a package by closing the Notebook, opening a Julia console, adding the package, reopening the Notebook, and then using the new package.

Pkg.add("DataFrames")
Pkg.add("RDatasets")
Pkg.add("Gadfly...
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