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Learning Jupyter

You're reading from   Learning Jupyter Select, Share, Interact and Integrate with Jupyter Notebook

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Product type Paperback
Published in Nov 2016
Publisher Packt
ISBN-13 9781785884870
Length 238 pages
Edition 1st Edition
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Table of Contents (11) Chapters Close

Preface 1. Introduction to Jupyter 2. Jupyter Python Scripting FREE CHAPTER 3. Jupyter R Scripting 4. Jupyter Julia Scripting 5. Jupyter JavaScript Coding 6. Interactive Widgets 7. Sharing and Converting Jupyter Notebooks 8. Multiuser Jupyter Notebooks 9. Jupyter Scala 10. Jupyter and Big Data

Julia Vega plotting


Another popular graphics package is Vega. The main feature of Vega is the ability to describe your graphic using language primitives, such as JSON. Vega produces most of the standard plots. Here is an example script using Vega for a pie chart:

#Pkg.add("Vega")
#Pkg.add("Compat")
#Pkg.add("Patchwork")
using Vega
using Compat
using Patchwork
Patchwork.load_js_runtime()
stock = ["chairs", "tables", "desks", "rugs", "lamps"];
quantity = [15, 10, 10, 5, 20];
piechart(x = stock, y = quantity)

The generated graphic produced in Jupyter is shown in the following screenshot:

Note

Vega gives you the option on the resultant display to Save As PNG.

Julia PyPlot plotting

Another plotting package available is PyPlot. PyPlot is one of the standard Python visualization libraries and is directly accessible from Julia. We can take this small script to produce an interesting visualization:

#Pkg.add("PyPlot")
using PyPlot
precipitation = [0,0,0,0,0,0,0,0,0,0,0.12,0.01,0,0,0,0.37,0,0,0,0,
...
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