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Learning Jupyter

You're reading from Learning Jupyter

Product type Book

Published in Nov 2016

Publisher Packt

ISBN-13 9781785884870

Pages 238 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Dan Toomey

Table of Contents (16) Chapters

Learning Jupyter

Credits

About the Author

About the Reviewer

www.PacktPub.com

Preface

1. Introduction to Jupyter

2. Jupyter Python Scripting

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

Apache Spark

One of the tools we will be using is Apache Spark. Spark is an open source toolset for cluster computing. While we will not be using a cluster, the typical usage for Spark is a larger set of machines or cluster that operate in parallel to analyze a big data set. An installation guide is available at https://www.dataquest.io/blog/pyspark-installation-guide. In particular, you will need to add two settings to your bash profile: SPARK_HOME and PYSPARK_SUBMIT_ARGS. SPARK_HOME is the directory where the software is installed. PYSPARK_SUBMIT_ARGS sets the number of cores to use in the local cluster.

Mac installation

To install, we download the latest TGZ file from the Spark download page at https://spark.apache.org/downloads.html, unpack the TGZ file, and move the unpacked directory to our Applications folder.

Spark relies on Scala's availability. We installed Scala in Chapter 7, Sharing and Converting Jupyter Notebooks.

Open a command-line window to the Spark directory and run this...

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Authors (1)

Dan Toomey

Dan Toomey has been developing application software for over 20 years. He has worked in a variety of industries and companies, in roles from sole contributor to VP/CTO-level. For the last few years, he has been contracting for companies in the eastern Massachusetts area. Dan has been contracting under Dan Toomey Software Corp. Dan has also written R for Data Science, Jupyter for Data Sciences, and the Jupyter Cookbook, all with Packt.

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