Jupyter provides the ability to use a variety of languages when developing a Notebook. Each of the languages is supported through the use of an engine that provides all of the programmatic interface from the coded language instruction that you write with the underlying Notebook. Several of the popular languages in use are Python, R, Julia, JavaScript, and Scala. In this chapter, we will show you the steps to add these engines to your Jupyter installation so that you can script your Notebook in the language you like.

Jupyter was originally derived from Python with the IPython project. At that time, Python 2 was the predominant version available for use. For every installation of Jupyter, the default engine provided is Python 2. There are a number of changes involved when moving from Python 2 to Python 3, especially in the underlying libraries that you may be invoking, where parameter and usage changes have occurred.

We will cover the installation of the Python 3 engine and make sure it is running with a script.

R is both a programming language and a software environment geared toward statistical computing and associated graphics. R has a clean syntax, providing access to a large set of statistical packages, publicly available for free use.

We can add the R engine to our Jupyter installation using Anaconda Navigator or the command line.

R is included with the R Studio option in Anaconda Navigator, as shown in this partial screenshot:

As noted in the screen, the R Studio installation includes...

Julia is a high-level programming language. It was built with high performance in mind, especially with regard to numerical computing. As such, it is a custom-made language for data science use.

We will now cover the steps to add the Julia engine and execute a Julia script under Jupyter.

I know that in previous instances of Jupyter, Julia was automatically installed as an engine in non-Windows environments. I do not have access to another environment, so we will walk through the steps to installing Julia...

JavaScript is another engine that can be used with Jupyter. Actually, it is node.js rather than JavaScript. node.js is a superset of JavaScript. node.js is an extension of JavaScript that is expected to be run on a server. As with JavaScript, node.js is cross-platform.

We will go over the steps to install the engine and run a small script.

First, we need to install node.js. It is available from node.js.org. From this site, you can download and install node.js on your machine directly....

Scala is described as a general-use language that is concise and elegant. Scala is an extension of the Java language and can call upon Java libraries. Its syntax is very similar as well.

We now go over the steps to install the Scala engine and run a Scala script.

The Scala engine implementation is immature. There may be better installations available for non-Windows environments, but for Windows I followed the following instructions, where someone had built a version of the Scala engine for Windows that...

Spark is an Apache project that provides an open source computing framework specially geared toward cluster computing. For our purposes, it provides a language called Spark that can be used to access Hadoop information sets.

We install the Spark engine and execute a Spark Jupyter script to show its working, as follows.

Generally, installing Spark involves two steps:

• Installing Spark (for your environment)
• Connecting Spark to your environment (whether standalone or clustered)

The Spark installations are...