Using Node.js Data Science notebooks

Introduction

Notebooks are where data scientists process, analyse, and visualise data in an iterative, collaborative environment. They typically run environments for languages like Python, R, and Scala. For years, data science notebooks have served academics and research scientists as a scratchpad for writing code, refining algorithms, and sharing and proving their work. Today, it's a workflow that lends itself well to web developers experimenting with data sets in Node.js.

To that end, pixiedust_node is an add-on for Jupyter notebooks that allows Node.js/JavaScript to run inside notebook cells. Not only can web developers use the same workflow for collaborating in Node.js, but they can also use the same tools to work with existing data scientists working in Python.

pixiedust_node is built on the popular PixieDust helper library. Let’s get started.

Table of Contents

• Running Node.js notebooks in Watson Studio
• Running Node.js notebooks in a local environment

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Running Node.js notebooks in Watson Studio

Creating a custom runtime environment

A runtime environment in Watson Studio (IBM's Data Science platform) is defined by its hardware and software configuration. By default, Node.js is not installed in runtime environments and you therefore need to create a custom runtime environment definition. [Learn more about environments...]

• Open Watson Studio in your web browser. Sign up for a free account if necessary.

• Create a project. [Learn more about projects...]

• In this project, open the Environments tab. A list of existing environment definitions for Python and R is displayed.

• Create a new environment definition.

  • Assign a name to the new environment definition, such as Python 2 with Node.js.
  • Enter a brief environment description.
  • Choose the desired hardware configuration, such as a minimalist free setup (which is sufficient for demonstration purposes).
  • Select Python 2 as software version. (Python 3 is currently not supported by pixiedust_node.)

• Customize the software definition.

  • Add the nodejs conda package dependency, as shown below:

    # Please add conda channels here
    channels:
    - defaults
    
    # Please add conda packages here
    dependencies:
    - nodejs
    
    # Please add pip packages here
    # To add pip packages, please comment out the next line
    #- pip:
    

  • Save the environment definition. It should look as follows:

  

You can now associate notebooks with this environment definition and run Node.js in the code cells, as illustrated in the getting started notebook.

Note: An environment definition is only available within the project that it was defined in.

Loading the getting started notebook

The getting started notebook outlines how to

• use variables, functions, and promises,
• work with remote data sources, such as Apache CouchDB (or its managed sibling Cloudant),
• visualize data
• share data between Python and Node.js.

In the project you've created, add a new notebook from URL:

• Enter any notebook name.
• Specify remote URL https://raw.githubusercontent.com/IBM/nodebook-code-pattern/master/notebooks/nodebook_1.ipynb as source.
• Select the custom runtime environment Python 2 with Node.js. you've created earlier.

Follow the notebook instructions.

You should be able to run all cells without making any changes.

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Running Node.js notebooks in a local environment

Setup

Prerequisites

To get started with nodebooks you'll need a local installation of

• PixieDust and its prerequisites
• A Python 2.7 kernel with Spark 2.x. (see section Install a Jupyter Kernel in the PixieDust installation instructions)
• Node.js/npm

Installing the samples

To access the samples, clone this repository and launch a Jupyter server on your local machine.

 $ git clone https://github.com/IBM/nodebook-code-pattern.git
 $ cd nodebook-code-pattern
 $ jupyter notebook notebooks/

Running the samples

Open nodebook_1 to learn more about

• using variables, functions, and promises,
• working with remote data sources, such as Apache CouchDB (or its managed sibling Cloudant),
• visualizing data
• sharing data between Python and Node.js.

No notebook changes should be required to complete all steps.

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Optional data source customization

Some of the nodebook code pattern examples access a read-only Cloudant database for illustrative purposes. If you prefer you can create your own copy of this database by replicating from remote database URL https://56953ed8-3fba-4f7e-824e-5498c8e1d18e-bluemix.cloudant.com/cities. [Learn more about database replication...]

Credits

This code pattern is based on a series of blog posts that were first published by Glynn Bird on medium.com.

License

Apache-2.0