Notebooks are where data scientists process, analyze, and visualize 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, thanks to the pixiedust_node library. Based on the popular open-source pixiedust Python productivity library, pixiedust_node makes it possible to use existing JavaScript/Node.js packages, access remote data sources (such as Apache CouchDB/Cloudant) and even share data between Python and Node.js.

To make it easy to get started we’ve published a Jupyter Notebook code pattern, which you can run in Watson Studio or a local Anaconda installation. Give it a try!

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