PixieDust: Easy access and visualizations for Jupyter notebooks in the cloud

PixieDust is an open-source Python helper library that works as an add-on to Jupyter notebooks to improve the user experience of working with data. It also provides extra capabilities that fill a gap when the notebook is hosted on the cloud and the user has no access to configuration files. (Check out the project's github repository.)

Its current capabilities include:

  • packageManager. Lets you install Apache Spark™ packages inside a Python notebook. This is something that you can't do today on hosted Jupyter notebooks, a gap that prevents developers from using a large number of Spark package add-ons. Note that you can install the packages or plain jars in your Notebook Python kernel without the need to modify a configuration file:


  • Visualizations. One single API called display() lets you visualize your Spark object in different ways: table, charts, maps, and so on. This module is designed to be extensible, providing an API that lets anyone easily contribute a new visualization plugin.


  • Export. Easily download data to .csv, html, json, etc. locally on your laptop or into a variety of back-end data sources, like Cloudant, dashDB, GraphDB, Object Storage, and so on.


  • Scala Bridge. Use Scala directly in your Python notebook. Variables are automatically transferred from Python to Scala and vice versa:

First define the variable in Python...

pythonVar = “pixiedust”  

Then in Scala...

val demo =  

import org.apache.spark.streaming._  
demo.startTwitterStreaming(sc, Seconds(10))


val __fromScalaVar = “Hello from Scala”  

And back to Python to use the Scala variable...

  • Extensibility. Create your own visualizations using the PixieDust extensibility APIs. If you know HTML, CSS, and JavaScript, you can write and deliver amazing graphics without forcing notebook users to type one line of code. Use the shape of the data to control when PixieDust shows your visualization in a menu.


This sample visualization plugin can use d3 to show the different flight routes for each airport:


  • Embed Applications. Encapsulate your analytics into compelling user interfaces better suited for line-of-business users:


Note: PixieDust currently works with Spark 1.6 and Python 2.7.

Note: PixieDust currently supports Spark DataFrames, Spark GraphFrames, and Pandas DataFrames, with more to come. If you can't wait, write your own today and contribute it back.


You Might Also Enjoy

Kevin Bates
Kevin Bates
9 months ago

Limit Notebook Resource Consumption by Culling Kernels

There’s no denying that data analytics is the next frontier on the computational landscape. Companies are scrambling to establish teams of data scientists to better understand their clientele and how best to evolve product solutions to the ebb and flow of today’s business ecosystem. With Apache Hadoop and Apache Spark entrenched as the analytic engine and coupled with a trial-and-error model to... Read More

Gidon Gershinsky
Gidon Gershinsky
a year ago

How Alluxio is Accelerating Apache Spark Workloads

Alluxio is fast virtual storage for Big Data. Formerly known as Tachyon, it’s an open-source memory-centric virtual distributed storage system (yes, all that!), offering data access at memory speed and persistence to a reliable storage. This technology accelerates analytic workloads in certain scenarios, but doesn’t offer any performance benefits in other scenarios. The purpose of this blog is to... Read More