pixie dust overview

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PixieDust: an Open Source Library that simplifies and improves Jupyter Python

Notebooks


PixieDust: an Open Source Library that simplifies and improves Jupyter Python

Notebooks

Jupyter + Pixiedust = 1. PackageManager2. Visualizations3. Cloud Integration4. Scala Bridge5. Extensibility6. Embedded Apps

https://github.com/ibm-cds-labs/pixiedust


1/6 - Package ManagerInstall Spark packages or plain jars in your

Notebook Python kernel without the need to modify configuration file

Install GraphFrames Spark Package

Uses the GraphFrame Python APIs


2/6 - Visualizations

Call the Options dialog

Performance statistics

Panning/Zooming options

One simple API: display()


3/6 - Cloud IntegrationEasily export your data to csv, json, html, etc. locally on your laptop or into a cloud-based

service like Cloudant or Object Storage


4/6 - Scala BridgeExecute Scala code directly from your python Notebook

%%scalaval demo = com.ibm.cds.spark.samples.StreamingTwitterdemo.setConfig("twitter4j.oauth.consumerKey",”XXXXX")demo.setConfig("twitter4j.oauth.consumerSecret",”XXXXX")demo.setConfig("twitter4j.oauth.accessToken",”XXXXX")demo.setConfig("twitter4j.oauth.accessTokenSecret",”XXXXX")demo.setConfig("watson.tone.url","https://watsonplatform.net/tone-analyzer/api")demo.setConfig("watson.tone.password",”XXXXX")demo.setConfig("watson.tone.username",”XXXX”)

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

pythonVar = “pixiedust”Define Python variable

println(pythonVar) Use the python var in Scala

val __fromScalaVar = “Hello from Scala” Define scala variable

print(__fromScalaVar) Use the scala var in Python


5/6 - ExtensibilityEasily extend PixieDust to create your own visualizations using HTML/CSS/JavaScript

Customized Visualization for GraphFrame Graphs


6/6 - Embed Apps in NotebooksEncapsulate your analytics into compelling User

Interfaces better suited for Line of Business Usersfrom pixiedust_twitterdemo import *twitterDemo()

pixie dust overview

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