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Pixie dust overview

PixieDust overview

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Pixie dust overview

  1. 1. ©2016 IBM Corporation IBM Data Science Experience PixieDust: an Open Source Library that simplifies and improves Jupyter Python Notebooks
  2. 2. ©2016 IBM Corporation IBM Data Science Experience PixieDust: an Open Source Library that simplifies and improves Jupyter Python Notebooks Jupyter + Pixiedust = 1. PackageManager 2. Visualizations 3. Cloud Integration 4. Scala Bridge 5. Extensibility 6. Embedded Apps https://github.com/ibm-cds-labs/pixiedust
  3. 3. ©2016 IBM Corporation IBM Data Science Experience 1/6 - Package Manager Install 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
  4. 4. ©2016 IBM Corporation IBM Data Science Experience 2/6 - Visualizations Call the Options dialog Performance statistics Panning/Zooming options One simple API: display()
  5. 5. ©2016 IBM Corporation IBM Data Science Experience 3/6 - Cloud Integration Easily export your data to csv, json, html, etc. locally on your laptop or into a cloud-based service like Cloudant or Object Storage
  6. 6. ©2016 IBM Corporation IBM Data Science Experience 4/6 - Scala Bridge Execute Scala code directly from your python Notebook %%scala val demo = com.ibm.cds.spark.samples.StreamingTwitter demo.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
  7. 7. ©2016 IBM Corporation IBM Data Science Experience 5/6 - Extensibility Easily extend PixieDust to create your own visualizations using HTML/CSS/JavaScript Customized Visualization for GraphFrame Graphs
  8. 8. ©2016 IBM Corporation IBM Data Science Experience 6/6 - Embed Apps in Notebooks Encapsulate your analytics into compelling User Interfaces better suited for Line of Business Users from pixiedust_twitterdemo import * twitterDemo()
  9. 9. ©2016 IBM Corporation IBM Data Science Experience https://github.com/ibm-cds-labs/pixiedust

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