Mapping data in Jupyter notebooks with PixieDust (sponsored by IBM)
Raj Singh offers an overview of PixieDust, a Jupyter Notebook extension that provides an easy way to make interactive maps from DataFrames for visual exploratory data analysis. Raj explains how he built mapping into PixieDust, putting data from Apache Spark-based analytics on maps using Mapbox GL.
| Talk Title | Mapping data in Jupyter notebooks with PixieDust (sponsored by IBM) |
| Speakers | RAJ SINGH (IBM Cloud Data Services) |
| Conference | JupyterCon in New York 2017 |
| Conf Tag | |
| Location | New York, New York |
| Date | August 23-25, 2017 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
The Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter stack is built from the ground up to be extensible and hackable. The Developer Advocacy team at IBM Watson Data Platform has developed PixieDust, an open source library of useful time-saving and anxiety-reducing tools designed to ease the pain of charting, saving data to the cloud, and exposing Python data structures to Scala code. Raj explains how the team built mapping into PixieDust, putting data from Apache Spark-based analytics on maps using Mapbox GL. You’ll learn how to programmatically extend Jupyter notebooks, use the Mapbox API, and combine Python with JavaScript using the Jinja2 Python template engine. Along the way, you’ll discover how PixieDust can help you automate some of the drudgery of data exploration and find out how to join the PixieDust developer community. This session is sponsored by IBM.
