Online Program

Return to main conference page
Thursday, May 30
Data Science Techologies
Project Jupyter
Thu, May 30, 1:30 PM - 3:05 PM
Regency Ballroom AB

JupyterLab: An Extensible and Flexible Platform for Collaborative Data Science (305085)

*Brian Ellison Granger, Cal Poly / Project Jupyter 

Keywords: open source, data science, interactive, collaboration, statistics

Project Jupyter is an open-source project that exists to develop software, open standards, and services for interactive and reproducible computing. The main application developed by the project is the Jupyter Notebook, a web-application that allows users to create documents that combine live code with narrative text, mathematical equations, and visualizations. Since its creation in 2011, the Jupyter Notebook has become a widely-used, open standard for developing, sharing, communicating, and reproducing computational work in scientific computing and data science.

In this talk, I will describe JupyterLab, a new user interface for the project focused on extensibility, collaboration, and flexibility. JupyterLab enables you to work with documents and activities such as Jupyter notebooks, text editors, terminals, and custom components in a flexible, integrated, and extensible manner. You can arrange multiple documents and activities side by side in the work area using tabs and splitters. Documents and activities integrate with each other, enabling new workflows for computational research and data science.

JupyterLab also offers a unified model for viewing and handling data formats. JupyterLab understands many file formats (images, CSV, JSON, Markdown, PDF, Vega, Vega-Lite, etc.) and can also display rich kernel output in these formats. JupyterLab extensions can customize or enhance any part of JupyterLab, including new themes, file editors, and custom components. And importantly, it uses the the same server and notebook document format as the classic Jupyter Notebook.